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People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.

Photocatalyst-loaded hydrogels show great potential for effective degradation of pollutants. In this work, a novel composite photocatalyst preparation method is proposed to prepare a gel composite photocatalyst using hydrothermal reaction and in situ synthesis method for rapid degradation and removal of heavy metal Cr present in water bodies. Sodium alginate was chosen as the hydrogel carrier of photocatalytic material to improve the homogeneity and stability of the composite photocatalyst. The H 2 O 2 generated from the hydrolysis of aluminum chloride during the reaction process can accelerate the photoreaction, so this experiment has high efficiency of autocatalysis. The experimental results showed that the degradation and removal of heavy metal Cr could reach 75% under the conditions of dosage of 0.08 g, pH 4, temperature 35 °C, initial concentration of 40 mg/L, and light intensity of 15 W. This work provides a low-cost and convenient method for constructing novel hydrogel carriers with high photocatalytic stability and efficiency.

Using calcium polysulfide as the reducing agent, synthetic zeolite as the adsorbent, and cement as the curing agent, the dual-index orthogonal test method was used to determine the best remediation dosage of chromium-contaminated soil. On this basis, through the dry–wet cycle test, the durability of the chromium-contaminated soil after repair is analyzed from the perspectives of unconfined compressive strength, toxic leaching concentration, quality loss, and microscopic characterization. Test results showed that the optimal ratio for the joint repair of chromium-contaminated soil was 3 times the amount of CaS 5 , 15% synthetic zeolite, and 20% cement. With the increase in the number of wet–dry cycles, the unconfined compressive strength of the composite preparation combined to repair chromium-contaminated soil was first increased and then reduced, and the concentration of Cr(VI) and total chromium in the leachate was first decreased and then increased. The higher the chromium content of the contaminated soil was, the lower the unconfined compressive strength, and the higher the leaching concentration of Cr(VI) and total chromium were. With the increase in cycle times, the cumulative mass-loss rate of composite preparations for repairing chromium-contaminated soil gradually increased, and the higher the chromium content was, the higher the cumulative mass-loss rate, which was less than 2%, reflecting the combination of composite preparations for repairing chromium-contaminated soil to have good durability. Microscopic and macroscopic results are consistent with each other.

Background Previous studies indicate that physical activity significantly alleviates anxiety among college students, and that psychological resilience and social support may mediate this relationship; however, existing evidence remains limited. This study aims to examine the mediating roles of psychological resilience and social support in the relationship between physical activity and anxiety in college students, and to analyze potential moderating factors. Methods Eight databases were searched without restrictions on language or publication time to obtain relevant coefficients. Meta-analytic structural equation modeling (MASEM) was applied to verify the mediating roles of psychological resilience and social support. In addition, one-stage MASEM was employed to assess the moderating effects of female proportion and sampling methods. Results Twenty-nine articles were included, comprising a total sample of 36,159 participants and 90 effect sizes. Physical activity had a significant negative effect on college students’ anxiety (β = –0.262, 95% CI: –0.281, –0.243). Psychological resilience (β = –0.152, 95% CI: –0.163, –0.142) and social support (β = –0.048, 95% CI: –0.055, –0.042) served as mediators. Female proportion and sampling methods exhibited moderating effects on the model. Sensitivity analysis and publication bias tests supported the robustness of the findings. Conclusion Increasing college students’ physical activity levels effectively reduces anxiety, and physical activity indirectly alleviates anxiety through enhanced psychological resilience and social support. Future research should focus on causal relationships among variables and on differential effects of gender and sampling methods, thereby providing more effective mental health support for college students. Trial registration This study has been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD420251129622).

1,1,1-Trichloroethane (1,1,1-TCA) is a typical organochloride solvent in groundwater that poses threats to human health and the environment due to its carcinogenesis and bioaccumulation. In this study, a novel composite with nanoscale zero-valent iron (nZVI) supported by polycaprolac-tone (PCL)-modified biochar (nZVI@PBC) was synthesized via solution intercalation and liquid-phase reduction to address the 1,1,1-TCA pollution problem in groundwater. The synergy effect and improvement mechanism of 1,1,1-TCA removal from simulated groundwater in the presence of nZVI@PBC coupling with Shewanella putrefaciens CN32 were investigated. The results were as follows: (1) The composite surface was rough and porous, and PCL and nZVI were loaded uniformly onto the biochar surface as micro-particles and nanoparticles, respectively; (2) the optimal mass ratio of PCL, biochar, and nZVI was 1:7:2, and the optimal composite dosage was 1.0% (w/v); (3) under the optimal conditions, nZVI@PBC + CN32 exhibited excellent removal performance for 1,1,1-TCA, with a removal rate of 82.98% within 360 h, while the maximum removal rate was only 41.44% in the nZVI + CN32 treatment; (4) the abundance of CN32 and the concentration of adsorbed Fe(II) in the nZVI@PBC + CN32 treatment were significantly higher than that in control treatments, while the total organic carbon (TOC) concentration first increased and then decreased during the culture process; (5) the major improvement mechanisms include the nZVI-mediated chemical reductive dechlorination and the CN32-mediated microbial dissimilatory iron reduction. In conclusion, the nZVI@PBC composite coupling with CN32 can be a potential technique to apply for 1,1,1-TCA removal in groundwater.

Background In paddy fields, the noxious weed barnyard grass secretes 2,4-dihydroxy-7-methoxy-2 H -1,4-benzoxazin-3(4 H )-one (DIMBOA) to interfere with rice growth. Rice is unable to synthesize DIMBOA. Rice cultivars with high or low levels of allelopathy may respond differently to DIMBOA. Results In this study, we found that low concentrations of DIMBOA (≤ 0.06 mM) promoted seedling growth in allelopathic rice PI312777, while DIMBOA (≤ 0.08 mM) had no significant influence on the nonallelopathic rice Lemont. DIMBOA treatment caused changes in the expression of a large number of glutathione S- transferase (GST) proteins, which resulting in enrichment of the glutathione metabolic pathway. This pathway facilitates plant detoxification of heterologous substances. The basal levels of GST activity in Lemont were significantly higher than those in PI312777, while GST activity in PI312777 was slightly induced by increasing DIMBOA concentrations. Overexpression of GST genes ( Os09g0367700 and Os01g0949800 ) in these two cultivars enhanced rice resistance to DIMBOA. Conclusions Taken together, our results indicated that different rice accessions with different levels of allelopathy have variable tolerance to DIMBOA. Lemont had higher GST activity, which helped it tolerate DIMBOA, while PI312777 had lower GST activity that was more inducible. The enhancement of GST expression facilitates rice tolerance to DIMBOA toxins from barnyard grass root exudates.

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.

To effectively address excessive SO 4 2- , Cr(VI), total chromium and F - in the groundwater of acidic mining areas, a facultative anaerobic bacterium, Citrobacter, with sulfate-reducing properties, tolerance to hexavalent chromium and the ability to reduce Cr(VI) to Cr(III) was isolated and domesticated. Based on microbial immobilization technology, a nano-ZrO 2 polyacrylamide hybrid material was prepared as an embedding agent to form nano-ZrO 2 polyacrylamide Citrobacter (ZPC) particles. ZPC was microscopically characterized, and the removal performance and mechanism of ZPC for SO 4 2- , Cr(VI), total chromium and F - in groundwater were analyzed. The results of single-factor tests showed that the optimal reaction conditions included a reaction temperature of 35°C, Citrobacter dosage of 35% (volume ratio) in the particles and hybrid material dosage of 300 mL; under these conditions, the removal rates of SO 4 2- , Cr(VI), total chromium and F - were 70.5%, 100%, 100% and 93.3%, respectively, and the pH value increased from 4.6 to 8.07. On this basis, the effects of the reaction layer type, influent hydraulic load and influent concentration on the removal efficiency of polluted groundwater were studied through dynamic experiments. The experimental results showed that ZPC particles were better than Citrobacter as a reaction layer; the optimal influent hydraulic load was 3.0 m 3 /(m 2 ·d); the selectivity of ZPC particles to anions and anionic groups was different; and the order of adsorption selectivity was F - > Cr(VI) > SO 4 2- .

This research explores a phenomenon that we see nearly every day and has implications for how we view people in other nations: Different media outlets may report the same international events either in terms of the nation (e.g., “Russia invades Ukraine”) or in terms of the leader (e.g., “Putin invades Ukraine”). Five studies, conducted during the 2022 Russia-Ukraine Conflict and involving both field and experimental data, find that readers of nation-framed news about the conflict had worse impressions of the people in the associated nation (Russians) than readers of the corresponding leader-framed version. We explain the psychology behind this framing effect and identify its moderators. Our research underscores the importance of responsible media practices in shaping global perceptions.

To solve the problem of high fluoride, iron and manganese concentrations in groundwater, serpentine (Srp) was modified by metal salt impregnation, acid–base activation and calcination, and the effects of these three modifications on the removal performance of Srp were compared. Specifically, the effects of the calcined serpentine (Csrp) dose, reaction time, pH, and temperature on the removal performance of F−, Fe2+ and Mn2+ on Csrp were analysed. An isothermal adsorption model and adsorption kinetic equation were established and confirmed through SEM, EDS, XRD and FTIR spectroscopy to analyse the mechanism of removing F−, Fe2+ and Mn2+ by Csrp. The results show that when 3 g/L Csrp was used to treat water samples with 5 mg/L F−, 20 mg/L Fe2+, and 5 mg/L Mn2+ (pH of 6, reaction temperature of 35 °C, and time of 150 min), the removal rates of F−, Fe2+, and Mn2+ were 94.3%, 99.0%, 98.9%, respectively. The adsorption of F−, Fe2+ and Mn2+ on Csrp follows the quasi-second-order kinetic equation and Langmuir isotherm adsorption model. After five cycles of regeneration of Csrp, Csrp can still maintain good properties of fluoride, iron and manganese removal.