Search Results (443)

Campylobacter is a leading foodborne bacterial pathogen, and poultry production is a major reservoir contributing to human exposure. Reducing Campylobacter at farm level is therefore critical to limit downstream contamination. This systematic review and meta-analysis aimed to identify and quantitively summarise the current interventions and control measures applied in poultry farms to control the contamination and bird colonisation by Campylobacter. The Scopus electronic database was accessed to collect primary research articles that focused on observational studies and in vivo experiments, reporting results on Campylobacter concentrations or prevalence in both non-intervened and intervened groups. A total of 4080 studies were reviewed, from which 112 were selected and included in the meta-analysis according to predefined criteria, yielding 1467 observations. Meta-regression models were adjusted to the full data set and by intervention strategy based on the type of outcome measure (i.e., concentration and prevalence). In general terms, the results reveal that the effectiveness to reduce Campylobacter colonisation vary among interventions. A highly significant effect (p < 0.001) was observed in interventions such as organic acids, bacteriophages, plant extracts, probiotics, and organic iron complexes added to feed or drinking water; although drinking water was proven to be a more effective means of administration than feed for extracts and organic acids. In contrast, interventions such as chemical treatments, routine cleaning and disinfection, and vaccination showed both lower and more heterogeneous effects on Campylobacter loads. Vaccination effects were demonstrated to be driven by route and schedule, with intramuscular administration, longer vaccination periods and sufficient time before slaughter linked to greater reduction in Campylobacter colonisation. Probiotics, plant extracts and routine cleaning and disinfection were associated with lower Campylobacter prevalence in flocks. Meta-regression models consistently showed that the interventions were proven more effective when the sample analysed was caecal contents in comparison to faeces (p < 0.001). Overall, the findings of this meta-analysis study emphasise the application of a multi-barrier approach that combines targeted interventions with robust biosecurity and hygiene measures in order to reduce Campylobacter levels in poultry farms. Full article

Background/Objectives: Birth preparedness and complication readiness (BPCR) is a cornerstone of maternal health strategies designed to minimize the “three delays” in seeking, reaching, and receiving skilled care. In Ethiopia, uptake of BPCR remains insufficient, and little evidence exists on how individual- and community-level factors interact to shape preparedness. This study assessed the determinants of BPCR among women of reproductive age in Hawela Lida district, Sidama Region. Methods: A community-based cross-sectional study was conducted among 3540 women using a multistage sampling technique. Data were analyzed with multilevel mixed-effect negative binomial regression to account for clustering at the community level. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported to identify determinants of BPCR. Model fitness was assessed using Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC), and log-likelihood statistics. Results: At the individual level, women employed in government positions had over three times higher expected BPCR scores compared with farmers (AIRR = 3.11; 95% CI: 1.89–5.77). Women with planned pregnancies demonstrated higher BPCR preparedness (AIRR = 1.66; 95% CI: 1.15–3.22), as did those who participated in model family training (AIRR = 2.53; 95% CI: 1.76–4.99) and women exercising decision-making autonomy (AIRR = 2.34; 95% CI: 1.97–5.93). At the community level, residing in urban areas (AIRR = 2.78; 95% CI: 1.81–4.77) and in communities with higher women’s literacy (AIRR = 4.92; 95% CI: 2.32–8.48) was associated with higher expected BPCR scores. These findings indicate that both personal empowerment and supportive community contexts play pivotal roles in enhancing maternal birth preparedness and readiness for potential complications. Random-effects analysis showed that 19.4% of the variance in BPCR was attributable to kebele-level clustering (ICC = 0.194). The final multilevel model demonstrated superior fit (AIC = 2915.15, BIC = 3003.33, log-likelihood = −1402.44). Conclusions: Both individual- and community-level factors strongly influence BPCR practice in southern Ethiopia. Interventions should prioritize women’s empowerment and pregnancy planning, scale-up of model family training, and address structural barriers such as rural access and community literacy gaps. Targeted, multilevel strategies are essential to accelerate progress toward improving maternal preparedness and reducing maternal morbidity and mortality. Full article

This study proposes HGBRCond, a machine learning model for conductivity prediction in controlled biodegradation processes. Eight regression algorithms were evaluated using experimental data (n = 424) from a micro-pilot treatment system. HGBRCond, based on Histogram-Gradient Boosting Regression (best performing ML model), achieved optimal performance (R² = 0.877 ± 0.011, RMSE = 10.235 ± 0.54 µS/cm) through 10-fold cross-validation. Unlike standard HGBR and previous conductivity models that lack comprehensive validation frameworks, HGBRCond integrates rigorous statistical validation (cross-validation, sensitivity analysis, confidence intervals) with multi-level interpretability (Morris screening, SHAP analysis, feature importance), achieving a 6.8% performance improvement over standard gradient boosting approaches while addressing mechanistic interpretability gaps present in prior work. However, limitations constrain direct potential industrial applicability: limited dataset (n = 424), narrow conductivity range (285–360 µS/cm), strong dissolved oxygen dependence, sensitivity across two critical parameters, constant flowrate, and validation restricted to controlled conditions. These constraints require model recalibration for potential industrial application. Future work will focus on model validation across extended operational ranges using industrial samples and full-scale testing to establish applicability beyond controlled experimental settings. Full article

Background: The COVID-19 pandemic has put other infectious diseases, especially in children, into a new perspective. Our study focuses on two important viral infections: COVID-19 and influenza, which often present with similar clinical symptoms. Taking into consideration that the pathophysiology and systemic impact of the two viruses are distinct, which can lead to measurable differences in laboratory values, this study aimed to analyze laboratory features that differentiate between COVID-19 and influenza virus infections in pediatric patients. Methods: We statistically analyzed the routinely available laboratory data of 98 patients with influenza virus and 78 patients with COVID-19. Afterwards, the classification and regression tree (CART) method was performed to identify specific clinical scenarios, based on multilevel interactions of different features that could assist clinicians in evidence-based differentiation. Results: Significant differences between the two groups were observed in ALT, eosinophils, hemoglobin, and creatinine. Influenza-infected infants presented significantly higher leukocyte, neutrophil, and basophil counts compared to infants infected with COVID-19. Regarding children (over 12 months), significantly lower levels of ALT and eosinophil counts were observed in those with influenza compared to those with COVID-19. Furthermore, the CART decision tree model identified distinct profiles based on a combination of features such as age, leukocytes, lymphocytes, platelets, and neutrophils. Conclusions: After further refinement and application, such machine learning-based, evidence-driven models, considering the large scale of clinical and laboratory variables, might help to improve, support, and sustain healthcare practices. The differential decision tree may contribute to enhanced clinical risk assessment and decision making. Full article

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

This study examines the perceived mental health of Ukrainian female forced migrants in Ireland through the lens of the socio-ecological model (SEM). Using binomial logistic regression on a 2023 online survey dataset (N = 656), it explores multi-level predictors across individual, relationship, community, and societal domains. Results indicate that individual-level factors explain the largest proportion of variance in perceived mental health (Nagelkerke R² = 0.399). Employment status, self-rated physical health, and coping strategies were key determinants: part-time employment and good physical health were associated with higher odds of good perceived mental health. In contrast, avoidant coping and worsening health were associated with poorer outcomes. Relationship-level factors (R² = 0.194) also contributed significantly; lack of social support and deteriorating family or friendship ties were linked to poorer mental health, whereas participation in refugee meetings was strongly protective. Community-level factors (R² = 0.123) revealed that unstable housing, living with strangers, and declining neighbourhood relationships were associated with reduced mental well-being. At the societal level (R² = 0.168), insufficient access to psychological support and excessive exposure to Ukrainian news were associated with poorer outcomes, while moderate news engagement was protective. The findings highlight the multifaceted nature of refugees’ perceived mental health, emphasising the interdependence of personal resilience, social connectedness, and systemic support. Full article

Traditional oilfields face challenges such as high energy consumption, imprecise control, and lax management in mid-to-late development stages, leading to increased costs and reduced efficiency. To address these issues, this work aims to develop an intelligent optimization framework for intermittent pumping by explicitly integrating physical mechanisms with data-driven modeling. Specifically, we propose a data–physics dual-driven method that combines physics-based parameters derived from seepage mechanics with data-driven feature selection using Pearson correlation analysis to identify nine key production factors. An improved CatBoost regression framework is developed through systematic preprocessing, including data cleaning, cubic polynomial feature expansion, F-value screening, and Z-score normalization. The model is further enhanced using Bayesian hyperparameter optimization, a weight adaptation mechanism, and an attention-based multi-level architecture. The novelty of this work lies in the unified dual-driven optimization strategy and the enhanced CatBoost framework that jointly improve prediction accuracy and model generalization. Experimental results demonstrate that the proposed method can accurately predict pumping operation times. Compared with the original CatBoost model, the MAE of the large-interval model decreases by 56.94%, while that of the small-interval model decreases by 16.23%. In addition, the accuracy of the large-interval model increases by 4.1%, and that of the small-interval model increases by 1.22%. These improvements show that the enhanced CatBoost model significantly strengthens predictive performance. This approach provides a reliable basis for optimizing pumping schedules, reducing energy consumption, and promoting intelligent and refined oilfield management. Full article

Accurate and real-time detection of polyps in colonoscopy is a critical task for the early prevention of colorectal cancer. The primary difficulties include insufficient extraction of multi-scale contextual cues for polyps of different sizes, inefficient fusion of multi-level features, and a reliance on hand-crafted anchor priors that require extensive tuning and compromise generalization performance. Therefore, we introduce a one-stage anchor-free detector that achieves state-of-the-art accuracy whilst running in real-time on a GTX 1080-Ti GPU workstation. Specifically, to enrich contextual information across a wide spectrum, our Cross-Stage Pyramid Pooling module efficiently aggregates multi-scale contexts through cascaded pooling and cross-stage partial connections. Subsequently, to achieve a robust equilibrium between low-level spatial details and high-level semantics, our Weighted Bidirectional Feature Pyramid Network adaptively integrates features across all scales using learnable channel-wise weights. Furthermore, by reconceptualizing detection as a direct point-to-boundary regression task, our anchor-free head obviates the dependency on hand-tuned priors. This regression is supervised by a Scale-invariant Distance with Aspect-ratio IoU loss, substantially improving localization accuracy for polyps of diverse morphologies. Comprehensive experiments on a large dataset comprising 103,469 colonoscopy frames substantiate the superiority of our method, achieving 98.8% mAP@0.5 and 82.5% mAP@0.5:0.95 at 35.8 FPS. Our method outperforms widely used CNN-based models (e.g., EfficientDet, YOLO series) and recent Transformer-based competitors (e.g., Adamixer, HDETR), demonstrating its potential for clinical application. Full article

Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. To overcome these limitations, this study proposes a data-driven optimization framework for sidetrack well placement. It utilizes machine learning techniques trained on a large-scale synthetic dataset generated from field-informed numerical simulations, to establish a robust machine-learning proxy model. Four predictive models—Linear Regression, Polynomial Regression, Random Forest, and a Backpropagation (BP) Neural Network—were systematically compared, among which the Random Forest model achieved the best predictive accuracy. After hyperparameter optimization, a robust prediction model for sidetracking performance was established, achieving a Mean Squared Error (MSE) of 0.0008 (Root Mean Squared Error, RMSE, of 0.0283) and an R² of 0.8059 on the test set. To further optimize well placement, a mathematical model was formulated with the objective of maximizing the production enhancement rate. Three optimization algorithms—the Multi-Level Coordinate Search (MCS), Differential Evolution (DE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES)—were evaluated, with the DE algorithm demonstrating superior performance. By integrating the optimized Random Forest predictor with the DE optimizer, a systematic methodology for sidetrack well placement optimization was developed. A field case study validated the approach, showing significant improvements, including a reduced water cut and an incremental cumulative oil production of 82.7 tons. This research demonstrates the simulation-based feasibility of intelligent sidetrack well placement optimization and provides practical guidance for future sidetracking development strategies. Full article

Accurate early warning of rainfall-induced landslides poses a critical challenge in geological disaster risk management. Conventional deterministic rainfall threshold models often overlook the heterogeneity of regional geological conditions, while landslide susceptibility assessment is plagued by uncertainties in selecting non-landslide samples. To address these issues, this paper took Zhushan County in Hubei Province as the study area, and the semi-supervised random forest (SRF) model was adopted to conduct landslide susceptibility assessment. The critical rainfall (Effective Rainfall-Duration, EE-D) threshold curves were constructed based on the antecedent effective rainfall (EE) and rainfall duration (D). Furthermore, EE-D threshold curves with different geological condition characteristics were established and analyzed according to the thickness, slope, and area of the landslides, respectively. By coupling the landslide susceptibility results with a classified multi-level rainfall threshold model, a spatiotemporally refined regional framework for tiered landslide early warning was developed. The results show that the SRF model solves the problem of non-landslide sample selection error in traditional supervised learning. The Area Under Curve (AUC) value reaches 0.91, which is better than the analytic hierarchy process, logistic regression, etc. Moreover, the models of landslide susceptibility and EE-D threshold can effectively achieve the hierarchical early warning of rainfall-induced landslide hazards. Full article

Background: Exercise therapy is essential in managing rotator cuff-related shoulder pain. Multimedia tools may enhance adherence and engagement, but their added value over traditional materials remains uncertain. Objective: To compare an exercise program delivered through paper-based materials with or without addition of multimedia animations in individuals with rotator cuff-related shoulder pain. Method: A single-center open-label randomized clinical trial was conducted in [Blinded] between April 2023 and December 2024 Patients with rotator cuff-related shoulder pain were included. Both groups received seven face-to-face exercise sessions with a physical therapist and were randomized into receiving or not multimedia animations. The main outcome measure was Shoulder Pain and Disability Index at 6-week follow-up. Other outcomes were pain intensity (rest, during movement and at night), patients’ satisfaction, perceived improvement and expectations and patients’ adherence to the exercise program. Furthermore, patients’ perceived usability, usefulness and satisfaction with multimedia animations were also measured. Subjects were followed for 24 weeks. Adequate multilevel regression models were implemented. Results: A total of 154 subjects were included (80 in the control group and 74 in the experimental group). Both groups improved over time, but there were no significant between-group differences regarding Shoulder Pain and Disability Index, pain intensity, patients’ satisfaction, perceived improvement or expectations. Subjects showed a decrease in adherence to exercise over time, without significant between-group differences. Conclusions: The implementation of multimedia animations may not provide additional benefits when a well-designed paper-based program and therapist support are already established. Full article

Background/Objectives: People with Alzheimer’s dementia (AD) experience distressing changes in social behaviour. However, little is understood about whether social behaviour is associated with support provided by, or familiarity with, conversation partners. We aimed to explore the association between support provided by, and familiarity with, conversation partners and the social behaviour of people with mild AD during conversation. Method: We designed an exploratory within-subjects study wherein conversations between 19 participants with mild AD and a familiar informant, followed by an unfamiliar researcher, were video-recorded and double-rated using two measures of social behaviour (Social Observation Inventory and Measure of Participation in Conversation—Dementia), and one measure of support from the conversation partner (Measure of Support in Conversation—Dementia). Multilevel linear regression with within-subject clusters was used to explore adjusted associations between support and familiarity and social behaviour. Results: Greater support in conversation was associated with more appropriate participation in social conversation of participants with AD. In fully adjusted models, every 1-point increase in MSC-D score was associated with a 0.29 (95% CI: 0.14 to 0.44) increase in MPC-D score and a 1.59 (95% CI: 0.87 to 2.32) increase in SOI score. Familiarity with the conversation partner was not associated with the social behaviour of the participants with AD. Conclusions: We found evidence for an association between social behaviour in AD and support provided by unimpaired conversation partners, but the numbers were small, and this should be interpreted cautiously. Future research should continue this hypothetical lead to expand our understanding of how support and familiarity influence social behaviour to inform potential interventions. Full article

Suburban green areas provide significant health, economic, social, and ecological benefits. They are a key element in advancing sustainability at local and regional scales. However, they become threatened in the presence of other competing land uses, neighborhood-change processes, and/or weak built-environment governance. Consequently, suburban green area loss and/or degradation is problematic. In this study, we tested whether socioeconomic decline is significantly correlated with loss or degradation of suburban green areas at a neighborhood scale. This phenomenon has been previously studied with a limited sample and methodology and needs further empirical documentation and more nuanced modeling and testing. We employed Social-Ecological System theory in scoping and framing this multidisciplinary study and informing multilevel panel-data regressions. This approach allowed us to identify key factors and lagged effects behind green area degradation in outer-ring suburbs of Los Angeles. In addition to internal socioeconomic factors, random components associated with ecological zonal distribution and county-level clustering registered significant variability in their influence on greater likelihood of green coverage loss and degradation in declining outer-ring suburbs. Findings from this study can inform intelligent spatial planning, management, and monitoring of suburban areas, and showcase the value of a social-ecological system lens in suburban green infrastructure research, as well as contribute to SES theoretical development and research methodology at the neighborhood scale. Full article

General self-efficacy is identified as a modifiable determinant of adolescent mental health and well-being. This study sought to better understand how conditions in different environments of adolescents’ lives and socio-demographic factors are associated with adolescents’ general self-efficacy. We conducted a hierarchical multi-variable linear regression analysis based on survey data from 2021 of a large population-based sample of Norwegian adolescents (n = 15,040). We found that better Relation to peers (β = 0.20, 95% CI [0.18; 0.22]) and Academic/social relation to teachers (β = 0.13, 95% CI [0.11; 0.14]), Perceived neighbourhood safety (β = 0.08, 95% CI [0.06; 0.10]), and Participation in physical activities (β = 0.07, 95% CI [0.06; 0.09]) had medium to small positive associations with adolescents’ general self-efficacy, whilst Parental involvement, Participation in organized music/cultural leisure activities, and Perceived access to neighbourhood leisure arenas had negligible associations with general self-efficacy. Boys reported a stronger general self-efficacy than girls (β = −0.17, 95% CI [−0.19; −0.16]) and Age and Socio-economic status had small positive associations with general self-efficacy (β = 0.08, 95% CI [0.07; 0.10] and 0.04, 95% CI [0.02; 0.06], respectively). We found some small moderation effects by socio-demographic factors in the associations between environmental factors and general self-efficacy. Our findings suggest that general self-efficacy-promoting initiatives that target adolescents apply a multi-sectorial and multi-level approach and pay particular attention to gender differences. A focus on facilitating adolescents’ experiences of mastery and access to relevant successful role models and supportive behaviour by adults and peers in the various contexts seems to be of particular importance. Full article

Under the national “dual-carbon goal” and the pressing demand for sustainable development, eco-city construction and carbon reduction have become critical issues on China’s urban development agenda, closely aligned with the United Nations Sustainable Development Goals (SDGs). However, most studies focus on regional assessments, lacking national-scale evaluations and spatial heterogeneity analysis of obstacles. This study analyzes 280 Chinese cities using a multi-level evaluation system. Analytic hierarchy process (AHP) and entropy weight methods determine index weights, while the comprehensive evaluation method assesses ecological levels. The obstacle diagnosis model identifies key obstacle factors, and geographically weighted regression (GWR) analyzes spatial heterogeneity, computing carbon intensity to explore relationships with eco-cities development. The findings reveal that (1) the ecological level of Chinese cities exhibits a regional pattern of “high in the east, low in the west”; (2) the primary index-level obstacle factors include total per capita water resources, per capita green space area, college full-time faculty per 10,000 people, the proportion of tertiary industries in gross domestic product (GDP), and college students per 10,000 people; at the element level, the main obstacles are environmental bases, social services, economic potential, and innovative capacity; (3) the GWR model reveals that eastern regions should increase water resources, central regions expand green space, and western and northeastern regions enhance innovative capacity and social services to foster balanced development; and (4) carbon intensity follows a “low in the east, high in the west” pattern, with eco-cities scores significantly negatively correlated with carbon intensity (r = −0.235, p < 0.01). This study provides the first comprehensive national-scale evaluation of eco-cities development, providing reference for the construction of eco-cities. Full article