Browse Articles

When distress meets disability: social connectedness buffers functional impairment in university students

Predicting the potential suitable habitats of Rosa roxburghii and its key pest Grapholita molesta in China using the MaxEnt Model

Climate change can reshape the potential distributions of crops and their pests, as well as their spatial overlap, with important implications for agricultural security and pest management. Rosa. roxburghii , a key specialty crop in southwestern China, is threatened by its primary pest, Grapholita molesta . Here, we used occurrence records and WorldClim bioclimatic variables to model the potential distributions of both species under current (1970–2000) and future (SSP2–4.5 and SSP5–8.5, 2061–2080) climates via a parameter-optimized MaxEnt model, and analyzed spatial overlap across suitability levels. Model performance was assessed with ENMeval and the continuous Boyce index. Results show that the minimum temperature of the coldest month (bio6) is the main climatic constraint for both species, with R. roxburghii influenced by multiple factors and G. molesta primarily limited by winter low-temperature thresholds. Currently, R. roxburghii ’s suitable areas are smaller and mainly concentrated in southwestern and central China, whereas G. molesta has a much broader potential distribution across China. Although the overall co-occurrence area spans most of the suitable range of R. roxburghii , the majority of this overlap occurs in low-suitability zones, indicating a generally low probability of co-occurrence across much of the distribution. The overlap of highly suitable areas (HSA) between the two species is extremely limited, covering only ~28,800 km² (~0.3% of China’s land area), suggesting that potential high-risk areas are spatially restricted rather than widespread. Under future scenarios, R. roxburghii ’s highly suitable areas are projected to contract and fragment, whereas G. molesta ’s range expands northward. However, the overlap of moderate and highly suitable areas between the two species does not increase and even declines, suggesting that future pest risk is likely to become spatially restructured, concentrating in specific regions rather than intensifying across the entire distribution range. These findings elucidate differences in climatic niche and climate sensitivity between crop and pest, providing a scientific basis for targeted pest management and optimized R. roxburghii cultivation.

PANI-mediated wastewater treatment: mechanistic insights into heavy metal remediation pathways

Factors affecting feeding choices in infants and toddlers in northern Jordan: A cross-sectional study

Background Breastfeeding is a key determinant of infant health and survival; however, exclusive breastfeeding (EBF) rates remain low worldwide. Various maternal, infant, and socioeconomic factors influence the feeding practices. Objective The main objective of this study was to identify maternal, infant, and socioeconomic determinants of infant feeding practices during the first six months of life among mothers in northern Jordan. Methods A prospective cross-sectional study was conducted at Princess Rahma and Prince Rashid Hospitals in Irbid City, northern Jordan, from December 2023 to February 2024. Mothers of healthy infants aged 6–24 months participated in a survey that gathered information on their demographics, feeding practices, and other infant-related details. Statistical analyses were performed to identify the associations and key predictors of feeding type. Results Among the 508 mothers who participated in this study, 29.9% were exclusively breastfeeding, 46.5% used mixed feeding, and 23.6% opted for formula feeding. The key factors influencing these choices include maternal health issues, work hours, and infant birth weight. Maternal illness was identified as the strongest predictor of exclusive artificial feeding (AOR = 12.72; 95% CI: 4.10–39.45; P < 0.001). Low birth weight (<2.5 kg) was also associated with higher odds of artificial feeding (AOR = 4.75; 95% CI: 2.08–10.88; p < 0.001). Maternal employment significantly increased the likelihood of mixed feeding compared with EBF (AOR = 3.54; 95% CI: 1.65–7.60; p = 0.001). Surprisingly, no significant correlation was found between maternal education, family income, cultural factors, and feeding methods. Conclusion This study highlighted the low exclusive breastfeeding rate, emphasizing the need for improved support systems to encourage breastfeeding in the form of workplace accommodations and healthcare counseling to address barriers to its practice.

Integrating visual transformers with biomechanical constraints for action quality assessment in competitive sports

Psychometric properties of the Probability Bias Measure

Probability bias is the tendency to think that negative events are relatively likely to happen, and that positive events are relatively unlikely to happen. It is reliably associated with depressive and anxious symptom severity. Here, we describe the initial development and testing of the Probability Bias Measure, which is designed to be relatively brief and general, allowing its convenient use with different populations. In two samples of Turkish-speaking students ( N s = 228 and 170), probability estimates for 12 positive and 12 negative events both displayed adequate one-factor structure, and good internal consistency. More importantly, probability bias – the difference between a participant’s mean probability estimate for negative events, and their mean probability estimate for positive events – showed excellent convergent validity, in that it correlated with depressive and anxious symptom severity, positive and negative mood, hopelessness, and dispositional optimism. Furthermore, probability bias accounted for additional variance in depressive symptom severity, over and above that already accounted for by hopelessness and dispositional optimism, which supports its discriminant validity. These findings provide preliminary support for the validity of the Probability Bias Measure for research with Turkish-speaking populations; we also discuss initial evidence for the validity of its English version. The full measure is provided in Turkish and English.

SSMOT: a self-supervised multi object detection and tracking for indoor farm animals

Abstract Wearable sensors and cameras with advanced imaging technologies play a pivotal role in monitoring animal behavior for selective breeding, managing pedigree information, and tracking genetic traits. The integration of such technological advancements is instrumental for both individual farmers and large-scale industries seeking to enhance their breeding programs. This paper proposes a camera-based multiple-animal tracking, employing a tracking-by-detection methodology within the context of self-supervised learning. In essence, the proposed framework integrates an EfficientDet detector with D0-Backbone and undergoes a two-step training process. Initially, pre-training is conducted with unlabeled data employing three self-learning strategies: Barlow Twins, SimCLR (Contrastive learning), and Masked Auto Encoder (MAE). Subsequently, the model is fine-tuned using our custom-labeled dataset in the second step. The detection results are used as inputs to our tracker, which incorporates visual and spatial information to enhance the track-to-detect association mechanism. To evaluate the effectiveness of our framework, we conducted training and testing on a proprietary dataset obtained and labeled by an animal farm in Norway. We employed standard performance metrics, including commonly used tracking measures such as Multiple Object Tracking Accuracy (MOTA), Higher Order Tracking Accuracy (HOTA), Identification Metrics (IDF1), number of ID switches (IDSW), and number of track fragments (FRAG). The quantitative results indicate that the proposed framework enhances performance on HOTA, MOTA, and IDF1 by over 8.6% on average compared to state-of-the-art methods.

Analysis of the current situation and influencing factors of emotional labor among pediatric nurses: A cross-sectional study

Aim To examine the level and factors associated with emotional labor among pediatric nurses in Yichang, China. Methods From December 20–25, 2024, a cross-sectional descriptive online study was conducted among 307 pediatric nurses in Yichang, China. The survey included general information, emotional labor (assessed using the Emotional Labor Scale; higher scores indicate greater emotional labor), spiritual climate, and compassion satisfaction, and the influencing factors associated with emotional labor were analyzed. Results Pediatric nurses in Yichang had a total emotional labor score of 69.20 ± 9.40. Regression analysis showed that nurses without children had higher emotional labor levels than those with children (P < 0.05). Higher spiritual climate and compassion satisfaction scores were both significantly associated with higher emotional labor scores (P < 0.05). Conclusions The intensity of emotional labor among pediatric nurses in Yichang is high and is associated with parental status (having children), spiritual climate, and compassion satisfaction.

Biomechanical effect of implant thickness and screw diameter on CFR-PEEK subperiosteal implants: a three-dimensional finite element analysis

Genetic algorithm-based daily power output forecasting for energy storage power stations

Accurate day-ahead power generation forecasting is crucial for improving the operational efficiency of energy storage power stations and enhancing the reliability of power grid dispatch. To address the challenges of prediction inaccuracy stemming from the complex nature of energy storage power stations, the difficulties in quantifying charge-discharge energy losses, and the obstacles in extracting implicit features from historical power data, this paper proposes a hybrid forecasting method that integrates chaos theory, signal decomposition, and deep learning, optimized using an adaptive genetic algorithm. First, a refined loss model is established for the battery packs, power conversion systems (PCS), transformers, and station auxiliary power consumption, to quantify energy losses during operation. Second, chaotic phase-space reconstruction is applied to the historical power generation data to reveal its inherent dynamic characteristics. Subsequently, the reconstructed sequences are processed using the Ensemble Empirical Mode Decomposition (EEMD) method to obtain a series of Intrinsic Mode Function (IMF) components. Based on these components, a Peak-based Frequency Band Division (PFBD) method is employed to aggregate the IMFs into high-frequency and low-frequency feature components, thereby effectively extracting implicit information from the original power sequences. Subsequently, a hybrid forecasting model integrating a Convolutional Neural Network (CNN), a Long Short-Term Memory Network (LSTM), and a Multi-Layer Perceptron (MLP) – denoted as CNN-LSTM-MLP – is constructed. This model takes as its input a combination of the extracted implicit features and the outputs from the loss model. The CNN captures local spatial patterns, the LSTM learns long-term temporal dependencies, and the MLP performs feature integration and nonlinear mapping. Finally, an Adaptive Genetic Algorithm (AGA) is used to automatically optimize the hyperparameters of the hybrid model, thereby improving forecasting performance. Experiments using actual operational data from a 10 MW/20 MWh electrochemical energy storage power station demonstrate that the proposed method achieves excellent performance in day-ahead 24-hour forecasting. The Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) reach 6.41 MW², 7.68 MW, and 0.898, respectively. Both forecasting accuracy and goodness of fit significantly outperform those of the compared single or hybrid models. This study provides an effective solution that integrates data-driven approaches with physical models for accurate power forecasting in energy storage power stations.

Toward adaptive control power sharing and bus voltage regulation for DC microgrids

Abstract Decentralized control for the DC microgrid has made extensive use of droop-based control. But correct power sharing and necessary voltage management are both impossible with traditional droop control, which results in circulating current. A novel adaptive control method that achieves precise power sharing and suitable voltage regulation based on the loading condition is presented in this paper for multiple converter DC microgrid applications. Since the output currents of the distributed power sharing units are far lower than the top limits, the accuracy of the power sharing procedure is not a problem under light load conditions. Because the output currents of the scattered producing units increase in proportion to the load, precise current sharing is required during high load situations. As the load level rises, the recommended control strategy improves the equivalent droop gains and offers precise current sharing. The innovative and novel adaptive droop controller has reduced the variance in load current sharing by utilizing the principal current sharing loops to update the droop settings and verifying them exist. To eliminate the bus voltage fluctuation in the DC microgrids, the second loop also shifted the droop lines. A variety of input voltages and load resistances are used to test the proposed method. The performance and stability of the suggested approach are assessed in this work using a linearized model, and the findings are confirmed using a suitable model created in MATLAB/SIMULINK and with the principles of real-time simulation.

Acceptability of Suubi+Adherence intervention to improve ART adherence in Southern Uganda: A qualitative analysis

In Uganda, 150,000 young people were living with HIV by the end of 2023. While Antiretroviral therapy (ART) is effective in reducing HIV transmission, youth in SSA, including in Uganda, face greater challenges with ART adherence compared to adults. Poverty-focused interventions have been recommended to improve adherence and viral load suppression. In this manuscript from Suubi+Adherence-Round 2 study, we explored the acceptability of Suubi+Adherence, a combination intervention aimed at improving ART adherence among adolescents living with HIV in Uganda that was tested in a six-year longitudinal study called Suubi+Adherence (2012–2018). We conducted semi-structured in-depth interviews with 36 youths who participated in the Suubi+Adherence intervention, which comprised matched savings accounts, mentorship, financial management, and business development training. Interviews explored participants’ motivations for joining, their experiences with the intervention, and the facilitators and barriers to intervention attendance. Informed by the Theoretical Framework of Acceptability, data were analyzed using thematic analysis and Dedoose software. Our results showed that the primary motivation for participation was the opportunity to learn about savings and income-generating activities. Participants also appreciated the Wisepill -an Electronic Monitoring Device, which reminded them to take their medication. Key facilitators for intervention attendance included transport reimbursements, family support, and content relevance. Challenges included long travel distances and associated costs, session timing conflicts with household responsibilities, and other personal obligations. Despite these challenges, participants highlighted the practical relevance of the intervention content, which aligned with their real-life experiences and needs. Our findings offer valuable insights into the acceptability of a combination intervention aimed at improving ART adherence among youth. These results have important implications for HIV treatment programs and policy in Uganda, particularly given the country’s high HIV prevalence. The parent randomized clinical trial is registered in the clinical trials database (NCT03307226).

Stereoselective synthesis, VCD study and conformational analysis of C-glycosyl isochromans

Abstract Isochromans and aryl- C -glycosides are important structural motifs in many natural products and drug candidates with diverse therapeutic potentials. Here we present the first application of pyranosyl aldehydes in the oxa-Pictet–Spengler cyclization to conjugate the two pharmacophore motifs. Enantiomeric pairs of aryl-2-propanols were reacted with pyranosyl dialdoses and 1-formyl sugars in a BF 3 ·Et 2 O-mediated oxa-Pictet–Spengler reaction. The effects of the substitution pattern, configuration, and protecting groups of the reactants on the efficiency and stereochemical outcome of the reactions were studied, and the reaction conditions were optimized. A series of 1,3- cis -substituted 1-( C -glycosyl)isochromans was prepared in high yields with good to complete stereoselectivity from glucosyldialdoses and 1-formyl glucopyranosides. The reaction efficiency dropped significantly with galacto -dialdose derivatives due to steric hindrance, leading to lower yields and anomerization, which slightly limits the universality of the method. ROESY-NMR, X-ray diffraction, and VCD methods were used to determine the structure and the absolute configuration of the compounds. The method developed represents a straightforward and stereocontrolled route to a new chemotype of isochroman C -glycosides.

Residual-aided CSI-free end-to-end learning for multiuser MIMO

A paradigm shift from Channel State Information (CSI)-dependent architectures to intelligent, AI-native air interfaces is required as 6G wireless systems advance. Conventional Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems have substantial pilot overhead and computational complexity since they rely on explicit CSI for beamforming and interference management. This study suggests a novel Deep Unfolding Successive Over-Relaxation (DU-SOR) paradigm to overcome these constraints. In contrast to conventional end-to-end learning techniques that operate as “black boxes,” DU-SOR combines iterative residual refining with a sparse Graph Transformer. The network can intuitively solve the inverse problem without explicit channel matrix inversion thanks to this novel architecture, which uses graph priors to condition the signal estimation. Extensive empirical analyses show that the proposed framework accomplishes three main goals: (i) near-optimal performance, confirmed by a mutual information score of 0.98 at 20 dB SNR; (ii) mathematically proven scalable complexity, reducing the scaling order from 𝒪 ( K 3 ) to 𝒪 ( K log K ) via sparse attention mechanisms; and (iii) robust generalisation across various channel conditions (Rayleigh, Rician, 3GPP UMi). This work offers a scalable foundation for sustainable AI-native 6G receivers by combining sparse-graph efficiency with CSI-free operation.

Thermally enhanced efficacy of chemotherapy potentiates cisplatin-based hyperthermic intraperitoneal chemotherapy in ovarian cancer

A comparison of long-term maternal mortality associated with pathologic placental separation: Highlighting possible trends and mechanisms

Abruption and retention, two types of abnormal placental separation, are associated with significant morbidity and mortality. Though advancements in obstetric management have improved peripartum injury, patients who experience abnormal placental separation may be at risk for long-term complications. This study evaluates long-term maternal mortality in patients with abruption or retention compared to those with normal placental separation. In our cohort of 638,911 vaginal deliveries (625,890 normal, 5,435 abruption, 7,586 retention), the mortality rate was 6.4 per 1,000 in normal deliveries, 9.8 per 1,000 in abruption, and 12.0 per 1,000 in retention. When controlling for demographic factors (age, race, social determinants of health), placental retention was associated with a 95% increased mortality risk (HR 1.95, 95% CI 1.59–2.40, p < 0.001) and placental abruption with a 59% increased risk (HR 1.59, 95% CI 1.21–2.08, p < 0.001). After excluding deaths within 42 days, the association with retention remained significant (HR 1.93, p < 0.001) while abruption lost significance (HR 1.31, p = 0.089), suggesting abruption-associated mortality may be driven by acute complications. Piecewise Cox regression confirmed these temporal patterns, with retention showing persistently elevated risk across all follow-up periods. A variety of health outcomes were associated with either abruption, retention or in both abnormal placental separation groups. More research is needed to understand the mechanisms associated with abnormal placental separation and contributors to long-term mortality.

A lightweight DRR-YOLOv11s model for power equipment failure and personal protective equipment detection in hydropower stations

Targeting pancreatic cancer with combined inhibition of EGFR and RAF

Pancreatic cancer is the third leading cause of cancer-related death, with a 5-year survival rate of only 10%. Preclinical studies remain essential for identifying novel therapeutic strategies, discovering biomarkers, and deepening the understanding of disease biology. The most frequent driver mutation in pancreatic cancer is the G12D mutation in the KRAS gene, present in approximately 90% of the tumors. A recent study demonstrated complete regression of KRAS-driven pancreatic cancer upon systemic ablation up- and downstream signaling proteins EGFR and C-RAF. Building on these findings, we investigated the therapeutic benefit of combining the EGFR inhibitor erlotinib with the novel pan-RAF inhibitor LXH-254. The anticancer effects of this combination were assessed in vitro in murine and human pancreatic cancer cell lines by evaluating cell proliferation, cell death and phosphorylation of key signaling proteins. Subsequent in vivo studies were performed in an orthotopic murine pancreatic cancer model and in genetically engineered KPC mice, using daily oral administration of LXH-254 (35 mg/kg) and erlotinib (75 mg/kg). While the treatment robustly inhibited MAPK signaling and caused significant anti-proliferative effects in vitro, it did not improve survival or reduce tumor burden in either in vivo model. hese results contrast with previous reports of efficacy from monotherapies in xenograft models, highlighting the limitations of current preclinical approaches. Our findings underscore the need to develop more effective pathway-targeted inhibitors, and preclinical models that predict clinical outcomes more accurately.

The effect of artificial roughness on bed topographic downstream of a culvert in response to variations in longitudinal slope

LAMAS (Light, Activity, Meals, & Sleep) timings & burnout, anxiety, and depression in teachers: Protocol for a cross-sectional study

Background Teachers play a key role in society and make up ~1.5–2.5% of the working population. Yet, there is a teacher shortage in many countries and preventive occupational medicine strategies are called for. The primary objective of this project is to explore single and joint associations of the diurnal distributions of light, activity, meal, and sleep timing and work-related exposures with severity scores of burnout, anxiety, and depression in a cross-sectional study of secondary school teachers in Germany. Methods and analysis The study will involve a one-time collection of questionnaire-based data on sleep, burnout, anxiety, and depression, sensor-based data on light and activity over one week, and diary-based data on work, sleep, and meals over one week. time. The protocol has been registered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/U4R5M ). Discussion From a preventive occupational medicine perspective, identifying where and how light, activity, meal, and sleep timing may be targeted to mitigate burnout, anxiety, and depression could inform measures to be tested not only at the individual (micro) level, but also at systems (meso-institutions; macro-policy and society) levels.