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Despite many studies on the influence of cropping practices on soil microbial community structure, little is known about ecological patterns of rare and abundant microbial communities in response to different tobacco cropping systems. Here, using the high-throughput sequencing technique, we investigated the impacts of two different cropping systems on soil biochemical properties and the microbial community composition of abundant and rare taxa and its driving factors in continuous and rotational tobacco cropping systems in the mountain lands of Yunnan, China. Our results showed that distinct co-occurrence patterns and driving forces for abundant and rare taxa across the different cropping systems. The abundant taxa were mainly constrained by stochastic processes in both cropping systems. In contrast, rare taxa in continuous cropping fields were mainly influenced by environmental perturbation (cropping practice), while governed by deterministic processes under rotational cropping. The α-diversity indices of rare taxa tended to be higher than those of the abundant ones in the two cropping systems. Furthermore, the network topologies of rare taxa were more complex than those of the abundant taxa in the two cropping systems. These results highlight that rare taxa rather than abundant ones play important roles in maintaining ecosystem diversity and sustaining the stability of ecosystem functions, especially in continuous cropping systems.

One of the most important activities in any organization is the identification and selection of the right supplier. The responsible organizations determine the performance of their potential suppliers based on the attributes that are aligned with their sustainable development goals. Data regarding these attributes can be qualitative and/or quantitative, and not every supplier selection methodology can handle them simultaneously. Data envelopment analysis (DEA) is an influential methodology for measuring the suppliers' performance, especially when there are more than one input and/or output. However, the original DEA model cannot consider human judgment during evaluation. In this regard, the current study proposes a novel methodology with the aid of the Ordinal Priority Approach (OPA) of multiple attributes decision-making and the DEA. The proposed method, the DEA-OPA model, truly enjoys the advantages of both DEA and OPA models, making it more powerful than the original DEA for performance measurement. The proposed model was compared with the original DEA and OPA then retested through incomplete data when the experts lack enough knowledge about inputs and outputs. Finally, a pilot application has been executed in the paper industry, and comprehensive sensitivity analysis has been performed to illustrate the feasibility of the proposed model. The findings are of significant importance to responsible enterprises and organizational decision-makers.

A high-speed rail (HSR) system is directly related to people’s daily travel and safety. To make HSR systems work better for society, it is necessary to improve their resilience. Based on an in-depth literature review, six HSR system ontology-related variables and four resilience attribute-related variables were identified. Then, a questionnaire was designed and distributed to targeted respondents after a pilot survey, aiming to collect experts’ opinions on these driving variables. Subsequently, multiple regression analysis was conducted to check the relationship between driving variables and HSR system resilience. Finally, a fuzzy set qualitative comparative analysis (fsQCA) was carried out to identify the potential configurations of driving variables from a holistic perspective. The results show that all driving variables are significantly correlated to HSR system resilience. Moreover, seven variable configurations were identified and divided into three categories, i.e., strong HSR system ontology–weak resilience attributes, weak HSR system ontology–strong resilience attributes, and strong HSR system ontology–strong resilience attributes. This paper explores the effect of configuration of the driving variables on HSR system resilience, providing a holistic perspective for system resilience literature. The results can also help HSR system-related stakeholders understand the effect of variable combinations on resilience improvement.

This study assessed the relationships between maternal systemic lupus erythematosus (SLE) or rheumatoid arthritis (RA) and risk for autism spectrum disorders (ASDs) in offspring. Seven observational studies, including 25,005 ASD cases and 4,543,321 participants, were included for meta-analysis. Pooled results by using random-effects models suggested that maternal RA was associated with an increased risk for ASDs [odds ratio (OR) 1.39, 95% confidence interval (CI) 1.16–1.67], while maternal SLE was associated with an increased risk for ASDs only in western population (OR 1.91, 95% CI 1.02–3.57). Further study is warranted to confirm these results.

Background The advancement of the information age has led to the widespread use of the internet, accompanied by numerous internet-related issues that often correlate with various physical and mental health conditions, particularly among college freshmen. We examined the relationship between internet addiction (IA) and insomnia among these students, using emotion regulation (ER) and anxiety as mediators and gender as a moderating variable. Methods This cross-sectional study included 7,353 freshmen from a university in Jingzhou City, Hubei Province, China. Data were collected through an online self-administered questionnaire, including the Internet Addiction Test (IAT), the Emotion Regulation subscale (ER), the Generalized Anxiety Disorder 7-item scale (GAD-7), and the Insomnia Severity Index (ISI). Data analysis was conducted using SPSS 21.0 and PROCESS version 4.1 to test the hypothesized relationships among variables. Results In our survey, correlation analysis showed that ER was significantly negatively correlated with IA, anxiety, and insomnia; IA was significantly positively correlated with anxiety and insomnia (all p  < 0.01). The mediating effect analysis indicated that IA was a significant positive predictor of insomnia. ER and anxiety played a chain - mediating role in the development of insomnia (β = 0.039, 95% confidence interval = 0.035–0.043). The moderating effect analysis showed that the interaction term of IA and gender had a significant negative predictive effect on ER (β = -0.014, 95% confidence interval [-0.027, -0.001]) and insomnia (β = -0.022, 95% confidence interval [-0.036, -0.007]). Males (direct effect: β = 0.048, 95% confidence interval = [0.037, 0.059]) had a stronger predictive ability for the level of insomnia than females (direct effect: β = 0.026, 95% confidence interval = [0.014, 0.037]). Females (indirect effect 1: β = 0.015, 95% confidence interval = [0.010, 0.020]; indirect effect 2: β = 0.041, 95% confidence interval = [0.037, 0.045]) had a stronger predictive ability for the level of insomnia through the level of IA than males (indirect effect 1: β = 0.014, 95% confidence interval = [0.009, 0.018]; indirect effect 2: β = 0.037, 95% confidence interval = [0.033, 0.041]). Conclusion IA can exacerbate insomnia in college freshmen by compromising their ER, subsequently triggering anxiety symptoms. The process differs by gender, suggesting tailored strategies for each. These findings may play crucial roles in promoting the physical and mental well-being of college freshmen.

Background: In any country, supply chain management is crucial to the economy. Big data-driven (BDD) implementation can be used in different disciplines, especially in construction supply chain management (CSCM). While BDD has a lot of opportunities for optimizing workflows, reducing costs, and improving collaboration among stakeholders to enhance efficiency and decision-making, its adoption is fraught with significant barriers. Thus, identifying these challenges is an important research concern. Methods: This study adopts a systematic review methodology aligned with PRISMA guidelines, combining bibliometric and thematic analyses to explore the integration of BDD approaches in CSCM. A comprehensive search of the Scopus database was conducted, focusing on articles published between 2014 and 2024 with a multi-phase screening process until 62 relevant studies were adopted. Results: This study summarizes the challenges associated with integrating BDD into CSCM and presents solutions to solve them and a framework for implementing BDD in CSCM. Moreover, providing future directions that require further consideration and research. Conclusions: By overcoming these barriers, the construction supply chain will be able to adopt big data for improving efficiency and reshaping CSCM. This study provides a clear view of how CSCM scholars and practitioners should develop along with promising research on BDD.

The Distinguishing Coefficient (ξ) is an important parameter of Grey Relational Analysis (GRA), a flagship multi-criteria decision making (MCDM) model of Grey System Theory, an intelligent and multifaceted field developed by Chinese scientists in 1980s. However, the scholars widely assume ξ = 0.5. The current study questions this practice. Also, some scholars have argued that the variation in ξ doesn’t influence the ranking of the factors through GRA. On contrary, the study demonstrates, the variation in ξ can influence the ranking. This has been shown through a case involving primary data concerning the perceived relative importance of Project Management Knowledge Areas (PMKAs). This study is significant for the analysts of uncertain systems, represented by grey or fuzzy systems, who intend to use GRA for intelligent multi-criteria decision making. It encourages ξ – driven sensitivity analysis of GRA model before interpreting the results. The study reveals, by tailoring the value of ξ a point can be achieved where the ranking obtained through GRA can be made most comparable to the other MCDM methods. For comparative analysis of the GRA based results the study deployed three other MCDM techniques; Analytic Hierarchy Process, Best Worst Method and Simple Additive Weighting.

The most important problems that occur during the extraction of knowledge from data streams are related to the properties characterizing “BigData,” namely high speed of information flow (velocity), variety of used forms, variability of data and diversity of information accuracy diagnosis methods (veracity). The use of online or sequential learning methods offers a specialized solution for solving real-time data processing problems. Data are provided without a clear knowledge of their particular inherent characteristics. Conventional approaches focus on applying heuristic or logical analysis rules. They fail to effectively handle new patterns (produced as a function of time) and to consider the dynamic change rate of their characteristics. In most cases, these methods approximate, by creating general rather than clear imprints of knowledge, which is hidden in the flows. Moreover, their function requires significant computational resources. This paper introduces (to the best of our knowledge, for the first time in the literature) the implementation of a specialized online reservoir computing architecture for smart city infrastructure protection which has low requirements in computing resources; it is efficient and suitable for real-time data flow analysis. More specifically, it describes the development of an echo state network, comprised of analog neurons with sparse random connections at the input levels and at the dynamical reservoir. Its training at the output level is performed with the recursive least square method. A complex data set was selected for the testing of the proposed model, which fully simulates the digital attacks that can be faced by the mechatronic equipment used in smart water supply networks located in the front end of the smart cities.

Knowledge loss (KL), the disappearance of critical knowledge once a project ends, remains a persistent threat to the sustainability of organizational performance and competitiveness despite ongoing efforts to implement knowledge retention (KR) methods in construction organizations. This study presents a new research model to examine why KL occurs and how valuable project knowledge can be effectively retained. From the conservation of resources (COR) perspective, we aim to investigate how project urgency and temporariness, referred to as project features (PFs), influence knowledge loss through members’ knowledge withholding (KW) behavior, how this association is affected by their psychological emotions (fears), and the contingent role relational resources, namely project team interaction (PTI), plays in this association. Data were collected from a sample of 469 construction experts with extensive experience in international engineering projects undertaken by Chinese international companies. Partial least squares path modeling (PLS-PM) analysis using SmartPLS 4 was employed to empirically test the proposed theoretical model. The results show that KW behavior is a critical driver of KL and serves as a mediator of the impact of PFs on KL. PFs were found to be positively associated with members’ KW behavior. This linkage was partially mediated by fear of failure (FF), while fear of losing uniqueness (FLU) showed no significant mediating effect. PTI played a moderating role in the relationship between KW and KL. Based on these findings, minimizing KL requires management to focus on reducing FF by fostering a climate of mistake tolerance, and subsequently strengthening PTI to promote effective knowledge exchange. The results of this study offer new theoretical and practical insights into KL risk management within construction organizations.

The stability assessment of loess slopes is of great significance for slope reinforcement and safety assessment. This research studies the main factors affecting the stability of the loess slope through the summary and analyzes the failure cases of the loess slope in Shaanxi Province. The importance of influencing factors was studied through numerical simulation method, sensitivity analysis method, and gray correlation analysis method, and the weight value method was given. On this basis, we have developed the fuzzy comprehensive evaluation model method for assessing the stability of loess slopes based on the principle of maximum membership degree. Finally, the method was applied to the stability analysis of the actual loess slope, and the rationality and correctness of the loess slope stability evaluation method proposed in this paper were demonstrated. The results showed that, for the Shaanxi loess slope, the probability of instability of the positive slopes is far greater than that of negative slopes; the greater the slope gradient, the more unstable the loess slopes. Collapse mainly occurs in the range of 10–40 m slope height. There is a significant positive correlation between rainfall and the probability of loess landslides. The degree of correlation between the factors influencing slope stability and the safety factor can be categorized from strong to weak as follows: slope inclination > internal friction angle > height of the slope > gravitational forces > cohesion > Poisson’s ratio > modulus of elasticity, and the influence of Poisson’s ratio and elastic modulus can be ignored. The fuzzy comprehensive evaluation method based on the gray correlation degree method established in this paper was used to evaluate the stability of the loess slopes. The evaluation results attested to the actual data of slope monitoring. The evaluation method proves reasonable and feasible and can be well applied to the stability analysis of the loess slopes.