Search Results (2,379)

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). 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

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

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

The design of aircraft electrical wiring interconnection systems (EWISs) is central to ensuring the safe and reliable operation of aircraft. The calculation of the temperature rise characteristics of aircraft wire harnesses is one of the key technologies in EWIS design, directly affecting the safety margin of the system. However, existing calculation methods generally face a bottleneck in the balance between speed and accuracy, failing to meet the requirements of actual engineering applications. In this paper, we conduct an in-depth study on this issue. Firstly, a finite element harness model is established to accurately obtain the convective heat transfer coefficients of wires and harnesses. Based on the analysis of the influencing factors of the thermal network model for a single wire, an improved thermal resistance hierarchical wire thermal network model is proposed. A structure consisting of series thermal resistance within layers and iterative parallel algorithms between layers is proposed to equivalently integrate and iteratively calculate the mutual thermal influence relationship between each layer of the harness, thereby constructing a hierarchical harness thermal network model. This model successfully achieves a significant improvement in calculation speed while effectively ensuring useable temperature rise results, providing an effective method for EWIS design. Full article

In the design of multiprocessor systems, evaluating the reliability of interconnection networks is a critical aspect that significantly impacts system performance and functionality. When quantifying the reliability of these networks, extra connectivity and extra diagnosability serve as fundamental metric parameters, offering valuable insights into the network’s resilience and fault-handling capabilities. In this paper, we investigate the 1-extra connectivity and 1-extra diagnosability of the n-dimensional enhanced folded hypercube-like network. Through analysis, we show that the 1-extra connectivity of this network is $2n+2$. Moreover, for $n>5$, we determine its 1-extra diagnosability under both the PMC model and the MM${}^{\ast }$ model to be $2n+3$. These results show that as the dimension n increases, both the 1-extra connectivity and 1-extra diagnosability of the network approach approximately twice the value of traditional diagnosability metrics. This provides quantitative insights into the reliability properties of the enhanced folded hypercube-like network, contributing to a better understanding of its performance in terms of connectivity and fault diagnosis. Full article

Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article

In the last two decades, the prevalence of South Asian migrant workers has significantly increased in the UAE’s construction sector, and they are under huge debt. Although researchers heavily stress the role of employers in migrant workers’ debt, the role of debt before departure has not been investigated. Thus, this study bridges this gap in the literature in the context of South Asian construction migrant workers. The objective of this study is to investigate how informal recruitment fees and debt arrangements contribute to bonded labor and dependency among migrant workers. A qualitative approach was used to conduct in-depth interviews with 30 South Asian migrants employed in the construction sector. This article highlights how pre-migration debt—which is often accrued through informal loans and exploitative recruitment fees—has been underexplored in migration studies. Drawing on interviews with 30 South Asian laborers, this study identifies five interconnected themes: pre-migration debt bondage, exploitative lending practices, lack of legal recourse, emotional manipulation, and a cycle of dependency. While UAE labor policies have improved, the real vulnerabilities lie in the informal recruitment systems and weak oversight in migrant workers’ countries of origin. Consequently, five themes were generated from the analysis: pre-migration debt bondage, exploitative lending practices, no legal recourse, emotional manipulation, and cycles of dependency. This study contributes to our existing knowledge by revealing the experiences of migrant construction workers from South Asia in the UAE. While the UAE has established one of the region’s most progressive legal frameworks to protect migrant workers and set clear labor standards, many exploitative practices occur outside its jurisdiction, particularly in the workers’ countries of origin. This study underscores that the root of the problem lies in weak enforcement and informal recruitment networks in sending countries, not in UAE policy itself. Addressing these challenges requires coordinated international action to ensure that migrant protection begins well before arrival. Full article

Despite extensive prevention strategies in Spain since the 1980s, gender-based violence, including among adolescents, remains prevalent, as observed in the Romance SUCC-ED Project (R&D&I Operating Programme ERDF Andalusia 2014–2020). This research study investigates the dimensions, meanings, relationships, and practices shaping the culture of romance in digital Andalusian adolescence (12–16 years) and its potential impact on school trajectories in Compulsory Secondary Education. Based on the premise that equality-focused relationship education is key to preventing gender violence, the study employs an ethnographic methodology with 12 Andalusian school case studies (4 out of them are located in rural areas) and 220 in-depth interviews (126 girls, 57.3%; 94 boys, 42.7%). This article aims to empirically explain gender violence in early adolescence by analysing the culture of romance as an explanatory factor. Findings reveal an interconnected model where dimensions (love, couple, sexuality, pornography, social networks, and cultural references), meanings (constructed by adolescents within each of them), relationships (partner), and practices (control and jealousy) reinforce romanticised femininity and dominant masculinity, thus explaining the high incidence of gender-based violence among students in the study. Full article

This study explores sustainable rural tourism entrepreneurship within the Urlaub am Bauernhof (UaB) cooperative network in Austria, offering an integrated model that unites financial, social, environmental, institutional, and marketing dimensions. Employing exploratory factor analysis (EFA) and Structural Equation Modeling (SEM) on data from 393 farm-based accommodation stakeholders, this research identifies sustainable entrepreneurship as comprising six interconnected dimensions: Economic Resilience and Diversification, Sociocultural Integration, Environmental and Regional Commitment, Market Visibility and Strategic Communication, Quality Assurance and Institutional Support, and Perceived Value and Branding. This multidimensional and hierarchically structured framework reflects the complex yet coherent nature of sustainability-driven entrepreneurship in cooperative tourism networks. The findings confirm the multidimensional nature of sustainable entrepreneurship and support the hypothesized structural relationships. The UaB network is presented as a transferable model that demonstrates how cooperative frameworks can enhance sustainability, regional identity, and rural revitalization, offering valuable insights and practical guidance for rural regions in the Western Balkans, where economic challenges, depopulation, and underdeveloped tourism infrastructure prevail. By illustrating a successful cooperative approach rooted in sustainability and regional identity, this study contributes to policy-making aimed at fostering resilient, culturally rich, and environmentally responsible rural tourism entrepreneurship in transitioning contexts. Full article

This article first constructs a multi-layer deep learning neural network to help understand the structural characteristics of communication data, thereby learning complex functions and obtaining the predicted network values. At the same time, signal transmission is achieved through the interconnection of neurons, the representation performance of which is enhanced through activation functions; this completes the modeling of IoT communication models. Then, we use the analytic hierarchy process to construct a deep learning autoencoder and extract the feature elements of network communication reliability parameters. Finally, we use the obtained total reliability indicators as features for automatic coding and evaluate the mapping relationship between indicators. The results show that the success rates of handovers in deep leaning-based IoT communication based are all greater than 99.6%. The predicted transmission rate can reach a maximum of 99.5%, achieving error free communication output and improving fidelity. Full article

The development of lightweight composites with superior mechanical properties and electromagnetic interference (EMI) shielding performance is essential for various structural and functional applications. This study investigates the effect of hybrid nanocarbon (graphene nanosheet (GNS) and carbon nanotube (CNT)) reinforcements on the properties of magnesium (Mg) matrix composites. Specifically, the GNS-CNT hybrid, which forms a three-dimensional interconnected network structure, was analyzed and compared to composites reinforced with only GNSs or CNTs. The objective was to determine the benefits of hybrid reinforcements on the mechanical strength and EMI shielding capability of the composites. The results indicated that the GNS-CNT/Mg composite, at a nanocarbon content of 0.5 wt.% and a GNS-CNT ratio of 1:2, achieved optimal performance, with a 55% increase in tensile strength and an EMI shielding effectiveness of 70 dB. The observed enhancements can be attributed to several key mechanisms: effective load transfer, which promotes tensile twinning, along with improved impedance matching and multiple internal reflections within the GNS-CNT network, which enhance absorption loss. These significant improvements position the composite as a promising candidate for advanced applications requiring high strength, toughness, and efficient electromagnetic shielding, providing valuable insights into the design of high-performance lightweight materials. Full article

Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unknown vulnerabilities, leveraging the complexity of physical and digital system interconnections, making them difficult to detect. The integration of legacy ICS networks with modern computing and networking technologies has expanded the attack surface, increasing susceptibility to cyber threats. Anomaly detection systems play a crucial role in safeguarding these infrastructures by identifying deviations from normal operations. This study investigates the effectiveness of deep learning-based anomaly detection models in revealing operational anomalies that could indicate potential cyber-attacks. We implemented and evaluated a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze ICS telemetry data. The CNN-LSTM model excels in identifying time-dependent anomalies and enables near real-time detection of cyber-attacks, significantly improving security monitoring capabilities for IIoT-integrated critical infrastructures. Full article