Search Results (65)

Adolescents are heavy users of digital media but often lack critical skills, increasing their vulnerability to harmful online content. The integration of game elements into learning and training offers a promising strategy to support positive behavioural change and strengthen adolescents’ skills. This paper describes the development of a conceptual framework for Dress-DIGITARIAN, a serious game aimed at improving health literacy, coping skills, and self-esteem, grounded in Self-Determination Theory (SDT). The framework was constructed to generate higher-order understanding through a multi-level process: analyzing general theory (SDT), integrating mid-range models (the Octalysis framework), and incorporating empirical insights derived from two data collection phases with the target population. This integrative approach informed and guided the game’s design through participatory methods. Developed through collaboration between schools and research institutions, this approach bridges theory and practice by aligning game mechanics with adolescents’ psychological needs. It also underscores the value of involving adolescents in research, not only to enhance scientific rigour but also to empower them as agents of change capable of contributing to health promotion policies and educational innovation. This study does not report the results of a completed intervention or outcome evaluation, which will be conducted in the sixth phase at the end of the current school year. Future research is needed to assess the model’s effectiveness and scalability and to identify areas for further refinement. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

Advances in artificial intelligence (AI), particularly agentic AI, have created opportunities to enhance global sustainability by improving the efficiency and accuracy of environmental monitoring and response systems. Agentic AIs autonomously plan and execute towards specific goals with minimal or no human intervention; however, accessing environmental data is challenging and requires expertise due to inherent fragmentation and the diversity of data formats. The Model Context Protocol (MCP) is an open standard that allows AI systems to securely access and interact with diverse software tools and data sources through unified interfaces, reducing the need for custom integrations while enabling more accurate, context-aware assistance. This study introduces WeatherInfo_MCP, an interface that provides the required expertise for AI agents to access National Weather Service (NWS) data. Built on a service-oriented architecture, the system uses a centralized engine to handle robust geocoding and data extraction while providing AI agents with simple, independent tools to retrieve weather data from the NWS API. The system was validated through 14 unit tests and 23 comprehensive protocol compliance tests against the MCP 2025-06-18 specification, achieving a 100% pass rate across all categories, demonstrating its reliability when working with AI agents. We also successfully tested our model alongside a memory MCP to showcase its performance in a multi-MCP environment. While in its earliest version, WeatherInfo_MCP connects to the NWS API, its modular design and compliance with software development and MCP standards facilitate immediate expansion to additional environmental data and tools. WeatherInfo_MCP is released as an open-source tool to support the sustainable development community, enabling broad adoption of AI agents for environmental use cases. Full article

The rapid evolution of Generative AI (GenAI) has created the conditions for developing innovative solutions that disrupt all fields of human-related activities. Within the healthcare sector, numerous AI-driven applications have emerged, offering comprehensive health-related insights and addressing user questions in real time. Nevertheless, most of them use general-purpose Large Language Models (LLMs); consequently, the responses may not be as accurate as required in clinical settings. Therefore, the research community is adopting efficient architectures, such as Multi-Agent Systems (MAS) to optimize task allocation, reasoning processes, and system scalability. Most recently, the Model Context Protocol (MCP) has been introduced; however, very few applications apply this protocol within a healthcare MAS. Furthermore, Retrieval-Augmented Generation (RAG) has proven essential for grounding AI responses in verified clinical literature. This paper proposes a novel architecture that integrates these technologies to create an advanced Agentic Corrective RAG (CRAG) system. Unlike standard approaches, this method incorporates an active evaluation layer that autonomously detects retrieval failures and triggers corrective fallback mechanisms to ensure safety and accuracy. A comparative analysis was conducted for this architecture against Typical RAG and Cache-Augmented Generation (CAG), demonstrating that the proposed solution improves workflow efficiency and enables more accurate, context-aware interventions in healthcare. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

Chronic atrophic gastritis (CAG) is a key precursor in the Correa cascade leading to gastric cancer and is driven by long-standing Helicobacter pylori infection, autoimmune reactions, environmental exposures, and persistent inflammation. Emerging evidence indicates that mild to moderate atrophy and part of intestinal metaplasia exhibit a degree of reversibility when etiological eradication, microenvironmental optimization, and regenerative stimulation are achieved. This review summarizes recent advances in the pathological basis, evaluation systems, therapeutic mechanisms, and clinical management strategies of CAG. Reversibility is closely related to residual glandular reserve, stem-cell plasticity, and effective mitigation of chronic inflammation. Current assessment tools integrate OLGA/OLGIM histological staging, high-quality endoscopy with AI assistance, and serological biomarkers. Fundamental interventions include early H. pylori eradication, mucosal protective agents, micronutrients, and small-molecule drugs targeting inflammation, oxidative stress, and epithelial regeneration. Novel strategies such as mesenchymal stem cells, exosomes, and focal endoscopic therapies demonstrate regenerative potential in preclinical studies. Traditional Chinese medicine provides multi-target regulation of inflammation, apoptosis, microecology, and stem-cell-related pathways, contributing to histological improvement. Contemporary guidelines emphasize early eradication, risk-stratified surveillance, and comprehensive intervention. Future directions focus on unified evaluation criteria, long-term prospective studies, multimodal combination regimens, and integration of AI-based risk modeling to achieve precise, cancer-preventive CAG management. Full article

Background: Cancer-related fatigue (CRF) is one of the most common and burdensome symptoms faced by patients with cancer, yet effective drug-based treatments remain limited. In recent years, phytotherapeutic agents have drawn attention as complementary options, supported by plausible anti-inflammatory, antioxidant, and immunomodulatory mechanisms. Methods: We performed a systematic review and meta-analysis to quantitatively synthesize randomized controlled trial evidence on the efficacy of phytotherapeutic interventions for cancer-related fatigue and to assess the certainty of evidence. Databases were searched from inception, with the final search update completed in October 2025. Eligible studies included adults with CRF and compared herbal interventions with placebo controls. Standardized mean differences (SMDs) were pooled using a DerSimonian–Laird random-effects model. We also evaluated risk of bias (RoB 2), publication bias, and certainty of evidence using GRADE. This systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines. Results: Fourteen trials were included, studying agents such as Paullinia cupana, Panax ginseng, multi-herbal Traditional Chinese Medicine formulations, and other botanical extracts. Overall, phytotherapy provided a modest improvement in CRF (SMD = 0.31; 95% CI, 0.08–0.53; p = 0.022), though heterogeneity was substantial (I² = 56.7%). In subgroup analyses, only the group of “other formulations” demonstrated significant benefit; ginseng and guaraná did not demonstrate statistically significant effects. Most trials had high or unclear risk of bias, and the certainty of evidence was rated very low. Conclusions: Current evidence does not firmly support phytotherapeutic agents as effective treatments for CRF, hindered largely by methodological weaknesses, heterogeneous interventions, and imprecise effect estimates. Even so, the biological rationale and the variability in clinical responses point toward an opportunity for the emerging field of precision herbal oncology. Well-designed, multicenter trials are essential to determine whether phytotherapy can meaningfully contribute to CRF management. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

Despite growing research on sustainable aviation, multi-airport systems, and environmentally constrained capacity allocation, critical gaps persist. Existing studies often treat passenger choice, airline competition, and airport regulation in isolation, or evaluate environmental policies such as carbon taxation only as macro-level constraints. Consequently, the endogenous feedback among pricing, capacity reallocation, and regulatory intervention in shaping equilibrium outcomes within multi-airport systems remains underexplored, particularly within a unified dynamic framework that links low-carbon policies to operational decision-making. This study develops such a dynamic framework to support the sustainable transition of carbon-constrained multi-airport regions. Focusing on the Beijing–Tianjin multi-airport system and China’s “Dual Carbon” goals, we construct a three-layer iterative equilibrium game integrating passenger airport choice (modeled using a multinomial logit specification), airline capacity reallocation (formulated as an evolutionary game internalizing carbon taxes), and airport slot regulation (implemented through a multi-objective mechanism balancing economic revenue, hub connectivity, and environmental performance). An agent-based simulation of the Beijing/Tianjin–Nanchang route demonstrates robust convergence to a stable systemic equilibrium. Intensified competition reduces fares and improves accessibility, while capacity shifts from higher-cost Beijing airports to Tianjin Binhai Airport, whose market share rises from 10.6% to 34.0%. Airport utilization becomes more balanced, total airline profits increase slightly, and both total and per-passenger CO₂ emissions decline, indicating improved carbon efficiency despite demand growth. The results further identify a range of carbon-tax levels that jointly promote emission reduction and traffic rebalancing with limited profit loss. Full article

We introduce Causal Incentive Design (CID), a framework that applies causal inference to canonical single-stage principal–agent problems (PAPs) characterized by bilateral private information. Within CID, the operating rules of PAPs are formalized using an additive-noise causal graphical model (CGM). Incentives are modeled as interventions on a function space variable, $\Gamma$, which correspond to policy interventions in the principal–follower causal relation. The causal inference target estimand $V(\Gamma )$ is defined as the expected value of the principal’s utility variable under a specified policy intervention in the post-intervention distribution. In the context of additive-Gaussian independent noise, the estimand $V(\Gamma )$ decomposes into a two-layer expectation: (i) an inner Gaussian smoothing of the principal’s utility regression; and (ii) an outer averaging over the conditional probability of the follower’s action given the incentive policy. A Gauss–Hermite quadrature method is employed to efficiently estimate the first layer, while a policy-local kernel reweighting approach is used for the second. For offline selection of a single incentive policy, a Functional Causal Bayesian Optimization (FCBO) algorithm is introduced. This algorithm models the objective functional $\gamma \mapsto V\left(\gamma \right)$ using a functional Gaussian process surrogate defined on a Reproducing Kernel Hilbert Space (RKHS) domain and utilizes an Upper Confidence Bound (UCB) acquisition functional. Consequently, the policy value $V\left(\gamma \right)$ becomes an interventional query that can be answered using offline observational data under standard identifiability assumptions. High-probability cumulative-regret bounds are established in terms of differential information gain for the proposed FBO algorithm. Collectively, these elements constitute the central contributions of the CID framework, which integrates causal inference through identification and estimation with policy search in principal–agent problems under private information. This approach establishes a causal decision-making pipeline that enables commitment to a high-performing incentive in a single-shot game, supported by regret guarantees. Provided that the data used for estimation is sufficient, the resulting offline pipeline is appropriate for scenarios where adaptive deployment is impractical or costly. Beyond the methodological contribution, this work introduces a novel application of causal graphical models and causal reasoning to incentive design and principal–agent problems, which are central to economics and multi-agent systems. Full article

Alzheimer’s disease (AD), the leading cause of dementia worldwide, is characterized by progressive neuronal loss, amyloid-β (Aβ) aggregation, tau hyperphosphorylation, oxidative stress, neuroinflammation, cholinergic dysfunction, and gut–brain axis dysregulation. Despite advances in anti-amyloid therapeutics, current interventions provide only modest symptomatic relief and face limitations in accessibility, cost, and long-term efficacy. Plant-derived bioactive compounds, rooted in traditional medicine systems such as Ayurveda and Traditional Chinese Medicine, have gained increasing attention as multi-target therapeutic agents due to their pleiotropic actions, relative safety, and ability to cross the blood–brain barrier. This review synthesizes mechanistic and translational evidence on major phytochemicals, including withanolides (Withania somnifera), curcumin (Curcuma longa), ginkgolides and bilobalide (Ginkgo biloba), bacosides (Bacopa monnieri), ginsenosides (Panax ginseng), crocin/safranal (Crocus sativus), epigallocatechin-3-gallate (Camellia sinensis), rosmarinic acid (Salvia officinalis, Melissa officinalis), and asiaticosides (Centella asiatica). These compounds exert neuroprotective effects by inhibiting Aβ aggregation, reducing tau phosphorylation, scavenging reactive oxygen species, attenuating NF-κB-mediated inflammation, modulating cholinergic signaling, enhancing synaptic plasticity via brain-derived neurotrophic factor/cAMP response element-binding protein (BDNF/CREB) activation, and regulating gut microbiota. Multi-target approach analyses underscore their synergistic potential in targeting interconnected AD pathways. However, translation remains hindered by poor oral bioavailability, rapid metabolism, and variability in clinical outcomes. Advances in delivery platforms, including liposomes, bilosomes, solid lipid nanoparticles, and nanostructured lipid carriers, are improving stability, blood–brain penetration, and therapeutic efficacy in preclinical models. Collectively, plant-derived phytochemicals serve as promising, affordable, and multi-modal candidates for reshaping AD management, bridging traditional knowledge with modern therapeutic innovation. Full article

Background: Interstitial myocardial fibrosis is a crucial pathological feature of many cardiovascular disorders. Myocardial fibrosis resulting in extracellular volume (ECV) expansion can be quantified via cardiac MRI (CMR) with T₁ mapping before and after minimally invasive gadolinium (Gd) contrast agent administration. However, longitudinal repetitive ECV measurements are challenging in rodents due to the prolonged scan time with cardiac and respiratory gating that is required for conventional T₁ mapping and the invasive nature of the rodent intravenous lines. Methods: To address these challenges, the objective of this study is to establish a fast, free-breathing, and gating-free ECV procedure using a minimally invasive subcutaneous catheter for in-scanner Gd administration that can allow longitudinal repetitive ECV evaluations in rodent models. This is achieved by the (1) IntraGate sequence for free-breathing, gating-free cardiac imaging; (2) minimally invasive subcutaneous in-scanner Gd administration; and (3) fast T₁ mapping with a varied flip angle (VFA) in conjunction with (4) triple jugular vein blood T₁ normalization. Additionally, full cine CMR (multi-slice short-axis, long-axis 2-chamber, and long-axis 4-chamber) was acquired during the waiting period to assess comprehensive cardiac function and strain. Results: We successfully established a minimally invasive fast ECV quantification protocol to enable longitudinal repetitive ECV quantifications in rodents. Minimally invasive subcutaneous Gd bolus administration induced a reasonable dynamic contrast enhancement (DCE) time course, reaching a steady state in ~20 min for stable T₁ quantification. The free-breathing gating-free VFA T₁ quantification scheme allows for rapid cardiac (~2.5 min) and jugular vein (49 s) T₁ quantification with no motion artifacts. The triple jugular vein T₁ acquisitions (1 pre-contrast and 2 post-contrast) immediately flanking the heart T₁ acquisitions enable accurate myocardial ECV quantification. Our data demonstrated that left-ventricular myocardial ECV quantification was highly reproducible with repeated scans, and the ECV values (0.25) are comparable to reported ranges among humans and rodents. This protocol was successfully applied to the ischemia–reperfusion injury model to detect myocardial fibrosis, which was validated by histopathology. Conclusions: We established a simple, fast, minimally invasive, and robust CMR protocol in rodents that can enable longitudinal repetitive ECV quantification for cardiovascular disease progression. It can be used to monitor disease regression with interventions. Full article

Background: Pancreatitis, encompassing acute (AP), severe acute (SAP), and chronic (CP) forms, is a life-threatening inflammatory disorder with limited therapeutic options. Current management is largely supportive, highlighting the urgent need for novel interventions targeting underlying molecular pathways. Aim: This review summarizes recent advances in the pathogenesis of pancreatitis, focusing on calcium dysregulation, ferroptosis, and microRNA-mediated mechanisms while exploring the therapeutic potential of phytochemicals as disease-modifying agents. Summary: Aberrant calcium signaling, iron-dependent lipid peroxidation, and microRNA imbalance drive acinar cell injury, inflammatory cascades, and pancreatic fibrosis. Phytochemicals, including flavonoids, terpenoids, alkaloids, and phenolics, have shown protective effects in preclinical models through multi-targeted mechanisms. These include suppression of NF-κB-driven inflammation, activation of the Nrf2/HO-1 antioxidant pathway, modulation of ferroptosis via GPX4 and iron efflux, regulation of calcium signaling, and modulation of microRNA expression. Importantly, several phytochemicals attenuate acinar cell death, reduce cytokine release, and limit fibrosis, thereby improving outcomes in experimental pancreatitis. However, poor solubility, bioavailability, and pharmacokinetic limitations remain significant barriers. Emerging strategies such as nanotechnology-based formulations, prodrug design, and pharmacokinetic profiling, as well as bioavailability studies, may enhance their clinical applicability. Conclusions: Phytochemicals represent a promising reservoir of multitarget therapeutic agents for pancreatitis. Their ability to modulate oxidative stress, inflammatory and calcium signaling, ferroptosis, and microRNA networks highlights their translational potential. Future studies should focus on clinical validation, bioavailability optimization, and advanced delivery platforms to bridge the gap from bench to bedside. Full article

Wastewater-based epidemiology (WBE) played a vital role during the COVID-19 pandemic by providing early warnings of outbreaks through SARS-CoV-2 RNA detection in sewage. Many rural communities did not benefit from WBE because limited centralized sewer infrastructure challenged conventional WBE surveillance strategies. We present a multi-agent computer model simulating COVID-19 spread in a U.S. county with both sewered and non-sewered zones to assess the performance of WBE in this setting. We evaluate how the sewage service status of the first SARS-CoV-2 carrier, cross-zone community mobility, and WBE detection thresholds influence outbreak detection timing at the county’s wastewater treatment plant under basic reproduction numbers (R0) of 4 and 8. Our key findings include that (1) a detection threshold of 10 gc/mL can identify outbreaks up to six days earlier than a threshold of 50 gc/mL; (2) outbreaks originating in non-sewered zones are detected 1–2 days later, compared with outbreaks in sewered zones; and (3) cross-zone community mobility impacts detection timing only when outbreaks begin in non-sewered zones. Furthermore, once detected, disease prevalence can increase by five- to eleven-fold within the following week. These results underscore the importance of WBE sensitivity and tailored surveillance strategies in both sewered and non-sewered zones of a community. Strengthening WBE capabilities at local treatment facilities can improve early outbreak detection, thereby supporting timely public health interventions. Full article