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Exploring factors contributing to antibiotic resistance: A cross-sectional empirical study in Bangladesh
Antibiotic resistance is a growing public health concern, particularly in low- and middle-income countries such as Bangladesh. This cross-sectional empirical study analyzed primary survey data collected from 254 respondents in Bangladesh using Structural Equation Modeling (SEM). Structural Equation Modeling (SEM) with Smart-PLS 4.0 was employed to analyze the relationships between key variables, ensuring accurate results despite non-normal data. The findings reveal that economic pressures, inadequate diagnostics, and cultural misconceptions are key contributors to antibiotic overuse and misuse. The study is grounded in the Health Belief Model and Ecological Systems Theory, offering a framework to understand these behaviors. It underscores the need for targeted policies, improved diagnostic resources, and heightened public awareness to address the growing problem of antimicrobial resistance. The findings indicate that economic incentives, diagnostic uncertainty, healthcare infrastructure, and sociocultural beliefs significantly influence antibiotic resistance within the surveyed population.
State estimation and dynamic compensation cooperative sliding mode control for space flexible manipulators
Disease burden of asbestos-related diseases in China (1990–2023) based on GBD estimates: A call for stronger labor protection laws
Background Asbestos exposure remains a persistent occupational hazard in China, yet updated national estimates of asbestos-related diseases (ARDs) after 2019 are scarce. This study quantified long-term trends and demographic patterns of ARDs from 1990 to 2023 using Global Burden of Disease data and joinpoint regression. Methods We analyzed incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for asbestosis and asbestos-attributable cancers (mesothelioma, tracheal/bronchus/lung cancer, laryngeal cancer, and ovarian cancer). Absolute numbers and age-standardized rates were assessed overall and stratified by sex and age. joinpoint regression identified significant temporal inflection points. Results The absolute burden of ARDs increased continuously from 1990 to 2023. Age-standardized prevalence and incidence rates of asbestosis peaked in 2001, while mortality and DALY rates peaked in 2004. Major turning points for asbestos-attributable cancers occurred around 2010–2011, marking historical peaks followed by declines. A modeled increase in mortality and DALYs was observed from 2020 to 2022 across nearly all ARDs. Males consistently demonstrated higher burdens than females, and older adults (≥65 years) carried the greatest burden, with a secondary mesothelioma peak at 55–59 years in males. Conclusions Although ARD indicators have declined from historical peaks, a statistically modeled increase was observed in 2020–2022, warranting continued public-health attention. These findings aim to provide evidence for clinicians, epidemiologists, and policymakers to strengthen occupational disease prevention, reinforce labor protection laws, and improve asbestos-control policies in China.
A privacy-preserving distributed protocol for secure data sharing in untrusted supply chain
Knowledge, attitudes and behaviours of nurses about antibiotic use and antibiotic resistance in Oman
Background Antimicrobial resistance (AMR) is a significant global health challenge, with organizations worldwide emphasizing the importance of addressing inappropriate antibiotic use and resistance. The aim of this study was to investigate nurses’ knowledge, attitudes, and behaviour on antibiotic use and resistance in Oman. Methods This cross-sectional study used a questionnaire designed by the European Centre for Disease Prevention and Control, which was distributed to nurses working in Oman’s Ministry of Health. Results A total of 424 nurses responded to the survey. A total of 64.2% and 48.8% of the respondents accurately acknowledged the lack of efficacy of antibiotics against viruses and the common cold, respectively. Nevertheless, a significant majority of 93% of participants were able to provide accurate responses regarding excessive use of antibiotics and associated adverse effects. Out of the surveyed nurses, 59% demonstrated awareness of the Oman National Action Plan on antibiotic resistance. Of those who responded, 54.7% wanted to know which antibiotics are used for specific medical conditions, 52.1% wanted to know more about antibiotic resistance, 42% wanted to learn about the proper usage of antibiotics, and 30% were interested in the links between humans, animals, and environmental health. Conclusion The study’s results should be used to enhance education and increase nurses’ capabilities and understanding regarding antibiotic use and antibiotic resistance. Strengthening capabilities, opportunities, and motivation is essential to empower nurses as frontline contributors in the global fight against antibiotic resistance.
A multi-source heterogeneous massive operation and maintenance data collection method for substations based on cloud-edge collaboration and artificial intelligence
Embedded timing and alert device triggered by total dissolved solids (TDS) for monitoring disinfection duration in acidic electrolyzed oxidizing water
To address the absence of a reliable timing and alerting tool for acidic electrolyzed oxidizing water (AEOW) disinfection of medical instruments in the Central Sterile Supply Department (CSSD), we developed an embedded timing and alert device triggered by total dissolved solids (TDS) sensing. Built around an Arduino development board, the device integrated a TDS sensor module, a rechargeable battery, and a wireless charging coil. Encapsulated with potting adhesive, it achieved an IP67 protection rating. It can identify the AEOW environment through TDS value detection to automatically start/stop timing, and is equipped with features including light alerts, data storage, and traceability. Medical instruments disinfected with AEOW in the CSSD of a hospital were selected as the test objects. Digital kitchen timers were used as the control group in September 2025, and TDS-triggered timers were adopted as the experimental group in October 2025. A comparative analysis was conducted on the disinfection timing execution rate and disinfection duration compliance rate between the two groups. The results demonstrated that the disinfection timing execution rate of 94.92% (655/690) and the disinfection duration compliance rate of 98.26% (678/690) in the experimental group were higher than those of 84.88% (713/840) and 93.69% (787/840) in the control group, with statistically significant differences (P < 0.001). Characterized by low cost and simple operation, the TDS-triggered timer provides a reliable, automated, and traceable timing solution for AEOW disinfection in CSSD. It also facilitates digital management of the disinfection process, and is expected to improve the quality of AEOW disinfection and enhance patient safety.
A GIS-based AHP approach integrating geospatial and magnetic data for groundwater potential mapping in a structurally complex arid region, Egypt
Abstract Groundwater exploration in arid regions of the Eastern Desert of Egypt requires an integrated understanding of the structural, geomorphic, and subsurface controls governing recharge and storage. This study presents a GIS-based groundwater potentiality model for a structurally complex rift-related zone along the southern Esh El Mellaha Block, between the Gulf of Suez and northern Red Sea. Sixteen topographical, meteorological, hydrological, and surface geological factors were systematically integrated with particular magnetic basement-depth modeling and subsurface fault architecture, which were weighted using the Analytical Hierarchy Process (AHP). Results demonstrate that regional tectonic configuration and structural geometry, rather than surface geomorphic factors alone, exert the primary control on groundwater distribution. High-potential zones are concentrated within major structural lows, including the Tarboul syncline, West Hurghada trough, and El Gouna fan system, where thick Quaternary deposits and enhanced infiltration prevail. The ENE-trending Bali Shear Zone acts as a key conduit for focused recharge by enhancing fracture permeability and linking the Gulf of Suez and Red Sea structural domains. Model validation yielded an AUC of 0.80, with balanced sensitivity and specificity (0.74), indicating reliable predictive performance. Single parameter sensitivity analysis confirms the robustness of the model and indicates that structural and geological factors exert the strongest control on groundwater potential distribution. The study emphasizes the role of structural architecture in groundwater assessment and supports future sustainable water-resource development in arid extensional tectonic settings.
Phylogeography and population genetics of the white spotted eagle ray, Aetobatus laticeps Gill, 1865, in the Eastern Tropical Pacific
The Eastern Tropical Pacific (ETP) Spotted Eagle Ray, Aetobatus laticeps Gill, 1865, is an understudied species with limited information on its ecology and conservation status. Only in the last 10 years has this lineage been re-described as a distinct species from the formerly widespread Aetobatus narinari species complex (Euphrasen, 1790) (ANSC). The sampling in the studies that divided the ANSC was not geographically balanced, with most individuals (50) collected from the Atlantic and only (5) from the ETP. Given the vast extent and environmental heterogeneity of this region, it is possible that a significant portion of the genus’s genetic variation, and possibly even the presence of undescribed species, is being overlooked. Because eagle rays have relatively low fecundity and are subject to targeted and incidental fisheries, they are highly susceptible to population declines. Consequently, elucidating the genetic diversity, distribution range, genetic variability and interconnectivity of this species is crucial to assess its conservation status correctly. Through a mixed genetic marker approach, we assessed ETP eagle rays’ phylogeny, genetic diversity and phylogeography. First, we confirmed that eagle rays in this region correspond to A. laticeps. Second, we found low levels of genetic variability across all markers, coupled with a high degree of geographic-genetic structure. Finally, our data suggested three potential mechanisms that could explain the population structure we encountered: a) Isolation by distance b) Isolation by philopatry, and c) Isolation by depth. To date, this is the first comprehensive study of the genetic diversity of eagle rays in the Eastern Tropical Pacific, shedding light on a species that has long been overlooked. Our findings suggest that this species is more susceptible to direct and bycatch fishing pressures, as well as other indirect human impacts. Therefore, we recommend a re-evaluation of the conservation status of this species.
Machine learning approach for mechanical property assessment of industrial waste-filled epoxy–jute composites
Abstract The growing demand for sustainable materials has stimulated the development of bio-based composites, yet the combination of natural fibers and industrial waste fillers in polymer matrices has not been exploited synergistically. In line with Sustainable Development Goal 12 (SDG-12), the paper focuses on reusing the steel industry by-product Linz–Donawitz (LD) sludge to enhance the mechanical properties of epoxy composites when used with the jute fiber. Six composite specimens with constant jute fiber loading (20 wt%) and a range of LD sludge content (0–25 wt%) were prepared using hand lay-up technique. The best composition (60 wt% epoxy, 20 wt% jute, 20 wt% LD sludge) resulted in tensile strength of 61.84 MPa (28.8% better than neat epoxy), flexural strength of 31.81 MPa (41.8% better) and impact strength of 18.026 kJ/m 2 . Interfacial defects and agglomeration of particles led to a decrease in mechanical properties beyound 20 wt% sludge. This experimental data was used to train four machine learning models to forecast mechanical properties given compositional inputs. On training data, XGBoost achieved R 2 = 1.0000 with near-zero errors (MAE = 0.0005 MPa, RMSE = 0.0008 MPa). However, when trained on a small dataset of six specimens, this perfect fit is mostly due to memorization of the training data, as opposed to predictive power. The findings suggest the risk of overfitting, mainly in the cases of Decision Tree and Gradient Boosting models. More realistic estimates of model performance are given by cross-validation (R 2 = 0.94 ± 0.04 in the case of XGBoost). The ML models can thus be used to analyze exploratory composition-property trend analysis in this particular composition space, as opposed to extrapolative prediction. These results both validate the possibility of hybrid composites that use industrial waste to obtain mechanical performance equivalent to standard natural fiber composites and indicate that waste can be valorized, although any assertion of ML predictive capacity should be carefully hedged due to limitations in the datasets.
Post-COVID spirometric abnormalities in workers with intermittent high-altitude exposure: A cross-sectional study in Peru
Introduction Persistent pulmonary sequelae after SARS-CoV-2 infection remain a concern in workers exposed to environmental and physiological stressors such as intermittent high-altitude hypoxia. Few studies include pre-pandemic spirometry or focus on this occupational group. Methods A cross-sectional study was conducted in 400 sea-level-born workers intermittently exposed to altitudes >2500 m, all with confirmed COVID-19. Only A/B-quality spirometry from 2024 was included. Sociodemographic, clinical, and occupational variables were assessed, and adjusted prevalence ratios (aPR) were estimated using Poisson regression with robust variance. Results Overall, 72.2% of workers showed spirometric abnormalities (40.5% mixed, 20.8% restrictive, 11.0% obstructive). Independent predictors included obesity (aPR 1.35; 95% CI 1.19–1.53), higher Charlson index (aPR 1.49; 95% CI 1.33–1.68), ≥ 5 years of inhalant exposure (aPR 1.64; 95% CI 1.43–1.89), ≥ 7 years of intermittent high-altitude exposure (aPR 1.81; 95% CI 1.60–2.05), and severe COVID-19 (aPR 1.65; 95% CI 1.41–1.91). Conclusions Over 70% of participants showed abnormal spirometry was found in post-COVID-19 workers with intermittent high-altitude exposure. Respiratory function monitoring should be reinforced in this occupational group, especially among those with higher clinical and environmental risk factors.
Clinical and epidemiological changes in severe viral respiratory infections in pediatric patients after the COVID-19 pandemic in Spain
Automated robotic arm system for real-time multi-parameter quality assessment of raw milk in cheese manufacturing
Ensuring the quality of raw milk is critical for consistent cheese manufacturing, yet traditional laboratory-based testing methods are slow, labor-intensive, and impractical for decentralized rural supply chains. This study presents a portable, fully automated robotic arm system for real-time, multi-parameter milk quality assessment. The system integrates pH, total dissolved solids (TDS), temperature, density, and color sensors into a single testing cycle of under five minutes. A four-degree-of-freedom robotic arm ensures precise and repeatable probe positioning, reducing contamination and accommodating varied container types. An AI-based Support Vector Machine (SVM) classifier, trained on multi-sensor data, achieved 97.1% classification accuracy, outperforming static threshold logic, particularly in borderline cases. Environmental control features, including an LED-based optical chamber and temperature-compensated TDS readings, improved robustness in non-climate-controlled rural conditions. Laboratory tests showed high agreement with ISO-calibrated references for pH and TDS. Field trials at rural milk collection centers in Sri Lanka demonstrated over 96.5% agreement with laboratory classifications. Although individual sensor readings (e.g., pH, temperature) are rapid, the integration of automated handling, sensor switching, and AI-driven classification reduced total testing time per sample by approximately 35% compared to manual workflows. The modular design allows for scalability, easy maintenance, and adaptability to resource-limited environments. By enabling rapid, non-destructive, and chemical-free testing, the system addresses critical challenges in rural dairy networks, improving decision-making, reducing spoilage risks, and supporting higher quality assurance standards in cheese production workflows.
Triply‐Linked N‐Confused Porphyrin Dimers: Cross Conjugation‐Mediated Expansion of π‐Conjugation
ABSTRACT meso ‐ meso ′, α ‐ α ′, β ‐ β ′‐Triply‐linked N‐confused porphyrin (NCP) dimers were synthesized via stepwise oxidative coupling reactions of 5,10,15‐triaryl‐NCP, followed by nickel or silver metalation. The novel dimers possess extended π‐conjugation throughout the entire fused dimer structures, leading to Hückel 36π‐antiaromaticity and 38π‐aromaticity for the nickel and silver complexes, respectively. Theoretical investigations using the gauge‐including magnetically induced current (GIMIC) method reveal the pivotal role of NCP cross‐conjugation and NH tautomerism in forming the global π‐conjugation circuit. Redox reactions induce a unique switching of the π‐conjugation circuits, transforming delocalized π‐conjugation into localized π‐conjugation in the NCP macrocycle, giving rise to an antiaromatic domain at the bay area.
Linear stability and dispersive soliton propagation in nonlinear media subject to parabolic phase modulation
Abstract This work investigates dispersive optical solitons governed by a perturbed cubic–quartic nonlinear Schrödinger equation with parabolic self-phase modulation, a model of direct relevance to high-capacity fiber-optic systems where simultaneous higher-order dispersion and nonlinear perturbations shape pulse dynamics. The model is physically motivated by fibers with intensity-dependent refractive index profiles, where the interplay between fourth-order chromatic dispersion and parabolic (cubic–quintic) nonlinearity generates wave structures that the standard Kerr approximation cannot capture. To extract exact traveling-wave solutions, we employ the improved modified extended tanh-function method (IMETFM), which is selected for its ability to handle multi-parameter auxiliary equations and yield a wider diversity of solution families than classical expansion methods such as the tanh-function or $$G'/G$$ -expansion approaches, without requiring the integrability of the underlying system. Our analysis produces five families of exact solutions: bright solitons, dark solitons, exponential-type solutions, singular periodic waves, and solutions expressed in terms of Weierstrass elliptic functions. For each family, explicit existence conditions and free-parameter restrictions are stated. The parametric constraints governing solution validity are derived and physically interpreted in terms of the dispersion, nonlinearity, and perturbation coefficients. Graphical representations of the spatial and temporal profiles illustrate the distinct propagation features of each solution type. A linear stability analysis, conducted via perturbation theory, yields an explicit eigenvalue dispersion relation and identifies a critical wavenumber threshold at which modulational instability sets in. The stability criteria provide actionable guidelines for maintaining soliton integrity under weak disturbances in practical optical environments. The results have direct implications for optical fiber communications, ultrafast signal processing, and dispersion-engineered photonic waveguides. The novelty lies in the simultaneous treatment of the parabolic law nonlinearity, fourth-order dispersion, and perturbative effects within a unified algebraic framework, yielding solution families including Weierstrass elliptic solutions that have not previously been reported for this model. Future work will address numerical validation, extension to stochastic and variable-coefficient models, and higher-dimensional soliton dynamics.
A study of push and pull factors influencing employee retention in education sector in China: Using PLS-SEM and multi-group analysis
Employee retention remains a pressing issue and it reflects a key psychological decision-making outcome shaped by work-related stressors and organizational resources. Drawing on the Push-Pull framework, this study examines how pull factors (training and development, compensation, and empowerment) and push factors (job dissatisfaction, job burnout, and peer turnover influence) affect employee retention, while testing the moderating role of engagement. Using survey data from 568 educators in Guangdong Province, Partial Least Squares Structural Equation Modeling and Multi-Group Analysis were employed. Results show that pull factors positively predict employee retention, whereas push factors negatively affect it. Engagement moderates the relationship between training and development and employee retention. The multi-group analysis results indicate that only job burnout shows a significant difference between public and private schools. The findings are important for education policymakers and school administrators. Targeted retention policies that integrate material incentives and psychological engagement can help stabilize the teaching workforce.
Contextual elaboration shapes object recognition memory across levels of childhood adversity in healthy adults
Abstract Childhood adversity is a known risk factor for psychopathology across the lifespan. One proposed mechanism involves long-term alterations in hippocampal memory systems, leading to disruptions in the integration of episodic memory within its contextual framework. It is therefore essential to investigate how individuals exposed to early life stress use contextual information during memory formation. We conducted an experimental study in healthy adults ( n = 76), manipulating the depth of contextual encoding. Participants viewed object-background pairs under either contextual or object-focused conditions, followed by a surprise memory test assessing object recognition and mnemonic discrimination. Childhood adversity was measured using the Childhood Trauma Questionnaire. Contrary to our initial hypothesis, contextual elaboration did not enhance recognition performance, but appeared to increase cognitive demands during encoding. We propose that increased contextual processing shifted attentional allocation away from object-specific information, thereby limiting recognition performance. Differences in recognition outcomes across conditions may further reflect variations in the strength and discriminative value of familiarity-based memory signals under differing encoding demands. We additionally observed a descriptive association between higher levels of childhood adversity and reduced object recognition performance under object-focused encoding conditions. These findings suggest that associations between childhood adversity and memory performance may emerge in a task-dependent manner and can be situated within broader theoretical accounts emphasizing the role of contextual factors in episodic memory implicated in vulnerability to mental health outcomes following early life stress.
Large-scale cryptic proteome mining revealed potential phage-mediated host-pathogen genetic exchange in Mycobacterium tuberculosis
Background Due to inevitable evolution, clinical strains of Mycobacterium tuberculosis (Mtb) exhibit distinct phenotypes and differ significantly from laboratory strains. This divergence is driven by the acquisition of diverse mutations, intragenomic recombination, and potentially phage-mediated genetic exchange. Further investigation is therefore required to better understand these differences, especially regarding the emergence of novel open reading frames (ORFs). Methodology A large-scale whole-genome sequencing (WGS) dataset from tuberculosis (TB)-endemic countries was assembled into contigs. These contigs were then used to mine the cryptic proteome through in silico predictions, using an emerging state-of-the-art deep learning protein language model, ProtBERT. Structures of emerging ORF proteins were predicted using colabfold2. In addition, Ramachandran plot analysis and molecular dynamics simulation (MDS) were performed to assess structural validity and stability. Results Most small cryptic proteins were derived from PE, PPE, and PE-PGRS family genes. Notably, a protein cluster consisting sequences of 101–300 amino acids showed no similarity with the proteins from the reference Mtb H37Rv strain but contained phage-derived domains and sequences homologous to host (primates) DNA. Sub-stratification of this cluster revealed the presence of domains like reverse transcriptases and other phage-associated proteins. BLASTp hits of these proteins showed similarity between these proteins and the host proteome. Structural validation showed that the Phi/Psi angles of modelled proteins were within the accepted ranges. Moreover, MDS of medoids of subclusters displayed stable root mean square deviation (RMSD) and radius of gyration (RGYR) profiles, supporting the structural plausibility of these proteins. Importantly, one sub-cluster showed a higher presence in drug-resistant Mtb strains. The co-occurrence of phage-related domains and host DNA strongly suggests illegitimate phage-mediated lateral transfer of host nucleic acids into the genome of Mtb. Conclusion The majority of the small ORFs are found to be nested within annotated genes. Particular emphasis has been given to the incidental finding of phage-host chimeric ORF signatures within the Mtb genome. This study provides computational evidence supporting the structural stability of these proteins. Thus, it can be speculated that such proteins may contribute to pathogenicity, survival within the host, or molecular mimicry mechanisms.