Search Results (18,093)

Background: Mobile health has been increasingly integrated into tuberculosis care to support patient education, communication, and treatment engagement. However, evidence remains limited regarding whether positive engagement with mHealth is associated with knowledge, attitudes, and practices among patients with multidrug-resistant tuberculosis. This study aimed to examine the association between positive mHealth engagement and knowledge, attitude, practice, and total KAP among patients with multidrug-resistant tuberculosis, and to evaluate the psychometric properties of the engagement score used as the primary exposure variable. Methods: A cross-sectional study was conducted among patients with multidrug-resistant tuberculosis. A positive mHealth engagement score was constructed from 12 mHealth-related items after harmonizing item directionality so that higher scores indicated more favorable engagement. The 12 items reflected five behavioural domains: intensity of use, ease and acceptability of use, functional engagement (communication with providers, access to health information, and perceived benefit for disease self-management), continuity of use, and barriers to sustained engagement. The composite score was computed as the mean of the 12 standardised items, with higher values indicating more positive engagement. Internal consistency was assessed using Cronbach’s alpha and corrected item–total correlations, and structural validity was explored using principal component analysis. Adjusted linear regression models were used to examine associations between the engagement score and Knowledge, Attitude, Practice, and total KAP scores, controlling for age, sex, and occupation. Sensitivity analyses were performed after excluding a poorly performing item, and tertile analyses were used to assess dose–response patterns. Results: The positive mHealth engagement score showed good internal consistency, with a Cronbach’s alpha of 0.852. One item demonstrated poor psychometric performance, and Cronbach’s alpha increased to 0.864 after its exclusion. The data were suitable for dimensionality assessment, with a Kaiser–Meyer–Olkin value of 0.870 and a significant Bartlett’s test. Principal component analysis identified a dominant first component explaining 43.29% of the total variance. Using the refined score, higher positive mHealth engagement was significantly associated with higher Knowledge scores (β = 2.06; 95% CI: 1.28–2.85; p < 0.001), higher Attitude scores (β = 4.68; 95% CI: 3.30–6.06; p < 0.001), and higher total KAP scores (β = 6.68; 95% CI: 4.62–8.74; p < 0.001), whereas no significant association was observed for the Practice score (β = −0.07; 95% CI: −0.63 to 0.49; p = 0.804). In tertile analyses, Knowledge, Attitude, and total KAP scores increased significantly across engagement levels, while Practice scores did not. Conclusions: Positive mHealth engagement was associated with better knowledge, attitudes, and overall KAP among patients with multidrug-resistant tuberculosis, but not with practice. These findings are associative; the cross-sectional design does not permit causal conclusions. The engagement score demonstrated good reliability and acceptable structural validity and may be a useful summary measure for evaluating patient interaction with mHealth interventions in tuberculosis care. Integrated strategies combining mHealth with clinical follow-up, adherence counseling, and structural support may be needed to translate informational and attitudinal gains into practice change. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

Background: Accurate identification and positioning of thoracic devices on chest radiographs is critical for patient safety in intensive care. Multimodal large language models (LLMs) offer potentially generalizable automated evaluation, but their performance in this domain is underexplored. Methods: Three multimodal LLMs (GPT-4o, gpt-4o-2024-08-06; Gemini 3.1 Flash Lite Preview; Claude Sonnet 4.6) were evaluated on 4813 chest radiographs from the RANZCR CLiP dataset for device presence and positioning of ETT, NGT, CVC, and Swan–Ganz catheters. Performance was quantified with 95% Wilson confidence intervals, balanced accuracy, MCC, Cochran’s Q, Bonferroni-corrected McNemar, and Cohen’s/Fleiss’ kappa. Six additional analyses were performed: a blinded paired reader study (n = 377; two board-certified radiologists, blinded to ground truth and to all LLM outputs), external validation on PadChest (n = 200, device-presence detection only—PadChest lacks granular position labels), three-variant prompt-sensitivity analysis (n = 103), repeat-inference stability across three runs (n = 50), systematic error taxonomy, and a failure-case analysis. Results: Device-presence performance varied widely across models; abnormal-position sensitivity was uniformly poor (MCC ≤ 0.028; balanced accuracy 0.41–0.53). Inter-model agreement was poor to slight (Fleiss’ κ: 0.005–0.383 for presence; −0.280 to −0.025 for classification). Radiologists numerically outperformed all three LLMs in 42/42 paired comparisons; the superiority was statistically significant after Bonferroni correction in 33/42 (32/42 at p < 0.001). PadChest replicated the negative finding for device-presence detection (malposition not externally validated). Prompts and inference stochasticity introduced 2–3× sensitivity swings and run-to-run κ from 0.20 to 0.85. Case failures concentrated systematically in multi-device cases (p < 0.0001) but not in abnormal-position cases (p = 0.14). Conclusions: Current general-purpose multimodal LLMs are not yet reliable for autonomous thoracic-device assessment; their failure patterns are structurally characterizable across models, prompts, and case types and support, at most a circumscribed role, as adjunct device-presence screening tools. The findings do not generalize to purpose-built, regulator-approved clinical AI systems. Full article

The three-dimensional (3D) texture of pavement surfaces critically influences skid resistance, noise, and rolling resistance, but high-resolution 3D acquisition is time-consuming and requires specialized equipment. This study investigates the use of a conditional generative adversarial network (cGAN) to predict 3D pavement texture from more efficiently acquired 2D reflectance intensity images. Co-registered 3D height maps and intensity data were captured using a high-precision line laser scanner. The intensity images were preprocessed into three representations: raw intensity, histogram-equalized, and watershed-segmented images. Four input configurations, each stacking three channels of these representations, were evaluated to determine the optimal input. Additionally, the proposed cGAN was compared with mainstream image-to-image translation models using the best-performing input. Model performance was assessed using root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The results show that the configuration using only histogram-equalized images achieved the best overall performance (SSIM = 0.4065). In the model comparison, the proposed cGAN attained the highest SSIM. These findings indicate that the proposed approach can produce 3D texture maps that capture the main structural features of pavement surfaces, suggesting its potential for efficient surface characterization. Full article

Background: Late-onset neonatal sepsis (LONS) remains a major cause of morbidity in preterm neonates admitted to the neonatal intensive care unit (NICU), yet the contribution of vitamin D status to neonatal infectious susceptibility remains uncertain. Objective: To evaluate clinical and demographic variables and serum vitamin D levels assessed at birth in preterm neonates with and without LONS. Methods: A retrospective observational cohort study was conducted in a tertiary NICU in northeastern Mexico between May 2023 and October 2024. Preterm neonates (<37 weeks of gestation) with serum 25(OH)D measured within the first hour of life were included. Vitamin D status was classified as sufficient (≥30 ng/mL), insufficient (20–29 ng/mL), or deficient (<20 ng/mL). LONS was defined as sepsis occurring after 72 h of life. Comparisons between neonates with and without LONS were performed using Fisher’s exact test for categorical variables and Student’s t-test or Mann–Whitney U test for continuous variables, as appropriate. Results: Twenty-nine preterm neonates were included (mean gestational age: 32.0 ± 2.6 weeks; mean birth weight: 1748 ± 545 g). The mean serum 25(OH)D level at birth was 35.5 ± 13.0 ng/mL. LONS occurred in 31% (9/29) of neonates, of which 55% were microbiologically confirmed. No significant differences were observed in vitamin D levels between neonates with and without LONS (35.0 ± 12.0 vs. 35.7 ± 13.7 ng/mL; p = 0.899). Vitamin D deficiency was not associated with LONS (OR 1.13, 95% CI 0.09–14.28). The prevalence of vitamin D deficiency was low (10%) in this cohort. Conclusions: A clear association between serum 25(OH)D levels at birth and the development of LONS could not be demonstrated in this small exploratory cohort. Given the limited sample size and low prevalence of vitamin D deficiency, further multicenter prospective studies are required to better understand the potential relationship between vitamin D status and neonatal infectious outcomes. Full article

Background/Objectives: Acute exercise can influence executive function, but the neurophysiological responses linking exercise to cognitive change remain unclear. Functional near-infrared spectroscopy (fNIRS) provides a feasible method for assessing prefrontal oxygenation during movement-based exercise. This study examined whether prefrontal oxygenated hemoglobin (oxy-Hb) during exercise was associated with subsequent changes in inhibitory control after aerobic and game-based exercise in young adults. Methods: Twenty-four healthy young adults completed aerobic and game-based exercise conditions in a randomized, counterbalanced, within-subject design. The aerobic condition consisted of jogging, whereas the game-based condition consisted of a pickleball-based activity. Exercise intensity was monitored during both conditions. Prefrontal oxy-Hb was recorded during exercise using fNIRS, and inhibitory control was assessed before and after each condition using an Eriksen Flanker task. The primary behavioral outcome was Flanker cost improvement, and the primary fNIRS outcome was mean baseline-corrected prefrontal oxy-Hb during exercise. Results: Exercise intensity was comparable between conditions. Greater mean prefrontal oxy-Hb during exercise was significantly associated with greater improvement in Flanker cost (β = 3.71 ms per 0.01 μM, 95% CI [2.13, 5.30], p < 0.001). Game-based exercise elicited a higher mean prefrontal oxy-Hb during exercise than aerobic exercise. No significant condition difference was observed for Flanker cost improvement. Conclusions: Prefrontal oxygenation during exercise was associated with subsequent improvement in inhibitory control. These findings suggest that neurophysiological responses during exercise may account for some between-person variability in acute exercise-related cognitive benefits. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

This research proposes a conceptual framework that employs generative artificial intelligence (AI) to automatically generate dynamic biomimetic façade designs for reducing building carbon emissions. Biomimetic façades show strong carbon-reduction potential; however, their application remains limited by interdisciplinary requirements and time-intensive optimization processes. Existing studies primarily rely on traditional multi-objective optimization for energy performance, while machine learning integration and carbon-oriented evaluation remain limited in biomimetic façade research. To address this gap, this study proposes an AI system for biomimetic façade generation in tropical climates by combining reinforcement learning–based multi-objective optimization with deep learning–based parameter prediction models. A carbon payback assessment method integrating operational and embodied carbon is further proposed to evaluate carbon reduction performance. Preliminary validation through pilot experiments and K-fold cross-validation achieved an average RMSE of 8.7% and an average R² value of 0.547, while façade parameter prediction for new building conditions could be completed within approximately 10 s. Simulated cases also indicated that the generated façade strategies generally remained within predefined carbon payback thresholds under different material configurations. The framework supports carbon-oriented biomimetic façade design and early-stage low-carbon design decision-making. Full article

Physical inactivity and declining health-related physical fitness among college students are growing global public health concerns. High-intensity interval training (HIIT) is a time-efficient strategy to improve multiple components of health-related physical fitness. Emerging evidence suggests that exercise timing may influence physiological responses and adherence through circadian rhythm regulation; however, its feasibility in college settings, particularly in China, remains unclear. This study aims to evaluate the feasibility and preliminary effectiveness of an eight-week time-specific HIIT programme among Chinese college students, and to compare outcomes between morning and evening training. In this mixed-methods feasibility randomized controlled trial, approximately 72 students with low physical activity levels and intermediate chronotype will be randomly assigned to a morning HIIT group, evening HIIT group, or control group. Intervention groups will complete three HIIT sessions per week for eight weeks. Primary outcomes include feasibility indicators (recruitment, retention, adherence, and data completeness). Secondary outcomes assess changes in body composition, cardiorespiratory fitness, muscular strength, endurance, and flexibility. Quantitative data will be analysed using descriptive and repeated-measures methods, while qualitative interviews will be thematically analysed. Findings will inform the feasibility and design of future large-scale trials and contribute to chrono-exercise research in college populations. Full article

Background: Chronic obstructive pulmonary disease (COPD) is commonly managed alongside multimorbidity, polypharmacy, recurrent treatment escalation, and older age, all of which increase vulnerability to drug–drug interactions (DDIs). We aimed to synthesize the main DDI domains relevant to COPD pharmacotherapy and to distinguish harmful DDIs from beneficial combination therapy and formal compatibility findings. Methods: We performed a narrative review using structured literature searches and citation tracking to evaluate COPD-related studies. We prioritized direct COPD-specific DDI evidence, while also including mechanistic, class-specific, and contextual studies when direct evidence was lacking. Retained evidence included observational cohorts, prescribing studies, pharmacokinetic trials, case reports, and systematic reviews. Results: The reviewed literature indicates that DDI vulnerability in COPD is driven less by isolated drug pairs than by overall regimen complexity, multimorbidity, aging, fragmented prescribing, and high-intensity treatment periods such as exacerbations, hospitalization, and discharge. Key DDI domains included cardiopulmonary co-treatment, QT-related vulnerability, and potential or clinically relevant interactions amplified during exacerbations. Inhaled therapies are not universally interaction-free, particularly with strong metabolic inhibitors. Psychotropics, frailty, dementia, and palliative care further increase clinical complexity. However, beneficial bronchodilator combinations and formal compatibility studies demonstrate that not all multidrug COPD regimens are harmful. Conclusions: In COPD, DDI assessment should focus on the full treatment regimen and not be limited to a set of iconic drug pairs. Clinicians must focus on exacerbation-related prescribing, QT-active drugs, theophylline exposure, psychotropic co-medication, and vulnerable subgroups such as older, frail, and palliative patients. Pharmacist-supported drug review, drug reconciliation, and selective deprescribing are key strategies for reducing clinically relevant DDI burden in COPD. Full article

Background/Objectives: Complex chronic patients represent a heterogeneous and high-risk population, for whom accurate prognostic tools are essential to guide clinical decision-making, optimize resource allocation, and support tailored interventions. The PROFUND index was developed for mortality prediction in polypathological patients, but its performance has not yet been evaluated in an ambulatory integrated care model. Methods: A retrospective observational study was conducted using two cohorts. Cohort H included complex chronic patients admitted to the Internal Medicine Department between March 2023 and February 2024. Cohort A comprised complex chronic patients followed by a multidisciplinary chronic care program between November 2016 and December 2023. PROFUND scores were derived from electronic health records. Discrimination for 12-month mortality was assessed using Kaplan–Meier curves, log-rank tests, and receiver operating characteristic curve analysis. Calibration was evaluated by comparing observed mortality with expected mortality based on the original PROFUND index and improved through intercept and slope recalibration. Results: A total of 660 patients were included in cohort H and 540 in cohort A. One-year mortality was 38.0% and 30.2%, respectively. Discriminatory performance was good in hospitalized patients (AUC 0.760; 95% CI 0.724–0.797) and moderate to good in ambulatory patients (AUC 0.705; 95% CI 0.656–0.754). Calibration analyses demonstrated systematic overestimation of mortality, particularly in the ambulatory cohort and intermediate–high risk strata, while recalibration improved agreement between predicted and observed risks. Conclusions: The PROFUND index provides useful risk stratification for 12-month mortality in CCP across care settings but overestimates absolute risk, particularly in ambulatory case management populations. Local recalibration may improve prognostic accuracy, support individualized care planning, and advance care planning discussions and allocation of multidisciplinary follow-up intensity. Full article

Bivalve mollusks efficiently bioaccumulate human enteric viruses, posing significant food safety risks. This study assessed the prevalence of Norovirus (NoV GI and NoV GII), Hepatitis A virus (HAV), and Hepatitis E virus (HEV) in 59 samples of live mussels (Mytilus galloprovincialis) collected from the Bulgarian Black Sea coast between July 2022 and July 2023. Viral detection was performed using one-step real-time reverse transcription-polymerase chain reaction (RT-qPCR) following ISO 15216-2 standards, with a mean extraction efficiency of 4.06%. Norovirus GII was the most prevalent pathogen, with detection peaks following intense rainfall events in July 2023. In contrast, all samples tested negative for HAV and HEV. The analysis showed no significant correlation between E. coli contamination levels and the presence of NoV (Mann–Whitney U test, p = 0.565). The viral RNA was detected in several samples that otherwise complied with regulatory bacterial standards for direct consumption (≤230 MPN/100 g). In conclusion, within the limitations of the evaluated sample size and the specific geographically unbalanced sampling design, NoV GII was the predominant genogroup detected. These results suggest that current bacterial indicators may be insufficient to ensure viral safety in these products. In this regard, national control authorities need to undertake timely policies and measures for better and adequate surveillance, control and prevention of viruses in the different parts of the food chain. Full article

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies. Full article

Ongoing global climate change and intensified human activities have increased the frequency and intensity of droughts, posing a serious threat to global ecosystems and agricultural sustainability. However, the seasonally differentiated effects of droughts on cropland phenology and productivity, especially in Northeast China, remain insufficiently understood, limiting the assessment of agro-ecosystem vulnerability and the development of effective adaptation strategies. In this study, the standardized precipitation evapotranspiration index (SPEI) was used to assess the frequency and severity of extreme drought in Northeast China based on run theory. Cropland phenology parameters and productivity were derived from time-series MODIS normalized difference vegetation index (NDVI), and gross primary productivity (GPP) products, which were smoothed using a Savitzky–Golay (S–G) filter. Correlation analyses were conducted to examine regional associations between SPEI-defined drought conditions and cropland phenology and productivity. Results show that: (1) Drought events occurred frequently in the central and southern parts of Northeast China, particularly in the Songnen Plain (5.22 events per decade) and the Liaohe Plain (4.89 events per decade); (2) the Songnen Plain showed significant increases (Sen’s slope > 0, p < 0.05) across all drought metrics over 2001–2020, which coincided with LOS shortening (−0.18 d a⁻¹) and GPP decline (−9.12 g C m⁻² a⁻¹); in contrast, the Sanjiang Plain exhibited slight declines (Sen’s slope, p > 0.05) in drought metrics, resulting in LOS lengthening (0.06 d a⁻¹) and GPP increases (7.84 g C m⁻² a⁻¹); and (3) drought impacts were strongly season-dependent, with autumn droughts showing a stronger association with reductions in crop productivity in local areas of Northeast China. These findings highlight the need to account for crop responses to drought events, which is essential for developing measures to cope with drought and protecting regional food security. Full article

Although the effectiveness of plant-derived essential oils (EOs) against several insect pests is well-documented, their high volatility presents a challenge. In this study, the potential to enhance the insecticidal activity of Satureja hortensis L. EO, an accessible natural agent, through nanoemulsification was assessed against the cosmopolitan pest Spodoptera frugiperda (J. E. Smith, 1797). The nanoemulsion of the EO (NEEO) was prepared using Tween 80 as the emulsifying agent and high-intensity ultrasonication. Oral bioassays indicated that the NEEO was more toxic (LC₅₀ = 0.922%) than the pure EO (LC₅₀ = 1.186%). Sublethal exposure to LC₃₀ of the NEEO caused evident reductions in preadult survival, developmental time, fecundity, and oviposition period, as well as the population growth parameter net reproductive rate (R₀). The exposure to the NEEO increased catalase (CAT), glutathione S-transferase (GST), and superoxide dismutase (SOD) actions and inhibited α-esterase (α-NE), β-esterase (β-NE), and cytochrome P450 (CYP450) actions. Both the NEEO and EO inhibited acetylcholinesterase (AChE) and Na⁺/K⁺-ATPase, with higher inhibition in the NEEO group. Generally, S. hortensis NEEO enhanced toxicity, intensified physiological perturbations, and caused greater negative impacts on population growth parameters. Consequently, nanoemulsification of S. hortensis EO can be considered an effective method to strengthen the insecticidal potential of this natural agent. Full article