Search Results (1,589)

Mercury (Hg) is a ubiquitous element in the environment that may pose a threat to human health due to its toxicity, high mobility through the food chain, and long-lasting persistence. Organic Hg compounds, particularly methylmercury, are more toxic than inorganic mercury due to their easy absorption and persistent retention within the organism. Although natural attenuation can occur in soil through various processes, excessive levels of Hg cause pollution that can adversely affect agricultural soil, making remediation necessary to either remove or stabilize Hg within the soil. This review primarily aims to summarize key remediation strategies—chemical, biological, and physical—developed in recent years for agricultural soil remediation. It discusses the influencing factors, advantages, limitations, mechanisms, and practical applications of these soil remediation technologies. The published literature focuses on identifying plant species and microorganisms capable of remediating Hg-contaminated soils. Emerging amendments, such as biochar and nanomaterials, have been tested for treating mercury (Hg)-polluted soils primarily by immobilizing mercury and reducing its bioavailability and methylation. Ex situ remediation technologies are effective for Hg-contaminated soils but are often costly, labor-intensive, detrimental to soil quality, and generate hazardous secondary waste. In contrast, in situ technologies treat Hg directly within the soil, preserving the soil matrix and its biota. According to the literature, remediation of Hg-contaminated agricultural soils can be compatible with food crop production only if the bioavailable Hg fraction is sufficiently reduced and crop uptake remains below food safety limits. The gap between laboratory trials and actual field applications in Hg-contaminated soil remediation mainly arises from differences in scale, complexity, and the uncertainty of real-world conditions, which often reduce the efficiency and predictability of treatments. This review aims to provide a practical reference for improving the effective remediation of Hg-contaminated soils in the future. Full article

Hyperconnected IoT ecosystems have become crucial for organizational operations; yet, existing governance structures remain fragmented, are technology-centric, and not well-equipped to manage the risks, compliance pressures, and resilience needs of IoT. This paper presents an integrated, theory-based information security governance model that is tailored for IoT-driven organizations. A conceptual synthesis is performed through integrating five theoretical anchors: governance theory, socio-technical systems theory, risk governance theory, institutional/compliance theory, and resilience/adaptive capacity theory. These theoretical lenses are used to derive essential governance constructs and to develop a modular architecture tailored to IoT security needs. The model’s validity is grounded in theoretical integration rather than empirical testing, consistent with the nature of conceptual research. The integrated model provides six interdependent governance dimensions: strategic governance, operational governance, technical oversight, compliance alignment, risk governance, and resilience/adaptation, anchored by an ecosystem coordination layer. It provides structured decision rights, continuous risk monitoring, regulatory legitimacy, and native adaptive capabilities toward dynamic cyber-physical threats. This research addresses a known gap in the literature on IoT governance by providing an integrated, theoretically validated governance model that systematically connects the rationale and operational mechanisms of governance for resilient, future-proof IoT adoption. The model is further operationalized through a five-level maturity structure, enabling organizations to assess and progressively enhance governance capabilities. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

The fall armyworm (Spodoptera frugiperda) is a major global migratory pest. Its increasing insecticide resistance poses a severe threat to food security. Developing biopesticides such as SfMNPV is critical for sustainable control. Nevertheless, the early molecular mechanisms underlying the S. frugiperda midgut response to oral SfMNPV challenge remain poorly understood. This study utilized high-throughput transcriptome sequencing to systematically characterize the dynamic transcriptional profiles of the larval midgut at 1, 12, and 24 h after oral SfMNPV inoculation. Results showed that the midgut transcriptional response to SfMNPV is time and stage-specific. During this period, the physical midgut barrier underwent remodeling, with core components of the peritrophic matrix downregulated at 1 h, followed by the basal lamina at 12 h, alongside the activation of cytoskeleton genes during 12–24 h. Concurrently, sustained endoplasmic reticulum stress, autophagy, and ubiquitin system responses occurred from 12 to 24 h. At the metabolic level, the defense system exhibited a functional succession, shifting from ABC transporters and UDP-glycosyltransferases at 1 h to glutathione S-transferases and superoxide dismutase at 12–24 h. Additionally, the midgut tissue exhibited a cascade transition from pro-apoptotic signaling at 1 h to compensatory regenerative repair mediated by the Wnt, mTOR, and Hippo pathways at 12–24 h. This study elucidates the molecular process of barrier damage, homeostatic imbalance, and tissue remodeling during early oral SfMNPV challenge. These findings provide a global perspective on baculovirus-host interactions and establish a theoretical foundation for designing novel biopesticides targeting the midgut interaction. Full article

Heavy metal pollution poses a serious threat to water resource security and ecological health, due to its high toxicity, persistence, and bioaccumulation. Accordingly, it is crucial to develop efficient, low-cost, and environmentally friendly adsorption materials. Biomass-based materials, as a widely available, renewable, and low-cost natural organic resource, exhibit significant advantages for water pollutant adsorption and removal due to their unique porous structures and abundant active functional groups. This review systematically summarizes the classification strategies, fabrication methodologies, and adsorption performances of biomass-based materials for aqueous heavy metal ion removal. Key factors governing adsorption behavior, including solution pH, temperature, initial ion concentration, and adsorbent dosage, are critically analyzed to elucidate structure–property–performance correlations. Particular emphasis is placed on the underlying adsorption mechanisms, encompassing physical adsorption, surface complexation, ion exchange, electrostatic interactions, and synergistic interfacial effects. By integrating recent advances in material design and mechanistic understanding, this review provides a comprehensive framework bridging fundamental research and practical implementation, and highlights future opportunities for engineering next-generation sustainable biomass adsorbents toward efficient heavy metal ion decontamination. Full article

With the rapid development of Internet technology, information security has become increasingly important. Cryptographic analysis techniques, especially side-channel analysis (SCA), pose a significant threat to security systems. The latest SCA technology mainly utilizes the physical leakage signals generated during the operation of encryption devices, such as power consumption, temperature and electromagnetic radiation. These signals themselves carry the physical characteristics of the device, which are related to the encryption algorithm. Among them, the power consumption trace remains the main target of modern SCA research. However, such trajectories often bring about some analytical difficulties, such as the data sequence being too long, the feature points being distributed sparsely, and the internal relationships of the data being complex. These challenges hinder effective analysis. While Transformer architectures are good at capturing long-range dependencies in sequential data, their high computational complexity limits practical deployment. To address this, we propose replacing the self-attention (SA) module in Transformers with a focal modulation module. This modification significantly reduces computational complexity and reduces computational operations during feature extraction, enabling efficient and accurate side-channel attacks. Experimental results on benchmark datasets (ASCAD, AES_RD, AES_HD, DPAv4) demonstrate the superiority of our approach. The proposed method achieves a reduction in training time compared to standard Transformer models, and achieves superior key recovery performance, outperforming existing state-of-the-art models. Full article

The increasing prevalence of multidrug-resistant (MDR) pathogens, particularly avian pathogenic Escherichia coli (APEC) and methicillin-resistant Staphylococcus aureus (MRSA), poses a severe threat to the breeding industry and human health. To develop novel antibiotic alternatives, we adopted a “converting virulence into therapy” strategy by leveraging the type VI secretion system (T6SS) of the APEC strain ACN17-20. Guided by the structural analysis of T6SS Protein 00145, we rationally designed a series of amphipathic α-helical polypeptides. Among them, polypeptide A7 emerged as a lead candidate, exhibiting potent broad-spectrum antibacterial activity with negligible cytotoxicity against mammalian cells. Mechanistic studies revealed that A7 exerts a rapid bactericidal effect through a dual mode of action: physical disruption of bacterial membrane integrity leading to cytoplasmic leakage, and induction of lethal oxidative stress via reactive oxygen species (ROS) accumulation. Furthermore, A7 demonstrated excellent efficacy in eradicating pre-formed bacterial biofilms, addressing the challenge of persistent infections in breeding environments. In a mouse sepsis model induced by APEC and MRSA, A7 treatment significantly improved survival rates (60–80%), reduced bacterial loads in vital organs, and attenuated the systemic cytokine storm (TNF-α and IL-1β), thereby alleviating immune-mediated tissue damage. In conclusion, this study identifies polypeptide A7 as a safe therapeutic agent with a dual mechanism of action, providing a promising strategy to combat MDR infections and reduce antibiotic dependence. Full article

This paper presents a conceptual architecture for an AI-Driven Adaptive Encryption Device (AI-AED), a tri-modular hardware platform embodied in a registered industrial design. The device integrates a Secure Input Module, an AI-Enhanced Central Processing Unit with biometric authentication, and a Secure Output Module connected by unidirectional buses. We formalise the adaptive encryption policy as a constrained Markov decision process (CMDP) over a discrete action space of 216 cryptographic configurations, with safety constraints that provably prevent convergence to insecure states. A formal threat model based on extended Dolev–Yao assumptions with four physical access tiers defines attacker capabilities, and anti-downgrade safeguards enforce a monotonically non-decreasing security floor during threat escalation. An information-theoretic analysis shows that adaptive algorithm selection contributes an additional entropy term H(α) to ciphertext uncertainty, upper-bounded by log₂(|L_enc|) ≈ 1.58 bits, while noting this represents increased attacker uncertainty rather than a strengthening of any individual cipher. A component-level latency model estimates 0.91–1.00 ms pipeline latency under normal operation and 3.14–3.42 ms under active threat, including integration overhead. Simulation validation over 1000 episodes compares a tabular Q-learning baseline against the proposed Deep Q-Network operating on the continuous state space: the DQN achieves 82% fewer constraint violations, 6× faster threat response, and more stable policy switching, demonstrating the advantage of continuous-state reinforcement learning for safety-critical adaptive encryption. All claims are positioned as theoretical contributions requiring empirical validation through prototype implementation. Full article

The transition to AI-driven Cyber–Physical Systems has fundamentally reshaped transportation, introducing systemic risks that transcend traditional physical boundaries. Unlike prior reviews focused on isolated technological domains, this survey proposes a novel “End-to-End” analytical framework that models the causal propagation of vulnerabilities from physical sensing hardware to human cognitive responses. Synthesizing 140 research contributions (2017–2025), we evaluate the paradigm shift from deterministic control to Generative AI and Large Language Models (Transportation 5.0). To substantiate our framework, we introduce a structured cross-layer threat matrix and mathematically formalize the technology–cognition cascade, explicitly mapping how physical layer perturbations, such as optical jamming, bypass digital edge security to trigger hazardous behavioral reactions in human drivers. We conclude that ensuring the resilience of next-generation infrastructure requires a unified analytical architecture that formally bounds hardware constraints, algorithmic safety, and human trust. Full article

Background: Black transgender women experience disproportionately high rates of violent victimization rooted in intersecting systems of oppression, including cisgenderism and anti-Black racism. Although victimization is linked to psychological distress, the mental health impacts of intersectional violence, which targets overlapping marginalized identities, remain understudied. Objectives: To examine the associations between anticipated and experienced intersectional victimization and psychological distress among Black transgender women. Methods: Online survey data from 151 Black transgender women (age ≥ 18) in the United States (US) between October 2021 and February 2024 were analyzed using t-tests and multivariate linear regressions. Results: In models controlling for age, employment, and US region, experienced sexual, physical, and threats of intersectional violence, as well as anticipated intersectional violence, were associated with increased post-traumatic stress disorder (PTSD) symptom severity, in separate models. Conversely, only experienced sexual intersectional violence and anticipated intersectional violence were associated with greater depressive symptom severity. When all violence variables were included simultaneously, experienced intersectional sexual violence and anticipated violence remained significantly associated with PTSD and depressive symptoms in separate models. Conclusions: Service providers who work with Black transgender women should routinely assess for anticipated and experienced intersectional victimization to guide person-centered interventions. Further research is needed to distinguish the effects of intersectional victimization from opportunistic victimization and to inform the adaptation of targeted mental health interventions. Full article

Anna Burns’ No Bones has extensively documented its depiction of trauma during the Troubles; less attention has been paid to the systematic mechanisms through which pervasive psychosocial harm is quietly administered and normalised. This article moves beyond readings of individual suffering to diagnose a collective condition, arguing that Burns constructs a veritable architecture of harm: a meticulously designed system operating not through overt aggression alone, but through the mundane, yet powerfully insidious, interplay of social forces governing everyday life. This synthesis reveals how these forces converge to produce what Achille Mbembe terms a death-world: a state of being where populations are subjected to conditions that confer upon them the status of the living dead. Within this necropolitical landscape, the protagonist Amelia’s routines are dictated by shrinking spatial affordances, while incessant rumour functions as a policing mechanism that enforces social death long before physical death is a threat. This analysis demonstrates that harm is not an atmospheric byproduct of conflict, but the very logic of this architecture, which compels the community to participate in its own subjugation. Ultimately, by mapping this architecture, this article reframes Burns’ novel from a historical text of the Troubles into a trenchant meditation on the governance of populations under duress. It offers a vital framework for understanding how quiet harm is spatially engineered, a dynamic with profound relevance for contemporary studies of carceral geographies, algorithm-driven social control, and the politics of atmospheric violence. It posits Burns’ work as a crucial resource for theorising the invisible structures that shape and constrain modern life. Full article

Vigilance is used to detect distant threats in many species of birds. Allocation of time to vigilance is shaped by the social and physical environment of individuals, but little research has focused on how weather variables affect vigilance. Wind speed, in particular, can influence vigilance by decreasing manoeuvrability during escape, increasing energy costs or by decreasing the ability to communicate and assess risk. We examined how wind speed influenced vigilance in sentinels of a cooperative breeder, the Florida Scrub-Jay (Aphelocoma coerulescens). Sentinels in this species occupy vantage points to monitor their surroundings and can devote all their time to vigilance during sentinel bouts. We found that head turns in sentinels, which allow individuals to monitor different areas, became more frequent under windier conditions. Wind speed is not likely to affect manoeuvrability in sentinels that are already close to cover. Energy costs during high wind likely are not as relevant to sentinels as they may be to other group members, since sentinels do not forage. We conclude that the ability to assess risk visually was probably compromised by movements in the vegetation caused by wind. The study highlights behavioural adjustments to weather-related environmental variability. Full article

Background: Breast cancer poses not only a physical health threat but also significant emotional and identity challenges for women, particularly regarding femininity and body image. Understanding how patients adapt psychologically can guide effective psychosocial interventions. Objective: This study aimed to evaluate psychological adaptation, coping strategies, illness acceptance, and body image in women with breast cancer and identify factors associated with better adjustment. Methods: A cross-sectional study was conducted among 30 women aged 22–66 undergoing treatment at the Wielkopolskie Centrum Onkologii, Poland. Standardized tools included the Mini-MAC scale (coping strategies), Acceptance of Illness Scale (AIS), and Body Image Scale (BIS). Descriptive statistics and correlations were analyzed. Results: Most participants exhibited a constructive coping style, with positive redefinition and fighting spirit being predominant. Some women simultaneously showed elements of a destructive coping style, including helplessness and hopelessness, indicating complex emotional reactions. Overall, participants demonstrated high illness acceptance, despite notable body image-related discomfort, particularly shame, reduced perceived attractiveness, and appearance-related anxiety. While age did not correlate significantly with coping or body image, a significant negative association was found between age and illness acceptance, with younger women showing better adjustment. Conclusions: Psychological adaptation to breast cancer is multidimensional and individualized, dependent on personality traits, internal resources, and social support. Findings highlight the need for holistic, patient-centered psychosocial care, addressing both emotional adaptation and body image-related distress, including support for intimacy and prosthetic interventions. Individualized strategies can improve quality of life and functional outcomes during and after cancer treatment. Full article