Search Results (4,484)

Tropospheric ozone (O₃) pollution is a major concern in urban environments because of its toxicity for both people and vegetation. This paper review provides an overview of atmospheric mechanisms, as well as the potential and best management practices of urban greening for reducing O₃ pollution in cities. Urban greening has often been proposed as a cost-effective solution to reduce O₃ pollution, but its effectiveness depends on careful species selection and integration with broader air quality management strategies. Ozone is a secondary pollutant and the volatile organic compounds emitted by vegetation (BVOCs) can play a prominent role in O₃ formation. A list of recommended and to-avoid species is given here to drive future planting at city scale. Planting low BVOC-emitting species and combining greening with reductions in anthropogenic emissions are key to maximizing benefits and minimizing unintended increases in O₃. Public and non-public institutions should carefully select plant species in consultation with expert scientists from the early stages, e.g., by considering local conditions and pollutant dynamics to design effective greening interventions. Collaborative planning among urban ecologists, atmospheric scientists, and municipalities is thus crucial to ensure that greening interventions contribute to overall air quality improvements rather than inadvertently enhancing O₃ formation. Such improvements will also translate into plant protection from O₃ stress. Therefore, future directions of research and policy integration to achieve healthier, O₃-resilient urban ecosystems are also provided. Full article

In the era of digital economy, computing power centers, serving as core infrastructure that aggregates computing resources and supports digital transformation, have seen their competitiveness formation mechanism and evaluation methods become important research directions in the field of economics and management. Breaking away from fragmented analyses, this study, based on a complex systems perspective, dissects the formation mechanism of computing power center competitiveness and extracts key influencing factors. Utilizing the entropy weight-TOPSIS-gray correlation method, a fully quantifiable evaluation system for computing power center competitiveness is developed, effectively enhancing the practicality, reusability, and comparability of the evaluation approach. Through an empirical analysis of 35 computing power centers in China, the research found that computing power is the primary influencing factor of competitiveness and pointed out that due to different resource advantages, there are also significant differences in the competitiveness level and development path of computing power centers. Based on these findings, and centered on the dual-wheel drive of technology and cost, four development pathways for computing power centers are proposed: strengthening technological advantages, optimizing cost structures, implementing targeted government policies, and fostering industrial ecosystem synergy. This provides a methodological framework and policy toolkit for enhancing the competitiveness and achieving sustainable development of computing power centers in various countries and regions. Full article

Habituation is a fundamental form of non-associative learning that allows organisms to filter out repetitive, non-salient stimuli but declines with age. While the kynurenine pathway (KP) of tryptophan metabolism is implicated in psychiatric and neurodegenerative diseases, its role in age-related habituation deficits has been overlooked. This review proposes a systems-level framework suggesting that age-related, chronic inflammation KP dysregulation is a key driver of habituation deficits. We present evidence showing that neurotoxic metabolites from the kynurenine-3-monooxygenase (KMO)-dependent branch drive a self-reinforcing cycle of oxidative stress, excitotoxicity, and glial reactivity that destabilizes the neural circuits required for habituation. This framework redefines KP modulation as context dependent: metabolites such as kynurenic acid (KYNA), which can be disruptive when elevated in youth, may become compensatory under the oxidative load of aging. Our findings that genetic KMO deletion preserves habituation in aged and old mice provide the first direct in vivo evidence supporting this model. We propose that inhibiting the KMO branch preserves habituation not by simply altering metabolite levels but by restoring homeostatic balance across neuroimmune, redox, and plasticity networks. KMO thus emerges as a critical node for maintaining cognitive resilience, offering a therapeutic target for preserving brain function during aging. Full article

A finite-element model of the direct-drive coreless permanent-magnet generator is used to simulate faults individually. Each fault case—rotor magnet demagnetization, a stator inter-turn short circuit, static eccentricity, and dynamic eccentricity—is introduced into the finite-element analysis (FEA) model separately, rather than in combination. For each isolated fault scenario, the stator current signals are processed using the Extended Park’s Vector Approach (EPVA) and the electromagnetic torque is examined in the frequency domain. The EPVA spectra and torque harmonics exhibit unique features for each fault type, allowing for clear discrimination among faults. These results demonstrate that modeling and analyzing faults one at a time yields distinct diagnostic signatures. Full article

Digital video surveillance systems are now common in the security infrastructure of modern times, but proprietary file systems provided by large manufacturers are a major challenge to the work of the forensic investigator. This paper proposes a forensic recovery methodology of Hikvision and Dahua surveillance systems by utilizing three major innovations: (1) adaptive temporal sequencing, which dynamically changes gap detection thresholds; (2) dual-signature validation with header–footer matching of DHFS frames; and (3) automatic manufacturer identification. The strategy puts into practice direct binary analysis of proprietary file systems, frame-based parsing and automatic video reconstruction. Testing on 27 surveillance hard drives showed a recovery rate of 91.8, a temporal accuracy of 96.7% and a false positive rate of 2.4%—the lowest of the tools tested with statistically significant improvements over commercial tools (p < 0.01). Better results with fragmented streams (87.2 vs. 82.4% with commercial tools) meet key forensic needs of determining valid evidence chronology. The open methodology offers the necessary algorithmic transparency to be court-admissible, and the automated MP4 conversion with metadata left intact makes the integration of forensic workflow possible. The study provides a scientifically validated approach to proprietary surveillance formats, which evidences technical innovativeness and practical usefulness to digital forensics investigations. Full article

Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there are still shortcomings in personalized prediction (particularly in accounting for individual differences in driving experience, cognitive abilities, and physiological responses). To gain a comprehensive understanding of the characteristics and applicability of take-over time prediction methods, this review covers four aspects: literature search information, factors influencing take-over time, data acquisition and processing methods, and take-over time prediction methods. Through literature search, research hotspots in recent years have been summarized, revealing the main research directions and trends. Key factors influencing take-over time, including driver factors, autonomous driving systems, and driving environments, are discussed. Data preprocessing stages, including data acquisition and processing, are systematically analyzed. The advantages and disadvantages of classical statistical, machine learning, and cognitive architecture models are summarized, and the shortcomings in current research are highlighted (for instance, the limited generalizability of models trained predominantly on simulator data to real-world driving scenarios). By thoroughly summarizing the strengths and weaknesses of existing research, this review explores under-researched areas and future trends, aiming to provide a solid theoretical foundation and innovative research perspectives for optimizing take-over time prediction, thereby promoting the widespread application and efficient development of autonomous driving technology. Full article

Improper irrigation and fertilization can easily lead to soil nutrient imbalance, inhibit microbial reproduction, and thereby reduce soil quality and crop yield. This study conducted winter wheat planting experiments in 2023–2025, setting three muddy water (sediment-laden irrigation water) treatments of different sediment concentrations (3, 6 and 9 kg·m⁻³), irrigation levels (0.50–0.65, 0.65–0.80 and 0.80–0.95 FC), and nitrogen application rates (100, 160 and 220 kg·ha⁻¹). An L9(3³) orthogonal experimental design was applied to evaluate the influence of water and nitrogen regulation on soil properties, microbial community structure, and wheat productivity. The results showed the following: Among these treatments, the T5 treatment (6 kg·m⁻³, 0.65–0.80 FC, 160 kg·ha⁻¹) significantly improved the root zone environment, and the total nitrogen (TN), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and soil organic carbon (SOC) content also increased significantly. T5 also enhanced the diversity and network complexity of bacterial and fungal communities. Notably, genera such as Lysobacter, Lasiobolidium, and Ascobolus became central to nitrogen transformation and nutrient cycling. Structural equation modeling revealed the interdependent mechanism between soil quality, microorganisms, and wheat yield: NO₃⁻-N and SOC drive improvements in soil quality, while microbial community structure and network complexity are key to yield increases, with fungal communities making the largest direct contribution to yield (R² = 0.93). The T5 treatment increased two-year yields by 21.34–24.96% compared to conventional irrigation and fertilization (CK2), improved irrigation water use efficiency by 56.40–57.51% and peak nitrogen agronomic efficiency. The synergistic effect of “soil quality optimization–enhanced microbial activity–efficient utilization of water and nitrogen–high wheat yield” has been achieved, providing a theoretical basis and practical reference for scientific water and nitrogen management and sustainable yield increase in winter wheat in the Yellow River Basin and similar areas. Full article

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future perspective in Industry 4.0. There are various approaches to PdM, each offering its own set of advantages and disadvantages which are single and hybrid approaches to carrying out diagnostics and prognostics in PdM. In this paper we will compare these approaches according to different aspects such as complexity of data and interpretability of results. Moreover, we also discuss the barriers to successful adoption, such as data quality, system complexity, and the need for workforce training. Finally, this paper concludes by identifying future research directions in response to scientific problems, which will drive the next wave of innovation in predictive maintenance solutions. Full article

Sensor sampling errors and inverter dead-time effects introduce significant nonlinear disturbances into dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive systems with sinusoidal excitation, leading to pronounced alternating current (AC) and direct current (DC) disturbances. These disturbances severely compromise the stability and reliability of the current control loop, ultimately degrading the overall driving accuracy of the system. To effectively address this issue, this paper proposes a novel interference suppression strategy based on bi-subspace predictive current control. Specifically, the proposed approach optimizes modulation through two-step virtual-vector-based predictive current control (VVPCC) operation to achieve disturbance decoupling. Building upon this foundation, a model-assisted discrete extended state observer (DESO) is incorporated into the fundamental subspace, whereas a discrete vector resonant controller (DVRC) with pre-distorted Tustin discretization is applied to the secondary subspace. Modeling analysis and experimental results demonstrate that, compared with the classical VVPCC method, the proposed bi-subspace VVPCC method has good steady-state performance and enhanced robustness in the presence of disturbances. Full article

Due to low passenger retention rates, autonomous Ride-hailing Vehicles (ARVs) face a critical bottleneck in commercialization, especially in the Chinese market. Based on 312 survey responses from Wuhan, this study systematically explored the mechanisms influencing customers’ continuance usage intention toward autonomous Ride-hailing Vehicles (ARVs), by integration of Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The empirical findings revealed that perceived usefulness, trust in technology, perceived value, perceived price fairness, and psychological ownership exert significant positive effects on sustainable usage intention, with trust in technology demonstrating the strongest direct effect. In contrast, concerns about safety equality demonstrate a significant negative impact. Trust in technology serves as an indirect mediator and emerges as a necessary condition in high-intention fsQCA configurations. Building on all insights, the study proposed a four-dimensional “Technology-Psychology-Safety-Economy” (TPSE) driving model, established a novel theoretical framework for user behavior research in intelligent transportation, and offered empirical guidance for differentiated corporate strategies and technology adoption. Full article

As global urbanization accelerates, cities increasingly shape economic growth and environmental outcomes, making sustainable urban and transport planning critical. Sustainable city branding (SCB) is emerging as a strategic tool that not only enhances a city’s global competitiveness but actively drives urban sustainability by integrating environmental, social, and economic dimensions aligned with the UN Sustainable Development Goals (SDGs). However, the direct link between SCB and transport planning remains largely unexplored, limiting actionable policy. This study introduces a novel conceptual framework connecting SCB with transport planning, positioning public transportation as a key lever for sustainable urban development. It identifies core interactions between city branding and sustainable mobility, proposes methodologies to evaluate SCB effectiveness, and addresses potential risks, challenges, and research gaps. A policy roadmap for decision-makers based on the framework is outlined. This roadmap is structured into three phases spanning a five-year program. In Phase 1, cities should lay the foundation by integrating SCB into municipal transport and sustainability plans and establishing measurable indicators aligned with the SDGs. Phase 2 focuses on engagement and experimentation, encouraging the creation of participatory branding platforms and the implementation of pilot projects, such as green mobility corridors or climate-resilient transit hubs. Finally, Phase 3 emphasizes monitoring and scaling, utilizing digital technologies for real-time tracking, evaluating pilot outcomes, and expanding successful initiatives based on key performance indicators, including ridership growth, carbon reduction, and citizen engagement. By linking SCB explicitly to transport planning and providing a concrete roadmap, this study offers a unique contribution to both urban sustainability research and practical policy-making, enabling cities to simultaneously strengthen their brand, enhance mobility, and achieve measurable sustainability outcomes. Full article

Macroprudential policy, as an important instrument for counter-cyclical regulation, plays a crucial role in enhancing urban economic resilience. Based on this, this paper empirically examines the influence of macroprudential policy on urban economic resilience and its optimization paths using data from 284 prefecture-level cities in China from 2011 to 2023. The research findings indicate that macroprudential policy significantly enhances urban economic resilience, and the conclusion still holds after various robustness tests. Further analysis reveals that the main transmission channels include stimulating digital finance development, promoting industrial structure upgrading, and deepening regional integration. Notably, this effect is particularly pronounced in smart cities, big data pilot zones, and cities with less fiscal pressure. Additionally, the test results of spatial spillover effects show that the direct effect of macroprudential policy on the economic resilience of cities is relatively significant, while the indirect effect is relatively weak. Finally, empirical tests have proved that the improvement of urban economic resilience can further drive regional innovation capability. This study provides empirical support and theoretical references for improving China’s “dual-pillar” regulatory framework and enhancing urban economic resilience. Full article

Low-complexity domains (LCDs) are protein regions characterized by a simple amino acid composition and low sequence complexity, as they are typically composed of repeats or a limited set of a few amino acids. Historically dismissed as “garbage sequences”, these regions are now acknowledged as critical functional elements. This review systematically explores the structural characteristics, biological functions, pathological roles, and research methodologies associated with LCDs. Structurally, LCDs are marked by intrinsic disorder and conformational dynamics, with their amino acid composition (e.g., G/Y-rich, Q-rich, S/R-rich, P-rich) dictating structural tendencies (e.g., β-sheet formation, phase separation ability). Functionally, LCDs mediate protein–protein interactions, drive liquid–liquid phase separation (LLPS) to form biomolecular condensates, and play roles in signal transduction, transcriptional regulation, cytoskeletal organization, and nuclear pore transportation. Pathologically, LCD dysfunction—such as aberrant phase separation or aggregation—is implicated in neurodegenerative diseases (e.g., ALS, AD), cancer (e.g., Ewing sarcoma), and prion diseases. We also summarize the methodological advances in LCD research, including biochemical (CD, NMR), structural (cryo-EM, HDX-MS), cellular (fluorescence microscopy), and computational (MD simulations, AI prediction) approaches. Finally, we highlight current challenges (e.g., structural heterogeneity, causal ambiguity of phase separation) and future directions (e.g., single-molecule techniques, AI-driven LCD design, targeted therapies). This review provides a comprehensive perspective on LCDs, illuminating their pivotal roles in cellular physiology and disease, and offering insights for future research and therapeutic development. Full article

Sensors are a key component of virtual reality (VR) technology, as they enable motion tracking, interaction with the environment, and realistic representation of user behaviour in virtual space. VR technology is gaining increasing importance in engineering, offering new ways to support research, analysis, and training. This article examines its applications in four key areas: surveying and geomatics, mining, environmental protection, and occupational safety. The study is based on a review of the scientific literature indexed in the Scopus database, with the aim of highlighting both the potential of VR and directions for its future development. The findings indicate that VR provides effective tools for analyzing, interpreting, and visualizing complex geospatial data. It enables realistic simulations of mining processes, supports the monitoring of environmental impacts, and facilitates environmental education by creating engaging, immersive experiences. In occupational safety, VR allows hazard scenarios and accident events to be reproduced in a safe yet highly realistic environment, significantly enhancing the effectiveness of training. This is made possible through the integration of sensors with virtual reality, further enhancing immersion in the environment. Despite these advantages, several barriers have been identified. They include technological challenges, insufficient numbers of trained specialists, health and ergonomics concerns, resistance to organizational change, ethical considerations, and limited funding. It is clear that the future of VR in engineering will be shaped by continuous technological progress combined with growing attention to behavioural aspects of training and user interaction. These trends are expected to drive the creation of increasingly advanced and effective tools. The article thus provides a foundation for further exploration of VR as an integral part of engineering practice. Full article

The soil of abandoned rural residential land is often deficient in organic matter and low in nutrient content, which limits agricultural productivity. Organic carbon input (OCI) is recognized as an effective strategy to enhance soil quality, yet it remains unclear which active carbon and nitrogen fractions drive yield enhancement and how their cycles are coupled. A three-year field experiment included five treatments: an unfertilized control (CK) and four OCI levels applied at an equal total N rate of 270 kg N ha⁻¹: 0.51 t ha⁻¹ (T1), 0.77 t ha⁻¹ (T2), 1.02 t ha⁻¹ (T3), and 2.56 t ha⁻¹ (T4). Compared with CK, T1–T4 treatments significantly increased dissolved organic carbon (DOC) by 56.04–137.25%, readily oxidizable organic carbon (ROC) by 56.46–85.29%, particulate organic carbon (POC) by 35.26–50.17%, microbial biomass carbon (MBC) by 33.87–49.90%, acid-hydrolyzable ammonium nitrogen (AN) by 21.54–30.66%, acid-hydrolyzable amino sugar nitrogen (ASN) by 11.05–24.21%, acid-hydrolyzable amino acid nitrogen (AAN) by 23.56–31.92%, and rice yield by 44.50–69.56%. Overall, among T1–T4 treatments, T2 and T3 treatments performed best in improving soil fertility and rice yield in the current study. Structural equation modeling (SEM) analysis indicated that ROC significantly influenced total hydrolyzable nitrogen (THN), which in turn was the main direct determinant of rice yield. Collectively, these findings demonstrate that a medium OCI rate (0.77–1.02 t ha⁻¹ in the current study) at 270 kg N ha⁻¹ delivers the most balanced improvement in soil C-N cycling and yield formation, providing a sound theoretical and practical basis for optimizing organic fertilization strategies in abandoned rural residential land soil. Full article