Search Results (176)

Mental disorders (MDs) constitute significant risk factors for self-harm and suicide. The incidence of MDs has been increasing annually, primarily due to inadequate diagnosis and intervention. Early identification and timely intervention can effectively slow the progression of MDs and enhance the quality of life. However, the high cost and complexity of in-hospital screening exacerbate the psychological burden on patients. Moreover, existing studies primarily focus on the identification of individual subcategories and lack attention to model explainability. These approaches fail to adequately address the complexity of clinical demands. Early screening of MDs using EEG signals and deep learning techniques has demonstrated simplicity and effectiveness. To this end, we constructed a Dual-Branch Network (DBN) leveraging resting-state Quantitative Electroencephalogram (QEEG) features. The DBN is designed to enable the detection of multiple categories of MDs. Firstly, a dual-branch feature extraction strategy was designed to capture multi-dimensional latent features. Further, we propose a Multi-Head Attention Mechanism (MHAM) that integrates dynamic routing. This architecture assigns greater weights to key elements and enhances information transmission efficiency. Finally, the diagnosis is derived from a fully connected layer. In addition, we incorporate SHAP analysis to facilitate feature attribution. This technique elucidates the contribution of significant features to MD detection and improves the transparency of model predictions. Experimental results demonstrate the effectiveness of DBN in detecting various MD categories. The performance of DBN surpasses that of traditional machine learning models. Ablation studies further validate the architectural soundness of DBN. The DBN effectively reduces screening complexity and demonstrates significant potential for clinical applications. Full article

Background/Objectives: There has been growing interest in understanding the impact of social determinants of health (SDoHs) on health outcomes. Since 2015, healthcare providers have begun to document patients’ SDoH systematically using ICD-10 Z-codes. Methods: We extracted claims data from a nationally representative hospital chargemaster database for 586,929 eligible HF patients between January 2019 and December 2021. We investigated the association between SDoH Z-codes and 30-day hospital readmission for heart failure (HF) patients in U.S. hospitals using a Chi square test and adjusted odds ratios from logistic regression models. Results: We found that four major SDoH Z-code categories and five specific sub-Z-code factors within the major categories are significantly associated with 30-day readmission for HF patients. We also found that patients with two or more SDoH Z-codes have a higher risk of readmission than those with one. Conclusions: Our study indicates that ICD-10 Z-codes are useful in identifying SDoH risk factors for hospital readmission among HF patients. Policymakers and healthcare providers should consider Z-codes when assessing HF readmission risk and developing interventions to lower HF readmission rates. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

The link between ESG and financial performance is still under debate. In this study, we explore which aspects of ESG specifically drive market valuations through both systematic and idiosyncratic risk channels. We analyze the impact of the three core ESG pillars, 10 subcategories, and associated controversies on market valuations in the energy sector. This analysis reveals that the environmental factor has a stronger impact (regression coefficient = 0.05) than the governance factor (regression coefficient = 0.003), emphasizing the need to prioritize environmental performance in ESG strategies. The positive coefficients for environmental resource use (0.005) and innovation (0.008) indicate that investments in efficiency and clean technologies are beneficial, while the negative coefficient for emissions (−0.004) underscores the risks associated with poor emissions management. These findings suggest that environmental risks currently outweigh governance risks for the energy sector, reinforcing the importance of aligning governance practices with environmental goals. To maximize ESG effectiveness, energy firms should focus on measurable improvements in resource efficiency, innovation, and emissions reduction and transparently communicate this progress to stakeholders. The evidence suggests that energy firms approach the ESG landscape differently, with sustainability leaders benefiting from higher valuations, particularly when ESG efforts are aligned with core competencies. However, many energy companies under-invest in value-creating environmental initiatives, focusing instead on emission management, which erodes value. While they excel in emission control, they lag in innovation, missing opportunities to enhance valuations. This underscores the potential for ESG risk analysis to improve portfolio performance, as sustainability can both create value and mitigate risks by factoring into valuation equations as both risks and opportunities. This study uniquely contributes to the ESG–financial performance literature by disentangling the specific ESG dimensions that drive market valuations in the energy sector, revealing that value is created not through emission control but through strategic alignment with eco-innovation, governance, and social responsibility. Full article

As climate change intensifies, assessing vulnerability at territorial levels such as cities, countries, and regions is essential for effective adaptation planning. This study evaluates the applicability of the United Nations Environment Programme and South Pacific Applied Geoscience Commission’s Environmental Vulnerability Index (EVI) for coastal regions in South Korea. By adapting and localizing 50 international indicators and a Geographic Information System framework, this research developed a Korean Coastal Vulnerability Index and used spatial regression analysis to compare results to historical water-related disaster data from 2010 to 2019. The findings reveal that contrary to South Korea’s global classification of “extremely vulnerable”, most coastal counties appear relatively resilient when viewed through the localized model. Sub-index analyses indicate that ecological and anthropogenic damage factors show the strongest correlation with past disasters among the hazard, resistance, and damage categories. While the model’s explanatory power was modest (R² = 0.017), the regression nonetheless provides meaningful insight into how global indices can reflect local vulnerability patterns. The regression results confirm that based on historical hazard records, the international model effectively predicts Korean coastal vulnerability. It demonstrates the potential of scaling down global models to fit national contexts, offering a replicable approach for countries lacking localized vulnerability frameworks. It advances climate adaptation research through methodological innovation, policy-relevant spatial analysis, and theoretical insights into the multidimensional nature of vulnerability. The results support more precise, data-driven resilience planning and promote international collaboration in climate risk management. Full article

In the present study, dam site suitability mapping was carried out for the Purna sub-basin of the upper Tapi basin. Constructing dams in strategically chosen locations is a crucial water management approach to alleviate flood risks and water scarcity. Selecting appropriate dam sites requires considering criteria such as precipitation, elevation, soil properties, slope, geomorphology, geology, lithology, stream order, distance from a road, and fault tectonics. To address this complex problem, integrating Multiple-Criteria Decision-Making (MCDM) techniques with Geographic Information System (GIS) has become increasingly prevalent. Among these techniques, the Analytic Hierarchy Process (AHP) is particularly effective for addressing water-related challenges. In this study, we developed a Dam Site Suitability Model (DSSM) by evaluating nine thematic layers: precipitation, stream order, geomorphology, geology, soil, elevation, slope, land use and land cover (LULC), and major fault tectonics. The AHP technique was employed to assign weights to these thematic layers, which were then used in an overlay analysis to create a suitability map with five classes ranging from high to low suitability. This study revealed that approximately 14% of the Purna sub-basin falls into the very high suitability category, while 27.2% is classified as highly suitable. This cost-effective approach not only simplifies the traditional method of dam site selection but also enhances decision-making accuracy. This methodology can be universally applied to identify potential dam sites, aiding flood mitigation and addressing water scarcity exacerbated by global and regional climate change. The DSSM, leveraging GIS and the AHP, can significantly improve dam management and promote sustainable, environmentally responsible water resource management practices worldwide. Full article

Gender-based violence is a social problem that affects the health of women in all countries, cultures, ages and economic status; its complexity requires a transdisciplinary approach. However, this study will emphasize the care provided by nursing in emergency services. Objectives: To explore the experiences of nursing professionals in the emergency area in relation to the approach to gender-based violence considering care management skills. Methods: A qualitative study with semi-structure interviews was conducted; the saturation was reached with the participation of 20 nursing professionals from emergency rooms. The study employed qualitative content analysis and the software QCAmap for organization and extraction of analysis. Results: The category called “Specific Approaches to Risk and Vulnerability” was characterized by its comprehensiveness, evident in its association with experiences in screening, follow-up, measures to prevent re-victimization, and ensuring privacy. The most relevant subcategories, based on the redundancy, were empathy, which encompasses affective aspects; education on forms of abuse not recognized by the victim; and multidisciplinary and multisectoral action to address patients’ needs effectively. Conclusions: Nursing professionals valued both the psychological and physical aspects of patients, highlighting the importance of multidisciplinary coordination and the protection of integrity. Awareness and offering help are key interventions during the activation of protocols for addressing gender-based violence. Full article

Massive land use changes in Indonesia driven by deforestation, agricultural expansion, and urbanization have significantly increased landslide susceptibility in upper watersheds. This study focuses on the Sumber Brantas and Kali Konto sub-watersheds where rapid land conversion has destabilized slopes and disrupted ecological balance. By integrating remote sensing, Cellular Automata-Markov (CA-Markov), and Random Forest (RF) models, the research aims to identify optimal land use scenarios for mitigating landslide hazards. Three scenarios were analyzed: business as usual (BAU), land capability classification (LCC), and regional spatial planning (RSP) using 400 field-validated landslide data points alongside 22 topographic, geological, environmental, and anthropogenic parameters. Land use analysis from 2017 to 2022 revealed a 1% decline in natural forest cover, which corresponded to a 1% increase in high and very high landslide hazard areas. From 2017 to 2022, landslide risk increased as the “High” category rose from 33.95% to 37.59% and “Very High” from 10.24% to 12.18%; under BAU 2025, they reached 40.89% and 12.48%, while RSP and LCC reduced the “High” category to 44.12% and 34.44%, respectively. These findings highlight the critical role of integrating geospatial analysis and machine learning in regional planning to promote sustainable land use, reduce landslide hazards, and enhance watershed resilience with high model accuracy (>81%). Full article

Food safety is a global issue that can be enhanced by collaboration with reliable suppliers. Given the complexities of international supply chains, identifying reliable suppliers is often challenging and resource-intensive. Integrating artificial intelligence (AI) offers a valuable opportunity to improve efficiency in this process. The aim of the present study was to develop a quantitative supplier assessment scheme for implementation in an AI-supported database. The framework developed incorporates different indicators, including the hazard risk, incident category level, vulnerability of a commodity, audit performance, logistic performance index, gross domestic product (GDP) growth, and GDP per capita. Each indicator is evaluated according to its own distinct assessment. Ultimately, the sub-assessments are integrated into the calculation of a supplier’s overall risk score. Hereby, it is possible to set individual weightings for each indicator. Manual testing using an exemplary selected supplier yielded promising results, indicating that the next steps involve implementation into an AI-supported database. It can be concluded that such an assessment framework can be an effective method for the identification of reliable suppliers. A future challenge will be to establish incentives to make audit data freely available, as these are often restricted and cannot be considered in the supplier risk assessment. Full article

Background/Objectives: The 2023 revision of the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) simplified the subcategorization of category III into two groups: “AUS-nuclear” and “AUS-other”. The aim of this study was to investigate the risk of malignancy (ROM) of individual BSRTC categories with a particular emphasis on the “AUS-nuclear” and “AUS-other” subcategories and to check whether the low-risk follicular-cell-derived thyroid neoplasm (LRTN) interpretation or EU-TIRADS class of the nodule modify ROM. Methods: The analysis covered the FNA results of 18,225 nodules in 12,470 patients. The rate of malignancy (the upper limit of ROM) was established on the basis of the assessment of 1660 nodules treated surgically in 978 patients. Results: In the broadest variant, with all LRTNs regarded as malignant, the ROM for subsequent categories was as follows: I: 0.4–3.5%, II: 0.1–1.3%, III: 3.8–17.7%, IV: 23.3–27.8%, V: 79.6–90.1%, and VI: 86.3–100.0%. In AUS-nuclear nodules, the ROM was 10.5–28.9%, while in AUS-other nodules, it was 2.2–12.2%. The exclusion of NIFTP or all LRTNs from cancers mainly affected the ROM of AUS-nuclear nodules: 9.4–25.9% or 8.6–23.7%, respectively. EU-TIRADS 5 class increases the ROM in AUS-nuclear nodules to 78.3%, OR: 15.7 and in AUS-other to 40.7%, OR: 6.6. Conclusions: The 2023 BSRTC is a welcome step towards simplification of the way nodules are classified within category III. The AUS-nuclear subcategory is associated with a two-times-higher incidence of malignancy than the AUS-other regardless of LRTN interpretation and EU-TIRADS class of the nodule. The EU-TIRADS 5 class of the nodule is helpful in the identification of category III nodules with a high risk of malignancy. Full article

Background: Incretin mimetics, including glucagon-like peptide-1 receptor agonists (GLP-1 receptor agonist) and dipeptidyl peptidase-4 (DPP-4) inhibitors, have been increasingly utilized for glycemic control in patients with type 2 diabetes (T2D). Studies have demonstrated additional improvements in weight loss, cardiovascular health, and renal outcomes. Animal studies have shown an association between GLP-1 receptor agonists and C-cell proliferation and elevated calcitonin, resulting in an FDA black box. Insulin resistance in patients with T2D, along with the use of other glucose control medications, confounds the relationship between incretin mimetics and thyroid cancers. The true effect of incretin mimetics on thyroid cancer remains uncertain and speculative due to this confounding. Methods: This retrospective cohort study compared patients with T2D, who were new users of incretin mimetics, to new users of metformin. Study patients used no other anti-diabetes medications beyond the study medications. The risks of incident thyroid cancer and subsequent thyroidectomy were quantified using Cox proportional hazards regression models fitted with adjustments for demographic and medical covariates over a three-year study period. Medullary thyroid cancer (MTC) and multiple endocrine neoplasia type II (MEN2) cases were quantified. Results: Of the 91,394 patients, 28 incretin mimetic users had a diagnosis of thyroid cancer, and nine of these patients underwent a subsequent thyroidectomy procedure. No incretin mimetic user was diagnosed with MTC or MEN2. There was no statistically significant effect on the overall incretin mimetic category (1.28 aHR, 0.83–1.96), the incretin mimetic subcategories of GLP-1 receptor agonists (1.35 aHR, 0.80–2.29), or DPP-4 inhibitor (0.62 aHR, 0.33–1.17) users in developing thyroid cancer within three years of drug initiation. Similarly, no association was found between the overall incretin mimetic category (1.02 aHR, 0.49–2.10), the subcategories of GLP-1 receptor agonists (1.26 aHR, 0.54–2.96), or DPP-4 inhibitors (0.32 aHR, 0.08–1.37) and a subsequent thyroidectomy. Conclusions: In this real-world cohort study, exposure to incretin mimetics overall or through the incretin mimetic subcategories of GLP-1 receptor agonists and DPP-4 inhibitors was not associated with risks of thyroid cancer or thyroidectomy compared to metformin users. Full article

E-government and transparency are significantly improving public service management by encouraging trust, accountability, and the massive participation of citizens. On the one hand, e-government has facilitated online services to address bureaucratic processes more efficiently. On the other hand, transparency has promoted open access to public information from the State so that citizens can understand and track aspects of government processes more effectively. However, as both require extensive citizen information management, these initiatives may significantly compromise privacy by exposing personal data. To assess these privacy risks in a concrete scenario, we analyzed 21 public institutions in Ecuador through a proposed taxonomy of 6 categories and 17 subcategories of disclosed personal data on their online portals and websites due to LOTAIP transparency initiative. Moreover, 64 open-access systems from these 21 public institutions that accomplish e-government principles were analyzed through a proposed taxonomy of 8 categories and 77 subcategories of disclosed personal data. Our results suggest that personal data are not handled through suitable protection mechanisms, making them extremely vulnerable to manual and automated exfiltration attacks. The lack of awareness campaigns in Ecuador has also led many citizens to handle their personal data carelessly without being aware of the associated risks. Moreover, Ecuadorian citizens’ privacy is significantly compromised, including personal data from children and teenagers being intentionally exposed through e-government and transparency initiatives. Full article

Background: Globally, approximately 65% of adolescents undergoing antiretroviral therapy (ART) adhered to their treatment, whereas only 55% achieved viral suppression in 2023. The low rate of viral suppression is concerning, as elevated viral loads are associated with a heightened risk of opportunistic infections, progression to advanced HIV disease, increased mortality, and greater HIV transmission rates. We conducted this scoping review to identify the facilitators and barriers to ART adherence among adolescents and young adults (AYAs) in sub-Saharan Africa (SSA). Methods: We conducted this scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist. We searched for peer-reviewed articles published in English from 2014 to 2024 across the SCOPUS, ScienceDirect, PubMed, Africa Journals Online, and Google Scholar databases. Two reviewers independently selected the articles and extracted the data. We used NVivo to develop codes and categories of facilitators and barriers. Results: We used 30 articles reporting on studies conducted in 13 countries in this review. The total number of participants in the studies was 12,250. Sixteen articles reported on qualitative studies, nine on quantitative studies, and five on mixed-methods studies. This scoping review identified various personal (14 articles), interpersonal and social (15 articles), healthcare system-related (9 articles), medication-related (7 articles), and economic (2 articles) factors that facilitate ART adherence among AYAs. Additionally, the scoping review also identified various personal (28 articles), interpersonal and social (13 articles), healthcare system-related (14 articles), medication-related (20 articles), school- or work-related (6 articles), and economic (14 articles) factors that hinder ART adherence among AYAs. Conclusions: Enhancing ART adherence in AYAs requires multiple strategies, including the reduction of internalized stigma, implementation of community awareness campaigns, harm reduction approaches for AYAs who misuse substances, comprehensive education on HIV, and the provision of support from school staff and leadership, alongside the adoption of differentiated service delivery (DSD), which encompasses home-based ART delivery, refills at private pharmacies, community ART distribution centers, and patient-led community ART refill groups, as well as multi-month dispensing practices. Full article

Sub-watershed prioritization using morphometric analysis and multi-criteria decision-making (MCDM) techniques is a systematic approach to identifying and ranking sub-watersheds based on their susceptibility to soil erosion. This helps in implementing targeted soil conservation measures. In this study, sub-watersheds in the Narangi basin are prioritized by employing morphometric analysis integrated with advanced MCDM techniques, including additive ratio assessment (ARAS), complicated proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and the technique for order preference by similarity to ideal solution (TOPSIS). Weights for various MCDM methods are determined using the criteria importance through an inter-criteria correlation approach (CRITIC: criteria importance through inter-criteria correlation method), while geospatial techniques ensure precise spatial analysis. The results provide a unified ranking of sub-watersheds, revealing that sub-watershed 3 (SW3) and SW9 are in the high-priority soil erosion category; SW1, SW2, SW5, and SW8 are medium-priority; and SW4, SW6, SW7, and SW10 are low-priority. This comprehensive and sustainability-oriented approach equips decision-makers with robust tools to identify and manage sub-watersheds at risk of soil erosion, ensuring the long-term sustainability of land and water resources. This study aligns with sustainable development goal 15 (life on land) and promotes sustainable land use practices to combat soil degradation. Full article

Background: Kidney transplantation (KTx) is a safe procedure that improves the life expectancy and quality of life of patients requiring it. However, despite the known benefits for patients who receive a kidney transplant, non-adherence to immunosuppressive medication is an unsolved problem, reflected mainly by graft rejection. Objective: The aim of this study is to systematically review the existing literature on adherence factors to medication after renal transplantation. Methods: A systematic literature review of studies published since 2010 was conducted in three databases. Records for the search were limited to publications from 2010 to 2024, available in full-text. The search was carried out in July 2024. In total, 2632 abstracts were downloaded from the different databases. Inclusion criteria were papers of any type (quantitative or qualitative) whose objective was the identification of predictors of adherence for patients who were prescribed immunosuppressive medication after kidney transplantation. Results: The predictors of adherence to treatment found in the systematic review were grouped into the following categories of the World Health Organization classification: socio-economic factors, factors related to the treatment/therapy, patient-related factors, disease-related factors, and health care system factors. Most of the studies were excluded, and in the end, 30 were included in the final analysis. According to these studies, a set of strong predictors was identified, but discrepancies among the variables of gender in young patients, pre-emptive transplantation, and the time of the transplantation were detected. Conclusions: In this study, we identified specific predictors and directions for the association of those predictors with adherence to immunosuppressive medication for patients after KTx. Further research should consider conducting reviews for different patient sub-groups on medication adherence and the development and validation of a screening instrument for adherence/non-adherence factors that clinicians could use as a detection tool for subjects at risk of low adherence. Full article