This paper develops a geospatial framework for climate risk stress testing in California with applications to banking and climate-exposed sectors such as agriculture, real estate, and tourism. The study integrates physical hazard mapping, sector-specific exposure analysis, and scenario-based financial risk assessment to evaluate how wildfires, drought, flooding, extreme heat, and transition risks may affect regional economic activity and financial stability. The framework is intended to support portfolio monitoring, climate scenario analysis, and institutional readiness under emerging disclosure and risk-management standards. In addition, the paper provides a survey-based implementation guide for benchmarking current climate-risk practices and data needs across industry and academic stakeholders.
With the rapid development of Internet of Things and artificial intelligence technologies, flexible wearable sensors have shown great potential in human-machine interaction and health monitoring fields. However, traditional hydrogel sensors face challenges such as water loss, freezing, and the use of toxic initiators during the preparation process, which lead to biological safety issues. To address these challenges, this paper proposes a green, initiator-free polymerization strategy based on the deep eutectic solvent system composed of choline chloride (ChCl) and D-sorbitol. By utilizing the property that the nitrogen-containing quaternary ammonium group in the ChCl molecule can generate free radicals upon ultraviolet irradiation, this study achieves the rapid polymerization of acrylamide in an initiator-free manner. The prepared eutectogel exhibits high transparency (≈96%), skin-fitting elastic modulus, good antifreezing performance, suitable breathability, and broad-spectrum adhesion. The flexible strain sensor constructed based on the eutectogel has high sensitivity, wide detection range, and good fatigue resistance. Combined with machine learning algorithms, this sensor system achieves a high accuracy (98.5%) of recognizing Curwen gestures. This research provides an innovative approach for developing safe, reliable, and environmentally adaptable intelligent wearable devices and has broad application prospects in intelligent music education and modern human-machine interaction.
This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration is investigated via the Johansen and Hatemi-J cointegration tests. Long-run coefficients are subsequently estimated using the DOLS and FMOLS estimators. The robustness of the empirical findings is further assessed using the ARDL approach. In addition, an interaction term is constructed to measure the impact of renewable energy in alleviating inflationary pressures arising from energy imports. The results show that energy imports and exchange rate have an increasing impact on inflation, while renewable energy and the interaction term have a decreasing impact. DOLS, FMOLS, and ARDL results support each other. Moreover, in both models, the impact of renewable energy in mitigating inflationary pressures stemming from energy imports is stronger than the direct disinflationary impact of renewable energy.
This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.
In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. However, there are two main challenges to the existing REC schema: 1) The RECs have not been globally adopted due to inconsis
This paper introduces the Future Atmospheric Conditions Training System (FACTS), a novel platform that advances climate resilience education through place-based, adaptive learning experiences. FACTS combines real-time atmospheric data collected by IoT sensors with curated resources from a Knowledge Base to dynamically generate localized learning challenges. Learner responses are analyzed by a Generative AI powered server, which delivers personalized feedback and adaptive support. Results from a user evaluation indicate that participants found the system both easy to use and effective for building knowledge related to climate resilience. These findings suggest that integrating IoT and Generative AI into atmospherically adaptive learning technologies holds significant promise for enhancing educational engagement and fostering climate awareness.
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.
To evaluate comparative outcomes of artificial Intelligence (AI)-based and traditional-based teaching in medical education. The literature search was carried out in CENTRAL, CINAHL, Web of Science, MEDLINE, and EMBASE to identify randomized controlled trials (RCTs) comparing AI-based versus traditional-based teachings in medical education. Estimate of effect size was determined for knowledge score, skills score, and teaching satisfaction score via fixed-effect modelling. Fourteen RCTs enrolling 1116 students who received AI-based teaching (n = 558) or traditional-based teaching (n = 558) were included. The use of AI-based teaching was associated with significantly higher knowledge score (standardized mean difference (SMD): 0.36, 95% CI, 0.24-0.49, P < .00001), skills score (SMD: 0.78, 95% CI, 0.57-0.99, P < .00001), and teaching satisfaction score (SMD: 0.97, 95% CI, 0.66-1.29, P < .00001) compared to the traditional-based teaching. Subgroup analyses with respect to the practical course, theoretical course, duration of course shorter or longer than 1 week were consistent with the main analyses. Meta-regression analysis demonstrated that practical course significantly increased estimate effect for knowledge score (P = .002) and skills score (P = .0001). Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. However, the available evidence may be subject to publication and reporting bias with high between-study heterogeneity. Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings. Key messages What is already known on this topic: Growing evidence from randomized controlled trials demonstrated positive impact of artificial intelligence (AI) in medical education when compared to the traditional approaches. What this study adds: Meta-analysis of best available evidence (level 1a) indicates that AI-based teaching significantly improves student's knowledge, skills, and satisfactions compared to traditional teaching. How this study might affect research, practice, or policy: This study suggests that Future studies should evaluate AI-based teaching in postgraduate settings including speciality and even subspecialties trainings.
We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on electroencephalography (EEG), termed EEGChat. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to be able to generate language. Our work thus demonstrates the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.
The pervasive adoption of Generative Artificial Intelligence tools in professional settings raises a question that those responsible for training can no longer avoid: what happens to expert intuition - that form of embodied, tacit knowledge built through years of practice and error - when the most cognitively demanding tasks are systematically delegated to a machine? This article argues that the risk is not technical but formative: it concerns the mechanism through which deep professional competence is constructed, with concrete implications for the design of professional development pathways in healthcare organisations.
Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electronic health records and imaging. New technologies have tried to overcome obstacles to implement guidelines and recommendations. This review aims to describe the recent progress in the use of machine learning and new technologies in the field of nutrition of the critically ill. Increase in data availability, ability to extract these data and analyze them using machine learning has allowed data scientists together with ICU specialists to improve nutritional screening and assessment and to predict occurrence of obstacles like enteral feeding intolerance or refeeding hypophosphatemia. In addition, new technologies can ensure nasogastric tube positioning and enteral feeding efficacy. Integrated platforms can integrate nutritional needs with most adequate prescriptions and modulate the nutritional administration according to the patient's tolerance and requirements. Analysis of continuous recording of imaging obtained from ultrasound can also predict gastric intolerance. Using machine learning, numerous algorithms and nomograms have been suggested to predict enteral feeding intolerance but validation of these predictions is still required. New technologies integrating energy requirements and delivery of the optimal enteral feeding are very promising.
Embodied intelligence-artificial intelligence instantiated in physical or virtual bodies that can perceive, communicate, and interact with users and their environments-has been increasingly applied in health care. However, the evidence base remains fragmented because of inconsistent terminology, diverse embodiment forms, and limited synthesis of application domains, target populations, care settings, acceptability, and effectiveness. This fragmentation constrains conceptual clarity and translation into routine health care practice. This scoping review aimed to systematically map the applications of embodied intelligence in health care by classifying embodiment forms, identifying major functional domains, describing target populations and implementation settings, and synthesizing the available evidence on acceptability and effectiveness. This scoping review followed the Arksey and O'Malley framework, with enhancements by Levac et al, and was reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) guidelines. Seven electronic databases were searched from database inception to December 2025, supplemented by gray literature searches and backward citation screening. Eligible studies were primary empirical studies published in English or Chinese that examined embodied intelligence in health care contexts. Two reviewers independently screened records and charted data using a pilot-tested standardized form. Descriptive statistics and thematic synthesis were applied. No formal critical appraisal was conducted because the aim was to map the breadth and characteristics of the evidence base. A total of 83 studies were included. Five embodiment forms were identified: virtual humanoid agents (32/83, 38.6%), physical humanoid robots (32/83, 38.6%), virtual animal-shaped agents (1/83, 1.2%), physical animal robots (13/83, 15.7%), and mechanical robots (5/83, 6%). Applications clustered into 3 functional domains: health management and health education (40/83, 48.2%), mental health promotion (37/83, 44.6%), and physiological health promotion (6/83, 7.2%). Older adults were the most frequently targeted population (45/83, 54.3%). Interventions were mainly implemented in home settings, care homes, laboratories, and hospitals. Twenty-two randomized controlled trials reported generally beneficial effects on health behaviors, mental health outcomes, or cognitive function, although outcome measures were heterogeneous. Twelve studies examined acceptability and generally reported favorable user acceptance. This scoping review provides the first comprehensive synthesis of embodied intelligence in health care using a unified classification of forms, functional domains, populations, and application settings. The findings indicate that embodied intelligence is most mature in "health management and health education" and "mental health promotion," with increasing real-world deployment in home and care home settings. By consolidating fragmented evidence and standardizing terminology, this review offers a practical foundation for clinicians, nurses, and policymakers to support the implementation of embodied intelligence in routine health care. Evidence is limited by heterogeneous outcome measures, many lab-based evaluations, and the absence of formal quality appraisal, underscoring the need for standardized outcome measures, rigorous randomized controlled trials, and longitudinal evaluations to enable scalable and ethically grounded real-world adoption.
The increasing complexity of healthcare systems management requires the development of advanced methodologies to support efficient resource allocation, service delivery, and strategic planning. Artificial intelligence has emerged as an important tool in this domain, offering capabilities to model demand, predict capacity requirements, and inform operational decisions through data-driven insights. This article provides a comprehensive scoping review of AI-based approaches for demand and capacity modelling in healthcare systems. Specifically, it examines AI methods applied to key demand prediction tasks, including outpatient activity, emergency department attendances, hospital admissions and readmissions, and length of stay, as well as capacity planning for beds, workforce, equipment, and other critical resources. The review reports trends in AI models design, learning paradigms, performance reporting, and data usage, and highlights the relationship between demand modelling and downstream capacity predictions. In addition, the paper analyses data infrastructure requirements, commonly used datasets, and the growing role of explainable AI in supporting transparency and trust. Despite recent advances in the field, the integration of AI into healthcare systems faces significant challenges, including concerns related to privacy, ethics, data quality, interpretability, bias, scalability, policy variation, and data interoperability. Addressing these challenges is essential to develop sustainable, fair and resilient healthcare systems. Our review highlights the current state and gaps in the literature, and proposes future directions for advancing the use of AI in healthcare management systems are reviewed.
Artificial intelligence is now embedded across the scientific research and publishing ecosystem, influencing discovery, analysis, knowledge translation, authorship, peer review, and editorial workflows. In cardiovascular and biomedical sciences, these developments offer substantial opportunities to accelerate knowledge generation, integrate complex datasets, and improve efficiency and consistency. At the same time, they introduce new risks related to bias, transparency, data integrity, and authorship responsibility, potentially endangering trust in the scientific record. This commentary examines the evolving role of AI in biomedical publishing, with particular attention to generative models and machine learning tools. We review both benefits and limitations, highlight risks such as fabricated content, biased outputs, and erosion of accountability, and discuss why traditional detection approaches are insufficient. Instead, we argue for a shift toward transparency, provenance, and enforceable human responsibility as the core principles guiding AI use, ensuring that AI strengthens rather than undermines scientific rigour and public trust. We outline practical expectations for authors, reviewers, editors, and publishers, with emphasis on reporting standards, reproducibility under rapidly evolving model versions, and the conflict-of-interest implications of AI tooling for the editorial process itself.
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