Search Results (14,875)

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear models to capture yield variability within mixed pixels. Based on a single-season (2025) field experiment, this study developed a UAV multispectral imagery-based yield estimation framework integrating multiple machine-learning algorithms. Shapley additive explanations (SHAP) and partial dependence plots (PDP) were used to interpret the spectral–yield relationships under different spatial configurations. The predictive performance of linear regression and eight nonlinear algorithms was compared using 20 spectral features. Ensemble learning outperformed linear approaches in all intercropping scenarios. In the maize–soybean 3:2 pattern, the GBDT model delivered the highest accuracy (R² = 0.849; NRMSE = 9.28%), whereas in the 4:2 pattern with stronger shading stress on soybean, the random forest model showed the greatest robustness (R² = 0.724). Interpretation results indicated that yield in monoculture systems was mainly driven by physiological traits characterized by visible-band indices, while yield in intercropping systems was dominated by structural and stress-response traits represented by near-infrared and soil-adjusted vegetation indices. The generated centimeter-scale yield maps revealed clear strip-like spatial variability driven by interspecific competition. Overall, explainable machine learning combined with UAV multispectral data shows promise for within-season yield estimation in intercropping systems and can support spatially differentiated precision management under the sampled conditions. Full article

Droughts are one of the most significant hazards that affect human life due to the imbalanced distribution of water across the world. Some parts of the world are usually dry, and meteorological conditions affect these regions rapidly. In water-scarce regions, droughts significantly put at risk socio-economic stability and food security, which may cause a major challenge to sustainable development. Therefore, a precise definition of drought and the identification of early warning signals can help to minimize the negative effects of droughts, especially in terms of agriculture. In this study, drought signals of three major agricultural provinces of Turkey, namely Antalya, Şanlıurfa, and Konya, were investigated. For this purpose, the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Evaporative Demand Drought Index (EDDI), and Vegetation Condition Index (VCI) were computed for each province. A composite score index was proposed for the evaluation of multiple indices together. All datasets were obtained from remote-sensing products to ensure reproducibility. A dataset for the 2003–2023 period was used. The monthly precipitation derived from CHIRPS data and potential evaporation (PEV) data were obtained from the ERA5-Land. Therefore, the SPEI and EDDI values were calculated by using ERA5-Land PEV values but not the evapotranspiration. The Normalized Difference Vegetation Index (NDVI) values for each province were obtained from the MODIS/Terra MOD13A3 v061. The Mann–Kendall test and Sen’s slope were applied to the computed time series to detect the trends. As a result, the dry and wet periods were identified for each province individually. The VCI was found to have an increasing trend for all tested provinces. Overall, from a future perspective, the most vulnerable province in terms of meteorological drought was indicated to be Antalya. Full article

Coal continues to play a significant role in Poland’s electricity generation system, making the sustainable management of environmental impacts from hard coal mining a critical challenge during the ongoing energy transition. In line with the European Green Deal and circular economy principles, reducing and managing mining-related waste emissions is an important component of sustainable development in regions undergoing a gradual phase-out of fossil fuel extraction. This study analyzes rock mass and dust emissions associated with underground hard coal mining in Poland over the period 2017–2025 using the most recent statistical data, including estimates for 2025 based on the first three quarters of the year. The scale, structure, and trends of emissions are examined to assess their implications for environmental sustainability, resource efficiency, and long-term land use. Particular attention is paid to the relationship between declining coal production and the relatively slower reduction in waste rock emissions, which indicates increasing contamination of extracted material and poses challenges for sustainable mining practices. The results show that while total coal output has decreased substantially, reductions in rock mass emissions have been less dynamic, highlighting the need for improved waste management strategies from a sustainability perspective. The study demonstrates that increasing the utilization of mining waste, through underground use and circular economy applications, can reduce environmental pressure, support compliance with sustainability policies, and mitigate long-term impacts on post-mining regions. Although the analysis focuses on Poland, the findings provide transferable insights for other countries seeking to balance energy security, mining sector restructuring, and sustainable development objectives during the transition away from fossil fuels. Full article

Addressing the challenges of insufficient reserve capacity allocation and wind power uncertainty-induced security and economic concerns under high wind power penetration, this paper develops an integrated energy–reserve market clearing model for regional electricity markets. Firstly, a comprehensive day-ahead market clearing mechanism is designed, encompassing market participant bidding, security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), nodal marginal price calculation, and market settlement. Secondly, a SCUC model targeting the minimization of total system operating costs and a SCED model targeting the minimization of energy and reserve procurement costs are established, comprehensively incorporating constraints, such as power balance, unit output and ramping limits, reserve requirements, and network power flows, with nodal marginal prices calculated using the Lagrangian multiplier method. Finally, simulation verification is conducted using a modified IEEE 30-bus system as a case study. Results demonstrate that the proposed model effectively coordinates wind power integration with system reserve requirements, achieving economically optimal dispatch while ensuring grid security and stability. Thermal units obtain substantial market revenues by providing reserve ancillary services, while wind units achieve high revenues through zero marginal cost advantages, fully validating the model’s effectiveness and economic efficiency under high wind power penetration conditions. The research findings provide theoretical foundations and practical guidance for constructing electricity spot market mechanisms adapted to large-scale renewable energy integration. Full article

The Dubai Government has made significant investments in digital learning through platforms such as Al Mawrid and Bayanati, enabling widespread access to employee training and upskilling. However, there remains a major gap in translating accumulated learning into intelligent workforce restructuring. This paper proposes “SkillChain DX,” a policy-driven framework that applies artificial intelligence (AI) to dynamically map employee-acquired skills to evolving job roles across departments, developed using a conceptual design science and policy analysis approach. The framework integrates blockchain to ensure secure, tamper-proof verification of skill credentials across diverse training platforms. To validate feasibility, a pilot prototype was implemented using sentence-transformer models for semantic skill inference and cryptographic hashing mechanisms for decentralized credential verification. Experimental evaluation across six controlled scenarios demonstrated an average role-matching accuracy of approximately 82%, blockchain transaction throughput exceeding 1000 operations per second, and near-instant credential verification with over 99% performance improvement compared to manual processes. The findings demonstrate that integrating AI-driven skill inference with decentralized credential verification can significantly enhance internal mobility, role alignment, and workforce planning at a policy level. The study benchmarks international practices and outlines a practical implementation path for the Dubai Government using only publicly available technologies and case studies, positioning SkillChain DX as one of the first integrated AI–blockchain policy frameworks tailored to public sector human resources (HR) transformation in Dubai. The proposed system framework bridges the current disconnect between training access and organizational transformation, supporting a proactive, transparent, and skills-first public sector, while offering actionable policy insights for future government HR modernization. Full article

Unmanned aerial vehicle (UAV) applications are growing fast in different sectors, such as agricultural, commercial, academic, leisure, and health fields. However, drones pose a significant threat to public safety due to their ability to transmit information, particularly when used in an unauthorized or malicious manner. In fact, in order to protect citizens’ privacy and prevent accidents in high-traffic areas due to poorly controlled flights, no-fly zones for drones have been established in the legislation of a number of countries. Most common UAV detection techniques are based on radio frequencies, which identify drones and their models by monitoring radio frequency signals. However, differentiating between multiple UAVs of the same model is their main limitation. This article fills this gap by proposing a method for physically tracking the communication frames of a registered UAV in the presence of another UAV of the same model. A measurement campaign was conducted to collect real-world RF communication signals from two DJI MAVIC 2 Zoom, two DJI Air2S, and two DJI Phantom drones. This measurement was performed inside and outside an anechoic chamber in order to study the UAV’s communication without any interference and in the presence of other communications. Through detailed statistical analysis, we characterized features such as communication duration, time intervals between communications, signal strength, and patterns in communication timing sequences. Our analysis revealed unique, identifiable patterns for each UAV, even within identical models. Based on these results, we developed an automated system that links communication frames to the corresponding registered drones. The proposed method fills gaps in drone detection and surveillance models, providing valuable information for applications in the fields of security and airspace management. This research lays the foundation for drone identification solutions, thereby addressing a major limitation of current detection technologies. Full article

Education on the Big Food Concept, as a strategic framework for ensuring national food security and promoting high-quality agricultural development, represents a key nexus between ideological and political education and quality-oriented education for college students. Based on survey data from 1268 students across six provinces in China, this study utilized the Delphi method, the analytic hierarchy process (AHP), and structural equation modeling (SEM) to develop a four-dimensional evaluation system encompassing cognitive, affective, value, and behavioral dimensions. It examined the relationship and underlying mechanism through which Big Food Concept education influences student behavior. The results indicate that college students’ overall understanding of the Big Food Concept remains at a moderate level, with particularly limited awareness of diversified food supply systems. The weights of the dimensions in the educational evaluation system were as follows: behavioral dimension (0.342) > cognitive dimension (0.287) > value dimension (0.221) > affective dimension (0.150). Big Food Concept education shapes student behavior through the sequential pathway of cognitive enlightenment, affective resonance, and value internalization, with value internalization demonstrating the strongest mediating effect (β = 0.413, p < 0.001). The evaluation system developed in this study is a practical tool for assessing the effectiveness of Big Food Concept education in higher institutions, while the identified mechanism provides a theoretical basis for implementing targeted educational practices. Full article

Agriculture serves as a critical foundation for livelihoods, food security, and sustainable development across the Sahara–Sahelian region. However, this vital sector faces mounting pressures from recurrent armed conflicts that systematically undermine its resilience and long-term sustainability. This study provides a comprehensive analysis of agricultural technical efficiency across 23 African countries in the Sahara–Sahelian region from 2009 to 2021, employing a robust bias-corrected bootstrap Data Envelopment Analysis approach. The findings reveal a concerning regional deterioration, with technical efficiency declining at an average annual rate of 1.7% throughout the study period. Conflict-affected countries demonstrated distinctive vulnerability patterns, exhibiting both higher average efficiency levels (0.875) and greater volatility, with annual declines of 1.8%. Sub-regional analysis highlights the Sahel’s particular fragility, where efficiency decreased by 2.2% yearly, nearly double the decline rate observed in North Africa. The most severe efficiency losses were recorded in countries experiencing intense and protracted conflict, notably Burkina Faso (4.0%) and Mali (3.5%), underscoring the severe association between conflict exposure and the erosion of agricultural productive capacity. These findings underscore the importance of developing integrated strategies that simultaneously address security challenges, climate adaptation, and institutional reform for effective resilience-building. Policy recommendations highlight the importance of enhanced regional connectivity, knowledge transfer, and targeted investments in agricultural capacity building—all aligned with both Sustainable Development Goals and the African Union’s Agenda 2063 objectives for achieving sustainable agricultural transformation in conflict-affected regions. Full article

Carbon sinks have been widely recognized as critical components of climate change mitigation and achieving carbon neutrality. With rapid urbanization, protecting and optimizing urban carbon sinks remain major challenges. This study uses Zhengzhou as a case study and analyzes 2000–2023 land-use data with the InVEST model to quantify carbon stocks and identify high-value carbon-sink areas. Circuit theory was further integrated to delineate ecological security patterns and inform optimization strategies. The results show a net decrease of 19.12 × 10⁶ t in carbon storage from 2000 to 2023, with the most rapid decline occurring during 2015–2020. Spatially, high-value carbon storage clustered in forested, high-elevation areas in the southwest, whereas low values predominated in the urban core. Carbon-sink source areas continued to shrink: fragmentation increased in the east, the west remained relatively stable, and the central area was highly fragmented. Corridor analysis indicated that the mean corridor length first increased and then decreased, accompanied by an expansion of pinch points and barrier areas. The study developed a systematic optimization framework that establishes a “Two Cores, Five Carbon-Sink Areas, Multiple Corridors” security pattern and proposes targeted conservation measures. The proposed methodology and findings offer a transferable basis for managing urban carbon sinks in rapidly developing regions and support both ecological security and climate-change mitigation, promoting sustainable urban development. Full article

Rapid urbanization has led to severe landscape fragmentation and ecosystem degradation in the Gansu Section of the Yellow River Basin (GSYRB). Focusing on this region, this study identified the spatial distribution of key ecological elements; consequently, an integrated “source–corridor–pinch point” ecological network was constructed. The findings aim to optimize the regional ecological security pattern. Ultimately, this study provides a scientific basis for the sustainable development of the study area and similar regions. This study revealed ecological trends based on four periods of land use data (1993–2023). We identified ecological source areas through MSPA and ecosystem service evaluations, and constructed resistance surfaces using spatial PCA. By applying circuit theory, we extracted ecological corridors—incorporating width attributes—and identified pinch points, thereby establishing a comprehensive ecological network. The results show that: (1) Over the past 30 years, construction land area expanded significantly, while cultivated land and water body areas contracted, and grassland and forest areas increased slowly. (2) Both the landscape fragmentation index and connectivity index exhibited a downward trend, while the landscape diversity index decreased first and then increased, indicating a systemic transformation in the landscape pattern. (3) A total of 260 ecological source areas, 694 ecological corridors (linear pathways connecting ecological source areas), and 371 ecological pinch points (critical bottleneck sections within corridors where connectivity is most vulnerable to disruption) were identified, forming an overall network structure with uneven spatial distribution. The ecological network spatial pattern constructed in this study based on ecosystem service assessment and circuit theory can effectively identify key ecological elements and their spatial heterogeneity characteristics, providing scientific reference for optimizing regional ecological security patterns and biodiversity conservation. Full article

Background/Objectives: We conducted this study to develop a generic amiodarone tablet pharmaceutically equivalent to the reference drug. This development is crucial for securing a stable supply chain for this orphan drug, which currently faces domestic market instability. Amiodarone, a national essential medicine, often experiences unstable supply due to its limited profitability. Methods: To secure this stable supply chain, we employed a factorial design, utilizing a Quality by Design (QbD) approach, to create the most suitable formulation. Initially, we observed a limitation where the formulation exhibited a flowability of 25% based on the Carr’s Index, which exceeded the target of 20%. To address this challenge, we incorporated lactose monohydrate during the pre-mixing stage rather than the post-mixing stage. Subsequently, we identified the binder content and the amount of granulation solvent as Critical Material Attributes (CMAs), and we performed a Design of Experiments (DoE). Result: Based on these investigations, we determined that the optimal prescription utilizes 5.71% povidone K25 and 40 mg/T of purified water. The final formulation successfully achieved an excellent flowability of 15.8%. Furthermore, this formulation showed a dissolution and bioequivalence PK profile equivalent to the reference drug in pH 1.2, 4.0, and 6.8 buffer solutions, each containing 1% Tween 80. Conclusion: Ultimately, the developed formulation is anticipated to establish a stable domestic supply chain and concurrently reduce national healthcare costs. These research findings also establish the groundwork for future continuous manufacturing implementation. Full article

The Internet of Things (IoT) is increasingly embedded in critical infrastructures across healthcare, energy, transportation, and industrial automation, yet its pervasiveness introduces substantial security and resilience challenges. This paper presents a comprehensive review of recent advances in IoT resilience, focusing on developments reported between 2022 and 2025. A layered taxonomy is proposed to organize resilience strategies across hardware, network, learning, application, and governance layers, addressing adversarial, environmental, and hybrid stressors. The survey systematically classifies and compares more than forty representative studies encompassing deep learning under adversarial attack, generative and ensemble intrusion detection, hardware and protocol-level defenses, federated and distributed learning, and trust and governance-based approaches. A comparative analysis shows that while adversarial training, GAN-based augmentation, and decentralized learning improve robustness, their evidence is often confined to specific datasets or attack scenarios, with limited validation in large-scale deployments. The study highlights challenges in benchmarking adaptivity, cross-layer integration, and explainable resilience, concluding with future directions for creating antifragile IoT systems that can self-heal and adapt to evolving cyber–physical threats. Full article

Water distribution systems (WDSs) are vital components of public infrastructure, ensuring the safe supply of drinking water. However, they are increasingly exposed to technical failures, contamination events, natural disasters, and cyberattacks. This review analyses global risks to water distribution systems (WDSs), focusing on biological, chemical, and cyber threats, and compares international approaches to detection, monitoring, and crisis management. Special attention is given to advanced technologies, such as sensors, digital modelling, and innovative disinfection methods, that enhance resilience and enable rapid contamination response. Case-based insights from the Czech Republic illustrate the strengths of a system with consistently high water quality standards while also revealing vulnerabilities linked to ageing infrastructure, limited digitalisation, and emerging risks related to climate change and cybersecurity. The review further highlights differences in international hygiene standards and regulatory frameworks and their implications for water safety. Future research priorities include: (I) predictive modelling and machine learning for contamination dynamics; (II) advanced disinfection combining UV, ozone, and nanomaterials; (III) systematic study of biofilms and microbial resistance; (IV) monitoring and risk assessment of pharmaceuticals, PFASs, and other emerging contaminants; (V) development of rapid, low-cost sensors and biosensors for real-time detection; and (VI) socio-technical studies addressing risk communication and public trust in drinking-water systems. Recommendations focus on systematic infrastructure renewal, enhanced monitoring and predictive modelling, and stronger integration of crisis preparedness and cybersecurity. Overall, the results underline the need for sustained investment, technological innovation, and cross-sector cooperation to ensure long-term water security. Full article

The applications of nanofluids are widely beneficial in heat transmission and cooling systems. Nanofluid viscosity and thermal conductivity have a substantial effect on heat transfer applications and on devices such as solar and geothermal systems. Machine learning models enable faster, less expensive modeling of nanofluid thermophysical properties. These models are secure for future studies and in the development of nanotechnology. In this review, shape, size, temperature, and volume concentration are considered as inputs to develop several machine learning methods, such as artificial neural networks, support vector regression, decision trees, and random forests. These models were analyzed by comparing their R² values, and the results indicated that machine learning-based models generally exhibited more reliable performance than the other approaches. The observation in this review was that thermal conductivity increases with temperature and volume fractions, whereas viscosity decreases with size, temperature, and volume fractions. To determine the optimal nanoparticle type, size, and concentration for specific applications such as data center cooling and high-heat-flux electronics, future research may employ ML-based optimization techniques. Full article

Background: Household food insecurity (HFI) adversely affects child development by restricting caloric intake, dietary diversity, and food quality. Since diet is a key factor influencing the gut microbiome, HFI may negatively impact health by altering microbial communities. However, direct evidence linking HFI to changes in the gut microbiome is limited. Therefore, we investigated the effects of HFI as a composite variable and used individual HFI assessment questions as specific proxies for dietary deprivation on the gut microbiome in a group of Ethiopian schoolchildren. Methods: Fecal samples were collected from 57 school-aged children in Ethiopia, and microbial profiles were established using 16S rRNA amplicon paired-end sequencing. Food insecurity was assessed using the Household Food Insecurity Access Scale (HFIAS). Results: We observed no significant differences in alpha diversity across food security status (Wilcoxon p > 0.05). However, beta diversity analysis revealed a significant shift in microbiome composition between food-secure and food-insecure individuals (Bray–Curtis dissimilarity; PERMANOVA, p < 0.05). Further analyses of individual HFIAS questions as specific proxies for dietary deprivation showed that limited dietary variety, consumption of disliked foods, and reduced meal size were each associated with significant changes in microbial compositions (PERMANOVA; all q < 0.05). Differential abundance analyses consistently identified Sutterella as significantly more abundant among food-insecure participants (composite model q = 0.11; component-specific models q < 0.05). Additionally, a microbial feature-based machine learning model accurately predicted food security status (AUC = 0.81), with Sutterella emerging as the top predictive feature. Conclusions: Our findings suggest that food insecurity metrics are associated with alterations in gut microbial composition. The consistent enrichment of Sutterella in food-insecure children in this study suggests the need for future mechanistic studies to explore its role in mediating the effects of food insecurity. Full article