Search Results (8,501)

To investigate the effects of seasonal grazing on ground-dwelling insect communities in desert steppe, this study conducted a controlled experiment in the desert steppe of Yanchi County, Ningxia, during 2022–2023. Five grazing regimes were established: spring-summer grazing (Sp+Su), spring-autumn grazing (Sp+Au), summer-autumn grazing (Su+Au), year-round continuous grazing (Annual), and no grazing (Control, CK). Insects were collected using pitfall traps and categorized into herbivorous and predatory functional groups. Combined with monitoring of vegetation community structure, we analyzed the regulatory mechanisms of grazing on insect diversity. The results showed that different grazing regimes had significantly divergent effects on herbivorous and predatory insects. Herbivorous insect diversity was significantly highest under the Annual grazing regime, while Sp+Au grazing effectively controlled herbivorous insect occurrence, resulting in the lowest abundance. Predatory insects exhibited the highest abundance but the lowest diversity under Su+Au grazing, whereas the CK regime increased their species richness. Beta diversity analysis indicated that total replacement diversity (Repl) was dominant, suggesting that grazing primarily influenced community structure by altering species composition rather than changing species number. Non-metric multidimensional scaling (NMDS) results revealed clustering characteristics of insect community structures under different grazing regimes. Redundancy analysis (RDA) and generalized additive models (GAMs) identified vegetation height and predatory insect abundance as key factors driving changes in herbivorous insects. Vegetation density and biomass exhibited nonlinear regulatory effects on herbivorous insects. Based on these findings, we recommend adopting either a hybrid strategy of “year-round continuous grazing combined with seasonal rest” or specifically the “spring + autumn” (Sp+Au) grazing regime. These approaches aim to synergistically achieve the goals of pest control and biodiversity conservation in desert steppe ecosystems. Full article

The severe damage caused by the fall webworm Hyphantria cunea is closely related to its internal microbiota. However, due to the widespread use of antibiotics and their environmental persistence, the specific effects of various antibiotics on the microbiome and fitness of H. cunea larvae remain ambiguous. This study investigated the impacts of three antibiotics (tetracycline, rifampicin, and kanamycin) on microbiome assembly, functional traits, and host fitness. Our findings revealed that each antibiotic distinctly altered the microbial community: tetracycline primarily decreased bacterial diversity (e.g., reduced abundance of Actinomycetota) and suppressed host fecundity; kanamycin lowered microbial evenness (e.g., decreased Bacillota) and diminished pupal weight; whereas rifampicin significantly restructured the community (e.g., increased Pseudomonas and decreased Bacillota), enhanced functional traits such as biofilm formation and stress tolerance, and imposed multidimensional adverse effects on fitness (prolonged developmental duration, reduced pupal weight, and decreased hatching rate). Alterations in microbiome diversity, structure, and function were tightly correlated with the differential impacts of antibiotics on host fitness. This research elucidates the mechanisms by which antibiotics disrupt host–microbe interactions in H. cunea, offering a theoretical foundation for understanding antibiotic ecological repercussions and devising microbe-based green pest control strategies. Full article

Trajectory prediction is a critical component of autonomous driving decision-making systems, directly impacting driving safety and traffic efficiency. Despite advancements, existing reviews exhibit limitations in timeliness, classification frameworks, and challenge analysis. This paper systematically reviews multi-agent trajectory prediction technologies, focusing on generating future position sequences from historical trajectories, high-precision maps, and scene context. We propose a multi-dimensional classification framework integrating input representation, output forms, method paradigms, and interaction modeling. The review comprehensively compares conventional methods and deep learning architectures, including diffusion models and large language models. We further analyze five core challenges: complex interactions, rule and map dependence, long-term prediction errors, extreme-scene generalization, and real-time constraints. Finally, interdisciplinary solutions are prospectively explored. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

This paper provides a comprehensive bibliographic and bibliometric review of water, sanitation, and hygiene (WASH) in global urban areas, employing the Proknow-C methodology. The study categorizes WASH into four main themes: sustainability, urban areas, indicators and index, and urban planning, allowing for a detailed analysis of several multidimensional aspects. The review underscores the importance of providing basic infrastructure to adopt an integrated, sustainable, and socially inclusive approach, showcasing the resilience and adaptability of the WASH sector in tackling the dynamic challenges of urbanization. It is noticeable that the WASH area has undergone significant development, moving from a focus primarily on infrastructure to a more holistic approach. In general, the WASH framework is globally characterized by high irregularity/inequality in provision and access. The relationship between urban vulnerabilities and WASH is very clear, but also multifaceted and complex, and there is a crucial need to combine behavior change with infrastructure development while addressing economic challenges and prioritizing investments in WASH. The improvement of WASH conditions in urban areas should focus the interplay between urban development policies and the provision of WASH services, while focusing also on the role of multi-sectoral collaboration, stakeholder engagement, and policy implementation in overcoming barriers to effective WASH delivery. Full article

Drawing on experience–satisfaction–loyalty, this study models how specific digital retail interface attributes translate into behavioural outcomes. Survey data from Portuguese online shoppers were analysed with PLS-SEM to test a formative–reflective framework linking Interactivity and Technologies, Trust–Security–Privacy, Fulfilment and Service Quality, Usability and Web Design, Personalisation and Customisation and Omnichannel Integration to customer experience (CX), customer satisfaction (CS), customer loyalty (CL) and electronic word of mouth (eWOM). The model explains 62.6% of CX, 70.1% of CS and 66.7% of CL. CX is strongly associated with CS and CS, in turn, with CL; associations with eWOM are non-significant, revealing a theoretical blind spot around advocacy. Interactivity and Technologies, Trust–Security–Privacy and Fulfilment and Service Quality emerge as the most significant antecedents of CX, whereas Omnichannel Integration is inert. The findings advance digital commerce theory by decoupling advocacy from evaluative satisfaction and by reconceptualising integration as multidimensional. Practically, they prioritise investment in interactive, secure and fulfilment capabilities while signalling that loyalty is not associated with advocacy. This study concludes by outlining measurement refinements and longitudinal avenues to capture social–motivational drivers of eWOM. Full article

This article explores an integrative framework for medical rehabilitation that combines regenerative medicine, systemic homeostasis, and microbiome modulation to optimize recovery and long-term health. Moving beyond conventional rehabilitation approaches focused on symptomatic recovery, this multidimensional paradigm emphasizes cellular repair, physiological balance, and microbial health as interdependent pillars of effective recovery. The framework leverages advancements in stem cell therapy, immune system modulation, and microbiota-targeted interventions to address both immediate functional restoration and long-term systemic resilience. By highlighting the synergistic interplay between these components, this article provides actionable insights into transforming medical rehabilitation into a proactive and holistic endeavor, paving the way for enhanced therapeutic outcomes and sustained patient well-being. Full article

In the context of a rapidly changing global energy landscape and mounting pressures on energy security, enhancing the resilience of the oil and gas industry (OGI) has become a critical task for safeguarding China’s energy security. This study develops a multidimensional resilience indicator system—comprising recovery, adaptability, responsiveness, and innovation—and, based on OGI data for 2001–2022, employs the entropy method to quantitatively assess resilience by sub-dimension and development stage. Leveraging a backpropagation (BP) neural network, we construct a dynamic simulation model to produce long-term, multi-scenario forecasts of China’s OGI resilience for 2023–2032, enabling comparison of development potential across scenarios. The results indicate that overall resilience exhibited a fluctuating upward trend and reached a medium-strength resilience level by 2022, with innovation and recovery gradually emerging as the dominant drivers. Forecasts show that under the green-transition scenario, resilience will improve the most, increasing by 5.49% by 2032 and reaching the threshold for strong resilience earlier than under other scenarios. These findings offer actionable insights for enhancing the reliability and sustainability of energy supply chains in the face of climatic and geopolitical challenges. Full article

In this paper, an intelligent simulation method for chloride ion diffusion behavior in marine concrete is established based on a physical information neural network. The dimensionless constraint equation is constructed to solve the influence of different physical parameter dimensions on the generalization ability of the model. The performance of the simulation method is verified by field measured data. The influence of different exposure ages and chloride ion diffusion coefficients on chloride ion diffusion behavior is quantified. The temporal and spatial distribution characteristics of chlorine ion (C) in concrete under a multi-dimensional diffusion state are analyzed, and the reliability model is further constructed to evaluate the degradation law of the service performance of marine concrete. The results show that the dimensionless physical information neural network model can effectively simulate the diffusion behavior and spatial–temporal distribution of C in marine concrete. The maximum error between the predicted value and the experimental value obtained by the method proposed in this paper is less than 15%. The dimension problem of high-order nonlinear equations can be solved by Non-PINN, with the maximum error value less than 5%. The spatial–temporal distributions of C on different exposed surfaces under a multi-dimensional diffusion state are independent of each other. The service performance of marine concrete will increase with an increase in slag content and protective layer thickness, and decrease with an increase in surface chloride ion concentration. Full article

Chemsex is a phenomenon involving the intentional use of psychoactive substances before or during sexual activity, especially among men who have sex with men (MSM). It is associated with various health risks, including substance dependence, risky sexual behaviors, and both mental and somatic disorders. Despite its growing prevalence and public health relevance, chemsex lacks a clear definition and is not recognized as a distinct diagnostic entity. This narrative review synthesizes clinical, epidemiological, and technological evidence on chemsex; argues for its classification as a form of mixed addiction; and preliminarily proposes diagnostic criteria for a potential entity in the International Classification of Diseases, 12th Revision (ICD-12). This paper highlights key psychotropic substances used in chemsex, patterns of use, and their neurobiological, psychological, and behavioral consequences. It explores the relationship between chemsex and compulsive sexual behavior disorder (CSBD), current diagnostic frameworks (ICD-10 and ICD-11), and challenges in clinical practice. Therapeutic strategies discussed include cognitive behavioral therapy (CBT), digital interventions, and emerging applications of artificial intelligence (AI) in prevention and treatment. Attention is also given to epidemiological trends, sociocultural influences, and barriers to seeking help. This review concludes by identifying research gaps and advocating for a more integrated, multidimensional approach to classifying and treating chemsex-related syndromes. Full article

Work happiness is commonly described as an umbrella concept encompassing job satisfaction, engagement, and emotional attachment to the workplace. However, few studies have explored its underlying sources and emotional experiences, raising questions about its conceptual clarity and measurement. This exploratory inductive mixed-methods study investigates whether work happiness can be better understood by distinguishing between its sources (antecedents) and emotional expressions (outcomes). In the qualitative phase, 23 part-time adult students from Norway’s public and private sectors reflected on moments of work happiness and the emotions involved. Thematic analysis identified five source-related themes, which informed the development of 49 items. These items were tested in a quantitative survey distributed to 4000 employees, yielding 615 usable responses. Exploratory factor analysis (EFA) revealed six conceptually coherent source dimensions—such as autonomy, recognition, and togetherness—and one emotional dimension. Regression analysis demonstrated statistically significant associations between source factors and emotional experiences, offering initial support for a dual-structure model of work happiness. Notably, the findings revealed a dialectical interplay between individual (“I”) and collective (“We”) sources, suggesting that work happiness emerges from both personal agency and social belonging. While promising, these findings are preliminary and require further validation. The study contributes to theory by proposing a grounded, multidimensional framework for work happiness and invites future research to examine its psychometric robustness and cross-contextual applicability. Full article

Background/Objectives: Frailty is a multidimensional syndrome characterized by diminished physiological reserves, reduced mobility, and increased fall risk. While clinical assessments are commonly used to screen for frailty, they may not capture minor deficits in postural control. Center-of-pressure (CoP) metrics from force plates provide objective markers of postural control, yet their role in frailty screening remains underexplored. This study aimed to investigate the associations between functional performance measures and CoP-based metrics to identify predictors of frailty among older adults. Methods: Eighty-three adults aged ≥ 55 years with a history of falls were classified as frail or pre-frail based on modified Fried criteria. Functional assessments (Timed Up and Go (TUG), grip strength, Berg Balance Scale [BBS], Falls Efficacy Scale [FES]) and CoP metrics (mean velocity, sway path; eyes open/closed) were evaluated. Both unadjusted and age-adjusted logistic regression models were used to identify independent predictors of frailty. Results: Increased TUG time and number of falls were the strongest risk factors for frailty, while increased sway path and CoP velocity were protective. In particular, sway path under eyes-closed conditions showed the strongest protective association (OR = 0.323, p < 0.001). Additionally, fear of falling (OR = 1.078, p = 0.013) emerged as a significant psychological factor, consistently associated with increased frailty risk regardless of physical performance. Correlation analysis supported these findings, showing that better functional performance was linked to lower frailty risk. Conclusions: CoP sway path and mean velocity independently predict frailty status and offer added value beyond traditional clinical tools. These findings highlight the importance of incorporating instrumented balance assessments into frailty screening to capture nuanced postural control deficits and guide early intervention strategies. Full article

A novel stretchable flexible electrode capable of simultaneously detecting isotropic three-directional strain and normal pressure has been developed. Inspired by the recursive symmetry of the Peano-Gosper fractal, the electrode integrates liquid metal (EGaIn) microchannels within a PDMS matrix to achieve uniform strain distribution and mechanically robust conductive pathways under large deformation. Within a strain range of 0–60%, the electrode exhibits highly consistent three-directional responses, with resistance variation across axes kept below 4% and a gauge factor (GF) standard deviation of only 0.0252. The device demonstrates low hysteresis (minimum DH = 0.94%), good cyclic durability, and reliable electromechanical stability. For normal pressure sensing (0–20 kPa), it provides a linear response (R² ≈ 0.99) with a moderate sensitivity of 0.198 kPa⁻¹. Wearable tests on the wrist, finger, and fingertip confirm the electrode’s reliable operation in multidimensional mechanical monitoring. This integrated fractal–liquid metal design offers a promising route for multifunctional sensing in applications such as soft robotics, human–machine interaction, and wearable electronics. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

This study develops and operationalizes a multi-dimensional framework for sustainable building retrofit that aligns with national 2050 net-zero objectives. First, we conduct a scoping review of international standards (e.g., ISO), sustainability reporting guidelines (GRI G4), and peer-reviewed studies to define an indicator system spanning three pillars—environmental (carbon neutrality, resource circulation, pollution management), social (habitability, durability/safety, regional impact), and economic (direct support, deregulation). Building on this structure, we propose a transparent 0–3 rubric at the sub-indicator level and introduce the Sustainable Building Retrofit Index (SRI) to enable cross-case comparability and over-time monitoring. We then apply the framework to seven countries (United States, Canada, United Kingdom, France, Germany, Japan, and South Korea), score their retrofit systems/policies, and synthesize results through radar plots and a composite SRI. The analysis shows broad emphasis on carbon neutrality and habitability but persistent gaps in resource circulation, pollution management, regional impacts, and deregulatory mechanisms. For South Korea, policies remain energy-centric, with relatively limited treatment of resource/pollution issues and place-based social outcomes; economic instruments predominantly favor direct financial support. To address these gaps, we propose (i) life-cycle assessment (LCA)–based reporting that covers greenhouse gas and six additional impact categories for retrofit projects; (ii) a support program requiring community and ecosystem-impact reporting with performance-linked incentives; and (iii) targeted deregulation to reduce uptake barriers. The paper’s novelty lies in translating diffuse sustainability principles into a replicable, quantitative index (SRI) that supports benchmarking, policy revision, and longitudinal tracking across jurisdictions. The framework offers actionable guidance for policymakers and a foundation for future extensions (e.g., additional countries, legal/municipal instruments, refined weights). Full article