Search Results (40,294)

Background: This study examines the impact of endometriosis on sexual function, focusing on disease localization, pain severity, and psychosocial factors. It integrates the rASRM and Enzian classification systems to explore anatomical contributions to sexual dysfunction. Methods: In a cross-sectional study, 102 patients with confirmed endometriosis completed an online evaluation. Of these, 77 had surgical and histological confirmation, and 25 had no prior surgery. Thirty-five participants were using hormonal therapy. Validated instruments assessed sexual function, pain intensity (VAS), and psychosocial variables. Analyses included univariate tests and multivariate logistic regression. Results: Deep infiltrating endometriosis involving the sacrouterine ligaments, cardinal ligaments, and pelvic sidewall (Enzian B) was associated with sexual dysfunction, highlighting the anatomical utility of the Enzian system. Surprisingly, those with sexual dysfunction reported lower pain scores (p = 0.024 *). In multivariate analysis (R² = 0.281), no individual factor, including Enzian B involvement or EHP scores, remained significant. No associations were found between sexual dysfunction and anxiety, depression, stress, or relationship satisfaction, though pain was inversely correlated with anxiety (p = 0.025 *). Conclusions: Sexual dysfunction in endometriosis appears multifactorial, not solely driven by lesion burden or pain. A multidisciplinary approach is recommended, addressing anatomical, psychological, and behavioral dimensions. Full article

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires $\times 57.1$ and $\times 31.3$ more Multiply and ACcumulate (MAC) and $\times 100.8$ and $\times 28.3$ more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Full article

This research proposes a method based on the greatest and shortest eigen fuzzy sets of fuzzy relations to evaluate the effectiveness of policies and strategies implemented in urban settlements aimed at increasing the livability and well-being of citizens. This assessment is performed by extracting population census data collected at the beginning and end of the analyzed period and grouping them by subzone, that is, for each of the urban areas into which the urban settlement is divided. The greatest eigen fuzzy set (GEFS) and the smallest eigen fuzzy set (SEFS) are determined symmetrically as min-max and max-min solutions of fuzzy relations; they are calculated to estimate the average impact of urban strategies on generating symptoms of growth in citizen well-being during the investigated period. The method is implemented on a GIS (Geographic Information System) platform and was tested to assess the effectiveness of local policies applied between 2011 and 2021 on the growth of employment rates and educational attainment in the city of Naples (Italy), partitioned by neighborhood. Our model, unlike other fuzzy-based models for evaluating the effectiveness of actions and strategies to improve the quality of life in urban settlements, does not allow for subjective interpretations based on the knowledge or experience of different stakeholders, but relies solely on measurements over time of characteristics collected during census activities. Furthermore, it is integrated into a GIS-based platform, providing additional capabilities for identifying the urban areas where the impact of local strategies and policies has been most significant and those most critical. The test results show that the proposed framework can be a valuable tool for supporting decision makers in evaluating the effectiveness of local actions and policies aimed at improving the livability and well-being of citizens. Full article

Multimedia learning effectiveness varies widely across cultural contexts and individual learner characteristics, yet existing educational technologies lack computational frameworks that predict and optimize these interactions. This study introduces the Multimedia Integration Impact Assessment Model (MIIAM), a machine learning framework integrating cognitive style detection, cultural background inference, multimedia complexity optimization, and ensemble prediction into a unified architecture. MIIAM was validated with 493 software engineering students from Zimbabwe and South Africa through the analysis of 4.1 million learning interactions. The framework applied Random Forests for automated cognitive style classification, hierarchical clustering for cultural inference, and a complexity optimization engine for content analysis, while predictive performance was enhanced by an ensemble of Random Forests, XGBoost, and Neural Networks. The results demonstrated that MIIAM achieved 87% prediction accuracy, representing a 14% improvement over demographic-only baselines (p < 0.001). Cross-cultural validation confirmed strong generalization, with only a 2% accuracy drop compared to 11–15% for traditional models, while fairness analysis indicated substantially reduced bias (Statistical Parity Difference = 0.08). Real-time testing confirmed deployment feasibility with an average 156 ms processing time. MIIAM also optimized multimedia content, improving knowledge retention by 15%, reducing cognitive overload by 28%, and increasing completion rates by 22%. These findings establish MIIAM as a robust, culturally responsive framework for adaptive multimedia learning environments. Full article

Background/Objectives: According to the World Drug Report 2025, cannabis is the most abused drug in the world, being sold in illicit markets in various physical forms ranging from herbal cannabis to cannabis resin and liquid cannabis. Currently, the methods used for cannabis identification are largely based on the morphological features and chemical content of the product. In this respect, identification could be severely impacted if the product is highly fragmented or pulverised. As such, DNA-based molecular techniques offer a viable alternative detection approach. In this study, we have developed a robust DNA testing method for cannabis identification, with high sensitivity and specificity. Methods/Results: Two plant DNA barcode regions, rbcL and matK, were successfully amplified in a cohort of 54 cannabis plant samples. DNA sequences obtained from these samples were blast-searched against GenBank and resulted in returned matched identity of at least 99% compared to their corresponding Cannabis sativa reference sequences. In addition, the amplification of two cannabis-unique markers, the tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes, produced amplicons with expected sizes only in cannabis samples; these amplicons were not detected in those plants closely related to cannabis. Sequence comparison of the majority of samples yielded at least 97% matched identity against C. sativa reference sequences in GenBank. The THCAS and CBDAS markers detected only the cannabis DNA in varying levels of cannabis–hops and cannabis–tobacco DNA mixtures. Lastly, the use of the four markers could effectively differentiate between cannabis and non-cannabis in 27 blinded samples, including 18 actual casework samples. Conclusions: In conclusion, these four genetic markers can be used to discriminate cannabis from other plant species at the genus level, especially in challenging forensic samples lacking morphological features which therefore cannot be determined by traditional detection methods. As such, this method can complement existing techniques to identify a myriad of cannabis samples. Full article

Background/Objectives: We wished to determine whether patients of providers subscribing to Health Information Exchange (HIE) services experience a reduction in emergency room visits and inpatient admissions compared to those who do not subscribe and whether the type of HIE service (online access to records via an HIE portal and/or integration with their electronic health record (EHR) and push notifications) a provider uses further impacts utilization. Methods: The research team utilized the Colorado All-Payer Claims Database (CO APCD) from 2017 to 2019, intentionally excluding the impact of COVID-19 on HIE services. A pre–post analysis was conducted 15 months before and after the provider subscribed to HIE solutions to quantify the effects when compared to control clinics. Results: Subscription to HIE solutions was associated with lower inpatient (IP) admissions than those in a control group for all insurance payer types (p = 0.001). The reduction in Medicaid patients was more pronounced when their providers subscribed to either service alone or in combination and was associated with significantly fewer ED and IP admissions than those for control clinics (p = 0.003, p < 0.05 for HIE portal alone and p < 0.0001, p = 0.002 for both HIE portal and integrated EHR solutions). HIEs enable providers to coordinate care and access patients’ previous test results and clinical histories, potentially reducing the need for high-cost emergency department and inpatient services. This, in turn, appears to substantially impact low-resource individuals, who have historically been the highest users of these services. Conclusions: HIE provider subscription is associated with a reduction in patients’ use of inpatient care after enrolling in HIE products across all insurance types. Medicaid patients experienced the most significant decrease in both emergency room (ED) visits and inpatient admissions when compared to control clinics. Full article

Titanium dioxide nanostructures are extensively produced and utilized in various industries. Concerns have been raised about this material’s less researched environmental impact. This study investigates the indirect toxicity of TiO₂ nanoparticles (NPs) on ant communication via the photocatalytic degradation of ant trail pheromones. Foraging experiments with Lasius niger demonstrated that TiO₂-treated pathways were avoided by ants, suggesting trail pheromone degradation. Photocatalytic tests confirmed the degradation of the pheromone component (R)-(-)-mellein under UV-A irradiation in the presence of Evonik Aeroxide P25 TiO₂. The nanosized titania was characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), and diffuse reflectance spectroscopy (DRS). These findings indicate that TiO₂ NPs can disrupt ant communication, potentially leading to significant ecological consequences. Full article

Wastewater contamination of freshwater ecosystems is a major driver of the spread of antibiotic resistance (AR). This preliminary study investigated the impact of wastewater pollution on the AR profiles of bacterial communities in the Oued–Zénati waterway, Algeria, across a pollution gradient. From September 2017 to May 2018, water samples were collected from an upstream reference site (P1), a site downstream of urban and hospital discharges (P2), and a downstream recovery site (P3). Physicochemical and microbiological analyses revealed a critical pollution hotspot at P2, with fecal coliform concentrations reaching 9.5 × 10⁵ MPN/100 mL, nearly 40 times higher than at P1. From a representative subset of 33 bacterial isolates characterized in this study, susceptibility testing showed a high prevalence of resistance, with observed trends matching the pollution gradient. Specifically, 100% of isolates from the polluted sites (P2 and P3) were resistant to ampicillin, and 60% of isolates from the hotspot (P2) were resistant to amoxicillin/clavulanic acid. Conversely, all isolates remained susceptible to gentamicin. These initial findings suggest that direct wastewater discharge is creating a significant reservoir for AR, highlighting potential risks to public and environmental health and underscoring the urgent need for improved wastewater management infrastructure. Full article

Porcine epidemic diarrhea (PED), a severe and highly contagious disease induced by porcine epidemic diarrhea virus (PEDV), impacts pigs across all age groups but has a particularly high lethality in neonatal piglets, with mortality rates reaching 80 to 100%, leading to substantial economic losses in the swine industry. In this investigation, 128 intestinal samples obtained from 65 large-scale pig farms in eight prefectures of Guangdong Province were screened by RT-qPCR between 2022 and 2024. Of these, 50 samples (39.06%) tested positive for Porcine Epidemic Diarrhea Virus (PEDV). The complete S1 genes of 31 representative strains were sequenced. Phylogenetic analysis revealed G2c as the exclusive dominant lineage (29/31, 93.6%), with single representatives of G2a and G2d. Nucleotide identity among the local strains ranged from 88.9 to 100% and 88.1 to 93.5% to prototype CV777 and from 91.2 to 99.1% to vaccine strain AJ1102. The COE neutralizing epitope (aa 499–638) carried 26 substitutions versus AJ1102; T499I/S, A520S/L, F539L, K566N and F615L were most prevalent. The SS2 epitope was fully conserved, whereas SS6 showed three low-frequency changes (S766P, S769F, G770V). Six distinct N-glycosylation patterns were identified relative to AJ1102. The predominance of G2c, accompanied by marked epitope drift and altered glycosylation, indicates the need for further investigation into vaccine efficacy. Continuous surveillance and the careful evaluation of G2c-based vaccine candidates are warranted. Full article

Background/Objectives: Precision medicine is transforming care for children with cancer, but raises new challenges. We explored parents’, oncologists’ and scientists’ perspectives on three aspects of a precision medicine trial for poor prognosis childhood cancer: data sharing, requests for additional tumor biopsies, and confidentiality. Methods: Data were collected through PRISM-Impact, a psychosocial sub-study within the Zero Childhood Cancer Program’s PRISM trial. Parents completed questionnaires at enrolment and one year later, and an optional interview after receiving their child’s trial results. Bereaved parents completed a questionnaire six months after bereavement (T1B). Oncologists and scientists were interviewed one year following trial commencement. Quantitative data were analyzed descriptively, and qualitative data thematically. Results: Parents (n = 126) considered additional tumor biopsies acceptable when risks were low and their child or oncologist supported the request. Oncologists (n = 26) emphasized weighing risk–benefit, ensuring parents felt fully informed, and research value. Most parents supported data sharing (≥89–96%), including after bereavement, despite potential privacy concerns. Parents supported overseas and interstate testing, and scientists having access to identifiable health information. Scientists (n = 10) found working with identifiable data emotionally challenging. Conclusions: Parents, oncologists, and scientists showed high acceptance of procedural aspects of precision medicine. Future trials should address privacy concerns and ensure informed consent recognizes that parents’ high acceptability of procedures may be linked to their hopes for benefit, reinforcing the need for informed consent. Full article

This study examines how supply chain structure diversification drives high-quality enterprise development in the digital economy. Using panel data from Chinese listed non-financial firms (2009–2023), we find that diversification of both suppliers and customers significantly improves firms’ total factor productivity (TFP), and the results remain robust after controlling for endogeneity. Mechanism analyses show that diversification enhances innovation capability, sustainability performance, and risk resilience, while digital supply chains strengthen these effects by improving information flow and coordination. Heterogeneity tests reveal that the impact is greater for firms with higher operational efficiency, cultural synergy, and information transparency. Overall, the findings highlight that diversified and digitally integrated supply chains are essential for innovation-driven, resilient, and sustainable enterprise growth. Full article

The current study investigates the influence of timber-to-concrete connection types on the behaviour of timber–concrete composite (TCC) structures employing metal web timber joists. Two groups of laboratory specimens were prepared, each comprising four samples with push-joisted beams joined by oriented strand board (OSB) and cast with a concrete layer. One group utilised compliant timber-to-concrete connections via perforated steel tape angles, while the other employed rigid connections through epoxy adhesive and granite chips. The specimens, consisting of two 1390 mm long beams of grade PS10 timber, were tested under three-point bending. Experimental results and finite element analyses demonstrated that specimens with compliant connections exhibited 14–16% greater maximum vertical displacements but only a marginal 1.79% reduction in load-carrying capacity compared to those with rigid connections. Findings indicate that connection compliance markedly affects stiffness and deflection but has a minor impact on ultimate strength. These insights can guide optimisation of TCC members with metal web joists, balancing structural performance and design requirements in sustainable timber construction. Full article

Background/Objective: Aging is associated with a decline in metabolic health, including impaired glucose regulation. Both diet and biological sex impact metabolic health, yet sexual heterogeneity in diet response is understudied. We report on exploratory analyses of sex-specific associations between diet and insulin sensitivity, insulin resistance, and android and intermuscular fat composition in older adults. Methods: This secondary analysis uses baseline data from a previously completed clinical trial (n = 96), the MASTERS study. An oral glucose tolerance test (OGTT) was used to calculate insulin resistance and insulin sensitivity as measures of metabolic function, while dual-energy x-ray absorptiometry and computed tomography were used to assess body composition. Univariate analyses were used to identify sex-specific associations between metabolic health and single nutrients, as well as other dietary components. Feasible solutions algorithm (FSA) modeling was employed to identify food groups that were most associated with insulin sensitivity. Results: In men, greater intakes of vegetable protein (p < 0.0001) and whole grains (p = 0.001) were associated with higher insulin sensitivity, while refined grains (p = 0.003) and conjugated linoleic acids (p < 0.001) were negatively associated. In women, insulin sensitivity was positively associated with alcohol (p < 0.001) and xylitol (p = 0.007). FSA modeling identified whole grains, nuts, and seeds as food groups that predicted higher insulin sensitivity in men, while alcohol remained the strongest predictor in women. Conclusions: Men showed higher insulin sensitivity with plant-based diets, while alcohol intake was the dietary factor most associated with insulin sensitivity in women. The findings of these exploratory analyses support the need for sex-specific clinical trials and dietary guidance for aging populations. Full article

The paper addresses tribocorrosion resistance studies of components made from the iron alloy Fe (0.21% C, 0.8% Si, 1.29% Mn, 1.34% Cr) using the Wire Arc Additive Manufacturing (WAAM) technology. The authors have developed their original heat treatment technology, where oil baths are used, leading to an increase in impact strength. This treatment combines processes such as austenitising, martensitic hardening, tempering, and austempering. As part of the research tests, tribocorrosion wear measurements were conducted in a 3.5% NaCl solution on both heat-treated and non-treated samples. The measurements showed lower tribocorrosion wear in the samples subjected to the novel heat treatment. Full article

The rapid digitalization of banking services has transformed consumer interactions, necessitating a deeper understanding of the factors influencing online banking adoption. This research investigates the factors influencing consumer adoption in a country undergoing rapid digital transformation but still facing gaps in digital skills and infrastructure—Romania. The objective of the study is to analyze how key variables such as ease of use, security, speed, usefulness, and social pressure influence online banking behavior of Romanian consumers, especially the most digitally engaged ones. The study utilizes a multi-method empirical approach, hypothesis testing, binary logistic regression for prediction modeling, and segmentation analysis to offer a comprehensive view of customer behavior. The findings identify essential adoption drivers and separate customer profiles, providing useful information for financial organizations aiming to enhance their digital strategy. Perceived ease of use and perceived security are primary factors influencing adoption; nevertheless, decision tree analysis indicates that speed and usefulness have a more significant impact than logistic regression implies, but social pressure unexpectedly serves as an impediment. These results highlight the necessity for banks to customize their digital services, harmonizing security and user-friendliness with improved efficiency and usefulness to promote broader adoption in emerging digital economies like Romania. Full article