During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (N-a, in g m(-2)) and chlorophyll (Chl(a), in mu g cm(-2)) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is vital for the accurate prediction of crop photosynthetic capacity. We conducted a two-year field study on winter wheat and paddy rice in Eastern China, measuring the leaf maximum carboxylation rate (V-cmax25), maximum electron transport rate (J(max25)), N-a, and Chl(a) every 7-10 days from greening to maturity. We analyzed vertical variations in these parameters in the upper (T-1), middle (T-2), and lower (T-3) canopy layers and explored relationships between N-a/Chl(a) and V-cmax25. The results showed significant vertical variations: V-cmax25 and J(max25) in T-1 were higher than in T-2, and T-2 was higher than T-3. The vertical distribution of N-a and V-cmax25 was more pronounced than that of Chl(a). The correlation between N-a and V-cmax25 increased from T-1 to the lower layers, while the V-cmax25-Chl(a) correlation decreased. A single V-cmax25 estimation model based on N-a performed well across layers (R-2 = 0.619, RMSE = 15.751 & micro;mol m(-2) s(-1)). Differentiating T-1 from T-2/T-3 improved the Chl(a)-based models. N-a was better than Chl(a) for characterizing the V-cmax25 vertical variation, with the Chl(a)-based models requiring separation of T-1 from T-2/T-3. This study provides key insights for remote sensing of photosynthetic parameters and improves the understanding of crop canopy photosynthesis.

The three-way decision-making method in hesitant fuzzy environments is widely recognized as a powerful tool for addressing uncertainties and ambiguities in the decision-making process. Moreover, prospect theory objectively depicts the effect of decision-makers’ psychological behavior on the decision-making process. Therefore, this article intends to establish a novel multi-attribute three-way decision-making method in a hesitant fuzzy environment by incorporating prospect theory to account for decision-makers’ irrational behaviors. Specifically, to overcome the limitations of existing distance formulas for hesitant fuzzy elements, we introduce a novel formula for computing the distance between hesitant fuzzy elements at first. On this basis, a novel method for computing the conditional probability in hesitant fuzzy environments is proposed. Concurrently, a relative loss function is developed for each scheme by integrating the hesitant fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method with prospect theory, which is utilized to address real-life multi-attribute decision-making problems. Finally, through case and comparative analysis, we demonstrate scientific validity and superior performance of the proposed method, and prove the reliability and robustness of the proposed method through parametric sensitivity analysis. The successful implementation of these methods has fully demonstrated the tremendous potential and advantages of artificial intelligence technology in dealing with complex decision-making problems.

Nutrient resorption from leaves and translocation to twigs and other woody tissues during leaf senescence is a crucial strategy for plant nutrient conservation. The nutrients retained in twigs provide essential resources for new growth, especially when root nutrient acquisition is restricted by low soil temperatures during early spring. However, the interconnections between leaf nutrient resorption and twig nutrient accumulation during autumn, and their influence on spring phenology, remain poorly understood. We selected 20 woody species with a wide range of leaf traits in a common garden and investigated the relationships among leaf resorption efficiency, twig accumulation efficiency of nitrogen (N) and phosphorus (P), and autumn-spring phenology. We found that leaf N resorption (54.27%) was significantly higher than leaf P resorption (42.42%). Additionally, twig N accumulation efficiency (40.00%) was significantly higher than twig P accumulation (18.37%), with both positively correlated with leaf nutrient resorption efficiency. Species with acquisitive traits exhibited higher N and P resorption efficiency, along with higher P accumulation efficiency in twigs. Soil fertility had a relatively minor influence on both leaf nutrient resorption and twig nutrient accumulation. In addition, autumn and spring phenological events were linked to plant internal nutrient dynamics. Species with later leaf shedding and shorter leaf fall duration in autumn tended to exhibit greater leaf P resorption efficiency. Furthermore, species with higher twig nutrient accumulation efficiency showed earlier bud break and a longer period of leaf-out in the subsequent spring. Modular network analysis and structural equation model further indicated that leaf nutrient resorption was strongly related to leaf economic traits, while leaf nutrient resorption was indirectly linked to bud-break timing through twig nutrient accumulation. Synthesis. Our findings suggest that plant internal nutrient dynamics are not only consequences of phenology but may also influence phenological timing. These results highlight the importance of nutrient resorption and storage strategies in regulating seasonal growth patterns and indicate that internal nutrient cycling may affect plant performance and ecosystem functioning under varying environmental conditions.

In the context of global urbanization, the rise of super tall buildings has brought new challenges to safety management. The development of artificial intelligence technology provides a new solution for the safety warning of super high-rise buildings. Random forest model is an integrated learning algorithm, which has good anti-noise ability and excellent prediction performance, can handle a large amount of data, and is not easy to overfit, so it is very suitable for constructing safety early warning system of super tall buildings. The comprehensive safety early warning system of super tall building constructed by random forest model algorithm can monitor and early warning the safety state of super tall building in real time, effectively reduce the safety risk of super tall building, and has important practical application value. In view of this, this study designed and verified a comprehensive safety early warning system that integrates real-time monitoring, data analysis and intelligent early warning, aiming at comprehensively monitoring building health status and timely preventing safety hazards. This paper discusses the overall design of safety early warning system of super tall buildings from the aspects of system architecture, key technology and early warning algorithm, and verifies the effectiveness of the system through typical building cases. The research focuses on intelligent early warning algorithm development, early warning level design and user interface optimization. Through technological innovation, this research builds a real-time, accurate and reliable warning platform for super tall buildings, which makes an important contribution to the safety, durability and sustainability of super tall buildings, lays a foundation for the construction of urban safety environment, and has important practical significance for the sustainable development of cities.

In the rice–wheat rotation system, two types of grains share the same land and supply chain. To study the green optimization of this grain supply chain, it is necessary to fully consider the production decisions of wheat and rice. Based on existing agro-food supply chain models, this paper considers the impact of straw returning and deep tillage on crop yield and greenhouse gas (GHG) emissions, and constructs a bi-objective mixed integer linear programming model that includes economic and environmental benefits. The results of the cases indicate that deep tillage is required before wheat sowing, while rice cultivation does not require deep plowing. Rice straw needs to be returned to the field, while wheat straw is best used as animal feed. Combining the inventory and transportation strategies in the results, this study can give supply chain decision-makers 94.6

In today’s rapidly evolving workplace environments, the integration of bioinformatics with occupational health data presents a unique opportunity to enhance employee well-being and optimize workplace safety, especially from the perspective of biomechanics. Existing systems often fail to account for individual genetic factors and the biomechanical aspects of the work environment when assessing occupational health risks, resulting in an increase in workplace-related health problems and less effective health treatments. The primary objective of this study is to develop a planning decision support model that integrates bioinformatics and occupational health data to recognize health risks and generate tailored interventions for employees. Incorporating biomechanics, we explore the impact of physical factors such as workstation ergonomics, repetitive motion patterns, and force exertion levels in the work environment on employee health, and analyze their relationship with genetic predispositions. For example, we study how specific genetic traits may interact with biomechanical stressors to increase the likelihood of musculoskeletal disorders. Initially, study data were collected from diverse sources, including bioinformatics databases and occupational health records, ensuring a comprehensive dataset for effective model training and validation. Data cleaning and Z-score normalization were used in the data preparation stage. Feature extraction was performed using Linear Discriminate Analysis (LDA) to reduce dimensionality from preprocessed data. Data fusion was accomplished by sharing information between bioinformatics and occupational health datasets, enabling a more comprehensive decision support model. The study proposed a Dynamic Bacterial Foraging fine-tuned Efficient Adaptive Boosting (DBF-EAdaBoost) method that integrates dynamic bacterial foraging optimization with adaptive boosting techniques to significantly enhance classification performance in bioinformatics and occupational health data analysis. The proposed algorithms offer high accuracy (0.93), precision (0.987), brier score (0.100), AUC (0.92), and log loss (0.314) in forecasting potential health issues based on workplace exposures, biomechanical factors, and genetic predispositions. To enhance the practicality of the research, a more detailed explanation of the implementation process and advantages of the proposed DBF-EAdaBoost algorithm is provided. Consider including real-world case studies to demonstrate the model’s application and the actual effectiveness of health interventions in real workplace environments. For instance, we can present a case where the model was applied in a manufacturing plant to predict and prevent musculoskeletal disorders among workers by analyzing their biomechanical workloads and genetic profiles, and implementing appropriate ergonomic interventions. The planning decision support model serves as a significant tool for public health officials, policymakers, and occupational health professionals, promoting data-driven decisions that enhance health outcomes.

Limitations regarding phosphorus (P) are widespread in ecosystems. Understanding the impacts of the wetland types on microbially mediated soil P availability and cycling is essential for the effective management of wetlands. In this study, the Beidagang wetland, Baodi paddy field, and Dahuangpu wetland in Tianjin, China were chosen as representatives of the coastal wetland (B), constructed wetland (R), and swampy wetland (W), respectively. Sequential P extraction and metagenomics approaches were adopted to explore the soil P fraction and microbially regulated P cycle. Proteobacteria were the predominant microbes-related soil P cycle. IMPA, gph, rsbU_P, ugpQ, and glpK genes were dominant in organic P (Po) mineralization, while gcd, ppa, and ppx genes were dominant in inorganic P (Pi) solubilization. The salinity, NO3--N concentration, the ratio of total carbon to total nitrogen (TC/TN), total carbon (TC), and the ratio of soil organic carbon to total P (SOC/TP) were the co-drivers of microbially mediated P cycle processes. Microbial network complexity-relate P cycle was the lowest in the coastal wetland. Salinity and NO3--N exhibited a significant negative relation to the abundance of most genes-relate Pi solubilization and a remarkable positive correlation with the abundance of many genes-relate Po mineralization. These findings demonstrated that Po mineralization tended to occur in habitats with high salinity and nutrient imbalances, whereas the dissolution of Pi was prone to occur in low-salinity environments with relatively balanced soil nutrients. This study improves understanding of how salinity and soil nutrients jointly shape microbial-regulated soil P cycle in different types of wetlands.

Given the increasing popularity of livestreaming and agricultural e-commerce, it is important to understand what influences knowledge sharing among competing local farmer-livestreamers. Using ordinal logistic regression and agent-based modeling, we examine several drivers of livestreaming knowledge sharing in an agricultural cluster. Our results show that peer effects and peer interaction can speed up knowledge sharing among local farmer-livestreamers. Furthermore, regional knowledge-sharing activities organized by local governments can meaningfully motivate farmer-livestreamers to share their livestreaming skills and knowledge. To promote knowledge sharing in agricultural clusters, local governments also can use communication platforms and education to change the idea of some farmer-livestreamers that knowledge sharing only benefits their local competitors. Highlighting several drivers for the evolution of livestreaming knowledge sharing, our study provides a theoretical basis for leveraging knowledge sharing to enhance the competitiveness of agricultural clusters in cyberspace.

Robotaxi services (RTS) represent a transformative shared mobility solution empowered by autonomous driving technology. However, consumer ambivalence toward new technologies may hinder the widespread adoption of RTS. Few studies have examined in-depth consumers’ two-sided psychology in accepting RTS. Drawing on behavioral reasoning theory and technical readiness, this study examines the relative influence of reasons for and against RTS acceptance among Chinese consumers and the importance of each reason. Using questionnaire data from 450 Chinese consumers, a dual-stage analysis combining partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was employed to validate causal hypotheses and determine the significance of factors. Dual-path analysis revealed a strong positive effect of “reasons for” and a slightly weaker negative effect of “reasons against” on consumers’ attitudes and intentions toward RTS acceptance. Among the “reasons for”, innovativeness was the most significant determinant of RTS acceptance, followed by perceived benefits, subjective norms, and optimism. Among the “reasons against”, insecurity emerged as the main psychological barrier to RTS acceptance, with discomfort also playing a significant role, whereas interaction barriers were found to be insignificant. This study emphasizes the importance of identifying both pro- and anti-adoption reasons in the mass marketing of RTS. These findings offer policymakers and industry stakeholders valuable insights to comprehensively assess consumer psychology and promote RTS adoption.

A growing body of literature identifies information constraints as a potential barrier to farmers’ adoption of sustainable agricultural practices (SAPs) and information acquisition (IA) as the key to breaking the dilemma. Due to the heterogeneity of information, channels and farmers, IA has different effects on farmers’ sustainable behaviour in different SAP scenarios. This study systematically reviews the mechanisms, empirical cases, models and methods of the impact of IA on farmers’ adoption of SAPs. Results show that IA plays a crucial role in promoting SAPs, and exposure to different information sources/channels usually has differential effects on farmers’ intention or behaviour to adopt SAPs. It is worth noting that the positive effect of the use of modern information channels (especially the Internet and smartphone) on SAPs is gradually being demonstrated in empirical studies across countries, and the effect of Internet or smartphone use on farmers with different resource endowment characteristics may also be heterogeneous. According to further analysis, traditional and modern information channels should complement each other and be fully integrated into SAPs extensions. This study also discusses the gaps that need to be bridged in IA-to-SAPs and describes possible future research directions. These findings will contribute to the scientific design of information interventions and environmental policies in SAPs extension services.

Information sources (ISs) are crucial for enhancing green behaviour among farmers. However, few in-depth studies have been conducted on the differential effects of different ISs. This poses a challenge for the design of efficient information intervention strategies. This study designed an extended theoretical framework of planned behaviour to examine the differential effects of different ISs on farmers' intentions to adopt organic fertilisers. Survey data from 361 Funing watermelon farmers in China were analysed using partial least squares structural equation modelling. The results showed that the utilisation of personal ISs (both formal and informal) had significant positive effects on intentions, whereas impersonal ISs did not play a significant role. Among these, personal informal ISs were the strongest informational determinant. Moreover, psychological factors played a key mediating role between ISs and intentions. Attitude was the strongest direct psychological factor, followed by perceived behavioural control and subjective norms. These findings provide theoretical and practical insights for policymakers to design targeted information interventions to promote the adoption of organic fertilisers by farmers. When relying on formal ISs, the Chinese government should focus on the role of informal ISs.

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (phi 0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for phi 0 (Topt_phi0). The roles of modified APAR and phi 0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and phi 0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m- 2 d-1, -0.14 g C m- 2 d-1, 1.77 g C m- 2 d-1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr -1 during 1981-2021, in the upper range of existing GPP products.

Participation in cooperatives has been shown to enhance sustainable practices among farmers. However, further investigation is needed into how to sustainably improve the behavior of members post-joining. Given the decentralized nature of farmer cooperatives, it is challenging for managers to enforce strict regulations that govern the sustainable actions of all members. Using propensity score matching, this study examines how varying degrees of organizational imprinting affect sustainability practices within cooperatives. These findings indicate that an environmentally friendly organizational imprinting significantly bolsters sustainable crop protection behaviors among cooperative farmers. The establishment and reinforcement of such eco-friendly imprints are identified as effective strategies for the sustainable management of farmer cooperatives, particularly for those that do not emphasize sustainable conduct among members. This research broadens the application of the organizational imprinting theory to loosely coupled systems and offers novel insights into the sustainable governance of agricultural cooperatives.

Different from the previous studies on social and economic impacts, this study focused on the assessment of psychological factors on farmers' application of organic manure. We explored the psychological evaluation based on the extended theory of planned behavior (TPB), which consists of attitude (AT), perceived behavior control (PBC), subjective norm, moral norm (MN), environmental risk perception (ERP), and perceived policy effectiveness (PPE). Further, we explored the moderating effects of PPE. We studied 235 tea growers in China to verify the model and analyzed the psychological factors in their decisions regarding organic manure application. The results showed that by incorporating psychological factors, such as MN, ERP, and PPE, the extended TPB's ability to explain farmers' intention to apply organic manure increased by 6%. The results also confirmed that psychological factors (ERP, PPE, AT, PBC, and MN) positively influenced farmers' inclination to use organic manure. Finally, PPE was found to have a negative mediating effect on attitude and intention. Given the influence of these psychological factors (PBC, ERP, and PPE), we discovered that increasing the policy publicity, raising the policy subsidy, and promoting the popularization of sustainable agriculture and environmental awareness, are essential to encourage farmers' utilization of organic manure.

In the digital age, human-algorithm interaction has become ubiquitous. The popularity of short-form video apps has led to greater access to and experience with algorithmic recommender systems, but it has also exposed their potential problems and dark sides. However, there is still limited knowledge about the dark side of over-recommendation from the perspective of user perception, especially regarding the negative impact on users' psychology and behavior. Drawing on social cognitive theory, this study constructed a theoretical model to investigate how information environment factors influence users' cognitive dissonance and discontinuance intention under the antecedent dimension of perceived over-recommendation. The model was tested using partial least squares structural equation modeling on 322 valid questionnaires from users of Chinse mass short video apps (eg, Douyin). The results showed that when users perceived over-recommendation, information narrowing, information redundancy, perceived overload, and privacy invasion significantly increased their cognitive dissonance, ultimately leading to discontinuance intention. Notably, cognitive dissonance fully mediated the relationship between the information environment and discontinuance intention, and self-efficacy did not play a significant moderating role. Additionally, the local path effects varied significantly across groups with different characteristics. To maintain the sustainability of short video platforms, it is crucial to explore moderate recommendation mechanisms. User-centered functionality improvements and differentiated behavioral interventions can help mitigate negative psychology and discontinuance intention among users.

Soil plays a crucial role as a significant reservoir for antibiotic resistance genes (ARGs). Despite the extensive research conducted in this area, there remains a need for further investigation, particularly concerning diverse types of agricultural soil. This comprehensive study examined Fusarium wilt diseased and healthy soil samples to investigate the spectrum of ARGs and identify bacteria potentially harboring these genes. The level of antibiotic resistance (AR) in the two soil types was detected by using culture plate counts (PC). We quantified and identified antibiotic resistant bacteria (ARB) against nine antibiotics. From the PC results, the relative number of ARB (number of ARB/total number culturable bacteria) resistant to erythromycin, lincomycin, sulfonamides, streptomycin sulfate, kanamycin, and enrofloxacin in the diseased soil was significantly higher than that in the healthy soil (One-way ANOVA, p < 0.01). The GeoChip (version 5.0) was employed to identify ARGs and antibiotic biosynthetic genes (ABGs). Interestingly, despite the presence of similar ARGs in both soil types, the Fusarium wilt diseased soil demonstrated a significantly elevated abundance of the tetracycline (tetX) resistance gene compared to the healthy soil. A robust correlation (r >= 0.8) between multidrug efflux pumps and ARGs indicates that natural antibiotics as selective pressures for transportation mechanisms. Our analysis identified diverse ARB phylotypes and multiple ARGs in both soil types. Incorporating these soil types into ARGs risk as-sessments and relevant monitoring is essential for a comprehensive understanding of AR dynamics in various environments.

To control plastic pollution, it is imperative to encourage and facilitate waste mulch film (WMF) recycling by farmer cooperative members. We investigate the WMF recycling behavior of farmers in several randomly selected regions from the main vegetable-producing provinces in China. Propensity score matching is used to study the impacts of face-to-face, telephone, and social media communication on WMF recycling behavior of farmers. Our study shows that face-to-face communication is still the most important channel to promote farmers’ recycling behavior. Therefore, while it is more efficient to promote WMF recycling through social media in the Internet age, cooperative managers should not underestimate the role of face-to-face communication which is the preferred communication channel for many small farmers according to our analysis. Cooperative managers can better promote their members’ WMF recycling behavior through both social media and face-to-face communication.

Increased atmospheric nitrogen (N) deposition significantly disturbs ecosystem N cycle. Although foliar interception and uptake of N deposition can provide an important alternative N supply to forest ecosystems, the mechanisms regulating foliar N uptake from wet deposition are not fully understood. Here, we selected 19 woody species with a wide range of plant traits from different functional groups and conducted a 15 N isotope labelling experiment through brushing 15 NH 4 + and 15 NO 3 − solution on canopy leaves. Our findings demonstrate that leaves can directly absorb N from wet deposition within a few hours. The average leaf 15 N recoveries were 10% and 28% under 15 NH 4 + and 15 NO 3 − treatments across species, respectively, while twig N recoveries were only 1%–7% of leaf N recoveries. Differences in foliar N uptake efficiency among species were closely associated with leaf traits but were little influenced by meteorological conditions or soil nutrient status. Specifically, plants with higher leaf N concentration, larger specific leaf area and lower wax concentration exhibited higher leaf N recovery. Our results indicated that tree canopies could directly absorb N from atmospheric deposition. We highlight the critical role of leaf traits in determining canopy foliar N uptake, which may consequently influence plant competition under elevated N deposition.