Search Results (7,402)

Background/Objectives: Sleep is pivotal for recovery, immunity, and energy restoration; however, sleep problems exist in elite athletes. Nutrition and supplementation strategies can play both a positive and negative role in sleep quality and quantity. Elite athletes experience unique psychological and physiological demands above non-elite athletes and may require different nutrition strategies to promote sleep. Nutrient interventions and their effect on sleep in elite athletes is an emerging area, with further research warranted. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews and Joanna Brigg’s Institute Reviewer’s Manual for Scoping Reviews were utilised to assess the available evidence on nutrition strategies used to promote sleep in elite athlete cohorts, and we tried to identify the interventions that could be best researched in the future. NUtrition QUality Evaluation Strengthening Tools (NUQUEST) was used to enhance rigour and assess risk of bias in studies. The Paper to Podium (P2P) Matrix was used to offer practitioners practical recommendations. Results: 12 studies met the inclusion criteria for nutrition interventions or exposures to promote sleep in elite athletes. The median participant group size was 19 and study designs were considered together to ascertain potential sleep promoting strategies. Kiwifruit, Tart Cherry Juice and high dairy intake, limited to females, have demonstrated the highest potential to promote sleep in elite athletes, despite limited sample sizes. A-lactalbumin, carbohydrate pre-bed, casein, tryptophan, probiotic and meeting energy demands showed varying results on sleep quality in elite athletes. Conclusions: Kiwifruit, Tart Cherry Juice and dairy consumption offer potential nutritional interventions to promote sleep in elite athletic populations, while protein-based interventions may have a ceiling effect on sleep quality when elite athletes are already consuming >2.5 g·kg⁻¹ body mass (BM) or are already meeting their sleep duration needs. Full article

The equilibrium between electricity demand and consumption is vital to ensure the stability of the transmission and distribution systems grid (TS & DS) and to ensure the stable operation of the electrical system. The aim of this review study is to highlight the current legislative and technical situation and the possibilities for managing peak loads, decentralization, sharing, storage, and sale of electricity generated from renewable sources in Slovakia. The European Union′s (EU) goal of achieving carbon neutrality by 2050 and a minimum of 42.5% renewable energy consumption by 2030 brings with it obligations for individual member states. These are transposed into national strategies. The current share of renewable sources in Slovakia is approximately 24% and the EU target by 2030 is probably unrealistic. Water resources are practically exhausted; other possibilities for increasing the share of renewable energy sources (RES) are in photovoltaics, wind, and thermal sources. Due to long-term geographical and historical development, electricity production in Slovakia is based on large-scale solutions. The move towards decentralization requires legislative and technical support. The review article examines the possibilities of increasing the share of RES and energy sharing in Slovakia, and examines the legislative, economic, and social barriers to their wider application. At the same time as the share of renewable sources in electricity generation increases, the article examines and presents solutions capable of ensuring the stability of electricity networks across Europe. The study formulates diversified strategies at the distribution network level and the consumer and building levels, and identifies physical (various types of electricity storage, electromobility, electricity liquidators) and virtual (electricity sharing, energy communities, virtual batteries) solutions. In conclusion, it defines the necessary changes in the legislative, technical, social, and economic areas for the most optimal improvement of the situation in the area of increasing the share of RES, supporting the decentralization of the electric power industry, and sharing electricity in Slovakia, also based on experience and good examples from abroad. Full article

This study evaluates the energy performance of a BOGE C 22-2 oil-injected rotary screw compressor under real industrial conditions. Using direct measurements with a power quality analyzer and thermodynamic modeling, key performance indicators such as compression work, mass flow rate, compressor efficiency, and motor efficiency were determined. The results revealed actual efficiencies of 27–48%, significantly lower than the expected 60–70% for this type of equipment, mainly due to partial-load operation and low airflow demand. A low power factor of approximately 0.72 was also observed, caused by a high share of reactive power consumption. To address these inefficiencies, the study recommends the installation of an automatic capacitor bank to improve power quality and the integration of a secondary variable speed compressor to enhance performance under low-demand conditions. These findings underscore the importance of assessing compressor behavior in real-world environments and implementing techno-economic strategies to increase energy efficiency and reduce industrial electricity consumption. Full article

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. Full article

Demand-side management has been demonstrated as an efficient and feasible method to unlock the flexibility on the demand side and support the flexible regulation of power systems. In integrated energy systems (IES) of buildings, through energy storage systems (ESS) and demand response methods, the utilization rate of renewable energy can be effectively improved, and the stability of the grid can be enhanced. However, the traditional energy usage methods of IES have limited responsiveness to the power system. Moreover, existing flexible energy usage strategies based on demand response rarely consider the impact of ESS in IES on energy usage strategies. Addressing the aforementioned issues, this paper proposes a flexible energy usage strategy based on ESS and demand-side management. This strategy takes into account the daily energy production and consumption of IES, as well as the relationship between user load and the grid, forming a hierarchical scheduling mechanism for energy usage. To fully explore the impact of ESS capacity on flexible energy usage scheduling strategies, the scheduling role of ESS is quantified in terms of photovoltaic utilization rate, responsiveness, and overall cost. The results indicate that implementing the flexible energy scheduling strategy in the system increases the annual PV self-consumption by 35.29%. With higher ESS capacity, the PV self-consumption rate (SCR) can be maximized, improving by up to 4.07%. The system’s response capability is enhanced after adopting the scheduling strategy and improves further with increasing ESS capacity. Regarding costs, although applying this strategy leads to a rise in ESS operational loss costs during its functioning phase, the overall system costs decrease by approximately 65.13%, with a capacity-based variation of about 1.48%. Full article

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research. Full article

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article

To achieve the carbon neutrality target, China has proposed “dual control” policies on provincial energy consumption. However, inter-provincial trade drives significant embodied energy flows beyond local demand. How do we identify key energy consumers driving through other provinces? And how does energy, especially from coal, flow to other provinces? Current studies analyzed regional and sectoral energy flow, which are always separated. And seldom was attention paid to coal flow. Intending to identify the critical energy-consuming province in China and investigate how energy and coal flow out from it, this study applied the EE-MRIO model to measure energy and coal embodied in provincial trades. The results suggest the following: (1) The energy embodied in provincial trade was mostly from energy-rich regions to provinces that lacked energy but had developed economies. Shanxi is a critical embodied-energy export province; (2) neighboring provinces and economically developed provinces drive the most embodied energy from Shanxi, and embodied energy mainly flows from the energy sectors and high-energy-intensity sectors; and (3) the provincial and sectoral coal flow in Shanxi presents consistent characteristics of embodied energy flow. We contributed to understanding the energy equity affected by embodied energy flow and propose energy consumption as a relieving measure. Full article

Background: Metabolic flexibility, the ability to efficiently switch between fuel sources in response to changing nutrient availability and energy demands, is recognized as a key determinant of metabolic health. In a recent randomized controlled human feeding trial, overweight individuals receiving American black elderberry juice (EBJ) demonstrated improvements in multiple clinical indices of metabolic flexibility, but the mechanisms of action were unexplored. The objective of this study was to utilize RNA sequencing to examine how EBJ modulates the transcriptional response to fasting and feeding, focusing on pathways related to metabolic flexibility. Methods: Overweight or obese adults (BMI > 25 kg/m²) without chronic illnesses were randomized to a 5-week crossover study protocol with two 1-week periods of twice-daily EBJ or placebo (PL) separated by a washout period. RNA sequencing was performed on peripheral blood mononuclear cells from 10 participants to assess transcriptomic responses collected at fasting (pre-meal) and postprandial (120 min post-meal) states during a meal-challenge test. Results: The fasted-to-fed transition for EBJ showed 234 differentially expressed genes following EBJ consumption compared to 59 genes following PL, with 44 genes shared between interventions. EBJ supplementation showed significantly higher enrichment of several metabolic pathways including insulin, FoxO, and PI3K–Akt signaling. KEGG pathway analysis showed 27 significant pathways related to metabolic flexibility compared to 7 for PL. Conclusions: Our findings indicate that short-term elderberry juice consumption may promote metabolic flexibility in overweight adults. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article

With the growing global deployment of Fiber-to-the-Home (FTTH) networks driven by the demand for ensuring high-capacity broadband services, mobile network operators (MNOs) face challenges of excessive energy consumption (EC) of wired optical access networks (OANs). This paper presents a comprehensive review of methods aimed at improving the energy efficiency (EE) of wired access passive optical networks (PONs) and active optical networks (AONs). The most important energy management and power-saving methods for Optical Line Terminals (OLTs) and Optical Network Units (ONUs), as key OAN components, are overviewed in the paper. Special attention in the paper is further given to analyzing the impact of a constant increase in the number of subscribers and average data rate per subscriber on global instantaneous power and annual energy consumption trends of FTTH Gigabit PONs (GPONs) and FTTH point-to-point (P-t-P) networks. The analysis combines the real ONU/OLT device-level power profiles and the number of installed OLT and ONU devices with data traffic and subscriber growth projections for the period 2025–2035. A comparative EE analysis is performed for different MNO FTTH OAN architectures and technologies, point-of-presence (PoP) subscriber capacities, and GPON-to-P-t-P subscriber distribution ratios. The findings indicate that different FTTH PON and AON architectures, FTTH technologies, and PON-to-AON subscriber distributions can yield significantly different EE gains in the future. This review paper can serve as a decision-making guide for MNOs in balancing performance and sustainability goals, and as a reference for researchers, engineers, and policymakers engaged in designing next-generation wired optical access networks with minimized environmental impact. Full article

Glazed façades play a decisive role in building energy performance, particularly in high-radiation equatorial climates. This study examines the thermal behavior and economic feasibility of three glazing systems—10 mm monolithic clear glass, laminated solar-control glass, and selective double glazing—applied to the Plataforma Gubernamental Norte, the largest institutional building in Ecuador. Dynamic simulations using DesignBuilder with the EnergyPlus engine assessed solar gains, HVAC demand, and operative temperatures, complemented by a sensitivity analysis of SHGC, U-value, and Tvis. Results indicate that selective double glazing reduced annual HVAC consumption by 78.21% (110.6 MWh), while laminated glazing achieved a 55.40% reduction. SHGC and U-value emerged as the most influential parameters, whereas Tvis had no impact on energy loads. Despite strong technical performance, the economic analysis revealed payback periods exceeding 235 years under Ecuador’s subsidized tariff (USD 0.10/kWh), compared to the 18–25 years commonly observed in Europe. This highlights the “efficiency paradox”: advanced glazing solutions deliver significant energy savings but remain financially unfeasible in subsidy-driven contexts. The findings underscore the need for policy reforms to better align façade design strategies with energy resilience, an issue particularly relevant after Ecuador’s 2024 electricity crisis and ongoing debates on subsidy elimination. Full article

In response to the rising demand for sustainable transportation, electric vehicles (EVs) are increasingly regarded as viable alternatives to conventional vehicles. This study investigates the intention of Malaysian consumers to choose EVs as their preferred mode of transportation. Consumption values were conceptualized as a multi-dimensional construct comprising functional value, symbolic value, emotional value, novelty value, and conditional value. This study examines the relationships between these consumption values, consumer attitudes, and intention to purchase EVs. In addition, this study also examines the mediating role of attitude and the moderating role of infrastructure readiness. Data were gathered using a proportionate stratified sampling method from 264 respondents in Klang Valley, Malaysia. Of the twelve (12) hypotheses tested, four (4) were supported. The analysis indicates positive relationship between attitude and emotional value with consumers’ intention to purchase EVs. Consumers’ attitudes mediate the relationship between functional value, emotional value, and intention to purchase EVs. Infrastructure readiness does not moderate the relationship between consumers’ attitudes towards EVs and their purchase intentions. This study enhances the existing knowledge of consumers’ multifaceted value views about EVs and offers practical guidance for marketers and serves as a reference for policymakers to improve the marketability of EVs. Full article

Future wireless networks are expected to deliver enhanced spectral efficiency while being energy efficient. MIMO and other non-orthogonal transmission schemes, such as non-orthogonal multiple access (NOMA), offer substantial theoretical spectral efficiency gains. However, these gains have yet to translate into practical deployments, largely due to limitations in current signal processing methods. Linear transceiver processing, though widely adopted, fails to fully exploit non-orthogonal transmissions, forcing massive MIMO systems to use a disproportionately large number of RF chains for relatively few streams, increasing power consumption. Non-linear processing can unlock the full potential of non-orthogonal schemes but is hindered by high computational complexity and integration challenges. Moreover, existing message-passing receivers for NOMA depend on specially designed sparse signals, limiting resource allocation flexibility and efficiency. This work presents NL-COMM, an efficient non-linear processing framework that translates the theoretical gains of non-orthogonal transmissions into practical benefits for both the uplink and downlink. NL-COMM delivers over 200% spectral efficiency gains, enables 50% reductions in antennas and RF chains (and thus base station power consumption), and increases concurrently supported users by 450%. In distributed MIMO deployments, the antenna reduction halves fronthaul bandwidth requirements, mitigating a key system bottleneck. Furthermore, NL-COMM offers the flexibility to unlock new NOMA schemes. Finally, we present both hardware and software architectures for NL-COMM that support massively parallel execution, demonstrating how advanced non-linear processing can be realized in practice to meet the demands of next-generation networks. Full article