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Anion exchange membrane water electrolyser showing the chemical structure of hydroxyl-conductive 2D hBN-based anion exchange membrane (AEM). The developed AEMs exhibit high hydroxyl conductivity, superior mechanical and electrochemical stability.

The Arctic climate system is in great distress, warming faster than the rest of the world and transforming more rapidly than previously anticipated. Sustained and harmonized multidisciplinary observations at key locations are needed to fill knowledge gaps and evaluate the ongoing climate change impacts on the complex Arctic marine system. For more than a decade, the Distributed Biological Observatory (DBO) has functioned as a “detection array” for ecosystem changes and trends in the Pacific sector of the Arctic Ocean. This long-term collaborative initiative builds on active involvement of scientists conducting in situ observations within marine disciplines to systematically document how the arctic marine ecosystem is transforming with environmental change. The DBO concept is currently being expanded into other sectors of the Arctic, including Davis Strait and Baffin Bay, the Atlantic Arctic gateway area, and the East Siberian Sea. Through increased collaboration and joint practices, findings from these regional areas can leverage to pan-Arctic perspectives and improve our understanding of the entire Arctic Ocean. Common practices are now being developed, including key phenomena and relevant indicators to study. Also, we strive towards harmonized routines for sampling, analysis and data sharing to increase the comparability across both disciplines and regions, and to improve the usability of our in-situ observations also for the modelling and remote sensing scopes. An ambition is, moreover, to expand from today's predominantly open-sea coverage towards coastal regions, to the benefit of both local communities and researchers. The process of establishing a pan-Arctic DBO network is to a large part facilitated by the EU Horizon project Arctic PASSION (2022-2025). Here, we present the latest developments and shared priorities, as well as our vision of how to incorporate our efforts into other parallel processes aiming to strengthen the pan-Arctic observing system towards, during and beyond the upcoming IPY.

OBJECTIVE: There is a growing correlation between the rise in infectious diseases and climate change; however, little is known about the interactions and mixed effects of climate factors on infectious diseases. METHOD: We conducted a retrospective longitudinal study spanning 108 consecutive months from 2014 to 2022 in Can Tho, Vietnam to identify common infectious diseases (excluding tuberculosis, HIV, and COVID-19) and their associations with climate change and determine which common diseases presented concurrently with the COVID-19 period using multivariate linear regression, receiver operating characteristic (ROC) curve analysis, Bayesian kernel machine regression (BKMR) and orthogonal partial least squares discriminant analysis. RESULT: The five infectious diseases with the highest average incidence rates per 100,000 people were diarrhea; hand, foot, and mouth disease (HFMD); dengue fever; viral hepatitis; and influenza. Positive associations with humidity were observed for dengue fever and HFMD. Temperature was positively associated with malaria. Negative associations were found between humidity and both chickenpox and tetanus. Diarrhea (AUC = 0.79; 95 % CL = 0.70–0.87) and dengue fever (AUC = 0.74; 95 % CL = 0.62–0.83) emerged as the most influential diseases both before and during the COVID-19 period. In our BKMR analysis, we found a significant association between the combined influence of temperature and humidity and the occurrence of dengue fever and HFMD, especially when all climate factors were at or above their 60th percentile relative to their values at the 50th percentile. Temperature emerged as the primary driver associated with the occurrence of infectious diseases. CONCLUSION: These findings underscore the importance of implementing robust surveillance, prevention, and control measures by public health authorities in Can Tho. Initiatives like vaccination campaigns, vector control programs, public education on hygiene practices, and strengthening healthcare infrastructure are crucial for mitigating the spread of infectious diseases and safeguarding public health in the region.

Hydrothermal liquefaction (HTL) presents a promising pathway for converting wet biomass resources into biofuels, offering significant advantages over conventional methods. However, numerous challenges must be addressed for HTL scale-up, including energy provision for the endothermic process, heat and mass transfer limitations, slurry concentration and pumpability challenges, char and coke formation, and continuous phase separation. This review explores key strategies such as autothermal HTL, which improves process efficiency and reduces external energy requirements by coupling exothermic and endothermic reactions within the same reactor, thereby simplifying reactor design and reducing operational costs. Additionally, multistage HTL processes are highlighted for their ability to optimize biocrude quality and yield by fractionating biomass conversion stages, resulting in higher energy returns on investment and better-quality biocrude. Solvothermal HTL and integration techniques for aqueous phase are also discussed. Furthermore, the HTL patent landscape is discussed to provide insights into current technological advancements. This review aims to offer a comprehensive understanding of process intensification in HTL, highlighting innovative solutions to enhance the efficiency and scalability of the process for sustainable biofuel production. ; This is a pre-proof of an article published as Mazhkoo, Shahin, Salman Soltanian, Habeeb O. Odebiyi, Omid Norouzisafsari, Mitchell Ubene, Aneela Hayder, Omid Pourali, Rafael Santos, Robert C. Brown, and Animesh Dutta. "Process intensification in hydrothermal liquefaction of biomass: A review." Journal of Environmental Chemical Engineering (2025): 115722. doi: https://doi.org/10.1016/j.jece.2025.115722.

Abstract Background Learning Health Systems (LHS), in which continuous and equitable improvements support optimization of healthcare practices, outcomes, experience, and costs, offer enormous potential for health system transformation. Within the LHS model, evaluation of health innovations assists in question identification, data collection, and targeted action, which facilitates continuous improvement. Evaluation that catalyzes learning may contribute to health innovation implementation, refinement, and sustainability, however, there is little consensus as to why certain evaluations support learning, while others impede it. Methods Embedded in the implementation science literature, we conducted a realist synthesis to understand evaluative contextual factors and underlying mechanisms that best support health system learning and sustainable implementation of innovations. We sought to understand whether evaluations can ‘work’ to support learning and sustainability, in which contexts, for whom, and why. Working with an Expert Committee comprised of leaders in evaluation, innovation, sustainability, and realist methodology, we followed a five-stage process of: 1. Scoping the Review, 2. Building Theories, 3. Identifying the Evidence, 4. Evidence Selection and Appraisal, and 5. Data Extraction and Synthesis. Our Review Team and Expert Committee participated in iterative cycles of results interpretation and feedback. Results Our synthesis includes 60 articles capturing the mechanisms and contextual factors driving learning and sustainability through evaluation. We found that evaluations that support learning and sustainability incorporate favourable organizational preconditions and focus on implementing rapid cyclical feedback loops that contribute to a culture of innovation and evaluation sustainability. Our findings have been organized into 6 Context-Mechanism-Outcome Configurations (CMOCs): 1. Embracing Risk & Failure; 2. Increasing Capacity for Evaluation; 3. Co-Producing Evaluation; 4. Implementing Learning Feedback Loops; 5. Creating Sustainability Culture; and 6. Becoming a Learning Organization. We have also translated findings into a series of Action Strategies for evaluation implementation to support health systems learning and sustainability. Conclusions We identified key contextual factors and underlying mechanisms that make evaluations ‘work’ (or ‘not work’) to support learning and sustainability. Findings support the operationalization of LHS by translating CMOCs into Action Strategies for those tasked with completing evaluations with a view toward health system learning and innovation sustainability.

A significant energy release over a short time is achieved in replicable experiments involving the interaction of deuterium gas with constantan specimens. The experiments were carried out in a gas chamber where the injected deuterium interacted with heated specimens: (i) Many replicable experiments were performed at initial temperatures in the range of 666–681 °C. The temperatures of the specimens began to increase ~8 s after the beginning of deuterium injection as additional increases of 358–382 °C reached after ~30 s. The released excess power was in the range of 183–209 W, its density ranged from ~114–130 W/g, and the ratio of (output power)/(input power) was ≈ 3.76–3.91. (ii) Several replicable experiments were performed at initial temperatures of 950 °C. In all these experiments, explosive evaporation of the wires occurred immediately after the beginning of deuterium injection. The released excess momentary power was greater than 3400 W, its density was 2280 W/g, and the ratio of (output power)/(input power) was ≈ 16 and greater. The outcomes found were as follows: (a) the released excess power was not of electrical origin; (b) the released excess power of chemical origin was less than ~0.18% of the total released excess power; (c) the significant density of the released excess power; and (d) helium release, correlating with the energy release, was observed. The conclusion that the released energy is of nuclear origin was drawn.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.