Search Results (3,109)

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

Perfluoroalkyl substances (PFASs) possess immunosuppressive properties. However, their association with rheumatoid arthritis (RA) risk remains inconclusive across epidemiological studies. This study integrates population-based and mechanistic evidence to clarify the relationship between PFAS exposure and RA. We analyzed 8743 U.S. adults from the NHANES (2005–2018), assessing individual and mixed exposures to PFOA, PFOS, PFNA, and PFHxS using multivariable logistic regression, Bayesian kernel machine regression, quantile g-computation, and weighted quantile sum models. Network toxicology and molecular docking were utilized to identify core targets mediating immune disruption. The results showed that elevated PFOA (OR = 1.63, 95% CI: 1.41–1.89), PFOS (OR = 1.41, 1.25–1.58), and PFNA (OR = 1.40, 1.20–1.63) levels significantly increased RA risk. Mixture analyses indicated a positive joint effect (WQS OR = 1.06, 1.02–1.10; qgcomp OR = 1.26, 1.16–1.38), with PFOA as the primary contributor. Stratified analyses revealed stronger effects in females (PFOA Q4 OR = 3.75, 2.36–5.97) and older adults (≥60 years). Core targets included EGFR, SRC, TP53, and CTNNB1. PFAS mixtures increase RA risk, dominated by PFOA and modulated by sex/age. These findings help reconcile prior contradictions by identifying key molecular targets and vulnerable subpopulations, supporting regulatory attention to PFAS mixture exposure. Full article

Anaerobic germination (AG) is a pivotal trait for successful direct-seeded rice cultivation, encompassing rainfed and irrigated conditions. Elite rice cultivars are often vulnerable to flooding during germination, resulting in poor crop establishment. This drawback has led to the exploration of AG-tolerant rice landraces, which offer valuable insights into the genetic underpinnings of AG tolerance. Over the years, substantial progress has been made in identifying significant quantitative trait loci (QTLs) associated with AG tolerance, forming the basis for targeted breeding efforts. However, the intricate gene regulatory network governing AG tolerance remains enigmatic. This comprehensive review presents recent advances in understanding the physiological and genetic mechanisms underlying AG tolerance. It focuses on their practical implications in breeding elite rice cultivars tailored for direct-seeding systems. Full article

The coastal developments in the Middle East put low priority on tsunami risk assessment due to the rare occurrence and absence of genuine tsunami track records on the coastline in the past. Tsunami-vulnerable coasts, including the east coast of the UAE, need to prepare for, and pay attention to, the impact of future tsunamis due to increased earthquake activity in the region. This study investigated the tsunami characteristics of the nearshore from hypothetical tsunami conditions by applications of numerical modeling and Artificial Neural Network (ANN) methods. The modeling results showed that the maximum tsunami depth at the shore was highest in Khor Fakkan and Mirbih for the given tsunami boundary conditions, while the tsunami withdrawal was greater on the southern bathymetry compared to that on the northern bathymetry when the tsunami period increased. ANN results confirmed that the still sea depth and seabed slope were more important than the tsunami period when predicting the maximum tsunami depth at the shore. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic limitations. Full article

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building efforts. Grounded in participatory feminist research, this study employed a multi-method qualitative approach, including semi-structured interviews and oral history narratives, with 60 women in two climate-vulnerable provinces. Data were analyzed through thematic coding, CATWOE (Customers, Actors, Transformation, Worldview, Owners, Environmental Constraints) analysis, and descriptive statistics. The findings identify nine major climate-related events disrupting livelihoods and reveal a limited understanding of HLR as a long-term, transformative concept. Adaptation strategies remain short-term and focused on immediate survival. Barriers to HLR include financial constraints, limited access to agricultural resources and technology, and entrenched gender norms restricting women’s leadership and decision-making. While local governments, women’s associations, and community networks provide some support, gaps in accessibility and adequacy persist. Participants expressed the need for financial assistance, vocational training, agricultural technologies, and stronger peer networks. Strengthening HLR among agrarian women requires gender-sensitive policies, investment in local support systems, and community-led initiatives. Empowering agrarian women as agents of change is critical for fostering resilient rural livelihoods and achieving inclusive, sustainable development. Full article

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. 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

Urban infrastructure, such as water supply networks, sewage systems, and electricity networks, is essential for the functioning of cities and, consequently, for the well-being of citizens. Despite its essentiality, the distribution of infrastructure in urban areas is not homogeneous, especially in cities in developing countries. Socially vulnerable areas often face significant deficiencies in sewage and road paving, exacerbating urban inequalities. In this regard, urban planners must consider the multiple elements of urban infrastructure and assess the compensation levels between them to reduce inequality effectively. In particular, the complexity of the problem necessitates considering the multidimensionality and heterogeneity of urban infrastructure. This complexity qualifies the operational framework of composite indicators as the natural solution to the problem. This study develops a new weighting system for the balanced expansion of urban infrastructures through composite indicators constructed by the Ordered Weighted Average operator. Implementing these weighting systems provides an opportunity to analyze urban infrastructure from different perspectives, offering transparency regarding the weaknesses and strengths of each perspective. This prevents unreliable representations from being used in decision-making and provides a solid basis for allocating investments in urban infrastructure. In particular, the study suggests that adopting weighting systems that prioritize intermediate values and avoid extreme values can lead to better resource allocation, helping to identify areas with deficient infrastructure and promoting more equitable urban development. Full article

Among the numerous compounds released as a result of human activities, endocrine-disrupting chemicals (EDCs) have attracted particular attention due to their widespread detection in human biological samples and their accumulation across various ecosystems. While early research primarily focused on their effects on reproductive health, it is now evident that EDCs may impact neurodevelopment, altering the integrity of neural circuits essential for cognitive abilities, emotional regulation, and social behaviors. These compounds may elicit epigenetic modifications, such as DNA methylation and histone acetylation, that result in altered expression patterns, potentially affecting multiple generations and contribute to long-term behavioral phenotypes. The effects of EDCs may occur though both direct and indirect mechanisms, ultimately converging on neurodevelopmental vulnerability. In particular, the gut–brain axis has emerged as a critical interface targeted by EDCs. This bidirectional communication network integrates the nervous, immune, and endocrine systems. By altering the microbiota composition, modulating immune responses, and triggering epigenetic mechanisms, EDCs can act on multiple and interconnected pathways. In this context, elucidating the impact of EDCs on neurodevelopmental processes is crucial for advancing our understanding of their contribution to neurological and behavioral health risks. Full article

As veganism becomes more popular, the number of vegan pregnant women and children is steadily increasing. During vegan pregnancy and early childhood, there is a high risk for nutrient deficiencies that may impair child development. External factors, such as healthcare advice, social networks, and social environments, that affect the diet of vegan pregnant women, parents, and their children, as well as their approach towards dietary supplementation, have not yet been investigated. Various sources of information, combined with a lack of expertise, sparse food and nutritional health literacy, and qualitatively heterogeneous information provision by medical experts, unsettle vegan pregnant women and parents and affect their dietary choices and potentially the health of their children. The VedieS study aims to investigate potential connections between external influences and associated impacts on a vegan diet and the intake of dietary supplements (DS) of pregnant women and children. Two surveys are being conducted within the study: one targeting 1000 vegan pregnant women and parents, and another targeting 60 experts in each of five healthcare groups: gynecologists, pediatricians, general practitioners, pharmacists, and dietitians. This study is the first to examine how socio-economic, social, and further informational factors influence dietary practices during vegan pregnancy and childhood. It highlights the need for reliable, expert-led guidance, as current information sources are often inconsistent and may put these vulnerable groups at risk. Full article

Introduction: Breakfast has emerged as a critical factor in preventing cardiovascular diseases, driven not only by its nutritional content but also by its alignment with circadian rhythms. However, gaps remain in the literature regarding its clinical impact and thematic evolution. Objective: To characterize the global scientific output on the relationship between breakfast quality and cardiovascular health through a systematic bibliometric analysis. Methodology: The PRISMA 2020 protocol was applied to select 1436 original articles indexed in Scopus and Web of Science (1957–2025). Bibliometric tools, including R (v4.4.2) and VOSviewer (v1.6.19) were used to map productivity, impact, collaboration networks, and emerging thematic areas. Results: Scientific output has grown exponentially since 2000. The most influential journals are the American Journal of Clinical Nutrition, Nutrients, and Diabetes Care. The United States, United Kingdom, and Japan lead in publication volume and citations, with increasing participation from Latin American countries. Thematic trends have shifted from traditional clinical markers to innovative approaches such as chrononutrition, digital health, and personalized nutrition. However, methodological gaps persist, including a predominance of observational studies and an underrepresentation of vulnerable populations. Conclusions: Breakfast is a dietary practice with profound implications for cardiometabolic health. This study provides a comprehensive overview of scientific literature, highlighting both advancements and challenges. Strengthening international collaboration networks, standardizing definitions of a healthy breakfast, and promoting evidence-based interventions in school, clinical, and community settings are recommended. Full article