Search Results (30,872)

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

Background/Objectives: Eleocharis ussuriensis Zinserl. is a perennial riparian sedge widely distributed in Northeast Asia and a dominant component of freshwater vegetation in South Korea. However, the intraspecific genetic structure of this species across contrasting hydrological habitats remains insufficiently understood. This study aimed to develop novel SSR markers from whole-genome data and investigate genetic variation and population structure among E. ussuriensis populations in South Korea. Methods: Twenty-one novel simple sequence repeat (SSR) markers were developed from whole-genome sequence data and applied to analyze genetic variation in 120 individuals from 6 populations. Genetic diversity, differentiation, and gene flow were estimated using allele-frequency-based metrics, and population genetic structure was further evaluated using spatial information derived from geographic coordinates. Results: A total of 201 alleles were detected, with a mean polymorphism information content (PIC) of 0.759, indicating high marker informativeness. Mean genetic diversity across populations showed observed heterozygosity (Ho = 0.360) and expected heterozygosity (He = 0.281), while multilocus genotype ratios (G/N) ranged from 0.30 to 1.00 among populations. Genetic differentiation was substantial (F_ST = 0.373–0.669; Jost’s D = 0.540–0.997). Mantel tests revealed that genetic differentiation was significantly correlated with geographic distance (r = 0.67, p < 0.001). Both allele-frequency-based and spatially explicit approaches suggested genetic structuring among populations. Conclusions: The results suggest spatial tendencies in genetic structure among populations, reflecting patterns of allele distribution across regions. These findings provide baseline information on genetic variation in E. ussuriensis and may contribute to a better understanding of its ecological dynamics. Full article

Water scarcity is becoming an increasingly critical global challenge, driven by climate change, rapid population growth, pollution, and unsustainable water use. Drought further intensifies this crisis by reducing water availability across agricultural, environmental, and socio-economic systems. In this context, nanotechnology has emerged as a promising tool for improving water management and enhancing drought resilience. This review examines the role of nanotechnology in drought mitigation and water conservation through multiple pathways, including the enhancement of plant drought tolerance, improvement in soil water retention, the development of smart irrigation and nano-sensing systems, and the expansion of water resources through purification, desalination, and wastewater reuse. In addition, the broader drought–water nexus is discussed to position nano-enabled approaches within existing water management strategies. While numerous studies report improvements in water-use efficiency, stress tolerance, and treatment performance under controlled conditions, significant limitations remain. These include concerns related to environmental safety, nanotoxicity, scalability, cost, and the gap between laboratory findings and field-level applications. Overall, nanotechnology should be considered a complementary approach rather than a stand-alone solution for addressing water scarcity under drought conditions. Future research should focus on long-term environmental impacts, techno-economic feasibility, and large-scale field validation to support the safe and effective integration of nanotechnology into sustainable water management systems. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Groundwater is a key strategic resource underpinning water security, and its effective management requires reliable, high-frequency monitoring data. In mountainous regions such as the flysch Carpathians in southern Poland, natural springs are particularly sensitive indicators of aquifer system dynamics. This study analyzes the role of springs in the national groundwater observation and research network and identifies barriers to the implementation of automated monitoring of spring discharge. The research covered 28 springs operating within the regional monitoring network of the Polish Geological Institute—National Research Institute in the Carpathian region. Classical hydrogeological spring classifications were applied and complemented with proprietary criteria addressing formal-legal, technical, and environmental conditions affecting the feasibility of automation. The results show that all of the analysed springs exhibited a Meinzer’s variability index (V) exceeding 100%, and numerous objects showed a coefficient of variation (CV) above 150%, providing quantitative evidence that standard weekly manual measurements statistically fail to capture rapid flow dynamics and peak discharge events. To bridge the gap between hydrodynamic observations and monitoring logistics, this study introduces a novel methodological contribution: the F-T-S-N screening framework. This proprietary, multi-criteria classification quantifies Formal-legal, Technical, Structural, and Nature-environmental barriers to telemetry implementation. The application of this framework demonstrates that the main obstacles to modernization are non-technological. The proposed classification serves as a practical, transferable tool that supports the rational planning of monitoring network automation in other mountainous regions with similar hydrogeological conditions. Full article

Against the backdrop of climate change and rapid urbanization, urban water resource management faces increasing complexity. Based on the OECD’s Twelve Principles of Water Governance, this paper aims to clarify the interaction relationships and relative priorities among dimensions and indicators, providing a structured basis for decision-making in urban water governance. The DANP method is applied to construct an analytical framework encompassing three major dimensions: effectiveness, efficiency, and trust and participation, together with twelve indicators. This study moves the OECD framework from a static structure toward a causal network. By identifying causal relationships and calculating local and global weights, the analysis reveals the driving structure and evaluation focus of governance elements. The results indicate that, from a causal perspective, efficiency (D₂) and effectiveness (D₁) are the primary driving dimensions of the governance system, while trust and participation (D₃) is primarily an outcome dimension. Weight analysis reveals that trust and participation have the highest weight, at 35.28%, reflecting the strong emphasis placed on transparency, public participation, and accountability in urban water governance evaluations. At the indicator level, Monitoring and Evaluation (C₁₂) ranks highest with a global weight of 9.44%. Overall, the study offers theoretical and practical insights for coordinating institutional efficiency and governance legitimacy in climate adaptation contexts. Full article

Grasslands are key terrestrial ecosystems in which root dynamics regulate soil carbon and nutrient cycling. Although grazing constitutes the predominant land use practice in grassland ecosystems, its impacts on root dynamics remain inadequately elucidated, particularly across a gradient of grazing intensities. In this two-year field experiment, an improved root window method was applied to investigate the effects of four grazing intensities (no grazing, light grazing, moderate grazing, heavy grazing) on root production, root mortality, root standing crop, root turnover, and root lifespan in the saline–alkaline grassland in northern China. The results showed that root production and root mortality exhibited pronounced seasonal dynamics, with peaks in June and August for root production and in September for root mortality. These seasonal patterns were primarily driven by precipitation and were not significantly altered by grazing intensity. Moderate grazing significantly increased root production by 51.2% through changes in soil bulk density and selective livestock grazing, supporting the intermediate disturbance hypothesis. Root turnover was predominantly shaped by plant community composition and interannual precipitation, as opposed to grazing intensity. Overall, these findings indicate that moderate grazing promotes root growth, providing important insights into the sustainable utilization of saline–alkali grassland resources. In other words, appropriate measures must be taken to effectively manage grazing activities in the fragile saline–alkaline grasslands of northern China. Full article

As the most critical binding constraint in the Yellow River Basin, the endowment and allocation efficiency of water resources significantly influence the stability and sustainability of urban economic systems. However, the direction, intensity, and heterogeneity of the impacts of water resource abundance and water use efficiency on urban economic resilience remain unclear. Therefore, to explore the intrinsic relationship between water resources and urban economic resilience and to identify effective pathways for enhancing urban risk resistance, this paper employs a fixed-effects model to empirically examine the differential impacts based on panel data from 78 prefecture-level cities in the Yellow River Basin from 2011 to 2023. The results show that: (1) Water resource abundance exerts a significant inhibitory effect on urban economic resilience, while water use efficiency exhibits a significant promoting effect. (2) Market demand, government intervention and opening up exacerbate the negative impact of water resource abundance and also strengthen the positive impact of water use efficiency. (3) The negative impact of water resource abundance is significant only in resource-based cities, water-abundant cities, cities in the lower reaches, and cities with high economic development, high urbanization, and high technology input. In contrast, the positive impact of water use efficiency is significant in most cities, and it is more pronounced in resource-based cities, water-abundant cities, cities in the middle reaches, and cities with high economic development and high urbanization. These findings provide important insights for enhancing urban resilience and promoting sustainable development. Full article

This article presents the design and development of an interactive architecture oriented toward the management of traditional vallenato, a musical genre recognized as an Intangible Cultural Heritage of Humanity by UNESCO. Architecture combines the principles of contextual awareness and the use of massive open online courses (MOOCs) to face the current challenges of preservation, dissemination, and teaching of this cultural expression, threatened by commercialization and the loss of its traditional roots. Through a modular structure, adaptive technological tools are integrated to capture, process, and use contextual information, personalizing learning experiences and strengthening the link between communities and their cultural heritage. The proposal consists of several functional layers, including context management, user profiles, educational resources, and a persistence unit, each designed to ensure the interoperability and sustainability of cultural data. In addition, the capacity of architecture to be used in other cultural contexts is highlighted, expanding its impact on different artistic manifestations and heritages worldwide. This article includes a comparative analysis with other existing models, highlighting the advantages of this solution in terms of customization and adaptability. Finally, opportunities for improvement and expansion are explored, as well as the pending challenges in the implementation of this technological tool in educational and cultural environments. Full article

In urban areas, green spaces have become the main refuge for biodiversity, providing essential habitat and resources for urban-adapted species. However, scientific evidence on the fitness consequences of urban green space management for urban populations remains scarce, limiting our ability to design successful conservation and management strategies. Here, we assess the fitness consequences of different levels of management practices in green spaces (i.e., high for areas with continuous intervention such as regular mowing and irrigation, and low for areas with minimal, sporadic maintenance) based on a 19-year long-term monitoring of the Eurasian Tree Sparrow (Passer montanus), a species with high behavioural plasticity in response to human-altered habitats. We formulated a unistate capture–mark–recapture model to estimate age-dependent survival while accounting for uncertainty in recapture probability. Furthermore, by means of GLMMs, we tested if the level of management influences reproductive parameters (i.e., breeding failure, number of eggs, nestlings, fledglings, brood number from the same year, breeding success). We found that high urban green space management caused a decline in adult survival, but we found no effect on juvenile survival. We also found lower breeding failure, a greater number of eggs, and larger brood numbers in the low management areas, but no differences were found in the number of nestlings and fledglings. Consequently, we found no differences in overall breeding success. Our results highlight the reduction in survival in a near-threatened passerine species due to routine green urban space management, in addition to differences in reproductive parameters depending on the degree of green urban space management. Overall, we confirm that the same species show several reproductive strategies with different breeding effort to reach similar breeding success, whatever the human context is. However, birds pay the cost in adult survival, and probably in shortening life span. Therefore, the management of urban green spaces has a negative impact on biodiversity in cities. It is necessary to review the management practices of these urban areas and promote practices that are friendly to biodiversity. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

Despite the availability of clinical guidelines aimed at managing pregnancy complications, maternal deaths related to obstetric emergencies remain unacceptably high in South Africa, especially in rural provinces like Limpopo. These preventable deaths are often linked to delayed response to complications, poor adherence to protocols, and lack of essential resources. The study aimed to explore the experiences of health professionals regarding the implementation of maternal guidelines used to manage obstetric emergencies. The study adopted a qualitative, descriptive, and explorative design. Data were analysed thematically, and trustworthiness was maintained throughout the research process. Sixteen participants from four selected hospitals in a rural area of South Africa (Vhembe District, Limpopo Province) were purposively sampled and interviewed using semi-structured interviews; data were analysed thematically. The findings highlighted multiple critical barriers to guideline implementation, including shortages of printed clinical protocols, inconsistent patient follow-up, poor referral systems, infrastructure deficits, medication stock-outs, and negative staff attitudes. Most doctors and midwives working in maternity units lacked training on the Essential Steps in the Management of Obstetric Emergencies (ESMOE), resulting in insufficient knowledge and skills to manage obstetric emergencies. Therefore, there is an urgent need for comprehensive ESMOE training for all doctors and midwives in maternity units. Full article

This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient Chinese literature. By extracting structured metadata from canonical texts, we construct a dense, bidirectional graph schema. Diverging from resource-intensive neural architectures, our system abandons heavyweight vector embeddings in favor of a highly optimized, template-based heuristic matching engine natively implemented in Java. This purely symbolic approach ensures deterministic execution, zero-dependency deployment, and seamless operation on standard CPU-only servers. To handle complex historical inquiries, the framework integrates a context-aware dialogue manager for multi-turn anaphora and ellipsis resolution, alongside a synergistic tiered caching mechanism. Extensive evaluations on a benchmark of 13,652 annotated queries demonstrate that the system achieves an exceptional intent recognition accuracy of 97.14%, robust context retention, and ultra-low response latency (≤17 ms). Ultimately, this architecture provides a sustainable, highly reproducible, and cost-effective paradigm for the semantic exploration of classical textual heritage, exceptionally suited for small-to-medium cultural institutions. Full article

Stochastic inputs are essential for incorporating hydrological variability in water resources assessment, planning, and management. However, most studies focus on the generation of precipitation and temperature, precipitation and streamflow, and precipitation and evaporation, with limited incorporation of groundwater levels. This study assessed the ability of the Variable Length Block (VLB) bootstrapping model for simultaneously generating stochastic sequences of rainfall, evaporation, and groundwater levels. The performance of the model was assessed by comparing single statistics of historical time series located within the box plots of 100 annual and monthly stochastically generated time series. The model preserved eight of the nine statistics adequately, except for skewness, across all variables, with historical values for evaporation and groundwater levels falling below and above the interquartile range for 12 months. All the historic statistics for rainfall, evaporation, and groundwater levels were within the interquartile ranges of the box plots for 83, 71, and 71% of the time, respectively. The historic statistics for rainfall, evaporation, and groundwater levels were within the box plot ranges for 100, 98, and 99% of the time, respectively. These findings indicated reasonably successful generation, and the VLB generator was therefore considered applicable for the stochastic generation of multiple hydrometric data types. Full article

This study examines how disruptive technologies reshape workforce skill requirements and organizational responses. As tools such as Artificial Intelligence, the Internet of Things, and cloud infrastructures become embedded in everyday operations, employees increasingly confront evolving competence demands. Drawing on data from 622 employees across Greece, Spain, and Italy, the study proposes and tests a structural model linking disruptive technology exposure with perceived skill gaps, organizational readiness, strategic HR alignment, and skill update intention. The findings show that disruptive technology exposure is positively associated with perceived skill gaps, which in turn relate to organizational readiness, strategic HR alignment, and stronger skill update intention. These results highlight the importance of coordinated organizational and HR mechanisms in supporting continuous learning. Full article