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Progressive damage mechanism and multiscale characterization of hard sandstone-coal composite structures under loading

Under geological conditions characterized by deep high-stress zones, intense mining disturbances, and hard roof strata, primary fractures on and within coal bodies are highly susceptible to expansion and propagation. This study investigates the failure characteristics of coal bodies under the combined effects of hard roof strata and sandstone fracture water. Taking the 11129 working face at Zhangji Coal Mine as the study site, this research examines the macro- and micro-scale load-bearing capacity and damage characteristics of coal bodies. The results indicate that: (1) During coal seam mining, the thick layer of hard sandstone roof directly overlying the seam exhibits a large span with minimal deformation while bearing the load of the overlying weak rock strata. By employing pre-splitting blasting technology, the initial roof fracture step length was reduced to 45 m. Stress concentration occurred 5–20 m ahead of the coal face, with peak stress reaching 33.8 kPa. Peak strain appeared 11m behind the coal face, registering a value of 594.3. (2) During the moment of stress-bearing or fracture, coal-rock strata undergo energy accumulation and dissipation. Before the advance distance reaches the minimum roof fracture step length, relatively hard rock layers bear their own weight and the load from overlying softer strata. As the span length of the hard rock increases, the accumulated energy rises. When the internal energy accumulation reaches the rock’s load-bearing limit, the roof fractures, transferring the force to the goaf and releasing the energy. (3) Under dynamic loading, coal sample cracks exhibit a progressive evolution from “development to penetration to failure.” At low impact intensity (0.3 MPa), the incident energy for samples with varying coal-to-rock ratios ranges from 172.64 to 240.89 J, with dissipated energy accounting for 0.249 to 0.44 of the total. When the impact pressure increased to 0.7 MPa and the rock content in the sample rose, the incident energy increased from 265.95 J to 326.87 J, an increase of approximately 60.92 J. The proportion of dissipated energy remained within the range of 0.249 to 0.418. The magnitude of the impact pressure had no significant effect on the degree of energy dissipation. (4) A pre-splitting blast pressure relief scheme employing fan-shaped hole groupings was proposed. Numerical results from the working face indicate that the pre-splitting blast achieved the anticipated roof control effect.

Pruning-fruit retention and plant models strategies to improve growth, quality, and yield of netted melon in Arid regions

Netted melon ( Cucumis melo var. reticulatus ) exhibits strong environmental adaptability and high economic value, making it a widely favored horticultural crop in local markets. In the arid regions of Northwest China, optimizing traditional monoculture planting patterns can fully exploit the production potential of netted melon. Economic benefits and environmental adaptability can be enhanced by adjusting plant spacing and pruning-fruit retention strategies, thus promoting sustainable agricultural development in arid Gobi regions. A field experiment was conducted with two factors: plant spacing and pruning-fruit retention strategies. Three plant spacing were set at 55, 65 and 75 cm, and three pruning-fruit retention methods included two vines with two fruits, three vines with two fruits and three vines with three fruits. The effects of different planting configurations on the growth yield and fruit quality of Gobi netted melon was evaluated. A Back Propagation Neural Network-Non-dominated Sorting Genetic Algorithm II (BP-NSGA-II) model was employed to simulate and optimize planting strategies. Increasing the number of retained vines and fruits moderately delayed plant development and postponed harvesting time. Enlarged plant spacing increased vine diameter and leaf number but inhibited vine elongation and leaf area expansion. Increasing the number of retained vines suppressed vine thickening but promoted leaf number expansion, and increased fruit retention number inhibited vine diameter growth. The effect of plant spacing on yield showed a decreasing trend as plant spacing increased, whereas the three vines with two fruits treatment consistently produced higher yields than the other pruning-fruit retention methods. Increasing plant spacing improved the comprehensive fruit quality, and the three-vine two-fruit method resulted in superior overall fruit quality. By employing BP-NSGA-II multi-objective optimization, the optimal planting configuration was determined to be a plant spacing of 70 cm with a pruning-fruit retention strategy of two vines with two fruits. Under these conditions, according to the model, the optimized growth duration would be 107 days, the maximum yield would reach 48.68 t hm -2 and the optimized fruit quality compliance index (Ci) would be 0.80. This strategy effectively achieves early maturity, high yield and superior fruit quality, contributing to the sustainable development of agriculture in arid Gobi regions.

Early clinical and laboratory markers associated with post-COVID respiratory syndrome: A retrospective analysis

Background Post-COVID-19 respiratory syndrome remains a significant concern, yet early clinical and laboratory markers at the time of admission are not well established. Identifying laboratory markers associated with this condition could help guide clinical management and long-term monitoring. This study aimed to determine which laboratory findings at admission significantly differ between COVID-19 survivors with and without post-COVID respiratory syndrome (PCRS) and assess their potential as markers. Methods A retrospective comparative study was conducted on COVID-19 survivors who has history of hospitalization at Persahabatan National Referral General Hospital, Jakarta, in 2020–2021, divided into case (PCRS, n:43) and control (nonPCRS, n:42) groups. Demographic data, vital signs, and laboratory findings were analyzed, including complete blood count, kidney and liver function, electrolytes, blood gas analysis, D-dimer, and C-reactive protein (CRP). Results Compared with controls, cases demonstrated significantly higher neutrophil percentages, neutrophil-to-lymphocyte ratio (NLR), blood urea nitrogen (BUN), potassium levels, and respiratory rates, along with lower lymphocyte and eosinophil percentages at admission. After Benjamini–Hochberg correction for multiple testing, respiratory rate, potassium, BUN, and neutrophil percentage remained statistically significant. In adjusted multivariable logistic regression models controlling for age, sex, body mass index, and markers of disease severity (SpO₂ and/or respiratory rate), potassium and respiratory rate showed consistent independent associations with case status across several models, while NLR retained a modest association only in models incorporating SpO₂. No significant differences were observed for D-dimer or CRP. Conclusion Neutrophilia, lymphopenia, increased NLR, elevated BUN, potassium levels, and higher respiratory rates at admission were associated with post-COVID respiratory syndrome. Among these, potassium levels and respiratory rate showed more consistent associations after adjustment for demographic factors and disease severity markers. These findings describe admission characteristics linked to post-COVID-19 Respiratory syndrome. Larger prospective studies with serial measurements are needed to confirm their clinical relevance and prognostic value.

Evaluation of alkali residue-stabilized soil in road construction with optimization of mechanical properties and environmental risk assessment

The effective utilization of alkali residue (AR) is a central issue and an urgent challenge for the green transformation of the soda ash industry. While research has primarily focused on the mechanical performance of AR-based materials, their long-term environmental risks, particularly under dynamic conditions, have been relatively overlooked. This study evaluates AR-stabilized soil for road construction by simultaneously optimizing its mechanical properties and conducting a comprehensive environmental risk assessment for shallow groundwater contamination. Through laboratory tests, we identified the AR substitution rate that maximizes the California Bearing Ratio (CBR) and water stability. To address the limitations of traditional static leaching tests, we integrated material degradation and contaminant transport models to dynamically simulate pollutant release and migration under realistic, non-steady conditions. Our findings reveal that an AR substitution rate between 10% and 50% yields optimal mechanical performance. However, environmental risk analysis indicates a significant increase in risk beyond 35%, with manganese (Mn) and nickel (Ni) exposure concentrations potentially exceeding Class III groundwater quality standards. Sensitivity analysis confirms the AR substitution rate as the most critical factor influencing environmental risk. Therefore, we recommend strictly controlling the AR substitution rate to ≤35% in road construction, providing a science-based, dual-criteria (mechanical and environmental) guideline for the safe and sustainable utilization of industrial waste in geotechnical engineering.

Evaluating the quality of social media content on metabolic dysfunction associated steatotic liver disease: An experience from a lower middle-income country

Introduction Metabolic dysfunction associated with steatotic liver disease (MASLD)/non-alcoholic fatty liver disease (NAFLD) represents a significant public health concern. Social media (SoMe) increasingly influences health perceptions in lower-middle-income countries, with one-third of Sri Lanka’s population using SoMe for health information. Assessing MASLD content quality on SoMe is therefore important. Aims & methods This cross-sectional study assessed accuracy, completeness, and quality of MASLD content across Facebook, YouTube, TikTok, Instagram, and X in Sinhala, English, and Tamil from Sri Lanka (January 2005-December 2024). Board-certified gastroenterologists independently reviewed posts using standardised scales for accuracy (0–3), completeness (0–5), and global quality score (GQS) (0–5). Posts were categorised by source profile and content type, with user interactions analysed. Results Analysis included 289 posts: 158 (54.7%) YouTube, 101 (34.9%) Facebook, 14 (4.8%) TikTok, 11 (3.8%) X, 5 (1.7%) Instagram. Languages: 214 (74.0%) Sinhala, 54 (18.7%) Tamil, 21 (7.3%) English. Content sources: undisclosed identity (36.0%), non-healthcare persons (26.0%), healthcare professionals (22.1%), alternative healthcare professionals (14.2%), healthcare institutions (1.7%). Health promotion (61.9%) was the predominant content type. Mean accuracy was 1.78/3 (59.3%), with healthcare professionals scoring highest (2.35/3, 78.5%) versus others (51.0–55.1%; p < 0.001). Completeness averaged 2.1/5 (42%), with English content scoring higher than Sinhala and Tamil. GQS averaged 2.4/5 (48.4%). 82% of posts were classified as “Rotten” (<60% score for each metric). Facebook and YouTube showed significantly higher completeness and GQS (p < 0.05). User engagement metrics showed no correlation with content quality. Conclusion Most SoMe content originated from non-healthcare sources. Healthcare professionals delivered the most accurate content. Facebook and YouTube showed relatively higher content quality scores, though comparisons are limited by the small number of posts from other platforms. Overall quality remained suboptimal across platforms, with 82% failing adequate standards. User engagement didn’t correlate with quality. These findings highlight the need for improved quality control and health literacy initiatives for MASLD information on SoMe platforms.

Antioxidant and antibacterial potential of bioactive extraction from Cadaba glandulosa leaves

This study investigated the bioactive components and explored the antioxidant and antibacterial properties of the methanolic leaf extracts of Cadaba glandulosa (MLCG). The observed activity is related to the diverse chemical composition of the extract as determined by gas chromatography-mass spectrometry (GC-MS) analysis, which tentatively identified 18 distinct compounds. Notable compounds include methyl dodecanoate, methyl tetradecanoate, 9,12-octadecadienoyl chloride, hexadecanoic acid methyl ester, palmitoleic acid, anethole, brefeldin A and oleic acid. The antioxidant tests showed a significant scavenging activity of 88.2% at a concentration of 381.5 µg/mL, which underlines the effectiveness of the extract in neutralizing free radicals. The total phenolic content in MLCG was found to be 79.5%, corresponding to 250.8 mg of gallic acid equivalents (GAE)/ mL.The antibacterial activity of MLCG showed variability between bacterial strains, with the strongest inhibition observed against Staphylococcus aureus and Streptococcus pyogenes , both Gram-positive bacteria. The extract showed moderate activity against Gram-negative bacteria such as Escherichia coli and Pseudomonas aeruginosa , while the least activity was observed against Klebsiella pneumoniae . In this study, the impressive antioxidant and antibacterial properties of MLCG underline the therapeutic potential of Cadaba glandulosa as a natural source of antioxidant and antibacterial agents.

Protocol for a cluster-randomized control trial of a remote workplace resilience intervention for early care and education providers: The OnWARD trial

Work-related stressors take a toll on individuals’ health and well-being, a toll which is often heavier for under-resourced, low-paid segments of the essential workforce who serve our communities. Resilience programs have arisen as a promising workplace strategy to improve mental health and well-being. However, emerging programs are constrained by time and resource-intensive implementation strategies that are challenging to scale for marginalized segments of the workforce, including early childcare education (ECE) staff. The goal of this 15-month cluster randomized control trial is to compare change in resilience assets and resources for ECE staff in centers (n = 80 consisting of 640 ECE workers) randomly assigned to either a remotely delivered resilience (intervention) or physical activity (attention control) program. Measures will be collected at four timepoints: baseline (0 months), post-intervention (3 months), and long-term maintenance (9 and 15 months). Secondary outcomes will include changes in well-being, physical activity, organizational support, absenteeism, and turnover. Additionally, we will explore potential moderators of the treatment effects. The RE-AIM Framework will be used to determine the potential for individual (staff) and organizational (center) level reach, adoption, implementation, and maintenance of the two programs. Results will fill key gaps of prior resilience work by focusing on an underserved population in critical need of well-being resources with implications for the feasibility and impact of remote programming in other segments of the workforce. Trial Registration: This trial is registered with the ClinicalTrials.gov registry (NCT06919952) and approved by the Institutional Review Board (IRB) at the University of North Carolina at Chapel Hill (IRB# 25−0016).

Dense retrieval and reranking for referenced provisions in electric power audit systems

Electric power audits require practitioners to describe an audit issue and justify the final opinion by citing an appropriate referenced provision. In practice, the referenced provision should be retrieved from an authoritative provision corpus rather than generated, because correctness and traceability are critical in audit workflows. This paper proposes a dense retrieval and reranking framework for referenced provision retrieval in electric power audit systems. The method follows a two-stage pipeline: a two-tower dense retriever efficiently recalls a small candidate set (top-20) from a large provision corpus, and a one-tower scoring model performs fine-grained reranking by jointly modeling the audit problem description and each candidate provision. To strengthen semantic matching under audit-specific contexts, the audit issue category is incorporated into the reranking input. Experiments are conducted on a Chinese electric power audit text dataset, demonstrating that the proposed retrieval–reranking design provides an effective and practical solution for accurate referenced provision retrieval.

Time-lapsed colposcopy image-based segmentation of cervical lesion areas

Engineered cementitious composites with nano calcium carbonate and corona waste mask fibers for sustainable 3D printing applications

Skin-to-skin contact significantly impacts maternal anxiety, mother-infant bonding, and autonomic function in infants with congenital heart disease and their mothers

Efficient cosine-windowed cross-correlation for intermediate deformable image registration

The role of BLZF1 in lung adenocarcinoma and its value as a diagnostic and prognostic biomarker

Model-order-reduced spectral-element method for high-accuracy and fast 3-D transient electromagnetic forward modeling with SAI-Krylov

Associations of the hs-CRP/HDL-C ratio with cardiovascular metabolic multimorbidity: a large cross-sectional study

A data-driven life cycle cost model for tender evaluation of metro pantograph carbon strips

Study on detachment mechanism of rice wet extruded mixtures adhering to cleaning sieve for combine harvester

Case-matched comparison of combined phacoemulsification with ab-interno trabeculectomy via Kahook dual blade and trabectome in a Caucasian population

Abstract The purpose of this retrospective study was to compare the outcomes after combined phacoemulsification and ab-interno trabeculectomy via Kahook Dual Blade (KDB) and Trabectome, being represented in two groups of patients of Caucasian ethnicity with matched baseline criteria. We included 60 eyes of 49 participants being treated for cataract, of which 30 eyes underwent additional ab-interno trabeculectomy via KDB (Kahook group) and 30 eyes received additional Trabectome surgery (Trabectome group). For this comparative analysis, the Kahook group and Trabectome group were matched at a 1:1-ratio, based on the following criteria: preoperative IOP, maximum known preoperative IOP, preoperative medication score, cup/disc-ratio, follow-up time, best-corrected visual acuity and age. Successful surgery was defined by three scores: IOP at longest follow-up < 21 mmHg (Score A) or < 18 mmHg (Score B) without re-surgery and an IOP reduction > 20% or IOP ≤ 15 mmHg without re-surgery and an IOP reduction ≥ 40% (Score C). Furthermore, we compared postoperative IOP, as well as medication score, and side effects between both groups. Both surgical techniques led to a relative IOP reduction of 29% within their respective groups. Specifically, preoperative IOP decreased from 19.5 ± 5.0 mmHg to 13.8 ± 3.9 mmHg in the Kahook group, and from 19.8 ± 4.5 mmHg to 14.0 ± 3.9 mmHg in the Trabectome group during an average follow-up period of 23–24 months. There was no statistical significant difference noted. Both the KDB and Trabectome yielded similar success rates, according to Score A (67% vs. 70%), Score B (63% vs. 67%) and Score C (33% vs. 23%). There were no severe side effects notes in either group. In conclusion, the KDB and Trabectome showed similar IOP-lowering properties and safety profiles within our two matched groups of Caucasian patients.

Prevalence of heart failure with preserved ejection fraction in patients with ischemia and non-obstructive coronary arteries

Deployment of a machine learning-based predictive system for childhood diarrhea in Sub-Saharan Africa

Abstract Diarrhea remains a leading cause of child mortality in Sub-Saharan Africa, necessitating advanced predictive tools for early intervention. Despite the growing adoption of machine learning in healthcare, gaps persist in deploying models as scalable, real-world solutions. This study developed an end-to-end machine learning framework to predict diarrhea among children under five in SSA, integrating rigorous model development with Flask-based deployment for practical use. Using nationally representative Demographic and Health Surveys (DHS) data from 27 SSA countries (2016-2024), we preprocessed data (handling missing values, feature selection, and SMOTE for class imbalance), trained a Random Forest classifier (optimized via RandomizedSearchCV), and deployed the model as a RESTful API with Flask. The final model demonstrated strong predictive power, with 79.6% accuracy and a particularly high recall of 84.1%, meaning it is exceptionally effective at identifying true diarrhea cases. Most importantly, the model is no longer just a research output; it is a deployed, interactive system ready for practical application. This work successfully demonstrates a complete pipeline from data to deployment, offering a tangible solution that can aid public health decision-making. We have proven that it is possible to close the gap between machine learning research and real-world implementation. To build on this foundation, future work should focus on enhancing the model’s interpretability for health workers, adopting more scalable deployment technologies like FastAPI and Docker, and conducting rigorous field validation with community stakeholders to ensure these tools truly meet the needs of those they are designed to serve.