Search Results (5,590)

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

Drosophila melanogaster shares approximately 75% of human disease-related genes and possesses sophisticated genetic toolkits, including GAL4/UAS, CRISPR-Cas9, and RNA interference (RNAi), making it a rapid, cost-effective, and genetically tractable in vivo platform for natural products (NPs) discovery. This review systematically summarizes the modeling strategies, pathological mechanisms, and therapeutic applications of Drosophila models for six major human diseases, including type 2 diabetes, nephrolithiasis, inflammatory bowel disease, cancer, Alzheimer’s disease, and Parkinson’s disease. Cross-disease analysis identifies five evolutionarily conserved signaling networks—IIS/PI3K/Akt/FOXO, JNK/JAK/STAT, Nrf2/Keap1, mTOR/TORC1, and IMD/Toll—as common molecular targets of bioactive NPs, providing a unified mechanistic framework for understanding their multi-target pharmacological activities and broad therapeutic potential. Critically, we propose a generalizable integrated stepwise pipeline: high-throughput fly screening of crude extracts, bioassay-guided isolation of active monomers, genetic mechanistic dissection via RNAi and mutant rescue, and layered validation in human cells and selective mammalian models. This pipeline addresses key challenges in NPs research, including the identification of bioactive constituents and mechanistic validation, while improving screening efficiency and translational potential. Overall, this review establishes a multi-disease-applicable framework linking disease modeling, conserved signaling mechanisms, and translational pharmacology, providing practical guidance for future mechanism-driven NP discovery and preclinical development using Drosophila. By leveraging Drosophila genetics to bridge evolutionary conservation and human pathology, this framework offers a powerful, paradigm-shifting strategy to accelerate mechanism-driven NP discovery and preclinical development. Full article

In the context of the global green development wave and the rapid iteration of digital technology, digital empowerment has become the core driving force for high-end manufacturing enterprises to achieve green transformation. At present, China’s manufacturing industry is facing the dual pressures of tightening resource and environmental constraints and industrial upgrading. How to break the bottleneck of green development through digital technology innovation has become a key issue to be solved urgently. Based on the techno-economic paradigm, green development theory and value creation theory, this study constructs a theoretical analysis framework for the green development of a digital-enabling manufacturing industry and deeply analyzes the mechanisms of digital technology (such as big data, Internet of Things, artificial intelligence, etc.) in optimizing energy allocation, improving production efficiency and reducing environmental emissions. By selecting 303 manufacturing enterprises of different scales in China as samples, the structural equation model is used for empirical tests. The results show that (1) digital empowerment has a significant positive impact on the green value performance of manufacturing enterprises, and (2) green development plays an intermediary role between digital empowerment and the green value performance of enterprises; that is, digital technology indirectly promotes green development by improving energy conservation and emission reduction, green innovation and green upgrading of enterprises. The research reveals the internal logic of digitally enabling the green development of Chinese manufacturing enterprises and provides a theoretical basis and implementation path for enterprises to formulate the innovation mechanism of digital–green development. Full article

The pathogenesis and progression of liver diseases are characterized by marked zonal heterogeneity, yet conventional research paradigms have long overlooked this intrinsic spatial logic. Ferroptosis and cuproptosis have been widely implicated in liver disease; however, their precise intralobular distribution and zonal susceptibility patterns remain poorly defined. We present a narrative synthesis of the literature on the spatial zonation of hepatic metabolism, redox homeostasis, and metal handling, and assess their potential roles as determinants of region-specific cell death vulnerability. We propose the novel “spatial redox–metalloptosis axis” hypothesis. The pericentral zone (Zone 3), characterized by hypoxia, high cytochrome P450 activity, and a redox environment that may favor lipid peroxidation under specific pathological conditions, is hypothesized to form a ferroptosis-susceptible niche under metabolic stress. Conversely, the periportal zone (Zone 1), characterized by active copper handling and oxidative phosphorylation-dependent metabolism, is hypothesized to be preferentially vulnerable to cuproptosis (proposed hypothesis; direct zone-resolved evidence of cuproptosis execution in Zone 1 is currently absent). Ceruloplasmin is proposed as a candidate molecular link between copper and iron metabolism. We further identify shared molecular hubs and a hypothesized spatial redox–metalloptosis axis linking these two regulated cell death modalities, while direct biological crosstalk remains to be demonstrated. We also highlight critical technological, mechanistic, and translational gaps. This review aims to shift liver disease research from viewing the liver as a homogeneous organ to a functionally compartmentalized zoned ecosystem, providing a testable theoretical framework for deciphering region-specific liver injury and developing spatially informed therapeutic strategies. Full article

Cancer cachexia is a multifactorial systemic syndrome characterized by progressive muscle loss, with or without adipose tissue depletion, that cannot be reversed by conventional nutritional support. It affects cancer patients and is associated with reduced treatment tolerance, impaired physical function, poor quality of life, and increased mortality. The understanding of cachexia has evolved recently, from the perception of a simple nutritional disorder to a complex immune–metabolic syndrome, based on tumor–host interactions, systemic inflammation, metabolic dysregulation, and multi-organ dysfunction. This review summarizes the progression of cachexia research, highlighting key findings involving inflammatory cytokines, proteolytic pathways, mitochondrial dysfunction, and immune dysregulation. The development of therapeutic strategies is examined, from early nutritional and appetite-stimulating interventions to contemporary targeted therapies, including ghrelin receptor agonists, cytokine inhibitors, and anabolic agents. Despite advances in mechanistic understanding, numerous trials targeting single pathways have failed to produce meaningful functional or survival benefits, underscoring the limitations of reductionist approaches. Emerging evidence supports a paradigm shift toward multimodal, biomarker-guided, and patient-centered interventions that address the interconnected biological mechanisms underlying cachexia. Particular emphasis is given to novel immunomodulatory strategies, including agents such as R-ketorolac, which may restore immune homeostasis and target the root causes of cachexia. It is hypothesized that future therapeutic success will likely depend on integrated approaches combining immunological, metabolic, nutritional, and rehabilitative interventions. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

Conventional heavy metal remediation technologies are constrained by low efficiency, secondary pollution risks, and limited scalability. Dissolved organic matter (DOM), with its green, cost-effective complexation properties, has become a promising pathway for pollution control. Existing patent bibliometric studies in this field suffer from single-database bias, limited causal quantification of policy impacts, and incomplete depiction of global–local technological heterogeneity. To address these gaps, this study maps the technological innovation landscape of DOM interactions with four typical heavy metals (Cd, Pb, Cu, Zn) during 2004–2024, using a complementary dual-database framework combining Derwent and IncoPat. We integrate a three-dimensional “time–region–technology” analytical framework with interrupted time series analysis (ITSA), after standardized data processing including family deduplication and citation normalization. Cross-validation confirms that China contributes the largest share of global patent output (46.6% in Derwent, 55.0% in IncoPat). Three milestone environmental policies in China exert sequentially intensifying causal effects on patent growth (all p < 0.05), forming a closed-loop mechanism of policy orientation, funding support, technology transfer, and international diffusion. We identify a pronounced global–local technological divergence: global frontier innovation centers on digital basic research, whereas local innovation in China prioritizes engineering applications. Core patents advance the field through cross-domain technology adaptation, and the representative technical paradigm (exemplified by patent CN101168852A) has been industrially validated. These findings provide empirical support for engineering translation and policy optimization in DOM-based heavy metal remediation. Full article

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic knowledge base (SKB) a critical cornerstone. However, effectively selecting appropriate content from massive cloud-based knowledge repositories for edge deployment remains a significant challenge. This paper conducts systematic research to address the key issues in the flow deployment of SKBs at the edge, including insufficient adaptation to personalized preferences, inadequate timeliness management, and the complexity of multi-objective optimization. First, a comprehensive system model is constructed, integrating user preferences, knowledge relevance, transceiver matching degree, and the Age of Information (AOI). Second, the Generative Adversarial Network (GAN)-assisted Preference-based Reinforcement Learning (GaPbRL) algorithm is proposed. The experimental results demonstrate that this method outperforms traditional schemes in terms of knowledge-base hit rate, transceiver matching degree, and algorithm convergence speed, while significantly reducing the overhead of manual fine-tuning. This study provides a robust framework for the personalized and efficient cloud–edge collaborative deployment of SKBs. Full article

Engineering education research (EER) draws on a wide range of philosophical traditions, yet the ontological, epistemological, and axiological (OEA) commitments that shape how knowledge is produced are rarely made explicit in published work. This study investigated the prevalence and nature of OEA positioning across a purposive sample of 54 papers published in 2024 in three Q1 engineering education journals: the Journal of Engineering Education, the European Journal of Engineering Education, and the Australasian Journal of Engineering Education. Using critical realism as a metatheoretical framework, we developed an OEA coding instrument and applied it through an AI-assisted abductive coding process, assigning ontological, epistemological, and two-level axiological codes to each paper and assessing their internal coherence. The overwhelming majority of papers carry implicit rather than declared OEA commitments, a pattern consistent across journals and methodologies. The field is genuinely philosophically plural, with ontological positions ranging from naïve realism to social constructionism and critical realism, but the lack of clarity in this space potentially has consequences for knowledge transfer to practice, cumulative knowledge-building, and the coherence of individual studies. Full article

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance. Full article

This study examines the mediumistic paintings of Guo Fengyi (郭鳳怡 1942–2010) through the lens of Daoist inner contemplation (neiguan 內觀) and traditional Chinese medicine. Guo developed distinctive self-cultivation techniques, including Ziran Chaoneng Gong (自然超能功 Natural Superpower Qigong), Qi’e Gong (企鵝功 Penguin Qigong 氣功), and Liu Ling Shuzi Gong (劉陵數字功 Liu Ling Numerology Qigong), which resonate with ancient Chinese practices of Zhouyi (周易) divination, shamanic ritual, and Daoist visualization. Through sustained spiritual practice, she experienced what she understood as mystical revelations transmitted by the High Masters. Vivid visionary images repeatedly emerged in her consciousness, accompanied by an overwhelming sense of being guided by an unseen spiritual agency to depict an unknown transcendent realm. Following these experiences, she created nearly one thousand mediumistic paintings. Her work delves into inner landscapes of consciousness and energetic experience, expanding visual art research into the domain of “inner vision”. This study argues that Guo’s art challenges ocular-centric creative paradigms: her mode of “seeing” operates through the supersensory “eye of spirit” rather than physical sight, and artistic creation becomes a process of receiving higher-dimensional messages rather than singular self-expression. Her practice offers a critical reference for recovering the spiritual dimension in contemporary art and provides theoretical perspectives for comparative studies of international mediumistic artists such as Hilma af Klint (1862–1944) and Georgiana Houghton (1814–1884). Full article

Ride quality—encompassing vehicle comfort, vibration isolation, and noise, vibration, and harshness (NVH)—has become a key competitive differentiator in modern automobiles. This paper presents a comprehensive update to the foundational literature on passenger car ride quality, capturing the rapidly growing literature and new technological paradigms that have emerged over the past decade. Established approaches to human vibration response, vehicle dynamics modelling, and road surface characterisation are examined within the ISO 2631 framework. This review critically surveys advances driven by battery electric vehicle (BEV) powertrains—where the absence of internal combustion engine noise unmasks motor whine, inverter switching noise, and tyre–road excitation, lowering the perceptual ride–NVH boundary from ~25 Hz toward 15–18 Hz—as well as intelligent semi-active and active suspension technologies, deep reinforcement learning for suspension control, machine learning for ride quality prediction, and connected vehicle infrastructure enabling predictive preview control. Key research gaps are identified: the absence of validated ISO 2631 frequency weightings for autonomous vehicle postures, the lack of standardised open benchmark datasets for cross-study comparison, and the unresolved sim-to-real validation gap for data-driven suspension controllers. Ten priority research directions are proposed for the coming decade. Full article

Pediatric acute myeloid leukemia (AML) is a highly heterogeneous malignancy, with cytogenetic and molecular abnormalities playing a critical role in determining prognosis and guiding treatment decisions. Despite therapeutic advances, patients with high-risk genetic mutations and translocations continue to experience suboptimal outcomes. As new targeted therapies emerge, the treatment of pediatric AML could undergo a paradigm shift, where “one-size-fits-all” chemotherapy is no longer the only frontline approach. Identifying genetic markers inform risk stratification and have greater impact on shaping the therapeutic approach, including the integration of targeted therapies such as FLT3 and menin inhibitors into frontline therapy. Furthermore, pediatric AML treatment options are being driven by recent discoveries in adult AML, broadening their clinical trials to include pediatric patients, in part due to the RACE for Children Act that went into effect in August 2020. This review identifies the most prevalent high-risk cytogenetic lesions in pediatric AML, emphasizing their incidence, prognostic significance, and implications for clinical management. By synthesizing current research on these key genetic abnormalities and their associated therapies, we aim to provide an updated perspective on the evolving landscape of high-risk pediatric AML management that can then lead to the establishment of an agile framework to rapidly evaluate, approve, and deploy novel agents. Full article

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking. Full article

The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems. Full article