Search Results (785)

Precision agriculture has become a major direction of agricultural technological development in recent decades, addressing efficiency, environmental, and economic challenges simultaneously. Input optimization based on site-specific data collection—particularly variable-rate nutrient application, precision irrigation systems, and targeted crop protection—has been shown to generate measurable cost and resource savings. The aim of the study is to explore and systematically evaluate the economic impacts influencing precision technology in crop production. Although the technical and environmental benefits of precision technologies are widely documented, their economic performance and farm-level profitability remain inconsistently interpreted. The study is based on a systematic literature review of peer-reviewed English-language journal articles retrieved from the Web of Science, Scopus, ScienceDirect, and JSTOR databases. Study selection and evaluation were conducted in accordance with the PRISMA 2020 methodological framework. The literature indicates that precision technologies achieve average input savings of 8–20% and yield increases of 2–6%, while reported return on investment (ROI) values typically range between 5% and 15%. Economic viability is strongly dependent on farm size, with most studies identifying profitability above 100–200 ha. Additional benefits include improved management of soil heterogeneity, enhanced nutrient-use efficiency, and reduced excess input application, although adoption remains constrained by high investment costs and technological complexity. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

The COVID-19 pandemic highlighted both the transformative potential of mRNA vaccines and the structural challenges associated with their supply chains. Unlike traditional vaccine platforms, mRNA vaccines depend on highly specialized raw materials, including plasmid DNA (pDNA), nucleotides, enzymes, and lipid nanoparticles (LNP), that are produced by a limited number of global suppliers. These dependencies, combined with platform-specific manufacturing processes and stringent cold chain requirements, introduce vulnerabilities across production, distribution, and regulatory oversight. This narrative review examines the distinctive features of mRNA vaccine supply chains and identifies key challenges and opportunities across three interconnected domains: manufacturing systems, logistics and distribution, and regulatory governance. Drawing on literature published between January 2021 and March 2026, the review synthesizes evidence on supply chain bottlenecks revealed during the COVID-19 pandemic, including upstream raw-material dependencies, limitations in manufacturing scale-up, cold chain constraints, and regulatory fragmentation. Particular attention is given to the implications of these challenges for low- and middle-income countries, where infrastructure, technical capacity, and regulatory resources may limit participation in mRNA vaccine production and deployment. The review also highlights emerging strategies to strengthen supply chain resilience, including diversification of input suppliers, development of regional manufacturing hubs, improvements in vaccine thermostability, regulatory harmonization initiatives, and the use of digital technologies for supply chain management. By integrating insights from manufacturing, logistics, and regulatory perspectives, this study contributes to a better understanding of the structural characteristics shaping mRNA vaccine supply chains and identifies priority areas for strengthening global preparedness for future health emergencies. Full article

Artificial intelligence presents a critical paradox for clean technology: while enabling unprecedented environmental optimization, AI deployment demands massive resource inputs that threaten to offset benefits. As global AI infrastructure investment approaches $500 billion annually, data center electricity consumption is projected to exceed 1000 TWh by 2030. We conducted a systematic literature review of 73 peer-reviewed empirical studies (2021–2025) to develop an Environmental Asset-Cost Framework categorizing AI’s impacts across five asset categories (energy optimization, production enhancement, green innovation, resource conservation, precision applications) and five cost categories (energy consumption, water use, e-waste, infrastructure, supply chain extraction). Our analysis reveals three critical insights: First, AI’s environmental impact follows a synthesized S-curve heuristic—a pattern derived from convergent but methodologically diverse evidence strands—characterized by initial emission reductions (0–2 years), mid-term rebound effects (2–5 years), and conditionally projected long-term optimization (5+ years). Second, geographical context creates 10–60× variation in outcomes; regions with high renewable electricity and water abundance achieve net benefits within 2–3 years, while fossil fuel-heavy, water-stressed regions may never reach net positive outcomes. Third, the rebound effect is predictable and manageable through strategic interventions. Our framework provides actionable deployment guidance, demonstrating that achieving AI’s net environmental benefits requires renewable energy infrastructure development before AI deployment, alternative cooling technologies, and policy frameworks incorporating temporal dynamics. Full article

The challenge of software vulnerabilities persists globally, despite the widespread availability of advanced security tools and comprehensive developer guidelines. This issue is not the result of professional negligence, but rather the complex and non-intuitive nature of secure coding. This research takes on the massive data silos in the security industry by providing a comprehensive review of best practices drawn from 35 reputable academic and corporate sources. Authentication, cryptography, input validation, and deployment hardening are some of the key development domains into which these technologies are organized. We conduct a comprehensive analysis of each practice, elucidating the specific security issue it addresses, prevalent implementation patterns, and potential hazards, in addition to serving as a checklist. Simple precautions, like not using passwords that are hardcoded, and more involved methods, such correctly encoding output and configuring access controls effectively, are all part of the range of practices. We assert that despite the prevalent usage of tools like as static analyzers, numerous vulnerabilities persist due to developers’ insufficient training in integrating security considerations into their coding practices. This work aspires to serve as a comprehensive, organized resource that supplies developers with the necessary context and guidance to make informed, security-oriented decisions along the software development lifecycle. The aim is to develop a more extensive resource than those presently accessible, which can also assist educators or security teams during code instruction or evaluation. Full article

The expansion of China’s digital economy (DE) has begun to reshape agricultural production in ways that extend beyond efficiency gains, raising important questions about its implications for the long-term sustainable intensification of cultivated land use (SCU). Drawing on panel data from 31 provincial-level regions between 2011 and 2023, this study examines how digital development influences cultivated land sustainability from the perspectives of productivity, resource efficiency, and system resilience. The results indicate that digital advancement is closely associated with higher land productivity and more efficient input use, with digital industrialization playing a particularly pronounced role. Its contribution to land system resilience, however, appears more limited, likely because ecological stability and structural risk-buffering mechanisms respond slowly to technological change. Further analysis suggests that agricultural industrialization (AID) and Rural financing capacity (RFC) function as important transmission channels through which digital development shapes land-use outcomes. Notably, the effects are not uniform. The influence of digital development becomes more evident after 2015, when digital infrastructure and policy support deepened nationwide. Regional differences are also apparent: while the eastern region has already absorbed much of the early digital dividend, stronger marginal gains remain possible in central and western China, where agricultural modernization and digital integration are still unfolding. These findings underscore the importance of strengthening rural digital infrastructure, enhancing farmers’ digital capabilities, and improving digitally enabled financial services to support sustainable land use, particularly in less-developed regions. Full article

This review paper synthesizes the application and evolution of environmentally extended input–output (EEIO) analysis in agricultural research, drawing on 647 publications (Scopus and Web of Science, 1978–2025) following the PRISMA method and using the Bibliometrix package in the R statistical computing environment. EEIO has become a leading method for assessing system-level environmental impacts by quantifying direct and indirect flows across complete supply chains. Bibliometric and thematic analyses reveal accelerated growth since 2015 and four principal domains of enquiry: emissions embodied in trade, water-resource management, energy and climate impacts, and the sustainability of agri-food supply chains. EEIO’s principal value lies in its capacity to support production- versus consumption-based accounting and to reveal intersectoral trade-offs that single-sector approaches overlook. However, standard EEIO frameworks remain constrained by fixed technical coefficients, coarse sectoral aggregation, and uncertainty in environmental extensions, which limit their capacity to resolve farm-scale processes, structural change, and feedbacks. To enhance analytical rigor and policy relevance, we advocate hybridization with life-cycle and farm-level data, development of higher-resolution multi-regional EEIO tables, incorporation of stochastic and scenario analyses, dynamic formulations to capture technological change, and adoption of open-data standards with transparent reporting. Advancing these priorities will improve comparability, reproducibility and the practical uptake of EEIO for evidence-based transitions in agricultural systems. Full article

With the advancement of agricultural modernization, issues related to resource conservation, intensive utilization, and green, low-carbon development have become increasingly prominent. To enhance water and fertilizer use efficiency in Henan Province and promote green, low-carbon, and sustainable agricultural development, field experiments were conducted during 2023–2024. The experiment employed a randomized complete block design with three replications. Each plot measured 30 m² (5 m × 6 m), totaling 36 plots. An IoT-based real-time coordinated water-fertilizer regulation technology, driven by continuous WSH-TDR310S sensor monitoring of soil moisture and nitrogen status with automated threshold-based control logic, was implemented. By transforming the traditional static scheduling approach into a dynamic feedback mechanism driven by real-time sensor data, the synchronization between resource supply and crop demand was achieved. This study aimed to elucidate the response characteristics of summer maize growth dynamics and farmland N₂O emissions under the proposed regulation strategy. The experiment included three levels of water deficit (mild, moderate, and severe) and three fertilization levels (low, medium, and high), resulting in a total of nine real-time water–fertilizer coordinated regulation treatments, along with three local border irrigation control treatments. The results showed that under real-time water–fertilizer regulation, plant height, stem diameter, and leaf area index of summer maize exhibited unimodal variation patterns, with the medium irrigation–medium fertilization (B2) treatment performing optimally. Compared with the border-irrigation medium-fertilization control (D2), plant height and stem diameter under the B2 treatment increased significantly. Cumulative farmland N₂O emissions increased with higher irrigation and fertilization levels, with the border-irrigation high-fertilization treatment producing the highest emissions. Yield formation was mainly governed by structural growth traits, with plant height showing the strongest predictive ability, followed by stem diameter, whereas leaf area index showed weaker explanatory power. Summer maize yield exhibited a unimodal response to both irrigation and nitrogen input levels. Compared with the D2 treatment, the B2 treatment increased grain yield by 41.33%, while achieving water-saving and fertilizer-saving rates of 38.10% and 35.75%, respectively, thereby achieving an optimal balance between high yield and efficient water–fertilizer utilization. These findings provide theoretical support for summer maize production in the North China Plain and contribute to the promotion of green and sustainable agricultural development. Full article

Background/Objectives: Municipal hospitals in transitional health systems operate under structural resource constraints that complicate managerial decision-making and shape patient perceptions. This study examines how patients interpret resource allocation and evaluate the ethical and legitimacy consequences of alternative strategic priorities. Methods: A qualitative research design was employed using semi-structured patient interviews. Participants were recruited using purposive sampling based on predefined inclusion criteria: age over 18, hospitalization for digestive symptoms, undergoing diagnostic investigations, and provision of informed consent. Thematic analysis identified key expectation domains related to technological modernization, workforce capacity, infrastructure, and relational communication. These themes were translated into core governance variables and integrated into a conceptual simulation model comparing three allocation scenarios: technological investment, human resource expansion, and status quo preservation. Results: Findings show that patient evaluations extend beyond satisfaction to include distributive fairness, symbolic modernization, and institutional legitimacy. Simulation findings suggest that technological investment strengthens symbolic legitimacy and perceived equity but may increase workload and fiscal exposure; workforce expansion enhances relational justice and operational stability yet leaves modernization gaps; and status quo preservation maintains short-term fiscal balance while risking gradual legitimacy erosion. Conclusions: The study demonstrates that satisfaction metrics alone are insufficient for governance evaluation. Integrating ethical analysis, organizational legitimacy theory, participatory input, and systems thinking provides a structured framework for assessing resource allocation trade-offs in resource-constrained municipal hospitals. Full article

Rainbow trout (Oncorhynchus mykiss) farming represents a significant opportunity for developing sustainable regional aquaculture and food systems. This study assesses its potential using the Vologda Region in Northwest Russia as a case study. The methodology involved analyzing the compatibility of the species’ ecological requirements with local hydrochemical conditions and evaluating production dynamics from 2016 to 2024 through trend analysis. The results confirm that key water bodies in the region provide suitable conditions for industrial-scale cage farming. Production exhibited exponential growth, increasing from 10 to 994 tonnes over the eight-year period, transitioning from a rapid expansion phase (2016–2020) to a phase of stable, sustainable growth (2021–2024) with annual increases of 100–150 tonnes. A SWOT analysis identified major strengths, including abundant water resources and government support, alongside critical challenges such as technological lag, dependence on imported inputs, and skilled labor shortages. The findings underscore the substantial potential of trout aquaculture to serve as a pillar of a localized food system in the region. Realizing this potential over the long term will require targeted investments in modern technology, value-added processing, and workforce development to mitigate existing constraints. Full article

Extremely Large-Scale Multiple-Input Multiple-Output (XL-MIMO) is positioned as a transformative technology for sixth-generation (6G) networks, effectively turning base stations into high-resolution sensing and communication hubs. However, the practical deployment of XL-MIMO is hindered by the “curse of dimensionality,” specifically the prohibitive overhead associated with Channel State Information (CSI) sensing and feedback, alongside the computational latency of massive antenna arrays. To resolve the conflict between high-resolution sensing requirements and limited bandwidth resources, this paper proposes a novel two-stage beamforming architecture that synergizes physics-aware dimensionality reduction with deep learning. First, by exploiting the inherent sparsity of XL-MIMO channels in the angle-delay domain, we design a Spatial–Frequency Concentration Block (SFCB). This module functions as a hard-attention sensing mechanism, performing efficient source-end dimensionality reduction on raw CSI at the User Equipment (UE) via precise feature extraction and adaptive energy truncation. Second, we develop a highly adaptable Direct Integrated Precoding Network (DIP-I). Departing from the conventional “sense-reconstruct-then-precode” paradigm, DIP-I learns end-to-end mapping to directly regress the optimal precoding matrix at the Base Station (BS). Comprehensive simulations utilizing the COST 2100 and QuaDRiGa hybrid channel models demonstrate that, under a massive 512-antenna configuration, the proposed framework achieves exceptional beamforming gain. Furthermore, it significantly reduces sensing data overhead and inference latency, offering a superior trade-off between spectral efficiency and hardware resource consumption for future 6G sensing-communication integrated systems. Full article

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

Natural gas hydrates, as an abundant potential energy resource, are widely present in marine sediments. In this paper, a novel method using color recognition technology is proposed for reconstructing marine hydrate reservoirs. By identifying the red, green, and blue values of image colors within the study area’s grid, numerical values are assigned and translated into geological parameters. These parameters are then input into the Computer Modeling Group software to establish heterogeneous reservoirs, and numerical simulations are conducted. The results indicate that this method successfully establishes a correspondence between color features and geological parameters. The reconstructed model images exhibit a high degree of consistency with the original images, allowing for precise parameter readings. The method was applied to hydrate reservoirs in the second trial production area of the South China Sea, the Shenhu SH2 area, and the Nankai Trough. The cumulative gas production obtained through numerical simulation of the reconstructed models closely matched the known production data, with discrepancies of 3.5%, 0.9%, and 7.6%, respectively. These findings confirm the reliability of the model, providing valuable insights for future studies on heterogeneous hydrate reservoirs and extending its application prospects to heterogeneous oil and gas reservoir research. Full article

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article

Green financial inputs (GFI) play an important role in promoting green innovation in the manufacturing industry, and accurately evaluating GFI utilization efficiency and its industry heterogeneity is crucial for optimizing green resource allocation. To address this, this study applies a three-stage Data Envelopment Analysis (DEA) model, using panel data of 29 Chinese manufacturing sectors from 2011 to 2024. This model eliminates the interference of environmental factors and statistical noise via the Stochastic Frontier Analysis (SFA) in the second stage, thus obtaining more reliable efficiency evaluation results. The empirical results show that: (1) GFI can effectively improve manufacturing green innovation efficiency (GIE), but the overall utilization efficiency remains at a low level; (2) there exists significant industry heterogeneity, with technology-intensive industries performing best in GFI utilization efficiency, followed by capital-intensive industries, and labor-intensive industries the worst; (3) environmental regulation and green financial market environment significantly improve GFI utilization efficiency, while government green finance support and market structure have no significant effects on it; (4) after eliminating external disturbances, the real GFI utilization efficiency tends to be stable, and the efficiency decline in 2023–2024 is mainly caused by external shocks. Corresponding targeted implications are put forward to optimize GFI allocation and promote balanced green development of China’s manufacturing industry. Full article