Search Results (866)

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

A general high-order linear consensus protocol is proposed for coupling topologies defined by directed graphs with partial relative state information and a reference model with lobal/local gains. Necessary and sufficient second-order stability bounds for the cases of relative position feedback with reference velocity and relative position and velocity feedback are then reviewed. Next, new necessary and sufficient stability bounds are obtained for third-order consensus for three cases of feedback of full and partial relative state information. The stability bounds obtained, unlike in prior studies, allow for the gains to be conveniently selected in a sequential manner and are shown to utilize those for second-order consensus. Comparisons with conservative stability bounds from previous studies are shown, and illustrative examples of the proposed consensus protocols and the obtained stability bounds are provided. Full article

Our goal was to identify and understand perspectives of different stakeholders in the field of climate policy and test a process of co-creative policy development to support the implementation of climate protection measures. As the severity of climate change grows globally, perceptions of climate science and climate-based policy have become increasingly polarized. The one-solution consensus or compromise that has encapsulated environmental policymaking has proven insufficient or unable to address accurately or efficiently the climate issue. Because climate change is often described as a wicked problem (multiple causes, widespread impacts, uncertain outcomes, and an array of potential solutions), a clumsy solution that incorporates ideas and actions representative of varied and divergent worldviews is best suited to address it. This study used the Theory of Plural Rationality, which uses a two-dimensional spectrum to identify four interdependent worldviews as well as a fifth autonomous perspective to define the differing perspectives in the field of climate policy in Austria. Stakeholder inputs regarding general worldviews, climate change, and climate policy were evaluated to identify agreeable actions representative of the multiple perspectives. Thus, we developed and tested a co-creative process for developing clumsy solutions. This study concludes that while an ideological consensus is unlikely, agreement is more likely to occur on the practical level of concrete actions (albeit perhaps for different reasons). Findings suggested that creating an ecological tax reform was an acceptable policy action to diverse stakeholders. Furthermore, the study illuminated that the government is perceived to have the most potential influence on climate protection policy and acts as a key “broker”, or linkage, between other approaches that are perceived to be more actualized but less impactful. Full article

The Pacific Alliance has established itself as one of the most dynamic regional economic integration initiatives, standing out for its pragmatic and consensual approach to trade, capital and people liberalisation. However, between 2020 and 2025, the bloc faced both opportunities and challenges arising from the international situation, including global tensions, internal political crises and the need for technological adaptation. Against this backdrop, this study aims to conduct a systematic review of the scientific literature published between 2020 and 2025 on the Pacific Alliance, identifying the predominant theoretical approaches and the main findings on the integration process. This systematic review followed the PRISMA methodology, which contributed fundamentally to this research by providing a structured, transparent and rigorous framework for a deeper and more informed understanding of the integration process. The results show that, despite some progress, structural limitations persist, such as asymmetries between countries, institutional obstacles and superficial integration in economic and social aspects, as well as fragmentation in academic production and little incorporation of geopolitical perspectives. This study contributes to a critical understanding of the current state and future challenges of the Pacific Alliance, offering inputs for the formulation of public policies and future research in the field of Latin American integration. Full article

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber–physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly constant velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens. 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

Buffer zones around protected areas (PA) face complex governance challenges as territorial transitions accelerate globally, yet limited consensus exists on their definition, ecosystem service (ES) priorities, and management strategies. This study employed a three-round Delphi methodology with 23 transdisciplinary experts to build consensus on buffer zone governance around Cerro Castillo National Park in Chilean Patagonia, using the IPBES ecosystem services framework to structure the analysis. Round 1 employed open-ended questions to explore expert perspectives, Round 2 evaluated 56 statements and 15 strategic components using structured questionnaires, and Round 3 refined non-consensus items. Experts achieved 76.7% overall consensus across three thematic areas: PA interface conceptualization (79.2% consensus on 24 statements), ES assessment (91.2% consensus on 34 statements), and territorial transition strategies (15 components evaluated). Water-related services achieved unanimous agreement across multiple IPBES categories, revealing their potential as boundary objects bridging conservation and development perspectives. Educational approaches and voluntary compliance emerged as high-feasibility strategic components, while regulatory frameworks showed high importance but implementation uncertainty. The study demonstrates that structured expert consultation can identify collaborative pathways for buffer zone governance, with water services providing concrete entry points for multi-stakeholder cooperation and education-based strategies offering promising implementation pathways for sustainable territorial transitions. 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

Natural resource assets inherently integrate tripartite synthesis of legal, economic, and ecological attributes. They serve dual critical functions as foundational elements supporting the evolution of new-quality productive forces and pivotal mechanisms safeguarding ecosystemic integrity. It has become a global consensus and direction of action to advance comprehensive supervision of natural resource assets and practice the concept of “Community of Life for Human and Nature”. Under the background of the super-ministry system restructuring in China, comprehensive supervision of natural resource assets remains challenged by system fragmentation in supervision objectives and multifaceted interest conflicts among stakeholders. In light of this, this research focuses on the theoretical justification and system optimization of the comprehensive supervision of natural resource assets in China. Using comparative analysis and normative analysis methods, we validate the system’s function on the comprehensive supervision of natural resource assets, summarize foreign experiences, and ultimately aim to explore the optimization pathway of the legal system for the comprehensive supervision of natural resource assets. The results show the following: (1) The choice of the legal system for the comprehensive supervision of natural resource assets emerges as the functional product aligning societal objectives, the rational paradigm for achieving efficient resource allocation, and the adaptive response to the external effects of common property. (2) The system supply of comprehensive supervision of natural resource assets in foreign countries is characterized by normative convergence in conceptual elements and typological categorization in objectives and objects. Therefore, this research recommends that, in order to optimize the system of the comprehensive supervision of natural resource assets in China, (1) in terms of protection of source, natural resource assets should be categorized, with operational natural resource assets focusing on management and public welfare natural resource assets focusing on conservation. (2) In terms of valuation, the economic valuation of natural resource assets should be integrated with ecosystem service assessments to enhance fair market equity. (3) In terms of method, the big data center should be established to enable the synergistic integration of technological innovation and system reforms. (4) In terms of subject, requiring the participation of various government departments, non-governmental organizations, the general public, and other parties could realize the connection of different legal bases for the comprehensive supervision of natural resource assets and the balance of multiple rights and interests, which should help to achieve balanced resource efficiency and biodiversity conservation and safeguard national ecological security. Full article

Regenerative design (RD) is attracting attention as a concept that goes beyond sustainability. However, RD has been criticized as an overly theoretical and abstract approach. This study constructs a multiscale RD approach in urban areas by combining the theoretical frameworks of an adaptive planning approach based on the complex adaptive systems (CAS) theory and transformative capacities (TC) through the case study of Shimokita-Senrogai. The study’s main contribution is to materialize the process for a multiscale RD approach in urban areas, where it is difficult to reach consensus among diverse stakeholders immediately. The main finding is identifying the necessary conditions for implementing an RD approach that enhances TC by adapting to urban uncertainties from global climate change to local civic dynamics through the agency of more-than-human actor networks. Based on these, this study proposes a methodology to visualize actors, their activity ranges, bases, and ecosystemic flows across multiple territorial scales beyond the development site and its vicinity. Full article

Sustainability within the rice and wheat supply chain is integral to attaining the UN’s Sustainable Development Goals (SDGs), as they are the two most consumed grains as food. Rice and wheat cultivation significantly impacts the environment, with the agricultural sector employing 27% of the global workforce and contributing 4% to the world’s GDP, thereby affecting social and economic sustainability. Developing a sustainability index for the wheat and rice supply chain is a complex endeavor, as it depends on various factors such as the location of growers, farming methods, the target audience, and the stakeholders involved. This index must be derived from an optimal selection of indicators to avoid information overload while covering all essential sustainability aspects. There are different methods, such as life cycle assessment, energy analysis, ecological footprint, and carbon footprint, being used to assess sustainability, with indicator-based assessment emerging as a comprehensive approach. This study utilised the Triple Bottom Line (TBL) to identify optimal sustainability indicators in the wheat and rice supply chain. A systematic literature review was initially conducted, followed by an expert opinion survey to determine the required indicators. The literature review unveiled a wide array of indicators used across studies, often contingent on each study’s specific objectives. While some consistency existed in environmental indicators, discussions on social and economic dimensions within the wheat and rice supply chain were limited. Analysis of the expert opinion survey revealed a consensus on most selected indicators, albeit with variations based on experts’ geographical locations. The final set of optimal indicators identified can serve as a foundation for developing a sustainability index, implementing a sustainability information management system, and formulating policy initiatives in the rice and wheat supply chain. Full article

Lead poisoning is a significant public health issue, contributing to 0.6% of the global disease burden and disproportionately affecting developing countries. Vulnerable populations, such as children, pregnant women, and low-income communities, remain at high risk, often exposed to lead levels exceeding safe thresholds. While the problem is global, this review focuses specifically on the Americas, regions with diverse regulatory landscapes and persistent environmental lead exposure. Regulatory frameworks vary widely, and the lack of global consensus on acceptable blood lead levels leaves important gaps in protection. This review compiles and updates knowledge on emerging sources of lead exposure in the region, evaluates advancements in regulatory approaches, and analyzes the molecular impacts of lead on human health. Using the Comparative Toxicogenomics Database (CTD), lead was found to interact with 3448 genes, including those linked to inflammation and oxidative stress, and is associated with 4401 diseases and 799 disrupted pathways. These findings emphasize the need for regionally tailored interventions, strengthened policies, and further research on its health impacts. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

Background: Surgical resection remains central to the curative treatment of locally advanced gastric cancer (GC), yet global variability persists in defining resectability, particularly in complex scenarios such as multivisceral invasion, positive peritoneal cytology (CY1), or oligometastatic disease. The Intercontinental Criteria of Resectability for Gastric Cancer (ICRGC) project was developed to address this gap by combining expert surgical input with artificial intelligence (AI)-based reasoning. Methods: A two-stage prospective survey was conducted during the 2024 European Gastric Cancer Association (EGCA) meeting. Fifty-eight surgical oncologists completed a 36-item questionnaire on resectability, strategy, and quality metrics. Subsequently, they reviewed AI-generated responses based on current clinical guidelines and completed a second round. Concordance between human and AI responses was classified as full, partial, or discordant, and changes in surgeon opinions were statistically analyzed. Results: Substantial agreement was observed in evidence-based domains. Seventy-nine percent of surgeons agreed with AI on distinguishing technical from oncological resectability. In cT4b cases, 61% supported restricting multivisceral resection to high-volume centers. Similar alignment was found in CY1 (54%) and N3 nodal disease (63%). Partial concordance appeared in areas requiring individualized judgment, such as peritonectomy or bulky-N disease. After AI exposure, surgeon responses shifted toward guideline-consistent decisions, including increased support for cytoreductive surgery only when CC0/1 was achievable and stricter classification of R2 resections as unresectable. Following AI exposure, 27.1% of surgeons changed at least one answer in alignment with AI recommendations, with statistically significant shifts observed in items related to surgical margin definition (p = 0.015), anatomical resection criteria (p < 0.05), and hospital stay benchmarks (p = 0.031). Conclusions: The ICRGC study demonstrates that AI-driven consensus modeling can replicate expert reasoning in complex surgical oncology and serve as a catalyst for harmonizing global practice. These findings suggest that AI-supported consensus modeling may complement expert surgical reasoning and promote greater consistency in decision-making, particularly in controversial or ambiguous cases. Full article

Intubated critically ill patients are susceptible to secretion accumulation because of compromised airway clearance. Various airway clearance interventions are employed to prevent complications arising from mucus retention. This Delphi study aims to collect global opinions in an international expert panel of ICU professionals on the usefulness of these various airway clearance interventions. A steering committee performed a literature search informing the formulation of statements. Statements are grouped into two distinct parts: (1) Humidification and Nebulization, and (2) Suctioning and Mucus mobilization techniques. For each part, a diverse panel of 30–40 experts will be selected, with concerted effort to involve experts from various medical specialties involved in airway clearance methods. Multiple choice questions (MCQs) or 7-point Likert-scale statements will be used in the iterative Delphi rounds to reach consensus on various airway clearance interventions. Rounds will continue until stability is achieved for all statements. Consensus will be deemed achieved when a choice in MCQs or a Likert-scale statement achieves ≥75% agreement or disagreement. Starting from the second round of the Delphi process, stability will be assessed using non-parametric χ² tests or Kruskal–Wallis tests. Stability will be defined by a p-value of ≥0.05. Full article