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Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents.

In order to solve the problem that the skid resistance of concrete pavement in tunnel deteriorates rapidly, which is easy to cause traffic accidents, the anti-skid rapid elevation technology of surface treatment is proposed. Wear tests were used to investigate the effects of concrete surface roughness, properties of modified emulsified asphalt binder and anti-skid fine aggregate type on long-term skid resistance of treated surfaces. The results show that the four coarsening methods of fine milling, milling, grooving and brooming can improve the skid resistance of concrete, and the skid resistance durability of fine milling and milling is better. The adhesive property of modified emulsified asphalt is the best when the content of water-based epoxy resin is 20%. In different aggregates, the anti-skid effect is better when silicon carbide is used as anti-skid aggregate and the particle size is 0.6mm:0.3mm = 2:3. The method of fine milling of concrete surface + spraying epoxy emulsified asphalt + spreading silicon carbide can effectively improve the anti-skid performance of the original concrete pavement, and the feasibility of the scheme is verified by the test road. The research results have a good reference value for improving the skid resistance of tunnel concrete pavement.

The rheological properties of asphalt can well reflect its road performance, but the rheological properties of warm mix high viscosity asphalt (HVA) are unclear. In order to study the effect of warm mixing agent on rheological properties of HVA, two kinds of warm mixing agent EC120 (EC) and Evotherm M1 (M1) were selected to prepare warm mix HVA. The rheological properties of warm mix HVA at high temperature (135~195°C), medium temperature (0~80°C) and low temperature (-6~18°C) were studied by Brinell rotary viscosity test, dynamic shear rheological test (including temperature scanning, frequency scanning, linear amplitude scanning) and bending beam rheological test. The test results show that both EC and M1 have good viscosity reduction effect on HVA at high temperature, and can effectively reduce the construction temperature. At medium temperature, M1 can effectively improve the fatigue resistance of HVA, and the fatigue life can be increased by about 30% when the dosage is 0.6%. EC can increase the rutting factor of HVA and improve its resistance to deformation, but it will reduce its fatigue performance. When the dosage is 4%, the fatigue life will be reduced by about 9%. At low temperature, M1 can reduce the creep stiffness S, increase the creep rate m, and improve the low temperature performance of HVA, while EC has the opposite effect, weakening the low temperature performance of HVA. The results are helpful to understand the rheological properties of warm mix HVA and promote its application.

Background/Objectives: Alkaline phosphatase (ALP) is a crucial enzyme in numerous pathological processes and a significant biomarker in clinical diagnostics. Conventional ALP detection methods are hampered by reliance on complex sample pretreatment, sophisticated instrumentation, time-consuming procedures, and high costs. This study aimed to develop a simple, rapid, and cost-effective colorimetric sensing method for ALP detection with enhanced resistance to matrix interference in biological samples. Methods: We designed a colorimetric assay based on bimetallic gold–platinum nanocatalysts (AuPt NPs) exhibiting peroxidase-like (POD-like) activity. The detection principle involves a dual-reaction cascade: (1) Alkaline phosphatase (ALP) catalyzes the conversion of trisodium L-ascorbic acid-2-phosphate (AA2P) into ascorbic acid (AA), and (2) the generated AA reduces oxidized 3,3′,5,5′-tetramethylbenzidine (oxTMB) produced by the catalytic activity of AuPt NPs. This method was evaluated for its detection performance in diluted human serum without complex sample pretreatment. Results: AuPt NPs exhibited resistance to biological matrix interference, enabling sensitive detection of ALP. The assay showed a linear ALP detection range of 0–90 mU·mL−1 (R2 = 0.994) and a limit of detection of 3.91 mU·mL−1. In spiked human serum, recoveries were 95.45–111.97%, with negligible interference from ions and biomolecules. Conclusions: We developed a simple, rapid, and reliable colorimetric sensor for ALP detection based on AuPt NPs. It overcomes limitations of conventional methods, holding great potential for clinical diagnostics and point-of-care applications.

A two-species interacting system, motivated by the triblock copolymers theory, is studied on a flat torus, the quotient space of the complex plane by a lattice. The free energy of the system, which contains both short-range and long-range interactions, admits disc–disc-like stationary points. The relative displacement of the disc centres in a stationary point is related to Green’s function of the Laplace operator on the flat torus. When restricted to disc–disc configurations with relative displacements equal to half periods, the free energy is minimized with respect to the lattice and its half periods. The resulting optimal lattice depends on a single parameter. As this parameter varies, the optimal lattice may be rectangular, square, rhombic, or hexagonal. This is in sharp contrast to single-species systems where optimal lattices are always hexagonal.

The total phenolic content (TPC) from Cassia javanica L. petals were extracted using ethanolic solvent extraction at concentrations ranging from 0 to 90% and an SCF-CO2 co-solvent at various pressures. Ultrasound-assisted extraction parameters were optimized using response surface methodology (RSM). Antioxidant and anticancer properties of total phenols were assessed. An SCF-CO2 co-solvent extract was nano-encapsulated and applied to sunflower oil without the addition of an antioxidant. The results indicated that the best treatment for retaining TPC and total flavonoids content (TFC) was SCF-CO2 co-solvent followed by the ultrasound and ethanolic extraction procedures. Additionally, the best antioxidant activity by β-carotene/linoleic acid and DPPH free radical-scavenging test systems was observed by SCF-CO2 co-solvent then ultrasound and ethanolic extraction methods. SCF-CO2 co-solvent recorded the highest inhibition % for PC3 (76.20%) and MCF7 (98.70%) and the lowest IC50 value for PC3 (145 µ/mL) and MCF7 (96 µ/mL). It was discovered that fortifying sunflower oil with SCF-CO2 co-solvent nanoparticles had a beneficial effect on free fatty acids and peroxide levels. The SCF-CO2 method was finally found to be superior and could be used in large-scale processing.

Inflammatory bowel disease, a typical chronic inflammatory disease of the gastrointestinal tract, make up a growing share of the global disease burden. This study firstly evaluated the anti-inflammatory effects of corn protein hydrolysate (CPH) using a cell model of tumor cell necrosis factor-α (TNF-α)-induced inflammation and a mouse model of colitis induced by dextran sodium sulfate. CPH digests significantly inhibited the expression of cyclooxygenase-2 and inducible nitric oxide synthase, and reduced the secretion of interleukin-8 in TNF-α-induced inflammation in Caco-2 cells. In mice, CPH digests significantly improved the body weight loss, clinical scores, shortening of the colon and histological symptoms, and decreased the myeloperoxidase activity, and down regulated the expression of TNF-α, and interleukin-6 in the colon. The above results indicate that the CPH can potentially be used as a health food/nutraceutical for the treatment/management of intestinal inflammation.

With the growing adoption of artificial intelligence (AI) in organizational performance management, AI feedback has increasingly supplemented or replaced leader-delivered evaluations. While prior research has addressed issues of fairness and accuracy in AI assessments, relatively little is known about how employees emotionally and behaviorally respond to negative performance feedback (NPF) from different sources. Building on Affective Events Theory, this study investigates how leader versus AI elicits distinct emotions, shame and anger, and how these emotions subsequently influence employees’ job crafting. Two studies were conducted to test the proposed model. Study 1 employed a scenario-based experiment to compare employees’ emotional reactions. Results indicate that leader NPF evokes greater shame, whereas AI NPF induces stronger anger. Study 2 used survey data from nine enterprises in China to further test the underlying mechanisms. Results show that shame and anger mediate the effects of leader and AI NPF on promotion-oriented and prevention-oriented job crafting, respectively. Moreover, leader trust weakens the relationship between leader NPF and shame, while algorithm aversion strengthens the relationship between AI negative feedback and anger. This study advances understanding of the emotional mechanisms underlying employees’ responses to negative feedback and offers practical insights for designing effective human–AI feedback systems in organizations.

Background Long-term recovery of patients undergone cervical spine surgery is of paramount importance to improve their quality of life. In this study we aimed to evaluate the effects of foot massage on relieving pain and anxiety of patients with anterior cervical discectomy and fusion (ACDF). Methods Enrolled patients undergone ACDF and diagnosed with anxiety disorder at least six months before surgery were treated with 10-min foot massage on a daily basis for four weeks using sweet almond oil. Patients were assessed by neck pain visual analog pain scale (NP-VAS), neck disability index (NDI) and self-rating anxiety scale. Results More significant relief in NP-VAS was observed in patients who received foot massage treatment. No significant difference in NDI reduction was seen in patients with or without the treatment. Intervention group demonstrated less anxiety during follow-up ( p  = 0.021) compared to the control group and more reduction compared to baseline ( p  = 0.046). In terms of quality of life, while both groups demonstrated improvement in pain relief ( p  = 0.015 for the intervention group and p  = 0.037 for the control group), only the intervention group showed improved mental function ( p  = 0.031). Conclusion This study found that foot massage was effective in alleviating pain and anxiety, while improving quality of life in patients undergone ACDF, indicating that this intervention should be considered in the clinical management of these patients.

To examine the influence of hydrometeorological factors on forest ecosystems, this study focused on the growth response of the Qinghai spruce ( Picea crassifolia Kom.) to hydrometeorological factors, such as soil moisture, relative humidity, vapor pressure deficit, temperature, precipitation and wind speed, in a mountainous watershed. The Dayekou watershed, which is situated in the Qilian Mountains, was used to study the increase in stem diameter based on the daily-monthly fluctuations, cumulative growth, and stem diameter expansion in response to hydrometeorological parameters. The stem diameters of six dominant trees (categorized in three classes) were recorded using the Dendrometer DRL26 tree stem diameter growth monitor and combined with hydrometeorological monitoring data. The influence of hydrometeorological factors on stem diameter growth was statistically analyzed. The results indicated that the daily fluctuation of stem diameter growth of Qinghai spruce exhibited a parabolic pattern, which could be divided into three stages: contraction (from 10:00 to 21:00), expanding (from 21:00 to 5:00 the following day), and growth (from 5:00 to 11:00 the following day). The monthly stem diameter growth also exhibited a trend, which could be divided into three stages: initial growth (May), rapid expansion (June-July) and slow growth (August–October). At a 40 cm depth, soil water content, air humidity, and atmospheric pressure all showed positive correlations with stem diameter growth ( P  < 0.01), while saturated water pressure differential, wind speed, and photosynthetically active radiation showed negative correlations ( P  < 0.01). Our results demonstrated that relative air humidity, soil moisture, air temperature, and atmospheric pressure at a 40 cm depth had the highest impact on the Qinghai spruce’s growth in stem diameter. Changes in these hydrometeorological factors due to potential climate change will affect forest growth in the future.