Search Results (1,097)

Highly pathogenic avian influenza (HPAI) remains a persistent threat to global poultry production and public health. Current vaccine platforms show limited cross-clade efficacy and often fail to induce mucosal immunity. Recent advances in microbiome research reveal critical roles for gut commensals in modulating vaccine-induced immunity, including enhancement of mucosal IgA production, CD8⁺ T-cell activation, and modulation of systemic immune responses. Engineered commensal bacteria such as Lactococcus lactis, Bacteroides ovatus, Bacillus subtilis, and Staphylococcus epidermidis have emerged as promising live vectors for antigen delivery. Postbiotic and synbiotic strategies further enhance protective efficacy through targeted modulation of the gut microbiota. Additionally, artificial intelligence (AI)-driven tools enable predictive modeling of host–microbiome interactions, antigen design optimization, and early detection of viral antigenic drift. These integrative technologies offer a new framework for mucosal, broadly protective, and field-deployable vaccines for HPAI control. However, species-specific microbiome variation, ecological safety concerns, and scalable manufacturing remain critical challenges. This review synthesizes emerging evidence on microbiome–immune crosstalk, commensal vector platforms, and AI-enhanced vaccine development, emphasizing the urgent need for One Health integration to mitigate zoonotic adaptation and pandemic emergence. Full article

Despite the widespread accessibility of vaccines and antivirals, seasonal influenza virus epidemics continue to pose a threat to public health. In this study, we constructed a recombinant replication-deficient simian adenovirus type 25 vector carrying the full-length hemagglutinin (HA) of the H1N1 influenza virus, named rSAd25-H1. Both systemic and mucosal humoral immune responses, as well as the protective efficacy, were assessed in mice immunized via the intramuscular (IM) or intranasal (IN) route. A single-dose IM or IN administration of rSAd25-H1 elicited a robust systemic IgG antibody response, including hemagglutination inhibition antibodies. As expected, only IN immunization was able to induce IgA production in serum and respiratory mucosa. Notably, a single dose of rSAd25-H1 at the highest dose (10¹⁰ viral particles) conferred complete protection against lethal homologous H1N1 challenge in mice despite the route of administration. These findings demonstrate the potential of simian adenovirus type 25-based vectors as a promising candidate for intranasal vaccine development targeting respiratory pathogens. Full article

The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms. Full article

Turbidity currents pose significant threats to offshore seabed infrastructures, including subsea hydrocarbon production facilities and submarine communication cables. These powerful underwater flows can damage pipelines, potentially causing hydrocarbon spills that endanger local communities, the environment, and negatively impact energy production infrastructures. Therefore, a comprehensive understanding of the spatio-temporal development and destructive force of turbidity currents is essential. While numerical computation of 3D flow, sediment transport, and substrate exchange is possible, field-scale simulations are computationally intensive. In this study, we develop a simplified morphodynamic approach to model the flow properties of channelized turbidity currents and the associated trends of sediment accretion and erosion. This model is applied to the Baco–Malaylay submarine system to investigate the dynamics of a significant turbidity current event that impacted a submarine pipeline offshore the Philippines. The modeling results align with available seabed assessments and observed erosion trends of the protective rock berm. Our simplified modeling approach shows good agreement with simulations from a fully 3D numerical model, demonstrating its effectiveness in providing valuable insights while reducing computational demands. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality of disruptive agents. This paper presents a game-theoretic approach to protecting power system infrastructure against deliberate attacks, taking into account the effects of demand response. The interaction between the disruptive agent and the system operator is modeled as a leader–follower Stackelberg game. The leader, positioned in the upper-level optimization problem, must decide which elements to render out of service, anticipating the reaction of the follower (the system operator), who occupies the lower-level problem. The Stackelberg game is reformulated as a bilevel optimization model and solved using a metaheuristic approach. To evaluate the applicability of the proposed method, a 24-bus test system was employed. The results demonstrate that integrating demand response significantly enhances system resilience, compelling the disruptive agent to adopt alternative attack strategies that lead to lower overall disruption. The proposed model serves as a valuable decision-support tool for system operators and planners seeking to improve the robustness and security of electrical networks against disruptive agents. Full article

Plant diseases resulting from pathogens and pests constitute a persistent threat to global food security. Pathogenic infections of plants are influenced by environmental factors; a concept encapsulated in the “disease triangle” model. It is important to elucidate the complex molecular mechanisms underlying the interactions among plants, their pathogens and various environmental factors in the disease triangle. This review aims to highlight recent advancements in the application of systems biology to enhance understanding of the plant disease triangle within the context of microbiome rising to become the 4th dimension. Recent progress in microbiome research utilizing model plant species has begun to illuminate the roles of specific microorganisms and the mechanisms of plant–microbial interactions. We will examine (1) microbiome-mediated functions related to plant growth and protection, (2) advancements in systems biology, (3) current -omics methodologies and new approaches, and (4) challenges and future perspectives regarding the exploitation of plant defense mechanisms via microbiomes. It is posited that systems biology approaches such as single-cell RNA sequencing and mass spectrometry-based multi-omics can decode plant defense mechanisms. Progress in this significant area of plant biology has the potential to inform rational crop engineering and breeding strategies aimed at enhancing disease resistance without compromising other pathways that affect crop yield. Full article

Small news media organizations are increasingly reshaping the news media system in Latin America. They are stepping into the role of watchdogs by investigating issues such as corruption scandals that larger outlets sometimes overlook. However, this journalistic work exposes both journalists and their organizations to a range of security threats, including physical violence, legal pressure, and digital attacks. In response, these outlets have developed coping strategies to manage and mitigate such risks. This article presents an exploratory study of the approaches adopted to protect information and data, ensure the safety and well-being of journalists, and maintain organizational continuity. Based on a series of in-depth interviews with leaders of award-winning news organizations for their investigative reporting, the study examines a shift from a competitive newsroom model to a collaborative approach in which information is shared—sometimes across borders—to support investigative reporting and strengthen security practices. We identify strategies implemented by small news organizations to safeguard their journalistic work and propose an integrative model of news safety encompassing the following three areas of security: physical, legal, and digital. This study contributes to the development of the newsafety framework and sheds light on safety practices that support media freedom. Full article

This research examines the relationship between environmental, social safety and governance risks, and the mitigation strategies of the host country to enhance project performance in the China–Pakistan Economic Corridor (CPEC). The study concludes that the timely and effective completion of CPEC projects is challenged by environmental, social safety, and governance (ESG) risks, including environmental degradation, security threats, and governance issues. Based on the data of 618 respondents from Pakistan and using Structural Equation Modeling (SEM) through SMART PLS 4, the study investigates the impact of sustainable environmental practices, safety and security measures, governance risk mitigation actions, and project management systems on the project performance of CPEC projects. The results show that mitigation efforts implemented by the host country reduce the ESG investment risk and yield a positive effect on the project performance. Hence, this paper will show the importance of proactive measures such as sustainable development practices, security risk management systems, and transparent governance practices in matching challenges and enhancing project benefits. This research reinforces the potential for these risks to be mitigated through the adoption of innovative technologies. Innovation in environments, social protection, and governance frameworks can greatly mitigate the negative impacts of risks, directly improving the outcomes of project delivery. Infrastructure projects are extremely challenging to manage, and this study gives key hints for enhancing project safety and risk management in those types of infrastructure projects for practitioners, policymakers, project managers, and other stakeholders to establish innovative, sustainable strategies. Full article

Plastic debris is a pervasive and persistent threat to marine ecosystems. Microplastics (plastics < 5 mm) are increasing in a variety of marine habitats, including open water systems, shorelines, and benthic sediments. It remains unclear how microplastics distribute and accumulate in marine systems and the extent to which this pollutant is accessible to marine taxa. We examined subtidal benthic sediments and beach sediments in critical nearshore habitats for forage fish species—Pacific sand lance (Ammodytes personatus), Pacific herring (Clupea pallasi), and surf smelt (Hypomesus pretiosus)—to quantify microplastic concentrations in the spawning and deep-water habitats of these fish and better understand how microplastics accumulate and distribute in nearshore systems. In the San Juan Islands, we examined an offshore subtidal bedform in a high-flow channel and beach sites of protected and exposed shorelines. We also examined 12 beach sites proximate to urban areas in Puget Sound. Microplastics were found in all samples and at all sample sites. Microfibers were the most abundant, and flakes were present proximate to major shipyards and marinas. Microplastics were significantly elevated in Puget Sound compared to the San Juan Archipelago. Protected beaches had elevated concentrations relative to exposed beaches and subtidal sediments. Microplastics were in higher concentrations in sand and fine-grain sediments, poorly sorted sediments, and artificial sediments. Microplastics were also elevated at sites confirmed as spawning habitats for forage fish. The model results indicate that both current speed and proximate urban populations influence nearshore microplastic concentrations. Our research provides new insights into how microplastics are distributed, deposited, and retained in marine sediments and shorelines, as well as insight into potential exposure in benthic, demersal, and shoreline habitats. Further analyses are required to examine the relative influence of urban populations and shipping lanes and the effects of physical processes such as wave exposure, tidal currents, and shoreline geometry. Full article

In the face of escalating cyber threats to critical infrastructures, achieving robust cyber resilience has become paramount. This paper proposes an endogenous security-oriented framework for cyber resilience assessment, specifically tailored for critical infrastructures. Drawing on the principles of endogenous security, our framework integrates dynamic heterogeneous redundancy (DHR) and adaptive defense mechanisms to address both known and unknown threats. We model resilience across four key dimensions—Prevention, Destruction Resistance, Adaptive Recovery, and Evolutionary Learning—using a novel mathematical formulation that captures nonlinear interactions and temporal dynamics. The framework incorporates environmental threat entropy to dynamically adjust resilience scores, ensuring relevance in evolving attack landscapes. Through empirical validation on simulated critical infrastructure scenarios, we demonstrate the framework’s ability to quantify resilience trajectories and trigger timely defensive adaptations. Empiricalvalidation on a real-world critical infrastructure system yielded an overall resilience score of 82.75, revealing a critical imbalance between strong preventive capabilities (90/100) and weak Adaptive Recovery (66/100). Our approach offers a significant advancement over static risk assessment models by providing actionable metrics for strategic resilience investments. This work contributes to the field by bridging endogenous security theory with practical resilience engineering, paving the way for more robust protection of critical systems against sophisticated cyber threats. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article

Addressing privacy concerns is one of the key challenges facing the development of Internet of Things (IoT)-based systems (IoTSs). As IoT devices often collect and process personal and sensitive information, strict privacy policies must be defined and enforced to keep data secure and safe, ensuring security and regulatory compliance. Any data breach could compromise the security of the system, leading to various types of threats and attacks, some of which could even endanger human life. Therefore, it is crucial to design and build a comprehensive and general privacy framework for the development of IoTSs. This framework should not be limited to specific IoTS domains but should be general enough to support and cover most IoTS domains. In this paper, we present a framework that assists developers by (i) enabling them to build IoTSs that comply with privacy standards, such as the General Data Protection Regulation (GDPR), and (ii) providing a simplified and practical approach to identifying and addressing privacy concerns. In addition, the framework enables developers to implement effective countermeasures. Full article

Meloidogyne enterolobii, a highly virulent and broad-host-range plant-parasitic nematode, poses an increasing threat to global agricultural production. By inducing the formation of nutrient-rich giant cells in host roots and deploying a diverse array of effector proteins to modulate plant immune responses, this nematode achieves efficient colonization and invasion, resulting in impaired crop growth and significant economic losses. In recent years, global climate warming combined with the rapid development of protected agriculture has broken the traditional geographical limits of tropical and subtropical regions, thereby increasing the risk of M. enterolobii occurrence in temperate and high-latitude areas. Concurrently, conventional chemical control methods are increasingly limited by environmental pollution and the development of resistance, steering research toward green control strategies. This review systematically summarizes the latest research progress of M. enterolobii in terms of ecological diffusion trends, pathogenic mechanisms, and green control, and explored the feasibility of integrating multidisciplinary technologies to construct an efficient and precise control system. The ultimate aim is to provide theoretical support and technical supports for green and sustainable development of global agriculture. Full article