Search Results (1,230)

In the U.S., youth face barriers to high-quality culture-centered health education and healthcare, leading to unintended pregnancy and sexually transmitted infections. This health burden falls disproportionately on youth of color in Southern states. Researchers partnered with Fact Forward to design, implement, and evaluate the “Chat About It: We’re in your corner” 10-week health communication campaign. The culture-centered health communication campaign aimed to increase awareness and use of sexual health services with a focus on youth of color in South Carolina. The study implemented a campaign evaluation pretest–posttest web-based survey among young people (ages 18–24) in South Carolina. The campaign resulted in over one million total impressions. Over two-thirds of respondents who saw the campaign indicated that it prompted them to perform a positive health behavior, with the most common behavior being discussing sexual health with a partner. Respondents with a higher PME score were over seven times more likely to seek sexual health services after seeing messages from the campaign. Findings highlight the importance of formative audience research and message testing in campaign design. By developing health communication campaigns in partnership with the target audience, health communicators can enhance engagement and improve perceived message effectiveness and campaign acceptability. Health communication campaigns may help address knowledge gaps in sexual health education. Full article

Energy efficiency (EE) has emerged as the paramount and cost-effective key strategy for achieving climate and energy objectives. Nonetheless, energy efficiency measures (EEMs) are frequently hindered by various barriers. Barriers, and to a lesser extent drivers, have been thoroughly examined across several contexts and sectors; nevertheless, research on barriers and drivers in Tunisia is still lacking. Thus, in the present paper, we explore the key barriers and drivers affecting industrial energy efficiency in Tunisia using qualitative analysis. Semi-structured interviews were performed with a set of industrial firms. The study included the major external key stakeholders. The findings show that economic barriers resulting from high investment costs, limited access to capital, and a lack of incentives are major impediments to the adoption of energy efficiency measures in Tunisia and that technical, institutional, regulatory, informational, awareness, and behavioral barriers may further stymie investment in these measures. This study’s main drivers are cost reductions, subsidies, management commitment, and awareness campaigns. The results offer Tunisian policymakers a useful resource for understanding the barriers to energy efficiency that exist today and creating new policies to get over them. The findings are also a useful resource for other countries. Full article

Autonomous navigation and mooring in confined waters require a navigation solution that remains reliable when individual sensing channels are delayed, unavailable, or environmentally degraded. A risk- and integrity-aware architecture was developed for joint processing of RTK-GNSS, inertial and heading measurements, short-range radar, LiDAR, camera, AIS, ultrasonic ranging, propulsion feedback, environmental, and mooring-line tension data. Asynchronous time alignment is combined with sensor-quality assessment, innovation-based fault detection and isolation, covariance adaptation, active-set reconfiguration, protection-level monitoring, and risk-dependent allocation of authority between automation and the operator. The system was evaluated during a five-day campaign comprising 40 runs, four operational phases, 480 synchronized evaluation epochs, and 16 controlled single-sensor or combined sensor-degradation events. Under nominal conditions, horizontal-position and heading RMSE were 0.043 m and 0.176°, respectively. All 64 fault-active diagnostic records were identified; mean event-log detection and controlled-recovery latencies were 3.17 s and 6.34 s. Horizontal protection-level coverage was 99.3% for single-fault epochs and 100% for combined-fault epochs. One combined-fault docking run contained five consecutive aborted decision epochs, while no unsafe-autonomy event was recorded. The measurements therefore indicate bounded degradation of the navigation solution and conservative transfer of authority when sensing integrity decreases. Because all injected-fault runs were acquired under adverse weather whereas nominal runs were acquired under calm or moderate conditions, the condition-class RMSE differences reported below are descriptive and must not be interpreted as isolated causal effects of sensor faults. Full article

Indoor exposure to fine particulate matter (PM2.5) is a major environmental-health concern in Northern China, where severe ambient pollution, coal-based district heating and diverse residential building stocks coexist. Previous studies have been constrained by small numbers of independent residences, limited geographic coverage, and predominantly pairwise (bivariate) analyses. This study presents a seasonal monitoring campaign covering 48 residences across eight Northern Chinese cities. Paired indoor and outdoor PM2.5 was measured at 5 min intervals and aggregated to 31,094 valid hourly observations (from 32,256 scheduled hourly slots after exclusion of 1162 h with missing or invalid 5 min readings) over seven consecutive days in four seasons, alongside building, behavioural and meteorological covariates. Because repeated hourly measurements are clustered within residences, the primary analysis is a linear mixed-effects model (LMM) with a residence-level random intercept and a first-order autoregressive residual structure (marginal R² = 0.71; conditional R² = 0.91; intra-class correlation = 0.34), supported by ordinary-least-squares (OLS) models with CR2 finite-cluster-robust standard errors and a daily average sensitivity analysis. Adjusted LMM coefficients indicate that outdoor PM2.5 (β = 0.872, p < 0.001), window-open fraction (β = 0.548, p < 0.001) and cooking events (β = 0.046, p < 0.001) were positively, and air-purifier operation (purifier-on fraction β = −0.902, p < 0.001) was negatively, associated with indoor PM2.5; continuous purifier operation was associated with 59.4% lower indoor PM2.5 (an observational association, not a causal effect). Building-level infiltration factors (F_inf) averaged 0.28 ± 0.15. Estimated outdoor contributions to indoor PM2.5 ranged from 54.8% to 66.3% across an assumed penetration factor of p = 0.6–1.0 (60.7% at p = 0.8), peaking in the heating season. The mass-balance analysis estimated a median deposition rate of 0.53 h⁻¹ and a median air change rate of 0.52 h⁻¹ (window-open periods > 2 h⁻¹); these are model-derived, not directly measured quantities. These findings provide multivariable, cluster-aware evidence for envelope airtightness, informed window operation and correct air-purifier use to reduce residential PM2.5 exposure in Northern China. Full article

Coordinating multi-agent unmanned aerial vehicle (UAV) swarms is challenging in disaster scenarios where communications are dynamic, unreliable, and subject to UAV losses. While distributed artificial intelligence has enabled unprecedented levels of autonomous multi-agent coordination, most methods implicitly take communication topology as a given, not accounting for its limited maintenance in such scenarios. As a result, communication fragmentation can undermine autonomous mission progress during highly dynamic communications conditions. This paper proposes the Adaptive k-Truss-Constrained Agentic AI Framework (ATAC), an AI-based decentralized coordination framework for multi-agent UAV systems, which explicitly factors in graph-theoretic structural considerations during decision-making. The swarm is modeled as a graph, where an adaptive k-truss backbone is maintained during dynamic communication conditions to preserve triangle-based redundancy. Each agent acts as a graph-aware AI entity which bases its decentralized decisions on local information and descriptors of the communication backbone. A closed-loop evolutionary process is used to rebuild the backbone after significant communication link losses while UAVs make mission progress decisions based on information from the current backbone, enabling continuous adaption of the swarm structure to the communication state. The efficacy of the proposed framework is demonstrated through a comprehensive simulation campaign which includes communication link losses, UAV failures, adaptive truss selection, ablation studies, reward sensitivity analysis, and computational performance assessments. ATAC is compared to alternative graph-aware coordination approaches, showing consistent improvements in maintaining communication, preserving backbone structure, enabling triangle-based connectivity, and overall structural recovery while still achieving high-levels of mission progress during dynamic disaster response scenarios. The results highlight the effectiveness of explicitly tying AI-driven decentralized decision-making to maintenance of a graph-theoretic backbone structure for resilient UAV swarm coordination. Full article

The management and consultation of heterogeneous spectral datasets present significant challenges in multidisciplinary research, where records from different campaigns, instruments, and scientific domains must remain linked to consistent metadata, geographic provenance, and analytical tools within a unified framework. This paper presents the design, implementation, and operational use of the INGV Spectral Library, a web-based spatial data infrastructure integrating structured metadata management, georeferenced Web-GIS consultation, and browser-native spectral preprocessing within a single environment. The system relies on a three-tier architecture and organizes spectral records through a domain-aware metadata model associating each entry with geographic, thematic, and domain-specific descriptors. Consultation is supported through two complementary access modes: an interactive map enabling spatial exploration and direct map-to-record navigation, and a metadata-driven filtered table supporting progressive retrieval across domains including geology, mineralogy, environmental surveys, and cultural heritage. Integrated preprocessing tools—first derivative, continuum removal, and sensor spectral response function-based resampling—operate directly within the environment without export to external software. A controlled ingest and harmonization workflow ensures metadata consistency and spectral validity prior to record exposure. The paper discusses the platform’s geoinformation design principles, spatial consultation model, and current limitations, contributing a practical example of georeferenced spatial data management applied to multidisciplinary spectral archives. Full article

Veterinary institutions need practical methods to translate the Sustainable Development Goals (SDGs) into profession-specific priorities. This study developed a two-phase priority-setting framework linking systematic SDG screening with multi-criteria decision analysis. First, all 169 SDG targets were screened using four veterinary-relevant dimensions. The 48 retained targets were synthesized into 27 preliminary concepts, refined through two rounds of structured internal expert evaluation, and harmonized by the research team into 27 final decision alternatives. Second, Budgetary Burden, Accessibility and Scope, Implementation Timeline, and Sustainability Impact were weighted using the Analytic Hierarchy Process (AHP), and the 27 alternatives were ranked using TOPSIS. Robustness was assessed through Monte Carlo criterion-weight perturbation, score aggregation, and leave-one-expert-out analyses. Budgetary Burden (0.4025) and Sustainability Impact (0.3526) received the largest weights. Prevention of Zoonotic Diseases ranked first (C_i = 0.8272), followed by Awareness-Raising Campaigns, Animal Welfare Inspections on Commercial Animal Farms, and Audits of Food Safety and Production Process Compliance Standards. Criterion-weight perturbation and score aggregation largely preserved the highest-priority set, whereas leave-one-expert-out analysis showed greater sensitivity to panel composition. The framework should therefore be interpreted as a transparent, context-dependent, priority-setting tool rather than as an externally validated or universally applicable hierarchy. Full article

Predicting the Remaining Useful Life (RUL) of railway wheels is challenging because wheel–rail degradation is cumulative, stochastic, and influenced by operating conditions. This study evaluates a multi-indicator prognostic framework using real in-service measurements acquired with a CALIPRI C42 optical profilometer (NextSense GmbH, Graz, Austria). The database comprises 80 wheels from ten vehicles of the same rolling-stock type, monitored during five monthly measurement campaigns, and includes flange width (Fw), flange height (Fh), and the flange-gradient dimension (qR). The Gamma process and first-passage formulation are established tools; the contribution of this work is their common application to all three indicators on the same in-service fleet and the benchmarking of long-horizon probabilistic results against an AR(1) short-term predictor embedded in the First-Passage Auto-Regressive (FP-AR) framework using the same dataset. Median Gamma-based RUL values were 21.2–22.0 months for Fw, 13.7–17.6 months for Fh, and 5.7–9.5 months for qR, with qR showing the largest relative percentile dispersion. For one-step prediction, the FP-AR benchmark achieved global MAE/RMSE values of approximately 0.368/0.502 mm for Fw and 0.0187/0.0216 mm for Fh; qR was more difficult to predict, with global MAE/RMSE values of approximately 0.575/0.991 mm. Under the adopted intervention thresholds, these results identify qR as the most variable and operationally constraining indicator under the studied Fès–Marrakech service conditions. The proposed dual-model analysis therefore provides a position-specific, uncertainty-aware basis for comparing wheel-profile degradation indicators, while its maintenance implications remain fleet- and route-specific pending validation in additional operating contexts. Full article

Antimicrobial resistance poses a critical challenge to global public health. Urinary tract infections are one of the leading causes of morbidity in Mexico. This study aimed to identify the most prevalent bacterial pathogens in urinary bacterial isolates and to determine their antimicrobial and selected multidrug-resistance pattern profiles in outpatient and inpatient isolates at a tertiary care hospital in Mexico City. In this retrospective laboratory-based surveillance study, a census of 3434 urine samples collected between January and December 2023 was analyzed. Bacterial identification and antimicrobial susceptibility testing were performed using a VITEK 2 XL automated system. Escherichia coli was the predominant uropathogen in both groups, followed by Enterococcus spp. and Klebsiella spp. A significant disparity was observed in the resistance profiles: E. coli resistance to third-generation cephalosporins and fluoroquinolones was higher in inpatient isolates than in outpatient isolates. The selected multidrug-resistance patterns were generally higher in the inpatient isolates than in the outpatient isolates. Although common enteric pathogens with lower resistance levels predominated in the outpatient isolates, inpatient isolates required coverage targeting higher resistance. These findings reflect the need to tailor the treatment to the patients’ clinical context, launch awareness campaigns for the strategic use of antibiotics, and strengthen epidemiological surveillance. Full article

Chronic kidney disease (CKD) is an emerging public health problem, and low public awareness may result in late diagnosis and poor outcomes. We evaluated baseline knowledge of CKD among participants in a community awareness campaign and immediate knowledge change following a focused educational intervention. The study was designed as a biphasic pre- and post-intervention study that took place over the course of a 3-day public campaign that coincided with World Kidney Day on 13 March 2025. The campaign consisted of six educational stations on important topics of CKD, blood-pressure and blood-glucose measurements, with access to nephrologist consultation. Participants completed a validated questionnaire prior to and after attending the campaign. The changes in knowledge about kidney function, risk factors, symptoms, diagnostic methods, and treatment were evaluated. A total of 134 responses were received; 102 participants were eligible and completed both assessments. Knowledge scores and several item-level response proportions were higher immediately after campaign participation than before participation. The knowledge of diabetes mellitus as a risk factor for CKD increased from 34.3 to 80.4%, the knowledge of blood tests for renal assessment increased from 52.0 to 80.4%, and the knowledge of pharmacological treatments that slow down the progression of CKD increased from 52.9 to 78.4%. The percentage of people who think that herbal medicines are effective to treat CKD decreased from 19.6% to 8.8%. No demographic or clinical predictors of change in overall knowledge were statistically significant, although differences were seen in specific knowledge domains. These findings show higher CKD knowledge immediately after participation in a structured community campaign. Controlled studies with longer follow-up are needed to determine causality, durability, behavior change, and clinical impact. Full article

(1) Background: Indonesia is one of the world’s largest consumers of asbestos, with widespread use of asbestos-cement roofing. Java Island, characterized by high population density and seismic vulnerability, presents a complex risk landscape for asbestos exposure. This study aimed to develop a district-level asbestos exposure risk map for Java by integrating asbestos roof prevalence, population density, and seismic hazard. (2) Methods: A descriptive, semi-quantitative approach was applied using secondary data from national statistics and geological hazard portals. Three variables—asbestos roof prevalence, population density, and seismic hazard—were categorized into five ordinal levels (very low to very high) and combined using GIS-based risk matrices to generate composite exposure risk maps. Sensitivity and statistical analyses were conducted to assess the robustness and internal consistency of the risk classification. (3) Results: Of 119 districts/municipalities, 43 (36%) were classified as very high risk and 45 (38%) as high risk under the primary GIS-based risk matrix classification. Major metropolitan corridors, including Greater Jakarta and Bandung, showed elevated risk where substantial asbestos use coincided with very high population density, whereas several southern and inland districts gained priority because of higher seismic hazard. Sensitivity analyses indicated that the classification was generally robust to moderate changes in weighting and threshold boundaries, although the predefined population density thresholds provided limited discrimination within Java. (4) Conclusions: Asbestos exposure risk in Java is both widespread and heterogeneous, shaped by the intersection of infrastructure, demographics, and seismic vulnerability. The map offers a practical tool for prioritizing asbestos control, public awareness campaigns, health surveillance, and disaster risk reduction measures in Indonesia. Full article

Asthma is one of the most prevalent chronic respiratory diseases worldwide, and environmental pollutants such as desert dust play a significant role in triggering respiratory sensitization and aggravating asthma symptoms. In this study, a mathematical model is proposed to investigate the dynamics of dust-induced asthma progression. The population is divided into unaware susceptible individuals, aware susceptible individuals, dust-sensitized individuals, and asthmatic individuals, while two additional variables describe dust concentration in the human population and the environment. Fundamental qualitative properties of the model, including positivity, boundedness, existence of equilibria, and stability conditions, are established. It is shown that the asthma-free equilibrium is locally and globally asymptotically stable whenever the dust-induced asthma threshold is below unity, whereas a unique asthma-persistent equilibrium exists when the dust-induced asthma threshold exceeds unity. To reduce the burden of asthma, an optimal control problem is formulated by incorporating three time-dependent interventions: awareness campaigns, medical intervention for dust-sensitized individuals, and environmental dust reduction. Pontryagin’s Maximum Principle is employed to characterize the optimal controls and derive the corresponding adjoint system. Numerical simulations, performed using the forward–backward sweep method, demonstrate that the proposed interventions significantly reduce the number of asthmatic individuals compared with the uncontrolled case. Furthermore, a cost-effectiveness analysis based on the Average Cost-Effectiveness Ratio (ACER) and Incremental Cost-Effectiveness Ratio (ICER) is conducted to compare seven intervention strategies. The results indicate that the medical intervention strategy is the most economically attractive option, while the combined strategy involving all controls achieves the largest reduction in asthma burden. Full article

Scalp hair offers a non-invasive matrix for retrospective paediatric biomonitoring, although interpretation is complicated by endogenous incorporation, exogenous deposition and element-specific analytical detectability. We characterised B, Ca, Co, Fe, Li, Mg, Mo, Ni and Se in archived scalp hair collected in 2001 from 120 children aged 6–9 years and 97 adolescents aged 13–16 years residing in urban–industrial Alcalá de Henares, Spain. Samples were analysed in 2025 by ICP–MS using a common quality-controlled analytical workflow, with left-censored observations evaluated using censoring-aware methods. B and Li were highly censored (89.2–94.8% and 86.7–99.0%, respectively), whereas Fe, Mg, Mo and Se were consistently quantified. Adolescents showed higher median Ca (325 vs. 168 µg g⁻¹; q = 1.43 × 10⁻⁹), Mg (24.0 vs. 9.05 µg g⁻¹; q = 8.91 × 10⁻¹⁹) and Ni (0.169 vs. 0.0597 µg g⁻¹; q = 2.10 × 10⁻⁵), but lower Fe (5.16 vs. 7.54 µg g⁻¹; q = 2.28 × 10⁻¹¹), Co and Mo than children. Sex-associated differences were also observed for several elements, although their magnitude and detectability varied between age strata. Residential heterogeneity was strongest among adolescents: median Ca in the mixed residential–industrial Zone IV was 967 µg g⁻¹ compared with 232–385 µg g⁻¹ in Zones I–III (q = 2.75 × 10⁻¹²), while median Mg was 113 versus 18.8–24.3 µg g⁻¹ (q = 2.26 × 10⁻⁶). These specimens, collected in 2001, stored dry in paper envelopes for approximately 24 years, and analysed together in the 2025 ICP–MS campaign, provide a distinctive historical paediatric biomonitoring baseline from a well-defined Spanish urban–industrial population. The findings support scalp hair as a screening-level matrix for investigating population patterns across developmental stage, sex and residential setting, while not supporting its use as a direct measure of nutritional status, internal dose or health risk. Full article

Background/Objectives: Blood–brain barrier (BBB) permeability is a major practical obstacle in central nervous system (CNS) drug discovery, because only a small fraction of drug-like molecules achieve sufficient brain exposure. Methods: We present DeepBBB, a graph-based, data-composition-aware screening workflow for predicting BBB permeability and for constructing BBB-focused screening libraries from commercial chemical space. Rather than introducing a new graph-learning architecture, the workflow combines standard graph convolutional and graph-transformer models with deliberate control of training-set composition, commercial-library filtering, chemical-space profiling, and prospective experimental evaluation. Three model variants were trained on the Blood–Brain Barrier Database (B3DB): a baseline classifier/regressor pair (DeepBBB_V1_BC/RG), a variant trained with a more strongly negative-enriched configuration (DeepBBB_V2_BC), and a graph-transformer counterpart (DeepBBB_trans_BC/RG). Because the sample-level split assignments and per-compound predictions from the original runs were not recoverable, the archived summary metrics are reported descriptively in the main text and are not used to support calibration, scaffold-level validity, or generalization. Applying the workflow to the ChemDiv collection (~1.5 million compounds) and the Enamine REAL lead-like space (~1.7 billion compounds) produced three progressively more stringently filtered BBB-focused libraries (21,991; 4,808,885; and 151,790 compounds). Results: Analysis of available processed data indicated that the predicted BBB-permeable set occupies a compact, BBB-compatible property region. Physicochemical, fragment, and scaffold summaries were interpreted descriptively at the constructed-library level. In a first prospective campaign, one of 12 tested candidate compounds was PAMPA-BBB-positive (all-tested molecular-level positive fraction 8.3%; exact 95% CI 0.2–38.5%). In a second campaign, five of 35 tested candidate compounds were PAMPA-BBB-positive (14.3%; exact 95% CI 4.8–30.3%); 13 compounds were not quantifiable and were not treated as ordinary CNS-negative measurements, and the two campaigns differed in compound source, selection strategy, and assay setting, so the numerical difference is reported descriptively rather than causally. Conclusions: Together, these results support the feasibility of BBB-focused computational filtering and a PAMPA-BBB evaluation workflow for CNS-oriented discovery. Full article

Background: Mental health issues among university students are a growing global concern, including in Malaysia. This exploratory study aims to examine mental health support systems in Malaysian universities using a dyadic perspective, capturing both students’ and counsellors’ experiences. This addresses a key gap in the literature, which has largely relied on single-perspective accounts. Methods: A qualitative exploratory design was employed using purposive sampling. Semi-structured interviews were conducted with 15 students receiving mental health support and 10 university counsellors. The data were analysed using hybrid deductive–inductive thematic analysis. Results: Three themes emerged from the findings. The first theme showed that students sought support once distress affected their daily functioning, valuing trust, safety, and emotional guidance, while counsellors described a parallel process of assessing student needs and facilitating longer term recovery. The second theme identified barriers shared by both groups, namely limited awareness of services and stigma, alongside additional constraints reported only by counsellors, including staffing shortages, unclear referral pathways, and limited institutional support. The third theme centred on strategies for improvement, including early assessment, awareness campaigns, digital accessibility, resilience building, and stronger collaboration among students, counsellors, and university stakeholders. Conclusions: This study contributes to the literature by integrating three complementary theoretical perspectives to explain university students’ mental health support experiences, support networks, and help-seeking behaviour. The findings also provide practical guidance for university administrators, counsellors, and policymakers seeking to develop more accessible, proactive, and student-centred mental health support systems. As the study is based on qualitative, cross-sectional data, the findings should be interpreted as indicative rather than causal. Nonetheless, they suggest that strengthening such initiatives may contribute to more supportive and responsive mental health provision within higher education institutions. Full article