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.

A man in his 60s with multiple vascular comorbidities presented with sudden, painless vision loss in one eye. Although he had a high risk for atherosclerotic events, initial evaluation for stroke was negative for acute ischemia, but found to have markedly elevated inflammatory markers. Accordingly, giant cell arteritis was investigated and Ophthalmologic findings and fulfillment of the 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria supported the diagnosis of giant cell arteritis, despite a negative temporal artery biopsy. Management included high-dose glucocorticoids and delayed tocilizumab initiation due to the need for multiple vascular surgeries. Vision loss was irreversible, but systemic symptoms resolved and vascular interventions were successful. This case highlights the diagnostic and management complexities of biopsy-negative giant cell arteritis in patients with severe atherosclerotic vascular disease, emphasizing the importance of clinical judgment and established classification criteria when imaging and biopsy results are inconclusive.

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.

Extracurricular activities and academic performance are connected in every aspect of the education system. Daffodil International University is one of the top universities in Bangladesh that focuses on student improvement through extracurricular activities. Extracurricular activities help students improve skills like leadership, teamwork, and analytical abilities. Do extracurricular activities help English as a Foreign Language (EFL) students improve their academic performance? This evaluation aims to find out this question among Daffodil International University students. The study focused on both qualitative and quantitative data. Therefore, the data analysis followed a mixed method. The quantitative data focused on the students' participation in extracurricular activities. Respectively, the comparison between their participation and EFL course improvement. On the other hand, the qualitative data focused on the interviewee's experience. However, it's been proven that though extracurricular activities help students improve their other soft skills, they actually don't have as much impact on improving their EFL course curriculum performance.

A persistent problem in the pharmaceutical industry that has existed for centuries is the prevalence of counterfeit drugs, and the World Health Organization (WHO) estimates that millions of people are affected by this issue each year. In particular, 4 out of 10 drugs in poor or underdeveloped countries may be adulterated, which can lead to severe adverse events. To address this problem, many countries have implemented regulatory compliance measures such as pharmaceutical drug serialization, which requires the unique identification of every drug package manufactured. This enables tracking and tracing of individual packages throughout the supply chain and helps to prevent counterfeit drugs from entering the market. In this paper, we conduct a systematic review of the serialization process evaluation and its impact on the pharmaceutical industry. We discuss the benefits of pharmaceutical drug serialization, including its ability to improve drug security and reduce adverse events and investigations. We also examine the challenges associated with implementing serialization processes and the regulatory requirements necessary for compliance. Finally, we explore the various tracking and tracing technologies used in serialization processes and their effectiveness in preventing the distribution of counterfeit drugs. Overall, this paper highlights the importance of pharmaceutical drug serialization in ensuring the safety and efficacy of drugs in the healthcare industry, particularly in poor or underdeveloped countries where the problem of adulterated drugs is especially prevalent.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

The government of Mexico City carried out the introduction of palm trees in the 50s to increase the visual appearance of the main avenues. As a result of introduction of these exotic species, phytosanitary problems appeared. Currently there is scarce information on how to evaluate the health of these majestic monocots, in this sense, it is necessary to implement assessment scales to determine the health condition of the most frequently found species to advance on their care and management. The present study had the following objectives: 1) To determine the current state of health of palm trees by means of a scale of visual evaluation of the crown; 2) To know the diversity and structural characteristics of palm trees and 3) To determine the influence of composite variables on the ecosystem services such as the amount of shade provided. Health of palm trees were evaluated two times (dry and rainy seasons in 2022) on 35 transects of 200 m length. An imaginary circle divided into twelfths was overlapped on palm tree “crowns”, and through it, two absolute variables, Live Crown Ratio (LCR) and Crown Quality (CQ) were evaluated. Composite variables were also calculated. The 12/12 health scale adapted in the present study was useful. Four health categories were obtained for the Live crown ratio (LCR): 7.62% of the palm trees were in critical condition, 7.80% were in intermediate condition, 80.36% were in normal condition, and 4.20% were in excellent condition. Meanwhile, for crown quality (CQ), the percentages were 13.50%, 20.00%, 56.96% and 0.43%, respectively. The total height and “crown” diameter showed a positive correlation with the volume composite variables. The shadow area projected as an important ecosystem service increased as the health of the palm trees improved. This is the first study on palm trees health assessment in Mexico City.

Risks in Public Private Procurement mass housing project (PPP-MHP) initiatives are emerging and this requires early risk identification and allocation to achieve the goal and sustenance of the scheme. The study, being a follow-up of a Delphi survey, elicits the opinion of respondents on the probability of occurrence and severity of identified risks in PPP-MHPs in Nigeria. The study adopts a quantitative research design approach by administering structure questionnaire survey on identified PPP-MHPs partners in Abuja, Nigeria. Data analysis was performed using descriptive and inferential statistical tools such as Mean item score (MIS), standard deviation, and Kruskal Wallis analytical techniques with the aid of SPSS software packages. The findings show that all the listed risk factors were found to be extremely high, very high, high, or moderate in terms of occurrence while all the listed risk factors recorded a very high level of severity on the delivery of PPP-MHPs. The top ten (10) risk factors frequently associated with PPP-MHPs are non-availability of finance, high finance cost, non-involvement of the host community, poor execution of housing policies, corruption and lack of respect for law, wrong perception of housing need by low-income earners, Illegal title to land, land acquisition and site availability, level of demand for the mass housing projects and unstable value of local currency. The respondents differs significantly on 29 risk factors in terms of occurrence and 40 risk factors in term of severity. The study, therefore, recommends that risk management culture should be highly encouraged among the PPP Partners in the sector. The study intends to enumerate the rate of occurrence of some itemized risk factors and their severities on the delivery of PPP – procured mass housing projects in Nigeria and the need to bookmark these risk factors in ensuring the sustainability of the PPP mass housing scheme.

The study accessed the critical risk factors in public-private partnership (PPP)-procured mass housing project (MHP) delivery in Nigeria. The research design adopts a quantitative approach, using well-structured questionnaires distributed to stakeholders involved in PPP-MHPs i.e. consultants, in-house professionals, contractors, and the organized private sector (OPS) registered with PPP departments in the Federal Capital Territory Development Authority (FCDA) Abuja, Nigeria. The instrument relates to the background information of respondents and the risk peculiar to PPP-MHP. Sixty-three (63) risk factors were submitted for the respondents to rank using Mean Item score (MIS) for risk occurrence and its severity, while risk significance index (RI) was used to determine the risk impact. Fuzzy Synthetic Evaluation (FSE) method was subsequently applied to determine the risk criticality groups and the overall risk level in the sector. The fuzzy set theory deals with ambiguous, subjective and imprecise judgments peculiar to decision making in construction project risk assessment. It aims to provide a synthetic evaluation of an object relative to a fuzzy decision environment with multiple criteria that requires qualitative linguistic terms. The findings show that thirty-one (31) risk factors were critical in the sector while financial and micro-economic risk group is contributing most significantly to the overall risk level in PPP-MHPs in Nigeria. The top 10 risk factors in the sector include availability of finance, high finance cost, the unstable value of the local currency, lack of creditworthiness, influential economic events (boom/recession), high bidding cost, poor financial market, financial attraction to project investors, interest rate volatility, inflation rate volatility, corruption and lack of respect for the law, non-involvement of the host community and poor execution of housing policies. The implication for practice is that having known the risk group contributing most significantly to the overall risk level in PPP-MHPs, adequate financial and budgetary allocation should be made available before embarking on such venture so as to sustain the scheme in the country. The study is one of the recent researches conducted on housing, since the procurement option is novel in the sector. The study is of immense value to PPP actors in providing necessary information required to formulate risk response methods in minimize the identified risk impact sector.

There have been various concerns about the environmental impact of construction works. This generates a need to take a more proactive approach in evaluating the environmental impacts of construction operations and further explore ways to reduce the environmental impacts. Enormous opportunities exist within the building industry to achieve a reduction in greenhouse gas emissions. The aim of the study is to develop a framework for the evaluation of improvement opportunities for environmental impact for onsite and offsite building construction works using life cycle assessment (LCA) and value-stream-mapping concepts. Various tools for LCA exist; however, there is a need for the development of an LCA framework and improvement opportunities that can be localized to various communities to evaluate improvement opportunities for building construction. This study conducts a review of methods to evaluate the LCA of buildings on local construction sites. A procedure for establishing improvement opportunities is also developed. Based on the author’s knowledge and experience, including site visits, using value stream mapping (VSM) techniques, a conceptual framework of the present state map and future state map of residential construction works was developed. The study presents a procedure for the evaluation of improvement opportunities for the environmental impacts of construction operations.

The decision to choose between onsite and offsite construction is important in the effort toward sustainable construction. Offsite construction is often promoted as an environmentally friendly approach to construction operations. However, previous studies have shown that there is a lack of clarity on the environmental trade-offs between onsite and offsite construction. Factors that can affect the decision to build onsite or offsite include the availability of a local offsite manufacturing facility, the distance of the offsite factory to the final place of use, the proximity of the site to the local supply of material and labor, etc. This study provides a framework to apply the system dynamic modeling technique to evaluate how various factors can affect the environmental impact of the building construction phase (for onsite or offsite construction methods). The system dynamic model (using Vensim software) that was developed provides a platform that allows users to input variables such as the distance that is expected for transportation of labor, material, and equipment to both the onsite facility and the offsite construction location, factors associated with the use of equipment for construction, the distance needed for transportation of building panels or modules from the offsite facility to the final site, etc. Among other things, the model showed that an increase in the distance from the offsite yard to the final construction site increases the total impacts of transportation of completed modules. An increase in the number of trips for the transportation of material to the onsite construction location increases the total impact of onsite construction. In terms of the environmental impact of construction, none of the two methods of construction gives an absolute superiority over the other. The environmental performance of offsite and onsite depends on various associated factors. It is recommended that building practitioners review various factors that are peculiar to their projects to make an informed decision on the best construction methods.

Studies on the use of pesticides in southwest Nigeria have revealed a substantial rise in a variety of pesticide-related illnesses, including mental impairment and reproductive problems. Those who work in agriculture and are regularly exposed to pesticides are the most impacted. The World Health Organization (WHO) predicts that three million severe pesticide poisoning episodes occur globally each year, with at least 300.000 deaths and 99% of cases occurring in low- and middle-income nations. The effects of longer-term exposure to pesticides on health are not yet precisely estimated. Objectives: To this end, the objective of this study is to assess the knowledge of pesticide residues and common pesticides in grain-based food (brown and white beans, yellow and white maize, brown millet and rice) about pesticide use in selected markets of Southwest Nigeria. Methods: A total of 240 respondents were selected from four states (Ado-Ekiti, Ibadan, Osun and Ondo) with the aid of structured questionnaire and interview guide using purposive sampling technique. The markets were Oja-titun (market) Ile-Ife, Osun; Alesinloye Market Ibadan, Oyo; Oja Oba, Ado-Ekiti, Ekiti; and Oja Oba, Akure Ondo State. The identification and quantification of pesticide residues was done using a Varian 3800/4000 gas chromatograph mass spectrometer while 60 structured questionnaires were administered to respondents including food merchants, buyers/consumers as well as food vendors. Data were analysed using descriptive statistics. Results: The experiment was conducted from November 2020 to November 2021. Up to 50.4% never read instructions on pesticide application while a shared 24.8% read them always and occasionally. The number of respondents who never read the instructions were particularly high in Ekiti and Ondo, up to 52.5% of the respondents in Ekiti do not know whether or not they are exposed to pesticides during application. Also, majority of the respondents never attended any professional training on pesticide application, this amounted to 79.3%. Most people were not aware that unsafe pesticide application is harmful to human health. Similarly, a wide variety of symptoms were reported by respondents following pesticide application or consumption of grains, these included headaches and dizziness, itching and redness of the eyes, skin allergy, diarrhea, and stomach disorder, vomiting and loss of appetite, weakness of the body, asthma, permanent skin patches, shortness of breath, excessive sweating. Millet recorded the highest number of OCP residues while maize had the lowest. On the contrary, maize had the highest number of OPP and carbamate residues while millet had the lowest number of OPP residues. Meanwhile, maize, rice and beans had only one carbamate pesticide residue. Conclusions: According to the study's conclusions, farmers who can only get information from agricultural extension officials should receive urgent and immediate attention for raising awareness. Additionally, extensive IPM training programs must be created with the intention of disseminating precautions for protecting human health and a healthy agro-ecosystem. In order to find more effective pest management methods that utilize less pesticides, it is crucial to reevaluate the pesticide residues and common pesticides found in grains in the targeted markets. To reduce farmers' exposure to pesticides, it is also required to establish personal protective measures, special educational initiatives, and legislation promoting the use of safer pesticides. Significance and novelty: This study gives policymakers a comprehensive understanding of the ways that may be utilized to close the significant knowledge gap on pesticide residues in grains and provides an insight into the knowledge of pesticide residues and common pesticides in grains.

Life cycle assessment, LCA is one of the tools that is used to measure the environmental impacts of a process or an operation. Various studies have mentioned the benefits of mass timber in building construction. This study presents an evaluation of the LCA of certain mass timber in relation to concrete-based materials. Using Athena impact estimator for buildings, the study compared the results of an LCA study for a house that is designed with concrete beams, concrete columns, and concrete walls with brick in the envelope category (Material group 1) with those that are made with glulam beams, glulam columns, CLT walls with spruce wood bevel siding (Material group 2), and another building with LVL columns, LVL beams, CLT walls with spruce wood bevel siding (Material group 3). The results are in line with those that were reported by the majority of previous researchers. For the location that is being reviewed (Calgary, Alberta), the designs showed that construction with wood materials having mass timber components will have a better environmental performance than that for a building design with more concrete-based materials. The building design with more concrete-based material (group 1) showed 242% and 60% higher global warming and acidification potential respectively than the building with glulam beams and columns (material group 2). Except for ozone depletion potential, material group 2 (with glulam beams and columns) has a lower impact than material group 3 (with LVL/PSL beams and columns). The differences in impacts are more pronounced when the comparison is with design with more concrete-based products. This report further shows that LCA can be helpful during the preliminary design to evaluate the expected environmental impacts of the choice of different materials. This study recommends that material manufacturers and building contractors pay attention to LCA results to evaluate areas for continuous improvement.

Hand hygiene is crucial in preventing healthcare-associated infections. In this study, we aimed to quantify the accuracy of subjective evaluation of hand washing through visual inspection and objective evaluation through images. The participants were 24 consenting nursing students, and the study used black light and fluorescent paint to generate sketches and captured images of the unwashed areas, which were processed. Handwashing training was conducted once a week for four consecutive weeks. We collected data in the first and fourth training sessions. We found that the percentage of the unwashed palmar areas was significantly higher in the images than in the sketches (p<0.05). The percentage of the uncleaned area as recognized visually significantly increased for sketches (p=0.01) and decreased for images (p=0.009) in the fourth session. The difference between the percentages of the image and sketch area notably decreased in the fourth session (p=0.002). When we checked the recognition percentage of the unwashed area by dividing the right-hand palmar side into six areas, the fingertips had the highest percentage, and the ball of the thumb had the lowest percentage. The recognition of the unwashed areas was low when comparing the subjective visual assessment with the objective imagery assessment. In addition, the percentage of the unwashed areas decreased with repeated training, indicating a decrease in the difference between the subjective and objective ratings.

Introduction: Health-related quality of life in patients undergoing dialysis decreases over time, not only due to the treatment of the disease but also due to the reduction of physiological, psychological and social well-being. Aim: The aim of this research is to study social support and nursing care that patients with dialysis receive, their levels of fatigue and their quality of life. In addition, the effect of social support and nursing care on the patients'' levels of quality of life is examined. Methodology: A quantitative cross-sectional study was conducted using the questionnaires “Multidimensional Scale of Perceived Social Support”, Fatigue Assessment Scale (FAS), Missoula-VITAS Quality of Life Index and nursing care. The study involved 69 patients on dialysis. Results: Interpersonal relationships were associated with social support (p <0.01). Quality of life was associated with social support (p <0.05). Conclusions: It seems that there is a strong association between social support and quality of life in patients on dialysis.

The objective of this work is to materialize the learning of geobotanical concepts, and a methodology for the interpretation of the landscape, which allows the student to acquire practical knowledge, to obtain sufficient autonomy that allows him to join the labor market. Regarding the methodology, 100 field samples are taken for 3 years, and the participation of 60 students. The evaluation is carried out through reports on the field study. The landscapes of various areas of southern Spain are studied. For this we rely on the methodology previously established by other researchers, through which a complete diagnosis of a territory is reached, since the series and geoseries of vegetation are revealed. Being a study of natural reality, the abstract character presented by concepts such as sigmetum, sinassociation, series, geoseries, climatophilous, edaphoxerophilous, chain; it is perfectly clarified to the student, which makes the student progress efficiently, coming to the fore the acquisition of practical knowledge compared to theoretical ones. For this reason, practical teaching acquires preponderance, since it not only provides knowledge, but also development of cognitive and psychomotor skills, which are essential in the acquisition of skills and development of the individual's personality.

Objectives: There remains no consensus management for isolated distal deep vein thrombosis (IDDVT), with current data inconclusive and dating back to the warfarin era. In the current direct oral anticoagulant (DOAC) era, optimal management of IDDVT needs to be re-assessed. Methods: A retrospective evaluation of patients treated with therapeutic anticoagulation for IDDVT in the DOAC era (2013-2016) was compared with historically published data from the warfarin era (2011-2012). Results: 247 patients were evaluated, 103 from the DOAC era and 122 from the warfarin era. There were less provoked events in the DOAC cohort (45.6% vs 66.7%, p=<0.01). Overall rate of major bleeding was 1.6% with 1.0% in the DOAC era and 2.1% in the warfarin era (p=0.50). There was no difference in rates of VTE progression on treatment 5.8% vs 4.9% respectively (p=0.91). Overall risk of VTE recurrence post cessation was 5.3% (1.86 per 100 person years) with no difference between groups (5.8% vs 4.9%, p=0.74). Conclusions: Our data shows IDDVT is not always benign, with risk of extension despite treatment and long-term risk of VTE-recurrence. Therapeutic anticoagulation with DOAC in these patients was associated with a major bleeding rate of 1.0% in the DOAC cohort. Further clinical trials into the optimal IDDVT management in the DOAC era are necessary.

In order to evaluate the effect of Azospirillum brasilense and mycorrhizal fungi in the soil on the nutrition of the yacon crop (Smallanthus sonchifolius [(Poeppig & Endlicher) H. Robinson]), determinations of agronomic parameters and the health status of the plants were carried out, under greenhouse conditions. The tests were carried out at the time of the implantation of the culture: the propagules were inoculated with A. brasilense and with native mycorrhizal fungi, generating four treatments, including the control and the co-inoculation of the consortium of the microorganisms under study (T0: control or control without inoculation; T1: inoculation with native A. brasilense; T2: inoculation with native mycorrhizal fungi and T3: joint inoculation with A. brasilense and native mycorrhizal fungi. The results indicate that co-inoculation with A. brasilense and native mycorrhizal fungi increased plant growth in height, leaf area, biomass, dry matter and yields significantly in greenhouse production. It was determined that the application of the selected microorganisms has a plant growth promoting effect, increasing growth and productivity, greenhouse grown yacón.

The article considers the problems of theoretical substantiation of negative informational and psychological impact evaluation principles, and conducting of relevant researches in this field. At the same time, due to imperfections of theory and practice of negative informational and psychological impact evaluation, previous researches were conducted mainly by “mirroring” the ways of information counteraction, both for our own measures of information and psychological influence, and for the adversary. The rationale for the methodological approaches used in the organization of information and psychological influence is due to inability to access the adversary's target audiences freely and gather the necessary information, as well as the inadequacy of assessing the degree of change in their behaviour. Characteristics mentioned in article classify target audiences according to defined indicators of remote information control of social and individual behaviour of possible objects of influence. Using the formulated conditions and relying on the psychological and psychophysiological characteristics of individuals, a database of target audience behaviour vulnerabilities is forming, that reflects the dependence of indicators of social behaviour perception changes on the intensity of external negative informational and psychological influences. The algorithm of target audience’s information perception is developed based on the model of planned behaviour, in which the subject’s overestimation of small values of probabilities are compared in dynamic with decrease of big ones. The proposed algorithm of evaluation of information-psychological impact allows to receive initial data based on which the model of target audience behaviour will be designed in any environmental conditions.

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.

Meigs’ syndrome is a rare triad of a benign ovarian fibroma (or fibroma‑like tumor), ascites, and pleural effusion that resolves after tumor resection. A 53‑year‑old multiparous woman presented with progressive exertional dyspnea and right‑sided pleuritic chest pain. Respiratory and cardiac evaluations were initially unrevealing. Bedside assessment identified mild right basal dullness, and point‑of‑care abdominal ultrasound demonstrated mild free fluid and a solid right adnexal mass. Chest radiography confirmed a small right pleural effusion. Without computed tomography and without diagnostic paracentesis or thoracentesis, Meigs’ syndrome was suspected. The patient underwent laparotomy with total abdominal hysterectomy and bilateral salpingo‑oophorectomy. Histopathology confirmed an ovarian fibroma. Postoperatively, symptoms resolved dramatically, and follow‑up imaging demonstrated complete resolution of the pleural effusion and ascites. This case highlights the importance of considering gynecologic etiologies in unexplained pleural effusion and dyspnea, especially when accompanied by abdominal distension or pelvic pressure.

Background: Tobacco endgame, defined as elimination of commercial tobacco sales The U.S. tobacco control landscape is a complex, adaptive system shaped by diverse stakeholders, evolving products and regulations, shifting social norms, and the strategic countermeasures of a powerful industry. Managing such complexity requires more than isolated interventions—it demands a coordinated, enterprise-wide approach that accounts for dynamic interactions, feedback loops, and emergent risks. Objective: Drawing on complex systems thinking, Zachman enterprise architecture model, and public health best practices, we conceptualize tobacco control as an evolving enterprise progressing through six interconnected phases: (1) Conception & Initiation, (2) Policy & System Design, (3) Implementation & Operation, (4) Evaluation & Adaptation, (5) Consolidation & Endgame Transition, and (6) Sustainment or Sunset. Each phase incorporates governance structures, performance benchmarks, and transition criteria designed to manage interdependence and reduce systemic vulnerabilities. Results: The lifecycle framing emphasizes how tobacco control in the U.S. can evolve as a complex, adaptive enterprise—integrating public health objectives with legal, operational, and cultural change processes. This model supports strategic sequencing, cross-sector alignment, and risk mitigation against emergent industry tactics, enabling a resilient and measurable pathway to the endgame. Conclusions: Seeing tobacco control as a complex enterprise that operates under a lifecycle model may offer a roadmap for achieving and sustaining the tobacco endgame. Using this approach may enhance policy coherence, resource efficiency, and adaptability, ensuring tobacco endgame is achieved.

Background: Hepatocellular carcinoma (HCC) faces significant challenges in early diagnosis and prognostic assessment, necessitating novel molecular biomarkers. The role of GCN1 in tumorigenesis remains unclear, warranting systematic investigation of its clinical value. Methods: Utilizing multi-omics data from 164 HCC patients in the TCGA database, we comprehensively evaluated the diagnostic and prognostic value of GCN1 through differential expression analysis, Cox proportional hazards regression, and gene set enrichment analysis (GSEA). Results: GCN1 expression was significantly upregulated in tumor tissues (P<0.001), with ROC analysis demonstrating an AUC of 0.921 (95% CI: 0.893-0.950) for discriminating tumor from normal tissue. Clinical correlation analysis revealed that high GCN1 expression significantly associated with advanced T stage (OR=1.941, P=0.002) and AFP levels >400 ng/ml (OR=3.697, P<0.001). Multivariate survival analysis confirmed its independent prognostic value (HR=1.454, P=0.038). Functional analysis indicated GCN1 promotes tumor progression by regulating cell cycle (NES=2.385) and axon guidance (NES=2.307) pathways. Conclusion: This study first elucidates the dual clinical value of GCN1 in HCC, providing a theoretical foundation for developing novel diagnostic biomarkers and prognostic evaluation systems. Future research should validate its molecular mechanisms and explore potential targeted therapies.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an Internet of Things (IoT)-based Information System customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare Deep Learning-based Intradialytic Blood Pressure (BP)/Pulse Rate (PR) Predictive Models, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.