Search Results (6,726)

Background: Differentiating pediatric pneumonia from acute bronchitis remains a persistent clinical challenge due to overlapping symptoms, often leading to diagnostic uncertainty and inappropriate antibiotic use. Methods: This study introduces DAPLEX, a structured ensemble learning framework designed to enhance diagnostic accuracy and reliability. A retrospective cohort of 868 pediatric patients was analyzed. DAPLEX was developed in three phases: (i) deployment of diverse base learners from multiple learning paradigms; (ii) multi-criteria evaluation and pruning based on generalization stability to retain a subset of well-generalized and stable learners; and (iii) complementarity-driven knowledge fusion. In the final phase, out-of-fold predicted probabilities from the retained base learners were combined with a consensus-based feature importance profile to construct a hybrid meta-input for a Multilayer Perceptron (MLP) meta-learner. Results: DAPLEX achieved a balanced accuracy of 95.3%, an F1-score of ~0.96, and a ROC-AUC of ~0.99 on an independent holdout test. Compared to the range of performance from the weakest to the strongest base learner, DAPLEX improved balanced accuracy by 3.5–5.2%, enhanced the F1-score by 4.4–5.6%, and increased sensitivity by a substantial 8.2–13.6%. Crucially, DAPLEX’s performance remained robust and consistent across all evaluated demographic subgroups, confirming its fairness and potential for broad clinical. Conclusions: The DAPLEX framework offers a robust and transparent pipeline for diagnostic decision support. By systematically integrating diverse predictive models and synthesizing both outcome predictions and key feature insights, DAPLEX substantially reduces diagnostic uncertainty in differentiating pediatric pneumonia and acute bronchitis and demonstrates strong potential for clinical application. Full article

As vision and mission statements embody the directions set forth by an organization, their connection to the Sustainable Development Goals (SDGs) must be made explicit to guide overall decision-making in taking strides toward the sustainability agenda. The semantic alignment of these strategic statements with the SDGs is investigated in a previous study, although several limitations need further exploration. Thus, this study aims to advance two contributions: (1) utilizing the capabilities of LLMs (Large Language Models) in text semantic analysis and (2) integrating fuzziness into the problem domain by using a novel intuitionistic fuzzy set extension of the PROBID (Preference Ranking On the Basis of Ideal-average Distance) method. First, a systematic approach evaluates the semantic alignment of organizational strategic statements with the SDGs by leveraging the use of LLMs in semantic similarity and relatedness tasks. Second, viewing it as a multi-criteria decision-making (MCDM) problem and recognizing the limitations of LLMs, the evaluations are represented as intuitionistic fuzzy sets (IFSs), which prompted the development of an IF extension of the PROBID method. The proposed IF-PROBID method was then deployed to evaluate the 47 top Philippine corporations. Utilizing ChatGPT 3.5., 7990 prompts with repetitions generated the membership, non-membership, and hesitance scores for each evaluation. Also, we developed a cohort-dependent SDG–vision–mission matrix that categorizes corporations into four distinct classifications. Findings suggest that “highly-aligned” corporations belong to the private and technology sectors, with some in the industrial and real estate sectors. Meanwhile, “weakly-aligned” corporations come from the manufacturing and private sectors. In addition, case-specific insights are presented in this work. The comparative analysis yields a high agreement between the results and those generated by other IF-MCDM extensions. This paper is the first to demonstrate two methodological advances: (1) the integration of LLMs in MCDM problems and (2) the development of the IF-PROBID method that handles the resulting inherently imprecise evaluations. Full article

Shared cargo bike services hold significant potential for promoting sustainable urban mobility, yet their adoption remains limited—especially for private, everyday use. This study investigates how such systems can be more effectively integrated into urban transport by identifying key enablers and operationalizing them through a GIS-based multi-criteria analysis (MCA). Using a mixed-methods approach, expert interviews were conducted to explore success factors and barriers. Results highlight the dual function of shared cargo bikes: enabling occasional use while increasing long-term uptake by fostering trial and visibility. The study identifies both spatial and non-spatial enablers. Key spatial factors include high visibility, pedestrian flows, access to public transport and cycling networks, and placement in mixed-use areas. Non-spatial enablers include technical reliability, ease of use, strong visual identity, subsidies, and trial opportunities. The spatial enablers were operationalized into seven criteria in the MCA. Based on qualitative expert interviews and thematic analysis, the highest weights were assigned to visibility and pedestrian flows, followed by proximity to public transport and local centers, while lower weights were given to proximity to residences, population density, and access to cycle paths. The results offer guidance for station placement and demonstrate the role of shared cargo bikes in sustainable urban transport. Full article

Background/Objectives: Adolescence is a sensitive developmental window shaped by both vulnerabilities and adaptive potential. From an evolutionary standpoint, mental health difficulties in this period may represent functional responses to environmental stressors rather than mere dysfunctions. Despite increasing interest, integrative models capturing the dynamic interplay of risk and protective factors in adolescent mental health remain limited. This study presents a holistic, multi-level framework grounded in ecological and evolutionary theories to improve understanding and intervention strategies. Methods: A two-round Delphi method was used to develop and validate the framework. Twelve experts in adolescent mental health evaluated a preliminary draft derived from the literature. In Round 1, 12 items were rated across five criteria (YES/NO format), with feedback provided when consensus thresholds were not met. Revisions were made using consensus index scores. In Round 2, the revised draft was assessed across eight broader dimensions. A consensus threshold of 0.75 was used in both rounds. Results: Twelve out of thirteen experts (92%) agreed to join the panel. Round 1 item scores ranged from 0.72 to 0.85, with an average consensus index of 0.78. In Round 2, ratings improved significantly, ranging from 0.82 to 1.0, with an average of 0.95. The Steering Committee incorporated expert feedback by refining the structure, deepening content, updating sources, and clarifying key components. Conclusions: The final framework allows for the clustering of indicators across macro-, medium-, and micro-level domains. It offers a robust foundation for future research and the development of targeted, evolutionarily informed mental health interventions for adolescents. Full article

Fiber-reinforced composites are widely used in aerospace and other fields due to their excellent specific strength, specific stiffness, and corrosion resistance, and further study of their failure criteria is essential to improve the accuracy and reliability of failure behavior prediction under complex loads. There are still some limitations in the current composite failure criterion research, mainly reflected in the lack of promotion of three-dimensional stress state, lack of sufficient consideration of multi-modal coupling effects, and the applicability of the criteria under multiaxial stress and complex loading conditions, which limit the wider application of composites in the leading-edge fields to a certain degree. In this work, a generalized Mohr failure envelope function approach is adopted to obtain the stress on the failure surface as a power series form of independent variable, and the unknown coefficients are determined according to the damage conditions, to extend the Chang–Chang criterion to the three-dimensional stress state, and to consider the coupling effect between the fiber and matrix failure modes. The modified Chang–Chang criterion significantly enhances the failure prediction accuracy of composite materials under complex stress states, especially in the range of multi-axial loading and small off-axis angles, which provides a more reliable theoretical basis and practical guidance for the safe design and performance optimization of composite structures in aerospace and other engineering fields. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

Consumers are increasingly willing to choose more sustainable products, driven by affordability and sustainability considerations. However, they often face difficulties in understanding the multitude of product certifications and identifying “greenwashing” marketing claims. This highlights the need for a clear and harmonized sustainability scoring system that allows consumers to benchmark products. Sustainability encompasses three key pillars: environmental, social, and economic. Accurately scoring a product’s sustainability requires addressing a wide range of criteria within these pillars, introducing significant complexity. This study proposes a multicriteria methodology for scoring the sustainability of apparel products into an A to E label. The approach combines a life cycle assessment covering environmental impacts from “farm-to-gate”, with a social evaluation based on country-level social key performance indicators (KPIs) and factory-specific data aligned with the International Labour Organization (ILO). Additionally, the sustainability score incorporates the impact of product durability, as longer-lasting products can reduce environmental footprint and costs for consumers. The methodology is defined and validated through a case study of a white T-shirt produced with 50% recycled cotton and 50% organic cotton. The results demonstrate the comprehensive assessment of the T-shirt’s environmental and social impacts, providing a detailed sustainability score, highlighting the role of recyclability. This comprehensive sustainability scoring system aims to provide consumers with a clear, harmonized, and reliable assessment of product sustainability, empowering everyone to make informed purchasing decisions aligned with their values. It will also enable brands and retailers to calculate the sustainability score of their products, including in the scope of digital product passport, provided they can ensure traceability and transparency along the supply chain. Full article

Hybrid Renewable Energy Systems are pivotal for the sustainable energy transition, yet their design and operation present complex optimization challenges due to diverse components, stochastic resources, and multifaceted objectives. This systematic review formalizes the HRES optimization problem space and identifies critical research gaps. Employing the PRISMA 2020 guidelines, it comprehensively analyzes the literature (2015–2025) from Scopus, IEEE Xplore, and Web of Science, focusing on architectures, mathematical formulations, objectives, and solution methodologies. The results reveal a decisive shift from single-objective to multi-objective optimization (MOO), increasingly incorporating environmental and emerging social criteria alongside traditional economic and technical goals. Metaheuristic algorithms (e.g., NSGA-II, MOPSO) and AI techniques dominate solution strategies, though challenges persist in scalability, uncertainty management, and real-time control. The integration of hydrogen storage, vehicle-to-grid (V2G) technology, and multi-vector energy systems expands system boundaries. Key gaps include the lack of holistic frameworks co-optimizing techno-economic, environmental, social, and resilience objectives; disconnect between long-term planning and short-term operation; computational limitations for large-scale or real-time applications; explainability of AI-based controllers; high-fidelity degradation modeling for emerging technologies; and bridging the “valley of death” between simulation and bankable deployment. Future research must prioritize interdisciplinary collaboration, standardized social/resilience metrics, scalable and trustworthy AI, and validation frameworks to unlock HRESs’ potential. Full article

Muscle dysmorphia (MD) is a body image disorder characterized by an obsessive preoccupation with muscularity and compulsive behaviors such as excessive exercise, rigid dieting, and frequent body checking. MD has been linked to obsessive–compulsive traits and the use of anabolic–androgenic steroids (AASs), yet these associations have not been comprehensively synthesized. This systematic review and meta-analysis examined the relationships between MD, obsessive–compulsive symptomatology, and AASs or performance-enhancing drug use. Following PRISMA 2020 guidelines and PROSPERO preregistration (CRD42025640206), we searched four major databases for peer-reviewed studies published between 2015 and 2025. Ten studies (five quantitative, five qualitative) met the inclusion criteria. Meta-analytic findings revealed a moderate positive correlation between MD symptom severity and obsessive–compulsive traits (r ≈ 0.24), and significantly higher MD symptoms among AAS users compared to non-users (Cohen’s d ≈ 0.45). Odds of MD were markedly higher in steroid-using populations. Thematic synthesis of qualitative studies highlighted compulsive training routines, identity conflicts, motivations for AAS use, and limited engagement with healthcare services. These findings suggest that MD exists at the intersection of obsessive–compulsive psychopathology and substance-related behavior, warranting integrated interventions targeting both dimensions. The study contributes to understanding MD as a complex, multi-faceted disorder with significant clinical and public health relevance. Full article

A systematic fuzzy multi-criteria decision making (MCDM) framework is proposed to prioritize near-Earth asteroids (NEAs) for a boulder capture mission, addressing the requirement for rigorous prioritization of asteroid candidates under conditions of data uncertainty. Twenty-eight NEA candidates were first selected through filtering based on physical and orbital properties. Then, objective fuzzy weighting MCDM methods (statistical variance, CRITIC, and MEREC) were applied to determine the importance of criteria such as capture cost, synodic period, rotation rate, orbit determination accuracy, and similarity to other candidates. Subsequent fuzzy ranking MCDM techniques (WASPAS, TOPSIS, MARCOS) generated nine prioritization schemes whose coherence was assessed via correlation analysis. An innovative sensitivity analysis employing Dirichlet-distributed random sampling around reference weights quantified ranking robustness. All methodologies combinations consistently identified the same top four asteroids, with 2013 NJ ranked first in every scenario, and stability metrics confirmed resilience to plausible weight variations. The modular MCDM methodology proposed provides mission planners with a reliable, adaptable decision support tool for asteroid selection, demonstrably narrowing broad candidate pools to robust targets while accommodating future data updates. Full article

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met inclusion criteria. Studies focused on acute myeloid leukemia (34), acute lymphoblastic leukemia (23), and multiple myeloma (18). Other hematological diseases were less frequently studied. Methods included Support Vector Machines, Random Forests, and deep learning (28, 25, and 24 studies). Multi-omics integration was reported in 23 studies. External validation occurred in 31 studies, and explainability in 19. The median diagnostic area under the curve was 0.87 (interquartile range 0.81 to 0.94); deep learning reached 0.91 but offered the least explainability. Artificial Intelligence and machine learning show promise for molecular characterization, yet gaps in validation, interpretability, and standardization remain. Priorities include external validation, interpretable modeling, harmonized evaluation, and standardized reporting with shared benchmarks to enable safe, reproducible clinical translation. Full article

The selection of the most economically advantageous tender (MEAT) in public procurement procedures requires transparent evaluation systems capable of integrating heterogeneous criteria, including qualitative ones, to reconcile quality and cost. This systematic review analyzes 74 studies published between 1998 and 2023 to explore the application of multi-criteria decision analysis (MCDA) methods in public construction procurement. The vast majority of MCDA applications focus on the award phase, with constant growth over the last 10 years. However, applications in the prequalification and verification phases are much less frequent and remain under-represented. Geographically, Europe is the most active area in terms of publications, followed by China and some countries in the Asia-Pacific area. In these regions, MCDA has been employed more systematically over time, while in other areas (e.g., Africa, Latin America), applications are sporadic or absent. Analytic Hierarchy Process (AHP) is confirmed as the most widely used technique. Emerging techniques (such as BWM, MABAC, EDAS, VIKOR, advanced TOPSIS) show greater computational rigor and in some cases better theoretical properties, but are less used due to complexity, less practical familiarity and the lack of accessible software tools. The operationalization of environmental and social criteria is still poorly standardized: clear indications on metrics, measurement scales and data sources are often lacking. In most cases, the criteria are treated in a generic or qualitative way, without common standards. Furthermore, the use of sensitivity analyses and procedures for aggregating judgments between evaluators is limited, with a consequent risk of poor robustness and transparency in the evaluation. In order to consider proposing a framework or guidelines based on the review findings, a six-step operational framework that connects selection of criteria and their operationalization, choice of method based on the context, robustness checks and standard minimum reporting, with clear assignment of roles and deliverables, is provided. The framework summarizes and makes the review evidence applicable. Full article

Global energy demand is steadily increasing, accompanied by a growing emphasis on clean and renewable energy sources. Serbia possesses significant solar energy potential, with solar radiation levels among the highest in Europe—about 40% above the European average. Within this context, rural depopulation clusters offer attractive opportunities for solar energy development due to the availability of underutilized land. This study aims to identify optimal locations for solar power installations in Serbia’s depopulated areas by applying multi-criteria decision-making methods under uncertainty. A hybrid framework, combining fuzzy Analytic Hierarchy Process (fuzzy AHP) and fuzzy MULTIMOORA, was employed to evaluate potential sites. Fuzzy AHP was used to determine the relative importance of criteria, while fuzzy MULTIMOORA ensured a robust ranking of alternatives by addressing the vagueness in data and expert judgments. The analysis identified several high-potential brownfield locations, with the most suitable land class covering 5.01% (16.94 km²) of the examined cluster area (311.3 km²). These areas are typically characterized by flat terrain, high solar irradiation, and minimal environmental constraints, providing favorable conditions for solar farms. Among the assessed sites, location no. 9 consistently ranked highest across all three fuzzy MULTIMOORA variants: FRPA (z = 0.0588), FRS (y = 0.2811), and FFMF (p = 1.6748). The findings confirm that the hybrid fuzzy AHP–MULTIMOORA approach offers valuable support for informed decision-making on solar energy deployment in depopulated rural regions. Moreover, the utilization of rural brownfield sites contributes to the expansion of renewable energy, rural revitalization, and sustainable land management in Serbia. Full article

Background: Syndromic forms of obesity are uncommon, complicated illnesses that include early-onset obesity along with other clinical characteristics such as organ-specific abnormalities, dysmorphic symptoms, and intellectual incapacity. These syndromes frequently have a strong genetic foundation, involving copy number variations, monogenic mutations, and chromosomal abnormalities. Methods: Using terms like “syndromic obesity,” “genetic diagnosis,” and “monogenic obesity,” a comprehensive literature search was conducted to find articles published between 2000 and 2025 in PubMed, Scopus, and Web of Science. Peer-reviewed research addressing the clinical, molecular, or genetic aspects of syndromic obesity were among the inclusion criteria. Conference abstracts, non-English publications, and research without genetic validation were among the exclusion criteria. The whole genetic, clinical, diagnostic, and therapeutic domains were thematically synthesized to create a thorough, fact-based story. Research using chromosomal microarray analysis (CMA), whole-exome sequencing (WES), next-generation sequencing (NGS), and new long-read sequencing platforms was highlighted. Results: Despite the fact that molecular diagnostics, especially NGS and CMA, have made tremendous progress in identifying pathogenic variants, between 30 and 40 percent of instances of syndromic obesity are still genetically unexplained. One significant issue is the variation in phenotype across people with the same mutation, which suggests the impact of environmental modifiers and epigenetic variables. In addition, differences in access to genetic testing, particularly in areas with limited resources, can make it difficult to diagnose patients in a timely manner. Additionally, recent research emphasizes the possible contribution of gene–environment interactions, gut microbiota, and multi-omic integration to modifying disease expression. Conclusions: Syndromic obesity is still poorly understood in a variety of groups despite significant advancements in technology. Multi-layered genomic investigations, functional genomic integration, and standardized diagnostic frameworks are necessary to close existing gaps. The development of tailored treatment plans, such as gene editing and focused pharmaceutical therapies as well as fair access to cutting-edge diagnostics are essential to improving outcomes for people with syndromic obesity. Full article

Background: Hypertension remains a leading cause of cardiovascular morbidity and mortality globally, and insulin resistance (IR) and systemic inflammation are implicated in the pathogenesis of hypertension. Limited evidence exists on the predictive role of the triglyceride-glucose (TyG) index and its related indices (TyG-WHtR and TyG-WC) for hypertension. This study aimed to investigate these associations across multiple Chinese cohorts. Methods: Data from 31,224 participants (Fuqing, CHNS, CHARLS) were analyzed. TyG indices were calculated using fasting triglycerides, glucose, and anthropometrics. Hypertension was defined as SBP/DBP ≥ 140/90 mmHg, or physician diagnosis, or antihypertensive treatment. Logistic/Cox regression models were used to examine associations, adjusting for demographics, lifestyle, and metabolic factors. Mediation analysis quantified the role of neutrophil-to-lymphocyte ratio (NLR) in mediating the TyG–hypertension relationship. Results: Elevated TyG index and its obesity-adjusted variants consistently predicted incident hypertension across cohorts (all p < 0.001). Each 1-unit TyG increase was associated with 9–36% higher odds of hypertension in Fuqing (OR = 1.09–1.36). NLR mediated 20.4–29.4% of these associations (p < 0.001). Subgroup analyses revealed effect modifications by age, sex, and residence. Sensitivity analyses confirmed robustness when redefining hypertension thresholds (ACC/AHA criteria). Conclusions: TyG index and its related indices are robust predictors of (new-onset) hypertension, with NLR statistically accounting for approximately 25% of these associations in the mediation model. These findings underscore the interplay between metabolic dysregulation, inflammation, and hypertension and advocate for integrated biomarker strategies in risk stratification and prevention, while external validation in multi-ethnic populations is warranted. Full article