Search Results (1,616)

For historically marginalized groups and residents of low-resource rural communities, contact tracing is a critical tool for controlling the spread of communicable diseases. To improve its effectiveness, more research on identifying factors that influence an individual’s willingness to comply with contact tracers is needed. Therefore, we examined the association of race/ethnicity, contextual factors, and willingness to engage in contact tracing during the COVID-19 pandemic. The sample included 337 adults (56% Black/African American and 66% female). Approximately 80% of the participants indicated they would disclose the names of contacts. The results from the multivariate logistic regression analyses indicated lack of access to COVID-19 testing sites (aOR = 2.20; 95% CI = 1.08–4.48) and trust in health care providers (aOR = 7.57; 95% CI = 3.82–14.88) were significantly associated with willingness to share information with contact tracers. Race did not moderate the relationship between trust and engaging with contact tracers. The results suggest contact tracing is a viable strategy for mitigating disease transmission in rural communities, particularly when trust in health care providers is high and access to testing is limited, regardless of race. Public health officials should invest in maintaining contact tracing teams that include medical providers and prioritize building trusting relationships with all community members. Full article

Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model that augments a GoogLeNet/Inception-V1 backbone with a lightweight, spatial gating head inspired by ConvLSTM. Unlike static fusion (concatenation/summation) of multi-branch features, the proposed gated head performs per-location, learnable regulation of feature flow across branches, prioritizing diagnostically salient patterns while suppressing redundant activations. Experiments were conducted on the clinical-images subset of the Multimodal Augmented Skin Lesion Dataset (MASLD), an augmented derivative of HAM10000, using stratified train/validation/test splits, clinically motivated augmentation, and class-weighted optimization to mitigate skewed label frequencies. A controlled ablation study evaluates backbone choices and optimization settings and isolates the contribution of gated fusion relative to standard Inception heads. Across runs, the gated fusion strategy improves discriminative performance while remaining parameter-efficient, supporting the view that spatially adaptive regulation can enhance robustness on non-dermatoscopic clinical imagery. We further outline practical steps for calibration analysis and compression-aware deployment in clinical and edge settings. Full article

Voltage security assessment is increasingly challenged by stochastic demand growth and localized stress patterns that are not well represented by deterministic, single-snapshot analyses. This paper proposes a fully steady-state probabilistic stress-testing framework based on Monte Carlo simulation and Newton–Raphson AC power flow, jointly evaluating the minimum bus voltage magnitude $V_{min}$ (voltage-floor adequacy) and the scenario maximum Fast Voltage Stability Index $FVS{I}_{max}$ (worst-case line stress). Stress is injected selectively on screened weak buses by sampling a random stress footprint and intensity across three progressive levels (L1–L3), while preserving the local power factor. The approach is demonstrated on IEEE 14-, 30-, and 118-bus benchmark systems using $N=2000$ realizations per level, with 100% convergence across all cases. Across all systems, results show a consistent, monotone degradation of the voltage floor and a systematic increase in violation risk as stress intensifies. For the IEEE 14 system, the voltage-risk profile escalates rapidly, with $\mathbb{P}(V_{min}<0.90)$ rising from 0.16 (L1) to 0.54 (L3), while the worst-case FVSI tail strengthens markedly (p95 increasing from 0.1455 to 0.2081), indicating a growing likelihood of severe voltage-stress events. In contrast, the IEEE 30 and IEEE 118 systems exhibit milder shifts in central voltage levels but maintain substantial exposure relative to the 0.95 pu planning threshold, with $\mathbb{P}(V_{min}<0.95)$ reaching 0.79 and 0.74 at L3, respectively. Beyond risk magnitudes, the framework reveals a nontrivial structural phenomenon in worst-case line stress: as system size increases, stochastic stress outcomes become increasingly concentrated into a small number of dominant transmission corridors. Recurrence analysis at the highest stress level shows fragmented criticality in IEEE 14 (Top-3 lines sharing criticality), near-total dominance by a single corridor in IEEE 30 (>92% of cases), and complete dominance collapse in IEEE 118 (one corridor governing 100% of $FVS{I}_{max}$ events). These results demonstrate that probabilistic stress-testing can simultaneously quantify voltage-risk escalation and expose hidden structural bottlenecks that remain invisible under deterministic screening, providing a scalable diagnostic tool for planning-stage monitoring and reinforcement prioritization. Full article

Dimensioning the energy storage systems for a heavy-duty fuel cell hybrid electric vehicle is not straightforward. This study proposes a methodology to address this challenge, aiming to maximize efficiency while mitigating the aging effects on the energy storage systems. Various configurations of storage system ratios have been analyzed using the concept of hybridization percentage, which represents the ratio between the supercapacitor weight and the total weight of the energy storage elements. Simulations were conducted using models developed in AVL Cruise MTM. A case study is included to test the methodology, incorporating commercial components, a standard driving cycle, and a rule-based energy management strategy. The conclusions of this application example illustrate the types of results that can be obtained by using this hybrid energy storage system sizing methodology. Findings for this case study suggest that for cycles lacking extreme power peaks, non-hybridized configurations can be the optimal solution, as the battery size reduction outweighs the benefits of hybridization in terms of efficiency, achieving 76.08% without supercapacitors compared to 65.7% with a high hybridization grade of 32.4%, and overall cost. However, sensitivity analysis reveals that if the optimization weights are adjusted to prioritize aging over efficiency, the optimal configuration shifts to a 6.48% hybridization grade at a 0.3C threshold. Full article

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article

Multiple myeloma (MM) is a clonal plasma cell neoplasm characterized by autonomous immunoglobulin overproduction. Despite associations between serum metabolites and MM, causal mechanisms remain unclear. Here, we employed bidirectional Mendelian randomization (MR) using 452 serum metabolites to elucidate causal associations with MM risk. The inverse variance-weighted (IVW) method was prioritized, complemented by MR-Egger and weighted median (WM) analyses to address horizontal pleiotropy. Sensitivity analyses—including Cochran’s Q test, MR-Egger intercept evaluation, and leave-one-out (LOO) robustness checks—confirmed result stability. Pathway enrichment was performed using MetaboAnalyst 6.0. RNA-seq data were integrated to identify transcriptional regulators and signaling pathways mediating serum metabolite-driven MM. Among 21 metabolites significantly associated with MM, 8 exhibited protective inverse correlations, while 13 showed risk-enhancing effects. Sensitivity analyses further confirmed the validity of the observed relationships, while bidirectional MR confirmed no reverse causality. Pathway enrichment highlighted valine/leucine/isoleucine biosynthesis and biotin metabolism as pivotal pathways. Integrating transcriptomic data revealed 11 overlapping genes enriched in metal ion transmembrane transporter activity and glycosaminoglycan biosynthesis—chondroitin sulfate/dermatan sulfate. This study established a causal relationship between specific serum metabolites and MM and revealed that key genes may affect the development of MM through metabolic-epigenetic crosstalk, providing preliminary insights into potential therapeutic targets. Full article

Through Glass Via (TGV) technology has emerged as a promising solution for advanced packaging. While glass offers lower dielectric loss than silicon, its lower thermal conductivity raises concerns about electro-thermal coupling effects in high-power, high-frequency applications. Therefore, this study conducted an electro-thermal co-design of TGV grounded Coplanar Waveguide (CPW) and Radio Frequency (RF) TGV connected CPW structures. A high-power test platform was developed to investigate the electrical and thermal performance of these structures. The temperature distribution mechanism under high-power conditions was revealed. Under high power and high frequency, the decrease in surface conductivity affected by surface state and film layer composition leads to increased loss, triggering temperature rise and forming an electrothermal coupling loop. Under continuous wave operation (5–20 W), the temperature rise reaches 92.4 °C while insertion loss increases by only 0.4 dB. Under pulsed wave operation (25–100 W, 2.5% duty cycle), the temperature rise is merely 2.1 °C with insertion loss increasing by 0.3 dB. The quadruple-redundant design and reduces heat flux density, preventing localized hotspot formation. The pulse intervals suppress thermal accumulation, leading to lower temperature rise. Therefore, continuous wave applications should prioritize thermal management, while pulsed wave applications can focus on electrical performance optimization. Full article

Agile and flexible resource modelling is essential for informed decision-making in early-stage mineral project assessment, and in more advanced stages, particularly when compared with conventional deterministic geological modelling and single-estimate resource evaluations. This study presents a case of rapid scenario generation to view, interpret and test the impact of alternative geological and modelling assumptions, including the definition of geological domains, geological interpretation, grade estimation within domains, and the associated uncertainty. The workflows are implemented in Annapurna™ Resource, a cloud-native geostatistical platform designed to support agile, advanced, and multivariate modelling workflows. Focusing on the multi-commodity San Antonio–Potrerillos district, we demonstrate how rapid model construction enables the systematic evaluation of geological and statistical assumptions, contrasting deterministic estimates with probabilistic outcomes and testing their impact on estimated grades and tonnage under multiple scenarios for five elements: copper (Cu), molybdenum (Mo), gold (Au), silver (Ag), and arsenic (As). The approach provides quantitative measures of model reliability, identifies areas of high uncertainty, and supports the prioritization of new drilling to improve geological knowledge, exploration targeting, and resource classification. This case study highlights the value of fast-turnaround, probabilistic modelling not as a replacement for traditional resource reporting, but as a decision-support framework that enhances understanding of the geology, tests the sensitivity of assumptions, and accelerates learning throughout exploration and into operations. The main results suggest that additional drilling can be strategically placed to reduce the geological uncertainty derived from comparing the current interpretation with the probabilistic model built with indicator kriging. Furthermore, this has relevance in reducing the risk in the assessment of the metal content in each area of the deposit. Sensitivity analysis performed over key parameters of the estimation suggests that outliers’ treatment is the most impactful step during estimation. With current technological tools, it is possible to maintain a live resource model, which can be continuously updated to assess the impact of new data and decisions in near real time. Full article

Background/Objectives: Identification of tools to minimize opioid-related harms is critical in the U.S. The purpose of this study was to better understand community responder and healthcare provider perceptions and preferences regarding the design and function of a potential new opioid sensor device (OSD). Methods: Adults aged ≥ 18 years employed as community responders or healthcare providers in Alabama were recruited via email to participate in an anonymous online cross-sectional survey informed by the Unified Theory of Acceptance and Use of Technology (UTAUT). Primary outcomes were assessed via multiple-choice and 7-point Likert-type scales (1 = strongly disagree, 7 = strongly agree) and included the following topics: (1) past OSD utilization (4 items); (2) perceived importance of OSD design elements (15 items); (3) OSD function and cost preferences (3 items); and (4) UTAUT measures including perceived usefulness of OSDs (3 items), ease of use (4 items), social factors (4 items), resources (4 items), concerns (3 items), and intentions (3 items). Differences in UTAUT measures across professions were assessed via Mann–Whitney U tests, and predictors of OSD utilization intention were analyzed via multiple linear regression. Results: Respondents (N = 145) included pharmacists (40.0%), nurses (23.4%), physicians (14.5%), behavioral health (4.8%), social work (4.8%), and law enforcement (0.7%). Availability in hospital emergency departments was rated as the most important device element (mean [SD] score: 6.66 [0.80]), followed by sensitivity and specificity of the test (6.42 [0.98]), rapid detection time (6.42 [0.88]), ability to detect opioids in a broad range of substance (6.42 [0.93]), and availability in law enforcement offices (6.33 [1.08]). A 2–5 min detection time was rated as reasonable by 32.6% of respondents, with 53.0% preferring to pay <USD 15 per test. There were no statistically significant differences in UTAUT scale scores across professions. Perceived usefulness (β = 0.493; p < 0.001), social acceptance (β = 0.281; p = 0.023), and resource availability (β = 0.708; p = 0.002) were positive predictors and perceived ease of use was a negative predictor (β = −0.472; p = 0.007) of intention to use an OSD. Conclusions: Newly developed OSDs should consider prioritizing accessibility in hospital emergency departments and law enforcement offices, ability to detect a broad range of opioids, detection time between 2 and 5 min, and cost less than USD 15 per test. Future research may explore perspectives from a more diverse sample across multiple states and different professional roles. Full article

Complex transcriptional and epigenetic regulation, including variation in promoter-level cis-regulatory architecture, influences infertility. In this study, we performed a purely in silico analysis of the −1000 to −1 bp promoter regions (relative to the annotated TSS) of 13 human fertility-related genes using an integrated motif-discovery and annotation workflow (NNPP, MEME/STREME, Tomtom, FIMO/CentriMo, GOMo, and MethPrimer). Motif discovery identified multiple statistically supported de novo promoter motifs, and motif scanning mapped their occurrences across the analyzed promoters. Similarity searches against curated PWM databases did not yield significant matches under stringent criteria, consistent with divergent or under-represented motif patterns. Functional association analysis and CpG island profiling further highlighted promoter segments that merit prioritization for follow-up testing. As the analysis is purely in silico and restricted to a fixed promoter window, the identified motifs should be interpreted as candidate regulatory elements pending experimental validation. Full article

This paper investigates the impact of tubed propeller design on the energy efficiency and acoustic emissions of sub-250 g quadcopters. This study was motivated by the growing popularity of ultralight UAVs and the lack of experimental data addressing the trade-offs between noise, efficiency, and mass. Ten drone configurations with varying tube geometries and tip clearances were constructed using 3D-printed PLA+ frames and identical propulsion components. Experimental tests were conducted in a reverberation room to measure sound pressure levels and onboard energy consumption during hover. The results show that tubed configurations are 3–6.5 dB louder than untubed ones, with a noticeable shift toward higher frequencies. While tubes increased total power demand by 18–37% compared to the lightest design, they also reduced it by 3–17% relative to untubed drones of the same mass. The findings demonstrate that tubing improves aerodynamic efficiency only under same mass constraints and is most beneficial when mechanical protection is prioritized over noise and endurance. Full article

Scenario planning supports strategic decision-making under uncertainty by comparing multiple plausible futures. Impact indicators help to prioritize scenarios, while rank-based evaluations clearly communicate indicator relevance for participatory planning, policymaking, and resource allocation. Ensuring that rankings are both sensitive and robust is therefore essential. However, conventional statistical measures fail to fully capture ranking dynamics. They describe overall dispersion but cannot jointly assess the magnitude of rank shifts and the frequency with which items occupy specific ranks across scenarios. This study explores the novel application of Rao’s Quadratic Entropy (Rao’s Q) in scenario analysis to quantify ranking variability. A theoretical test demonstrates that Rao’s Q captures full variability in rankings and continuous values, suggesting it as a promising alternative to existing approaches. Rao’s Q is then applied to a climate change hotspot in Central Italy to evaluate changes in bio-energy landscape connectivity across forty-eight scenarios. Results reveal how land-use and climate changes affect landscape unit connectivity over time, identifying which are highly stable across scenarios or consistently critical, and thus highlighting planning priorities for mitigation, conservation, and sustainable urban development. Supported by openly available R code, this study demonstrates the relevance of Rao’s Q for participatory, scenario-based decision-making processes. Full article

With the significant increase in the number of motor vehicles in plateau regions, the adaptability and reliability requirements of diesel engines operating under high-altitude and cold conditions have become increasingly critical. In this study, a one-dimensional transient simulation model of the overall engine lubrication system was developed based on a physical experimental prototype. The multiphysics-coupled lubrication system was numerically modeled and analyzed, with particular emphasis on elucidating the influence mechanisms of high-altitude and cold environments on the startup performance of diesel engine lubrication systems. System responses under different ambient pressures (0.88 bar, 0.92 bar, 0.96 bar, and standard atmospheric pressure) and oil temperatures (30 °C, 55 °C, and 100 °C) were systematically investigated. In addition, variations in the opening degree of the oil pump pressure relief valve (closed, 4%, 30%, 60%, and 100%) were incorporated to reveal the governing effects of high-altitude and cold environments on lubrication system startup behavior. The results indicate that under high-altitude and cold conditions, the decrease in oil temperature is the dominant factor and exerts the most significant influence on the steady-state oil pressure and flow rate of the lubrication system. Variations in ambient pressure lead only to an equivalent shift in absolute oil pressure, with negligible effects on relative oil pressure, steady-state flow rate, response time, or filling rate. However, a reduction in atmospheric pressure leads to a decrease in the peak oil flow rate at the outlet of the oil pump. The opening degree of the pressure relief valve exhibits a nonlinear influence on the startup performance of the lubrication system, and significantly decreases the oil filling rate. This study innovatively develops a lubrication system performance prediction model under high-altitude, low-pressure, and low-temperature conditions. Calibrated using vehicle road-test data, the model quantifies for the first time the relative contributions of the three key factors to start-up lubrication performance, thereby providing a clear decision-making framework and prioritized improvement directions for the reliability-oriented design and safety threshold calibration of lubrication systems in high-altitude diesel engines. Full article

Background and Objectives: Early saphenous vein graft (SVG) failure remains a clinically significant limitation of contemporary coronary artery bypass grafting (CABG). Platelet function testing has been proposed to identify patients with an attenuated aspirin effect who may be at higher thrombotic risk. Therefore, this study aimed to determine whether preoperative aspirin non-responsiveness, assessed by the platelet function assay, is associated with early graft failure after CABG, as evaluated by CT coronary angiography. Materials and Methods: In this prospective observational study, consecutive patients undergoing elective, first-time isolated on-pump CABG with ≥1 SVG and preoperative aspirin therapy were enrolled. Platelet function was measured preoperatively using a point-of-care assay (ASPI, aspirin reaction units [ARU]), and patients were stratified as responders (<550 ARU) or non-responders (≥550 ARU). The primary endpoint was early SVG occlusion, detected by CT angiography performed before discharge after CABG. Secondary endpoints included postoperative cardiac and renal biomarkers, myocardial infarction, stroke, rehospitalization, and 30-day mortality. Results: Early CT-confirmed SVG occlusion occurred in 22/170 patients (12.9%) and did not differ between responders and non-responders (20/136 [14.7%] vs. 2/34 [5.9%]; p = 0.21). Cardiac biomarkers were similar between the groups for 4–24 h. Thirty-day mortality (1.5%), myocardial infarction (5.9% in each group), and stroke (2.2% vs. 5.9%) were infrequent and similar between groups. Rehospitalization was more common among non-responders, driven by deep wound infection (5.9% vs. 0%; p = 0.040). In exploratory analysis, females had a significantly higher early graft occlusion rate than males (27.3% vs. 8.6%; p = 0.004). Conclusions: Aspirin non-responsiveness, as assessed by ASPI testing, was not associated with early CT-confirmed SVG occlusion, and these data do not support intensifying antiplatelet therapy based solely on a single preoperative platelet-function measurement. Given the absence of serial postoperative platelet function measurements, future studies should prioritize postoperative platelet reactivity and different treatment strategies during the early window of graft vulnerability. Full article

This study examines the effectiveness of a purposefully constructed Foreign Language Learning Environment (FLLE) in developing foreign language communicative competence within Kazakhstani higher education. Focusing on four interrelated components—pedagogical resources, physical learning space, motivational strategies, and ICT integration—the study addresses the limited opportunities for authentic English communication characteristic of EFL contexts. A quasi-experimental design involving 69 undergraduate students was employed, with participants divided into experimental and control groups. Statistical analysis using the Mann–Whitney U test revealed significantly higher post-test results in the experimental group, particularly in speaking performance. The findings demonstrate that communicative competence development can be significantly enhanced when (1) instructional materials prioritize authentic, task-based communication, (2) classroom spaces are organized to facilitate face-to-face interaction, (3) motivational support is provided through speaking activities and extracurricular activities, and (4) ICT tools, including conversational AI, are used to extend communicative interaction beyond classroom time. Full article