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The research interest of urban researchers and geographers in the relationship between urban environments and happiness has been increasing. Previous studies have mostly focused on people’s long-term overall wellbeing. However, there is limited evidence that momentary happiness is associated with immediate urban environments. This study provides new evidence on this issue. 144 participants living in Guangzhou, China, were asked to repeatedly self-report their momentary happiness through ecological momentary assessment (EMA) and the day reconstruction method (DRM). The microenvironment variables were captured by portable sensors, while the built environment variables were captured by associating the GPS response locations with objective spatial data. The results indicate that momentary happiness is influenced by immediate microenvironment variables and built environment characteristics including temperature, noise, PM2.5, population, POI density, POI types and street intersections. On the other hand, the use of different sizes of contextual units affects the results. The built environment in 100 m buffers and the microenvironment has higher explanatory power for momentary happiness recorded by EMA than the built environment in 500 m buffers. Similarly, the temporality of the contextual influences also affects the results. Urban environment features have higher explanatory power for real-time momentary happiness recorded by EMA than recalled momentary happiness recorded by DRM. These results also strongly corroborate the results of recent studies on the uncertain geographic context problem (UGCoP) and partly explain the inconsistency in the results of past research. 城市研究人员和地理学家对城市环境和幸福感之间关系的研究兴趣一直在增加。以前的研究大多集中在人们的长期整体幸福感上。然而，有限的证据表明短暂的幸福感与直接的城市环境有关。这项研究提供了这方面的新证据。144名生活在中国广州的参与者被要求通过生态瞬间评估 (EMA) 和日间重建法 (DRM) 反复自我报告他们的瞬间幸福感。微环境变量由便携式传感器捕捉，而建筑环境变量则通过将全球定位系统响应位置与客观空间数据相关联来捕获。结果表明，瞬间幸福感受即时微环境变量和建筑环境特征的影响，包括温度、噪声、PM2.5、人口、兴趣点密度、兴趣点类型和街道交叉口。另一方面，使用不同的环境单位尺度会影响结果。100米缓冲区的建筑环境和微环境对EMA记录的瞬间幸福感的解释力高于500米缓冲区的建筑环境。同样，环境影响的暂时性也会影响结果。城市环境特征对EMA记录的实时瞬间幸福感的解释力高于DRM记录的回忆瞬间幸福感。这些结果也有力地证实了最近关于不确定地理环境问题 (UGCoP) 的研究结果，并部分解释了过去研究结果的不一致性。

Grape branches and leaves are promising feedstock for high-quality silage, yet their high moisture content limits effective fermentation. This study aimed to investigate the effect of cotton stalk addition on fermentation quality and microbial characteristics of high-moisture grape branches and leaves silage, providing a basis for the efficient utilization of these resources as animal feed. The experiment included six treatments with different levels of MG (cotton stalk) : 0% (MG0), 10% (MG10), 20% (MG20), 30% (MG30), 40% (MG40), and 50% (MG50). After 60 days of silage fermentation, increasing MG proportion significantly reduced silage moisture content ( P  < 0.05), while pH and acetic acid (AA) levels significantly increased ( P  < 0.05). Bacterial community analysis revealed that the dominant genera were mainly Lentilactobacillus , Bacteroides , unclassified_rumen_bacterium , and Romboutsia . Among these, Lentilactobacillus predominated in MG20 group, with a relative abundance of 92.15%. Correlation analysis indicated that Lentilactobacillus was significantly negatively correlated with potentially undesirable bacteria such as Romboutsia and unclassified_Clostridia_UGG_014 ( P  < 0.01). Network analysis showed that addition of MG altered the bacterial community structure, with MG20 group exhibiting the highest mean degree and graph density, suggesting enhanced microbial interactions. In conclusion, incorporating cotton stalk can significantly improve fermentation quality of high-moisture grape branches and leaves silage while optimizing its microbial community. The best fermentation performance was achieved with 20% cotton stalk addition.

Selenium-enriched yeast (SeY), a high-quality organic source of selenium, enhances antioxidant activity and intestinal health in swine. This study aims to evaluate the effects of varying dietary SeY levels on intestinal morphology, epithelial mucus production, antioxidant activity, and colonic bacterial communities in growing-finishing pigs. Thirty 90-day-old Duroc×Landrace×Yorkshire growing-finishing pigs (average body weight of 54.37±2.13 kg) were randomly assigned to five treatment groups. The control group (CON) was fed a basal diet, while the other four groups were fed the basal diet supplemented with SeY at 0.3, 1, 3, and 5 mg/kg, respectively, for an 80-day of feeding trial. The results showed that the addition of SeY at 0.3 mg/kg increased villus height, villus height/crypt ratio, and mucus production in the ileum, as evidenced by the increase in goblet cell number and mucus thickness ( P < 0.05). Furthermore, 0.3 mg/kg SeY up-regulated the mRNA expression levels of the MUC -1, claudin -1, occludin , and ZO -1 genes ( P < 0.05). In contrast, high-dose SeY at 5 mg/kg resulting in damage to mucosal morphology. Ileal antioxidant activity of SOD and GSH-Px, and jejunal mRNA expression of GPX -1 and GPX -4, were higher in response to SeY ( P < 0.05). Faecal Se excretion increased in SeY groups in a dose-dependent manner ( P < 0.05). SeY led to a significant difference in beta diversity among treatment groups ( P = 0.002) and led to a significant decrease in the concentrations of isobutyric and isovaleric acids when compared to the control group ( P < 0.05). The acetate, propionate, butyrate, and total short-chain fatty acids were positively correlated with the biomarker genera Agathobacter (SeY at 0.3mg/kg), while isobutyrate and isovalerate were negatively correlated with biomarker genera Lactobacillus (SeY at 0.3mg/kg) ( P < 0.05). Faecal accumulation of Se was positively correlated with the biomarker genera Alloprevotella (SeY at 3mg/kg) and Prevotellaceae _ UCG- 001 (SeY at 5mg/kg) and was negatively correlated with biomarker genera Agathobacter (SeY at 0.3mg/kg), Bacteroides (CON), and Faecalibacterium (CON) ( P < 0.05). In conclusion, SeY doses of 0.3 mg/kg have beneficial effects on intestinal health, whereas prolonged SeY doses up to 5 mg/kg may compromise the intestinal mucus function in growing-finishing pigs.

Environmental health effects during urbanization have attracted much attention. However, knowledge is lacking on the relationship between long-term cumulative residential environment and health effects on individuals during rapid transformations in urban physical and social space. Taking Guangzhou, China, as a case example, this study analyzed the relationship between long-term exposure to green environments and residents’ mental health under urban spatial restructuring. Based on a household survey in 2016, 820 residents who have lived in Guangzhou for more than 15 years were used as the sample. High-resolution remote sensing images were used to assess the long-term green exposure of residents. The results indicate that long-term green exposure in residential areas had a negative correlation with residents’ mental health (p < 0.05), and the correlation was strongest for the cumulative green environment in the last five years. However, this significant effect was moderated by income and residential location. Green exposure had a positive relationship with mental health for low income groups, and a negative relationship for middle and high income groups. In addition, residents living farther away from the city center were likely to have fewer green environmental health benefits. Residential relocation in a rapidly urbanizing and transforming China has led to the continuous differentiation of residential green environments among different income groups, which has also caused different mental health effects from green exposure. It provides empirical evidence and theoretical support for policymakers to improve the urban environment and reduce environmental health disparities by considering social differences and residential location.

Hexavalent chromium (Cr(VI)) contamination in water poses severe environmental and health risks, necessitating efficient and sustainable removal technologies. A water-based polyacrylic resin was synthesized via inverse emulsion polymerization using methyl methacrylate, acrylic acid, and maleic anhydride, thereby avoiding the use of organic solvents. Under optimal conditions (0.8 g dosage, pH 2, 318 K, 12 h), the resin achieves 98.73% Cr(VI) removal from 1 mg/L wastewater, following the pseudo-second-order kinetic model ( R 2  = 0.9927). Furthermore, the adsorption is well-fitted to the Langmuir model ( R 2  = 0.9911), yielding a calculated maximum adsorption capacity of 142.86 mg/g. FTIR analysis confirms chemisorption via Cr–O bond formation as the key mechanism. Thermodynamic analysis supports this chemisorption dominance, revealing an exothermic process (Δ H  = 138.47 kJ/mol) with high spontaneity (Δ G  < 0). Characterization via SEM/XRD shows the resin’s 3D porous structure maintains integrity post-adsorption. Significantly, acid–base elution enables high regeneration efficiency (> 93%) over 5 cycles without secondary pollution. These findings highlight the promising potential of the water-based polyacrylic resin as a macromolecular adsorbent for the efficient removal of Cr(VI) ions from wastewater, offering a viable solution for wastewater treatment.

Streptococcus equi subsp. zooepidemicus (S. zooepidemicus) is one of the important zoonotic and opportunistic pathogens. In recent years, there has been growing evidence that supports the potential role of S. zooepidemicus in severe diseases in horses and other animals, including humans. Furthermore, the clinical isolation and drug resistance rates of S. zooepidemicus have been increasing yearly, leading to interest in its in-depth genomic analysis. In order to deepen the understanding of the S. zooepidemicus characteristics and genomic features, we investigated the genomic islands, mobile genetic elements, virulence and resistance genes, and phenotype of S. zooepidemicus strain ZHZ 211 (ST147), isolated from an equine farm in China. We obtained a 2.18 Mb, high-quality chromosome and found eight genomic islands. According to a comparative genomic investigation with other reference strains, ZHZ 211 has more virulence factors, like an iron uptake system, adherence, exoenzymes, and antiphagocytosis. More interestingly, ZHZ 211 has acquired a mobile genetic element (MGE), prophage Ph01, which was found to be in the chromosome of this strain and included two hyaluronidase (hyl) genes, important virulence factors of the strain. Moreover, two transposons and two virulence (virD4) genes were found to be located in the same genome island of ZHZ 211. In vitro phenotypic results showed that ZHZ 211 grows faster and is resistant to clarithromycin, enrofloxacin, and sulfonamides. The higher biofilm-forming capabilities of ZHZ 211 may provide a competitive advantage for survival in its niche. The results expand our understanding of the genomic, pathogenicity, and resistance characterization of Streptococcus zooepidemicus and facilitate further exploration of its molecular pathogenic mechanism.

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

Osteosarcoma (OS), the most common malignant bone tumor in adolescents, exhibits marked genetic and cellular heterogeneity, contributing to its resistance to conventional therapies. Although surgical and chemotherapeutic interventions have advanced, survival rates remain stagnant, especially for patients with metastatic or treatment-refractory disease. Recent multi-omic studies have uncovered distinct molecular subtypes characterized by unique genomic, epigenomic, transcriptomic, and tumor microenvironmental signatures. The integration of bulk and single-cell transcriptomics, spatial profiling, epigenetic mapping, and proteomic analyses has provided unprecedented insights into the pathogenesis of OS and has facilitated the identification of novel biomarkers. This review comprehensively summarizes the current advances in the molecular subtyping of OS, highlighting subtype-specific oncogenic drivers, their implications in tumor progression and therapeutic resistance, and the potential for precision medicine strategies tailored to these molecular profiles.

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.

Considering the characteristics of equipment on underground fully mechanized coal mining face, a multirobot system, which takes heavy-duty mobile support robot (HMSR) as the pushing robot and middle trough (MT) as the manipulated object, is established. To overcome the problem of unstable communication and potential pressure loss, a memory-pushing fuzzy control strategy is proposed to achieve better practical performance without human-guided operations. The pushing dynamics without communication is derived to proof the convergence of the dynamic system, and the time-based memory-pushing fuzzy model is built for compensating the potential pressure loss. Finally, the proposed control strategy is simulated in virtual environment, which integrates our pushing dynamics, and an industrial experiment is demonstrated as well. Both the simulation and industrial experiments show the efficiency and feasibility of the proposed method.