Search Results (14,328)

Background/Objectives: Antibiotic resistance represents one of the major global health emergencies, driven by the inappropriate use of antibiotics and persistent misconceptions among adults attending general medical clinics. This study, conducted on 325 participants recruited from general medical clinics in the province of Salerno, aimed to assess their knowledge, perceptions, and behaviors regarding antibiotic use. Methods: A cross-sectional, quantitative observational survey was conducted using a structured questionnaire based on the WHO tool and adapted to the local context. Results: The results show that the majority of participants take antibiotics only when prescribed by a doctor (90.2%), but risky practices such as self-medication (10%) and early discontinuation of therapy (16%) persist. In addition, 72% of subjects demonstrate incomplete knowledge about the independent management of drugs, and 86% mistakenly believe that resistance is limited to the individual rather than the community. The descriptive analysis stratified by age showed higher levels of awareness among subjects under 30 years of age, compared to significant knowledge gaps and inappropriate behaviors in the over-65 age group. Conclusions: Despite a good awareness of the need for medical prescriptions and the collective importance of the phenomenon, there are still critical areas of knowledge and incorrect practices that can promote the spread of antibiotic resistance. The data collected underscore the urgency of targeted educational strategies differentiated by age group, integrated with multi-channel communication interventions, in order to promote the appropriate use of antibiotics and contain the impact of one of the most serious global health emergencies. Full article

Background: Multimodal large language models (LLMs) are increasingly being explored as clinical support tools, yet their capacity for orthodontic biomechanical reasoning has not been systematically evaluated. This retrospective study assessed their ability to analyze treatment mechanics and explored their potential role in supporting orthodontic decision-making. Methods: Five publicly available models (GPT-o3, Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4.0, and Grok) analyzed 56 standardized intraoral photographs illustrating a diverse range of active orthodontic force systems commonly encountered in clinical practice. Three experienced orthodontists independently scored the outputs across four domains—observation, interpretation, biomechanics, and confidence—using a 5-point scale. Inter-rater agreement and consistency were assessed, and statistical comparisons were made between models. Results: GPT-o3 achieved the highest composite score (3.34/5.00; 66.8%), significantly outperforming all other models. The performance ranking was followed by Claude (57.8%), Gemini (52.6%), GPT-4.0 (48.8%), and Grok (38.8%). Inter-rater reliability among the expert evaluators was excellent, with ICC values ranging from 0.786 (Confidence Evaluation) to 0.802 (Observation). Model self-reported confidence showed poor calibration against expert-rated output quality. Conclusions: Multimodal LLMs show emerging potential for assisting orthodontic biomechanical assessment. With expert-guided validation, these models may contribute meaningfully to clinical decision support across diverse biomechanical scenarios encountered in routine orthodontic care. Full article

Iran is a uniquely compelling case due to its ancient and diverse culinary heritage, coupled with a strategic national mandate to significantly boost tourism, making the development of this high-impact sector a crucial policy imperative. The present study adopts a scenario planning approach to first identify the key factors influencing food tourism in rural areas of Iran, then explores plausible future scenarios for rural tourism development, and finally ranks strategic alternatives for enhancing food tourism in these regions. Methodologically, the research combines a goal-oriented, descriptive-analytical approach with future study techniques. Data for the initial phase were collected through a literature review, field studies (surveys, interviews), and expert surveys, and subsequently analyzed using MICMAC and ScenarioWizard software tools. Strategic alternatives were then evaluated using Picture Fuzzy Sets (PFSs) and the COPRAS method based on six critical factors. The findings reveal that six primary factors—promotional activities, pricing, food quality, infrastructure, government support, and investment—play pivotal roles in advancing food tourism in rural Iran. Based on these six primary factors, the study constructs three future scenarios: optimistic, stagnant, and crisis-driven scenarios. In the third phase of the analysis, employing Picture Fuzzy COPRAS and Picture Fuzzy Analytic Hierarchy Process (PF-AHP), the results indicate that “food festivals and promotional campaigns” carry the greatest weight and are deemed the most influential in attracting tourists, whereas “investment” ranks the lowest. Following normalization and application of weights, COPRAS analysis identifies “improving the quality of tourism infrastructure” as the most effective strategy, receiving the highest score (464.0620). A sensitivity analysis further confirms that the overall ranking of the strategies remains stable despite changes in the criteria weights, with only minor shifts observed among mid-ranked alternatives. These results offer policymakers a practical decision-making tool to allocate limited resources efficiently and focus on high-impact strategies that support the sustainable development of food tourism in Iran’s rural areas. Full article

Accurate precipitation data are essential for effective water resource management. This study aimed to correct precipitation values from the ERA5_Ag reanalysis dataset using observational data from 20 meteorological stations located in the Tensift basin, Morocco. Five machine learning models were evaluated: MLP, XGBoost, CatBoost, LightGBM, and Random Forest. Model performance was assessed using RMSE, MAE, R², and bias metrics, enabling the selection of the best−performing model to apply the correction. The results showed significant improvements in the accuracy of precipitation estimates, with R² ranging between 0.80 and 0.90 in most stations. The best model was subsequently used to correct and generate raster maps of corrected precipitation over 42 years, providing a spatially detailed tool of great value for water resource management. This study is particularly important in semi−arid regions such as the Tensift basin, where water scarcity demands more accurate and informed decision−making. Full article

The growing demand for sustainable solutions in the plastics industry has highlighted the need to reintroduce post-industrial polymer waste into high-performance applications. This study focuses on the mechanical recycling of automotive scraps containing variable proportions of polycarbonate (PC), acrylonitrile–butadiene–styrene (ABS), and a commercial PC/ABS blend. After determining the composition of two representative batches, a screening of seven commercial compatibilizers and impact modifiers was performed to improve impact strength. Among them, an ethylene–methyl acrylate–glycidyl methacrylate (E-MA-GMA) terpolymer was identified as the most effective additive. Its influence was further investigated through a mixture design approach, varying the composition of the three polymer phases and the additive content (0–10 wt.%). The resulting response surface model revealed a significant increase in impact resistance in PC-rich formulations with increasing E-MA-GMA content, while ABS and PC/ABS showed more complex trends. Rheological, mechanical, and thermal analyses supported the observed behavior, suggesting improved matrix compatibility and reduced degradation during processing. The proposed model enables the prediction of impact performance across a wide range of compositions, offering a practical tool for the optimization of recycled blends. These findings support the potential of targeted compatibilization strategies for closed-loop recycling in the automotive sector. Full article

Background/Objectives: This systematic review with meta-analysis synthesizes current evidence on the effectiveness of non-immersive virtual reality (VR) interventions in enhancing gross motor function, balance, and functional independence in children with cerebral palsy (CP). Methods: A systematic search was performed across six databases (PubMed, Web of Science, Scopus, MEDLINE, CINAHL Complete, and Psychology and Behavioral Sciences Collection) to identify randomized controlled trials (RCTs) published up to July 2025. Primary outcomes included gross motor function (GMFM-D/E), balance (Pediatric Balance Scale, PBS), and functional independence (WeeFIM). Risk of bias was assessed using the RoB 2 tool, and the certainty of evidence was evaluated with GRADE. Results: From 1233 retrieved records, 13 RCTs involving 624 participants fulfilled the inclusion criteria. Pooled analyses demonstrated significant improvements with non-immersive VR in gross motor function (GMFM-D: ES = 2.04, p = 0.02; GMFM-E: ES = 2.02, p < 0.001), balance (PBS: ES = 1.34, p = 0.02), and functional independence (WeeFIM: ES = 0.99, p < 0.001). Conclusions: Non-immersive VR interventions were associated with meaningful gains in gross motor function, balance, and independence in children with CP. Significant differences were consistently observed in GMFM-D, GMFM-E, PBS, and WeeFIM outcomes when compared with control groups. Full article

Microbiota play a pivotal role in holobionts, influencing nutrient intake, growth, and overall health. In this context, microbial dysbiosis of Penaeus stylirostris larvae seem to be associated with huge larval mortalities in hatcheries in New Caledonia. To understand larval dysbiosis establishment, our purpose was to identify bacterial biomarkers, as bioindicators, related to a given larval stage and health condition. To this end, larvae were sampled daily to access their active microbiota through sequencing of the V4 region of the 16S rRNA molecule, while their stage and their health were also observed. We identified three biomarkers strongly related to healthy zoea, and some may act as probiotics or play key roles in larval ontogeny and nutrition. We also found six biomarkers linked to unhealthy zoea and eight related to healthy mysis. Biomarkers were mostly related to diseased shrimps (Lewinella) or healthy shrimps (Cognitishimia, Thalassolituus) or were known to prey on cells (P30B-42), suggesting that the larvae might be battling against detrimental conditions. No biomarker related to unhealthy mysis was identified. Finally, our data showed that bacterial bioindicators could be used as an effective biosurveillance proxy in hatcheries, to monitor larval development, and as an early warning tool to predict rearing outcomes. Full article

Utilizing a dataset of 190 risk factors spanning over three decades, we apply a swarm-based classification model to estimate factor velocity and analyze its implications for asset pricing. Our results show that slower-moving factors generate higher abnormal returns than their faster-moving counterparts, underscoring the critical role of price adjustment speed in market dynamics. Furthermore, our results suggest that trading frictions impede the rapid assimilation of information, contributing to the observed return patterns. This research offers new insights into return predictability and demonstrates the potential of swarm intelligence as a powerful tool for financial modeling. Full article

Dizygostemon riparius is an endemic tropical Brazilian species whose physiological responses to salinity are poorly understood. This study evaluated the effects of NaCl (0, 50, and 100 mM) on in vitro-grown D. riparius by integrating growth, anatomical, photosynthetic, and biochemical analyses. Salinity significantly reduced biomass and growth parameters, indicating impaired development. Photosystem II efficiency declined, as evidenced by decreases in the performance index and chlorophyll content, while anatomical changes such as epidermal thickening and reduced vascular bundles reflected structural adjustments under stress. At the biochemical level, salinity altered polyamine metabolism, with reductions in total free polyamines, suggesting potential limitations in defense mechanisms. Hyperhydricity observed under high salinity indicated a non-adaptive response. Overall, D. riparius displayed limited tolerance to salt stress, with physiological and biochemical impairments outweighing structural plasticity. This study provides the first data-driven characterization of salinity effects in this species and highlights the value of in vitro culture as a tool to investigate stress responses. Full article

Pneumonia is an acute respiratory infection caused by pathogens such as bacteria or viruses, and accurate early diagnosis is critical for reducing mortality. Chest X-ray (CXR) imaging serves as a conventional diagnostic tool. However, radiographic features of pneumonia often overlap with those of other pulmonary diseases and are subject to inter-observer variability. Traditional Convolutional Neural Network (CNN) models tend to capture redundant information during feature extraction, and single pre-trained models often exhibit limited generalization in multiclass classification tasks. This study proposes a multi-model ensemble learning framework based on multi-head attention mechanism. Firstly, the three pre-trained backbones—DenseNet-121, ResNet-50, and VGG-19—were fine-tuned through transfer learning by replacing their classification heads, adapting pooling layers, and optimizing the fully connected layers. Secondly, feature maps extracted from these tuned backbones were concatenated and fused using a multi-head attention mechanism; the fused representation was then refined by two consecutive multi-head attention layers and finally passed to a fully connected classifier to produce the ensemble prediction. Three task sets were constructed from a public Kaggle dataset: binary classification (normal vs. pneumonia), three-class classification (normal, COVID-19, viral pneumonia), and four-class classification (normal, lung opacity, viral pneumonia, COVID-19), achieving accuracies of 91.67%, 93.79%, and 90.60%, respectively. The results demonstrate that the proposed multi-head attention-based ensemble framework offers significant advantages for pneumonia multiclass classification, particularly by maintaining high recall and robustness in more complex scenarios such as four-class differentiation, indicating its potential as a clinical decision-support tool. Future work will involve expanding the dataset and evaluating the model’s generalizability across additional disease categories. Full article

Reliable simulations of any flow require proper preservation of the fundamental principles governing the mechanics of its motion, whether in differential or integral form. When these principles are solved in differential form, discretization schemes introduce errors by transforming the continuous physical domain into a discrete representation that only approximates it. This paper analyzes the numerical performance of the solver for supersonic chemically active flows, rhoCentralRfFoam, using integral conservation principles of mass, momentum, energy, and chemical species as a validation tool in a classical test case with a highly refined mesh under nonlinear pre-established reference conditions. The analysis is conducted on this specific test case; however, the methodology presented here can be applied to any problem under study. It may serve as an a posteriori verification tool or be integrated into the solver’s workflow, enabling automatic verification of conservation at each time step. The resulting deviations are evaluated, and it is observed that the numerical errors remain below $0.25\%$, even in cases with a high degree of nonlinearity. These results provide preliminary validation of the solver’s accuracy, as well as its ability to capture physically consistent solutions using only information generated internally by the solver for validation. This represents a significant advantage over validation methods that require external comparison with reference solutions, numerical benchmarks, or exact solutions. Full article

Background: Adjunctive antibiotics are frequently used alongside mechanical debridement (MD) for peri-implantitis, yet their additional clinical benefit remains uncertain. Objective: To systematically assess whether adding local or systemic antibiotics to non-surgical MD improves clinical outcomes in peri-implantitis. Methods: The review protocol was registered in PROSPERO (CRD42022380401). We included randomised controlled trials (RCTs) involving peri-implantitis patients treated with MD plus local or systemic antibiotics, compared to MD alone, with at least 3 months of follow-up. Searches were conducted in PubMed, Cochrane Library, LILACS, Web of Science, and Embase up to 9 April 2025. Eleven RCTs (634 patients) were included in the qualitative synthesis. The Cochrane RoB 2.0 tool evaluated the risk of bias. Random-effects meta-analyses of data from 10 studies, adjusting results to an equivalent 6-month follow-up time-frame, assessed treatment efficacy based on changes in probing pocket depth (PPD) and bleeding on probing (BoP), the primary outcomes. Meta-regressions examined the influence of mean patient age and implant-to-patient ratio on adjusted outcomes. Results: Systemic antibiotics resulted in generally greater PPD reduction and BoP reduction over MD alone or plus chlorhexidine, with the greatest benefits observed in amoxicillin-based multi-agent regimens and longer follow-up duration. Comparatively, local antimicrobial adjuncts performed less effectively on PPD reduction. No implant losses were reported, and adverse events were rare. Limitations: Some included trials had a high risk of bias and considerable heterogeneity. Follow-up was limited to the short term, and definitions of clinical “success” varied across studies. Conclusions: Adjunctive systemic antibiotics, particularly amoxicillin-based combinations, substantially improve short-term clinical outcomes of non-surgical peri-implantitis treatment compared to MD alone. Nevertheless, given the variability in study quality and potential risks associated with antibiotic use, their application should be judicious. Further long-term RCTs are warranted to confirm sustained efficacy and safety. Full article

Avocado (Persea americana Miller), a crop of major economic importance in Mexico, is threatened by several quarantine pests, and recent reports have suggested that the lance fly Neosilba batesi (Diptera: Lonchaeidae) may be responsible for significant yield losses. To clarify the role of this species, we surveyed avocados from six localities in Veracruz State on the Gulf coast of Mexico and identified lance flies using both morphological and molecular tools. None of the symptoms previously attributed to N. batesi infestation in Hass avocado were observed in any of the fruits inspected across the six localities. However, 90 fruits displayed clear signs of borer attack by Conotrachelus spp. or other primary pests, and 64 of these damaged fruits (60%) yielded lance flies. Hass avocados were rarely infested and hosted only N. batesi, whereas creole avocados (P. americana var. drymifolia) were hosts to N. batesi, N. glaberrima, N. recurva, and N. flavitarsis and an undescribed species (Neosilba sp.3) that was detected by analysis of the COI gene sequences of males. Additionally, Lonchaea cristula was reported for the first time emerging from creole avocado. Each avocado yielded an average of between 2.3 and 21.0 adult lance flies. Infestation was more frequent and numerous in fruits collected from the ground than in those harvested directly from trees, supporting the idea that lance flies preferentially exploit pre-damaged or fallen fruits. Indeed, lonchaeid eggs were frequently observed deposited on the periphery or inside oviposition holes created by other pests. Overall, our results indicate that Neosilba spp. act as secondary invaders in Veracruz, with no evidence of N. batesi behaving as a primary pest in this region. None of the avocados were infested by species of Tephritidae and none of the Neosilba species we identified appear to pose a threat to avocado production in Mexico. This study highlights the value of combining morphological and molecular tools for species identification and underscores the importance of differentiating between primary and secondary invaders in the context of avocado pest management. Full article

Fe₄₂Mn₂₈Cr₁₅Co₁₀Si₅ is a highly strain-hardenable high-entropy alloy (HEA) that is challenging to machine with traditional metal cutting tools. The electrochemical behavior of this HEA was examined in nitrate- and chloride-based electrolytes to understand the electrochemical machining (ECM) process. Potentiodynamic and potentiostatic tests were conducted on this alloy in 1 M and 2.35 M NaNO₃ solutions, with and without additions of 0.01 M nitric acid and 0.01 M citric acid. A 20% NaCl solution was also tested as an electrolyte. Nitrate solutions caused passivation of the HEA, while no passivation was observed in chloride solutions. Surface analysis with X-ray photoelectron spectrometry (XPS) indicated that adding citric acid helped reduce surface passivation. The Faradaic efficiency of ECM increased with higher applied voltage. The chloride solution showed higher Faradaic efficiency than nitrate-based solutions. Specifically, the Faradaic efficiency of 20% NaCl at 10 V is 57.4%, compared to 21.9% for 20% NaNO₃ + 0.01 M citric acid at 10 V. Electrochemical parameters, including anodic and cathodic exchange current densities, Tafel slopes, and corrosion current densities, were calculated from the experimental data. The corrosion current densities in the 20% nitrate solutions ranged from 2.35 to 3.2 × 10⁻⁵ A/cm², while the 20% chloride solution had a lower corrosion rate at 1.45 × 10⁻⁵ A/cm². These electrochemical parameters can help predict the dissolution behavior of the HEA in nitrate and chloride solutions and aid in optimizing the ECM process. Full article

Bumblebees (Bombus spp.) are essential pollinators in natural and agricultural ecosystems but face increasing threats across Europe from habitat loss, climate change, and intensive land use. Austria hosts 42 recognized bumblebee species, yet comprehensive molecular data have been lacking. Here, we present the first complete DNA barcode reference library for the Austrian bumblebee fauna, generated as part of the Austrian Barcode of Life initiative. This reference library includes 586 partial mitochondrial COI sequences. DNA barcoding successfully identified all species, with distinct Barcode Index Numbers (BINs) and no BIN sharing observed, demonstrating its reliability as a complementary method to traditional morphology-based identification. Intraspecific genetic diversity was generally low, though B. jonellus exhibited notable mitochondrial structure with a complex biogeographic pattern. Our results underscore the value of DNA barcoding as a straightforward tool for accurate species identification and biodiversity monitoring, even for non-experts, while also highlighting cryptic genetic variation within widely distributed species. This reference library provides a robust framework for taxonomic, ecological, and conservation research, and supports future metabarcoding-based monitoring efforts in Austria and beyond. Full article