Key Influencing Factors Research Articles

Integrating Convolutional Attention and Encoder–Decoder Long Short-Term Memory for Enhanced Soil Moisture Prediction

Soil moisture is recognized as a crucial variable in land–atmosphere interactions. This study introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory (CAEDLSTM) model to address the uncertainties and limitations inherent in traditional soil moisture prediction methods, especially in capturing complex temporal dynamics across diverse environmental conditions. Unlike existing approaches, this model integrates convolutional layers, an encoder–decoder framework, and multi-head attention mechanisms for the first time in soil moisture prediction. The convolutional layers capture local spatial features, while the encoder–decoder architecture effectively manages temporal dependencies. Additionally, the multi-head attention mechanism enhances the model’s ability to simultaneously focus on multiple key influencing factors, ensuring a comprehensive understanding of complex environmental variables. This synergistic combination significantly improves predictive performance, particularly in challenging climatic conditions. The model was validated using the LandBench1.0 dataset, which includes multiple high-resolution datasets, such as ERA5-land, ERA5 atmospheric variables, and SoilGrids, covering various climatic regions, including high latitudes, temperate zones, and tropical areas. The superior performance of the CAEDLSTM model is evidenced by comparisons with advanced models such as AEDLSTM, CNNLSTM, EDLSTM, and AttLSTM. Relative to the traditional LSTM model, CAEDLSTM achieved an average increase of 5.01% in R2, a 12.89% reduction in RMSE, a 16.67% decrease in bias, and a 4.35% increase in KGE. Moreover, it effectively addresses the limitations of traditional deep learning methods in challenging climates, including tropical Africa, the Tibetan Plateau, and Southeast Asia, resulting in significant enhancements in predictive accuracy within these regions, with R2 values improving by as much as 20%. These results underscore the capabilities of CAEDLSTM in capturing complex soil moisture dynamics, demonstrating its considerable potential for applications in agriculture and water resource monitoring across diverse climates.

Expansion of Pleioblastus amarus in tea plantations significantly enhances the appearance and nutritional composition of bamboo shoots but adversely affects palatability

The expansion of Pleioblastus amarus into tea plantations introduces environmental heterogeneity, significantly influencing the growth and quality of bamboo shoots. This study examined the effects of bamboo expansion on the appearance, nutrition, and palatability of bamboo shoots, utilizing partial least squares structural equation modeling (PLS-SEM) to identify key influencing factors. Results revealed that bamboo expansion increased shoot diameter, length, and fresh weight, enhancing overall size and edibility, particularly in the tea-bamboo mixed forest center zone (TBC), where appearance quality peaked. Nutritional analysis revealed substantial increases in protein, fat, starch, and vitamin C content after bamboo expansion, along with the improvements in amino acid score (AAS), essential amino acid index (EAAI), and nutritional index (NI), indicating elevated nutritional value. However, despite the rise in soluble sugars and flavor-enhancing amino acids, higher levels of total acids, oxalic acid, tannins, and cellulose diminished the palatability, notably in TBC site. PLS-SEM further indicated that while bamboo expansion positively influenced shoot appearance and nutrition, soil factors predominantly drove these changes and concurrently detracted from overall palatability. These findings provide a framework for enhancing bamboo shoot quality and optimizing management practices in tea plantation ecosystems.

A Fuzzy-Bayesian Network Approach Based Assessment of CoP System in Forging Higher Education Social Responsibility

Community of practice (CoP) has been seen as a pivotal support for higher education institutions to implement their social responsibilities. Even though this model is widely admired, assessing its effectiveness and sustainability still faces many challenges: (1) the absence of an appropriate index reveals the significance of CoP; (2) the difficulty of realizing quantitative assessment; and (3) the strategies to improve contribution sustainably by considering CoP development. To address these challenges, a comprehensive Higher Education Social Responsibility Contribution Index (HESRCI) is constructed by taking into account the CoP key influence factors. An FBN model is further developed for the purpose of assessing the various corresponding contributions quantitatively and investigating the potential interdependencies between influence factors. The effectiveness of the proposed approach is evidenced by the quantitative indication of CoP’s contributions to priorities. Research findings also highlight the significance of CoP governance, the mechanism of resource allocation, and team development, in particular, in facilitating the synergy between university development and sustainable socio-economic growth. In addition, it provides data support and a theoretical basis for higher education institutions to make more informed decisions when implementing industry-education integration strategies.

Research on Determining the Weights of Key Influencing Factors Based on Multi-Grained Binary Semantics

To effectively address the complexity of the environment, information uncertainty, and variability among decision-makers in the event of an enterprise emergency, a multi-granularity binary semantic-based emergency decision-making method is proposed. Decision-makers use preferred multi-granularity non-uniform linguistic scales combined with binary semantics to represent the evaluation information of key influencing factors. Secondly, the weights were determined based on the proposed method. Finally, the proposed method’s effectiveness is validated using a case study of a fire incident in a chemical company.

Remediation of Various Phenanthrene-contaminated Soils using Persulfate-Pseudomonas aeruginosa GZ7: Soil Properties, Radical Formation, and Microbial Community

In this study, the complex interaction of oxidizing conditions, soil composition, microbial activity, and phenanthrene (PHE) degradation was explored in persulfate (PS)-Pseudomonas aeruginosa GZ7 remediation of four PHE-contaminated soils: Paddy soil (PAS), Saline soil (SS), Red soil (RS), and Cinnamon soil (CS). Batch experiments displayed the optimal PS dosage was 0.750% (w/w), except for CS (0.125%), achieving PHE degradation rates of 54.79% ~ 81.92% on the 27-day. The zeta potential of soil clay component after 0.750% PS oxidation was lower than 0.125% and 2.000% PS, resulting in a higher PHE bioavailability and biodegradation. The electron paramagnetic resonance (EPR) spectroscopy analysis showed that the abundance of dominant hydroxyl radicals (·OH) in CS was 3 times higher than in PAS at 0.750% PS and PS to Fe2+ molar ratio of 2:1, indicating that a higher soil organic matter would decrease the content of ·OH thus diminishing PHE removal. Stepwise linear regression and sensitivity analysis indicated that the key influential factors in the PHE degradation rate were the residual PHE content after oxidation (60.12%) and hydrolase nitrogen content (30.97%). High-throughput sequencing results showed the bacterial diversity and richness in four soils decreased after combined remediation compared to pristine soil. Redundancy analyses revealed that the fluorescein diacetate (FDA) hydrolase enzyme was the key factor effect of soil bacterial community development in SS; and electrical conductivity and FDA hydrolase enzyme were the key factors in RS, CS, and PAS. This study may provide technical support for optimizing chemical conditions for PS-microbial remediation.

Exploring Disease Manifestations and Influencing Factors in Acute and Chronic Hepatitis B

Scope and Aims: The investigation of disease manifestations and influencing factors in both acute hepatitis B (AHB) and chronic hepatitis B (CHB) remains limited, with varying results. This study aimed to explore the factors influencing AHB and CHB and their disease manifestations within a Ghanaian population, with the goal of developing a control strategy. Methods: A retrospective study was conducted on 569 admitted hepatitis B cases. Demographic data and disease manifestations were compared between AHB and CHB patients. Logistic regression and correlation analyses were employed to identify the factors influencing the progression of the disease. Results: Significant differences were observed between AHB and CHB patients in terms of median age and hospitalization duration. Variations in age, gender, education level, and occupational distributions were statistically significant (P < 0.05) between the two groups. Symptoms such as fever, nausea, polydipsia, palpitation, anicteric presentation, anorexia, and itching were less common in CHB patients (P < 0.05), while abdominal pain, jaundice, and enlarged liver were more frequent (P < 0.05). CHB patients exhibited significantly higher levels of aspartate aminotransferase, viral load, bilirubin, prothrombin time, partial thromboplastin time, HBeAg, albumin, abdominal ultrasound findings, and globin (P < 0.05), while HBsAg and liver function test levels were significantly lower (P < 0.05) in CHB patients compared to AHB patients. Logistic regression identified age, gender, occupation, education level, and hospitalization duration as significant influencing factors. Conclusion: Males and the adult population represented a higher proportion of CHB patients, with a significant association between CHB and elevated clinical and laboratory characteristics. Age, gender, occupation, education level, and hospitalization duration were established as key influencing factors in the progression of hepatitis B.

The phosphorus export coefficients variability of specific land-use and the threshold response relationship with watershed characteristics in a subtropical hilly region

The magnitude of land-use phosphorus (P) export in subtropical hilly watersheds is subject to the collective influence of various watershed characteristics. However, the spatiotemporal dynamics of key watershed characteristics and their impacts on P exports remain unclear. Total phosphorus export coefficients (TPECs) can quantify the contribution of different land-use types to P exports. Here, we employed continuous observation data of typical subtropical hilly areas in the Laodao River watershed, China, from 2012 to 2019, as well as an enhanced export coefficient model combined with Bayesian statistical methods, to quantify the spatiotemporal variability and uncertainty of TPECs under various hydrological conditions in different land-use types (forest, cropland, and tea plantation). We then determined the key watershed characteristic factors influencing TPECs and threshold responses of TPECs to these factors. The three land-use types exhibited significant spatiotemporal differences in TPECs, i.e., under different hydrological regimes and within each catchment. Watershed characteristic factors explained 68.5–74.1 % and 33.3–50.4 % of TPEC variations under medium- and low-flow regimes, respectively, but only 1.9–3.8 % under the high-flow regime. Compared to topography and soil factors, landscape patterns had a higher individual impact on TPECs. Threshold response relationships existed between TPECs and key influencing factors under both medium- and low-flow regimes. Moreover, the thresholds corresponding to abrupt TPEC change points were comparatively consistent under medium- and low-flow regimes. These findings have practical applications for rapidly characterising critical P pollution source areas and formulating basin-scale land-use plans for water quality protection.

Distribution and key influential factors of comammox in drained and waterlogged soils of zoige plateau peatland

Peatlands are important carbon and nitrogen reservoirs, playing crucial roles in nitrogen cycling. During microbially-driven nitrogen cycling, nitrous oxide (N2O, 298 times global warming potential of CO2) can be emitted, exacerbating global warming. Complete ammonia-oxidizing bacteria (comammox), a newly discovered group of prokaryotes, can independently oxidize ammonia directly to nitrate, bypassing the nitrite stage, and thereby reducing N2O production associated with the traditional two-step nitrification process. However, information on comammox distribution and its key influential factors in plateau peatlands remains scarce. Thus, this study chose Zoige plateau peatland in China to collect soil samples from different soil types (drained and waterlogged), across different seasons (non-growing and growing), and at various depth (0–100 cm) to assess comammox abundance and community composition. Additionally, soil properties were analyzed and correlated with comammox abundance and community composition to identify the key factors affecting comammox distribution. Comammox abundance varied significantly across soil types, depth, and sampling seasons. Waterlogged soils demonstrated higher comammox abundance than drained soils. In waterlogged soils, comammox abundance showed higher during growing season than non-growing season, while the opposite trend was observed in drained soils. Regardless of soil types, comammox abundance decreased with increasing soil depth. In soils of Zoige plateau peatland, comammox clades A.1, A.2, A.3 and B.1 were identified, with clade B.1 dominating the comammox community. Both Nitrospira sp. CG24A (clade B.1) and Candidatus Nitrospira nitrificans (clade A.1) showed great seasonal variations. Soil properties, including moisture, pH, carbon, and nutrients, collectively influenced comammox abundance and diversity. Among these factors, NH4+-N was the main factor affecting comammox abundance, while moisture primarily drove community distribution. These findings provide valuable insights into comammox distribution, enhancing our understanding of its potential role in mitigating N2O emissions and thus nitrogen cycling in plateau peatland soils.

The critical role of education in shaping nurses' attitudes and intentions towards neonatal palliative care: A network analysis

BackgroundNeonatal palliative care is an essential component of comprehensive neonatal care; however, its implementation remains challenging worldwide, particularly in low- and middle-income countries due to limited resources, cultural barriers, and lack of training. ObjectivesThe aim of this study was to investigate the structural characteristics of neonatal nurses' attitudes towards neonatal palliative care and their intention to provide such care using network analysis to identify key influencing factors and interrelationships. DesignA multi-center cross-sectional study. SettingThe setting was 92 hospitals across 28 provinces in mainland China. ParticipantsA convenience sampling method was employed to recruit 893 neonatal nurses from October 2023 to February 2024. MethodsThe web-based survey included a sociodemographic questionnaire, the simplified Chinese version of the Neonatal Palliative Care Attitude Scale (NiPCAS), and a single question gauging participants' intention to provide neonatal palliative care. Network analysis techniques were used to examine the structural characteristics of the attitude network. ResultsA total of 767 valid questionnaires were received. The estimated network comprised 26 nodes representing individual NiPCAS items, with 150 non-zero edges out of a possible 325 connections. In-service education experience emerged as the most central and influential node, demonstrating the highest centrality (strength = 2.511; bridge strength = 3.144) and predictability (R2 = 0.475). This was followed by the ideal palliative care environment and staff support for palliative care. On average, 29.3 % of each item's variance could be accounted for by surrounding items. The strongest associations with the intention to provide neonatal palliative care were observed with beliefs about the necessity of palliative care in neonatal nurse education (edge weight = 0.29). ConclusionsThe findings highlight the pivotal role of in-service education experience in shaping nurses' attitudes towards neonatal palliative care, suggesting that educational interventions may significantly influence overall attitudinal structures. The strong associations between the intention to provide neonatal palliative care, and beliefs about the necessity of palliative care in neonatal nurse education, further reinforce the critical role of education in fostering positive attitudes and intentions. The significance of organizational and resource-related factors suggests that efforts to improve neonatal palliative care should focus on enhancing staff support and creating supportive work environments.

Benchmarking performance: A round-robin testing for liquid alkaline electrolysis

Liquid alkaline water electrolysis has gained considerable interest in recent years due to its promising role in an energy sector based on renewable energy sources. Its main advantage is the low investment cost of industrial alkaline water electrolyzers compared to other electrolysis technologies. A challenge remains in developing cost-efficient materials, stable in corrosive electrolytes, and offering competitive cell performance. Although there are many publications in liquid alkaline electrolysis, there is insufficient standardization of experimental conditions and procedures, reference materials, and hardware. As a result, comparability and reproducibility suffer, significantly slowing down research progress. This manuscript presents the initial efforts towards the development of such reference hardware and procedures within the framework of Task 30 Electrolysis in the Technology Collaboration Programme on Advanced Fuel Cells (AFC TCP) of the International Energy Agency (IEA). For this purpose, a homogenized setup including the electrolysis cell, functional materials, experimental conditions, and a test protocol was developed. The protocol and hardware were tested simultaneously at eleven different institutions in Europe and North America. To evaluate the success of this approach, polarization and run-in data were collected and analyzed for comparison, and performance differences were calculated. Significant disparities between the laboratories were observed and some key influence factors were identified: iron content in the electrolyte resulted to be a main source of deviation between experiments, along with temperature control and the conditioning of the cells. The results suggest that additional attention to detailed experimental conditions should be paid to obtain meaningful performance data in future research.

Community Assembly Mechanisms of Populus euphratica in Northwest China and Their Relationship with Environmental Factors

Populus euphratica is a key community-building species in the desert riparian forests of Northwest China, exhibiting exceptional resistance to stress and playing a vital role in soil and water conservation as well as maintaining ecological balance in arid regions. To investigate the ecological processes underlying the composition of P. euphratica communities and to identify their community construction mechanisms, this study analyses the species diversity and phylogenetic diversity of 58 P. euphratica communities, exploring their assembly processes and key influencing factors. This research aims to elucidate the relationship between community structure from the perspective of species evolution and analyse the construction mechanisms of P. euphratica communities across different clusters in arid environments. The results show that the species diversity of P. euphratica clusters in Northwest China is relatively low, and a significant correlation is noted with phylogenetic diversity (PD). The Shannon–Wiener and Margalef indices exhibit similar trends, whereas Simpson’s index show the opposite trends. Pielou’s index range from 0.7 to 0.85. Notably, the PD and species diversity of the P. euphratica–Haloxylon ammodendron association group (Group 4) is significantly higher (p < 0.05) compared to that of the other groups. Additionally, net relatedness index (NRI) and nearest taxon index (NTI) peaked in the P. euphratica–H. ammodendron association group (Group 4) and the Populus pruinosa–Tamarix ramosissima–Phragmites australis association group (Group 1) (p < 0.05). A Pearson correlation analysis indicated that PD was significantly positively correlated with Margalef’s index, Shannon–Wiener’s index, and Pielou’s index, but was significantly negatively correlated with Simpson’s index, while also being associated with environmental factors. Key factors influencing the diversity of P. euphratica communities in Northwest China include total phosphorus, pH, soil moisture content, total potassium, the mean temperature of the coldest quarter, precipitation of the wettest month, and precipitation seasonality. Soil factors primarily affected the Pielou and Simpson indices of species diversity, whereas climatic factors mainly influenced the Margalef and Shannon–Wiener indices. PD and structure were mainly influenced by climatic factors. The combined effects of soil and climatic factors play a crucial role in sustaining the diversity and ecological adaptation of these plant communities. In summary, P. euphratica communities may exhibit a significant ecological niche conservation in response to environmental changes, and competitive exclusion might be the primary process shaping community structure. Climatic factors were shown to be important regulators of community diversity and phylogenetic structure.

Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)

Methane (CH4) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH4 concentration (XCH4) at both national and international levels. This study investigates the correlation between atmospheric XCH4 concentrations (XCH4) and key influencing factors to identify the primary sources and sinks of CH4 across the Middle East (ME). Initially, XCH4 data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH4 across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH4 and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH4 variations. The results reveal significant spatial and temporal variations in XCH4 concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH4, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH4 concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH4 and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH4.

Spatial-Temporal Characteristics and Driving Factors of Surface Water Quality in the Jing River Basin of the Loess Plateau

Water quality safety in the water source constitutes a crucial guarantee for public health and the ecological environment. This study undertakes a comprehensive assessment of the water quality conditions within the Jing River Basin of the Loess Plateau, emphasizing the spatial and temporal characteristics, as well as the determinants influencing surface water quality in the Shaanxi section. We utilized data from seven monitoring stations collected between 2016 and 2022, employing an enhanced comprehensive Water Quality Index (WQI) method, redundancy analysis (RDA), and Spearman’s correlation analysis. The results show that the average annual WQI value of the water quality of the Shaanxi section of the Jing River increased from 68.01 in 2016 to 76.18 in 2022, and the river’s water quality has gradually improved, with a significant improvement beginning in 2018, and a series of water quality management policies implemented by Shaanxi Province is the primary reason for the improvement. The river’s water quality has deteriorated slightly in recent years, necessitating stricter supervision of the coal mining industry in the upper section. The river has an average WQI value of 73.70 and is rated as ‘good’. The main pollution indicators influencing the river’s water quality are CODMn, COD, BOD5, NH3-N, and TP. From the upstream to the downstream, the water quality of the river shows a pattern of increasing and then decreasing, among which S4 (Linjing Bridge in Taiping Town) and S5 (Jinghe Bridge) have the best water quality. The downstream part (S6, S7) of the Jing River near the Weihe River has poor water quality, which is mostly caused by nonpoint source contamination from livestock and poultry rearing, agricultural activities, and sewage discharge. Redundancy analysis revealed that the spatial scale of the 2500 m buffer zone best explained water quality changes, and the amount of bare land and arable land in land use categories was the key influencing factor of river water quality.

Towards a Risk-Based Follow-Up Surveillance Imaging Schedule for Children and Adolescents with Low-Grade Glioma.

The frequency and duration of imaging surveillance in children and adolescents with pediatric low-grade gliomas (pLGGs) aims for the early detection of recurrence or progression. Although surveillance of pLGGs is performed routinely, it is not yet standardized. The aim of the current review is to provide a comprehensive synthesis of published studies regarding the optimal frequency, intervals, and duration of surveillance. Several key influencing factors were identified (age, the extent of resection, the tumor location, the histological type, and specific molecular characteristics). However, the lack of consistent definitions of recurrence/progression and the extent of resection meant that it was not possible to perform a meta-analysis of the data from the 18 included articles. This review highlights the need for updating the definition of these terms for uniform and global use both in routine clinical practice as well as in upcoming trials. Thus, future studies on the heterogenous group of pLGGs will allow for the better tailoring of both the frequency and duration of imaging surveillance protocols in relevant settings.

Prediction of Shrimp Price Based on WOA-VMD-XGBoost Algorithm and SHAP Model

This research aims to construct a WOA-VMD-XGBoost-SHAP model to predict shrimp prices and analyze the nonlinear effects of key predictors. Firstly, the whale algorithm (WOA) is used to optimize the K-value and penalty parameter of the variational mode decomposition (VMD) to adaptively decompose the original price series and reduce the data noise. In addition, the trend, period, high and low frequency, and residual terms obtained from the decomposition of the original price series are used as inputs to the XGBoost model for training and testing. Finally, K-fold cross-validation and learning curves are used to test the model performance and analyze the nonlinear effects of key influencing factors in combination with the SHAP model. The results show that the Bayesian-optimized WOA-VMD-XGBoost model has excellent predictive performance with an R2 of 0.927, which is better than other benchmark models; the fluctuation of shrimp prices is cyclical, and the cyclical term accounts for 67% of the characteristic importance. The model can provide effective technical support and decision-making references for relevant management departments and enterprises to predict the price fluctuation of aquatic products.

Identification of Urban Renewal Potential Areas and Analysis of Influential Factors from the Perspective of Vitality Enhancement: A Case Study of Harbin City’s Core Area

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions.

Structural characteristics of plankton community in Dongting Lake and its relationship with water environmental factors

The plankton community structure and environmental factors in Dongting Lake were investigated during four seasons from September 2020 to August 2021. The results revealed 147 species from 58 genera and 7 phyla of phytoplankton and 84 species from 56 genera and 4 phyla of zooplankton in Dongting Lake. The characteristics of plankton communities varied with time and space. The temporal variation of phytoplankton abundance ranged from 43.5201 × 104 to 120.7968 × 104 cells/L and the spatial variation ranged from 18.6707 × 104 to 247.5542 × 104 cells/L. The temporal variation of zooplankton abundance ranged from 18 to 42 ind/L and the spatial variation ranged from 19 to 62 ind/L. The temporal and spatial variations of the abundance range values were much larger for phytoplankton than for zooplankton, with phytoplankton dominating. However, environmental factors in Dongting Lake vary more temporally than spatially, and drive more temporal than spatial variations in planktonic organisms. Based on the redundancy analysis WT, DO, and CODMn were the main environmental factors affecting the distribution of phytoplankton, while WT, CODMn, NO2–N, and Chl-a were the main factors affecting the distribution of zooplankton. WT and CODMn were the common key influencing factors.

Enhancing Emergency Cognitive Ability in College Students Under Emergencies: A Study of Influencing Factors and Hierarchical Relationships

This study addresses the need to enhance college students’ emergency cognitive ability by identifying key influencing factors and analyzing their hierarchical structure. To fill the gap in understanding these relationships, we used grounded theory to identify 12 influencing factors through a literature review, case analysis, and interviews. The interpretive structural modeling (ISM) method categorized these factors into three levels: direct, key, and root factors. Root factors such as risk awareness, crisis perception, and responsibility are core to the cognitive ability framework and have a profound impact on students’ responses to emergencies. Further, the matrix of cross-impact multiplications applied to classification (MICMAC) analysis categorized the factors based on driving force and dependency, showing strong interrelationships. The integration of ISM-MICMAC methods offers a novel approach to understanding the hierarchical influence among factors, enabling educational institutions and policymakers to design targeted emergency training programs. By incorporating information technology into the educational process, this research provides practical guidance for enhancing students’ preparedness and resilience in emergencies. The findings support policy development and the design of effective educational interventions, offering valuable insights for administrators, policymakers, and emergency management professionals in creating safer, more resilient educational environments.

Research Progress and Prospect of Antibiotic Resistance Gene Contamination in Soil-vegetable System

As a new environmental pollutant, the widespread existence of antibiotic resistance genes （ARGs） has brought about a series of environmental and human health problems. Livestock manure is considered to be an important repository of ARGs, and its resource utilization has potential environmental risks. Soil-vegetable systems are the core link in agricultural production, and it is also an important way for humans to communicate with the environment. The utilization of livestock and poultry manure in agricultural production leads to the proliferation and spread of ARGs in soil-vegetable systems. ARGs in soil-vegetable systems may be ingested by humans through the food chain and pose a threat to human health. Therefore, on the basis of briefly discussing the sources and hazards of ARGs in the soil-vegetable system, this study focused on the environmental behavior of ARGs in the soil-vegetable system； summarized the occurrence characteristics, migration rules, and key influencing factors of ARGs in the soil-vegetable system； and put forward suggestions and prospects suitable for the prevention and control of ARGs pollution in the soil-vegetable system in order to provide theoretical support for China's agricultural green development.

Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines

To effectively mitigate unsafe events and accident symptoms stemming from flight dispatchers’ human factors, this paper proposes a novel risk evaluation model to accurately identify and evaluate potential human risks associated with flight dispatchers. First, the HFACS (Human Factors Analysis and Classification System, HFACS) model is employed to construct a human risk assessment indicator system for flight dispatchers. Second, the hesitant fuzzy set is introduced to represent the uncertainty during experts’ evaluation, and the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) method is applied within a hesitant fuzzy environment to obtain rankings of human factors. Third, the hesitant fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory, DEMATEL)-ISM (Interpretive Structural Modeling, ISM) approach is constructed to analyze the correlation among human factors, leading to the establishment of a multi-level hierarchical structure model. Finally, a case study of risk assessment for human factors of flight dispatchers in Sichuan Airlines is conducted to demonstrate the effectiveness of the proposed method. The results revealed the flight dispatchers’ human factors associated with higher risks and identified the key factors with a larger impact on other factors in Sichuan Airlines. Subsequently, a multi-level hierarchical structure model comprising five layers is developed to investigate the internal correlations among human factors, facilitating the formulation of targeted improvement suggestions for the higher risk indicators and key influencing factors.