Search Results (7,636)

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

In most traditional quantum public-key cryptosystems, the public key held by the key management center (KMC) is a group of quantum systems. The public key is destroyed after a secret communication process, and so users must reconstruct the public key with the KMC after every communication process or hold many copies of the public key in the beginning. This requirement is an obstacle to the practical application of such quantum cryptosystems. This paper describes a quantum public-key cryptosystem with reusable keys using entangled states. Each user shares a set of entangled quantum systems with the KMC as that individual user’s (public key, private key) pair. Two users can exchange secret communications with the help of the KMC. Moreover, the states of the quantum systems revert to their original states. The user’s (public key, private key) pair is unchanged so that the keys are reusable. It is unnecessary for users to reconstruct the public key with the KMC or save many copies of the public key in the KMC. As a result, this public-key cryptosystem is much less expensive to manage and easier to realize in practice than most traditional quantum public-key cryptosystems. Full article

Caring for adult children with disabilities represents a prolonged and emotionally demanding experience for family caregivers, frequently associated with psychological distress and reduced well-being. This study examined the effects of a group-based parent training program on key psychological dimensions in caregivers of adults with disabilities. One hundred and nine caregivers participated in a psychoeducational intervention and completed measures of self-compassion, perceived self-efficacy, emotional maturity, empathy, and adult attachment. Results showed significant improvements in self-compassion and self-efficacy, with trends toward more secure attachment patterns, while empathy remained stable. Overall, findings suggest that parent training can foster emotional regulation and caregiving processes. Full article

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), is rapidly changing how higher education approaches teaching, learning, and assessment. In cybersecurity education, professional certification exams are key for measuring competence and helping professionals find better job offers, but there is little research on how GenAI systems perform in these exam settings. This study looks at how three popular LLMs, ChatGPT-5, Gemini-2.5 Pro, and Copilot-2.5 Pro, handle 183 practice questions from the CompTIA Security+ certification. The study used a two-phase evaluation: a domain-based assessment and a full-length practice exam that mirrors real certification tests. The researchers measured model performance with accuracy scores, chi-square tests for statistical differences, and an error taxonomy to spot patterns of mistakes important for education. All three GenAI systems scored above the passing mark, and there were no significant differences between them. Still, the error analysis showed ongoing conceptual and classification mistakes that did not show up in the overall accuracy scores. Our results show that GenAI systems can pass structured certification tests, but accuracy by itself does not fully measure professional skills. The study points out important issues for the reliability and validity of AI-based assessments in higher education and stresses the need for more realistic, concept-focused ways to evaluate GenAI in cybersecurity education. 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

Drought, a major abiotic stressor affecting global agricultural productivity, significantly reduces crop yields and threatens food security worldwide. As the primary organ for perceiving soil moisture signals and absorbing water, the crop root system architecture plays a pivotal role in plant adaptation to drought conditions. With the development of high-throughput imaging technologies (i.e., 2D/3D image acquisition), high-throughput genotyping platforms, and gene-editing technologies, significant progress has been achieved in the characterization of root traits and the dissection of molecular genetic regulatory networks underlying these traits in crops. This review comprehensively synthesizes recent advances in the phenotypic characterization, underlying molecular regulatory networks, and functional roles of key root architectural traits, including the root length, angle, density, and root hair development, in enhancing drought resilience. Finally, we discuss the existing challenges in the current research and provide an outlook on the future trend of integrating multi-omics, high-throughput phenomics, and genome editing technologies to breed new drought-resistant crop varieties with ideal drought-resistant root architectures. Full article

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks. Full article

Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive patient data. We propose a framework that integrates ensemble machine learning (ML) models with a formal differential privacy (DP) mechanism. Using a dataset of 5110 samples with clinical features, we evaluate Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CAT) for BS and HD prediction. To protect individual privacy, we apply the Gaussian mechanism of DP with two probabilities of failure (POF) parameters (10–5 and 10–6) and a privacy budget ranging from 0.5 to 5.0. A key novelty of this work is the application of Pareto frontier multi-objective optimization (PFMOO) to systematically identify the optimal trade-off between model accuracy and privacy constraints. Our approach successfully identifies optimal, privacy-preserving models: XGB achieves top performance for BS prediction (92.3% accuracy, 92.29% F1 score), with a POF of 10–6, while RF excels for HD detection (95.61% accuracy, 97.8% precision), with a POF of 10–5. Furthermore, we employ explainable AI (XAI) techniques, SHAP and LIME, to provide interpretability of the model decisions, enhancing clinical trust. This research delivers a robust, interpretable, and privacy-conscious framework for early disease detection, offering a significant advancement over existing methods by holistically balancing accuracy, data security, and transparency. Full article

The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) for identifying dummy power traces in side-channel analysis (SCA). A controlled benchmarking framework is developed to evaluate training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. To move beyond idealized simulation, hardware-relevant factors, including finite measurement budgets and device noise, are incorporated, and inference robustness under degraded operating conditions is assessed. The results show that VQCs can capture meaningful discriminative patterns in structured side-channel data, although robustness and performance depend strongly on encoding strategy, circuit depth, and measurement conditions. These findings provide an empirical assessment of the potential and limitations of QML for side-channel security and offer practical guidance for future research. Full article

With the advancement of 5G and the Internet of Things (IoT), traditional upper-layer authentication mechanisms are vulnerable to attacks, while quantum computing threatens cryptographic security. Radio frequency fingerprint identification (RFFI) offers a physical-layer solution by exploiting inherent hardware imperfections. However, in complex electromagnetic environments, narrowband and especially agile interference (characterized by low power and narrow bandwidth) can severely distort fingerprint features, rendering conventional detection algorithms ineffective. To address this challenge, this paper proposes a novel interference detection framework tailored for Orthogonal Frequency Division Multiplexing (OFDM) systems. First, a signal transmission model incorporating non-ideal hardware characteristics (e.g., DC offset, I/Q imbalance) is established. Based on this model, we design an agile interference detection algorithm comprising two key components: (1) a time-series anomaly detection method that fuses multi-domain expert features (fractal, complexity, and high-order statistics) with machine learning, demonstrating superior performance over the traditional CME algorithm under narrowband interference, and (2) a progressive search segmental detection algorithm that, combined with reconstruction error features extracted by an autoencoder, effectively identifies low-power agile interference by appropriately trading-off computation time for detection sensitivity. Finally, an OFDM simulation platform is developed to validate the proposed methods. The results show that the segmental detection algorithm achieves reliable detection at a jammer-to-signal ratio (JSR) as low as −10 dB, significantly outperforming existing approaches and enhancing the robustness of RFFI in challenging interference environments. Full article

While return migrant workers (RMWs) are increasingly viewed as key to rural development, their specific impact on grain production remains ambiguous. Clarifying this role is critical to manage the dual nature of their reintegration—leveraging valuable resources and knowledge while addressing complex reintegration challenges—to ensure national food security and advance agricultural modernization. Drawing on data from the 2018 China Labor-force Dynamics Survey (CLDS), this study explicitly tests the hypothesis that migration experience significantly reduces the likelihood that RMW households engage in grain production. The empirical results from probit models support this hypothesis, and this finding is robust across multiple specifications. Further analysis shows that migration experience significantly reduces land cultivation scales—especially among larger producers—and increases land abandonment. Additionally, it inhibits technology adoption or invest in agricultural technology. These results suggest that migration experience may weaken, rather than enhance, RMWs’ commitment to grain production, challenging the policy expectation that they can lead agricultural transformation. The study calls for more nuanced policy interventions that account for the structural constraints facing RMW households and their limited contribution to large-scale, efficient grain farming. Full article

Islands often suffer from relatively limited freshwater resources, and the effective utilization and distribution of water resources are a key issues for the sustainable development of island-based economies and societies. While island water security has been widely discussed, few studies trace the socio-technical construction of island water-supply systems across the stages of planning, construction, and operation. Integrating Actor-Network Theory with political ecology, this study investigates the water-supply infrastructure of Kinmen. Drawing on official archives, participant observation, and in-depth interviews, this research analyzes the collective actions mobilized to address Kinmen’s water scarcity following the lifting of martial law in 1992. These efforts jointly reshaped both water-supply practices and the infrastructural network. Over the past three decades, Kinmen’s water-supply system has transformed into a sophisticated technological network, integrating reservoirs, desalination plants, and advanced sewage infrastructure. The introduction of these technologies, which function as critical non-human actors within the system, marks a clear shift in how water is managed and distributed. However, the rapid expansion of water-intensive industries, especially tourism, liquor distilling, and cattle farming, has outpaced local ecological limits, precipitating the current water crisis. The study concludes that this shortage has been mitigated through the strategic integration of water sources, most notably the cross-strait pipeline from mainland China, which now provides more than 80 percent of the island’s water. This transition marks a profound shift in the island’s socio-technical and geopolitical network. Full article

Deep saline aquifers are key targets for secure CO₂ geological storage because of their petrophysical and geochemical characteristics. This study conducts two-dimensional radial numerical simulations of CO₂–brine flow and dissolution to examine plume migration and dissolution in saline aquifers while allowing porosity and permeability to evolve with pressure. The model outputs include reservoir pressure, porosity, permeability, gas saturation, and dissolved CO₂, with additional analyses of permeability anisotropy, initial reservoir pressure, and stratified sandstone–shale architecture. Simulations with evolving properties predict a smaller radial plume extent than simulations with fixed properties, together with a maximum pressure buildup of about 2 MPa near the injection well. In a homogeneous aquifer, porosity and permeability increase nonlinearly during injection and reach about 1.25 and 2.6 times their initial values near the injection well after 1200 days, whereas the increases are lower in the sandstone–shale case at about 1.16 and 2.0 times because shale interlayers confine the enhanced zone to the lower sandstone. Increasing permeability anisotropy shifts migration toward lateral spreading, and higher initial reservoir pressure reduces plume extent. Overall, the assumption of constant porosity and permeability tends to predict larger plume footprints and different pressure responses, with sensitivity controlled by anisotropy, initial pressure, and shale interlayers. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article