Browse Articles

Analysis of epidemiological characteristics and associated factors of congenital hypothyroidism in Henan Province

Household food waste from a settlement perspective in Cape Town South Africa

Abstract Food security, greenhouse gas emissions from the food supply chain, and waste disposal are three of the most pressing global challenges. Food waste impedes food security, environmental sustainability, and economic resilience, especially in rapidly urbanizing destitute settlements of the Global South, where structural vulnerabilities intensify the issue. This study examines the extent, nature, and drivers of household food waste in Wallacedene, a less privileged settlement on Cape Town’s periphery. Using a mixed-methods approach, we conducted 85 household surveys alongside a focus group discussion to explore food waste practices, perceptions, and challenges. Findings indicate that 85% of households discard edible food, predominantly vegetables, bread, and fruit. Key drivers include inadequate meal planning, restricted access to refrigeration and storage facilities, irregular income affecting purchasing habits, and limited awareness of food preservation techniques. While many participants reported guilt, sadness, and frustration over food waste, their ability to reduce it was constrained by structural and material barriers. This study underscores the intricate relationship between individual behaviors and systemic limitations, arguing that household food waste requires a multifaceted approach beyond behavior change campaigns. Effective interventions must integrate education, infrastructure improvements, and community-driven solutions adapted to local contexts. Additionally, households should align food disposal decisions with ethical and moral principles rooted in Ubuntu (Botho), reinforcing collective responsibility and minimizing waste through economic and socially inclusive food-sharing practices. These findings provide empirical evidence for food waste in low-income African settlements, offering actionable insights for policies supporting Sustainable Development Goal 12.3 of the United Nations.

Enhancing logistic regression classification: insights from simulation and real-world applications through ranked set sampling

Computational identification of Terminalia arjuna phytochemicals as potential 3α-HSD3 inhibitors

Retraction Note: Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

Process modeling and sludge characterization of electrocoagulation for the removal of oil-in-water emulsions and calcium from petroleum refinery wastewater

Abstract Electrocoagulation (EC) process efficiency for treating synthetic petroleum refinery wastewater was investigated. The novelty of this study lies in the simultaneous removal of oil-in-water emulsion and calcium ions using an integrated experimental–statistical–financial approach, combining response surface methodology (RSM) optimization with COMSOL Multiphysics simulation. The impact of independent variables on the removal rates of both contaminants was studied and optimized using the central composite design method. Analysis of variance was employed to evaluate the significance of the variables and the mathematical model determined by RSM. The optimal conditions were determined to be a pH of 9, a current density of 6.123 mA/cm 2 , an initial calcium concentration of 130 ppm, an initial oil content concentration of 588 ppm, a NaCl concentration of 2.5 g/l, and a total electrolysis time of 98 min. These conditions correspond to an oil content removal rate of 91.3% and a calcium removal rate of 72.9%. Energy consumption and total operation costs were calculated under these parameters to be 12 kWhm -3 and 10.32 EGPm -3 , respectively. Fourier Transform Infrared spectroscopy, Energy-dispersive X-ray spectroscopy, and Scanning electron microscopy characterization were performed on the resulting sludge and scum at the optimum conditions and its utilization was discussed. Furthermore, COMSOL Multiphysics software was used to simulate the voltage distribution across the proposed cell to understand the electrochemical features. Overall, the statistical, financial, and simulated study demonstrates the feasibility of the EC technique for oil refinery wastewater treatment.

Research on numerical simulation of surrounding rock stability of deep roadway with advanced strain softening model based on Hoek-Brown criterion

Ultrasound-assisted aqueous two-phase extraction of flavonoids from erigeron breviscapus: process optimization, structural characterization, antioxidant study, and DFT calculation

Validation of automated 5 mL thin liquid swallowing sound segmentation for estimating audio-derived pharyngeal clearance time

Intelligent monitoring and CNN-based performance evaluation of borehole-pipe-pump gas drainage systems in coal mines

Neural correlates of adversity-overcoming pup rescue behavior in female mice

Abstract Rescuing infants under threat is a fundamental parental behavior in mammals. However, the behavioral expression and neural correlates of adversity-overcoming infant rescue in non-parental female individuals remain poorly understood. In this study, we first established a novel pup rescue paradigm with scalable adversity, in which mothers and virgin female mice have to cross a water pool of varying depths (0, 3, or 20 mm) to retrieve pups into the nest. We unexpectedly found that virgin females were less averse to water and retrieved pups faster than mothers. Next, we implemented an additional hurdle by trapping pups into a tube, so that female mice had to cross the pool and open the tubes to rescue pups. The rescuer virgin females in this “trapped pup” rescue task showed increased neuronal activity in the anterior cingulate cortex, lateral septum, anterior commissural nucleus, basolateral amygdala, and dorsal raphe nucleus, compared with non-rescuers. The c-Fos + cell densities in these regions showed significant negative correlations with the latencies to rescue behaviors suggesting their positive impact on rescue. Given that the virgin females do not have genetic relations to the rescuee pups, our findings provide a basis for further analyses of adversity-overcoming altruistic behavior and its neural correlates.

The association between medicinal herbs consumption and body weight and composition: a hospital based cross-sectional study

Virtual reality to enhance risk management and safety in electrical substations

Evaluating routing stability and coordination in swarm-based multi-agent task-oriented dialogue systems

physically interpretable residual strength prediction of corroded pipelines via symbolic Bayesian networks

Abstract Residual strength assessment of corroded pipelines is essential for ensuring the structural integrity and safe operation of gas transportation infrastructure. Traditional empirical formulas and finite element analyses, while widely used, often lack adaptability, interpretability, or computational efficiency. Recent advances in machine learning have improved prediction accuracy; however, many models remain opaque, limiting their utility in safety-critical structural health monitoring (SHM) applications where transparency and physical insight are imperative. This study introduces a novel framework, Symbolic Bayesian Networks (SyBN), for physically interpretable residual strength prediction of corroded pipelines. SyBN combines a Bayesian Feature-Weighted Neural Network (BFW-NN) for high-accuracy prediction and uncertainty quantification with a Deep Symbolic Regression (DSR) component that generates explicit mathematical expressions representing the relationship between pipeline parameters and failure pressure. A key innovation lies in an adaptive gating mechanism that dynamically balances prediction accuracy and symbolic consistency based on sample complexity. Extensive experiments were conducted on a public benchmark dataset comprising both experimental and simulation-based measurements of pipeline burst pressure. SyBN achieved state-of-the-art performance, with an $$R^2$$ of 0.966, RMSE of 1.304 MPa, and MAE of 0.968 MPa, outperforming several classical and ensemble learning baselines. Feature importance analysis confirmed high consistency between Bayesian-derived feature weights and SHAP values, while ablation studies validated the necessity of each framework component. The SyBN framework provides an effective and interpretable solution for residual strength prediction in corroded pipelines, offering engineers explicit symbolic models that enhance transparency and support informed decision-making. This approach aligns well with the growing demand for explainable and trustworthy machine learning in SHM tasks, particularly in critical infrastructure systems.

Hunyuan zhuang improves interoception: evidence from an expert-novice study and a pilot randomized controlled trial

Global protein profiling of human milk using pre-enriched RNA-sequence libraries

Abstract The complex protein composition of human milk remains challenging to characterize due to technical limitations, yet such insights are crucial as early-life nutrition influences long-term health outcomes and is associated with pathological conditions such as obesity. In this study, we applied APTASHAPE, a high-throughput profiling method based on chemically modified protein-binding RNA molecules, to globally profile protein composition in skimmed human milk and to explore associations with maternal and infant characteristics. A total of 520 human milk samples collected at 3 days, 1 month, 2 months, and 3 months postpartum were analyzed. Ordinary least squares regression analysis of the discovery cohort identified discriminatory RNA sequence profiles reflecting the global protein composition and correlating with sampling time, maternal BMI, and parity. These associations were subsequently validated and confirmed in an independent test cohort. Furthermore, 14 candidate aptamers were subjected to protein pull-down assays followed by mass spectrometry, which identified C4b-binding protein and tenascin C as candidate targets associated with maternal BMI and sampling time. Overall, our findings highlight the dynamic nature of the human milk protein composition, with the greatest variation attributable to sampling time, while maternal BMI and parity accounted for more subtle differences.

Multi-scale effects of soil and water conservation on runoff and sediment transport in a Chinese loess plateau basin

Abstract Quantifying the multi-scale effects of Soil and Water Conservation (SWC) measures is essential for managing soil erosion and water resources in the Yellow River Basin. This study evaluates the multi-scale (daily, monthly, annual) impacts of SWC on runoff and sediment reduction in the Sanchuan River Basin from 1960 to 2019 by integrating a Random Forest (RF) model with SHapley Additive exPlanations (SHAP) analysis. Key findings include: (1) Runoff reduction showed distinct seasonal variation, peaking at 53.8% in July, whereas sediment reduction remained consistently high (> 84%) year-round. (2) SHAP analysis quantitatively demonstrated that antecedent rainfall (R1d) exerted a stronger influence than current-day rainfall (R0d) on both runoff and sediment responses, underscoring the importance of cumulative hydrological conditions—a finding robust despite moderate model R² values (0.552 for runoff, 0.452 for sediment). (3) The analysis revealed two distinct benefit thresholds: significant runoff reduction emerged during 2001–2003 when terrace coverage reached 4.74 × 10⁴ hm², while peak sediment reduction occurred in 2013–2015 when forest area attained 18.94 × 10⁴ hm². These thresholds, derived from polynomial trend analysis and validated by Pettitt change-point detection, mark periods when cumulative SWC implementation is associated with significant hydrological benefits. The study provides a mechanistic, data-driven framework for understanding SWC effects and offers scale-specific, quantitative targets for adaptive watershed management in erosion-prone regions.

Analysis of different methods to calculate tertiary regulation reserves for renewable energy in Japan

Excessive gamma and beta oscillations in manic states across mood and psychotic disorders

Abstract The clinical manifestations of mood and psychotic disorders encompass phasic and transdiagnostic features. The complexity of these symptoms may hamper the development of biomarkers for these diagnostic categories. In the present study, machine learning was employed to cluster their transdiagnostic clinical manifestations as a means of developing state-dependent biomarkers with electroencephalography (EEG). This data-driven clustering classified the items of multiple symptom scales into three symptom domains, which subsequently stratified patients with major depressive, bipolar, and schizophrenia spectrum disorders into distinct symptom state groups. The specific brain activity profiles of each stratum were then characterized by resting-state and auditory steady-state EEG paradigms. The resting-state readings of patients in manic states revealed significantly increased gamma and beta oscillations, whereas 40-Hz and 20-Hz auditory steady-state responses (ASSRs) showed no significant differences. These findings suggest that manic states are associated with heightened high-frequency oscillatory activity, represented by gamma and beta oscillations, without a concomitant increase in sensory-evoked information processing precision. Further investigations of excessive gamma and beta oscillations may facilitate the development of state-dependent biomarkers for the assessment, diagnosis, and treatment of manic state symptomatology in clinical psychiatry.