Search Results (173)

As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts. Full article

Climate change poses an escalating threat to food security in the Near East, a region characterized by water scarcity, rapid population growth, and heavy dependence on rainfed agriculture. Despite extensive research on climate change impacts on crop yields, the effects on rainfed crop suitability—the fundamental capacity of a region’s climate to support crop growth—remain insufficiently explored, particularly across transboundary river basins. This study assesses the impact of climate change on the suitability of seven rainfed crops in the Near East, specifically the Nile, Levant, and Tigris-Euphrates River basins. Using the EcoCrop model and climate projections for 2041–2060 under RCP 4.5 and 8.5 scenarios, we analyzed changes in crop suitability relative to a 1970–2000 baseline. Results project significant temperature increases (2.1–3.8 °C) and precipitation reductions (8–20%) in the Levant by mid-century, leading to alarming declines in crop suitability. While the Nile Basin is projected to gain substantial rainfall (+214 billion m³ under RCP 8.5 by 2050), the Fertile Crescent faces a significant rainfall decrease (−24 billion m³ under RCP 8.5 by 2050). Contrary to the negative impacts predicted for the Levant and parts of the Fertile Crescent, the Tigris-Euphrates basin shows potential suitability gains for maize and olives (up to +30% under RCP 4.5 for maize), with olives also showing increased suitability in other basins. However, the suitability of the remaining five rainfed crops is projected to decline across all basins under both emission scenarios. These findings highlight the complex and regionally diverse impacts of climate change on agricultural productivity in the Near East and provide critical information for cross-border food and water security policies. Full article

Given livestock’s crucial role in global food security and economic stability, the alarming threat of climate change calls for the implementation of effective mitigation strategies for climate-resilient livestock production. Management and nutritional strategies offer temporary relief, whereas genetic approaches represent a permanent solution. The role of genetic tools in enabling the development of climate-resilient livestock breeds is widely recognized. Genetic tools like microarrays, RNA-seq, omics, and GWAS can improve the understanding of livestock’s climate adaptability at a molecular level. These tools facilitate the identification of biomarkers for thermo-tolerance, bordering on climate-resilient livestock breeding. Among them, studies employing genome-wide association studies (GWAS) have increased in recent years. GWAS have the potential to improve the genetic basis of thermo-tolerance in heat-stressed livestock populations. GWAS have been used to identify candidate genes for complex and economically important traits in livestock. These include growth, reproduction, disease resistance, milk, meat, and wool production traits under heat stress conditions. This makes GWAS a useful tool for identifying biomarkers that can be incorporated in breeding programs through marker-assisted selection (MAS). The integration of these potential biomarkers into selection and breeding programs would allow GWAS to substantially refine breeding strategies, thereby advancing the climate-resilient potential and sustainability of the livestock sector. Furthermore, GWAS, when utilized along with emerging technologies like Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) for genomic prediction, can predict genetic aspects of livestock adaptation more efficiently and precisely. Thus, future studies should focus on integrated modeling approaches for improving the climate resilience of livestock without jeopardizing their production potential. Such an effort will contribute to sustainable livestock production as well as ensure food security for the growing human population amid changing climate conditions. Full article

This study investigates the comminution behavior and beneficiation potential of lithium-bearing ores, zinnwaldite from Cínovec (Czech-Germany border) and lepidolite from Villasrubias (Spain) by integrating mineralogical analysis and mechanical characterization. The research is driven by Europe’s need for secure lithium supply chains. In particular, it focuses on the challenges associated with low-grade, fine-grained lithium micas found in hard-rock ores, which offer significant potential to supply in Europe but also pose substantial processing challenges. QMA (Quantitative Microstructural Analysis) revealed distinct differences in the textural and structural characteristics of the studied ores. Zinnwaldite-bearing rocks are coarser-grained with high interlocking and roughness, while lepidolite-bearing samples showed finer grains, lower roughness, and more disseminated mica distribution, indicated by their low clustering degree. In terms of mechanical characterization, zinnwaldite-rich ores have the lowest compressive strength, while lepidolite-rich samples showed the highest values, attributed to their finer grain size and more cohesive structure. This suggests that lepidolite may require higher energy input and finer crushing stages to achieve the target liberation size. These features influenced the breakage behavior observed during mechanical testing and comminution and are essential for enabling selective comminution, separating mica from gangue material. This study contributes to analyzing the potential of European hard-rock lithium resources from the perspective of upstream comminution, which is an essential step influencing downstream energy consumption, reagent use, and overall recovery efficiency. The results of this research emphasize that selective comminution should not rely solely on mineral hardness contrasts but must incorporate microstructural parameters such as clustering, grain size distribution, and orientation. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article

This research explores the multifaceted dynamics of water (in)security in Pakistan-Occupied Jammu and Kashmir (POJK), examining the region as a vector of broader transboundary hydro-politics between India and Pakistan. The study begins by outlining the current state of water infrastructure and governance within POJK, highlighting key issues such as water scarcity, environmental degradation, and socio-political marginalization in access to water. It then transitions into a critical analysis of transboundary water management between India and Pakistan under the auspices of the Indus Waters Treaty (IWT), emphasizing how the unique geopolitical ambiguity of POJK complicates cooperative water governance. The paper contends that POJK’s water scarcity is both a humanitarian concern and a flashpoint for regional instability, warranting more inclusive, transparent, and robust green political governance frameworks. By bridging localized realities with transboundary water cooperation, this study offers a structured interpretive analysis of how water (in)security in POJK reverberates beyond its borders, informing the need for a deep ecological approach. Full article

In an era of rising geopolitical tensions, repeated global crises, and growing uncertainty in trade and finance, economic resilience has become a key priority for policymakers. This study presents an understanding by distinguishing regional resilience from global resilience, offering hardnosed explanations of both concepts and outlining mensurable indicators for each. Regional resilience is the capacity of an economy to endure and recuperate from shocks by way of strong, cost-effective connections in its region. These could be seen in terms of intra-bloc trade power, trade concentration, intra-regional investment flows and constant capital flows, which indicate the deep economical integration and interdependence. On the contrary, global resilience is concerned with the extent to which an economy is guarded by larger global diversification. It is quantified by the distribution of exports and investments geographically, the extent and diversity of trade partners, membership on global value chains, and the stability of the cross-border capital flows. Understanding the difference between these two forms of resilience has become increasingly important for policy design, especially in a period marked by repeated crises, geopolitical tension, and shifting trade and financial conditions. Countries must decide not only how open their economies should be, but also whether openness should be integrated regionally, diversified globally, or stable through a hybrid approach. Further, it argues that regional integration is peculiarly invaluable during region-wide disruptions such as pandemics, financial crises, or supply shortages, where integrated policies can reduce adjustment costs and protect demand and supply chains. However, global diversification becomes significant in areas such as energy and commodity security, where dependence on limited suppliers can magnify risks. Ultimately, most economies benefit from combining both approaches (a hybrid approach), adapting their strategy to the development stage, institutional strengths, and exposure to external shocks. Full article

Cryphalus dilutus is an emerging invasive pest of tropical and subtropical regions, with Mangifera indica and Ficus carica being its primary host plants. Larval damage caused by this insect can lead to severe tree wilting, posing a direct threat to agricultural production and ecological security. Native to South Asia, C. dilutus has established introduced populations in the Near East, Mexico, and other areas. In recent years, it has invaded multiple regions, including southern China and southern Italy. Given the widespread global distribution of host plants and the intensification of climate change, their distribution ranges are expected to expand. However, research assessing the potential global geographical distribution of this pest under climate change is lacking. In this study, we used the Random Forest model to predict the potential distribution range of C. dilutus. Under historical climatic conditions between 1970 and 2000, suitable climatic regions for C. dilutus were primarily distributed across southern China, southeastern Brazil, southeastern Mexico, the Congo Basin periphery, and the Iberian Peninsula, with a total area of 12,192.42 × 10⁴ km². The Temperature Annual Range and Precipitation of Warmest Quarter were identified as key environmental determinants that shaped its distribution. Under the future RCP4.5 climate scenario projected for the 2050s, the total suitable area for C. dilutus is projected to contract. Specifically, high-, medium-, and low-suitability areas are projected to decline by 52.77%, 62.39%, and 24.02%, respectively. While the total area of the very low zones is expected to increase, the total area of the suitable region has been reduced to 11,891.17 ×10⁴ km². Future climate change is expected to drive the distribution northward to high-altitude areas and inland areas. Model projections indicate a poleward expansion of the fundamental climatic niche, with climatic suitability increasing in high-latitude and high-altitude regions, such as Northern Europe and western North America. Conversely, current core tropical habitats in the Indian subcontinent and the Amazon Basin are projected to face significant habitat degradation due to thermal stress. Agricultural regions previously considered relatively safe due to climatic constraints, such as northern China, the midwestern United States, and Eastern Europe, may face new challenges from pest infestation. These findings underscore the importance of proactive monitoring and implementation of preventive measures. This provides crucial decision support for countries and regions to formulate precise pest control strategies and offers a theoretical basis for early monitoring and prevention of cross-border invasions on a global scale. Full article

Rapid agricultural expansion threatens water security in one of the world’s largest hydroelectric systems, the Itaipu dam, located on the Brazil–Paraguay border. Yet regional hydrological responses to land cover change and climate variability remain insufficiently characterized at management-relevant scales. The Upper Paraná River Basin (UPRB), which sustains agriculture, hydropower, and municipal water supply across both countries, exemplifies this challenge as accelerating cropland conversion raises concerns about long-term water availability. This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. We integrate GRACE/GRACE-FO (Gravity Recovery and Climate Experiment Follow-On), Terrestrial Water Storage Anomalies (TWSAs), MODIS (Moderate Resolution Imaging Spectroradiometer) land cover, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, and LandScan population density using Pettitt’s breakpoint test and Mann–Kendall trend analysis to detect temporal breakpoints and quantify co-variability between hydrology and land surface dynamics. Together, these methods identify a significant basin-wide shift in TWSAs in mid-2009, with storage increases of 151.6 cm at Itaipu and 103.1 cm at Yguazú Reservoir. Over the study period, cropland expanded from 13.5% to 37.9% of total land cover, while savanna declined from 28.1% to 24.2%. After 2009, correlations between land cover and TWSAs strengthened substantially, particularly for wetlands (r = 0.88), croplands (r = 0.73), and savannas (r = −0.81; all p < 0.001), indicating strong coupling between landscape transformation and basin-scale storage variability. Principal Component Analysis shows land use change explains 39–41% of TWSA variance, exceeding hydroclimatic contributions. Granger causality analysis reveals bidirectional coupling between wetlands and water storage at Itaipu, while cropland and savanna dynamics exert predictive influence on downstream hydrology in the Yguazú basin. Water balance decomposition further indicates a post-2009 regime shift, with residual storage transitioning from −10.6 to +4.7 and 78% greater runoff generation per unit precipitation, consistent with reduced infiltration capacity. Together, these findings underscore intensifying land–water feedback and the need for adaptive watershed management under expanding agriculture and climate variability. Full article

As a pivotal hub for the northward advancement of the Belt and Road Initiative and a strategic outpost for national security, the spatial patterns of towns in the border regions of northern Inner Mongolia exert a direct impact on the region’s sustainable development and long-term prosperity. This study focuses on 141 border towns situated along the Inner Mongolia stretch of China’s northern border. By leveraging analytical tools including kernel density analysis, standard deviation ellipse method, and nearest neighbor index analysis, it explores the distinctive characteristics of their spatial distribution. Furthermore, this study applies the Geodetector method to quantify the explanatory power of key influencing factors on the spatial differentiation of these border towns. The findings can be summarized as follows: (1) The border towns along the Inner Mongolia stretch of the northern border displayed a distinct heterogeneous distribution gradient characterized by prominent regional agglomeration and formed a three-tier spatial hierarchy. Specifically, the Bayannur–Hetao Plain Town Cluster served as the primary agglomeration core, supplemented by two secondary clusters, namely the Xing’an League–Southern Greater Khingan Range Town Cluster and the Hulunbuir–Border Port Town Cluster. In contrast, the Alxa League constituted a low-density peripheral belt with sparse town distribution. (2) Factor analysis via Geodetector revealed that the spatial distribution pattern of these border towns was primarily driven by the core mechanism of port-led urbanization. This core driver was synergistically reinforced by secondary factors such as mineral resource endowments, jointly shaping a complex spatial layout that partially transcended natural geographical constraints—a stark contrast to coastal ports, where development is dominated by innate natural geographic advantages. Full article

Groundwater is a critical strategic resource supporting agricultural production and ecological security in the transboundary river basins of Northeast China. However, intensified climate variability and rapid agricultural expansion over the past two decades have imposed increasing pressure on regional groundwater systems. In this study, we integrated GRACE-derived terrestrial water storage anomalies, GLDAS land surface data, meteorological datasets, land-use information, and agricultural statistics to construct a comprehensive assessment framework consisting of groundwater storage anomalies (ΔGWS), the GRACE Groundwater Drought Index (GGDI), and sustainability indicators—REL (Reliability), RES (Resilience), VUL (Vulnerability), and SI (Sustainability Index). By integrating GRACE-derived groundwater dynamics with sustainability indicators (REL, RES, VUL, and SI), enabling a basin-scale, long-term assessment of groundwater sustainability across Northeast China’s transboundary basins, and clarifying the relative roles of climatic variability and intensive human water use. We systematically examined the spatiotemporal evolution of groundwater conditions in the Heilongjiang, Suifen, Tumen, and Yalu River basins from 2002 to 2022, and quantified the relative roles of climatic and anthropogenic drivers. The results indicate that groundwater storage exhibited pronounced seasonal fluctuations alongside a persistent downward trend, with GGDI remaining predominantly negative after 2018, reflecting the development of structural groundwater drought. The SI declined markedly from 0.32 to 0.06, and areas with extremely low sustainability accounted for more than 90% of the study region in recent years. MIC-based dependence analysis showed that sown area (MIC = 0.98) and nighttime light intensity (MIC = 0.92) were the dominant drivers of groundwater degradation, exerting far greater influence than precipitation or potential evapotranspiration. These patterns highlight that policy-driven agricultural expansion and increased irrigation demand have surpassed natural recharge capacity, becoming the fundamental cause of long-term groundwater depletion. This study underscores the urgency of promoting agricultural green transformation, optimizing crop planting structures, improving irrigation efficiency, and enhancing ecological conservation to rebuild groundwater resilience. Moreover, coordinated cross-border groundwater monitoring and management will be essential for ensuring the sustainable use of water resources in Northeast Asia’s transboundary river basins. Full article

It is a hot topic to enhance the stability, security, and sustainability of industrial chains, against the backdrop of adjustments and rising uncertainty in global value chains. Using Chinese A-share listed firms from 2012 to 2022 as the research sample, this study treats the establishment of Cross-Border E-Commerce Comprehensive Pilot Zones (CBECCPZs) as a quasi-natural experiment and employs a difference-in-differences approach to empirically examine the impact of digital trade (DT) on industrial chain resilience (ICR) and its underlying mechanisms. The findings demonstrate that DT exerts a significantly positive effect on ICR, providing strong support for the long-term sustainability of the economic system. This conclusion remains robust after a series of robustness checks, including the incorporation of high-dimensional fixed effects, exclusion of confounding policy effects, adjustments to the sample, dimension-specific tests, consideration of lagged effects, and propensity score matching. Mechanism analysis reveals that DT strengthens ICR primarily by promoting firms’ digital transformation and improving human capital levels. The heterogeneity results suggest that the contribution of digital trade to resilience differs markedly across structural dimensions: the effect is more significant among firms located in eastern regions, state-owned enterprises, firms operating in regions with higher levels of digitalization, manufacturing firms, firms in more competitive industries, and firms with stronger internal control systems. From the perspective of ICR, this study elucidates the intrinsic mechanisms through which DT fosters high-quality development and sustainable economic growth. The findings provide robust empirical evidence for understanding the strategic role of DT in enhancing the security, stability, and sustainable operation of industrial chains and in building a modern industrial system that is autonomous, controllable, secure, and efficient. Moreover, the study offers important policy implications for governments seeking to advance DT institutional innovation and promote coordinated regional development, as well as for firms aiming to leverage DT to enhance long-term competitiveness and achieve sustainable development goals. Full article

Several state institutions are involved in border security management, including border guards, customs services, veterinary and phytosanitary supervision, and other institutions whose areas of responsibility overlap at border control points. In this study, we found that most EU member states still use sectoral systems, with varying degrees of cooperation. The authors emphasise the importance of providing a unified (comprehensive, integrated, and sustainable) approach to border security risk management. The study focuses on the security risk management of the external border. The authors explore a feasible methodological solution and provide recommendations for improving border security and common risk management at the tactical (one-year) level, based on an analysis of scientific literature and practical work experience, as well as surveys and empirical considerations. Quantitative and qualitative research methods are employed in the study. The study’s main findings demonstrate how methodological solutions can support sustainable risk management and provide essential risk assessment techniques. The authors propose a 5-level matrix to assess the impact of external border security risks. National and international agencies can apply the study’s outcome to facilitate mutual collaboration and enhance sustainable, common security risk management practices. Full article

As the threat from malicious UAVs continues to intensify, accurate identification of individual UAVs has become a critical challenge in regulatory and security domains. Existing single-signal analysis methods suffer from limited recognition accuracy. To address this issue, this paper proposes a high-precision individual identification method for UAVs based on FFS-SPWVD and DIR-YOLOv11. The proposed method first employs a frame-by-frame search strategy combined with the smoothing pseudo-Wigner–Ville distribution (SPWVD) algorithm to obtain effective time–frequency feature representations of flight control signals. Building on this foundation, the YOLOv11n network is adopted as the baseline architecture. To enhance the extraction of time–frequency texture features from UAV signals in complex environments, a Multi-Branch Auxiliary Multi-Scale Fusion Network is incorporated into the neck network. Meanwhile, partial space–frequency selective convolutions are introduced into selected C3k2 modules to alleviate the increased computational burden caused by architectural modifications and to reduce the overall number of model parameters. Experimental results on the public DroneRFb-DIR dataset demonstrate that the proposed method effectively extracts flight control frames and performs high-resolution time–frequency analysis. In individual UAV identification tasks, the proposed approach achieves 96.17% accuracy, 97.82% mAP50, and 95.29% recall, outperforming YOLOv11, YOLOv12, and YOLOv13. This study demonstrates that the proposed method achieves both high accuracy and computational efficiency in individual UAV recognition, providing a practical technical solution for whitelist identification and group size estimation in application scenarios such as border patrol, traffic control, and large-scale events. Full article