Search Results (574)

Thailand is facing a rapidly aging society, raising concerns about how retiring workers will maintain their quality of life. Insured persons in the social security system—especially voluntary members under Section 40 of the Social Security Act B.E. 2533 (1990), who are often informal workers—frequently lack formal retirement plans, underscoring the need for interventions that address financial security and subjective well-being (SWB) in later life. This study aimed to develop and evaluate a retirement planning program designed to enhance subjective well-being and improve the quality of life for pre-retirees in Songkhla Province. A Research and Development (R&D) design was employed in four phases. Phase 1 (R1) involved a needs assessment: survey data from 500 insured individuals (ages 40–60) were collected to identify gaps between current and desired retirement preparedness. Phase 2 (D1) utilized the needs assessment results and theoretical frameworks to design a Subjective Well-being Retirement Planning Program, encompassing financial, health, and psychosocial components. Content-relevance experts validated the draft program. Phase 3 (R2) involved implementing the program with 15 volunteer participants over four weekly workshops (each 3 h long) and evaluating its short-term pilot outcomes using pretest-posttest measures of subjective well-being. Phase 4 (D2) refined the program based on evaluation findings and expert feedback. Results indicated that following participation in the program, participants’ overall subjective well-being and all sub-dimensions (life satisfaction, positive and negative affect balance, sense of meaning, social connectedness, security, and health) were significantly higher than before (p < 0.001). Additionally, the proportion of participants classified as inadequately prepared for retirement (high-risk due to low planning) decreased markedly, suggesting increased readiness within the pilot group. Expert evaluations of the program design reflected a high content validity index and strong agreement on the program’s accuracy, appropriateness, and usefulness for the target group. In conclusion, the developed retirement planning program was associated with short-term improvements in subjective well-being and quality-of-life indicators among insured pre-retirees. This theory-informed program, developed through an R&D process, offers a model for supporting aging workers in the social security system, with implications for policymakers and practitioners seeking to promote healthy, happy, and secure retirements in an aging society. Full article

In emerging environments such as cloud computing and the Internet of Things (IoT), secure authentication and key negotiation play a crucial role in protecting data transmitted over public networks. However, many existing authentication protocols are still designed based on classical public-key cryptography primitives, and quantum computing may threaten their security. To address this challenge, we propose a post-quantum authentication and key agreement protocol that uses the lattice-based Kyber key encapsulation mechanism (KEM). Our proposed protocol integrates cryptographic authentication, smart card protection, and post-quantum key encapsulation mechanisms, enabling mutual authentication between users and servers and securely establishing session keys. The security of the protocol is formally analyzed in the Real-or-Random (ROR) model under the random oracle assumption and the IND-CCA security of the underlying KEM scheme. Furthermore, through informal security analysis, we have further demonstrated that the protocol possesses important security properties, including anonymity, untraceability, perfect forward confidentiality, and resistance to known attacks. In addition, the computational cost and communication overhead of the proposed scheme are evaluated and compared with several representative authentication protocols. The results show that the proposed protocol can provide strong security while maintaining low computational cost and communication overhead. Full article

Body area networks (BANs) require secure intra-body communication, yet sensor nodes are too resource-constrained for conventional public-key cryptography, and pre-shared key schemes conflict with plug-and-play clinical workflows. This paper introduces PhysioKey, a TinyML-based key agreement framework that derives symmetric session keys from physiological signals without pre-shared secrets or trusted third parties. A lightweight 1D-CNN (6320 parameters, INT8-quantized, 31.2 KB flash) extracts embeddings from ECG and PPG windows on ARM Cortex-M4 class devices, which are reconciled through fuzzy commitment with BCH error-correcting codes. Patient-level 5-fold cross-validation on PTB-XL (500 patients, dual-ECG) achieves EER of $7.8\%\pm 0.8\%$ with ROC AUC $0.978\pm 0.004$; on BIDMC (53 patients, ECG + PPG), a dual-encoder architecture reduces cross-modal EER to $30.6\%\pm 1.2\%$. Since standalone PhysioKey yields only 7–24 effective key bits, the recommended deployment mode is a hybrid PhysioKey + ECDH protocol providing 128-bit security while PhysioKey adds physical on-body authentication; standalone operation suits energy-constrained scenarios with its $27\times$ advantage over ECDH. HKDF-SHA-256 post-processing yields session keys passing all six NIST SP 800-22 tests (≥96% at the 1024-bit level). Full article

Small-scale inland fisheries in the Amazon are critical for food security, yet their sustainability is increasingly threatened by overexploitation and environmental degradation. In data-limited contexts such as the northern Ecuadorian Amazon, the absence of continuous monitoring constrains the development of adaptive management strategies. This study develops an integrated socio-ecological baseline to support the establishment of fisheries agreements in five Indigenous communities of the Napo and Aguarico rivers. Through a participatory monitoring approach, we generated reproductive parameters (gonadosomatic index, fecundity, size at first maturity), population structure metrics, and length–weight relationships for key subsistence species across three hydrological phases. Reproductive investment exhibited marked seasonality, with peak gonadosomatic indices during rising waters in most species, identifying a critical period for protection. Life-history strategies ranged from high-fecundity periodic strategists to low-fecundity equilibrium species, implying differentiated vulnerability to harvesting. Community perceptions prioritized large migratory catfish and floodplain habitats, aligning with biological indicators of vulnerability. High performance in technical training demonstrated the feasibility of long-term local monitoring systems. By linking biological indicators with local ecological knowledge, this study proposes a pathway from baseline assessment to adaptive co-management. The framework presented here provides a transferable model for strengthening sustainability, governance, and food security in tropical small-scale fisheries facing persistent data limitations. Full article

This paper studies the effects of leasing on credit risk and access to credit. The repossession of a leased asset is generally easier than the enforcement of collateral associated with securing a standard loan agreement. We argue that this greater efficiency in enforcement mitigates, ceteris paribus, the counterparty’s moral hazard. To test this hypothesis, we developed a credit rationing model in which income is privately observed and non-verifiable, and financial intermediaries share credit risk information about borrowers. Financial contracts that are more rapidly enforced, such as in leasing, enable the screening of relatively safer projects or credit rationing reduction. We provide empirical evidence consistent with this prediction for the Italian credit market and considerations for the effects of monetary policy variables on the model’s equilibrium. Full article

As a higher-layer protocol over a controller area network (CAN) or CAN with a flexible data-rate bus, Society of Automotive Engineers (SAE) J1939 has been widely adopted in commercial vehicles. Although it supports advanced diagnostics, complex data transmission, and network management in harsh environments, SAE J1939 lacks native authentication mechanisms. Consequently, in-vehicle communication remains vulnerable to replay, spoofing, and injection attacks. In practice, deploying a Security-designated Electronic Control Unit (SeCU) is often deemed necessary to provide robust authentication, as generating and distributing session keys is essential. However, this introduces a single point of failure and renders the SeCU a high-value target for attackers. To address these issues, we propose J1939-ADBE, an authentication and key-distribution scheme that operates without a centralized SeCU. The scheme is built on Authenticated Distributed Broadcast Encryption (ADBE), a tightly integrated construction that augments distributed broadcast encryption with publicly verifiable sender authentication in a shared bilinear setting. By leveraging ADBE, we eliminate the requirement for a SeCU while achieving the desired security goals. Using the Tamarin Prover, we formally verify in the Dolev–Yao model that J1939-ADBE satisfies injective agreement, session secrecy, known-key security, and forward secrecy. Furthermore, the broadcast nature of ADBE reduces the communication cost of key distribution from $\mathcal{O}\left(n\right)$ to $\mathcal{O}\left(\right|\mathcal{G}\left|\right)$, where n denotes the number of Electronic Control Units (ECUs) and $\left|\mathcal{G}\right|$ denotes the number of ECU logical groups. Experimental results show that our proposal is practical for authentication within SAE J1939 networks. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

Frequent major emergencies threaten the security of the feed grain import supply chain. Enhancing import resilience is essential for supporting a new development pattern. However, research on a dedicated system to evaluate the resilience of China’s feed grain imports remains limited. In addition, strategies to strengthen resilience based on country-specific distances are still underexplored. This study constructs a comprehensive indicator system for China’s feed grain import resilience, using data from 2000 to 2023. It empirically examines the impact of country distance on this resilience across four dimensions: geographic distance, economic distance, institutional distance, and cultural distance. The findings indicate that country distance has an inhibitory effect on China’s feed grain import resilience. This conclusion holds true even after testing various adjustments, such as changes to core explanatory and dependent variables, modifications in sample sizes, alterations in measurement methods, and the introduction of instrumental variables. Further analysis reveals that country distance undermines feed grain import resilience by significantly reducing trade efficiency. However, the Belt and Road Initiative (BRI) and Regional Trade Agreements (RTA) help mitigate the negative impact of country distance on resilience. To strengthen China’s feed grain import resilience, it is crucial to enhance cultural and institutional trust, improve trade efficiency, and optimize import distribution. This study provides empirical evidence to support the safety of China’s feed grain imports and promote efficient, mutually beneficial trade in feed grains with partner countries. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant challenge due to the open wireless environment, high drone mobility, and strict computational and energy constraints. Existing authentication mechanisms either rely on computationally expensive cryptographic operations or remain validated only at the protocol or simulation level, leaving a critical gap in practical, hardware-validated solutions suitable for resource-constrained drone platforms. This gap motivates the need for a lightweight, privacy-preserving authentication scheme that is both theoretically sound and experimentally deployable on real hardware. To address this, we propose a Physically Unclonable Functions (PUF)-assisted lightweight authentication scheme for IoD environments that binds cryptographic keys to each drone’s intrinsic hardware characteristics via PUFs. The scheme employs dynamically generated pseudo-identities to conceal permanent drone identities and prevent tracking, while authentication and key agreement are achieved using efficient symmetric cryptographic primitives, including SHA-256 for key derivation and updates, AES-256 for secure communication, and lightweight XOR operations to minimize overhead. Forward secrecy is ensured through rolling key updates, and periodic renewal of PUF challenges enhances resistance to replay and modeling attacks. To validate practicality, both software-based and hardware-based implementations were developed and evaluated. The software evaluation demonstrates a low communication overhead of 708.5 bytes and an average computation time of 18.87 ms. The hardware implementation on a Nexys A7-100T FPGA operates at 100 MHz with only 12.49% LUT utilization and low dynamic power consumption of approximately 182.5 mW. These results confirm that the proposed framework achieves an effective balance between security, privacy, and efficiency. The significance of this work lies in providing a fully hardware-validated, PUF-based authentication framework specifically tailored to the real-world constraints of IoD environments, offering a practical foundation for securing next-generation drone networks. Full article

In recent years, the Mediterranean basin has been characterized as a climate change hotspot due to its rapid transition to warmer conditions and the strong agreement among most climate models predicting a significant decrease in precipitation by the end of the 21st century. These robust signals of climate change highlight the region’s high susceptibility to hydrometeorological extremes, such as droughts, which are expected to become more frequent, prolonged, and intense. In this context, the study focuses on Greece, where rising water scarcity threatens critical sectors such as food security, energy production, public health, and, more broadly, the resilience of ecosystems. Future drought conditions were assessed using the 12-month Standardized Precipitation Index (SPI-12) for 58 meteorological stations during 2071–2100, based on high-resolution regional climate simulations under RCP4.5 and RCP8.5. Spatial drought variability was examined using Principal Component Analysis, while drought severity and duration were quantified through Run Theory. The results indicate increasingly prolonged and severe droughts by the late 21st century, particularly in eastern Crete and southeastern Peloponnese, highlighting the urgent need for targeted adaptation measures. Full article

The Medical Internet of Things (MIoT) has promoted smart healthcare through the deep integration of wearable devices, wireless communication, and cloud services. However, this framework faces security risks, as attackers may exploit public channels to impersonate legitimate devices or services and steal sensitive data. Therefore, establishing authentication between wearable devices and servers prior to data transmission is crucial. Existing schemes suffer from two critical drawbacks: vulnerability to quantum attacks and excessively high communication overhead, highlighting the need for improved solutions. The authors of this paper present a multi-factor identity authentication protocol to achieve post-quantum security and privacy protection. The scheme integrates lattice-based Kyber key encapsulation and a fuzzy commitment mechanism to secure biological templates and enable post-quantum key agreement. Additionally, hash functions and lightweight error correction codes are employed to reduce terminal communication overhead. The security of the scheme is rigorously proved in the Real-or-Random model, and the analysis confirms that the scheme satisfies common security requirements for wireless networks. The proposed scheme is also compared with existing schemes, and the results demonstrate that it achieves a balance between security and overhead. Full article

In Social Internet of Vehicles (SIoV) environments, fog computing plays a crucial role in supporting real-time services by reducing the latency inherent in cloud-based architectures. However, fog nodes are typically deployed in physically exposed roadside environments and can be operated by several system operators, making them vulnerable to physical compromise and unauthorized access. Despite these threats, many existing authentication schemes assume fog nodes to be fully trusted or honest-but-curious, allowing them to decrypt transmitted data using a session key shared among vehicles, fog nodes, and cloud servers. To overcome these limitations, this paper proposes a quantum-secure pairwise key agreement scheme that establishes distinct session keys for vehicle–fog, fog–cloud, and vehicle–cloud communications. This design effectively prevents the disclosure of sensitive information even in the event of fog node compromise. Furthermore, Physical Unclonable Functions (PUFs) are employed to mitigate physical capture attacks, while lattice-based cryptography based on the Module Learning with Errors (MLWE) problem is integrated to ensure resistance against quantum computing attacks. The security of the proposed protocol is rigorously validated through formal analysis using AVISPA, BAN logic, and the Real-or-Random (RoR) model, in addition to informal security analysis. Comparative performance evaluations against related schemes demonstrate that the proposed approach achieves a balance between efficiency and security, making it well suited for practical deployment in SIoV environments. Full article

Background/Objectives: Federated learning has emerged as a promising paradigm for enabling collaborative artificial intelligence in healthcare while preserving data privacy. However, most existing frameworks focus on isolated tasks and lack integrated pipelines that combine classification, segmentation, explainability, and automated clinical reporting. Methods: This study proposes FedIHRAS, a privacy-preserving federated learning framework designed for multi-institutional radiological analysis. The system integrates multi-task deep learning modules, including pathology classification using a modified ResNet-50 backbone, anatomical segmentation, explainability through Grad-CAM, and automated report generation supported by semantic aggregation using SNOMED CT. The framework employs confidence-weighted aggregation, differential privacy mechanisms, and secure aggregation protocols to ensure privacy and robustness across heterogeneous institutional datasets. Results: Experimental evaluation was conducted across four large-scale chest X-ray datasets representing simulated institutional nodes, totaling approximately 874,000 images. FedIHRAS achieved high diagnostic performance with strong cross-institutional generalization and demonstrated improved robustness under non-IID data distributions. Additional experiments showed favorable communication efficiency, effective privacy–utility trade-offs, and strong agreement with expert radiologist assessments. Conclusion: The proposed FedIHRAS framework demonstrates that federated learning can support scalable, privacy-preserving, and clinically meaningful radiological AI systems. By integrating multi-task learning, explainability, and automated reporting within a unified federated architecture, the framework addresses key limitations of existing approaches and contributes to the development of collaborative AI in healthcare. Full article

Amidst the polycrisis of 2018–2024, Asia-Pacific trade flows exhibited a structural resilience that contrasts with traditional theoretical predictions of severe trade contraction under high uncertainty. This study investigates these resilience dynamics using a structural gravity model estimated via the Poisson Pseudo Maximum Likelihood (PPML) approach. The analysis utilizes a balanced panel of 14 key regional economies (N = 4914), explicitly disaggregated into geographic (ASEAN-6 vs. non-ASEAN) and global value chain (high vs. low GVC intensity) subgroups to capture heterogeneous responses. The empirical results confirm that economic policy uncertainty (EPU) acts as a significant trade friction (β = −3.371), consistent with the wait-to-invest mechanism of real options theory. However, this effect is heterogeneous and significantly mitigated by institutional frameworks. We identify a robust institutional shield effect, where participation in trade agreements effectively neutralizes the adverse transmission of policy shocks (interaction coefficient = 3.396). Furthermore, this study uncovers a structural break during periods of extreme geopolitical conflict, characterized by a convex U-shaped relationship between uncertainty and trade. This pattern provides macro-level evidence of a behavioral shift in regional supply chains from a just-in-time cost-efficiency optimization model to a just-in-case security maximization paradigm, consistent with precautionary inventory accumulation. These findings underscore the critical role of modern trade pacts as institutional credibility anchors and the necessity of adaptive strategies in navigating heightened macroeconomic volatility. Full article