Search Results (243)

Vernacular housing in the Andean region embodies long-standing building knowledge, environmental adaptation, and forms of social organization rooted in rural life. Over recent decades, these dwellings have undergone visible transformations linked to migration, changing aspirations, and the growing presence of industrialized construction materials. Rather than disappearing, vernacular forms have increasingly merged with contemporary solutions, producing hybrid architectural landscapes whose local dynamics are still insufficiently documented. This study analyzes the material, typological, and functional transformation of rural housing in Las Lagunas and Quisquinchir, two Indigenous communities located in Saraguro, Loja, Ecuador. A total of 192 houses were recorded through field observation and a structured digital survey implemented with KoBoCollect. The information was processed in R using descriptive statistics, contingency tables, chi-square tests, Cramér’s V, and standardized residual analysis. The findings show that architectural change in both communities does not occur through a simple replacement of traditional housing by modern models. Instead, vernacular, hybrid, and modern/eclectic typologies coexist within the same rural setting, revealing uneven and locally specific processes of transformation. The clearest differences emerge in construction materiality. Las Lagunas preserves a stronger presence of traditional wall systems, especially adobe and bahareque, while Quisquinchir shows a broader incorporation of industrialized materials, particularly concrete block. Statistical analysis confirmed significant associations between community and wall material, as well as between typology and wall material, whereas the relationship between community and architectural typology was comparatively weaker. Functional changes were also identified through the reduction or reconfiguration of intermediate spaces such as portals, patios, and corridors, suggesting a gradual shift toward more enclosed and specialized domestic environments. These results contribute empirical evidence for understanding architectural hybridization in Indigenous rural territories and support conservation and planning approaches capable of recognizing continuity, adaptation, and change within evolving Andean built landscapes. Full article

Highway construction projects face interacting risks that affect time, cost, regulatory compliance, and delivery resilience, all of which are closely linked to sustainable infrastructure development. This study develops a hybrid decision-analytical and machine learning framework for sustainability-oriented risk assessment in highway construction under limited-data conditions. The framework combines (i) the Analytic Hierarchy Process (AHP) and tabular Generative Adversarial Networks (GANs) to structure and stress-test expert judgement, and (ii) Probability-Impact (P-I) scoring with a Bayesian Networks (BNs) to model dependencies and derive posterior weights for probability of occurrence, impact on time, and impact on cost across four headline risk factors: weather-related risks, lack of labour, design-related risks, and permitting/regulatory risks. AHP provides transparent and auditable priorities with consistency checks, while GAN-generated synthetic tables support diagnostics for central tendency (P50) and tail behaviour (P90) under data scarcity. The calibrated P-I scores parameterise BN conditional probability tables, enabling the updating of BN scores; and factor-level decomposition of expected contributions. The framework produces model-ready posterior weights that support early planning, contingency allocation, mitigation prioritization, scenario analysis, and subsequent simulation and optimization studies. In sustainability terms, the proposed approach helps project teams improve climate resilience, strengthen regulatory and environmental preparedness, and reduce inefficient use of time, cost, and project resources in data-constrained settings. The results show that permitting/regulatory risks have the highest contribution to probability of occurrence and time impact, while weather-related risks exert the greatest cost impact. The framework therefore offers a practical tool for supporting more resilient, transparent, and sustainable highway project delivery when large historical datasets or questionnaire surveys are unavailable. Full article

This study examines the phenomenon of pseudoconfident knowledge in the context of the everyday use of generative artificial intelligence. By pseudoconfident knowledge, we mean a response that is substantively plausible, rhetorically coherent, and outwardly persuasive but is treated and understood as knowledge before its actual reliability has been established. Of course, we do not use the term “pseudoconfident knowledge” to denote knowledge in the strict epistemological sense. Rather, it denotes a special form of AI-generated content that acquires the status of knowledge in the user’s perception before its reliability, source-based justification, or factual correctness have been established. The problem here is not that such an answer is already knowledge but that it is prematurely accepted as knowledge because of its coherence, completeness, and rhetorical confidence. The aim of the study is to identify the epistemic gap between the everyday operational integration of artificial intelligence and the user’s critical ability to distinguish between persuasiveness and justification. The theoretical framework combines approaches to AI literacy, epistemic vigilance, and contemporary forms of digital mediation in the circulation of knowledge. The empirical basis of the study is an online survey of AI users. The analysis was conducted using descriptive statistics, contingency tables, and methods for testing associations between categorical variables. The results show that the key differentiating factor is not the frequency of AI use, but the strategy used in handling its responses. More epistemically robust positions are associated with practices of comparison, editing, and verification, whereas uncritical acceptance of the answer is associated with greater vulnerability to pseudoconfident knowledge. We conclude that the spread of generative artificial intelligence is producing a new socioepistemic problem that calls for a shift in emphasis from simple instrumental literacy toward a culture of verification, doubt, and epistemic responsibility. Full article

Background/Objectives: Mesenteric panniculitis (MP) is a chronic inflammatory and sclerosing disorder of the mesenteric fat. This study evaluated the occurrence and MRI appearance of MP in patients with chronic inflammatory bowel disease and chronic inflammatory bowel conditions. Methods: In this retrospective single-center study, 312 individuals underwent standardized 3-T MRI Sellink examinations, including 252 patients with clinically confirmed chronic inflammatory bowel disease or chronic inflammatory bowel conditions and 60 control patients without intestinal inflammation. MP was diagnosed when at least three of five characteristic MRI findings were present: increased mesenteric signal intensity on fat-saturated fluid-sensitive T2 sequences, fat ring sign, pseudocapsule, embedded micronodules, and displacement of bowel loops. Group differences were analyzed using contingency table analysis with Monte Carlo exact testing; Pearson’s chi-square test and Holm-adjusted pairwise post hoc comparisons were additionally performed. A p-value < 0.05 was considered statistically significant. Results: MRI signs of MP were present in 221/312 patients (70.8%) overall, including 220/252 patients (87.3%) in the disease cohort and 1/60 patients (1.7%) in the control group. Among patients with MP, the underlying diseases/conditions were Crohn’s disease (104/220, 47.3%), sigmoid diverticulitis (88/220, 40.0%), and ulcerative colitis (28/220, 12.7%). The overall distribution of MP extent differed significantly among diagnostic groups (Monte Carlo exact test: p = 0.030), although adjusted pairwise comparisons were not statistically significant. Topographically, MP showed a clear predominance in the left upper quadrant. Conclusions: MRI-defined features consistent with mesenteric panniculitis are highly prevalent in this selected cohort of patients with chronic intestinal inflammatory conditions when standardized criteria are applied. These findings suggest that MP-like mesenteric changes may represent a common imaging correlate of chronic intestinal inflammation rather than a rare incidental finding. MRI enables consistent detection and topographic assessment of MP. Full article

This systematic literature review investigates the use of physics-informed neural networks (PINNs) in electromagnetics by examining peer-reviewed articles and conference papers. By integrating governing physical laws into the loss function of a neural network, PINNs offer a mesh-free method in scientific computing. Records published between 2020 and 2025 were retrieved from the databases Scopus, Web of Science, and IEEE Xplore. The initial dataset comprised 500 records, from which 292 unique publications were identified. These were screened, yielding a final set of 139 publications that met predefined eligibility criteria. The analysis reveals growth in research activity, with a pronounced increase from 2022 onward. The literature predominantly addresses electrodynamic problems, employs feedforward neural network architectures, and adopts physics-only training. Two-dimensional problem formulations dominate, with three-dimensional formulations concentrated almost exclusively in electrodynamics, and no publications addressing electroquasistatics were identified. Contingency tables show that methodological choices are not independent of problem characteristics: medium selection correlates with physics regime, and architectural diversity increases with spatial dimensionality. Based on these findings, priorities for future work include: addressing the gap in electroquasistatics, extending three-dimensional formulations to static and quasistatic regimes, broader architectural experimentation in lower-dimensional settings, and increased integration of labeled data in static electromagnetics. To support methodological consistency and reproducibility, a reporting checklist for future PINN-based electromagnetics publications is proposed. Full article

Background/Objectives: Cardiovascular diseases are a major cause of morbidity and mortality worldwide. Acute chest pain is a frequent reason for emergency department presentation and requires structured evaluation to identify life-threatening conditions. This study evaluated clinical characteristics, cardiovascular risk profile, risk stratification patterns, and hospitalization status in adults with acute chest pain. Methods: We conducted a retrospective study using registry data from Arad County Clinical Emergency Hospital between January 2021 and December 2024. Adult patients with documented acute chest pain were included according to predefined criteria. Demographics, comorbidities, clinical presentation, troponin values, hospitalization status, and HEART, and EDACS categories were extracted when available. The Marburg Heart Score was also assessed as an exploratory complementary score. Statistical analysis used descriptive statistics, contingency tables, and chi-square testing, with available-case analysis. Results: Overall, 2070 patients were included. Most patients were aged 35–54 or 55–69 years. Hypertension and diabetes mellitus were the most common comorbidities, and pressure-like chest pain predominated. In unadjusted analyses, HEART and EDACS categories were significantly associated with hospitalization status across all study years. Score categories were significantly associated with hospitalization status across all study years. Age was consistently associated with cardiovascular comorbidity burden and higher-risk score categories. Conclusions: Structured risk stratification scores were associated with hospitalization status, while age was associated with cardiovascular risk burden. Full article

Background/Objectives: Early identification of ST-segment elevation myocardial infarction (STEMI) at first medical contact remains challenging, as high-sensitivity troponin T may be insufficiently sensitive during the initial phase of myocardial injury. Readily available complete blood count (CBC)-derived inflammatory indices may provide complementary early diagnostic signals. This study aimed to evaluate whether baseline CBC-derived inflammatory indices differ between STEMI and NSTEMI and whether they provide adjunctive discriminatory information at presentation (0 h) in patients with acute coronary syndrome (ACS). Methods: A 12-lead electrocardiogram (ECG), high-sensitivity troponin T, and CBC were obtained at presentation from 252 patients with ACS (195 STEMI and 57 NSTEMI). Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and 2 × 2 contingency tables to determine the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios. Results: High-sensitivity troponin T demonstrated the highest specificity (84.44%) and PPV (92.93%), supporting its role as a confirmatory biomarker; however, its low sensitivity (50.83%) and NPV (29.92%) may reduce its utility during early assessment. In contrast, WBC and neutrophil counts demonstrated relatively favorable discriminatory performance at presentation (AUC > 0.72; Youden’s index > 0.40). Among composite indices, NLPR demonstrated the highest sensitivity (88.66%) and NPV (53.19%), along with the lowest negative likelihood ratio (0.25), suggesting potential adjunctive value during early assessment. NLR, SII, SIRI, and adjusted NLR showed moderate performance, with aNLR providing a balanced sensitivity (67.01%) and specificity (74.55%). Conclusions: CBC-derived inflammatory indices, particularly neutrophil-based markers such as NLPR, may provide adjunctive discriminatory information during the early assessment of patients with ACS, particularly at first medical contact when baseline hs-Troponin T sensitivity may still be limited. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Identifying similarities in data is fundamental to discovery in science. Measuring or ranking similarity is a key way of reducing the dimensionality of data, is at the heart of many data intensive algorithms and can also be used directly for some applications. This paper extends our understanding of a relatively simple similarity problem. Our primary application is spectral-based fault localisation (SBFL), in which a computer program is run with a large number of test cases and data is collected on which statements are executed in each test case. For each statement, the set of test cases in which it is executed is compared to the set of test cases that failed, and this is used to rank the statements to help locate bugs, an instance of what we call the similarity to a single set (STASS) problem. This paper is primarily theoretical but some contributions are validated with SBFL experiments. Set similarity is equivalent to similarity of binary vectors or two-by-two contingency tables. The problem is also equivalent to converting two-dimensional data with a “partial order”, such as points on a rectangular grid, to a one-dimensional total order. Even when the raw data is not binary, we are often interested in comparing binary classifiers for the data, such as diagnostic tests, and comparing binary classifiers is an instance of the STASS problem. More than a hundred set similarity measures have been proposed in the literature and hundreds of thousands have been evaluated for SBFL, but there is very little understanding of how best to choose a similarity measure for a given domain. This work discusses numerous properties and forms of symmetry that similarity measures can have. It refines previously identified properties so they are no longer incompatible, identifies new forms of symmetry, defines ordering relations over similarity measures, and proposes a new statistic that can be used to help choose a good similarity measure for a given domain. Full article

Background: Ultrasonographic assessment of the upper airway has emerged as a non-invasive method for evaluating obstructive sleep apnea (OSA), offering advantages including wide accessibility and absence of ionizing radiation. However, the diagnostic validity and standardized screening thresholds for ultrasonographic parameters remain unclear. Methods: A systematic literature search was conducted in PubMed, Scopus, and ScienceDirect from database inception to February 2026. Eligible studies enrolled adults with suspected OSA, used in-laboratory polysomnography (PSG) as the reference standard, and assessed upper-airway structures using ultrasonography. Studies reporting diagnostic performance metrics (sensitivity, specificity, AUC, or diagnostic thresholds) or quantitative associations with apnea–hypopnea index (AHI) were included. Risk of bias was assessed using QUADAS-2. Owing to methodological heterogeneity, findings were synthesized qualitatively. Results: Six studies (n = 473 participants) met the inclusion criteria. Evaluated parameters included tongue base thickness, lingual artery distance, lateral pharyngeal wall thickness, dynamic airway dimensional changes, and tongue stiffness. Three studies reported threshold-based diagnostic performance, although only one provided a complete diagnostic contingency table. Dynamic retropalatal percentage change demonstrated the highest diagnostic performance (AUC up to 0.989; sensitivity 97%; specificity 93.3%). Other studies demonstrated significant morphologic associations with OSA severity but lacked externally validated diagnostic thresholds. Conclusions: Ultrasonographic upper-airway assessment demonstrates promising structural and functional correlates of OSA. However, robust diagnostic accuracy evidence and standardized thresholds remain limited. Further prospective studies with standardized acquisition protocols and predefined diagnostic thresholds are required before ultrasound can be incorporated into routine OSA screening pathways. Full article

Background/Objectives: Chronic Obstructive Pulmonary Disease (COPD) is frequently associated with multiple comorbidities that influence prognosis. The comorbidome is a graphical representation of both the prevalence and strength of association of each comorbidity with COPD, allowing rapid identification of the most relevant risk factors. The aim of this study was to evaluate the association between comorbidities and mortality in patients with COPD using a comorbidome approach. Methods: We conducted a retrospective cohort study of 500 patients aged ≥40 years with COPD treated between 2005 and 2020 at Clínica Universidad de La Sabana (Chía, Colombia). Demographic variables, comorbidities, and mortality were recorded. The prevalence of each comorbidity was expressed as a percentage, and their association with mortality was assessed using odds ratios (OR) derived from univariate contingency tables with 95% confidence intervals (95% CI). The comorbidome was constructed by plotting the inverse odds ratio (1/OR) against the prevalence of each condition. Results: The mean age was 76.6 years (SD 11.3). Overall mortality was 28.4%. The most prevalent comorbidities were hypertension (45.2%) and smoking (38%). Comorbidities significantly associated with mortality in unadjusted analyses included congestive heart failure (OR: 4.28; 95% CI: 2.55–7.18), arrhythmias (OR: 2.86; 95% CI: 1.60–5.13), acute myocardial infarction (OR: 2.58; 95% CI: 1.52–4.38), moderate or severe renal disease (OR: 2.08; 95% CI: 1.07–4.04), peripheral vascular disease (OR: 1.94; 95% CI: 1.10–3.40), and hypertension (OR: 1.66; 95% CI: 1.12–2.46). Conclusions: The most prevalent comorbidities were hypertension and smoking. However, the conditions significantly associated with mortality in unadjusted analyses were congestive heart failure, arrhythmias, acute myocardial infarction, moderate or severe renal disease, peripheral vascular disease, and hypertension. Full article

Background: Fixed night work and rotating schedules including nights may contribute to migraine via sleep disruption and circadian misalignment, but evidence is inconsistent and definitions vary. This systematic review and meta-analysis compared past-year migraine prevalence in nurses working night-inclusive schedules versus day-only or non-night schedules. Methods: Following PRISMA 2020 and registered in PROSPERO (CRD420261304288), we searched PubMed, Scopus, Web of Science, CINAHL, and the Cochrane Library from inception to 3 February 2026 (English/Spanish). Observational studies in nurses (≥18 years) reporting past-year migraine prevalence by shift pattern were eligible. All included studies assessed past-year prevalence; pooled PRs reflect 1-year prevalence. Crude prevalence ratios (PRs) were calculated from contingency tables and pooled quantitatively. Risk of bias was assessed with the JBI prevalence checklist. Results: We identified 54 records; 4 studies were included (N = 3843) of which 3323 participants contributed to the comparative meta-analysis because complete disaggregated data were available to construct contingency tables. The pooled association between night-inclusive schedules and migraine prevalence was not statistically significant (PR = 0.95, 95% CI 0.82–1.10; I² = 0%). Secondary intensity contrasts were inconclusive (high vs. low: PR = 1.24, 95% CI 0.46–3.36; high vs. zero nights: PR = 0.85, 95% CI 0.38–1.93). Conclusions: Current nurse-specific evidence does not show a statistically significant difference in migraine prevalence between night-inclusive and non-night schedules; however, the small evidence base and limited generalizability preclude firm conclusions. Future longitudinal studies are needed to clarify this association. Full article

One of the first serious attempts to study the indices that assess the association between the variables of a 2 × 2 contingency table was undertaken in the mid-1880s. Central to this study is the 1884 tornado observation/prediction data collected by Seargent John Park Finley (1854–1943), while working for the US Army Signal Service, and the controversial index he proposed to evaluate the success of his tornado predictions, which he denoted i. Subsequent improvements to Finley’s index were proposed, all of which pre-date the development of association measures made by pioneers such as Sir Francis Galton and Karl Pearson. This paper discusses Finley’s data, his index i, and the improvements made to this index. We also give historical context to Finley and his successors and their place in the early development of contingency table analysis. Full article

This paper examines how companies in Slovakia and Poland perceive AI tool utilization and report changes in selected performance indicators after AI adoption (annual turnover, BIT, and employee error rates), and whether these assessments differ across firm demographics (country, company size, and length of operation). Using a CAWI survey of 865 firms and a contingency-table framework with Pearson’s chi-square tests and Cramer’s V effect sizes, we observe statistically significant—yet predominantly weak—associations between firm demographics and both AI utilization and self-reported performance changes. The findings provide actionable implications for managers and policy-support institutions seeking to accelerate AI adoption and value realization in central Europe, while acknowledging the limitations of cross-sectional self-reported data. Full article

The transition towards high-renewable power systems introduces high-dimensional nonlinearity and uncertainty, rendering traditional offline look-up table schemes prone to control mismatch against “unseen” contingencies. Meanwhile, existing response-driven approaches face a dilemma between the computational latency of physics-based optimization and the safety risks of end-to-end AI. To bridge this gap, this paper proposes a Response-Driven Optimal Emergency Control Framework that ensures both millisecond-level speed and rigorous physical constraints. First, a deep learning-based predictor is employed to extract spatiotemporal features from real-time PMU data, enabling high-fidelity prediction of stability margins. Crucially, instead of direct black-box control, the data-driven model is utilized to derive linear control sensitivities via a batch-processing perturbation mechanism. This transforms the intractable Transient Stability Constrained Optimal Power Flow (TSC-OPF) problem into a real-time solvable Linear Programming model. Case studies on a regional AC/DC hybrid grid demonstrate that the proposed framework achieves high prediction accuracy and effectively restores stability in mismatch scenarios where traditional schemes fail. Furthermore, the decision speed of the proposed method is significantly improved compared to traditional time-domain simulations, thus strictly satisfying the real-time requirements of the second line of defense. Full article