Search Results (659)

This review paper synthesizes the application and evolution of environmentally extended input–output (EEIO) analysis in agricultural research, drawing on 647 publications (Scopus and Web of Science, 1978–2025) following the PRISMA method and using the Bibliometrix package in the R statistical computing environment. EEIO has become a leading method for assessing system-level environmental impacts by quantifying direct and indirect flows across complete supply chains. Bibliometric and thematic analyses reveal accelerated growth since 2015 and four principal domains of enquiry: emissions embodied in trade, water-resource management, energy and climate impacts, and the sustainability of agri-food supply chains. EEIO’s principal value lies in its capacity to support production- versus consumption-based accounting and to reveal intersectoral trade-offs that single-sector approaches overlook. However, standard EEIO frameworks remain constrained by fixed technical coefficients, coarse sectoral aggregation, and uncertainty in environmental extensions, which limit their capacity to resolve farm-scale processes, structural change, and feedbacks. To enhance analytical rigor and policy relevance, we advocate hybridization with life-cycle and farm-level data, development of higher-resolution multi-regional EEIO tables, incorporation of stochastic and scenario analyses, dynamic formulations to capture technological change, and adoption of open-data standards with transparent reporting. Advancing these priorities will improve comparability, reproducibility and the practical uptake of EEIO for evidence-based transitions in agricultural systems. Full article

Intensive horticultural and fruit production in Chile relies on pesticides, raising concerns about compliance with residue limits and the continued availability of highly hazardous pesticides (HHPs). Recent national monitoring data from Chile indicate frequent detections of HHPs in plant-based foods and repeated exceedances of Maximum Residue Limits (MRLs). This study analyzed official datasets from Chile’s Ministry of Agriculture, combining food residue monitoring data from 2015 to 2023 with pesticide sales and import statistics as additional indicators of availability. Active ingredients were standardized to ISO names and CAS numbers and classified for HHP status based on FAO/WHO hazard criteria, with cross-referencing to the Pesticide Action Network (PAN). The results present surveillance indicators focusing on detection rates and MRL exceedance proportions. Between 2015 and 2023, residues were identified in 82.8% of the collected samples. The most frequently detected residues overall included fludioxonil, acetamiprid, pyrimethanil, fenhexamid, and boscalid, indicating a detection profile primarily characterized by fungicides with substantial contributions from insecticides. When restricting to HHPs classified residues, the most frequently detected HHPs included tebuconazole, captan, iprodione, spirodiclofen, chlorantraniliprole, and carbendazim, indicating a detection profile primarily characterized by fungicides, with significant contributions from insecticides. Records of exceedances were concentrated within a limited subset of residues, predominantly acetamiprid and dithiocarbonates, and were most frequently associated with apples, table grapes, cherries, blueberries, pears, and certain vegetables, notably leafy vegetables. The active ingredients classified within HHPs included fludioxonil, fenhexamid, tebuconazole, cyprodinil, and lambda-cyhalothrin. The findings support agronomic decision-making by emphasizing GAP/PHI reinforcement, targeted monitoring, and IPM-based substitution options for activities involving recurrent HHP detection. Full article

Open and reusable road-safety microdata remain scarce in Latin America, particularly when incident records combine detailed temporal information, geocoded event locations, and a clear pathway for extracting fatal outcomes. This article documents a curated administrative dataset for Medellín, Colombia, containing 702,540 reported road-traffic incidents recorded between 1 January 2008 and 31 August 2025. The dataset includes 13 variables describing incident identifier, date, time, incident class, severity, interpolated address, geographic coordinates (latitude and longitude), and planning-unit identifiers. Although the complete dataset contains three severity levels—property damage only, injured, and fatal—it also enables the construction of a fully reproducible fatality subset by filtering incidents classified as fatal, yielding 2762 records. The database covers 21 planning units (communes) in Medellín and includes named neighborhood information for 394 neighborhoods in the complete dataset and 274 neighborhoods in the fatal subset. Spatial completeness is high for administrative data: geographic coordinates are available for 93.63% of all records and 90.77% of fatal incidents. To keep the emphasis on dataset documentation, this data descriptor focuses on compact statistical tables and an illustrative grouped logistic regression model of fatal outcomes. The dataset, accompanied by a complete data dictionary and reproducible R script, is intended to support secondary research in road-traffic safety, spatial epidemiology, transportation planning, urban mobility, and public health. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. 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

The data plane and the control plane are targets for Distributed Denial of Service (DDoS) attacks in the Software-Defined Internet of Things (SDIoT). Currently available studies rely on observations from a single network layer which limits the cross-layer attack analysis. This paper presents a synchronized, phase-aware, and a multi-layer traffic collection framework mimicking SDIoT environments under diverse DDoS attack scenarios. The data collected are the metrics captured at host, switch, and controller layers during normal, attack, and post-attack phases with strict temporal alignment. For capturing diverse DDoS attack behaviors in SDIoT environments, representative data plane attacks including volumetric flooding and switch-level flow table saturation were used. Control plane level attack targeting the SDN controller was implemented. The evaluation was done using a Mininet-based SDIoT testbed with a POX controller. Each scenario is executed across five independent runs with statistical validation. The proposed framework enables reproducible and time-aligned multi-layer analysis through standardized orchestration and automated logging. Results indicate that SDIoT DDoS behavior demonstrates differently across traffic, state, and resource-level metrics, and that accurate characterization benefits from temporally aligned multi-layer monitoring rather than relying solely on packet rate analysis. 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 study presents a comprehensive GIS-based multicriteria decision analysis (MCDA) framework for identifying prospective groundwater abstraction sites in a 9 municipality region of South-East Poland (Podkarpackie Voivodeship), covering approximately 830 km². The analysis integrated hydrogeological parameters (aquifer thickness, quality, productivity, water table depth, protection degree, recharge zones) with spatial risk factors (contamination sources, exclusion zones) and population density patterns. The MCDA approach provides a decision support tool for municipal authorities tasked with water infrastructure planning under conditions of limited baseline data. The framework demonstrates the utility of a carefully specified GIS-MCDA framework to provide such support, while highlighting the need for improved data sharing to enable full statistical validation. Full article

Background/Objectives: Stigma and fear related to human immunodeficiency virus persist in healthcare settings and negatively influence professionals’ attitudes and the quality of care provided to people living with human immunodeficiency virus. This study aimed to evaluate knowledge, attitudes, and stigma toward people living with human immunodeficiency virus among healthcare professionals in Spain and to explore strategies to reduce stigma. Methods: An explanatory sequential mixed-methods study was conducted. In the quantitative phase, an online questionnaire based on the International Planned Parenthood Federation instrument was disseminated nationwide through social media using non-probability convenience sampling. Quantitative data from 1013 healthcare professionals were analyzed using descriptive statistics and non-parametric tests (Kruskal–Wallis, chi-square, Friedman) with appropriate corrections for multiple comparisons. In the qualitative phase, 12 participants were purposively selected for semi-structured interviews to explain quantitative findings. Qualitative data were analyzed using reflexive thematic analysis. Integration occurred through joint interpretation and a joint display table connecting quantitative patterns with qualitative themes. Ethical approval was obtained from the Clinical Research Ethics Committee of Bellvitge Hospital in Catalonia. Results: A total of 1013 healthcare professionals from diverse specialties participated, and twelve completed qualitative interviews. Knowledge regarding transmission, prevention, and treatment of human immunodeficiency virus was high. However, more than half reported no specific training and felt unprepared to care for people living with human immunodeficiency virus. Despite knowledge, fear of contagion was common. Attitudes were positive, with acceptance of caring for people living with human immunodeficiency virus and rejection of common misconceptions. Qualitative findings revealed persistent stigma linked to insufficient training and cultural prejudice. Integration of quantitative and qualitative data revealed that knowledge alone does not eliminate fear, and that the gap between theoretical understanding and clinical confidence represents a critical barrier to stigma-free care. Conclusions: Although healthcare professionals in Spain demonstrate knowledge about human immunodeficiency virus, stigma and fear remain prevalent. Targeted education and interprofessional training are needed to ensure respectful, inclusive, and stigma-free clinical care. 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

A smart classroom integrates emerging technologies such as the Internet of Things and cloud computing, optimizes resource allocation, and transforms classroom interaction. A smart classroom encourages students to participate in a pressing concern as Chinese institutions steadily promote the development and implementation of such classrooms. Identifying the key spatial factors that influence learning engagement is essential. Current work has identified learning factors for a smart classroom that encourage dealing with learning environments, perceptions, experiences, and engagement by following a learner-centered educational philosophy. A questionnaire was designed to collect data from the Yibin Campus of Chengdu University of Technology and data was collected by using a survey method. The statistical analysis was applied to 156 valid student perception samples, which were empirically explored. Four factors related to classroom infrastructure and design are examined: physical environment, spatial layout, table and chair design, and technological equipment. Among these, technological equipment has the strongest effect on learning engagement. The findings provide practical guidance for designers seeking to optimize smart classroom environments, thereby enhancing teaching quality and improving learning efficiency. Full article

Background/Objectives: Standardized reporting of astigmatism outcomes is essential for comparability, reproducibility, and interpretation of refractive surgery studies. Vectorial analyses based on established metrics are increasingly required by major journals, yet no freely available tool exists for generating publication-ready vector analysis tables with statistical comparisons. This study presents AstigMETRICS, a standalone application for automated calculation, formatting, and statistical comparison of standard vector metrics in refractive surgery. Methods: AstigMETRICS was developed in MATLAB and compiled as a standalone executable requiring no programming knowledge. The software accepts preoperative, intended, and postoperative astigmatism data in spreadsheet format for both refractive and corneal measurements. It calculates seven standard vector metrics following the Alpins method: the target-induced astigmatism (TIA), surgically induced astigmatism (SIA), difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS). Statistical comparisons are performed automatically using appropriate parametric or nonparametric tests for paired and unpaired study designs, with p-values and Cohen’s d effect sizes reported. Results: AstigMETRICS generates standardized tables reporting the means, standard deviations, and clinically relevant proportions (percentage of eyes with an ME within ±0.50 D or ±1.00 D, and an AE within ±15°). Three simulated datasets were created to validate the software functionality across common surgical scenarios: a contralateral eye laser vision correction, toric phakic IOL implantation, and cataract surgery with toric IOLs. The output tables are displayed in standardized format and saved as high-resolution TIFF images suitable for publication. The software is freely available and a download link is provided in this article. Conclusions: AstigMETRICS enables clinicians and researchers to perform standardized, reproducible astigmatism vector analyses with built-in statistical comparisons. This freely available tool simplifies outcome reporting and improves methodological consistency in refractive surgery research. Full article

Table grapes are commonly consumed fresh, and their market value is largely determined by ripeness, grape color, mineral composition, and variety-specific aroma. This study integrated physicochemical ripening indicators (°Brix%, pH, titratable acidity, maturity index), CIELAB color parameters measured on the outer skin and inner sections, multi-element mineral profiling following microwave-assisted digestion (ICP-MS), and volatile organic compound (VOC) profile by HS-SPME/GC-MS to characterize five table grape varieties (Thompson Seedless, Isabella, Mevlana, Pepita Alfonso, and Red Globe). Significant differences in ripeness were found among the varieties (p < 0.01). Isabella had the highest soluble solids content (22.91 °Brix%), while Pepita Alfonso had the highest maturity index (79.89) and the lowest titratable acidity (0.22%). Color measurements also showed significant differences among the varieties (p < 0.01). Thompson Seedless exhibited the highest peel lightness/yellowness and chroma values, while Pepita Alfonso and Red Globe had a darker, lower chroma profile. Color index values differed between the peel and the inner cross-section depending on the variety (p < 0.01). Mineral composition was found to be variety-specific (p < 0.01). The dominant macroelements among the samples were K, P, and Mg, and statistically significant differences were also determined in trace elements (p < 0.01). A total of 42 volatile organic compounds (VOCs) were identified. Aldehydes were dominant in the volatile fraction (39.07–64.96%), nonanal contributed a significant percentage, and terpenoids (floral aroma note) were found in the highest percentage in the Isabella variety (28.87%). PCA applied to the integrated physicochemical, color, and mineral dataset enabled the clear discrimination of the five table grape cultivars. Pepita Alfonso was positioned toward positive PC2, and Red Globe occupied the opposite segment. Thompson Seedless and Isabella were separated mainly along PC1, while Mevlana showed an intermediate profile. SIMCA class-distance results confirmed the visual separation. All pairwise interclass distances were above the decision threshold (ICD > 3), ranging from 62,922 (Red Globe–Mevlana) to 806,425 (Isabella–Pepita Alfonso). Findings indicated robust cultivar-level classification for authenticity and quality control purposes. Overall, the integrated multi-domain approach is considered to provide a solid foundation for variety differentiation and quality-oriented harvesting and market management. Full article

This study evaluates the capability of multi-temporal vegetation indices derived from Sentinel-2 imagery to indicate groundwater discharge in a forested mountainous sector of the Northern Apennines (Italy). The NDVI was computed from Level-2A surface reflectance data (10 m resolution) and analyzed over five growing seasons (2017–2021), encompassing recurrent summer droughts. Aridity conditions were quantified using the Standardized Precipitation–Evapotranspiration Index (SPEI) derived from long-term meteorological records. The methodological framework integrates cloud-masked satellite observations, drought characterization, and spatial statistical comparison between known spring discharge zones and randomly distributed forested control points. NDVI values extracted within 100 m radius buffers, centered on spring outlets, were systematically compared with those from control areas located outside the shallow-water-table influence zone. During periods of negative SPEI (moderate-to-severe drought), spring-centered buffers consistently exhibited higher NDVI values than control sites, with the NDVI contrast increasing under severe arid conditions. This pattern indicates enhanced vegetation resilience supported by shallow groundwater availability. The results demonstrate that vegetation health anomalies, when constrained by homogeneous land cover and a consistent hydrogeological setting, can serve as indicators of the groundwater discharge likelihood. The proposed workflow provides a reproducible and cost-effective tool to support hydrogeological reconnaissance and spring inventorying in rugged mountainous environments where field-based surveys are logistically demanding. Full article

Machine learning (ML), including deep learning, kernel-based classifiers, and ensemble methods, is increasingly used to support clinical diagnosis in medical imaging, biosignal interpretation, and electronic health record (EHR)-based decision support. Despite rapid progress, many diagnostic AI studies still rely on limited retrospective evaluation and single summary measures (e.g., accuracy or AUC), creating a gap between reported model performance and evidence required for safe clinical adoption. This review proposes TRIAGE, a clinically grounded evaluation framework designed to organize diagnostic AI testing as an evidence pipeline aligned with real clinical use cases (screening, triage, second reading, and confirmatory testing). We summarize core discrimination metrics derived from the confusion matrix (sensitivity, specificity, predictive values, likelihood ratios, diagnostic odds ratio, and F-scores) and highlight the importance of prevalence and spectrum effects for interpreting predictive value and clinical workload. We further review evaluation strategies for multi-class and multi-label diagnostic tasks using appropriate aggregation methods (micro, macro, and weighted averaging) and set-based measures such as Hamming loss, exact match ratio, and Jaccard/IoU. Because diagnostic deployment is threshold-dependent, we integrate representation curves (ROC, precision–recall, lift, and cumulative gain) with calibration assessment and clinical utility analysis, including calibration slope, Brier score, and decision-curve analysis. We also address robustness and fairness evaluation, leakage-resistant validation designs (patient-grouped splits, stratified and temporal validation, and external validation), computational constraints relevant to deployment (latency, throughput, and energy use), and statistically sound model comparison with multiplicity control. A structured TRIAGE checklist table summarizing the evaluation parameters described in this review is provided in the main text to support reproducible and clinically interpretable reporting. Full article