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Open Access February 06, 2026

Predictive Modeling of Public Sentiment Using Social Media Data and Natural Language Processing Techniques

Abstract Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled [...] Read more.
Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.
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Article
Open Access October 12, 2025

Assessment of Handling Practices and Awareness of Aflatoxin Contamination in Spices among Micro and Small-Scale Processors in Tanzania

Abstract Frequent consumption of aflatoxins-contaminated spices has been linked to serious adverse health effects among consumers. The likelihood of exposure to these toxins is influenced by the level of public awareness. Controlling aflatoxins contamination throughout the food chain is critical for public health. This study aimed to assess the handling practices and awareness of aflatoxin contamination [...] Read more.
Frequent consumption of aflatoxins-contaminated spices has been linked to serious adverse health effects among consumers. The likelihood of exposure to these toxins is influenced by the level of public awareness. Controlling aflatoxins contamination throughout the food chain is critical for public health. This study aimed to assess the handling practices and awareness of aflatoxin contamination among micro- and small-scale spice processors. A total of 60 processors from 4 districts of two regions of Tanzania were interviewed. The results showed that while 56.7% of interviewed processors were aware of aflatoxin contamination in spices primarily through training (38.3%) and mass media (30%). However, there were still misconceptions regarding the causes and effects of aflatoxins to human health. It was observed that, poor drying and storage practices, inadequate monitoring of processors aggravated the situation. Nonetheless, all interviewed processors expressed willingness to participate in training programs to ensure quality and safety along the chain. The study findings underscore the necessity for targeted interventions to reduce aflatoxin risks in the spice value chain. These should include strengthened food safety inspections and enforcement, as well as tailored training and support for micro and small-scale spice processors. Enhancing their knowledge and ability to adopt proper handling, drying and storage practices is critical for enhancing food safety and safeguarding public health.
Article
Open Access June 25, 2025

Performance and Validity of Knee Function Assessment Tools After Total Knee Arthroplasty: A Systematic Review

Abstract Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. [...] Read more.
Objective: To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. Methodology: A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. Results: All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. Conclusion: Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.
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Systematic Review
Open Access January 11, 2025

Exploring LiDAR Applications for Urban Feature Detection: Leveraging AI for Enhanced Feature Extraction from LiDAR Data

Abstract The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is [...] Read more.
The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.
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Article
Open Access November 16, 2024

Electrocution Cervical Myelopathy Presenting with Partial Brown Sequard Syndrome: A Case Report and Review of Literature

Abstract Background: Electrical injuries are underreported in literature, but they can affect the peripheral and central nervous system causing permanent disability. Aims and objectives: This case report aims to highlight cervical spinal cord injury secondary to electrocution, a rare cause of spinal cord injury. Case report: We report the case of a 54-year-old housewife who presented [...] Read more.
Background: Electrical injuries are underreported in literature, but they can affect the peripheral and central nervous system causing permanent disability. Aims and objectives: This case report aims to highlight cervical spinal cord injury secondary to electrocution, a rare cause of spinal cord injury. Case report: We report the case of a 54-year-old housewife who presented with transient loss of consciousness and right sided hemiparesis following electrocution, while at home. Results: The patient met clinical critera for partial Brown- Sequard syndrome, which to our knowledge, has not been previously reported. She showed significant improvement over a month and is currently under monitoring. Conclusions: Electrical injury is a rare cause of normal MRI myelopathy and the potential for immediate, delayed, and long- term neurological disability.
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Case Report
Open Access October 07, 2023

A Systematic Review of Observational Studies Focusing on Impact of Telehealth Consultation in Osteoporosis Management during the Pandemic

Abstract Background: The COVID-19 pandemic disrupted routine osteoporosis care due to clinic closures and limited in-person consultations. Telehealth emerged as an alternative model enabling remote care delivery and monitoring. However, previous reviews on telehealth either did not include the pandemic period or had a limited focus in scope. Evidence synthesized specifically for osteoporosis care [...] Read more.
Background: The COVID-19 pandemic disrupted routine osteoporosis care due to clinic closures and limited in-person consultations. Telehealth emerged as an alternative model enabling remote care delivery and monitoring. However, previous reviews on telehealth either did not include the pandemic period or had a limited focus in scope. Evidence synthesized specifically for osteoporosis care during the pandemic is needed but lacking. Methods: We systematically searched PubMed, MEDLINE, EMBASE, PsycINFO, Web of Science, and CINAHL for studies on telehealth for osteoporosis published between January 2021 and March 2023. Five studies met the inclusion criteria of: osteoporosis population, telehealth intervention, and COVID-19 pandemic timeframe. Data was extracted on study characteristics, COVID-19 outcomes, osteoporosis status, telehealth purpose, patient satisfaction, and clinical outcomes. Result: The five studies showed telehealth was used for monitoring data, delivering test results, adjusting medications, and assessments. Osteoporosis prevalence among telehealth users ranged 30-100%. High patient satisfaction was reported with telehealth versus in-person care. No major differences occurred in medication delays or fractures between telehealth and in-person groups. Conclusion: This review found telehealth enables effective osteoporosis care and monitoring during the pandemic, with high patient and provider satisfaction. However, more robust randomized controlled trials are needed to establish stronger evidence around telehealth's impacts on clinical osteoporosis outcomes. Implications: Though promising, further high-quality studies will help clarify telehealth's role in improving osteoporosis care and outcomes. Findings inform guidelines on integrating telehealth into routine management. Evidence on user perspectives optimizes telehealth implementation policies.
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Systematic Review
Open Access August 23, 2023

Determinants and Satisfaction Outcomes of Pregnancy Care in China: The Case of Ghanaian Women in Zhenjiang

Abstract The concept of maternity care satisfaction focuses on women's expectations and results in women having a positive attitude about the care received during pregnancy, childbirth and after birth. The proportion of births to Ghanaian migrant mothers in China is increasing, and there is an increasing demand for information regarding their reproductive health. To reduce maternal and neonatal morbidity [...] Read more.
The concept of maternity care satisfaction focuses on women's expectations and results in women having a positive attitude about the care received during pregnancy, childbirth and after birth. The proportion of births to Ghanaian migrant mothers in China is increasing, and there is an increasing demand for information regarding their reproductive health. To reduce maternal and neonatal morbidity and death rates, it is crucial for foreign women who use maternity services to be satisfied with their care. Ghanaian women's birth experiences in China might be harmed by language and cultural disparities. Little is known about their experiences in China's homogeneous society. A survey of 317 postnatal Ghanaian foreigners in Zhenjiang, China provided the study's data and was analyzed using IBM SPSS Statistics 25. The results showed that (76%) of postnatal foreigners were satisfied with delivery care. Though the satisfaction level was high, respondents raised the issues of poor communication (62.8%) and high cost of delivery care (52.4%) as some of the general experiences they faced. Healthcare providers’ strengthening routine monitoring of maternal and newborn health programs will help deliver more woman-centered care.
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Case Study
Open Access December 15, 2021

Dissemination and Exploitation of Regional Meteo-Hydrological Datasets through Web-based Interactive Applications: The SOL System Case Study

Abstract The effects of climate change are already being felt in several parts of the World. Variability of changing rainfall intensity, drought and weather patterns contribute to determining the vulnerability of many human activities such as agriculture. In the next future, climate change considerations will depend on having appropriate strategies such as strengthen implementation agencies working in a [...] Read more.
The effects of climate change are already being felt in several parts of the World. Variability of changing rainfall intensity, drought and weather patterns contribute to determining the vulnerability of many human activities such as agriculture. In the next future, climate change considerations will depend on having appropriate strategies such as strengthen implementation agencies working in a coordinated manner and with a data-driven approach in order to ensure monitoring, reporting and data verification. In this context, national and regional meteorological Services are facing with high demand for timely and quality information, services and products. A web-based interactive application with the aim of disseminating meteo-hydrological information at regional scale is described in this paper. The web application is built on a relational database and client-side programming has been used for implementing the user interface and controlling the web page behavior. The combination of PHP (Hypertext Preprocessor, a general-purpose scripting language, especially suited to server-side web development) and JavaScript (high-level object-oriented scripting language, nowadays the dominant client-side scripting language of the Web) has been chosen for this reason, since such software is free to use for everyone. The SOL system, developed on behalf of Marche region, Italy, was chosen as a case study, due to its multi-source data framework and because of the processing and public dissemination of several ad-hoc data elaborations.
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Case Study
Open Access January 16, 2026

Evaluating the Effectiveness of Occupational Health and Safety Management Practices in Improving Workplace Safety in Nigerian Construction Sites

Abstract The construction industry remains one of the most hazardous sectors globally, with Nigeria experiencing a high incidence of workplace accidents despite the adoption of Occupational Health and Safety Management (OHSM) frameworks. This study evaluated the effectiveness of OHSM practices in improving workplace safety across construction companies in Nigeria’s coastal cities. A cross-sectional design [...] Read more.
The construction industry remains one of the most hazardous sectors globally, with Nigeria experiencing a high incidence of workplace accidents despite the adoption of Occupational Health and Safety Management (OHSM) frameworks. This study evaluated the effectiveness of OHSM practices in improving workplace safety across construction companies in Nigeria’s coastal cities. A cross-sectional design was employed, combining quantitative surveys of construction workers (n = 1,400) with qualitative interviews of 35 managers and supervisors. Quantitative data were analyzed using SPSS version 28, while thematic analysis was applied to qualitative responses. Findings revealed a generally positive perception of OHSM, with 54.4% of workers rating OHS policy effectiveness as “Good” and 52.0% rating health outcomes as “Good.” However, accident frequency remained a concern, with 46.4% reporting accidents occurred “Occasionally” and 31.9% acknowledging them as “Frequent” or “Very Frequent.” Comparative analysis showed indigenous firms were rated higher in policy effectiveness and health outcomes but also reported slightly higher accident frequencies than international firms. Thematic analysis identified five key monitoring and evaluation strategies including routine inspections, regular training, audits, behavioural reinforcement, and access control, Also, five measures of OHSM effectiveness, including compliance observation, incident tracking, KPIs, employee feedback, and benchmarking. OHSM was found to positively influence project outcomes by reducing compensation costs, enhancing reputation, and improving supervision and quality of work. OHSM practices in Nigeria’s construction sector are perceived as effective in policy and health outcomes, yet accident rates remain a critical challenge. The study underscores the importance of continuous training, stricter enforcement, behavioural reinforcement, and systematic performance evaluation.
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Article
Open Access January 07, 2026

Pre-eclampsia’s Hidden Risk: Sudden Postpartum Bilateral Serous Retinal Detachment with Complete Visual Recovery

Abstract Introduction: Severe pre-eclampsia is a multisystem disorder associated with various ocular complications, however postpartum bilateral serous retinal detachment is uncommon and may threaten vision if not early recognized, thus requiring prompt management in order to prevent permanent visual loss. Clinical Description: A case of a 31-year-old woman, G3P0 with an in vitro [...] Read more.
Introduction: Severe pre-eclampsia is a multisystem disorder associated with various ocular complications, however postpartum bilateral serous retinal detachment is uncommon and may threaten vision if not early recognized, thus requiring prompt management in order to prevent permanent visual loss. Clinical Description: A case of a 31-year-old woman, G3P0 with an in vitro fertilization and previous miscarriages, developed severe pre-eclampsia at 34 weeks of gestation. She underwent an emergency cesarean section for maternal indication. On the second postoperative day, she develops sudden unilateral blindness and blurred vision in the contralateral eye. Ophthalmological examination showed normal optics discs while MRI revealed bilateral serous retinal detachment. She was managed conservatively with strict blood pressure control, magnesium sulphate therapy and anticoagulation with full recovery of vision over 3 weeks without need of surgical intervention. Discussion: Postpartum retinal detachment is uncommon, most often serous and reversible. This case highlights that conservative management focusing on strict blood pressure control and supportive care was sufficient to avoid surgical intervention. Timely diagnosis and coordinated multidisciplinary management ensured complete visual recovery. Conclusion: Bilateral serous retinal detachment is a rare but reversible postpartum complication of severe pre-eclampsia. With early recognition, close monitoring and conservative management can lead to complete restoration of vision.
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Case Report
Open Access September 04, 2025

Evidence-Based Protocols for the Prevention and Treatment of Prosthetic Joint Infection in Total Hip Arthroplasty: A Systematic Review

Abstract Objective: This systematic review aimed to identify, synthesize, and critically analyze the available evidence on clinical protocols used for the prevention and treatment of prosthetic joint infection (PJI) in total hip arthroplasty (THA), based on studies published between 2000 and 2025. Methods: The review was conducted according to PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, Web of Science, and Embase between January and April 2025. Eligible studies included clinical trials, cohort studies, case-control studies, systematic reviews, and meta-analyses published in English that addressed either preventive or therapeutic strategies for PJI in THA. Study selection, data extraction, and quality assessment were carried out independently by two reviewers. Due to the heterogeneity of the included studies, a qualitative synthesis was performed. Results: A total of 32 studies were included. Preventive measures identified in the literature comprised combined antibiotic prophylaxis (cefazolin and gentamicin), multimodal perioperative protocols such as ACERTO, nasal decolonization for Staphylococcus aureus [...] Read more.
Objective: This systematic review aimed to identify, synthesize, and critically analyze the available evidence on clinical protocols used for the prevention and treatment of prosthetic joint infection (PJI) in total hip arthroplasty (THA), based on studies published between 2000 and 2025. Methods: The review was conducted according to PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, Web of Science, and Embase between January and April 2025. Eligible studies included clinical trials, cohort studies, case-control studies, systematic reviews, and meta-analyses published in English that addressed either preventive or therapeutic strategies for PJI in THA. Study selection, data extraction, and quality assessment were carried out independently by two reviewers. Due to the heterogeneity of the included studies, a qualitative synthesis was performed. Results: A total of 32 studies were included. Preventive measures identified in the literature comprised combined antibiotic prophylaxis (cefazolin and gentamicin), multimodal perioperative protocols such as ACERTO, nasal decolonization for Staphylococcus aureus, silver-impregnated dressings, and structured post-discharge surveillance. Treatment strategies included DAIR (Debridement, Antibiotics, and Implant Retention), the DAPRI technique, one-stage and two-stage revision surgeries, muscle flap reconstructions, and protocols without spacers. These interventions were associated with significantly reduced infection rates and improved clinical outcomes when applied appropriately and in accordance with patient-specific factors. Conclusion: Effective prevention and treatment of PJI in total hip arthroplasty require a systematic and evidence-based approach. Integrated protocols—spanning preoperative optimization, meticulous intraoperative techniques, and rigorous postoperative monitoring—have proven effective in reducing infection incidence. In cases of established infection, surgical management must be tailored to the timing of infection, microbial profile, and host conditions. Two-stage revision remains the gold standard for complex infections, while one-stage revision and emerging techniques like DAPRI offer promising results in selected cases. This review contributes to the standardization of clinical practice and supports improved patient outcomes.
Systematic Review
Open Access March 20, 2025

Weaker Effects of Parental Education on Oral Nicotine Use of High School Students in Rural Areas: Marginalization-Related Diminished Returns

Abstract Background: Nicotine pouches, gummies, and candies have emerged as popular alternatives to traditional tobacco products among U.S. adolescents. While parental educational attainment is generally associated with youth substance use, marginalization-related diminished returns (MDRs) suggest that this effect may be weaker in marginalized populations, including non-Latino White communities. In [...] Read more.
Background: Nicotine pouches, gummies, and candies have emerged as popular alternatives to traditional tobacco products among U.S. adolescents. While parental educational attainment is generally associated with youth substance use, marginalization-related diminished returns (MDRs) suggest that this effect may be weaker in marginalized populations, including non-Latino White communities. In particular, place-based marginalization—such as neighborhood economic disadvantage and school-level poverty—may attenuate the benefits of parental education. This study examines MDRs in the relationship between parental educational attainment and nicotine pouch/gummy/candy use among non-Latino White 12th graders in the 2024 Monitoring the Future (MTF) study. Methods: This study analyzed nationally representative data from the 2024 MTF study, focusing on non-Latino White 12th graders who reported parental education levels and adolescents’ use of nicotine pouch/gummy/candy. Structural equation modeling (SEM) was used to estimate the effects of parental education on adolescents’ use of nicotine pouches, gummies, and candies, while adjusting for demographic covariates. Place-based marginalization was operationalized using rural vs urban /suburban residence. Interaction terms tested whether the effect of parental education varied based on place of residence. Results: Higher parental educational attainment was associated with lower use of nicotine pouches, gummies, and candies. However, this effect was significantly weaker in rural areas. Conclusion: Public health interventions should account for place-based disparities rather than assuming a uniform effect of SES factors. This study highlights the need for policy responses that address structural inequities beyond individual family SES.
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Original Article
Open Access March 12, 2025

Academic Aspirations of 12th Grade Students in the United States: Place-Based Diminished Returns of Parental Education in Rural Areas

Abstract Background: The Motivational Theory of Life-Span Development suggests that individual aspirations are shaped by both internal and external resources. Parental education is a key determinant of educational aspirations, yet its effects may vary by geographic location, demonstrating spatial patterns of Minorities’ Diminished Returns (MDRs). Objectives: This [...] Read more.
Background: The Motivational Theory of Life-Span Development suggests that individual aspirations are shaped by both internal and external resources. Parental education is a key determinant of educational aspirations, yet its effects may vary by geographic location, demonstrating spatial patterns of Minorities’ Diminished Returns (MDRs). Objectives: This study examines the association between parental education and aspirations for graduate or professional education among non-Latino White adolescents, with a specific focus on urban-suburban versus rural differences. Methods: Using data from the 12th-grade cohort of the Monitoring the Future (MTF) 2024 survey, we conducted multivariate analyses to assess the relationship between parental education and aspirations for graduate or professional education. We further examined whether this association was moderated by geographic location (urban-suburban vs. rural) to identify place-based MDRs. Results: Higher parental education was associated with greater aspirations for advanced education; however, this effect was weaker in rural areas compared to urban and suburban settings. These findings highlight that even among non-Latino White adolescents, rural residence diminishes the benefits of socioeconomic resources, providing evidence of place-based MDRs. Conclusion: Rural residents face a dual disadvantage—both lower socioeconomic status and weaker returns on those resources—necessitating targeted interventions beyond resource allocation. To address disparities in educational aspirations in rural areas, policymakers should focus on improving equitable access to educational opportunities and ensuring that these resources translate into comparable outcomes across different social and geographic contexts.
Article
Open Access March 09, 2025

Place-Based Diminished Returns of Parental Education on Adolescents’ Inhalant Use in Rural Areas

Abstract Background Adolescent substance use is often influenced by socioeconomic and geographical factors. While higher parental education is typically associated with lower substance use, these protective effects may be weaker for marginalized groups facing structural disadvantages that limit the utility and returns of their economic and social resources. Rural areas, characterized by fewer [...] Read more.
Background Adolescent substance use is often influenced by socioeconomic and geographical factors. While higher parental education is typically associated with lower substance use, these protective effects may be weaker for marginalized groups facing structural disadvantages that limit the utility and returns of their economic and social resources. Rural areas, characterized by fewer employment opportunities and limited recreational activities, may contribute to marginalization-related diminished returns (MDRs) of parental education on adolescent substance use, including inhalant use. Objectives This study applies the MDRs framework to examine whether the protective effect of higher parental education on current inhalant use (past 30 days) among 12th-grade American adolescents varies by geographic location. Specifically, we assess whether youth from highly educated families in rural areas are at a disproportionate risk of inhalant use compared to their urban and suburban peers. Methods Using data from the 2024 Monitoring the Future (MTF) study, a nationally representative survey of 12th-grade adolescents in the U.S., we tested main effects and statistical interactions between parental education and residence (rural vs. urban/suburban) in predicting the odds of inhalant use over the past 30 days. Logistic regression models, both with and without interaction terms, were applied to evaluate whether the protective effects of parental education varied by residence location, controlling for relevant demographic and socioeconomic factors. Results Findings indicate a significant interaction between parental education and rural residence. While higher parental education was associated with lower odds of inhalant use in urban and suburban areas, this protective effect was substantially weaker in rural settings. Adolescents from highly educated families in rural areas exhibited a higher-than-expected risk of inhalant use, suggesting that geographic marginalization attenuates the benefits of parental socioeconomic resources. Conclusions These results highlight the role of place-based marginalization in shaping adolescent substance use disparities, demonstrating that MDRs extend beyond race and ethnicity to location-based disadvantages. Rural youths from highly educated families may face unique structural and social challenges that counteract the protective effects of parental education. Public health efforts should consider place-based interventions that address the economic, recreational, and social limitations of rural environments to reduce substance use risk among high-SES adolescents residing in rural areas.
Article
Open Access March 08, 2025

A Case of Severe Pulmonary Aspergillosis Successfully Treated by Isavuconazole

Abstract Isavuconazole (ISCZ) is a novel antifungal agent that is expected to be effective against severe fungal diseases. A case of chronic pulmonary aspergillosis that was refractory to existing agents, such as micafungin, but was successfully treated by ISCZ, is presented.
Isavuconazole (ISCZ) is a novel antifungal agent that is expected to be effective against severe fungal diseases. A case of chronic pulmonary aspergillosis that was refractory to existing agents, such as micafungin, but was successfully treated by ISCZ, is presented.
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Commentary
Open Access February 26, 2025

Innovations and Challenges in Pharmaceutical Supply Chain, Serialization and Regulatory Landscape

Abstract The pharmaceutical supply chain has become increasingly complex and vulnerable to various risks, including counterfeit drugs, diversion, and fraud. As these challenges threaten patient safety and the integrity of global healthcare systems, serialization has emerged as a pivotal innovation in pharmaceutical logistics and regulatory compliance. Serialization involves assigning unique identifiers to [...] Read more.
The pharmaceutical supply chain has become increasingly complex and vulnerable to various risks, including counterfeit drugs, diversion, and fraud. As these challenges threaten patient safety and the integrity of global healthcare systems, serialization has emerged as a pivotal innovation in pharmaceutical logistics and regulatory compliance. Serialization involves assigning unique identifiers to individual drug packages, enabling precise tracking and authentication at every stage of the supply chain. This process provides unprecedented transparency, enhances product security, and facilitates real-time monitoring of pharmaceutical products as they move from manufacturers to end consumers. Despite its potential to revolutionize pharmaceutical traceability, the integration of serialization technologies faces numerous obstacles. These include high implementation costs, regulatory inconsistencies across regions, and the technological challenges of managing vast amounts of data. Moreover, the complex, multi-tiered nature of the global supply chain introduces additional risks related to data integrity, cybersecurity, and interoperability between systems. As pharmaceutical companies seek to navigate these challenges, innovations in serialization technology—such as blockchain, artificial intelligence (AI), the Internet of Things (IoT), and radio frequency identification (RFID)—are providing promising solutions to enhance efficiency, reduce fraud, and increase visibility. This manuscript explores both the innovative advancements and the key challenges associated with the integration of serialization in the pharmaceutical supply chain. It delves into the evolving regulatory landscape, highlighting the need for global harmonization of serialization standards, and examines the impact of serialization on securing pharmaceutical distribution networks. Additionally, the paper emphasizes the importance of collaboration among manufacturers, technology providers, and regulatory bodies in overcoming implementation barriers and realizing the full potential of serialization. As the pharmaceutical industry moves towards a more interconnected and data-driven future, serialization promises to play a central role in shaping the next generation of drug safety and supply chain management. By addressing the hurdles to adoption and leveraging emerging technologies, the pharmaceutical sector can create a more secure, transparent, and efficient supply chain that better serves public health and fosters greater trust among consumers and healthcare professionals alike.
Review Article
Open Access February 12, 2025

Unequal Benefits: How Parental Education Falls Short for Black and Latino Youth

Abstract Background: Parental education is a key determinant of academic performance, yet its protective effects may differ by race and ethnicity. The concept of Minorities’ Diminished Returns (MDRs) highlights the weaker association between socioeconomic resources and outcomes for marginalized populations, including Black and Latino youth. Objective: To investigate whether the [...] Read more.
Background: Parental education is a key determinant of academic performance, yet its protective effects may differ by race and ethnicity. The concept of Minorities’ Diminished Returns (MDRs) highlights the weaker association between socioeconomic resources and outcomes for marginalized populations, including Black and Latino youth. Objective: To investigate whether the positive association between parental education and school performance (letter grades) is weaker for Black and Latino youth compared to non-Latino White youth. Methods: Data were drawn from the Monitoring the Future (MTF) 2023 study. The sample included Black, Latino, and non-Latino White youth. The outcome was a nine-level continuous measure of academic performance based on self-reported letter grades, with higher scores indicating better performance. Multivariate regression models tested interactions between parental education and race/ethnicity in predicting grades, adjusting for confounders such as family income, gender, and school characteristics. Results: A total number of 7584 12th graders entered the study. Parental education was positively associated with school performance across all groups, but the magnitude of this association was significantly smaller for Black and Latino youth compared to non-Latino White youth. Even after controlling for socioeconomic and contextual factors, the racial and ethnic differences in the strength of this association persisted. Conclusions: Our findings provide evidence of Minorities’ Diminished Returns (MDRs) in the academic domain, with Black and Latino youth experiencing weaker benefits of parental education on school performance. These disparities suggest that structural barriers and systemic inequities undermine the translation of parental educational attainment into academic success for marginalized groups. Policy interventions must address these structural barriers to promote equity in educational outcomes.
Article
Open Access January 22, 2025

Tech Transformations: Modern Solutions for Obstructive Sleep Apnea

Abstract Recent advancements in the screening, diagnosis, and management of obstructive sleep apnea (OSA) have significantly improved patient outcomes. For screening, the use of home sleep apnea testing (HSAT) has become more prevalent, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. HSAT devices have shown good specificity and sensitivity, particularly in [...] Read more.
Recent advancements in the screening, diagnosis, and management of obstructive sleep apnea (OSA) have significantly improved patient outcomes. For screening, the use of home sleep apnea testing (HSAT) has become more prevalent, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. HSAT devices have shown good specificity and sensitivity, particularly in patients with a high pre-test probability of OSA. In terms of diagnosis, advancements in wearable technology and mobile health applications have enabled continuous monitoring of sleep patterns and respiratory parameters. These tools provide valuable data that can be used to identify OSA more accurately and promptly. Additionally, machine learning algorithms are being integrated into diagnostic processes to enhance the accuracy of OSA detection by analyzing large datasets and identifying patterns indicative of the condition. Management of OSA has also seen significant progress. Continuous positive airway pressure (CPAP) therapy remains the gold standard, but new developments include auto-adjusting CPAP devices that optimize pressure settings based on real-time feedback. Mandibular advancement devices and hypoglossal nerve stimulation are emerging as effective alternatives for patients who are CPAP-intolerant. Furthermore, lifestyle interventions such as weight management, positional therapy, and exercise have been shown to complement medical treatments, leading to better overall outcomes. This review article highlights these advancements that collectively contribute to improved patient adherence, reduced symptoms, and enhanced quality of life for individuals with OSA.
Review Article
Open Access January 09, 2025

Advances in the Synthesis and Optimization of Pharmaceutical APIs: Trends and Techniques

Abstract The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API [...] Read more.
The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.
Review Article
Open Access November 16, 2024

Digital Therapeutics: A New Dimension to Diabetes Mellitus Management

Abstract Digital therapeutics (DTx) play a transformative role in diabetes management by leveraging technology to provide personalized, data-driven medical interventions. These tools enhance self-management by offering continuous monitoring and real-time feedback on glucose levels, diet, and physical activity. This personalized approach helps patients adhere to treatment plans and make informed lifestyle [...] Read more.
Digital therapeutics (DTx) play a transformative role in diabetes management by leveraging technology to provide personalized, data-driven medical interventions. These tools enhance self-management by offering continuous monitoring and real-time feedback on glucose levels, diet, and physical activity. This personalized approach helps patients adhere to treatment plans and make informed lifestyle changes, leading to improved clinical outcomes such as reduced HbA1c levels and better overall diabetes control. The importance of DTx lies in their ability to make diabetes care more accessible and convenient. Mobile apps and telemedicine platforms enable patients to receive support and guidance from anywhere, reducing the need for frequent in-person visits. Additionally, DTx often include behavioral support features like reminders, educational content, and motivational tools, which are crucial for maintaining healthy habits and managing stress. Currently, the dynamics of DTx in diabetes are rapidly evolving, with increasing integration of artificial intelligence and machine learning to further personalize and optimize care. As the adoption of these technologies grows, they hold the potential to significantly improve patient outcomes and revolutionize diabetes management on a global scale. This article will focus on the benefits of novel digital therapeutics for prevention and management of type II diabetes that are currently available in the market.
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Open Access October 26, 2024

Exploring the Relationship between Teacher Training and Challenges in Delivering Effective Sex Education

Abstract This study aimed to explore the relationship between teacher training and challenges in delivering effective sex education in the Sagnarigu district in the Northern region of Ghana. The Social-cultural theory underpins the study. A descriptive survey research design was adopted for the study. The population of this study comprises teachers, head teachers and a School Improvement Support Officer [...] Read more.
This study aimed to explore the relationship between teacher training and challenges in delivering effective sex education in the Sagnarigu district in the Northern region of Ghana. The Social-cultural theory underpins the study. A descriptive survey research design was adopted for the study. The population of this study comprises teachers, head teachers and a School Improvement Support Officer (SiSo) in basic schools in the Gumani/Nyanshegu circuit in the Sagnarigu district of the Northern Region. This study adopted multi-sampling methods to select respondents. Random and purposive sampling techniques were used to select the study's 10 basic schools and 83 respondents. The main tools used for the data collection were the questionnaire and interview. The data was analysed using Statistical Package for Social Scientists (SPSS) software version 23 tools. Descriptive Statistical tools such as frequencies were used to gauge the number of occurrences of the studied variables. The qualitative data was also further analysed in line with the research questions to establish patterns of similarities and variations. These were then validated with the quantitative data for any possible contradictions in the findings. The study concludes that people's socio-cultural and religious beliefs and practices affect the teaching of sex education in school. It was observed that sex education could be misconstrued as an encouragement to young people to engage in early sexual promiscuity. Finally, the study concludes that there cannot be effective sex education without the appropriate logistics such as T.L.M.s, syllabus and time allocation for the subject. The study also acknowledges that the availability of these materials must go along with appropriate capacity building for teachers to be well-positioned to teach the subject. It is recommended that Sexuality education should be included in the curricular and academic calendar of basic schools, along with the provision of adequate T.L.M.s and the proper collaboration with appropriate agencies for the effective teaching of the subject. Sex education needs to be given maximum attention, just as any of the life skills subjects, with adequate investment and motivation to both teachers and pupils to ensure a meaningful impact. There should be regular monitoring, supervision, and assessment of the training of teachers and its influence on the teaching of the subject, as well as ensuring that ethical considerations regarding cultural and religious sensitivities and individual privacy issues are upheld.
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Open Access August 16, 2024

Perceived Prevalence of Pre-marital Sex in Ga Mashie

Abstract This study is aimed at examining the prevalence of premarital sex among adolescents between 13-19 years within Chorkor, Korle Gonno and Mamprobi communities in the Accra Metropolis. The study with a sample size of 268 used a descriptive approach and a combination of convenient and quota sampling methods. The study recommends the establishment of a school guidance and counselling unit to sensitize [...] Read more.
This study is aimed at examining the prevalence of premarital sex among adolescents between 13-19 years within Chorkor, Korle Gonno and Mamprobi communities in the Accra Metropolis. The study with a sample size of 268 used a descriptive approach and a combination of convenient and quota sampling methods. The study recommends the establishment of a school guidance and counselling unit to sensitize and advise students and parental monitoring of adolescent lives and the use of social media on mobile devices.
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Open Access July 16, 2024

Management of Saltwater Intrusion in Coastal Aquifers: A Review and Case Studies from Egypt

Abstract Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, [...] Read more.
Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, reduced recharge, rising sea levels, climate change, and other causes. Saltwater intrusion (SWI) is a prevalent issue that needs attention, as it significantly threatens groundwater quantity and quality. SWI happens when saline water infiltrates coastal aquifers, contaminating freshwater supplies. This review article aims to define SWI, explore its causes and influencing factors, and discuss various monitoring techniques. Additionally, it examines different modeling methods and management tools, including remote sensing, field surveys, modeling approaches, and optimization techniques. To mitigate the adverse effects of SWI, several control measures are outlined, along with their pros and cons. The final section reviews previous SWI studies and case studies from the Nile Delta, Sinai Peninsula, and North-West coast in Egypt. These studies offer suggestions, adaptations, and mitigation measures for future research.
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Review Article
Open Access June 21, 2023

Effectiveness of Environmental Solid Waste Management Policies and Practices for Sustainable Development

Abstract The purpose of this study was to examine the effectiveness of environmental solid waste management policies and practices for sustainable development in Komenda-Edina-Eguafo-Abrem Municipality in the Central Region of Ghana. The case study research design was adopted for the study. Using the simple random sampling procedure, 425 respondents comprising of 380 residents and 45 Zoomlion staff were [...] Read more.
The purpose of this study was to examine the effectiveness of environmental solid waste management policies and practices for sustainable development in Komenda-Edina-Eguafo-Abrem Municipality in the Central Region of Ghana. The case study research design was adopted for the study. Using the simple random sampling procedure, 425 respondents comprising of 380 residents and 45 Zoomlion staff were involved in the study. The data were analysed through the computation of frequencies, percentages, as well as means and standard deviations. On the waste management strategic action plan for sustainable development, it can be concluded that, regular monitoring system should be in place to ensure that households adhere to the solid waste management practices; and education/training programmes on solid waste management should be provided for employees so that they can appreciate the need for sustainable development practices. It can also be concluded that, most of the environmental management policies and practices of solid waste management were not effective in the KEEA Municipality because the residents were uncertain about the effectiveness of the environmental policies that have been put in place. Perhaps, the Assembly does not conduct regular monitoring to find out solid waste management practices of the various households. It could be that the Assembly does not enforce bye-laws on sanitation on appropriate solid waste management practices. It is recommended that, the Environmental Protection Agency (EPA), and the Assembly should conduct regular monitoring system in order to ensure that residents adhere to the solid waste management practices. Again, the Assembly should make available a reliable data on solid waste generation for households in the Municipality. It is also recommended that, the Environmental Protection Agency (EPA) and the Assembly should ensure proper enforcement of the bye-laws on sanitation on appropriate solid waste management practices. Residents of the KEEA Municipality should be encouraged by the Environmental Protection Agency (EPA) to consider reuse and recycling as important activities.
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Open Access January 15, 2023

Proposal for Didactic Innovation through the Monitoring of Threatened Biodiversity

Abstract Biodiversity Conservation is a priority issue for the scientific community, and a main subject in the Biology and Geology curriculum at secondary school level in Spanish Educational System. In the present didactic proposal, we use the demographic monitoring of an endangered plant species to illustrate a research tool for estimates of biodiversity loos in nature, the local endemic Astragalus tremolsianus [...] Read more.
Biodiversity Conservation is a priority issue for the scientific community, and a main subject in the Biology and Geology curriculum at secondary school level in Spanish Educational System. In the present didactic proposal, we use the demographic monitoring of an endangered plant species to illustrate a research tool for estimates of biodiversity loos in nature, the local endemic Astragalus tremolsianus Pau. The aim of the proposal is to bring students closer to a real experience, which brings together knowledge of Biology, Algebra and Trigonometry, through Information and Communication Technologies (ICTs). Showing a census to monitor the populations of a threatened high mountain flora species into the classroom is an innovative teaching activity that combines and uses knowledge from different disciplines, as well as demonstrating a real application of the knowledge acquired. The use of mathematical tools encompasses a number of skills that require the application of mathematical principles and processes in the scientific context. The introduction of the structure of scientific texts is another positive aspect of this didactic experience.
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Open Access December 14, 2022

Applying Artificial Intelligence (AI) for Mitigation Climate Change Consequences of the Natural Disasters

Abstract Climate change and weather-related disasters are speeded very fast in the last decades with the consequences bringing to humanity: insecurity, destructing the ecological systems, increasing poverty, human victims, and economical losses everywhere on the planet. The innovative methods applied to mitigate the magnitudes of natural disasters and to combat effectively their negative impact consist of [...] Read more.
Climate change and weather-related disasters are speeded very fast in the last decades with the consequences bringing to humanity: insecurity, destructing the ecological systems, increasing poverty, human victims, and economical losses everywhere on the planet. The innovative methods applied to mitigate the magnitudes of natural disasters and to combat effectively their negative impact consist of remote and earth constantly monitoring, data collection, creation of models for big data extrapolation, prediction, in-time warning for prevention, and others. Artificial intelligence (AI) is used to deal with big data, for calculations, forecasts, predictions of natural disasters in the near future, the establishment of the possibilities to escape the hazards or risky situations, as well as to prepare the human being for adverse changes, and drawing the different choices as assistance the right decision to be accepted. Many projects, programs, and frameworks are adopted and carried out the separate governments and business makers to common goals and actions for the formation of a friendly environment and measures for reducing undesired climate alterations and cataclysms. The aim of the article is to review the last programs and innovations applied in the mitigation of climate change using AI.
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Brief Review
Open Access November 21, 2022

An evaluation of Monitoring and Supervision in the Junior High Schools Curriculum Delivery in Ghana

Abstract Monitoring and supervision in schools is a very important aspect in the educational process. The purpose of the study was to examine monitoring and supervision of curriculum delivery in the Junior High Schools in Ejisu-Juaben Municipality of Ghana. Mixed method research approach was adopted for the study. The population f or this study was made up of teachers, head-teachers and the deputy director [...] Read more.
Monitoring and supervision in schools is a very important aspect in the educational process. The purpose of the study was to examine monitoring and supervision of curriculum delivery in the Junior High Schools in Ejisu-Juaben Municipality of Ghana. Mixed method research approach was adopted for the study. The population f or this study was made up of teachers, head-teachers and the deputy director in charge  of supervision in the Ejisu-Juaben Municipality. Purp osive and convenient sampling techniques were employed to select the one-hundred and eighty-four respondents for the study. The main instruments for data collection were questionnaire and observation. The study revealed that monitoring and supervision was more or less just conformance to stipulated regulations and that teachers and head-teachers must comply without necessarily ensuring staff development to reduce limitations. The study also indicated that there is high level of impact of monitoring and supervision on Junior High Schools’ curriculum implementation in Ejisu-Juaben Municipality over the past years. It is recommended that, f or good performance, appraisal should be done at least by the end of every school term to ascertain staff performance on their j ob. It is also recommended that, for improvement of curriculum implementation, school heads should improve on; frequency coordination of all departments of the organization of visiting less on sessions, checking teachers’ less on notes, inviting teachers to observe him/her teach and checking students’ assignments, class exercises and pupils project work to ensure regular marking of exercise takes place.
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Open Access May 26, 2022

Women and Places; Female Street Vendors, Territorial Identity and Placemaking

Abstract Street vending is a vital part of global urban life and not a local phenomenon. It can be found in various countries and forms; stationary and mobile. In Egypt, street vendors’ activities are considered illegal, an image of backwardness, blocking investors and tourism. This study aims at monitoring and investigates the female street vendors' role in placemaking in Heliopolis, Cairo. Challenging [...] Read more.
Street vending is a vital part of global urban life and not a local phenomenon. It can be found in various countries and forms; stationary and mobile. In Egypt, street vendors’ activities are considered illegal, an image of backwardness, blocking investors and tourism. This study aims at monitoring and investigates the female street vendors' role in placemaking in Heliopolis, Cairo. Challenging the authoritarian illegality aspect, literature review, observational walks, and spontaneous interviews are adopted in obtaining data and evaluating the female street vendors’ role in constructing a sense of place and identity. Female street vendors' expression, displaying arrangement, socio-cultural identities and chancy events create livable public places, territorial identities and a sense of place.
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Open Access February 17, 2022

Role of Digital Formative Assessment in Improving the Assessment and Monitoring of Students’ Learning and Their Significance During the COVID-19 Pandemic

Abstract Digital formative assessments have the features of digital learning environment and can be used by teachers to both empower students’ learning and adapt the next steps in the learning process of their students. They are effective tools that can help lecturers and tutors to both collect and analyze the required information and data for supporting the teaching and learning processes. Importantly, [...] Read more.
Digital formative assessments have the features of digital learning environment and can be used by teachers to both empower students’ learning and adapt the next steps in the learning process of their students. They are effective tools that can help lecturers and tutors to both collect and analyze the required information and data for supporting the teaching and learning processes. Importantly, digital formative assessments have been particularly critical during the COVID-19 pandemic due to increased students’ learning outside traditional classrooms, limited face-to-face classes and other students’ learning and assessment difficulties at many educational institutions worldwide. In this article, we describe the essential features and importance of digital formative assessments and their recently developed communication methods. We also discuss the significance of digital formative assessments in measuring students’ learning and skills in times of global crisis such as the COVID-19 pandemic.
Mini Review
Open Access October 28, 2021

Development of an Improved Solid Waste Collection System using Smart Sensors

Abstract Waste collection system has become a challenging task, occasioned by the overflowing garbage bins littered all over the environment, causing environmental hazard and further leading to incurable diseases which endanger life. The present-day waste collection system has proven to be inefficient, taking into consideration the advancement in the technologies on the rise in recent years as well as the [...] Read more.
Waste collection system has become a challenging task, occasioned by the overflowing garbage bins littered all over the environment, causing environmental hazard and further leading to incurable diseases which endanger life. The present-day waste collection system has proven to be inefficient, taking into consideration the advancement in the technologies on the rise in recent years as well as the continuous increase in population growth. As a result of this inefficiency observed, this work developed a model for electronic waste collection system in a telecommunication driven environment. In the system's implementation, PIC18F4620 based instrumentation, integrated with proximity sensor for external monitoring and level sensors for internal monitoring was adopted, while the controlling of the opening and closing of the cabins was implemented using a smart switching board. A remote reporting to the waste management authority so as to systematically plan route-map for garbage collection when the waste cabin is fully filled was done by deploying a 900MHz transmitter interfaced with the system’s controller. The result shows that with this model the waste cabin opens only on account of a user approaching the sensing distance of the system and the cabin is not filled. But when the cabin gets filled and a user approaches the sensing distance of the system, it directs the user to use the nearest waste cabin by displaying a message on the LCD (Liquid Crystal Display), while communicating with relevant authority for the evacuation of the cabin via SMS. It was obviously seen that the automation incorporated into the system had zero impact on the success rate of the system or system availability while introducing a latency of 5.6seconds, which is just 28.0% of the maximum allowable latency of this kind of system, while protecting the environment from environmental pollution and spread of diseases. This work highlights the potentials of (EWCS) Electronic Waste Collection System in monitoring and controlling waste disposal for healthy and clean environment.
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Open Access August 24, 2021

The Art of Shoot: The 3D Model Presents a Smart Digital Way Teaching of Basketball

Abstract Sport is an art form. Every athlete thinks, creates, obeys rules, plans, and produces tangible results. Like most art forms, basketball for learning, monitoring, and understanding the sport requires all five senses. With the same logic by which an architect plans to build a building, the basketball team prepares for the game mode, the systems, which they will have in a match. In summary the [...] Read more.
Sport is an art form. Every athlete thinks, creates, obeys rules, plans, and produces tangible results. Like most art forms, basketball for learning, monitoring, and understanding the sport requires all five senses. With the same logic by which an architect plans to build a building, the basketball team prepares for the game mode, the systems, which they will have in a match. In summary the players and the coaching staff think before they do. For this reason, in basketball it is important to create a philosophy and a system of values in the team. Values such as trust, solidarity, cooperation, ambition, consistency are important for building the mindset among stakeholders for the successful course of the team and for titles. Finally, sport produces knowledge. Basketball is an evolving and progressive sport. Adapting to modern requirements, studying, and monitoring new trends. For example, the specialization of players in Shoot, in speed in, power, strong, results in an increase in the ability of players to man-to-man attacks. On the other hand, the defensive function of both individual and team needs to deepen the proper treatment of powerful offensive players.
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Open Access August 16, 2021

When Water Turns Deadly: Investigating Source Identification and Quality of Drinking Water in Piwoyi Community of Federal Capital Territory, Abuja Nigeria.

Abstract Essentiality of water sustain life, and a satisfactory supply must be readily available to promote health, prolong life expectancy and prevent diseases. This study assesses the sources and quality of drinking water in Piwoyi community of Federal Capital Territory, Abuja, Nigeria. Thirty-five (35) Boreholes and Two (2) Sachet water were identified sources of drinking water in Piwoyi Community. Six [...] Read more.
Essentiality of water sustain life, and a satisfactory supply must be readily available to promote health, prolong life expectancy and prevent diseases. This study assesses the sources and quality of drinking water in Piwoyi community of Federal Capital Territory, Abuja, Nigeria. Thirty-five (35) Boreholes and Two (2) Sachet water were identified sources of drinking water in Piwoyi Community. Six Samples (5 Boreholes and 1Sachet water) were selected at random and analyzed in the laboratory. The Physicochemical parameters examined include electrical conductivity (EC), pH, temperature, turbidity, dissolve oxygen (DO), chloride, total hardness, alkalinity, nitrate, calcium, magnesium, phosphate, phosphorous, sulphate, sodium, potassium, fluoride, bicarbonate, nitrate-nitrogen, nitrite, copper, iron and zinc; and Microbiological parameters include Coliforms, E-coli and Faecal Strep. The results of analysis shows significant concentration of physicochemical and microbiological parameters in the samples of water analyzed according to Nigerian Standard for Drinking Water Quality thereby makes the water unsafe for drinking. Drinking from these sources of water will pose serious health risk to the people of Piwoyi Community. Therefore, the study helps to identify the contaminated locations as well as assist to follow emerging remedial measures toward controlling the contamination source in the community. It also recommends continuous monitoring and enforcement of environmental violations, aggressive sensitization on water sanitation and hygiene; adequate purification of water at domestic level; and government support on potable water supply and establish reasonable management strategies for sustainable water quality protection toward protecting public health.
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Open Access July 30, 2021

Air of Uncertainty from Pollution Profiteers: Status of Ambient Air Quality of Sawmill Industry in Ilorin Metropolis, Kwara State, Nigeria

Abstract We can’t stop breathing, but we can do something about the quality of air that we breathe. Clean fresh air is indispensable ingredient for a good life quality. Individuals poses the right towards expecting that the breathed air will not harm people. Thus, fighting air pollution will not only improve health outcomes, productivity, and well-being, it’s also essential toward reducing the emissions of [...] Read more.
We can’t stop breathing, but we can do something about the quality of air that we breathe. Clean fresh air is indispensable ingredient for a good life quality. Individuals poses the right towards expecting that the breathed air will not harm people. Thus, fighting air pollution will not only improve health outcomes, productivity, and well-being, it’s also essential toward reducing the emissions of greenhouse gas as well as fighting climate change. For examples, a third of the global population is at risk from unhealthy of ambient air pollutants concentrations, with the loss of approximately 6.4 million healthy-life-years attributed specifically to chronic exposure to ambient particulate matter. Expert panels have consistently rated air pollution as a greater health hazard than water pollution. Pollution of air is the leading source of unexplained and undiagnosed diseases, besides have remained associated with a variety of serious human health risks, and in fact, a threshold has not been established under which these pollutants exert no adverse effects. This study evaluates ambient air quality at major sawmill sites in Ilorin Metropolis, Kwara State, Nigeria. “Measurements of Air pollution were accurately carried out using direct reading, automatic in situ gas monitors; Hand held mobile multi-gas monitor with model AS8900 [Combustible (LEL), and Oxygen (O2)], BLATN with model BR – Smart Series air quality monitor (PM10, Formaldehyde) and air quality multimeter with model B SIDE EET100 (Dust (PM2.5), VOC, Temperature and Relative Humidity)”. The outcomes disclosed among others, the average concentrations of CO, O2 as well as other measured parameters for instance formaldehyde (HcHo) etc., they are also consistently low as well as within acceptable range in terms of National as well as Global monitoring standards for air quality indices. However, there are few exceptions for instance the average volatile organic compounds (VOCs) concentrations, PM2.5, PM10 as well as Combustible (LEL) respectively, which are higher when compared to National and Global standards. This high figure is due to pollutant amount existing in the sawmills air environment resulting from input of influents from activities of the sawmill. However, as a result, air pollution in the city of Ilorin is found to be increasingly polluted and are of major health concern because of their synergistic action. Due to the high evidences and values, it can lead to a remarkable rise in over-all figure of hospital visits/ patients’ admissions with acute respiratory illnesses as soon as air pollutants level remained high. Hence, there is the need for an aggressive control of ambient air pollution.
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Open Access July 21, 2021

Earth Observation Techniques to Assess Water Quality Monitoring in the Murray Darling Basin of Australia

Abstract The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based [...] Read more.
The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll - Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projects.
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Open Access October 15, 2022

Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity

Abstract The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even [...] Read more.
The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.
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Open Access December 27, 2021

Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent Systems

Abstract Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. [...] Read more.
Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.
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Review Article
Open Access October 29, 2022

Neural Networks for Enhancing Rail Safety and Security: Real-Time Monitoring and Incident Prediction

Abstract The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the [...] Read more.
The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the improvements in monitoring can be achieved using artificial intelligence algorithms such as neural networks. Neural networks have been used to achieve real-time incident identification in monitoring the track quality in terms of classifying the graphical outputs of an ultrasonic system working with the rails and track bed, to predict incidents on the rail infrastructure due to transmission channels becoming blocked, and also to attempt scheduling preemptive and preventative maintenance. In terms of forecasting incidents and accidents on board the trains, neural networks have been used to model passenger behavior and optimize responses during a train station evacuation. In tackling the incidents and accidents occurring on rail transport, we contribute with two methodologies to detect anomalies in real-time and identify the level of security risk: at the maintenance level with personnel operating along the railways, and onboard passenger trains. These methodologies were evaluated on real-world datasets and shown to be able to achieve a high accuracy in the results. The results generated from these case studies also reveal the potential for network-wide applications, which could enhance security and safety on railway networks by offering the possibility of better managing network disruptions and more rapidly identifying security issues. The speed and coverage of the information generated through the implementation of these methodologies have implications in utilizing prediction for decision support and enhancing safety and security on board the rail network.
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Open Access November 16, 2023

Zero Carbon Manufacturing in the Automotive Industry: Integrating Predictive Analytics to Achieve Sustainable Production

Abstract This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the [...] Read more.
This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.
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Open Access December 27, 2022

Building Scalable and Secure Cloud Architectures: Multi-Region Deployments, Auto Scaling, and Traffic Management in Azure and AWS for Microservices

Abstract The last few years have seen an increased adoption of cloud infrastructure, which has in turn led to a growth in large-scale distributed architectures in data centers to accommodate cloud resource elasticity and resiliency better. Selecting the right approach to build secure, scalable, and reliable cloud infrastructure within a budget is always a challenge. This text focuses on offering practical [...] Read more.
The last few years have seen an increased adoption of cloud infrastructure, which has in turn led to a growth in large-scale distributed architectures in data centers to accommodate cloud resource elasticity and resiliency better. Selecting the right approach to build secure, scalable, and reliable cloud infrastructure within a budget is always a challenge. This text focuses on offering practical solutions for designing and building a secure, scalable, and reliable cloud-based infrastructure where auto-scaling and multi-region deployments are the two key approaches to offer high availability. It covers designing secure and scalable microservices using cloud platforms. The content will provide an understanding of public cloud architecture, the design of microservices running on the cloud, and also the design patterns used in the cloud era. With real-world examples, you will learn how microservices can enable scalable distributed systems. Furthermore, you will be walked through multi-region deployments, auto-scaling, and traffic management in cloud environments, using a sample environment setup and useful tips and tricks for monitoring. Finally, you will see a mock implementation of cloud infrastructure on-premise for a private cloud or single-node cloud. By the end of this text, you will be able to build, manage, and deploy a highly scalable and reliable cloud-ready solution [1].
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Open Access November 16, 2022

AI-Driven Automation in Monitoring Post-Operative Complications Across Health Systems

Abstract Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health [...] Read more.
Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].
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Open Access December 27, 2019

The Role of Neural Networks in Advancing Wearable Healthcare Technology Analytics

Abstract Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical [...] Read more.
Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical healthcare technology, crawling through some industry giants. Wearable Healthcare Technologies are becoming more popular every day. These technologies facilitate collecting, monitoring, and sharing every vital aspect of the human body necessary for diagnosing and treating an ailment. At the advent of global digitization, health data storage and systematic analysis are taking shape to ensure better diagnostics, preventive, and predictive healthcare. Healthcare analytics powered by neural networks can significantly improve health outcomes, maximizing individuals' potential and quality of life. The breadth and possibilities of connected devices are getting wider. From personal activity monitoring to quantifying every bit of health statistics, connected devices are making an impact in measurement, management, and manipulation. In healthcare, early diagnosis could be a lifesaver. Data analytics can help in a big way to make moves and predictions to save lives. We are in another phase of the digitization era, "Neural Network and Wearable Healthcare Technology Analytics." A neural network could be conceived as an adaptive system made up of a large number of neurons connected in multiple layers. A neural network processes data in a similar way as the human brain does. Using a collection of algorithms, for many neural networks, objects are composed of 'input' and 'output' layers along with the layers of the neural network.
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Open Access December 27, 2019

Data Engineering Frameworks for Optimizing Community Health Surveillance Systems

Abstract A Changing World Demands Optimized Health Surveillance Systems – and How Data Engineering Can Help There is a growing urgency to manage the public health and emergency response practices effectively today, in light of complex and emerging health threats. Fortunately, a host of new tools, including big and streaming data sources, methods such as machine learning, new types of hardware like [...] Read more.
A Changing World Demands Optimized Health Surveillance Systems – and How Data Engineering Can Help There is a growing urgency to manage the public health and emergency response practices effectively today, in light of complex and emerging health threats. Fortunately, a host of new tools, including big and streaming data sources, methods such as machine learning, new types of hardware like blockchain or secure enclaves, and means of data storage and retrieval, have emerged. But, with these innovations comes a grand challenge: how to blend with, and adapt them to, the traditional public health practices. The long-in-place infrastructures and protocols to protect and ensure the welfare of communities are in need of change, or at least update, to enhance their marked longevity of impact directly on the health outcomes and community wellbeing they were designed to fortify. It is in this vein that the essay is written and composed. The investigation in this essay is to query what, particularly, might be the aspects and influences of the emerging veritable cornucopia of new data engineering frameworks that are either being developed specifically for health surveillance and wellness, or are available to be co opted from devices and services already thriving in the current market and research milieu. Knowing what these ways may be could well aid in molding their uptake and spread, ensuring their beneficial impacts on those communities who stand to gain the most. The essay is divided into several key segments. After this introduction, section two details the research methods. In the section that follows, the maximum health outcome potentials of these novel frameworks are reviewed. Part four of the essay takes a more critical approach, addressing how the success of these methods may be hindered and future research avenues. Lastly, the concluding information suggests some actions to take to aid best suit the implementation of these ways, and suggests some thoughts for further research after the completion of these inquiriestrand [1].
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Open Access December 21, 2016

Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media

Abstract The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) [...] Read more.
The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.
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Open Access December 27, 2021

Advancing Healthcare Innovation in 2021: Integrating AI, Digital Health Technologies, and Precision Medicine for Improved Patient Outcomes

Abstract Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial [...] Read more.
Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].
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Open Access December 27, 2020

Enhancing Regulatory Compliance in Finance through Big Data Analytics and AI Automation

Abstract This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without [...] Read more.
This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.
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Open Access December 27, 2020

Optimizing Unclaimed Property Management through Cloud-Enabled AI and Integrated IT Infrastructures

Abstract With unclaimed property assets reaching record levels, businesses have become, in some cases, overwhelmed and hamstrung by stagnant, unoptimized processes. That sentiment is compounded by ever-evolving regulatory changes, resulting in organizations struggling to hit compliance deadlines while delivering an optimal claimant experience. Often, early systems had periods of short-term success but are [...] Read more.
With unclaimed property assets reaching record levels, businesses have become, in some cases, overwhelmed and hamstrung by stagnant, unoptimized processes. That sentiment is compounded by ever-evolving regulatory changes, resulting in organizations struggling to hit compliance deadlines while delivering an optimal claimant experience. Often, early systems had periods of short-term success but are on the verge of obsolescence, resulting in stressed workflows and cumbersome integrations. Deploying an integrated IT infrastructure, supported by cloud-enabled AI, represents the quickest path to modernizing unclaimed property management. A fully integrated IT infrastructure is crucial to optimize the management of unclaimed property [1]. When lone solutions exist across an organization, companies miss out on automation opportunities generated through the interconnectedness of systems and data. AI presents organizations with the opportunity to traverse these gaps, enabling a vast library of applications to improve the perturbed workflows of unclaimed property teams. Automated data extraction, document comparison, fraudulent claim detection, and workflow completion analysis are just a few popular applications well suited for the unclaimed property space. In addition to the lagging technology currently deployed by many organizations, the unclaimed property landscape itself is evolving. Compliance issuance, asset availability, rates, the ability to collect fraudulently posted claims, and the claimant experience have all become hot-button items that are now front of mind for regulation agencies and businesses alike. Issuing duplication letters in a compliant manner, accommodating claimant inquiries regarding held assets, and managing, processing, and understanding the operational impact of rate changes are vexing problems many organizations now find themselves playing catch-up to address. The opportunity posed by cloud-enabled AI is furthered by economic, regulatory, and report cycle pressures on unclaimed property teams to do more with the same size or fewer resources. It’s now no longer simply a case of hitting the audit date deadline and checking off a box but an emerging priority for businesses at all sides of the market, from Fortune 500 to mid-market firms. In-house shared service teams are comfortable in areas of monitoring and curating business data; however, unclaimed property is an unknown territory with a learning curve, compliance gaps, and operational holes that, if ignored, stand to scale up exponentially. The combined fallout from regulatory changes and the recent pandemic have only made the situation riskier, with increased volatility in balancing time-sensitive tasks against stringent regulatory deadlines and growing claimant outreach.
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Open Access December 24, 2022

Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning

Abstract Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing [...] Read more.
Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.
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Open Access December 09, 2021

Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications

Abstract The banking sector has shown a strong interest in scaling out and utilizing the microservices architectural pattern within their payments domain, not only to manage increased transaction volumes, but also for compliance and risk-related control. Financial organizations are adopting containerization technologies like Kubernetes and Docker to align with the microservices paradigm. Containerization [...] Read more.
The banking sector has shown a strong interest in scaling out and utilizing the microservices architectural pattern within their payments domain, not only to manage increased transaction volumes, but also for compliance and risk-related control. Financial organizations are adopting containerization technologies like Kubernetes and Docker to align with the microservices paradigm. Containerization provides the foundation for automation and operational excellence of microservice-based applications by enabling continuous deployment and automated build-test-release cycles. However, deploying a Kubernetes cluster and the services it hosts in production is not sufficient to guarantee a secure and compliant operating environment. Kubernetes itself should be secured to protect workloads, and risks associated with the services being deployed must be managed continuously.
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Open Access December 27, 2021

Best Practices of CI/CD Adoption in Java Cloud Environments: A Review

Abstract The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality delivery of software. These DevOps methods are automated, and the code development, testing, and deployment processes are streamlined, which reduces the risk of integration, enhances productivity, and minimizes [...] Read more.
The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality delivery of software. These DevOps methods are automated, and the code development, testing, and deployment processes are streamlined, which reduces the risk of integration, enhances productivity, and minimizes human labor. To implement CI/CD, Java cloud applications can utilize cloud-native services such as AWS Code Pipeline, Azure DevOps, and Google Cloud Build, as well as tools like Jenkins, GitLab CI/CD, GitHub Actions, CircleCI, Travis CI, and Bamboo. Basic concepts of CI/CD include automation, regular integration, testing, intensive testing, constant feedback, and process improvement. Some of the major pipeline phases include deployment, monitoring, testing, artefact management, build automation, and source code management. Despite clear benefits, challenges remain, including infrastructure complexity, dependency management, test reliability, and cultural barriers, particularly in large-scale or enterprise Java projects. This work provides a thorough analysis of CI/CD procedures and resources, including frameworks, best practices, and challenges for Java cloud applications. It highlights strategies to optimize adoption, improve software quality, and accelerate delivery cycles.
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Open Access December 18, 2020

Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking

Abstract The global banking industry is subject to ever-growing regulatory requirements, designed to prevent financial tour de force repeats tearing through the world economy. The changes are incomplete and new rules being enacted each year. Implementing and executing these rules and regulations requires the guiding principles from senior management to reach the product desks in a clear and efficient way. [...] Read more.
The global banking industry is subject to ever-growing regulatory requirements, designed to prevent financial tour de force repeats tearing through the world economy. The changes are incomplete and new rules being enacted each year. Implementing and executing these rules and regulations requires the guiding principles from senior management to reach the product desks in a clear and efficient way. Technical systems must implement these rules. Differences in interpretation, implementation, and warnings must be addressed during normal operations. Most importantly, systems must provide warning alerts to management and the business as early as possible, to allow for proper handling. History has shown that the importance of early warnings has been overlooked repeatedly. Real-time capabilities are essential to meet these business needs. Organizations must therefore be ready to embrace a next-generation architecture that enables real-time alert and warning generation. Systems based on a streaming architecture, combined with systems enabling the real-time flow of events between domains supported by orchestration, provide a solid foundation to meet these requirements.
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Open Access December 27, 2020

Improving Data Quality and Lineage in Regulated Financial Data Platforms

Abstract Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data [...] Read more.
Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data quality is captured and monitored, examines the artefacts that record data lineage, reviews the roles and responsibilities of staff who implement the necessary processes, and maps areas where improvements are possible. The internal organization and processes of regulated data platforms are shaped not only by the capabilities prescribed by their technical architecture but also by the regulatory regimes under which they operate. These mandates, in particular, require rigorous examination of four aspects of data quality — accuracy, completeness, consistency, and timeliness — and detailed documentation of how data arrives in its final form in the repository. Although data monitoring, alerting, assessment, and remediation are well established, provenance capture remains an area ripe for further investment.
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Open Access December 26, 2021

Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics

Abstract Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine [...] Read more.
Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.
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Open Access December 27, 2023

MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms

Abstract Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, [...] Read more.
Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.
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