Search Results (1,230)

Urban historic districts often present archaeological sites and historic buildings in a fragmented way, posing significant challenges for public understanding and enhancing heritage value. Solely physical conservation fails to fully communicate their cultural significance, while excessive commercialization often results in the erosion of cultural authenticity and the displacement of local communities. Drawing from cultural capital theory in sociology and cultural economics, this study redefines historical districts as sustainable urban cultural capital, comprising habituated, objectified, and institutionalized components. A Value Chain Model of Cultural Capital (VCMCC) is developed, consisting of three stages: cultural resource excavation, cultural asset cultivation, and cultural capital management. This model aims to empower heritage adaptive reuse and foster synergy between cultural heritage and economic development. Utilizing an embedded single-case design with longitudinal ethnography, the research focuses on the Western Han Archaeological Sites Historical District (WHAS HD) in Hanzhong, China. It involves multiple rounds of mixed-data collection from 2023 to 2025, on which design-based research is performed. This study operationalizes VCMCC through a series of spatially and socially grounded strategies. In the cultural resource excavation stage, superior resources are identified through a systematic review of historical archives, archaeological reports, and local gazetteers, along with surveys of architectural remains and spatial mapping. In the cultural asset cultivation stage, these resources are transformed into experiential and communicable cultural assets via a “one courtyard, one strategy” approach for activating courtyard functions, developing dual-theme heritage routes, and deploying digital interpretation tools. In the cultural capital management stage, a multi-stakeholder community committee is established, and binding institutional safeguards are integrated to ensure sustainable heritage adaptive reuse. Concurrently, a baseline indicator system covering three dimensions, cultural, social, and economic benefits, is developed to provide benchmarks for future post-intervention benefit evaluation and verification. The proposed and implemented VCMCC model translates cultural capital theory from an abstract explanatory framework into an actionable pathway for heritage adaptive reuse, offering theoretical and methodological guidance for the adaptive reuse of similar small and medium-sized historic districts. Full article

Driven by increasingly severe drought stress associated with global warming, this study investigated a synthetic microbial community, SynCom-SASW01, with strong stress tolerance and plant growth-promoting potential, and systematically elucidated its mechanisms for enhancing drought resistance in wheat (Triticum aestivum L.). Dual-site field trials demonstrated that SynCom-SASW01 significantly alleviated drought-induced growth suppression, increasing grain yields by 10.42% and 8.52% at the Hohhot and Hulunbuir sites, respectively. This improvement was primarily associated with increased effective tiller number and enhanced root vigor. Physiologically, inoculation promoted root proline and glutathione accumulation and enhanced antioxidant enzyme activities, including superoxide dismutase, thereby reducing malondialdehyde levels. Environmental analyses showed that the consortium established rhizosphere “micro-reservoirs” through exopolysaccharide secretion, improving soil relative water content and the availability of alkali-hydrolyzable nitrogen and phosphorus. High-throughput sequencing revealed that SynCom-SASW01 reshaped the endosphere microbiome through early colonization priority effects, selectively enriching beneficial taxa such as Pseudomonas. Functional prediction indicated upregulated branched-chain amino acid biosynthesis, promoting osmotic adjustment and redox homeostasis. These findings provide a microbiome-based strategy for stabilizing wheat productivity in arid regions. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Endometriosis is increasingly recognized as a chronic systemic disorder extending beyond the classical estrogen-dependent paradigm, integrating metabolic, immune, fibrotic, and nociceptive pathways that sustain lesion persistence and refractory pelvic pain. We propose a mechanistic, translational hypothesis in which tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist, may modulate four interconnected pathological axes of refractory endometriosis—Warburg-type metabolic reprogramming with lactate accumulation, peritoneal immune dysfunction, NF-κB/NLRP3/TGF-β1-driven inflammatory–fibrotic remodeling, and persistent nociceptive sensitization—through three convergent molecular nodes: AMPK-associated signaling, GLP-1 receptor activity in peritoneal macrophages and spinal microglia, and the NF-κB/NLRP3/TGF-β1 axis. Particular emphasis is placed on the concept of “peritoneal incretin deficiency”, characterized by reduced peritoneal GLP-1 concentrations and increased expression of incretin-degrading proteases. This concept currently rests on a single, non-replicated case–control study, and the broader mechanistic chain is supported largely by indirect evidence extrapolated from adjacent inflammatory, metabolic, and neuroimmune disease models rather than by endometriosis-specific data. Direct experimental or clinical validation in endometriosis-specific models is currently absent. Accordingly, this article represents a hypothesis-generating framework rather than evidence of established efficacy, or a clinical treatment recommendation, intended to guide future mechanistic and prospective clinical investigation of incretin-based modulation as a potential adjunctive strategy in refractory endometriosis. Full article

The emergence of multimodal large language models (MLLMs) is opening a new avenue for explainable and interactive intelligent diagnosis in agriculture. However, generic MLLMs still face two major obstacles in plant disease recognition—insufficient fine-grained visual perception and misalignment between visual and linguistic features—which jointly limit diagnostic accuracy. To address these issues, we propose a Qwen2.5-VL-based full-chain fine-tuning framework termed dual-side synergistic low-rank adaptation. Unlike the mainstream paradigm that freezes the vision encoder, our method injects trainable LoRA adapters into both the vision encoder and the large language model, while establishing end-to-end gradient backpropagation across the entire multimodal pipeline. By using the supervision signal from autoregressive text generation (text-supervised visual learning), the framework directly drives deep optimization of visual representations, thereby enabling coordinated alignment between pixel-level perception and semantic-level understanding. We trained Qwen over CDDM and conducted in-domain (CDDM) and cross-domain (PlantVillage) experiments. The results show that the proposed 7B-parameter model achieves 98.8 and 96.0% diagnostic accuracy under in-domain and cross-domain scenarios, respectively. The recognition accuracy of Qwen in the case of cross-domain only decreases slightly, which demonstrates that the MLLM trained by our method exhibits excellent cross-domain recognition capability. This indicates that our method can significantly improve the robustness and generalization ability of MLLM in complex agricultural scenarios. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

A compact, low-profile dual-band feed network operating at 37–40 GHz (Ka-band) and 70–86 GHz (E-band) is presented for millimeter-wave backhaul applications. The proposed network integrates a quad-ridged orthomode transducer (OMT) and four ridge-waveguide diplexers into a three-layer all-metal stacked architecture, eliminating the cascaded inter-stage flanges of conventional feed chains and yielding a monolithic-like assembly that is mechanically robust and CNC-friendly for mass production. Stepped-impedance matching stubs in the OMT junction provide broadband matching across the widely separated bands, while compact ridge-waveguide T-junction diplexers, comprising stepped-impedance low-pass filters and rectangular high-pass paths, perform the spectral separation. Back-to-back measurements of the fabricated prototype demonstrate an insertion loss below 0.6 dB across both bands. The measured VSWR at the four output ports remains below 1.5 across both bands, and the port-to-port isolation exceeds 32 dB at the Ka-band and 45 dB at the E-band. The proposed network thus offers a highly integrated, low-loss solution for next-generation dual-band mmWave links. Full article

Under the dual constraints of global food security and ecological protection, conventional agriculture is hampered by low resource efficiency and sluggish environmental response. Crop digital twin technology establishes a dynamic virtual reality system that integrates crops, environment, and water to enable real-time interaction and optimization. Based on the existing literature, this paper reviews the concept, architecture, and core modules of this technology and summarizes its applications in precision irrigation and crop monitoring. There are three major bottlenecks that persist, including limited high-frequency multi-source sensing and spatiotemporal fusion, insufficient parameter calibration and dynamic updating, and weak cross-scale integration from plant to watershed. Water is increasingly recognized as the key constraint and control variable and acting as both the central physiological driver of crop growth and the mass-flow link that connects the soil–plant–atmosphere continuum. The spatiotemporal dynamics of crop water deficit, compensatory root water uptake, evapotranspiration feedback, and the hydraulic behavior of irrigation-district canal systems constitute the core hydrological processes that must be simulated within the digital twin. Synchronizing crop water demand, soil moisture dynamics, atmospheric evapotranspiration, and irrigation scheduling within a unified spatiotemporal framework establishes a complete sensing, diagnosis, prediction and regulation technical chain. This chain offers a core pathway for alleviating agricultural water scarcity, improving irrigation efficiency, and ensuring food security. Full article

Efficient monitoring of pork freshness is essential to minimize spoilage-related losses in the meat industry. To address the limitations of existing detection technologies, namely high cost, poor timeliness and high environmental sensitivity, this study developed a novel electronic nose system integrating a hybrid sensor array with dynamic gas path control. By combining metal oxide semiconductor (MOS) and electrochemical sensors (e.g., MQ137, MQ136), the system exhibits high sensitivity to the key volatile organic compounds (VOCs) released during pork spoilage, achieving a detection accuracy of over 90% in identifying spoilage stages. Combined with a dual-mode gas circuit design (solenoid valve switching time: 0.85 s), the reliability of the system was further demonstrated. This technology offers an economical and efficient real-time monitoring solution for slaughterhouses and cold chain logistics, providing a new low-cost scientific approach for pork freshness assessment. Full article

The roots of Baphicacanthus cusia (Nees) Bremek, commonly known as Nan-Ban-Lan-Gen, have been used for a long time in traditional Chinese medicine to manage inflammatory and infectious diseases. However, the structural features and bioactive potential of its polysaccharides have not been extensively studied. In the present study, a novel homogeneous polysaccharide (BcP-b2) was isolated from the roots of B. cusia, and its bioactivity was evaluated using an activity-guided purification strategy. Multi-dimensional structural analysis identified BcP-b2 as a highly branched galactoarabinan with a molecular weight of ~38.1 kDa, featuring a well-defined backbone and a variety of side chains. In vitro and in vivo assays demonstrated that BcP-b2 attenuated the accumulation of reactive oxygen species (ROS) and enhanced the activities of endogenous antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px). Additionally, BcP-b2 activated macrophages under basal conditions and alleviated lipopolysaccharide (LPS)-induced cytotoxicity and inflammatory mediator release. Transcriptomic and Western blot analyses revealed that these dual effects were achieved through the simultaneous suppression of the PI3K/Akt inflammatory axis and activation of the Nrf2/HO-1 antioxidant pathway, concomitant with enhanced nuclear translocation of Nrf2. These findings provide a molecular basis for the ethno-pharmacological use of Nan-Ban-Lan-Gen and identify BcP-b2 as a promising candidate for further investigation as a potential therapeutic agent. Full article

Against the backdrop of the dual-carbon goals and the Digital China initiative, the urgent need for enterprises to pursue green innovation and transformation is evident. Supply chain digitalization serves as a critical enabler for enterprises to achieve a low-carbon industrial transformation and high-quality development through the effective coordination of data resources across the entire chain. This study examines A-share listed companies from 2012 to 2023, leveraging the 2018 Supply Chain Innovation and Application Pilot Policy to construct a quasi-natural experiment. Employing a difference-in-differences approach with multiple mediation effects, it investigates the impact of supply chain digitalization on corporate green innovation and its transmission mechanisms. Findings reveal that supply chain digitalization significantly enhances corporate green innovation levels, with this effect being more pronounced in substantive innovation, western regions, and firms with high customer concentration. Mechanism tests reveal that supply chain digitalization promotes green innovation not only through independent pathways of enhancing information transparency and improving ESG performance but also via a chained mediation effect: “supply chain digitalization → information transparency → ESG performance → green innovation”. This study enriches theoretical research on the relationship between supply chain digitalization and green innovation from the dual perspectives of information and governance, offering insights for government initiatives to advance data sharing, implement differentiated policies, and establish green governance systems. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Scheduling in multi-layer industrial networks must remain stable even when the feedback mechanism of the environment changes inside a single production episode. The system can switch between a step-continuous regime with dense process feedback and a task-driven regime with sparse milestone feedback, so that the same state requires different behaviour before and after the switch. A regime-oblivious policy may therefore optimise the wrong action preference after a switch. We formulate this setting as a mode-switched multi-industrial-chain Markov decision process (MS-MIC-MDP) and prove that a single fixed action preference is necessarily suboptimal in at least one regime. We then propose BHERA, a belief-guided homeostatic estimation framework for regime adaptation. BHERA builds cross-layer representations, performs structured variational inference of slow and fast latent beliefs, estimates the posterior probability of the task-driven regime, and uses that posterior to regulate sample weights, entropy strength, return-prediction emphasis, and latent information capacity. A homeostatic feedback rule on the Kullback–Leibler (KL) divergence keeps the latent representation informative without allowing uncontrolled information growth, and we analyse it as a two-timescale stochastic approximation with an associated convergence argument and a per-iteration complexity bound. Experiments in a multi-layer industrial scheduling simulator show that BHERA achieves higher return, lower cost, and higher utility than CReSCENT, HiTAC-MuSE, Informed Switching, and WToE across all tested perturbations, with paired statistical tests confirming significance. Expanded ablations and parameter-sensitivity studies confirm the importance of regime belief, regime-balanced weighting, bootstrap prediction, homeostatic capacity control, and the dual-timescale latent split. Full article

Against the background of global climate change and China’s dual-carbon strategic goal, agricultural carbon emission reduction and low-carbon transformation have become urgent practical issues. As an important characteristic cash crop in China, apple cultivation faces significant carbon emission pressure, and an obvious spatial imbalance exists in carbon productivity across major producing areas. Using the Dagum Gini coefficient, kernel density estimation, and Markov-chain analysis, this study analyzes regional differences in and the dynamic distribution of carbon productivity in China’s main apple-growing provinces from 2008 to 2024. The results indicate the following: (1) Overall, carbon productivity in China’s apple industry shows an upward trend, with a “rising–declining–rising–declining” M-shaped evolution during the study period. (2) The main reason for the overall differences is variation between regions, which shows a continuous inverted V-shaped change pattern of “rising–declining–rising–declining–rising–declining–rising.” (3) High-carbon-productivity areas have a positive effect on surrounding areas, while low-productivity areas have a negative effect. Therefore, to improve carbon productivity in apple cultivation, it is essential to not only understand regional differences and their causes but also leverage the positive effects of neighboring high-carbon-productivity areas to positively influence local conditions. This will help achieve cross-regional collaborative improvement in carbon productivity in China’s main apple-producing provinces. Full article

With the growing global population, intensifying resource constraints, and deepening climate change impacts, agriculture faces dual challenges of ensuring food security and advancing sustainable development. Artificial intelligence (AI) has emerged as a transformative technology, penetrating the entire crop production chain and offering innovative solutions to traditional agricultural bottlenecks. This paper systematically reviews AI applications in five core domains: biotic stress monitoring, soil health management, precision operation, supply chain optimization, and climate-resilient agriculture. It further categorizes and analyzes four key technical pathways—deep learning, sensor fusion, data-driven methods, and hybrid modeling—while critically examining major challenges across data, technology, implementation, and ethics/policy dimensions. Future directions are discussed from technological innovation, scenario expansion, implementation guarantees, and sustainability orientation. Research findings show that AI has achieved technical validation in pest/disease detection, soil parameter modeling, and intelligent spraying, with accuracy exceeding 85% in some cases. However, regional data bias, insufficient model generalization, and the digital divide still hinder large-scale deployment. Moving forward, coordinated efforts in technological innovation and policy support are required to promote inclusive, standardized, and sustainable AI applications in crop production. Full article