Search Results (2,475)

GLP-1 receptor agonists and dual GLP-1/GIP agonists have significantly transformed the treatment of obesity, enabling clinically meaningful weight reduction and improvements in cardiometabolic parameters. However, clinical trial data indicate that cessation of therapy is associated with biologically driven weight regain and a partial loss of metabolic benefits. This phenomenon underscores the chronic nature of obesity and the limited durability of effects achieved through pharmacotherapy alone. Nevertheless, structured clinical frameworks describing how to maintain satiety and metabolic stability after GLP-1/GIP dose reduction or discontinuation remain limited. The aim of this narrative review is to discuss the mechanisms underlying weight regain following dose reduction or discontinuation of GLP-1/GIP pharmacotherapy and to present strategies supporting long-term metabolic stabilisation. Weight regain is driven in part by persistent metabolic adaptations, including a reduction in resting energy expenditure (adaptive thermogenesis), alterations in the hunger–satiety axis (increased ghrelin, reduced leptin signalling), and potentially incomplete restoration of adipose tissue and liver-related metabolic function, although direct evidence in this specific setting remains limited. Weight loss is often accompanied by a reduction in fat-free mass, which further lowers energy expenditure and increases susceptibility to a positive energy balance after treatment cessation. It remains unclear whether pharmacological suppression of appetite results in sustained normalisation of endogenous satiety regulation after treatment cessation, and its effects on gut microbiota function remain uncertain. In clinical practice, key priorities include preserving muscle mass (adequate protein intake, resistance training), maintaining dietary nutrient density, stabilising postprandial glycaemia, and ensuring sufficient intake of fermentable fibre to support short-chain fatty acid production and gut–brain signalling. GLP-1/GIP pharmacotherapy should be viewed as a component of an integrated model of obesity treatment. We propose that long-term weight stabilisation may require a transition from pharmacologically induced satiety to satiety supported by diet quality, preserved fat-free mass, and metabolic stability. Further research is needed to define optimal post-treatment strategies and to identify patients in whom therapy can be safely reduced or discontinued. This transition should be regarded as a conceptual framework and forward-looking hypothesis requiring validation in prospective studies. Full article

Background/Objectives: Traditional predictive equations derived from regression analyses exhibit varying degrees of accuracy in estimating resting energy expenditure (REE). AI models can increase the predictability of such equations, even for population-specific ones. This work aimed to improve the prediction of REE in a dataset of Saudi population-specific equations using suitable AI tools. Methods: The dataset from the previously published Saudi population-specific equation by Almajwal and Abulmeaty (AA) in 2019 was used to develop an artificial neural network (ANN)-based version to better predict REE in the adult population. Anthropometric and body composition parameters were used as proposed features. The proposed hybrid prediction model underwent an extensive two-stage, iterative training process. First, the Extreme Gradient Boosting (XGBoost) model is used to compute feature importance scores. Then, the most prominent features were identified and incorporated into the ANN model. These significant features were used to train the ANN model to capture nonlinear correlations among them and make accurate predictions. Subsequently, XGBoost and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) are used for their ability to provide a multi-layer abstraction of complex input data. Results: A total of 423 participants (208 male, 215 female) were divided into three non-overlapping sets: training (295, 70%), validation (64, 15%), and testing (64, 15%). The ANN model, combined with XGBoost, helped us to develop two equations: AA_ANN1= 2.47 × BMI + 11.9 × AdjBW + 962.5 and AA_ANN2 = 4.29 × age + 9.4 × fat mass + 15.71 × FFMI + 1289.3, where BMI is Body Mass Index (kg/m²), AdjBW is Adjusted Body Weight (kg), and FFMI is Fat Free Mass Index (kg/m²). The AA_ANN1 presented a Root Mean Square Error (RMSE) of 215 and an accuracy of 66.2%, whereas AA_ANN2 presented a lower RMSE of 193 and a higher accuracy of 71.4%. The ANN model was trained on the top 10 features ranked by XGBoost, achieving an average accuracy of 90.2%. Conclusions: The two new predictive equations, developed using an ANN combined with XGBoost, significantly improved REE prediction accuracy to 90.2%, achieved only with the full ANN model. Future external validation in an independent cohort is essential before clinical application of these equations. Full article

To mitigate large-scale blackout risks in urban distribution systems under typhoon-induced extreme weather and to reduce post-disaster restoration costs, this study proposes a resilience-oriented spatiotemporal co-optimization framework integrating transportation networks, power grids, and distributed energy resources. First, a city-scale typhoon spatiotemporal model is established, integrating static wind field, dynamic evolution, and trajectory-based mobility with urban-geometry-driven wind speed correction to characterize the spatiotemporal progression of extreme wind hazards. Second, the time-varying failure rates of distribution network components are quantified by explicitly accounting for network topology correlations, while the spatiotemporal dispatchability and output characteristics of distributed resources under disaster conditions are systematically modeled. Third, a pre-disaster proactive deployment model is formulated to minimize load curtailment costs and resource allocation expenditures. The model integrates active network reconfiguration with coordinated placement of distributed generation (DG) and mobile energy storage systems (MESSs), enabling resilience-enhancing pre-positioning strategies. Subsequently, a post-disaster restoration scheduling model is developed with the objective of minimizing unserved load. By embedding traffic flow constraints and optimal path computation under disrupted transportation conditions, the proposed framework realizes spatiotemporal coordination among MESSs, DG, and electric vehicles (EVs), thereby accelerating system-level recovery. Finally, the effectiveness of the proposed strategy is validated on a 51-node urban distribution system located in eastern coastal China, demonstrating significant improvements in restoration performance and resilience enhancement. Full article

The development of renewable energy is vital for addressing future climate change and environmental degradation. Nevertheless, the irregular and fluctuating essential features of wind power presents a considerable barrier to grid operational stability. Hence, precise prediction of wind energy output is crucial for improving power system management, boosting the reliability of the supply, and minimizing reserve expenditure. This study presents a predictive model designed for predicting short-term wind speeds using a stacking ensemble approach, which is based on an enhanced Multi-Feature Zebra Optimization Algorithm (IZOA-Stacking). In the data preprocessing phase, to minimize computational costs and prevent overfitting, a module tailored to the various features affecting wind power is developed for the IZOA-Stacking model. Grey relational analysis and Pearson correlation analysis are employed to determine and filter feature correlations. Critically, the preprocessing module demonstrates strong robustness: the One-Class Support Vector Machine (OneSVM) model is applied to identify and replace 100% of anomalous wind speed data, which leads to a substantial and measurable increase in feature correlation and overall model performance. For instance, when retaining wind speed features, the One-Class Support Vector Machine (OneSVM) model is employed to eliminate anomalous wind speed data. During model construction, a stacking ensemble learning strategy integrates multiple prediction models, including Long Short-Term Memory (LSTM) net-works, Extreme Gradient Boosting (XGBoost), ridge regression (RR), and Residual Networks (ResNets). This integration leverages the predictive strengths of each model. Additionally, the improved Zebra Optimization Algorithm (ZOA) optimizes the hyperparameters of each constituent model, further enhancing forecasting accuracy. The findings suggest that the proposed model demonstrates better performance than reference competitor models with regard to predictive accuracy. Full article

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton exchange membrane (PEM) electrolyzer (EL) and coal-to-hydrogen (C2H) technology are combined to produce hydrogen, and a mixed-hydrogen-source ammonia production model is constructed. The low-carbon characteristics of ammonia gas are used for thermal power mixed ammonia combustion. Secondly, to alleviate the operational burden on TPUs, a collaborative operating framework integrating a concentrating solar power (CSP) plant, an electric heater (EH), and an ammonia-coal co-fired power unit (ACCPU) is introduced. Furthermore, its low-carbon mechanisms during both peak and off-peak load intervals are thoroughly investigated. Thirdly, the ‘electricity–hydrogen–ammonia’ conversion link inside the deep excavation base and the reserve potential of the CSP plant are constructed, and a variety of flexible resource collaborative reserve models are constructed. Building upon this foundation, to account for the diverse uncertainties associated with load demand, wind, and PV generation, a fuzzy chance-constrained programming method is formulated. Seeking to enhance economic efficiency, the framework focuses on lowering the aggregate operational expenditures. Ultimately, the example results demonstrate that the presented approach effectively expands the accommodation capacity for renewable energy, lowers the base’s carbon emission, and alleviates the operational strain on TPUs. Full article

This article addresses the operational challenges facing maritime vessels in the context of decarbonization, with a focus on developing staged recommendations for the integration of power limitation systems and alternative fuels. The systematisation of existing decarbonization problems in the maritime sector and the establishment of their interrelationships constitute the framework for developing coherent decarbonization strategies for the industry. The analysis of alternative fuels identifies the key factors that drive fuel selection in practice. The analysis of contemporary energy consumption regulation technologies has shown that power limitation systems operating through controllable pitch propellers (CPP), integrated with electronic remote-control systems, provide the highest flexibility in managing propulsion characteristics without altering engine rotational speed. The comparative analysis of the engine power limitation (EPL) and shaft power limitation (SHaPoLi) systems has confirmed that SHaPoLi offers a greater potential for reducing fuel consumption and carbon dioxide (CO₂) emissions; however, it comes at higher capital expenditure at the implementation stage. Pairing power limitation with alternative fuels shows that deep cuts in the sector’s carbon footprint are within reach. The economic analysis of power limitation system deployment has revealed the potential for achieving considerable operational cost savings, with a balanced consideration of capital investments and operational benefits. Future research should target the optimisation of EPL and SHaPoLi systems and their integration with other energy-saving technologies. Transitioning to alternative fuels in parallel offers the greatest cumulative reduction in the sector’s carbon footprint. Full article

Purpose: Recent studies have shown that high-intensity interval training (HIIT) can improve cardiometabolic health in individuals with spinal cord injury (SCI); however, many barriers remain for individuals with spinal cord injury to participate in exercise such as lack of time, accessible equipment and facilities, and transportation. The use of telehealth interventions may be a form of exercise delivery that can ease the burden on the participant and lead to greater exercise participation. Thus, the purpose of this study was to determine the feasibility and efficacy of a home-based telehealth HIIT arm crank exercise training program for individuals with spinal cord injury. Methods: Participants were randomly assigned to 16 weeks of telehealth HIIT arm crank exercise training or a no-exercise control group. Body composition, resting energy expenditure (REE), blood lipids, insulin sensitivity, blood pressure, aerobic capacity (VO₂ max), and a qualitative interview were assessed at baseline and at 16 weeks post intervention. Results: Six individuals (four male and two female, mean age 52.7 ± 10.2 years) with spinal cord injury were recruited for this study. Four out of five HIIT participants showed improvements in aerobic capacity, insulin sensitivity, and resting energy expenditure. Three qualitative themes emerged: (1) convenience and perceived benefits were critical elements of engagement; (2) high-intensity exercise elicited time-sensitive responses; and (3) trainers played a key role in promoting strong program adherence. Conclusions: Overall, we found that this program could be easily implemented and per-formed at home in individuals with spinal cord injury. We also found that participants enjoyed the 1:1 training sessions with a telecoach and that the intervention was easy to adhere to, as demonstrated by participant attendance. There is a need for future randomized controlled trials to determine the efficacy of telehealth exercise training for improving cardiometabolic health in spinal cord injury. Full article

Regional electricity interconnections are increasingly recognised as enablers of cost-effective power system expansion, resilience and energy security in emerging economies. In East Africa, Kenya and neighbouring countries, namely Tanzania, Ethiopia, and Uganda, operate relatively low-carbon electricity systems; however, rapidly growing electricity demand and expanding thermal generation are placing upward pressure on grid emissions intensity. This study examines whether planned cross-border interconnections can mitigate this trajectory using OSeMOSYS Global v1.0.0, an open-source least-cost capacity expansion model, comparing stand-alone national power systems against an interconnected regional grid over 2022–2045. Results show that interconnection enables access to low-cost renewable electricity and facilitates surplus generation exports, maintaining system-wide carbon intensity within climate finance eligibility thresholds of 100 gCO₂/kWh. Outcomes are heterogeneous: Ethiopia and Kenya incur cost increases (+USD 481 million and +USD 568 million, respectively) attributable to transmission capital expenditure, whereas Tanzania and Uganda achieve net cost savings (−USD 590 million and −USD 891 million) alongside substantial emissions intensity reductions of 141.9 and 280.5 gCO₂/kWh, respectively. Regional emissions equity is preserved, with modest intensity increases in Ethiopia and Kenya offset by large reductions elsewhere. These findings strengthen the case for climate-financed regional transmission as a scalable and equitable mitigation strategy in East Africa. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This limitation becomes more pronounced in districts where buildings differ in load demand, photovoltaic (PV) production and battery energy storage system (BESS) behavior, while electricity prices and grid carbon intensity vary hourly. Conventional rule-based controllers can exploit patterns, but they require tuning and do not generalize across heterogeneous buildings. Existing centralized reinforcement learning methods improve adaptivity, yet they often learn compromise policies and scale poorly as the number of buildings increases. To address these issues, this paper proposes an AttentionKAN-based multi-agent reinforcement learning controller for district-level BESS scheduling. The method uses centralized training with decentralized execution, where each building is controlled by its own actor and a centralized critic models cross-building interactions through a multi-head query-key-value attention mechanism. To improve approximation accuracy under nonlinear and constrained battery dynamics, multilayer perceptron (MLP) blocks in the actor and critic are replaced with Kolmogorov-Arnold Networks (KANs), whose spline-parameterized univariate functions capture saturation effects, tariff discontinuities and couplings among state of charge, PV availability and carbon intensity. Implemented in CityLearn and evaluated on a residential net-zero community dataset, the proposed controller is assessed using building-level and district-level indicators for cost, ${\mathrm{CO}}_2$ emissions, peak demand, ramping and load shape. The learned policy charges during solar-rich hours and discharges during evening peaks, achieving the strongest performance among benchmark controllers, including an approximately 50% cost reduction versus the reference case and emissions reduction. From a sustainability perspective, the results indicate that coordinated multi-building BESS control can support low-carbon residential electrification through emission reduction, lowering electricity expenditure and improving renewable-energy utilization and providing grid-supportive flexibility through reduced peaks and ramping. Full article

The transition to renewable energy is a central objective of the European Union’s energy and climate policies, yet adoption rates differ significantly across Member States. This study analyses the economic, structural, and energy determinants of renewable energy adoption in the EU-27 over the period 2008–2023, using panel data models with country and year fixed effects and clustered standard errors. The results indicate that the relationship between renewable energy and its main determinants is limited and heterogeneous across countries. Most explanatory variables do not exhibit consistent and statistically significant effects across model specifications. In particular, research and development expenditure does not show a robust impact, while GDP per capita is associated with negative coefficients in several specifications, suggesting the presence of structural constraints and path dependency. Energy-related variables also display weak and unstable relationships. The findings suggest that renewable energy adoption is shaped by context-specific and heterogeneous dynamics rather than by uniform drivers. The study contributes by highlighting the limited explanatory power of standard macroeconomic indicators and supports the need for differentiated policy approaches across Member States. Full article

Japan’s import market for wood pellets has expanded rapidly since the introduction of the feed-in tariff (FIT) scheme in 2012, with imports exceeding six million tonnes in 2024, positioning Japan as the world’s second-largest wood pellet importer. Despite this expansion, empirical evidence on its demand structure remains limited. This study employs a Dynamic Linear Approximate Almost Ideal Demand System (Dynamic LA-AIDS) model incorporating demand inertia stemming from long-term fuel supply contracts to analyze Japan’s wood pellet import demand from 2012Q1 to 2025Q3. The results reveal a distinct two-tiered structure: North American pellets behave as a strategic necessity, exhibiting price-inelastic demand and a tendency toward a stable long-run procurement pattern following price and expenditure shocks, suggesting procurement practices that prioritize supply security under long-term contracts. In contrast, Vietnamese pellets behave as a price-sensitive commodity, displaying price-elastic demand and relatively sustained responsiveness following such shocks. These results indicate a dual procurement strategy under the FIT scheme that balances stability and cost flexibility. Importantly, the Japanese demand structure differs from the more uniformly price-inelastic patterns observed in the EU and South Korean markets, providing new insights into how institutional frameworks shape biomass allocation and market responsiveness in renewable energy systems. Full article

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation. Full article

The global seafood industry faces persistent challenges related to product quality, safety, and authenticity, driven by complex supply chains, increasing demand, and the perishable nature of aquatic products. Traditional analytical methods often fall short in providing rapid, comprehensive, and non-destructive insights into the intricate biochemical changes occurring in seafood. ¹H Nuclear Magnetic Resonance (¹H NMR) spectroscopy has emerged as a powerful and versatile tool for metabolomics, offering a holistic view of the low-molecular-mass compounds (metabolites) present in biological samples. The present study applied ¹H NMR for chemical fingerprint identification in mullets (Mugil liza) from Brazil. Dorsal muscle samples were taken from the fish during summer, autumn, and winter. The procedure involved freeze-drying the muscle tissue, thereafter extracting polar metabolites using designated solvents (methanol, water, and chloroform), and analyzing them using a 600 MHz spectrometer. As a result, 23 metabolites related to degradation biomarkers, essential metabolites, energy expenditure, and muscle structure were identified. The statistical analysis demonstrated a distinct separation between the geographical origins (RJ vs. SC), mostly influenced by variations in the concentrations of lactate, histidine, threonine, phenylalanine, and ornithine. Factors like fish size and seasonal variations did not markedly affect the overall metabolic profile, underscoring the reliability of these chemicals as stable origin indicators. The Principal Component Analysis identified two distinct groups of metabolites, establishing a profile for each geographical origin. The developed protocol can be applied to the processes of geographical identification. Thus, the ¹H NMR tool was efficient in determining metabolites that can be considered biomarkers in analyses for seafood traceability. Full article