Search Results (257)

The escalating threats of zoonotic diseases, antimicrobial resistance (AMR), climate change, and environmental degradation have intensified the need for a unified health approach. One Health—integrating human, animal, and environmental health—is critical for national and global health security. India, with its high population density, biodiversity, and socio-ecological complexity, stands poised to lead in operationalizing this integrated vision. This review analyzes India’s evolving One Health ecosystem, focusing on policy development, inter-ministerial collaborations, surveillance systems, grassroots implementation, and education. Institutions like the National Centre for Disease Control (NCDC), Indian Council of Medical Research (ICMR), and Department of Biotechnology (DBT) are discussed. We propose a strategic blueprint built on integrated surveillance (One Network), cross-sectoral governance (One Nation), and field-level implementation (One Health). Highlighting successful case studies and India’s role in global platforms, the article presents a roadmap to bridge fragmented efforts into a resilient, community-driven national mission to protect human, animal, and environmental health. Full article

Hybrid AC/DC microgrids (HMGs) are increasingly recognized as a solution for the transition toward future energy systems because they can combine the efficiency of DC networks with an AC system. Despite these advantages, HMGs still have challenges in protection, cybersecurity, and reliability. Conventional protection schemes often fail due to reduced fault currents and the dominance of power electronic converters in islanded or dynamically reconfigured topologies. At the same time, IEC 61850 protocols remain vulnerable to advanced cyberattacks such as Denial of Service (DoS), false data injection (FDIA), and man-in-the-middle (MITM), posing serious threats to the stability and operational security of intelligent power networks. Previous surveys have typically examined these challenges in isolation; however, this paper provides the first integrated review of HMG protection across three complementary dimensions: traditional protection schemes, cybersecurity threats, and artificial intelligence/machine learning (AI/ML)-based approaches. By analyzing more than 100 studies published between 2012 and 2024, we show that AI/ML methods in simulation environments can achieve detection accuracies of 95–98% with response times under 10 ms, while these values are case-specific and depend on the evaluation setting such as network scale, sampling configuration, noise levels, inverter control mode, and whether results are obtained in simulation, hardware in loop (HIL)/real-time digital simulator (RTDS), or field conditions. Nevertheless, the absence of standardized datasets and limited field validation remain key barriers to industrial adoption. Likewise, existing cybersecurity frameworks provide acceptable protection timing but lack resilience against emerging threats, while conventional methods underperform in clustered and islanded scenarios. Therefore, the future of HMG protection requires the integration of traditional schemes, resilient cybersecurity architectures, and explainable AI models, along with the development of benchmark datasets, hardware-in-the-loop validation, and implementation on platforms such as field-programmable gate array (FPGA) and μPMU. Full article

Bt Cry toxins remain the cornerstone of transgenic crop protection against Lepidopteran pests, yet field-evolved resistance, particularly in invasive species such as Spodoptera frugiperda and Helicoverpa armigera, can threaten their long-term efficacy. This review presents a comprehensive and unified mechanistic framework that synthesizes current understanding of Bt Cry toxin modes of action and the complex, multilayered regulatory mechanisms of field-evolved resistance. Beyond the classical pore-formation model, emerging evidence highlights signal transduction cascades, immune evasion via suppression of Toll/IMD pathways, and tripartite toxin–host–microbiota interactions that can dynamically modulate protoxin activation and receptor accessibility. Resistance arises from target-site alterations (e.g., ABCC2/ABCC3, Cadherin mutations), altered midgut protease profiles, enhanced immune regeneration, and microbiota-mediated detoxification, orchestrated by transcription factor networks (GATA, FoxA, FTZ-F1), constitutive MAPK hyperactivation (especially MAP4K4-driven cascades), along with preliminary emerging findings on non-coding RNA involvement. Countermeasures now integrate synergistic Cry/Vip pyramiding, CRISPR/Cas9-validated receptor knockouts revealing functional redundancy, Domain III chimerization (e.g., Cry1A.105), phage-assisted continuous evolution (PACE), and the emerging application of AlphaFold3 for structure-guided rational redesign of resistance-breaking variants. Future sustainability hinges on system-level integration of single-cell transcriptomics, midgut-specific CRISPR screens, microbiome engineering, and AI-accelerated protein design to preempt resistance trajectories and secure Bt biotechnology within integrated resistance and pest management frameworks. Full article

This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16). Full article

Cross-site scripting (XSS) attacks are among the threats facing web security, resulting from the diversity and complexity of HTML formats. Research has shown that some text processing-based methods are limited in their ability to detect this type of attack. This article proposes an approach aimed at improving the detection of this type of attack, taking into account the limitations of certain techniques. It combines the effectiveness of deep learning represented by convolutional neural networks (CNN) and the accuracy of classification methods represented by support vector machines (SVM). It takes advantage of the ability of CNNs to effectively detect complex visual patterns in the face of injection variations and the SVM’s powerful classification capability, as XSS attacks often use obfuscation or encryption techniques that are difficult to be detected with textual methods alone. This work relies on a dataset that focuses specifically on XSS attacks, which is available on Kaggle and contains 13,686 sentences in script form, including benign and malicious cases associated with these attacks. Benign data represents 6313 cases, while malicious data represents 7373 cases. The model was trained on 80% of this data, while the remaining 20% was allocated for test. Computer vision techniques were used to analyze the visual patterns in the images and extract distinctive features, moving from a textual representation to a visual one where each character is converted into its ASCII encoding, then into grayscale pixels. In order to visually distinguish the characteristics of normal and malicious code strings and the differences in their visual representation, a CNN model was used in the analysis. The convolution and subsampling (pooling) layers extract significant patterns at different levels of abstraction, while the final output is converted into a feature vector that can be exploited by a classification algorithm such as an Optimized SVM. The experimental results showed excellent performance for the model, with an accuracy of (99.7%), and this model is capable of generalizing effectively without the risk of overfitting or loss of performance. This significantly enhances the security of web applications by providing robust protection against complex XSS threats. Full article

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use patterns for 2040 under three scenarios: Natural Development (NDS), Ecological Protection (EPS), and Urban Development (UDS). Results indicated a consistent decline in agricultural land and an expansion of urban land across all scenarios, with the most pronounced urban growth under UDS (6.79%) and the largest ecological land area under EPS (5178.96 km²). Since 2000, the number of EN sources and corridors had decreased, with sources mainly concentrated along coastal areas. The source and corridor under UDS exhibited the highest area ratio (20.08%), while NDS showed the lowest (18.72%), with UDS demonstrating the strongest resilience. Through community detection, the UDS EN was divided into five ecological clusters, encompassing 127 intra-cluster corridors (2285.95 km) and 34 inter-cluster corridors (1171.32 km), among which the cluster near the Yellow River estuary was determined to be the most critical (Level 1). These findings will provide valuable insights for managing landscape fragmentation and biological habitat protection in YRDEEZ. Meanwhile, the multi-scenario simulations of ENs could play an important role in constructing ecological security patterns and protecting ecosystems. Full article

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is challenging due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Complex water networks face prominent contradictions among flood control, water supply, and ecological protection, and traditional scheduling models struggle to address multi-dimensional water security challenges. To solve this problem, this study proposes a multi-objective coordinated water resources scheduling model for complex water networks, taking the Taihu Lake Basin as a typical case. First, a multi-objective optimization indicator system covering flood control, water supply, and aquatic ecological environment was constructed, including 12 key indicators such as drainage efficiency of key outflow hubs and water supply guarantee rate. Second, a dynamic variable weighting strategy was adopted to convert the multi-objective optimization problem into a single-objective one by adjusting indicator weights according to different scheduling periods. Finally, a combined solving mode integrating a basin water quantity-quality model and a joint scheduling decision model was established, optimized using the particle swarm optimization (PSO) algorithm. Under the 1991-Type 100-Year Return Period Rainfall scenario, three scheduling schemes were designed: a basic scheduling scheme and two enhanced discharge schemes modified by lowering the drainage threshold of the Xinmeng River Project. Simulation and decision results show that the enhanced discharge scheme with the lowest drainage threshold achieves the optimal performance with an objective function value of 98.8. Compared with the basic scheme, it extends the flood season drainage days of the Jiepai Hub from 32 to 43 days, increases the average flood season discharge of the Xinmeng River to the Yangtze River by 9.5%, and reduces the maximum water levels of Wangmuguan, Fangqian, Jintan, and Changzhou (III) stations by 5 cm, 5 cm, 4 cm, and 4 cm, respectively. This model effectively overcomes technical bottlenecks such as conflicting multi-objectives and complex water system structures, providing theoretical and technical support for multi-objective coordinated scheduling of water resources in complex water networks. Full article

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

In the current digital landscape, where platforms process AI-generated content and intelligent network traffic on a large scale, it is the duty of such platforms to continuously measure the reliability, trustworthiness, and security of various data streams. Driven by this practical challenge, this research develops an effective decision-support mechanism in intelligent decision-making in big-data AI-generated content and network systems. The decision problem has considered several uncertainties, including content authenticity, processing efficiency, user trust, cybersecurity, system scalability, privacy protection, and cost of computing. The multidimensional uncertainty of AI-generated information and trends in network behavior are challenging to capture in traditional crisp and fuzzy decision-making models. To fill that gap, a new Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) methodology is proposed, based on multi-perspective expert assessment and better computational aggregation to improve the accuracy of rankings, symmetry, and uncertainty treatment. A case scenario comprising fifteen different alternative intelligent decision strategies and seven evaluation criteria are examined under the evaluation of four decision-makers. The PF-FUCA model successfully prioritizes the best strategies to control AI-based content and network activities to generate a stable and realistic ranking. The comparative and sensitivity analysis show higher robustness, accuracy, and flexibility levels than the existing MCDM techniques. The results indicate that PF-FUCA is specifically beneficial in settings where a large amount of data has to flow, a high uncertainty rate exists, and the variables of decision are dynamic. The research introduces a scalable and credible methodological conception that can be used to facilitate high levels of intelligent computing applications to content governance and network optimization. Full article

Drought is one of the most severe abiotic stresses limiting agricultural productivity and threatening global food security. As the central organelle responsible for photosynthesis and stress perception, the chloroplast is highly sensitive to drought, and its structural and functional stability directly determines plant adaptability. Recent studies have revealed that chloroplasts undergo pronounced ultrastructural alterations under drought stress, including thylakoid membrane shrinkage, disorganization of grana stacks, and accumulation of reactive oxygen species (ROS). Excessive ROS production causes oxidative damage to lipids, proteins, and nucleic acids, whereas moderate ROS levels act as retrograde signals to regulate nuclear gene expression. In parallel, calcium (Ca²⁺) oscillations and retrograde signaling pathways—such as those mediated by GENOMES UNCOUPLED PROTEIN1 (GUN), 3′-phosphoadenosine-5′-phosphate (PAP), and Methylerythritol cyclodiphosphate (MecPP)—integrate chloroplast-derived stress cues with nuclear responses. To counteract drought-induced damage, plants activate a series of antioxidant systems—both enzymatic (Superoxide Dismutase (SOD), Ascorbate Peroxidase (APX), Catalase (CAT)) and non-enzymatic (Ascorbic Acid (ASA), (Glutathione) GSH, tocopherols, carotenoids)—along with protective proteins such as fibrillins (FBNs) and WHIRLYs that stabilize thylakoid and membrane structures. In addition, autophagy and plastid degradation pathways selectively remove severely damaged chloroplasts to maintain cellular homeostasis. Exogenous substances, including melatonin, 5-aminolevulinic acid (ALA), and Zinc oxide (ZnO) nanoparticles, have also been shown to enhance chloroplast stability and antioxidant capacity under drought stress. In this review, we discuss the structural and functional changes in chloroplasts, signaling networks, and protective repair mechanisms under drought stress. Furthermore, we highlight future research prospects for enhancing plant stress resilience through multi-omics integration, application of functional regulators, and molecular design breeding. Full article

Recently, the record-level rise in Internet of Things (IoT) devices has produced unparalleled security challenges, particularly for resource-constrained devices operating under limited computational resources, memory, and power. In this context, traditional cryptographic methods not only fail but are also expensive and require extensive resources, given their static nature. In this article, an Adaptive Hybrid Cryptographic Framework (AHCF) is proposed to address the security challenges of resource-constrained IoT devices by adaptively balancing performance and protection levels, which can adaptively adjust cryptographic parameters based on the state of the device at a given time under a specific network environment and security needs. It also effectively balances security level and resource usage and employs low-overhead asymmetric key management with lightweight symmetric cryptography and machine learning-based predictors for the optimal selection of encryption schemes. Experimental testing on multiple IoT platforms has demonstrated its significant benefits, namely 42% less energy consumption, a 38% increase in processor speed, and improved security responsiveness over static deployments. This solution can be applied on boards with as little as 2 KB RAM and 16 KB flash and outperforms existing IoT standards and protocols. Full article

The construction of urban ecological security patterns (ESPs) is an effective approach for managing ecological space and preventing the uncontrolled expansion of urban areas, thereby safeguarding the ecological security of urban agglomerations. This study focuses on the Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZTUA), utilizing an ESP framework based on ecosystem services, ecological sensitivity, landscape connectivity, and resistance surfaces (SSCR). The spatio-temporal evolution and driving forces of ESP were analyzed for 2010, 2015, and 2020. Based on this, the ecological control zones of the CZTUA were delineated according to ecosystem importance, and appropriate ecological improvement strategies were proposed. The findings revealed the following: (1) The number of ecological sources in the CZTUA decreased from 26 to 23, while their total area expanded from 1113.6 km² to 3013.96 km², indicating a “point-to-patch” development trend. Ecological corridors showed a “decrease–increase”trend in number, but their total length consistently contracted from 1025.69 km to 536.25 km, with greater emphasis on the efficiency and effectiveness of connecting habitats. Ecological nodes decreased from 14 to 5, while their aggregate area increased from 290.6 km² to 1796.48 km², mirroring changes in ecological sources. (2) Ecological sources, corridors, and nodes in the CZTUA are primarily located in the eastern mountainous and hilly regions, with a trend of expansion toward the western areas. The spatial distribution of corridors and nodes is shaped by these sources, with dense areas exhibiting short-distance networks and dispersal areas showing long-distance linear patterns. Node distribution shifts from entry/exit areas of ecological sources and corridors to the sources themselves. (3) The spatio-temporal evolution of the ESP in the CZTUA is driven by a dual-wheel mechanism of “natural foundation-policy regulation,” where precipitation and potential evapotranspiration serve as the primary natural drivers, manifested through water conservation. (4) The region is divided into three control levels: the core protected areas focus on ecological protection in the eastern mountainous and hilly regions; the ecological buffer areas emphasize ecological coordination in transitional landforms such as hills, medium-undulating mountains, and platforms; the intensive development areas, mostly located in platform, plain, and some hilly areas, prioritize ecological optimization. The three-tier control zones implement strategies of strict protection, buffering and coordination, and optimized development, respectively, providing a direct basis for the refined management of ecological spaces. Full article