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It is well-known that energy consumption is increasing around the world on a daily basis. In the construction sector, a highly effective solution for reducing energy consumption involves exploring both modern and traditional buildings designed to adapt to climate changes. One promising approach is to use paper waste and bio-sourced materials as the basis for insulation. The purpose of this study was to improve the sustainability of buildings by using recycled waste materials that have a positive impact on the environment, people, and the economy. A novel insulating material composed of recycled paper waste and Ampelodesmos mauritanicus leaves and fibers was developed and used in non-load-bearing elements. The paper waste was transformed into pulp and mixed with the bio-sourced materials to create a composite material that exhibits excellent insulation properties. The resulting material is lightweight, durable, and cost-effective. Furthermore, different mechanical and thermal analyses were performed on specimens with varying dosage ratios. The results showed that the developed material has good thermal insulation, with a value of 0.027 W/m.K. [ABSTRACT FROM AUTHOR]

Overwintering insects are facing energetic challenges because of food shortage, low temperature, and desiccation stress. Paper wasps of the genus Polistes overwinter as mated adults (gynes) in hibernacula protecting them from predation, snow, and rain but barely from low environmental temperature. In different climates, they face differing overwintering temperature regimes, and therefore they may differ in their energy use. We investigated how much of energy resources built up until autumn is used during diapause dormancy in natural hibernacula by measuring lipid, glycogen, and free carbohydrate content in autumn and early spring in Polistes dominula from temperate European (Austrian) and warm Mediterranean (Italian) climate and Polistes gallicus from Mediterranean climate. Winter energy consumption amounted to ~ 339 and ~ 310 J per wasp in the Austrian and Italian Polistes dominula populations. The smaller Italian Polistes gallicus consumed ~ 247 J. This amounts to 2.62, 2.35, and 1.79 J per day. Of this, the energy demand was mainly fuelled by lipids (84%, 93%, and 90%, respectively), but glycogen stores contributed also considerably (16%, 6%, and 9%). Free carbohydrates decreased only by 0.7%, 1%, and 0.8%. While fat stores seem still sufficient in spring, the wasps depleted most of their carbohydrates. The energy reserves of 396, 400, and 147 J per wasp remaining in spring in the three populations seem sufficient to fuel rest or simple brood care activities for a whole summer but restrict foraging flights to a few hours (~ 3.5–6 h). Results suggest that energy supply might become challenging in expected future climate scenarios. [ABSTRACT FROM AUTHOR]

This study delves into the dynamic evolution of solids content in a pilot-scale through air drying tissue machine, aiming to enhance comprehension and refine optimization strategies for tissue manufacturing. It focuses on development of solids content throughout the process. Analyzing the interaction between process parameters and solids content provides deeper insights into water removal dynamics. The research employs solid contents monitoring techniques in pilot scale, offering a detailed view of solids content evolution from the wet web's inception to the final tissue product. It significantly highlights the impact of key parameters, particularly pulp refining, on solids content across all positions of the pilot machine. Additionally, the study emphasizes the influence of vacuum system configurations, identifying the relationship between energy expended in the vacuum system and evaporation energy. Furthermore, the results indicate that compression and air displacement dewatering in vacuum boxes are less speed-sensitive compared to heat-driven evaporation in the TAD- and Yankee cylinders. Solid contents at all positions in the tissue machine correlates between various dewatering elements. Optimizing site-specific dewatering and evaporation strategies can potentially save drying energy in the Yankee drying phase which hold promise for enhanced energy and resource utilization, aligning with demands for sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

A novel approach for paper drying using airborne ultrasound technology is presented. A unique experimental setup is developed, and a systematic study is conducted using 23 factorial design of experiments and Analysis of Variance. Three controlling factors are considered in the experiments including the initial moisture content, basis weight and refining condition. The outcome of the experiments is compared to a previous work on direct-contact ultrasonic drying of paper. The results confirm that similar to direct-contact, for airborne ultrasonic drying, the basis weight/thickness of the sample is the most important factor in ultrasonic drying and it is followed by the effect of initial moisture content. Using linear regression model, a correlation for predicting the total time of ultrasonic drying is provided. Quality of the dried samples is evaluated, and the permeability measurements confirmed the effect of pore characteristics on ultrasonic drying. The analysis for energy consumption reveals that airborne ultrasonic drying is more efficient at higher moisture contents. [ABSTRACT FROM AUTHOR]

There is an urgent need for energy sources with lower environmental impacts to dispose of waste generated by human activities. In this context, a holistic analysis of energy use of waste from agricultural and forestry activities, animal excreta, urban solid waste and sewage, waste from the pulp and paper industry and charcoal in Brazil is presented. Based on a bibliographic review, the current Brazilian panorama is presented in figures giving, a vision of national potential for harnessing bioenergy from this theoretical and technical potential. The QGIS3.24.2 Tisler geoprocessing tool was used to generate potentiality maps from census data of microregions and municipalities available in the IBGE database (2022) and in the Geocentric Reference System for the Americas (SIRGAS 2000). The results of map analysis by micro‐regions of the primary energy from agricultural residues highlight sugar cane, corn and soybeans crops, followed by wastewater and animal excreta. They stood out amont the studied sources of biomass with technical potentials of 53.17, 15.23 and 6.81 GW, respectively. With these results, it is concluded that Brazil could enable around 75 GW installed power, reusing all biomass, potentially 12.5 PJ, without considering the 16 GW already installed from sugarcane and paper black liquor plants. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ultrafine marble dust waste particles were obtained using the top–down approach. • Waste and by-products had positive effect on dry grinding of stone wastes. • It is economically feasible, also environmentally friendly. • These obtained ultrafine particles can be used effectively mineral filler in many areas. Waste management in marble processing plants is a considerable problem in countries with developed stone industries and trade. Research on the utilization of stone waste in different fields is still continuing. However, there are few studies on using this stone waste from processing plants as mineral fillers. Moreover, no study in the literature has investigated the influence of grinding aids on stone properties. The current research is the first to compare of the efficiency of different grinding aids on the waste calcitic and dolomitic marble in the production of mineral fillers. The objective of this work is to research the impacts of waste and by-products on the dry grinding of stone wastes as grinding aids in a vertical stirred media mill. The results were assessed in terms of various parameters: product particle size, powder flowability, material coating on the grinding media, and energy consumption. Remarkably, the study showed that the use of any of the grinding aids tested enhanced the grinding process in comparison with the no-aid condition. The best product fineness value was achieved with pomace oil followed by the by-product glycerin, among the grinding aids tested in both samples. Moreover, a higher media coating reduction and the highest energy saving were achieved with pomace oil. The most significant and lowest increase in the fluidity index was achieved with pomace oil and paper wastewater, respectively. Additionally, the Fourier transform infrared spectroscopy (FTIR) performed to reveal the interaction between particle surfaces and grinding aids demonstrated the activity of pomace oil and glycerin polar functional groups on particle surfaces. The analysis of the agglomeration phenomenon with SEM (scanning electron microscopy) micrographs showed that particle agglomeration could be eliminated with the pomace oil. Thermogravimetric analysis (TGA) was used to investigate changes in the mass of the samples' due to temperature changes. With this study, a significant contribution will be made to the ecosystem using waste marble powder whose storage problematic. [ABSTRACT FROM AUTHOR]

The research assessed waste-based briquettes consumption compared to conventional fuels in the Andes. Laboratory tests were conducted together with on-field analysis in Colquencha (Bolivia). The laboratory study shows that the performances of briquettes are better in terms of PM2.5 (933.4 ± 50.8 mg kg−1) and CO emissions (22.89 ± 2.40 g kg−1) compared to animal dung (6265.7 ± 1273.5 mgPM2.5 kg−1 and 48.10 ± 12.50 gCO kg−1), although the boiling time increased due to the lower fuel consumption rate and firepower compared to shrubs. The social survey organized with 150 Bolivian citizens suggested that low-income households are not able to pay for an alternative fuel: about 40% would pay less than 4 USD per month, while methane use for cooking is positively correlated with the income level (r = 0.244, p < 0.05). On field analysis suggested that local cookstoves are not appropriate for briquettes combustion since indoor air pollution overcomes 30 ppm of CO and 10 mgPM2.5 m-3. On balance, local small manufactures can be the main target for selling waste-based briquettes to reduce shrubs and wood consumption. However, briquettes production costs seem not yet competitive to natural easy-to-obtain fuels (i.e., animal dung). The research encourages the use of cellulosic and biomass waste-based briquettes in the Andean area for cooking, heating, or manufacturing and strongly advises policy-makers to introduce economic incentives for the recovery of secondary raw materials. • Analysis of waste-based briquettes use in Andean areas were conducted. • Laboratory analysis, social surveys, and on field evaluations are the methods. • Briquettes can be employed for reducing about 30% of the conventional fuels. • The research encourages the employment of waste-based briquettes in the Andes. • Incentives to briquettes production and consumption should be introduced. [ABSTRACT FROM AUTHOR]

In this paper, a wideband metasurface-loaded (MTS-L) rectenna system is proposed to capture electromagnetic (EM) energy at arbitrary azimuth angles. The radiation patterns of different modes in the original MTS configuration are analyzed using the characteristic mode theory, and potential modes with omnidirectional radiation are screened out. By the arrangement of patches, the roundness performance of the radiation pattern can be ameliorated, and the omnidirectional characteristic is obtained over a wide frequency band. Subsequently, the surface current density of the selected mode is carefully and artificially designed to facilitate probe excitation as well as refrain from introducing complex power-combining networks. A wideband rectifier circuit is designed as the load of the proposed antenna. Eventually, measured results show that it operates from 4.6 to 9.6 GHz with a fractional bandwidth of 70.4%, and the peak system efficiency is 52.2%. The proposed system demonstrates excellent potential for wireless power transmission and EM energy harvesting in indoor environments. [ABSTRACT FROM AUTHOR]

With the development of energy technology, hydrogen is gradually becoming widely used. China is also rapidly developing its own hydrogen energy industry in an effort to accomplish the "dual carbon target'. Hydrogen from methane is one of the mainstream sources, of which hydrogen from methane cracking is the decomposition of methane into hydrogen and solid carbon, with the solid carbon also having a high economic value. In particular, this technology does not emit carbon dioxide, which is very friendly to the environment, but it cannot avoid larger energy consumption. In contrast, hydrogen production from methane cracking driven by solar energy not only combines the characteristics of no CO 2 emission, but also greatly reduces the consumption of primary energy, which is the future development trend of hydrogen production from methane. This paper provides a brief overview of the various technological pathways for methane to hydrogen production in the context of China's actual development, focusing on the current status of the development and application of solar methane cracking to hydrogen production technologies at home and abroad. Among them, this paper mainly discusses the characteristics of solar reactors of different sizes and structures, and summarizes the results of related optimization studies. Meanwhile, the application of catalysts in solar reactors and the problem of carbon deposition are analyzed, and the practical application of solar methane cracking hydrogen production system is outlined. Finally, the future development of solar methane cracking hydrogen production technology is prospected from the national situation of China. [Display omitted] • Solar methane cracking for hydrogen can reduce emissions and energy consumption. • The current state of research on solar reactors is summarized. • Solar methane cracking requires higher performing catalysts. • Providing insights into the future development of solar methane cracking in China. [ABSTRACT FROM AUTHOR]

In this paper, a proper noun “Region” is used for nm‐scale n‐type dopant‐rich region in p‐type Si crystal. Using this Region, certain solar cells have been assumed. By resonance absorption between photon energy and potential barrier of the Region, the cell can absorb most photons for visible light frequency without passing loss or thermal loss. This light absorption mechanism is different from conventional band gap absorption. Despite this benefit, output voltage is anticipated to decline according to the principle of detail balance. To control the decline, two methods are proposed in this paper. Theoretical energy conversion efficiencies for several cases are calculated with an ideal condition. The calculation result is over 70% as a theoretical value. [ABSTRACT FROM AUTHOR]

Fuel cell electric buses (FCBs) have proven to be a technically viable solution for transportation, owing to various advantages, such as reliability, simplicity, better energy efficiency, and quietness of operation. However, large-scale adoption of FCBs is hindered by the lack of extensive and structured infrastructure and the high cost of clean hydrogen. Many studies agree that one of the significant contributors to the lack of competitiveness of green hydrogen is the cost of electricity for its production, followed by transportation costs. On the one hand, to reduce the investment cost of the electrolyzer, high operating hours should be achieved; on the other, as the number of operating hours decreases, the impact of the electricity costs declines. This paper presents an innovative algorithm for a scalable hydrogen refuelling station (HRS) capable of successfully matching and identifying the most cost-efficient levelized cost of hydrogen (LCOH) produced via electrolysis and connected to the grid, based on the HRS components' cost curves and the hourly average electricity price profile. The objective is to identify the least-cost range of LCOH by considering both the electric energy and the investment costs associated with a hydrogen demand given by different FCB sizes and electrolyzer rated powers. In addition, sensitivity analyses have been conducted to quantify the technology cost margins, and a cost comparison between the refuelling of an FCB fleet and the recharging infrastructure required for an equivalent fleet of Battery Electric Buse (BEB) has been performed. An LCOH of around 10.5 €/kg varying from 12 €/kg (2 FCB) to 10.2 €/kg (30 FCB) has been found for the best-optimized configurations. The final major conclusion of this paper is that FCB technology is currently not economically competitive. Still, a cost contraction of the electric energy price and the electrolyzer capital investment would lead to a 50% decrease in the LCOH. Furthermore, increasing renewable energies into the grid may shift the electricity cost curve, resulting in higher prices when the BEB recharging demand is more significant. This impact, in addition to the peak power load and longer recharging times, might contribute to bridging the gap with FCBs. • Techno-economic model for the sizing of an HRS with grid-connected electrolyzer. • The widespread diffusion of H2 technology in mobility needs both reliability and cost-effectiveness. • Investigation of various electrolysers rated powers and fuel cell buses fleet size. • An action on both CAPEX and OPEX would lead to potential cost margins improvement. • It is found that the best configuration that balances the CAPEX and OPEX coincides with an LCOH ranging from 12 to 10 €/kg. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS. [ABSTRACT FROM AUTHOR]

In this paper, the effects of different designs of pressure vessels on hydrodynamic parameters and the performance of seawater RO systems are investigated using computer simulations. Simulation results showed that designs using pressure vessels with four membranes in two-pass RO systems have a lower energy consumption, 5.29 and 0.33 kWh/m3 for the first and second passes, respectively, than those with two or three membranes. Also, designs No. 4 and No. 10 were the most efficient, 41 and 56% for the first and second passes, respectively, compared to other ones. The first and second passes required a lower permeate and feed pressure when using designs No. 3, No. 6, No. 9, and No. 12 where pressure vessels with two membranes were used. In addition, hybrid designs of two-pass RO systems were found to have the best efficiency and the lowest energy consumption among various other designs. Findings showed that in two-pass RO systems, the first pass consumes more energy than the second pass. Thus, the efficiency of the second pass in all designs of two-pass RO systems is more than the first pass. The results presented in this paper can be used to optimize the design of two-pass RO systems to ensure the optimum operation of such systems. [ABSTRACT FROM AUTHOR]

The software-defined network paradigm, ensembled with a wireless sensor network, has emerged as a promising phenomenon to enable multi-tasking, re-configuration, and scalability. Termed the software-defined wireless sensor network (SDWSN), it divides the network into two planes: data and control. The data plane consists of software-defined sensor nodes (SDSN) that sense monitoring activity and generate data. On the other hand, the control plane has controller/control Nodes (CN) which collect data from SDSN, perform data aggregation, and then transmit toward the sink node. These CNs consume more amount of energy as compared to SDSNs as they perform multiple tasks. Following this scenario, this paper proposes an energy-efficient multi-objective optimization approach to solve the CNs placement problem through a meta-heuristic algorithm by considering the nodes' location, energy, and load distribution. This paper presents a particle swarm optimization-based controller placement and re-placement (PSO-CPR) algorithm for SDWSN. The PSO-CPR elects SDSNs to become CN based on their distance, residual energy, and capacity in the network. Moreover, the placement of a CN rotates within the cluster to avoid its failure and balance energy consumption. The simulation results show improved CN placement with respect to the state-of-the-art algorithms in terms of average delay by 23.5–37.4%, energy consumption by 18.6–32.6%, and probabilistic load distribution by 17.7–54.1%. Moreover, the comparative study also indicates that PSO-CPR achieve promising result by reducing packet loss by 14.4–27.5% and network re-clustering period by 32.3–68.3% and enhance the network lifetime by 22.6–42.5%. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm's robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm's stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

High-pressure proton exchange membrane (PEM) water electrolysis for hydrogen production is a crucial method to achieve low energy consumption, high efficiency, minimal pollution, and seamless integration with storage systems. Despite its potential, the current application of high-pressure PEM water electrolysis faces several challenges. This paper provides a concise analysis of the research advancements in high-pressure PEM water electrolysis for hydrogen production, focusing on technical bottlenecks within high-pressure devices. It explores key issues such as gas cross-permeability, membrane degradation, membrane shedding, and hydrogen embrittlement encountered during high-pressure PEM water electrolysis for hydrogen production. Furthermore, this paper summarizes the latest research directions and proposed solutions based on existing findings, aiming to offer effective references for enhancing the safety, scalability, and industrial application of high-pressure PEM water electrolysis technology. [Display omitted] • State-of-art technology of high-pressure PEMWE for hydrogen production was discussed. • Gas cross-permeability, membrane degradation and hydrogen embrittlement of PEMWE were analyzed. • The latest research directions and proposed solutions for the PEMWE were presented. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications. [ABSTRACT FROM AUTHOR]

Vertical management auditing reforms achieve the centralization of audit institutions. The centralized structure of the auditing institutions affects their performance. This paper verifies the positive effect of vertical management auditing reform on carbon productivity through a difference-in-differences method. The conclusion still holds after robustness tests. Moreover, the policy effects only make sense when government officials face low promotion pressure or high public scrutiny pressure. The mechanism analysis shows that the vertical management auditing reform can improve carbon productivity by strengthening environmental regulation, promoting industrial structure rationalization, and improving regional green innovation and energy efficiency. This paper enriches the research on the relationship between auditing centralization and carbon productivity. [ABSTRACT FROM AUTHOR]

The behaviors of local governments can affect the enforcement of environmental regulations. Environmental regulation can influence the behaviour of energy-intensive enterprises. This paper examines the relationship between local government competition, environmental regulation and the investment efficiency of high energy consuming firms using a basic panel regression and moderating effects model based on data from 2011-2020 for high energy consuming firms in China. The paper found that local government competition and environmental regulation can promote increased investment efficiency in energy-intensive firms in the short term. However, environmental regulation is not beneficial to investment efficiency in the long run. Local government competition plays a positive moderating role in the impact of environmental regulation on the investment efficiency of high energy consuming firms. There is a threshold effect on the role of local government competition. When the level of government competition exceeds 2.097, it has a negative impact on the investment efficiency. We further test for heterogeneity and we find that this promotion effect is also more obvious in areas with high environmental taxes and low marketability. Finally, we provide constructive suggestions for the development of government and the improvement of the investment efficiency of high energyconsuming firms. [ABSTRACT FROM AUTHOR]

Coal and biomass are important feedstocks for carbon energy from thermochemical conversion process. Fully understanding the analytical technology that characterizes the changes in physicochemical properties and structural characteristics of coal and biomass during the thermochemical reactions is a key prerequisite for the realization of appropriate utilization of energy fuels. Modern in-situ process analysis technology can accomplish the in-situ detection of the experimental process, and therefore reflect the experimental process more accurately. Moreover, it is developing towards automation, intelligentization, and comprehensive detection. Based on the characteristics of each detection technology, this paper summarizes the basic principles, application scope and performance characteristics of the three advanced in-situ process analysis technologies: hyphenated technology, synchrotron radiation, and online analysis. The practicability and accuracy of each detection technology in coal and biomass research are compared and analyzed, and its latest application and development trend are elucidated. These tools not only make up for the shortcomings of traditional detection techniques in characterizing the in-situ reaction, but also provide complementary information on molecular microscopic changes during fuel thermal conversion. This review paper can provide insights for relevant researchers in the selection of analytical techniques, and promote in-depth study on microcosmic mechanism of fuel conversion. [ABSTRACT FROM AUTHOR]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time consensus control for a class of multi-agent systems subject to unknown bounded disturbances under undirected topology. Firstly, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid the non-differential problem. Secondly, a distributed fixed-time control protocol is designed, which can realise the fixed-time convergence and improve the system convergence rate independent of the system's initial state. Thirdly, the dynamic event-triggered mechanism is proposed, such that the triggering frequency of events is reduced and communication energy consumption can be saved. The stability analysis is presented by employing the Lyapunov theory and the Zeno phenomenon is excluded. Finally, the simulation examples are presented to show the effectiveness and advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

The paper presents an original method for synthesizing high-density composite ceramics with a Mo 2 C/Mo 3 Co 3 C composition using the technology of high-speed Spark Plasma Sintering (SPS) of a mechanically activated powder mixture of Mo 2 C-10 wt%Co. The activated homogeneous starting mixture was obtained by wet milling/activation of the initial powders of Mo 2 C and Co in an anhydrous isopropanol medium. The ceramic samples were consolidated by SPS under vacuum (10−5 atm.) at 1000, 1100, 1150, and 1200 °C with a heating rate of 87.3 °C·min−1 under constant external uniaxial pressing pressure of 50 MPa. The sintering kinetics were studied, the twofold nature of sintering was established, and the temperature range of active densification was determined. The ceramic synthesis is accompanied by a reactive interaction of the components, resulting in the formation of the Mo 3 Co 3 C phase and a change in the crystal lattice of the non-stoichiometric Mo 2 C x compound. The ceramic obtained in the temperature interval of the isothermal sintering stage of 1150–1200 °C is homogeneous in the distribution of Mo and Co, consists of a Mo 2 C/Mo 3 Co 3 C phase mixture, has a density value of 98.13 ± 0.5 % theoretical density, and has an average Vickers hardness value of ∼1526 HV. Increasing the sintering temperature to 1200 °C results in the formation of almost monolithic structures in the samples, but also induces the formation of large defects due to the collective consolidation of pores within the bulk material and increases the susceptibility of the ceramic to cracking. The paper presents preliminary results from electrochemical testing. The proposed method for synthesizing Mo 2 C/Mo 3 Co 3 C ceramics has potential for use in optimizing the composition and production methods of electrode materials for the realization of active components in heterostructured electrodes used for hydrogen evolution. [ABSTRACT FROM AUTHOR]

Mobile object detection and tracking is an important application of wireless sensor network. However, longevity of such application is a challenge as sensor nodes in wireless sensor network operates on battery power. Therefore, there is the requirement for increasing the lifetime of this application by optimal usage of energy while tracking. Of course, while optimizing the energy usage, tracking accuracy should not be compromised. A number of research works are carried out in the past to enhance the wireless sensor network lifetime but no work resolved the problem completely. This paper studies the issue of data routing to sink in energy constrained wireless sensor network based application where tracking information collected from different sensor nodes are processed to determine the object's presence in the monitoring area. This paper proposes an energy efficient hybrid artificial bee colony-modified Dijkstra (HABC-MD) algorithm for cluster based network for optimum usage of energy during routing of packet. The objective of this algorithm is to save the energy of the network by optimizing the number of node-to-node transmission while tracking. It uses metaheuristic approach based artificial bee colony algorithm for selection of cluster head in each round and heuristic approach-based Dijkstra algorithm for selection of optimum route between source node and sink for packet transmission. Modified Dijkstra algorithm considers both distance between source and destination and energy of the source node for determination of optimum route to sink. The performance of HABC-MD is compared with existing hybrid LEACH-Dijkstra algorithm and is found that performance of our algorithm is better in comparison to later algorithm. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

This paper aims to study the influence of novel macro encapsulated phase change materials (PCMs) on the mechanical and thermal properties of concretes. Thermal energy storage aggregates (TESAs) were prepared using vacuum impregnation. The PCMs were a lauric-myristic acid (LA-MA) eutectic mixture and coconut oil, pure or mixed, and are impregnated in diatomite as a supporting material. Thermal energy storage concrete (TESC) was made by replacing gravel in ordinary concrete with TESAs. Thermal and mechanical properties of prepared TESC were studied using differential scanning calorimetry, thermogravimetric analysis, Fourier transform infrared spectroscopy, compressive strength, and thermal performance analysis. The results showed that the melting temperature of coconut oil and the eutectic mixture of LA-MA were 25.2°C and 34.5°C, respectively. TESC with 80 wt.% of gravel replaced with TESA had a compressive strength of higher than 17 MPa, which indicates its usability as structural concrete. The results of thermal performance analysis also revealed that prepared TESC had the capability of storing thermal energy and can reduce energy consumption by damping temperature fluctuations caused by the outside environment. In this paper, a thermal energy storage concrete (TESC) was prepared by embedding phase change materials (PCMs) in diatomite used in TESC. Using this type of concrete in the building will reduce the energy consumption of the building and increase its thermal comfort. The produced concrete containing 80 wt.% of gravel replaced with diatomite saturated with PCM had a desirable 28-day cylindrical sample strength of 21 MPa. In the designed thermal test, the time required for this concrete to reach 40°C is almost twice that of concrete without PCM. The results show a reduction of the influence of outside temperature on the temperature inside the buildings made with this type of concrete. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous derivative of the Internet of Things (IoT) business, the use of various types of information sensing equipment needs to continue to increase. However, power supply conditions are getting tougher. On the other hand, as the function of the devices itself increases, the power consumption also increases, and the lack of energy demand is more obvious. To solve these problems, a dual-frequency RF energy harvesting system is designed in this paper. First of all, in order to expand the effective receiving range of RF energy, this paper designed a 920 MHz hexagonal windmill circular-polarized antenna, and added an energy receiving circuit in the 2.45 GHz band on this basis, which fully alleviated the problem of insufficient energy collection for small devices in the Internet of Things while ensuring a small volume wavelength ratio. In addition, in order to improve the energy conversion efficiency, this paper also designed a dual-frequency rectifier circuit based on conjugate separation matching, using the harmonic signal of 920 MHz signal to broaden the bandwidth of 2.45 GHz. The experimental results show that the peak conversion rates of 920 MHz and 2.45 GHz channels are 72.8%@0dBm and 38.7%@-2dBm, respectively. Overall testing in a Wireless Fidelity (Wi-Fi) room with a commercial Radio Frequency Identification (RFID) reader. The experimental results show that the system can steadily output 3.29 V–3.3 V voltage within 6 m charging distance, which is 20% higher than the output voltage of the single-channel receiving system, and the receiving angle is 36.84% larger. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

Energy demand is rising over time in both developing and developed countries. Therefore, finding new sources of energy is a prime concern now. For this effort, this paper presents the pyrolysis of onion (Allium cepa) shells in a reactor with a fixed bed for generating alternative liquid fuel. This paper also compares alternative fuel characteristics, including higher heating value, viscosity, density, pour point, and flash point, with conventional petroleum fuels at optimal process conditions. The work adopted pyrolysis to produce liquid fuel at a temperature range of 400–550 °C and utilized LPG to provide a heat source. The liquid product (fuel oil) was collected, and non-condensable gas was flared. The liquid product was tested for various properties, and the results of the analyses show that alternative fuel has a higher heating value of 12.227 MJ/kg, density of 800 kg/m3, viscosity of 4.3 cP at 30 °C, pour point below −6.2 °C, and flash point around 137 °C, with a variation due to the volatile matters. To obtain favorable conditions for pyrolysis, some parameters, including bed temperature, sample quantity, average particle size, and operating time, were varied and analyzed. The physio-chemical properties made the alternative fuels isolated from conventional petroleum fuels due to the variation in distillation temperature. This work shows that the fuel oil generated from the pyrolysis of onion shells could be considered an alternative source of fuel. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [ABSTRACT FROM AUTHOR]

In this paper, a combined air/water source heat pump water heater is presented. At ordinary time, the heater can be used for hot water heating in water-source mode to improve the energy efficiency. While in cold season, it gives priority to air-source mode for water heating to improve operational safety. Experiments were conducted to investigate the thermodynamic performance of the heater in each operation mode, and the experiment results were analyzed. The research indicated that its operation was reliable during the long-time running. This paper offers a promising energy-saving method in future. [ABSTRACT FROM AUTHOR]

This paper studies a local fractional vibration system with a damped non-linear term to reveal its frequency property and its energy consumption. A modification of He's frequency formulation is recommended for this purpose. Some examples are given to illustrate the solving process and the reliability of the method. Additionally, the effect of the initial conditions on the vibrating properties is elucidated. This paper offers a new window for fast and effective insight into local fractional vibration systems. [ABSTRACT FROM AUTHOR]

The micro-electro-mechanical system is widely used for energy harvesting and thermal wind sensor, its efficiency and reliability depend upon the pull-in instability. This paper studies a micro-electro-mechanical system using He-Liu [34] formulation for finding its frequency-amplitude relationship. The system periodic motion, pull-in instability and pseudo-periodic motion are discussed. This paper offers a new window for security monitoring of the system reliable operation. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 ×   10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents' energy literacy. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

Emulsion polymerization is an effective means of synthesizing high-molecular-weight polymers in order to reduce the consumption of fossil energy. This paper adopts isoamyl octanoate to replace white oil as the continuous phase of emulsion polymerization to synthesize the high molecular weight polyacrylamide emulsion RSE. S-80 and T-80 were chosen as the emulsifiers, and the test results showed that the HLB value is 5.72 when S-80/T-80 is 6.5. When S-80/T-80 is 6.5, the HLB value is 5.72. Water and isoamyl octanoate can produce ultra-low interfacial tension and stable arrangement in the interfacial membrane to form a stable emulsion, and the particle size is uniformly distributed in a normal manner. The test of the exothermic process of polymerization shows that the emulsion can be stabilized into nuclei, and polyacrylamide polymers with a molar mass of more than 18 million are obtained. The microstructure test observes that RSE molecules are in an aggregated state in the aqueous solution. A three-dimensional spatial network structure is formed, and the polymerization can be stabilized in a three-dimensional network structure after 120 °C, 170 s−1 shear, which still has good stability. After the variable shear performance test proved that the aqueous solution of RSE belongs to the typical non-Newtonian fluids, to shock test the viscoelasticity of different concentrations of RSE polymer, G′ > G″, the RSE solution to elasticity is dominant. In this paper, the capacity to form stable emulsions of isoamyl octanoate in the emulsion polymerization process is investigated to provide a basis for seeking the selection of a continuous phase with little environmental pollution and high functionality. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

Given the increasing severity of energy shortages, the exploration of effective strategies to optimize energy structures has become imperative. This requires careful consideration of energy consumption patterns, especially since these data are fundamental inputs for policy formulation. Given the uncertainty in the rate of change in energy consumption, this paper proposes an optimized multivariable grey Bernoulli model that is rooted in the grey Bernoulli model and incorporates background values and genetic algorithms. The grey Bernoulli model effectively linearizes nonlinear problems, thus simplifying computational procedures. In addition, to account for random fluctuations of relevant factors that may affect the model's predictions, this model introduces nonlinear correction terms that allow simulation and prediction values to adhere to the grey index law. The incorporation of background values enhances the model's ability to process information, providing it with a superior grasp of real data. Genetic algorithms can be used to refine the model's parameters, increasing its adaptability and precision. Finally, this paper applies the refined model to examine China's energy consumption patterns, validating its efficacy and versatility. Furthermore, energy consumption patterns for the next four years are forecast, with the analysis revealing that the growth rate of energy consumption from 2021 to 2024 shows a downward trend, particularly notable in 2024, where the growth rate is 1.64%. [ABSTRACT FROM AUTHOR]