Smartphone use among adolescents has surged globally, reshaping communication and learning patterns, especially in developing countries. However, the implications of such digital habits on students in rural or under-resourced areas remain underexplored. This study aims to examine the patterns of smartphone usage and its effects on learning among high school students in Tarutung, a developing region of North Sumatra, Indonesia. Utilizing a quantitative descriptive approach, data were collected from 358 students using structured questionnaires. The results show that 96.05% of students own personal smartphones regardless of socioeconomic background, with an average daily usage of 4 hours and 45 minutes. While 91.81% believe smartphones support their learning, 25.99% report declining academic performance. Alarmingly, 20.62% of students admitted involvement in cyberbullying activities, highlighting a critical digital risk impacting the school environment and student well-being. The study concludes that although smartphones offer educational benefits, their misuse can lead to negative academic, social, and psychological outcomes. This study recommends digital literacy curricula and structured cooperation between parents and educators to prevent risks while optimizing educational opportunities in smartphone use.

Background: Tobacco use remains a major global health concern, and understanding the factors that influence tobacco-related knowledge and support for tobacco control policies is critical for effective development of tobacco control policies that are accepted by the public. Objectives: This study introduces the rationale, design, methodology, and participants of the SMOKES Study (Study of Measurement of Knowledge and Examination of Support for tobacco control policies), which is conducted to evaluate tobacco use, tobacco-related knowledge and attitude, as well as support for tobacco control policies among college and university students. Methods: The SMOKES Study was designed to address significant gaps in literature by focusing on college and university students in a non-Western context. A multi-center, cross-sectional design was employed to collect data from a diverse sample of college and university students across different geographical provinces in Iran. The survey instrument incorporated a range of measures covering socio-demographic characteristics, university-related variables, family tobacco use status, personal tobacco consumption behaviors (including detailed assessments of cigarette, hookah, and electronic cigarette use), and attitudinal as well as knowledge-based assessments related to vaping. Support for tobacco control policies is also measured. Data were collected using an online survey that included self-administered questionnaires, enabling access to a large diverse sample. This study may be used to determine the prevalence of ever and current use of cigarettes, electronic cigarettes, and hookah, as well as examining the correlates of single, dual, and poly-tobacco use. The study also aims to assess the role of social determinants, attitudes, and ethnic/geographic differences in shaping these outcomes. Results: The study sample consisted of 2403 college and university students, including undergraduates enrolled in different academic programs from all faculties and disciplines. Participants were drawn from universities across 15 provinces, and 11 ethnic groups, ensuring a heterogeneous sample with respect to socio-demographic background, ethnicity, and institutional affiliation. This diversity enhances the generalizability of the findings and allows for the exploration of subgroup differences in tobacco use patterns and policy support. Conclusions: The SMOKES Study offers a framework for examining tobacco-related knowledge and the acceptability of tobacco control policies among a key part of the population, being college and university students. By providing detailed insights into the prevalence and correlates of tobacco knowledge, attitude, use, as well as the tobacco control policy support, the study lays the groundwork for tailored public health interventions and more effective tobacco regulation strategies particularly for college campuses in a non-Western setting.

Background: As a central component of the brain's reward system, nucleus accumbens (NAcc) plays a crucial role in reward salience and substance use behaviors. Changes in the NAcc are also relevant to higher rates of substance use of youth and adults from low-income backgrounds. Although resting-state functional connectivity (rsFC) of the NAcc provides valuable insights into the neural mechanisms underlying reward processing and the propensity for self-reported reward salience and substance use, research exploring the association between NAcc rsFC and brain networks beyond the default mode network (DMN) and prefrontal cortex (PFC) is limited. Objective: To investigate the role of the resting-state functional connectivity of the NAcc with the cingulo-opercular network, sensorimotor mouth network, and sensorimotor hand network in the association between socioeconomic status, self-reported reward salience, and future substance use. Methods: Data were obtained from the Adolescent Brain Cognitive Development (ABCD) study. NAcc rsFC with the cingulo-opercular network, sensorimotor mouth network, and sensorimotor hand network was assessed at baseline. Socioeconomic status was measured using family income. Self-reported reward salience was assessed using validated psychometric scales. Substance use outcomes were tracked longitudinally over the study period. Structural Equation Modeling was employed to examine the covariances between family income, NAcc rsFC, reward salience, and subsequent substance use. Results: Higher baseline family income was positively associated with baseline NAcc rsFC (B = 0.092, p < 0.001) and negatively associated with baseline reward salience (B = -0.040, p = 0.036) and future substance use (B = -0.081, p < 0.001). Baseline NAcc rsFC was strongly and positively associated with reward salience (B = 0.734, p < 0.001) and future substance use up to age 13 (B = 0.124, p < 0.001). Additionally, baseline reward salience was positively associated with future substance use (Covariance = 0.176, p < 0.001). Conclusion: The findings suggest that NAcc rsFC with brain networks beyond the DMN or PFC may contribute to the links between low parental socioeconomic status, reward salience, and substance use risk. Expanding the understanding of NAcc rsFC provides new insights into the neural mechanisms underlying these associations. These results have important implications for developing targeted interventions aimed at preventing substance use, particularly among low-income youth with heightened reward salience. Further research is needed to explore causal pathways and moderating factors influencing these relationships.

Background: Low socioeconomic status (SES) has been linked to higher rates of tobacco and marijuana use initiation; however, the contributions of environmental and neurocognitive factors remain underexplored. This study investigates a potential pathway connecting low SES, fine particulate matter (PM2.5) exposure, brain functional connectivity, and inhibitory control to increased tobacco and marijuana use initiation among adolescents. Objectives: To examine the mediating roles of PM2.5 exposure, resting-state functional connectivity between the right pallidum and the ventral attention network (P-VAN rsFC), and inhibitory control in the relationship between low SES and tobacco and marijuana use initiation. Methods: Data were drawn from the Adolescent Brain Cognitive Development (ABCD) study to assess associations between baseline SES, baseline PM2.5 exposure (based on zip code), baseline P-VAN rsFC, baseline inhibitory control, and subsequent tobacco and marijuana use initiation. Mediation models were used to determine whether PM2.5 exposure and changes in P-VAN rsFC act as pathways linking low SES to diminished inhibitory control and subsequent substance use initiation. Results: Low SES was associated with higher PM2.5 exposure, which, in turn, was linked to alterations in P-VAN rsFC. These alterations were correlated with lower inhibitory control, which significantly predicted tobacco and marijuana use initiation over time. Inhibitory control partially mediated the relationship between low SES and substance use initiation, indicating a complex pathway influenced by environmental and neurocognitive factors. Conclusions: This study identifies a potential mechanism linking low SES to tobacco and marijuana use initiation through environmental and neurobiological pathways. Understanding how PM2.5 exposure and neurofunctional connectivity impact inhibitory control can provide valuable insights for developing targeted interventions to reduce substance use among adolescents in low SES environments.

Background: Although some neural mechanisms underlying socioeconomic status (SES) disparities are known, the role of brain-wide resting-state functional connectivity in these effects remains less understood. Aim: This study aims to identify brain-wide resting-state functional connectivity signatures that may mediate the effects of SES on body mass index (BMI) and blood pressure in children, using data from the Adolescent Brain Cognitive Development (ABCD) study. Methods: Data were drawn from the ABCD study, a large, diverse cohort of children aged 9-10. Pre-processed resting-state functional MRI data were used, and factor analysis was conducted to extract a whole-brain connectivity factor. The first factor, capturing the greatest variance in brain-wide resting-state connectivity, was selected for further analysis in a structural equation model (SEM). This connectivity factor was tested as a potential mediator of the relationship between SES (measured by parental education, family income, and neighborhood characteristics) and two indicators of cardiometabolic health: BMI and systolic blood pressure. Results: Factor analysis revealed a robust first factor that accounted for a significant proportion of variance in brain-wide resting-state functional connectivity. This factor was significantly associated with SES, indicating that children from lower SES backgrounds exhibited distinct connectivity patterns. Additionally, the factor was linked to both BMI and systolic blood pressure, suggesting its relevance to cardiometabolic health. Mediation analysis showed that this connectivity factor partially mediated the relationship between SES and both BMI and systolic blood pressure. Conclusions: Brain-wide functional connectivity may be a mediator of SES effects on BMI and blood pressure in children. The first connectivity factor provides a promising neural signature linking SES with cardiometabolic risk. Comprehensive brain-wide approaches to functional connectivity may offer valuable insights into how social determinants of health shape neural and physical development in childhood.

This study investigated the use of digital design as a tool for assessing attitudes among male sex offenders by exploring how artistic expression can reveal complex emotional landscapes related to their crimes. The study utilized digital paintings, a type of digital design that involves creating artwork using digital tools and software. Digital painting tools allowed the researcher to simulate traditional painting techniques, while benefiting from the flexibility and versatility of digital media. It is essential to comprehend and address offenders’ attitudes towards sexual crimes to create prevention and rehabilitation plans that work. The effectiveness of digital paintings that illustrate the consequences of sexual crimes on offender, victim and the offender’s family was examined in this study. The study examined whether exposure to these visual representations could result in a quantifiable decrease in supporting attitudes toward sexual crimes by comparing participants' pre-test and post-test attitudes using the Crime Pics II tool. There is little research on how visual cues can change offenders' attitudes and perceptions, despite the urgent need for creative evaluation techniques in this delicate field. This study adds to the continuing conversation on crime prevention and offender rehabilitation by demonstrating how well digital design can change perceptions of sexual crimes. A total of 61 male convicts of defilement and rape were purposively sampled for the study. The study utilized an art exhibition and Rapid Serial Visual Presentation format to repeatedly display digital paintings to participants aged 18-45. The artwork produced in both monochrome and polychrome schemes and in realist and surrealist styles was designed to elicit immediate emotional and cognitive responses. Qualitative and quantitative analyses of the participants' responses and reflections provided important insights into their attitudes, including sentiments of regret and guilt as well as a sophisticated awareness of the wider effects of their behaviour. Monochrome paintings tended to portray darker emotions, whereas polychrome paintings showed a wider range of emotions, including hope and recognition of the harm done. The findings demonstrate that digital design can effectively support offenders' emotional processing and self-reflection, demonstrating its potential as a helpful tool in rehabilitative and assessment contexts.

Epigenetic studies, which can reflect biological aging, have shown that measuring DNA methylation (DNAm) levels provides new insights into the biological effects of social environment and socioeconomic position (SEP). This study explores how race, family structure, and SEP (income to poverty ratio) at birth influence youth epigenetic aging at age 15. Data were obtained from the Future of Families and Child Wellbeing Study (FFCWS) cohort, with GrimAge used as a measure of DNAm levels and epigenetic aging. Our analysis included 854 racially and ethnically diverse participants followed from birth to age 15. Structural equation modeling (SEM) examined the relationships among race, SEP at birth, and epigenetic aging at age 15, controlling for sex, ethnicity, and family structure at birth. Findings indicate that race was associated with lower SEP at birth and faster epigenetic aging. Specifically, income to poverty ratio at birth partially mediated the effects of race on accelerated aging by age 15. The effect of income to poverty ratio at birth on DNAm was observed in male but not female youth at age 15. Thus, SEP partially mediated the effect of race on epigenetic aging in male but not female youth. These results suggest that income to poverty ratio at birth partially mediates the effects of race on biological aging into adolescence. These findings highlight the long-term biological impact of early-life poverty in explaining racial disparities in epigenetic aging and underscore the importance of addressing economic inequalities to mitigate these disparities. Policymakers should focus on poverty prevention in Black communities to prevent accelerated biological aging and associated health risks later in life. Interventions aimed at eliminating poverty and addressing racial inequities could have significant long-term benefits for public health. Future research should explore additional factors contributing to epigenetic aging and investigate potential interventions to slow down the aging process. Further studies are needed to understand the mechanisms underlying these associations and to identify effective strategies for mitigating the impact of SEP and racial disparities on biological aging.

The operational performance of Active Pharmaceutical Ingredients (API) supply chains often suffers from significant disruptions attributed to inherent vulnerabilities. Despite theoretical discussions, empirical evidence validating these vulnerabilities remains sparse. This study endeavours to empirically substantiate the vulnerabilities arising from dynamic disruptions within the pharmaceutical supply chain. Its primary goal is to discern actionable insights that can inform the development of robust resilience strategies capable of effectively mitigating such disruptions. This study investigates vulnerabilities within the active pharmaceutical ingredient (API) supply chain in response to disruptions. Despite theoretical insights, empirical evidence validating these vulnerabilities remains limited. Through empirical analysis, this research aims to identify and elucidate the specific vulnerabilities exacerbated by dynamic disruptions in the API supply chain. The findings are intended to inform the development of resilient strategies capable of mitigating the impact of disruptions on pharmaceutical supply chains.

Myeloproliferative neoplasms (MPNs) are hematological disorders affecting myeloid stem cells. They are classified as Philadelphia (Ph) chromosome positive-chronic myeloid leukemia, and Ph-negative polycythemia vera, essential thrombocythemia, primary myelofibrosis, chronic neutrophilic leukemia, chronic eosinophilic leukemia, juvenile myelomonocytic leukemia, and MPN unclassifiable. This review is mainly focused on the Ph-negative MPNs namely, PV, ET, and PMF. These affect both males and females with a slight male predominance, with patients mainly presenting in the seventh decade. Patients often present with thrombotic events resulting in complications that lower survival rates. The major driver mutations that have been identified in MPNs are JAK2 Exon 14, JAK2 Exon 12, MPL Exon 10, and CALR Exon 9. The importance of these driver mutations gives due recognition to their inclusion into the 2022 diagnostic criteria of the MPN WHO Classification. However, other non-driver mutations have also been reported, especially in triple-negative cases. These mutations lead to downstream constitutive activation of the JAK/STAT signaling pathway, as well as the MAPK, and PI3K/Akt pathways. Insights into the molecular pathogenesis of MPN and its association with JAK2, CALR, and MPL mutations have identified JAK2 as a rational therapeutic target. Thus, as an approach to MPN therapy, JAK2 inhibitors, such as ruxolitinib, have been shown to effectively inhibit JAK2, and are currently in clinical trials in combination with other drug classes. This review comprehensively examines the molecular markers of the main Ph-negative MPNs, as well as diagnosis and treatment options.

This study examines the influence of socio-demographic factors on work-family conflict among management staff in star-rated hotels in the Accra Metropolis. The research aims to explore how variables such as gender, age, marital status, and educational background impact the experiences of work-family conflict among hotel managers. A quantitative research design was employed, and data were collected through structured questionnaires distributed to a purposive sample of hotel managers. The findings reveal significant associations between socio-demographic characteristics and work-family conflict, highlighting the complexities of balancing work responsibilities with family obligations in the hospitality industry. The implications of the study underscore the importance of tailored interventions to support the well-being of hotel managers, while the recommendations emphasise the implementation of work-life balance programs, support for family obligations, promotion of diversity and inclusion, and continuous training and development. It is recommended that hotel organisations create a supportive work environment that enhances the job satisfaction and productivity of their management staff. This study contributes valuable insights to the existing literature on work-family conflict in the hospitality sector and provides a foundation for future research endeavours in this area.

Delirium is a major complication most commonly observed in patients with advanced cancer. However, despite its prevalence, the early diagnosis, management, and prevention of this condition have not seen significant progress. Aim of this research is to provide insights into the prevalence of delirium, the optimization of interventions for managing delirium symptoms, their effectiveness and the impact of underlying factors on the reversibility of delirium in advanced cancer patients receiving palliative care. The review involved systematic searches of relevant databases including MEDLINE, CINAHL, ProQuest Nursing and Allied Health, and PsychInfo using refined search terms. Eight publications out of 614 studies originally searched were selected and critically reviewed. Their quality was assessed using Joanna Briggs Institute's Critical Appraisal Tool for Case Series. Data abstraction and content analysis were performed to synthesize the findings. Delirium is prevalent among advanced cancer patients in palliative care, with rates ranging from 10.3% to 24.1%. Pharmacotherapy and non-pharmacological interventions showed effectiveness in reducing delirium symptoms. Delirium was found to be reversible through palliative care interventions, antipsychotic medications, and exercise therapy. Effective delirium management is crucial in improving the quality of life of cancer patients. This review emphasizes the importance of subtype-specific treatments, standardized guidelines, and long-term follow-up studies. Implementing evidence-based individualized approaches to delirium management can optimize treatment efficacy and clinical outcomes in patients as well as improve the quality of care. Tailored interventions, standardized protocols, and further research are hereby recommended.

Both crucial success and critical failure factors are included in the current review work. The method relies on creating surveys to collect optional data. It describes the terms that are used to obtain research papers on the ERP deployment in Enterprise Business from databases and scholarly research. In order to enhance the quality of papers, it also includes the consideration and restriction criteria. At that time, a thorough audit of the available papers is conducted to determine the impact of ERP use in Enterprise Business. Important elements are found that determine whether ERP deployments are successful or unsuccessful, as well as how they actually affect Enterprise Business (insert actual success and failure variables here aside from impact). The time span during which research publications have been evaluated limits the scope of the study presented in this paper. One implicit drawback is that it only considers the state of the art in the field of study, without taking into account an empirical investigation. Nevertheless, its findings may prove advantageous, and the directions for future research aid in expanding the field of study. This work advances the body of knowledge regarding the potential benefits and drawbacks of ERP adoption for small and medium-sized enterprises. It uses a secondary data collection strategy to identify important success factors, important failure factors, and their impact. The insights will assist Enterprise Business, Enterprise Business' stakeholders, and ERP service providers in understanding the causes of success or failure and in taking the appropriate action.

The role of enterprise applications in pharmaceutical industries is driving the digital transformation of various critical processes, and one process benefiting from this innovation is pharmaceutical drug traceability. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology as an enterprise application has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding enterprise applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These enterprise applications can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this enterprise technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials.

The present study examines a sample of 140 juveniles. Study aimed to explore the relationship between interparental conflicts, irrational beliefs and mental health. Sample of the study was taken from borstal jail Faisalabad and district jail Lahore. Perceived interparental conflicts scale (CPIC), Irrational beliefs inventory (IBI) and mental health inventory (MHI) was use Results indicated that psychological distress has significant positive relationship with interparental conflicts and mental health. Furthermore, it was found that interparental conflicts significantly predict irrational beliefs. The study will give insights into as what type of interparental conflicts predicts irrational beliefs and different mental health problems in juveniles. It may help clinical psychologists/mental health practitioners to develop appropriate ways to manage interparental conflicts, irrational beliefs and mental health problems. Findings of the study may help mental health practitioners to develop appropriate assessment and treatment programs.

Factory owners and vehicle manufacturers increasingly opt for predictive analytics to inform their decisions. While predictive analytics have been proven to provide insights into the initiation of maintenance measures before a machine actually fails, the right models and features could have a significant impact on the budget spent and resources allocated. This means that financially oriented questions need to at least partially guide the decisions in the planning phase of data science projects. Data-driven approaches will play an increasingly important role, but only a few of the firms that were confident performed logistic regression models for predictive maintenance. Also, from the available knowledge, data-driven classification models connecting vehicle component failures and the occurrence of delays at the assembly line have not been published. This paper utilizes a real-world data-driven approach using classification models in predictive analytics by vehicle manufacturers and thereby links the financial implications of such data science projects to their results. We expand the existing literature on predictive maintenance and possess a unique dataset of newly launched series of vehicles, presented as-is. Our research context is of interest to researchers and practitioners in the automotive industry that manage and plan the final vehicle assembly with just-in-time principles, factoring the consequences of component failures on the assembly process. Key findings of this paper highlight that while minor tweaking of the models is possible, their potential input in decision-making processes for budget optimization is limited.

Self-service business intelligence (BI) platforms have become essential applications for exploring, analyzing, and visualizing business data in various domains. Here, we envisage that the business intelligence platform will perform automatic and autonomous data analytics with minimal to no user interaction. We aim to offer a data-driven, intelligent, and scalable infrastructure that amplifies the advantages of BI systems and discovers hidden and complex insights from very large business datasets, which a business analyst can miss during manual exploratory data analysis. Towards our future vision of autonomous analytics, we propose a collective machine learning model repository with an integration layer for user-defined analytical goals within the BI platform. The proposed architecture can effectively reduce the cognitive load on users for repetitive tasks, democratizing data science expertise across data workers and facilitating a less experienced user community to develop and use advanced machine learning and statistical algorithms.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

The current financial services sector is realising considerable changes in its operations due to development in technology and embracing of digital platforms. This evolution is changing the established concepts of business, consumers and channels of delivery of services. Financial services firms are changing the way they work through digital transformation due to developments in technology, changes in customer needs, and an increase in emphasis on sustainability. Understanding the opportunities, risks, and new trends in digital transformation is the focus of this paper. Opportunities include efficient real-time decision-making processes, increased transparency and better process controls, which are balanced by the threats of change management, dubious organization-technology fit, and high implementation costs. The study also examines recent advancements, including the application of machine learning and artificial intelligence, developments in mobile and online banking, integration of blockchain, and increasing focus on security and personalised banking. A literature review yields some findings from different studies on rural financial services, the evolution of the blockchain, drivers of digital transformation, cloud-based learning approaches, and emerging sustainability practices. All of these results suggest that more strategic planning, analytics, and more focus on ensuring that organisational objectives are met with transformations should be pursued. Hence, this research findings add to the existing literature in determining how innovative and digital technologies are likely to transform the financial services sector and advance sustainability.

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

Generative AI refers to deep learning technology that can automatically produce original text, images, audio, video, and other outputs. With its emerging capabilities, Generative AI can radically change the dynamics of key operational processes in most industries. In this document, we illustrate how it is possible to integrate Generative AI technologies into the Financial Reporting System (FRS) of a corporation. The integration will allow the FRS to deliver on demand concise and lucid insights to its associated users on what is happening in the corporation and different aspects of the corporation performance assessment, such as its liquidity, solvency, profitability, organizational structure, and share buy back decision. The integration will also facilitate the delivery of what-if analyses associated with different strategic and tactical decisions taken by the corporation management, such as capital budgeting and profit distribution decisions. The unique added value of several attributes of these insightful analytics is automating the responses to ongoing requests of the FRS users on the corporation. Generative AI capabilities are rapidly expanding. The integration can be applied not only for the corporate FRS but any FRS at the national or global levels delivered by a central bank or an accounting standards setter. Any of these FRS can be made into a unique “hub” for the integrated Generative AI technologies. An equally innovative possible generalized integration could put any corporate process at the center and its supporting FRS tasks and deliverables in its periphery.

Crime is a serious and widespread problem in their society, thus preventing it is essential. Assignment. A significant number of crimes are committed every day. One tool for dealing with model crime is data mining. Crimes are costly to society in many ways, and they are also a major source of frustration for its members. A major area of machine learning research is crime detection. This paper analyzes crime prediction and classification using data mining techniques on a crime dataset spanning 2006 to 2016. This approach begins with cleaning and extracting features from raw data for data preparation. Then, machine learning and deep learning models, including RNN-LSTM, ARIMA, and Linear Regression, are applied. The performance of these models is evaluated using metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RNN-LSTM model achieved the lowest RMSE of 18.42, demonstrating superior predictive accuracy among the evaluated models. Data visualization techniques further unveiled crime patterns, offering actionable insights to prevent crime.

This study aims to achieve a new understanding of how, why, and when consumer behavior is shaped, enacted, and experienced inside and across integrated digital ecosystems related to large-scale trackable goods, all in service of helping marketers optimize their business performance in the new economy. The pioneering understanding begins by exploring what motivates the choices of a homogeneous group of consumers to organize their consumption of national and store brand varieties of consumer package goods in a certain manner. Thereafter, the essay explores how, if at all, the other digital activities of consumers across various product-related digital spaces and on various platforms build expertise and interest in these products such that they exert an effect on the purchase choices for these products. The essay then advances to asking how online information seeking, in various product-related digital spaces, on various platforms, and from various sources, and taking place at various points in the purchase journey affects online-offline dynamics in purchasing these products. Thereafter, the research examines how paid digital communication in various product-related digital spheres and forms, enabled by consumer advertising engagement on various platforms, boosts the offline sales of these products. Finally, by employing a new methodology that combines consumer scanning data, self-reported online activity data, and transaction data collected from an ad-tech partner, the research presents a fresh set of marketing action levers and performance outcomes on chosen products. Along the way, progress is made on four under-investigated topics in the advertising literature – the role of consumer actors and their expertise in the online-offline purchasing dynamics for ads, advertising engagement, consumer digital spaces, and consumer digital activity investment.

While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. To this end, in this paper authors present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders [1]. Large information databases on various public issues exist, but their usage for public policy formulation and impact analysis has been limited so far, as no cloud-based service ecosystem exists to facilitate their efficient exploitation. With the increasing availability and importance of both public big and traditional data, the need to extract, link and utilize such information efficiently has arisen. Current data-driven web technologies and models are not aligned with the needs of this domain, and therefore, potential candidates for big data, cloud-based and service-oriented public policy analysis solutions should be investigated, piloted and demonstrated [2]. This paper presents the conceptual architecture of such an ecosystem based on the capabilities of state-of-the-art cloud and web technologies, as well as the requirements of its users.

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.