Search Results (493)

Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables. Full article

Forging represents a very important process in the metal processing industry, for which risk analysis and continuous improvement activities are required in order to satisfy customer requirements regarding product quality and mechanical properties. In practice, Process Failure Mode and Effects Analysis (PFMEA) is used for risk identification and assessment. However, since this analysis provides only recommended process improvement measures as an output, without prioritizing them according to technical, economic, and operational aspects, this study develops a Multi-Criteria Decision-Making (MCDM) approach based on the integration of the Analytic Hierarchy Process (AHP) and the Square-Root-Based Evaluation Method (SREM), extended through the application of Interval-Valued Pythagorean Fuzzy Numbers (IVPFNs) to model uncertainty in the assessments of the expert team. In this way, a decision-making framework was developed that enables the prioritization of process improvement measures identified through PFMEA in an exact and mathematically based manner. The proposed approach was tested through a case study conducted in a company primarily engaged in the production of forgings as a supplier to various industrial sectors. A total of six process improvement measures were considered and evaluated with respect to seven technical, economic, and operational criteria. The results of the study clearly demonstrated that the additional die-leading measure ranked first and represented the most stable solution, maintaining its leading position regardless of the changes introduced through the sensitivity analysis. Full article

Providing care for effective management of tuberculosis (TB) and diabetes mellitus (DM) is a challenge, particularly in resource-limited settings. This study assessed factors affecting the provision of care for patients with TB-DM in Addis Ababa, Ethiopia, using the six elements of the SELFIE—Sustainable intEgrated chronic care modeLs for FinancIng and performancE framework. A health facility-based cross-sectional study was conducted with randomly selected patients with TB-DM multi-morbidity. Data were analysed using the Statistical Package for Social Sciences (SPSS) version 27. Logistic regression was employed to identify factors influencing TB-DM care provision. A total of 357 respondents participated, with a response rate of 96.5%. The mean age of the respondents was 49.8 years. Only 13.4% of patients received good TB-DM services. Key factors influencing TB-DM care were (a) insufficient counseling on the proper use of medication (AOR = 2.6, CI: 1.1–6.6, p = 0.035) and the risk of TB for DM patients (AOR = 10, CI: 3.7–27, p < 0.001), (b) leadership and governance including supportive leadership for TB-DM care (77.1%), organized TB-DM care (62.5%), the presence of a care policy (77.1%), and continuity of care (58.3%), (c) workforce factors such as the presence of a multidisciplinary team (60.4%), TB-DM service coordinator (58.3%), HCWs with adequate knowledge (87.5%), (d) costs of health services are fair (75%) and (e) lack of technologies such as EMR (88%) and lack of medical products to treat TB-DM (64.7%) delays care for TB-DM. Most TB-DM patients in Addis Ababa had limited access to care. The continuous monitoring of services can facilitate the identification of care gaps, thereby guiding the implementation of improved interventions. Full article

Background: The integration of Artificial Intelligence (AI) and algorithms into healthcare is transformative, particularly in diagnosing rare diseases (RDs), enhancing the accuracy and speed of condition identification. Objectives: This systematic literature review investigates perceptions and attitudes toward the use of AI in Electronic Health Records (EHRs) for screening patients at risk of RD, aiming to understand patients’ and healthcare providers’ expectations and concerns. Methods: Following PRISMA guidelines, a systematic search was performed in December 2023. A search strategy developed by the research team in collaboration with an expert librarian, using the PICO framework, was applied. Searches were conducted in PubMed, Scopus, and Web of Science. The search strategy covered four main concepts: diagnostic techniques, medical records, AI, and attitudes toward these technologies. Results: The initial search retrieved 3348 articles after duplicate removal. However, no studies met the inclusion criteria. As a result, no eligible studies were identified, preventing risk-of-bias assessment or data synthesis. Discussion: The absence of relevant studies highlights the need for further research focusing on patient and healthcare provider attitudes toward AI-integrated EHRs, especially in RD and their early detection. Conclusions: The lack of studies on stakeholder attitudes toward AI in EHRs for RD screening represents an important research gap. Addressing this gap will improve the understanding and development of AI applications in healthcare, ensuring they meet user needs and ethical standards. Full article

Background: Abdominal examination (AE) is an important component of clinical assessment in Traditional East Asian Medicine (TEAM), providing information on abdominal shape, tenderness, stiffness, and skin color. However, conventional AE relies largely on subjective judgment and lacks standardized quantitative indicators. This study aimed to quantitatively characterize abdominal physical features associated with the cold pattern (CP) using objective abdominal examination devices and to explore their potential role in supporting standardized pattern-related assessment. Methods: A case–control study was conducted including 63 patients with functional dyspepsia and 60 healthy controls. Participants were classified according to CP status using a validated cold–heat pattern identification questionnaire. Abdominal features were quantified using a digital algometer and a depth-based geometric assessment system, measuring algometric (pressure tolerance, indentation depth, stiffness), geometric (abdominal depth and curvature), and chromatic (CIE L*a*b*) parameters. Group differences were analyzed using generalized linear models adjusted for confounders. A two-stage LASSO logistic regression with nested 10-fold cross-validation was applied. Results: Individuals with CP showed significantly lower pressure tolerance, indentation depth, stiffness, and CIE a* values, along with a flatter abdominal contour. The integrated model achieved a cross-validated ROC–AUC of 0.777 (95% CI, 0.674–0.872), indicating moderate discriminative performance. Conclusions: Quantitative algometric, geometric, and chromatic abdominal features were significantly associated with CP. Objective abdominal measurements may complement conventional AE by providing quantitative physical indicators that support more standardized and clinically relevant pattern-related assessment. These findings highlight the potential clinical utility of quantitative abdominal evaluation in improving diagnostic consistency. Trial registration: KCT0003369. Registered 23 November 2018. Full article

Diagnostic failure in invasive fungal infections (IFIs) remains a relevant and underrecognized cause of mortality, morbidity, delayed therapy, unnecessary antifungal exposure, and pharmacological selective pressure. Although major advances have been achieved in biomarkers, rapid diagnostic tests, molecular methods, imaging studies, and microbiological identification, timely diagnosis continues to be influenced by the interaction among host factors, pathogen-related factors, diagnostic tools, and healthcare system–related factors. This narrative review analyzes diagnostic failure in IFIs as a dynamic process that includes delayed, incorrect, and incomplete diagnosis. It examines its determinants and consequences in high-risk populations—critically ill patients, patients with hematologic diseases or hematopoietic stem cell transplant recipients, and neonates—as well as in invasive candidiasis, aspergillosis, mucormycosis, cryptococcosis, endemic mycoses, and infections caused by rare or emerging fungi. It also reviews how delayed sampling, decontextualized interpretation of biomarkers, incomplete microbiological identification, absence of antifungal susceptibility testing when clinically relevant, and fragmentation between clinical and laboratory teams contribute to adverse outcomes. Finally, it proposes a diagnostic-centered antifungal stewardship framework (AFSP-Dx) based on syndromic bundles, population-specific diagnostic algorithms, 48–72 h reassessment, and auditable indicators intended to support earlier recognition, more precise therapeutic decisions, and rational antifungal use. Full article

Manned–unmanned teaming (MUMT) represents a critical evolution in collaborative operations across domains including search and rescue, firefighting, surveillance, and defense. Despite widespread interest in MUMT capabilities, the field lacks a unified taxonomy for classifying and comparing system capabilities, hindering systematic development and technology integration. This paper presents a comprehensive framework for MUMT that addresses the fundamental challenge of organizing and assessing cognitive agent capabilities within human–machine teams. Building upon established automation frameworks, we propose a three-dimensional framework comprising information analysis and inference, decision-making, and action execution. Each dimension defines six hierarchical levels of teaming, ranging from human-only operations to fully autonomous cognitive agent capabilities. The framework distinguishes itself from existing taxonomies by explicitly modeling collaborative teaming rather than simple task delegation, incorporating transparency requirements, and addressing dynamic authority relationships between humans and cognitive agents. The proposed taxonomy provides researchers and engineers with a common vocabulary for MUMT development, enables gap analysis for technology roadmaps, and facilitates the identification of integration opportunities across organizational boundaries. Full article

Introduction: Nicotine dependence is a chronic disorder with both psychological and somatic components which, in the intensive care unit (ICU) setting, may affect the course of treatment through mechanisms related both to long-term nicotine exposure and to the consequences of its abrupt cessation. The aim was to collect and map the current knowledge on smoking-related complications, the prevalence of nicotine withdrawal symptoms in this group, and to identify and describe interventions used to prevent or alleviate nicotine withdrawal symptoms in patients hospitalised in the ICU. Methods: The review included sources retrieved from databases such as PubMed, CINAHL, Scopus, Web of Science, and the Cochrane Library, published in English, that met the PCC criteria, with no time restrictions. Results: Forty-four sources were included. Twenty-nine contributed evidence on smoking-related status as an exposure or associated factor, five explicitly focused on abrupt nicotine cessation or nicotine withdrawal syndrome, and fifteen addressed interventions; categories overlapped. Delirium was the most frequently investigated outcome in smoking-related exposure studies. Withdrawal-focused sources reported or discussed nonspecific manifestations, including agitation, restlessness, anxiety, craving, and delirium-like presentations, but no validated ICU-specific diagnostic approach or robust prevalence estimate was identified. NRT was the only intervention evaluated. Conclusions: Smoking-related status was associated with agitation and delirium in several observational studies; however, heterogeneous exposure definitions and inconsistent evidence syntheses preclude causal or general prognostic conclusions. Evidence specific to nicotine withdrawal syndrome was limited, and the effectiveness and safety of NRT remain uncertain. Implications for clinical practice included routine identification of nicotine dependence at ICU admission, early monitoring of withdrawal symptoms, individualisation of sedation management, careful and selective consideration of nicotine replacement therapy (NRT), education of the therapeutic team, planning of further care, and smoking cessation interventions. Full article

Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management. Full article

Background/Objectives: The Observational Clinical Human Reliability Assessment (OCHRA) system evaluates surgical performance by identifying intraoperative errors, yet evidence on error patterns and procedural safety in laparoscopic cholecystectomy (LC) remains limited. This study aimed to assess LC safety using established parameters and to describe intraoperative errors through the OCHRA system in patients with gallstone disease in Veracruz, Mexico. Methods: An observational, retrospective, analytical study was conducted between January 2022 and March 2025. Surgical videos from 11 surgical teams were reviewed. Intraoperative errors were classified using the OCHRA system across the three key steps of LC, while procedural safety was assessed through achievement of the Critical View of Safety (CVS) using the Doublet Photographic Score (DPS). Comparisons were performed according to the Parkland Grading Scale. Statistical analysis was conducted using SPSS version 26. Results: A total of 106 patients were included (67% women; mean age 45 ± 13 years; BMI 25.1 ± 3.2 kg/m²). Total LC was performed in 95% of cases and subtotal LC in 5%. Parkland grade 3 was the most frequent (32.1%). Overall, 3180 operative steps were evaluated, and 705 errors (22.1%) were identified. Procedural errors predominated across all phases (97–99%), mainly due to step repetition or additional steps, whereas execution errors were uncommon (1–3%). A satisfactory CVS was achieved in 54.7% of cases. No bile duct injuries were observed. Conclusions: The OCHRA system enabled detailed the identification of intraoperative error patterns and their relationship with surgical difficulty. Higher anatomical severity was associated with increased procedural errors and lower rates of adequate CVS achievement. These findings support structured video-based performance assessment as a complementary tool to established safety principles, with the potential to guide targeted training and improve surgical consistency in laparoscopic cholecystectomy. Full article

Background and Objectives: Water polo (WP) is a high-intensity, intermittent aquatic team sport that has been extensively investigated within sports science. While contemporary literature has examined the body composition and morphological characteristics of elite and international WP players, this study aimed to define the general body composition profile of world-class WP players and determine position-specific differences. Methods: The study involved 72 national team players from Serbia, Croatia, Greece, and Italy who participated in the Olympic Games, World Championships, or European Championships. Participants’ body composition was measured using the InBody 720 multichannel bioimpedance method. Ten different variables were examined to assess body structure regarding contractile and ballast components. Results: MANOVA revealed statistically significant differences in body composition across playing positions (Wilks’ lambda = 0.239, p < 0.000, η²_p = 0.402). The variables that had the greatest impact on the difference were: body mass, body fat and body mass index with the 47.0, 44.4, and 43.7% of explained total variance of the impact on the differences (p = 0.000), respectively. Conclusions: world-class WP players assigned to different playing positions differ significantly in body composition. These positional profiles should be considered in talent identification, selection procedures, training, and nutritional strategies to optimize performance models, considering the future evolution of the game at the highest competitive level. Coaches could use this information to initially select players for different specific positions based on anthropometric and body composition criteria. Full article

Introduction: The colossal European catfish (Silurus glanis) is the largest invasive freshwater fish on the Iberian Peninsula, reaching up to 2.8 metres and 130 kg in weight. Its large size makes it a highly valued target for recreational anglers, leading to repeated illegal introductions across several Iberian watersheds. Despite its appeal to anglers, this species is recognised as a high-impact invasive predator with substantial ecological consequences for European freshwater ecosystems. Recently, large catfish aggregations have been reported by anglers and environmentalists in several areas of Portugal and Spain. These impressive aggregations are frequently documented on videos and posted on social media networks (Facebook, WhatsApp groups, etc) or shared directly with our team members. Objective: Such records provide a valuable source of information for identifying the habitats and seasonal periods associated with aggregation behaviours and may therefore support more efficient management and population control actions. Methodology: We compiled information on European catfish aggregation events in Southern Iberia, namely date and location. The catfish aggregations were mapped, and their general habitat characteristics were described. Results: We recorded 10 catfish aggregation events, most of which occurred between May and June. These were generally located in transitional areas between lentic and lotic habitats, especially in narrower river sections. Possible explanations include hydromorphological constraints, seasonal environmental conditions, and species-specific behavioural responses, although these mechanisms require further investigation. Conclusions: Within the LIFE PREDATOR project, which focuses on the management of European catfish in the Tagus watershed, knowledge of aggregation locations is important to direct population control efforts aimed at reducing the abundance of this invasive fish. Moreover, the identification of common habitat characteristics may help predict other potential aggregation sites and improve the planning of future management actions. Full article

The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was conducted with 51 participants. Each participant completed hazard identification in four conditions: traditional–pre, GEN-AI–pre, traditional–post, and GEN-AI–post, with a short training session on hazard identification delivered between the pre- and post-stages. Effectiveness was measured using the F1 score, combining both precision and recall. For analysis, traditional and GEN-AI performance were compared at each stage using paired-sample t-tests, and the overall pattern was tested using a 2 × 2 repeated measures ANOVA. The results showed that GEN-AI support produced significantly higher performance than the traditional approaches at both stages (p < 0.05). The repeated measures ANOVA confirmed a strong overall method effect. However, the overall intervention effect was small, and the method × intervention interaction was negligible, with no statistically significant change over time (p > 0.05). Overall, the findings indicated that GEN-AI support improved hazard identification accuracy relative to traditional approaches in this dataset, with limited evidence of additional gains from the training intervention. This study contributes towards providing empirical evidence that GEN-AI improves hazard identification and strengthens proactive prevention, but final outputs need human validation. Full article

Cardiogenic shock (CS) is the most lethal complication of acute myocardial infarction (AMI), with a 30-day mortality of approximately 40–50% despite early revascularization. Temporary mechanical circulatory support (tMCS) devices, including the intra-aortic balloon pump (IABP), microaxial flow pumps (MAFP) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO), are used as adjunctive therapy in refractory shock, but evidence of a survival benefit is limited and often conflicting. The IABP-SHOCK II trial found no 30-day mortality reduction with IABP, supporting a Class III (no benefit) recommendation, whereas the DanGer Shock trial reported a 12.7% absolute mortality reduction at 180 days with the MAFP Impella CP in highly selected patients. In contrast, the ECLS-SHOCK and ECMO-CS trials showed no improvement in survival with early VA-ECMO and noted high complication rates. Real-world data reveal significant disparities between trial populations and clinical practice, highlighting limitations of current evidence, since many AMI-CS patients are older, in more advanced shock or have multiple comorbidities and would not meet typical randomized controlled trial (RCT) inclusion criteria. In clinical practice, in-hospital mortality with IABP or VA-ECMO often exceeds 50–60%. Given the heterogeneity of AMI-CS, rapid identification of appropriate tMCS candidates and personalized therapy are essential. Management guided by individual patient profile, hemodynamic stage and neurological status, supported by multidisciplinary shock teams, may improve timely triage, device selection and outcomes. This review emphasizes the need for individualized, protocol-driven care within structured shock systems to optimize tMCS use in AMI-CS. Full article

Background: Black Sub-Saharan African immigrants are among the fastest-growing immigrant populations in the United States, and their mental health needs, particularly with respect to depression, remain understudied. Cultural beliefs, linguistic frameworks, and coping practices in this population often diverge from Western psychiatric models, suggesting that conventional approaches may fail to capture how distress is experienced and expressed. Objective: This scoping review mapped literature on how Black Sub-Saharan African immigrant adults in the United States perceive, report, and respond to depression. Methods: Following PRISMA-ScR guidelines, six electronic databases were systematically searched for empirical studies published between 2000 and 2026. Two reviewers independently screened and extracted data using a standardized form. Data were analyzed using a narrative synthesis approach combining deductive thematic categorization across three predefined review domains with inductive identification of subthemes through iterative team discussion and consensus, with sociocultural, religious, linguistic, and structural factors examined as cross-cutting themes. Findings were synthesized narratively across three domains: perceptions of depression, reporting and communication, and responses to depression. Results: A total of 19 studies met the inclusion criteria (7 quantitative, 10 qualitative, 2 mixed methods; total N ≈ 1900), generating 24 themes. Perception themes highlighted cultural non-recognition of depression (12 of 19 studies), absence of equivalent terms in African languages (7 studies), spiritual explanatory models, and profound stigma. Reporting patterns showed predominant somatic symptom expression and very low disclosure to providers (2.6–4.2%), with depression prevalence ranging from 8.1% to 100% and no validated screening instrument identified for this population. Response themes emphasized religion and social support as primary coping strategies, with formal mental health utilization virtually absent due to structural, cultural, and intersectional barriers. Conclusions: Depression among Black Sub-Saharan African immigrants is widely experienced yet rendered invisible through interlocking cultural, linguistic, somatic, and institutional mechanisms, which this review terms an architecture of invisibility, leaving it largely unaddressed by formal mental health systems. The identification of only one intervention study underscores a substantial gap between documenting the burden of depression and advancing evidence-informed solutions. Culturally validated measures, faith-based intervention models, longitudinal designs, and attention to structural determinants are urgently needed. Full article