Search Results (337)

The Practical Byzantine Fault Tolerance (PBFT) algorithm, while fundamental to consortium blockchains, suffers from performance degradation and vulnerability of leader nodes in large-scale scenarios. Existing improvements often prioritize performance while lacking systematic consideration of the structural characteristics of the nodes and network coverage. In this paper, a new network topology-aware Byzantine fault-tolerant algorithm NetTopoBFT is proposed for the supply chain and other application scenarios that require strict transaction finality but moderate throughput. Firstly, it innovatively combines the weighted signed network with the consortium chain, constructs a two-layer Bayesian smoothing node evaluation model, and evaluates the nodes through the two-dimensional evaluation of ‘behavioral reputation plus structural importance’. Then, to reduce the risk of being attacked, it uses Verifiable Random Function (VRF) to decide the leader. Furthermore, it uses a duplicate coverage-driven waitlisting mechanism to enhance the robustness and connectivity of the system. Theoretical analysis and experiment results show that NetTopoBFT significantly improves the quality of consensus nodes under the premise of guaranteeing decentralization, realizes the simultaneous optimization of communication overhead, security and network coverage. It provides a new idea for designing consensus mechanism of consortium blockchains. Full article

Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged as a promising Layer 1 approach by partitioning blockchain networks into smaller, parallelized components, significantly enhancing processing efficiency while maintaining decentralization and security. In this paper, we have conducted a systematic literature review, resulting in a comprehensive review of sharding. We provide a detailed comparative analysis of various sharding approaches and emerging AI-assisted sharding approaches, assessing their effectiveness in improving TPS and reducing latency. Notably, our review is the first to incorporate and examine the standardization efforts of the ITU-T and ETSI, with a particular focus on activities related to blockchain sharding. Integrating these standardization activities allows us to bridge the gap between academic research and practical standardization in blockchain sharding, thereby enhancing the relevance and applicability of our review. Additionally, we highlight the existing research gaps, discuss critical challenges such as security risks and inter-shard communication inefficiencies, and provide insightful future research directions. Our work serves as a foundational reference for researchers and practitioners aiming to optimize blockchain scalability through sharding, contributing to the development of more efficient, secure, and high-performance decentralized networks. Our comparative synthesis further highlights that while Bitcoin and Ethereum remain limited to 7–15 TPS with long confirmation delays, sharding-based systems such as Elastico and OmniLedger have reported significant throughput improvements, demonstrating sharding’s clear advantage over traditional Layer 1 enhancements. In contrast to other state-of-the-art scalability techniques such as block size modification, consensus optimization, and DAG-based architectures, sharding consistently achieves higher transaction throughput and lower latency, indicating its position as one of the most effective Layer 1 solutions for improving blockchain scalability. Full article

Ensuring accessibility in urban public spaces is a key challenge for contemporary cities, particularly in the context of ageing populations, socio-spatial inequalities, and the global call for inclusive urban development. Despite its importance, accessibility is often treated as a cross-cutting issue rather than as a central objective in planning practice. This study examines how accessibility can be addressed in participatory urban public space design through a geodesign workshop conducted with architecture students from the University of Alicante. Focusing on the area along Line 2 of the TRAM light-rail network in Alicante, Spain, the workshop applied the geodesign framework in four iterative phases: system analysis, stakeholder role-play, design negotiation, and consensus building. The workshop participants represented six stakeholder groups with varying objectives and priorities, proposing micro-interventions in vulnerable urban areas aimed at improving walkability, surface conditions, and access to services. The role-play phase highlighted contrasting views on accessibility, particularly emphasised by groups representing older adults and people with disabilities. Negotiation revealed both alliances and tensions, while the final consensus reflected a moderate but meaningful inclusion of wide accessibility concerns. The resulting proposals showed spatial awareness of socio-territorial inequalities. The findings suggest that geodesign fosters critical thinking, collaboration, and empathy in future urban professionals; however, challenges persist regarding inclusivity, contextual adaptation, and integration into practice. Future work should explore long-term impacts and co-creation of accessibility standards. Full article

Blockchain technology has rapidly evolved beyond cryptocurrencies, underpinning diverse applications such as supply chains, healthcare, and finances, yet its security vulnerabilities remain a critical barrier to safe adoption. However, attackers increasingly exploit weaknesses in consensus protocols, smart contracts, and network layers with threats such as Denial-of-Chain (DoC) and Black Bird attacks, posing serious challenges to blockchain ecosystems. We conducted anomaly detection using two independent datasets (A and B) generated from simulation attack scenarios including hash rate, Sybil, Eclipse, Finney, and Denial-of-Chain (DoC) attacks. Key blockchain metrics such as hash rate, transaction authorization status, and recorded attack consequences were collected for analysis. We compared both class-balanced and imbalanced datasets, applying Synthetic Minority Oversampling Technique (SMOTE) to improve representation of minority-class samples and enhance performance metrics. Supervised models such as Random Forest, Gradient Boosting, and Logistic Regression consistently outperformed unsupervised models, achieving high F1-scores (0.90), while balancing the training data had only a modest effect. The results are based on simulated environment and should be considered as preliminary until the experiment is performed in a real blockchain environment. Based on identified gaps, we recommend the exploration and development of multifaceted defense approaches that combine prevention, detection, and response to strengthen blockchain resilience. Full article

Multi-agent systems (MAS) typically model interaction topologies using directed or undirected graphs when analyzing consensus convergence rates. However, as system complexity increases, purely directed or undirected networks may be insufficient to capture interaction heterogeneity. This paper adopts hybrid networks as interaction topology to investigate strategies for improving consensus convergence rates. We propose the Hermitian Kirchhoff index, a novel metric based on resistance distance, to quantify the consensus convergence rates and establish its theoretical justification. We then examine how adding or removing edges/arcs affects the Hermitian Kirchhoff index, employing first-order eigenvalue perturbation analysis to relate these changes to algebraic connectivity and its associated eigenvectors. Numerical simulations corroborate the theoretical findings and demonstrate the effectiveness of the proposed approach. Full article

Background: Breast cancer is the most common female cancer in Italy. Despite better survival rates, significant disparities in access to diagnosis, treatment, and follow-up persist across regions. We propose an integrated, multidisciplinary care model—the Breast Immunology Network (BIN)—to address these challenges. Methods: The model was developed through a two-phase expert consultation with key opinion leaders and stakeholders, aligned with national and European oncology guidelines. No new patient data were collected; this is a qualitative analysis based on expert consensus and existing literature. The proposed model integrates a Hub-and-Spoke cancer network structure with fully functioning multidisciplinary teams (MDTs), standardized care pathways (PDTA), and digital tools to ensure continuity of care. Results: Experts identified critical gaps in Italy’s breast cancer care: limited access to specialized centers, inconsistent adherence to screening programs, and delays in treatment initiation. The proposed BIN model aims to bridge these gaps by enhancing collaboration across all care levels, incorporating immunotherapy where appropriate, and defining key performance indicators (KPIs) for continuous quality evaluation. For example, quantitative targets include achieving ≥65% nationwide mammography screening adherence and ensuring ≥90% of patients are treated in certified Breast Units. Conclusions: The Breast Immunology Network offers a strategic framework to improve equity, quality, and timeliness of breast cancer care in Italy. Importantly, unlike existing Hub–Spoke or CCCN models, the BIN formalizes governance tools, harmonized eligibility criteria, and a national registry for immunotherapy. By uniting Breast Units and community services under shared governance, and by integrating innovations such as immunotherapy and telemedicine, the BIN model could significantly improve clinical outcomes and ensure more equitable care for all patients. Its implementation may serve as a reference model for other health systems seeking to optimize oncology pathways through multidisciplinary integration and advanced treatments. Full article

Objective: This study aims to explore the mechanism of regulated cell death-related genes in the development of endometrial carcinoma. Methods: Endometrial carcinoma-related datasets were yielded via the Cancer Genome Atlas and Gene Expression Omnibus databases, and regulated cell death-related genes were extracted from the literature. Differential expression analysis, weighted gene co-expression network analysis, and protein interaction analysis were performed to identify critical regulated cell death-related genes. Gene set enrichment analysis was used to identify the functional pathways involved in these critical genes. Afterward, the best clustering approach for tumor samples was yielded via consensus clustering analysis, and nomogram prediction models were built. Shiny Methylation Analysis Resource Tool was used to compare the expression levels of CpG methylation probes for critical genes between tumor and normal samples. Spearman correlation analysis was conducted to investigate the relationship between critical genes and various immune features. Eventually, immuno-infiltrative analysis was implemented, and potential therapeutic agents were screened targeting critical genes. The data were analyzed and visualized by R software using different packages. In addition, the expressions of critical genes were validated by quantitative real-time polymerase chain reaction and immunochemistry. Results: Four critical genes, namely GBP2, SLC11A1, P2RX7, and HCLS1, were identified, and they were involved in various functional pathways such as leukocyte-mediated cytotoxicity. There were substantial differences in CpG methylation in GBP2, SLC11A1, and HCLS1 between tumor and normal samples. As for immune features, all critical genes were positively connected with immunosuppressive factors such as TIGIT and most HLA molecules in endometrial carcinoma. The critical genes high/low expression groups of tumor samples showed different immune responses towards PD-1, PD-L1, and CTLA-4 immunotherapy. The infiltration of 24 immune cells, such as effector memory CD8⁺ T cells, was notably different between tumor and normal samples. Based on sensitivity analysis of chemotherapeutic agents, we found the highest positive correlation between SLC11A1 and “BI.2536” and the strongest passive correlation of HCLS1 and GBP2 with “Ribociclib”, as well as P2RX7 with “BMS.754807”. Quantitative real-time polymerase chain reaction suggested that the expression trends of GBP2, P2RX7, and HCLS1 were consistent with the results of bioinformatic analysis. Conclusions: Regulated cell death-related genes (GBP2, SLC11A1, P2RX7, and HCLS1) may play a role in endometrial carcinoma development, which can provide new ideas for the treatment and prognosis prediction of this disease. Full article

G8 rotaviruses are primarily associated with animals and infrequently cause infections in humans. The first detection of G8 strains in humans occurred around 1979, and since then, their presence has been sporadic, particularly in the United States (U.S.). During the 2016–2017 rotavirus surveillance season, the New Vaccine Surveillance Network (NVSN) identified 36 G8P[8] rotavirus strains across four sites in the U.S. This study presents the whole-genome characterization of these G8P[8] strains, along with comparative sequence analyses against the current vaccine strains, Rotarix and RotaTeq. Each strain exhibited a DS-1-like backbone with a consensus genotype constellation of G8P[8]-I2-R2-C2-M2-A2-N2-T2-E2-H2 and exhibited high genetic similarities to G8P[8] strains previously detected in Europe and Asia. Clinical analysis revealed no significant differences in hospitalization rates, length of stay, or severity scores between G8P[8] RVA-positive and non-G8P[8] RVA-positive subjects. Additionally, phylodynamic analysis determined the evolutionary rates and the most recent common ancestor for these strains, highlighting the importance of ongoing monitoring of rotavirus genotypes to assess the spread of these emerging G8P[8] strains. Full article

Background: Adaptogens, commonly used as traditional herbal medicinal products for the relief of symptoms of stress, such as fatigue and exhaustion, belong to a category of physiologically active compounds related to the physiological process of adaptability to stressors. They are used both as pharmaceuticals in medicine and as dietary supplements or nutraceuticals in nutrition, depending on the doses, indications to treat diseases, or support health functions. However, such a dual-faced nature of adaptogens can lead to inconsistencies and contradictory outcomes from Food and Drug regulatory authorities in various countries. Aims: This narrative literature review aimed to (i) specify five steps of pharmacological testing of adaptogens, (ii) identify the sources of inconsistencies in the assessment of evidence the safety, efficacy, and quality of multitarget adaptogenic botanicals, and (iii) propose potential solutions to address some food and drug regulatory issues, specifically adaptogenic botanicals used for prevention and treatment of complex etiology diseases including stress-induced, and aging-related disorders. Overview: This critically oriented narrative review is focused on (i) five steps of pharmacological testing of adaptogens are required in a sequential order, including appropriate in vivo and in vitro models in animals, in vitro model, and mechanisms of action by a proper biochemical assay and molecular biology technique in combination with network pharmacology analysis, and clinical trials in stress-induced and aging-related disorders; (ii) the differences between the requirements for the quality of pharmaceuticals and dietary supplements of botanical origin; (iii) progress, trends, pitfalls, and challenges in the adaptogens research; (iv) inadequate assignment of some plants to adaptogens, or insufficient scientific data in case of Eurycoma longifolia; (v) inconsistencies in botanical risk assessments in the case of Withania somnifera. Conclusions: This narrative review highlights the importance of harmonized standards, transparent methodologies, and a balanced, evidence-informed approach to ensure consumers receive effective and safe botanicals. Future perspectives and proposed solutions include (i) establish internationally harmonized guidelines for evaluating botanicals based on their intended use (e.g., pharmaceutical vs. dietary supplement), incorporating traditional use data alongside modern scientific methods; (ii) encourage peer review and transparency in national assessments by mandating public disclosure of methodologies, data sources, and expert affiliations; (iii) create a tiered evidence framework that allows differentiated standards of proof for traditional botanical supplements versus pharmaceutical candidates; (iv) promote international scientific dialogs among regulators, researchers, and industry to develop consensus positions and avoid unilateral bans that may lack scientific rigor; (v) formally recognize adaptogens a category of natural products for prevention stress induced brain fatigue, behavioral, and aging related disorders. Full article

Background: Tumor-educated platelets (TEPs) represent a promising biosource for non-invasive multi-cancer early detection (MCED). While machine learning (ML) has been applied to TEP data, the integration of explainability to reveal gene-level contributions and regulatory associations remains underutilized. This study aims to develop an interpretable ML framework for cancer detection using platelet RNA-sequencing data, combining predictive performance with biological insight. Methods: This study analyzed 2018 TEP RNA samples from 18 tumor types using seven machine learning classifiers. SHAP (Shapley Additive Explanations) was applied for model interpretability, including global feature ranking, local explanation, and gene-level dependence patterns. A weighted SHAP consensus was built by combining model-specific contributions scaled by Area Under the Receiver Operating Characteristic Curve (AUC). Regulatory insights were supported through network analysis using GeneMANIA. Results: Neural models, including shallow Neural Network (NN) and Deep Neural Network (DNN) achieved the best performance (AUC ~0.93), with Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) also performing well. Early-stage cancers were predicted with high accuracy. SHAP analysis revealed consistent top features (e.g., SLC38A2, DHCR7, IFITM3), while dependence plots uncovered conditional gene interactions involving USF3 (KIAA2018), ARL2, and DSTN. Multi-hop pathway tracing identified NFYC as a shared transcriptional hub across multiple modulators. Conclusions: The integration of interpretable ML with platelet RNA data revealed robust biomarkers and context-dependent regulatory patterns relevant to early cancer detection. The proposed framework supports the potential of TEPs as a non-invasive, information-rich medium for early cancer screening. Full article

As Saudi Arabia accelerates digital transformation under Vision 2030, the sustainable adoption of Health 4.0 technologies depends on equitable digital health literacy (DHL) and population-level readiness for eHealth engagement. Despite growing interest, empirical data on the behavioral, social, and contextual determinants of digital health adoption remain limited in Middle Eastern settings. This study investigates the readiness of Saudi adults for eHealth services, identifies key behavioral factors influencing digital tool adoption, and proposes an equity-centered, network-aware DHL framework to support inclusive and sustainable Health 4.0 implementation. A multi-phase, cross-sectional study was conducted among 430 Saudi adults using validated instruments including eHEALS, TRI 2.0, UTAUT, and EQ-5D. Quantitative analysis employed multiple linear regression (R² = 0.79), structural equation modeling (CFI = 0.96; RMSEA = 0.04), social network analysis (centrality scores), and network-based diffusion analysis (s = 0.17). Additionally, a three-round Delphi method (CI ≤ 0.25) ensured expert consensus on framework development. Significant predictors of digital health tool adoption included eHealth readiness (β = 0.18), perceived usability, and system trust. Social network metrics identified central actors who facilitated peer-driven behavioral diffusion, validated through NBDA modeling. Based on these findings, a comprehensive DHL Equity Framework was synthesized, integrating behavioral drivers, network diffusion pathways, and principles from the Triple Bottom Line (TBL) framework to mitigate structural disparities while addressing environmental, economic, and social dimensions of sustainable digital health access. The framework was also systematically mapped to relevant Sustainable Development Goals (SDGs), highlighting its alignment with global health and sustainability targets. This study presents a scalable and policy-relevant model to guide inclusive eHealth strategies in Saudi Arabia and similar developing contexts. The proposed framework advances national digital resilience, reduces inequities, and promotes sustainable Health 4.0 service delivery. Full article

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

This study examines the semantic dynamics and thematic shifts in artificial intelligence (AI) ethics over time, addressing a notable gap in longitudinal research within the field. In light of the rapid evolution of AI technologies and their associated ethical risks and societal impacts, the research integrates the theory of chance discovery with the KeyGraph algorithm to conduct topic detection through a keyword network built through iterative semantic exploration. ChatGPT is employed for semantic interpretation, enhancing both the accuracy and comprehensiveness of the detected topics. Guided by the double helix model of human–AI interaction, the framework incorporates a dual-layer validation process that combines cross-model semantic similarity analysis with expert-informed quality checks. An analysis of 24 authoritative AI ethics reports published between 2022 and 2024 reveals a consistent trend toward semantic stability, with high cross-model similarity across years (2022: 0.808 ± 0.023; 2023: 0.812 ± 0.013; 2024: 0.828 ± 0.015). Statistical tests confirm significant differences between single-cluster and multi-cluster topic structures (p < 0.05). The thematic findings indicate a shift in AI ethics discourse from a primary emphasis on technical risks to broader concerns involving institutional governance, societal trust, and the regulation of generative AI. Core keywords, such as bias, privacy, and ethics, recur across all years, reflecting the consolidation of an integrated governance framework that encompasses technological robustness, institutional adaptability, and social consensus. This dynamic semantic analysis framework contributes empirically to AI ethics governance and offers actionable insights for researchers and interdisciplinary stakeholders. Full article

Under the “Dual Carbon” goal, public opinion analysis is crucial for optimizing policy implementation and enhancing social consensus, yet it faces challenges such as insufficient multi-source data integration, limited causal modeling, and delayed interventions. This study proposes a collaborative framework integrating reinforcement learning-enhanced large language models (LLMs), diffusion models, and multi-agent systems (MASs). By constructing a four-dimensional causal network of “policy–technology–economy–public sentiment”, it analyzes multi-source data and simulates multi-agent interactions. The experimental results show that this framework outperforms Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and Susceptible Infected Recovered (SIR) models in causal inference, dynamic intervention, and multi-agent collaboration. Reinforcement Learning from Human Feedback (RLHF) optimizes LLM outputs for reliable policy recommendations, with pass@10 showing strong correlations. This study provides scientific support for “Dual Carbon” policymaking and public opinion guidance, facilitating the green and low-carbon transition. Full article

Background: Prostate cancer (PC) is the second leading cause of cancer-related death in men in the United States. A subset of patients develops non-metastatic, castration-resistant PC (nmCRPC), for which management requires a personalized consideration for appropriate treatment. However, there is no consensus regarding when to switch from androgen deprivation therapy (ADT) to more aggressive treatments like abiraterone or enzalutamide. Methods: We analyzed 5037 nmCRPC patients and employed a Weibull Time to Event Recurrent Neural Network to identify patients who would benefit from switching from ADT to abiraterone/enzalutamide. We evaluated this model using differential treatment benefits measured by the Kaplan–Meier estimation and milestone probabilities. Results: The model achieved an area under the curve of 0.738 (standard deviation (SD): 0.057) for patients treated with abiraterone/enzalutamide and 0.693 (SD: 0.02) for patients exclusively treated with ADT at the 2-year milestone. The model recommended 14% of ADT patients switch to abiraterone/enzalutamide. Analysis showed a statistically significant absolute improvement using model-recommended treatments in progression-free survival (PFS) of 0.24 (95% confidence interval (CI): 0.23–0.24) at the 2-year milestone (PFS rate increasing from 0.50 to 0.74) with a hazard ratio of 0.44 (95% CI: 0.39–0.50). Conclusions: Our model successfully identified nmCRPC patients who would benefit from switching to abiraterone/enzalutamide, demonstrating potential outcome improvements. Full article