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The variable pressure source system is widely used in renewable energy recovery scenarios. However, the instability of the new energy input and the nonlinearity of the power generation system can lead to problems such as more difficult tracking and control of the maximum power point and poor power generation quality. To address the problem of lower efficiency under varying inputs, which is prevalent in renewable energy generation, this paper establishes an energy recovery system model based on power conversion rectifier topology, designs a speed current double closed‐loop control strategy, proposes an online variable‐step maximum efficiency point tracking method based on experience curves, and tests the overall energy recovery effect through system simulation and comparative experiments. The simulation results show that the maximum efficiency point tracking method proposed in this paper reduces the number of optimization searches by 50% and improves optimization speed. The experiment results show that, under the drastic changes of the input, the method proposed in this paper could reduce the total harmonic distortion to 5.65%, improve the energy recovery efficiency by 10.976%, and reduce the fluctuation ratio of the voltage to 2.43%. This study can provide an important reference for the collection and utilization of new energy sources. [ABSTRACT FROM AUTHOR]

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study. [ABSTRACT FROM AUTHOR]

Energy consumption has risen much more while the economy has expanded so quickly. As a result, significant environmental contamination is created, which causes public worry. Under this background, enhancing energy efficiency and lowering pollution is particularly crucial for the fulfillment of sustainable economic development. Efficiency evaluation for energy and environment is an effective measure to achieve both energy saving and emission reduction as well as sustainable social development. In this paper, energy and environmental efficiency consists of energy utilization efficiency and pollution treatment efficiency. An improved two‐stage DEA model is proposed, which takes into account how stochastic and environmental factors affect decision‐making units (DMUs). Finally, the author analyses the panel data of 15 cities in Southwest China from 2010 to 2018, which proves the feasibility and effectiveness of this study. The results show Southwest China's poor energy and environmental efficiency as well as the stark differences among its cities. The scientific value revealed by this paper is as follows: (1) for the study of energy and environmental efficiency, this paper is more targeted at the differences between cities and (2) using an improved two‐stage DEA model to consider the effects of stochastic factors on DMUs. [ABSTRACT FROM AUTHOR]

The microthruster is generally chosen as the actuator of the drag‐free satellite control system, and the layout directly affects the stability of the satellite control. Due to the extremely rigorous objectives of drag‐free control, a design method for the redundant thruster layout is proposed in this paper to improve the control stability of the satellite and reduce the allocation error of the control system. First, the factors corresponding to the allocation errors of redundant thruster layouts are analyzed. And the minimum number of thrusters required for satellite control is determined through the calculation method of thruster redundancy. Then, the design criterion of reducing the allocation error and increasing the satellite control stability is put forward. To demonstrate the effectiveness and feasibility of the design method proposed in this paper, the space gravitational wave detection satellite is taken as a simulation case. Thrust distribution models corresponding to the designed thruster layouts are established, and the results show that a proper layout can not only improve stability but also reduce fuel consumption. The research is looking forward to providing a reference for designing the microthruster layout of the gravitational wave detection satellite in the future. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle platoons (CAVPs) are considered an effective way to alleviate traffic congestion, reduce the incidence of traffic accidents, and improve vehicle economy in the intelligent transportation system (ITS). Vehicles in the CAVPs can communicate with each other through V2X technology, which could optimize the economy of the platoon. Cooperative adaptive cruise control (CACC) can make effective use of the characteristics of CAVPs and contribute to resource conservation, ecological driving, and traffic system development. In this paper, a two-stage CACC method is proposed for CAVPs to reduce fuel consumption in the multislope road section. In the first stage, the optimal velocity profiles for the leader based on dynamic programming (DP) are planned according to the road information and the fuel consumption model. In the second stage, a vehicle longitudinal third-order differential dynamics model is utilized to build the platoon time-delay system considering communication delay and actuator delay. A feedback controller is developed for each vehicle considering the internal stability and the string stability of the CAVPs. Results show that the proposed method can save 5.33% of fuel consumption compared to the constant speed cooperative adaptive cruise control (CS-CACC) method and has a better tracking performance compared to the model predictive control (MPC) method. The CACC method proposed in this paper can provide a theoretical basis and data support for building an ecological CACC strategy for CAVPs. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

In the wake of the COVID-19 pandemic, prioritizing indoor air quality has emerged as a crucial measure for preventing infections. Effective ventilation is vital in mitigating airborne pathogen transmission and maintaining a healthy indoor environment by diluting and removing infectious particles from enclosed spaces. However, increasing the supply of pathogen-free air to enhance infection control can lead to a rise in energy consumption. Nevertheless, evaluating the overall efficacy of ventilation-based infection prevention strategies while considering their energy requirements has posed challenges. This scientific paper introduces the ICEE (Infection Control's Energy Efficiency) index, a newly developed simple integrated index to assess the effectiveness of ventilation strategies in reducing infection risks while accounting for associated energy demands. The paper reviews the current understanding of ventilation strategies, their impact on infection prevention, and their corresponding energy consumption. By employing a straightforward analytical approach, this metric offers a comprehensive framework to optimize ventilation systems for both infection prevention and energy efficiency. To quantify infection risk, a simplified equation model is utilized, incorporating factors such as ventilation effectiveness and filter efficiency, in case of recirculation. Energy demand is determined using approximations and relevant values from existing literature. Reference cases are defined, distinguishing between natural and mechanically ventilated scenarios, as these reference situations influence the energy-related effects of any implemented measures. The paper outlines the methodology employed to develop the index and illustrates its applicability through exemplary measures. The proposed index yields valuable insights for the design, operation, and retrofitting of ventilation systems, enabling informed decision-making towards fostering a healthier and more sustainable built environment. [ABSTRACT FROM AUTHOR]

Edge computing allows IoT tasks to be processed by devices with passive processing capacity at the network's edge and near IoT devices instead of being sent to cloud servers. However, 5G‐enabled architectures such as Fog Radio Access Network (F‐RAN) use smart devices to bring the delay down to even a few milliseconds. This is important, especially in latency‐sensitive applications such as online digital games. However, a trade‐off must be made between the delay and energy consumption. If too many tasks are processed locally on edge devices or fog servers, energy consumption increases because mobile devices such as smartphones and tablets have limited energy charges. This paper proposes a Deep Reinforcement Learning (DRL) method for offloading optimization. In designing states, we consider all three critical components of memory consumption, the number of CPU cycles, and network mode. This makes the modeling aware of the workload of the tasks. As a result, the model matches the requirements of the real world. For each mobile device that submits a task to the system, we consider a reward. It includes the total delay of tasks and energy consumption. The output of our DRL model specifies to which edge/fog/cloud device each task should be offloaded. The results show that the DRL technique produces less resource waste than RL when the number of tasks is very high. In addition, DRL consumes 30% less network resources than the FIFO method. As a result, DRL provides a better trade‐off between offloading and local execution than other methods. [ABSTRACT FROM AUTHOR]

This paper presents a tracking method to improve the performance of switched reluctance generators in areas with low wind speeds. The main contribution of this work is the proposal of an advanced control system that employs dynamic tracking techniques to optimize the energy efficiency and response of the switched reluctance generators under various operating conditions. The methodology encompasses the construction of the generator, the development of a wind turbine model, and the coupling of the turbine with the switched reluctance generators. Simulations are conducted using real wind speed data to evaluate the use of switched reluctance generators in wind energy generation. The performance of the switched reluctance generators is improved by applying a tracking technique that operates on the converter's turn‐off angle, which controls the generator's switching. The tracking and optimization techniques implemented in this work maximize the performance of switched reluctance generators in wind energy generation, both in standalone and self‐excited operations. The results demonstrate that satisfactory wind energy generation can be achieved using the SRG, with wind speeds of 5.40 m/s producing approximately 1.5 kW without optimization and around 1.65 kW with optimization, reaching about 70% efficiency with a custom‐built generator. This makes the technology viable for regions characterized by low wind speeds. [ABSTRACT FROM AUTHOR]

Seismic isolation technologies for large‐span structures have rapidly developed alongside the popularization of the seismic resilience concept. To produce a high‐efficiency isolation technology with lower energy dissipation demands, this paper proposes a novel inerter‐enabled isolator (IeI) and a tailored input energy reduction‐oriented design method. The inerter‐based damper within the IeI is developed by combining the dashpot, tuning spring, and two inerters to facilitate the optimization of inerter distribution. Assuming the large‐span structure remains linear, the overall seismic input energy of the large‐span structure with IeIs and its allocation in the superstructure and additional damping are quantified using stochastic energy analysis. The advantages of the IeI over the conventional linear viscous damper (LVD) isolator are elucidated through dimensionless parametric analysis. Based on the results of parametric analysis, an input energy reduction‐oriented design method is proposed for the IeI, along with an easy‐to‐follow diagram that helps with preliminary design in practical applications. The effectiveness of the IeI and the proposed design method is validated through a design case study of a benchmark large‐span structure. The results demonstrate that the IeI reduces the seismic response of large‐span structures by simultaneously employing the input energy reduction effect of grounded inerters with the damping‐enhancing effect of inerter‐based dampers. The proposed design method effectively balances the performance of controlling the large‐span structure and the isolator displacement. Under consistent control performance and isolator displacement constraints, the IeI requires much less damping coefficient and energy dissipation capacity than the conventional LVD isolator. Moreover, leveraging the damping enhancement and input energy reduction effects, the IeI achieves comparable control performance to the conventional LVD isolator, even under stricter isolator displacement constraints. [ABSTRACT FROM AUTHOR]

The Dedicated Bus Lane (DBL) is often adopted to ensure transit priority. This is because transit priority can effectively mitigate congestion at the signalized intersection. However, the DBL would cause heavy sacrifices from general vehicles when the frequency of buses is low. To address this issue, many studies were proposed to reduce general vehicles' sacrifice by converting DBLs into Bus‐Priority Lanes (BPLs). Such BPLs can be intermittently open to general vehicles. However, these studies cannot ensure absolute transit priority when general vehicles access BPLs. With the advance of Connected Automated Vehicle (CAV) technology, this paper proposes a Trajectory‐Based Control (TBC) method for connected automated traffic to approach signalized intersections considering absolute transit priority. A TBC controller is designed to control general vehicles' trajectories to access BPLs without interference with buses. The TBC controller can balance the multiple cost factors and ensure absolute bus priority. The proposed TBC controller is evaluated against the noncontrol baseline and the state‐of‐the‐art TBC. Sensitivity analysis is conducted under four different congestion levels. The results demonstrate that the proposed TBC method outperforms and has benefits in improving throughputs and fuel efficiency and reducing delays. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the effectiveness of ultrawideband localization in the path planning of multiple quadrotors performing inventory management in warehouses. As studies investigating warehouse automation became prevalent, researchers have looked into integrating quadrotors to aid inventory management with their ability to equip a wide array of sensors and reach high altitudes. Indoor localization has been one of the main foci of research where simultaneous localization and mapping (SLAM) and radio frequency identification (RFID) were commonly used due to their reliability and accuracy. Ultrawideband (UWB) can be viable for quadrotor navigation with their effectiveness even in nonideal lighting conditions and its relatively inexpensive infrastructure. A path planning algorithm was designed which optimizes quadrotor energy consumption and yaw movement. Simulations were performed to verify the feasibility of the system, and field experiments were carried out to assess the accuracy of UWB to localize the quadrotor. Results have shown that UWB maintained an accuracy of 0.43 m with 95% reliability, even if two quadrotors were used. Moreover, the quadrotor energy consumption was minimized successfully, with the maximum energy consumption difference below 23% for multiple inventory management. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) communication is a promising technology in fifth‐generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and network quality assurance. This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. The proposed algorithm aims to optimize overall throughput and maximize the signal‐to‐interference noise ratio (SINR) while ensuring low computational complexity. The study introduces the following two key techniques: staggered training and decentralized execution. Staggered training improves agent performance and minimizes computational complexity by training agents one at a time in a sequential manner. This allows agents to learn from each other's mistakes and avoid local minima. Decentralized execution enhances scalability and system robustness by enabling agents to learn and act independently without relying on communication with other agents. In the event of agent failure, the remaining agents can continue operating. The findings of this work demonstrate a significant improvement in energy efficiency (EE) and an enhancement in the quality of service (QoS) of the network. Overall, the algorithm proves to be a promising solution for resource allocation in multiagent D2D networks, offering notable improvements in EE and QoS while maintaining scalability for large networks. [ABSTRACT FROM AUTHOR]

This paper presents the feasibility analysis and study of integrated renewable energy (IRE) using solar photovoltaic (PV) and wind turbine (WT) system in a hypothetical study of rural health clinic in Borno State, Nigeria. Electrical power consumption and metrology data (such as solar radiation and wind speed) were used for designing and analyzing the integrated renewable energy system. The health clinic facility energy consumption is 19 kWh/day with a 3.4 kW peak demand load. The metrological data was collected from National Aeronautics and Space Administration (NASA) website and used to analyze the performance of electrical generation system using HOMER program. The simulation and optimization results show that the optimal integrated renewable energy system configuration consists of 5 kW PV array, BWC Excel‐R 7.5 kW DC wind turbine, 24 unit Surrette 6CS25P battery cycle charging, and a 19 kW AC/DC converter and that the PV power can generate electricity at 9,138 kWh/year while the wind turbine system can generate electricity at 7,490 kWh/year, giving the total electrical generation of the system as 16,628 kWh/year. This would be suitable for deployment of 100% clean energy for uninterruptable power performance in the health clinic. The economics analysis result found that the integrated renewable system has total NPC of 137,139 US Dollar. The results of this research show that, with a low energy health facility, it is possible to meet the entire annual energy demand of a health clinic solely through a stand‐alone integrated renewable PV/wind energy supply. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

This paper focuses on the active cell balancing of lithium-ion battery packs. An improved single-input, multioutput, bi-switch flyback converter was proposed to achieve effective balancing. The proposed topology simplifies the control logic by utilising a single MOSFET switch for energy transfer and two complementary pulses to control the cell-selecting switches. The proposed topology can decrease the number of switching devices as well as the size and cost of the system. The bi-switch flyback converter eliminates the need for a separate buffer circuit to minimise leakage and electromagnetic inductance. Losses and energy efficiency were analysed at each end of the proposed topology. The appropriate MATLAB simulations investigated the balancing characteristics of various state of charge (SOC) imbalances. A comparison is made between the balancing speed and energy transfer efficiency of the proposed topology and a conventional topology that employs a multi-input and multi-output flyback converter in a static mode. The results of the MATLAB simulation were validated by the OPAL-RT (OP5700) real-time simulator. The balancing data of the proposed topology were compared using MATLAB simulation and real-time simulation. This work may reduce the time required to assemble and commission the hardware for the proposed topology's real-time implementation. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs' lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis. [ABSTRACT FROM AUTHOR]

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model's superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

Solar photovoltaics are considered the practical solution to energy access and climate change issues, especially in tropical countries that receive relatively more sunlight throughout the year. However, questions arise on the reliability of these systems in providing sufficient supply to meet the users' electricity needs. This paper looks at the reliability of a solar project installed on two rooftops on an off-grid island in Cebu, Philippines, that provides increased electricity access to 11 households. PVSyst and HOMER Pro software analyzed solar PV systems performance and techno-economics. The simulations yielded the annual mean values of reference yield, array yield, final yield, array capture loss, system loss, performance ratio, and capacity factor are 5.66 kWh/m2/day, 3.51 kWh/kWp/day, 3.23 kWh/kWp/day, 2.15, 0.278, 57.10%, and 18.96%, respectively. The peak PV resource of 3.30 kWp/day can supply the 1.66 kWp/day of the consumer's electrical demand. It was concluded that the current installation could supply the electrical load demand of the residents; however, consideration for the potential increase in demand must be in place. While renewable energy sources are relevant in achieving 100% electrification in rural communities, their ability to address the energy demands of the users must be carefully considered in planning and design. [ABSTRACT FROM AUTHOR]

The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater "reuse" effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure's costs. [ABSTRACT FROM AUTHOR]

Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., "the difference edges," into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model. [ABSTRACT FROM AUTHOR]

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain-based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph-based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real-world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods. [ABSTRACT FROM AUTHOR]

The media access control (MAC) address is the basis of frame forwarding in data‐link layer. When MAC missing occurs in the software‐defined network (SDN), the processing method is the same as that of Ethernet, which is also broadcast. The broadcast will spread and cover part or even the entire network, result in a large number of frames, which leads to a sharp increase in network traffic, even cause congestion. To suppress the layer‐2 flooding, a new frame forwarding method is proposed, namely, multidevice search and path generation (MSPG). New approach will search multiple devices when MAC missing occurs and calculate a forwarding path; a set of flow table is generated to restrain the switch from broadcasting. Experiments and analysis show that in comparison with the existing frame forwarding methods in SDN, MSPG can effectively suppress flooding, greatly reduce the network load, minimize the traffic between data plane and control plane when MAC missing occurs, and reduce energy consumption significantly. The MSPG's path calculation and flow dispatch method also decrease the forwarding delay and are more suitable for large‐scale network. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the adaptive strategies and compensatory growth mechanisms of juvenile Epinephelus coioides under starvation stress. The fish with an initial body weight of 8.88 ± 0.06 g were starved for 0 day (F0, control), 3 days (F3), 6 days (F6), 9 days (F9), 12 days (F12), and 15 days (F15), followed by feeding periods of 30 days, 27 days, 24 days, 21 days, 18 days, and 15 days, respectively. The results are shown as follows: (1) E. coioides could achieve complete or partial compensatory growth by improving both feeding rate and feed conversion efficiency. (2) The viscerosomatic index and the hepatosomatic index of the test fish decreased linearly with the duration of starvation (P < 0.05), while the condition factor did not significantly decrease (P > 0.05). At the end of feeding, all the physical body indicators of the test fish recovered or exceeded the control levels. (3) The lipid and glycogen contents of the test fish decreased continuously throughout the entire starvation process, with noticeable protein consumption occurring in the later stages of starvation (P < 0.05). At the end of the feeding period, apart from a significant reduction in crude lipid contents in the F12 and F15 groups compared to the control group (P < 0.05), the biochemical composition of the test fish was restored or even surpassed that of the control group. (4) The activity of digestive enzymes could be stimulated by starvation, enabling the efficient utilization of energy storage substances. In conclusion, it is advisable to limit the period of starvation for juvenile E. coioides to 12 days, which means that the ratio of starvation period to feeding period should not exceed two‐thirds. [ABSTRACT FROM AUTHOR]

The dynamic autonomous mobile ad hoc networks (MANETs) allow nodes to move everywhere quickly and communicate without the support of a network infrastructure. The movement of mobile nodes in MANETs leads to several issues, such as changeable topology, energy limitations, path conservation, and bandwidth ineffectiveness. This would negatively impact the network's QoS. The primary aim of the present study is to select the optimal path in MANETs using the clustering technique for variable nodes (i.e., 100–1000). The network performance characteristic and energy efficiency of the present ORSMAN protocol are simulated in the ns3 simulator. The research results demonstrate that compared to the current OPSTSA, EAAORS, EERS, and OEERS techniques, the suggested ORSMAN has a higher packet delivery ratio, the lowest latency, the lowest jitters, and superior throughput. The maximum improvement in throughput is 29.81%, and in energy efficiency, it is 9.67% compared to current techniques. Additionally, a trade‐off study between energy efficiency and QoS reveals that the performance characteristics enhance QoS while reducing energy efficiency. This reduction may be attributed to variations in throughput, jitter, and latency. The total QoS performance was improved by 22.16% for the present study compared to the current techniques. [ABSTRACT FROM AUTHOR]

The energy supply in Ethiopia is dominated by biomass energy, mainly for household consumption. The development of gasification‐based gasifier baking stoves for energy demand is the subject of this study. The novelty of the gasifier baking stove is its ability to bake food more quickly while using less wood fuel. Biomass fuels are used inefficiently in poorly ventilated kitchens, resulting in indoor air pollution and the consumption of large amounts of wood fuel. Biomass gasifier baking stoves can improve fuel efficiency, baking time, and indoor air pollution while producing char as a byproduct. The proposed new gasifier study shows an average fuel use efficiency of 45% and time savings of 12% compared to Mirt stoves. Furthermore, the grate cover of the biomass gasifier stove is also used to regulate the airflow rate. The primary air that enters the reactor is controlled by a manually operated slide grate cover. The water boiling test and control cooking test were developed to mirror the performance of a modern baking and cooking stove. The baking stove was tested with Eucalyptus globulus as a feedstock. A technoeconomic assessment was conducted on a biomass gasifier baking stove for commercial purposes. For the technoeconomic assessment, we mostly employed the simple payback period projection. The economic analysis was conducted using Ethiopian supply and market rates, which may differ from region to region. The baking stove has an initial capital cost of about 15,100 ETB and a payback period of 2.49 years, which makes it economically viable for those living in remote locations. According to the study, the typical emissions from a biomass gasifier stove running on eucalyptus wood fuel are 0.24 ppm CO and 8.91 ppm CO2. The results of the stove's economic study demonstrate that using eucalyptus wood fuel results in a shorter payback period. The performance efficiency of the new gasifier stove in terms of wood fuel and time saving as compared to the Mirt stove is 45% and 12%, respectively. [ABSTRACT FROM AUTHOR]

The use of traditional biomass in developing countries such as Ethiopia accounts for approximately 90% of the total primary energy consumption at the household level. Such heavy dependency threatens the natural forest ecosystem and contributes to climate change. However, empirical evidence on the extent of fuelwood consumption is limited. This study was therefore carried out to investigate the status of firewood consumption in the Negele Arsi District of Oromia Region, Ethiopia, and its contribution to climate change. Door‐to‐door surveys and measurements were made to estimate the extent of firewood consumption and the resultant greenhouse gas (CO2e) emissions at the household level. The major type of woody species used as household energy sources and the amount of firewood saved while using the improved stove were also predicted through kitchen performance tests. The findings showed that 84.5% of the energy needed for home‐based activities was fulfilled by firewood. Eucalyptus species, Podocarpus facaltus and Acacia etbaica, were the most common tree species used as household energy sources in the district. It was found that 17.02 tons of firewood was consumed, and 18 × 103 tCO2e was emitted annually at the household level. Baking Injera using Mirt stove was found to reduce firewood consumption by approximately 25% compared to a traditional open stone stove. The study showed that firewood consumption is the main cause of deforestation in the district and has a large contribution to climate change. Hence, coordinated effort and appropriate interventions are needed to avert the situation. [ABSTRACT FROM AUTHOR]

Energy efficiency of coal‐water slurry mixing determines its application and transportation efficiency. This study compares the hydrodynamic characteristics and mixing performance of five different configurations of hydrocyclone mixers under turbulent flow conditions. Numerical calculations were used to investigate the velocity, pressure, and concentration fields of the mixer. Attempts were made to decrease the tangential inlet size and to design indentation structure to control the velocity and pressure fields inside the hydrocyclone mixer, thereby increasing the degree of mixing (DOM) and reducing mixing energy cost (MEC). The findings indicate that better mixing and dispersion can be achieved while reducing energy costs by designing the indentation structure and decreasing the size of the tangential inlet and outlet. It offers a reliable solution for uniformly mixing and dispersing coal‐water slurry with organic waste liquids and other highly concentrated fuels. [ABSTRACT FROM AUTHOR]

The global construction industry is shifting toward eco‐friendly building materials to reduce environmental impact while ensuring affordability, energy efficiency, and resource conservation. Interlocking compressed earthen bricks (ICEBs) offer a sustainable alternative to traditional bricks. However, ICEB composition significantly impacts their performance. This study investigates the interplay of stabilizer, water, and fine aggregate content to optimize ICEBs. Soil properties were assessed through particle size gradation analysis, Atterberg limits, and the modified Proctor test. ICEBs were manufactured using cement as a stabilizer, with various combinations of stabilizer proportions, water‐to‐soil ratios, and inclusion of fine aggregates. Compressive strength tests were conducted for each mix. Results showed that increased cement content enhanced ICEB compressive strength by promoting the formation of calcium silicate hydrate gel. Water content increments from 12% to 21% improved strength up to 18% but declined thereafter. Higher sand percentages initially reduced compressive strength but improved it with higher cement proportions. The optimal mix ratio was selected for ICEB production, followed by tests including flexural strength assessment, forensic investigations, curing regimes impact, and comprehensive durability and structural performance evaluation. Findings highlight ICEBs' potential as a sustainable alternative to conventional brick masonry. [ABSTRACT FROM AUTHOR]

Nowadays, dust exposure pollution is receiving a lot of attention due to its significant impact on public health. To investigate the impact of dust particle size and human respiratory strength on respiratory particle deposition patterns, data was collected through on‐site surveys. The study analyzed the equivalent respiratory strength, dust environment characteristics, and bronchial particle escape and deposition patterns of humans in fully mechanized mining faces at various operating times. This was done using ergonomic energy consumption simulation experiments and a fluid–solid interaction method of CFD‐DEM. The findings revealed that as humans worked continuously for 5, 15, 30, 45, and 60 min, their respiratory intensity corresponded to 8, 18, 30, 42, and 50 L/min, respectively. According to the field investigation and particle size analysis, the particle size distribution of 1~5, 5~10, 10~20, 20~30, and 30~40 μm particles accounted for 36%, 26%, 15%, 11%, and 10%, respectively. In general, the deposition rate of dust was highest in the main bronchus of the respiratory tract, followed by the trachea area. Particles ranging from 5 to 10 μm in size were observed to have a higher likelihood of escaping from the tertiary bronchioles and entering the secondary bronchial regions. Conversely, particles larger than 20 μm exhibited a deposition rate of up to 80% in the tertiary bronchial regions. It was noted that the bronchial deposition rate of particles of varying sizes increased with respiratory strength, with smaller particles showing greater sensitivity to changes in respiratory strength in terms of the deposition fraction. Among the different particle sizes, the deposition rate of 5–10 μm particles exhibited the most variation with increasing respiration intensity, ranging up to 17%. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) communication network is anticipated to satisfy the need for higher data rates, less latency, ubiquitous connectivity with minimum consumption of energy and acceptable quality of Service (QoS). The massive MIMO and heterogeneous network (HetNet) has evolved as a promising technology to address the aforementioned challenges. In the proposed framework, the massive MIMO technology at the macro base station and full duplex (FD) technology enabled at the small cell base station in HetNet are investigated. An optimal antenna selection strategy, user association (UA), and power allocation (PA) in a massive MIMO‐based HetNet with FD‐enabled small cells are proposed to optimize system sum rate. Initially, the objective function of the optimization problem is nonconvex and a mixed integer nonlinear programming problem. Then, the solution is obtained by transforming the problem into convex problem utilizing Lagrangian decomposition. Simulation results manifest the effect of the number of base station antennas, number of small cell base stations, self‐interference cancellation factor, and channel state information on system sum rate. Moreover, it is evident that the propounded algorithm is effective in terms of optimizing the system sum rate when compared to the conventional maximum signal‐ to ‐ interference ‐ plus ‐noise ‐ ratio (SINR) algorithm. [ABSTRACT FROM AUTHOR]

This study investigates the performance of a novel, single‐pot sawdust stove prototype designed for extended use applications requiring steady heat, such as daylong hot water provision, room heating, or bulk cooking for large gatherings. The prototype utilizes the rocket stove concept and prioritizes long burn times with minimal refuelling. The stove features a double‐walled, cylindrical combustion chamber made of mild steel (primary material) for efficient burning and heat retention. Fiberglass insulation (10 mm thick) is sandwiched between the walls to minimize heat transfer. An adjustable cooking platform, constructed with rods, pipes, and clamps, allows users to control the gap between the pot and the combustion chamber (currently set at 35 mm for a typical 200 mm diameter pot suitable for 3–5 people). This adjustability optimizes heat transfer for various pot sizes, improving overall cooking efficiency for a wider range of users. A heat spreader lid distributes heat evenly across the pot, while a sliding air inlet tray facilitates fire starting and airflow control. The combustion chamber dimensions (270 mm internal diameter and 420 mm height) are based on a prominent design by the Kisangani Smith Group's Sawdust Stove Design of Tanzania Kisangani. The overall external stove diameter is 280 mm, with a mild steel outer wall (2.0 mm thick) surrounding the insulated combustion chamber. Controlled tests compared the prototype to a traditional charcoal stove. The prototype achieved significantly higher thermal efficiency (20.6% vs. 13%), faster rice cooking time (24.8 minutes vs. 31 minutes), and lower specific fuel consumption during cooking (0.55 vs. 0.62). This novel design, with its adjustable features and improved insulation, offers a more efficient, user‐friendly, and sustainable cooking solution for regions with limited access to conventional fuels and abundant sawdust. This research contributes to the development of efficient and sustainable biomass cookstoves, particularly in sawdust‐rich areas of Zambia. [ABSTRACT FROM AUTHOR]

The need to replace nonrenewable sources is a global challenge, especially for emerging countries with financial constraints to invest in energy transition. In Brazil, electrical generation is predominantly renewable, since the hydraulic source is responsible for more than half of the domestic electricity supply. Other alternative sources, such as biomass, solar, and wind, complement the renewable portion of the Brazilian electrical matrix. However, periods of drought and climate disturbances threaten the stability of the electrical system and increase dependence on thermal generation. Based on the global movement to intensify the use of renewable sources, which are volatile and influenced by climate variations, the objective of this research is to predict electricity generation using the Heston model. This model, originally applied to predict option prices, assumes nonconstant volatility and has the ability to estimate the average value including the stochastic volatility process. The database is made up of monthly values of the volume of electrical energy generation from biomass, coal, fuel oil, hydraulic, industrial waste, natural gas, nuclear, solar, and wind sources. Compared to the Black‐Scholes model, the chosen method proved to be superior in predicting the generation from coal, hydropower, and solar energy, according to the MAE, MAPE, and RMSE statistics. We conclude that a financial model can be effectively applied to the energy sector and the Heston model can be a useful instrument to assist in the planning and management of the national electrical system. One suggestion for the Brazilian case is to encourage the use of alternative energies and low‐carbon sources, such as solar, wind, and natural gas, providing greater energy security and a more reliable electrical system. [ABSTRACT FROM AUTHOR]

Intersections are vital components of urban road traffic management, frequently facing persistent congestion challenges. Existing studies rarely combine multiobjective optimization with dynamic adjustment methods. This study introduces an innovative dual‐layer framework for traffic signal optimization. The first layer involves multiobjective optimization, addressing critical performance metrics such as delay, the number of stops, and fuel consumption. In the second layer, we propose a method that uses a fuzzy neural network to learn the correspondence between queue lengths and signal timings. This two‐tiered approach enables real‐time adjustments, achieving dynamic signal optimization. Applying this framework with real traffic flow data to a specific road intersection allows us to determine optimal signal timings dynamically. Extensive simulations using the SUMO software validate the efficacy of our approach in enhancing intersection performance. The timing strategy implemented within this framework leads to a substantial reduction in delay, ranging from 11.1% to 29.0%. The dual‐layer framework presented in this study contributes valuable theoretical insights into future research initiatives in this domain. [ABSTRACT FROM AUTHOR]

Buildings play a critical role in energy consumption, representing one of the primary consumers of power. Heating load (HL) and cooling load (CL) are essential for determining the energy efficiency of buildings. Several research projects attempt to address the critical challenge of enhancing energy efficiency in residential buildings, focusing on accurately estimating HL and CL using solutions that implement statistical prediction or typical building control management. This study, however, looked into advanced machine learning (ML) models for sustainable building design based on harnessing the potential of artificial intelligence and explainable AI (AIX) technologies. The proposed model was trained and tested using a dataset of 768 buildings based on feature engineering methods with various ML algorithms (including cutting-edge emotional neural learning (ENN), nonparametric kernel-based probabilistic models known as Gaussian process regression (GPR), and boosted tree (BT) algorithm). In addition, the output of the model was fed to standard building energy performance software (Ecotect) that utilizes the dataset from twelve different building shapes to perform various building energy efficiency analyses. The overall performance of the proposed model was measured using different performance metrics, including MAPE, MAE, RMSE, and PCC to measure the performance of HL- and CL-based building energy efficiency. The performance evaluation results indicate that the M3 variants, especially GPR-M3, consistently outperformed their counterparts across heating and cooling scenarios. The three models indicated reliability for modeling HL and CL. However, for HL, the GPR-M3 model emerged as the best model, outperforming GPR-M1 by 9.2% and GPR-M2 by 3.9%. Similarly, GPR-M3 is superior to CL, with the highest PCC at 0.9858, marking an 8.1% and 1.9% improvement over GPR-M1 and GPR-M2, respectively. [ABSTRACT FROM AUTHOR]

In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task's subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms. [ABSTRACT FROM AUTHOR]

This research showcases the seamless integration of solid oxide fuel cells (SOFC) with waste heat recovery systems, utilizing liquefied petroleum gas (LPG) as the primary fuel source. The focus of the study is on efficiently harnessing the cold energy from the LPG supply to produce substantial power for the entire system. To optimize energy utilization, a gas turbine (GT) and steam Rankine cycle (SRC) are integrated to effectively convert waste heat from the system into useful work and power. Additionally, a waste heat boiler (WHB) is incorporated to provide superheated vapor steam for seafarer accommodation. A detailed thermodynamic analysis and investigation of the proposed integrated system are performed. The simulations and optimizations of combined system are conducted using ASPEN HYSYS V12.1. Thermodynamic equations based on the fundamental laws of thermodynamics are employed to estimate system performance indicators, and the exergy destruction in major components is assessed to optimize the system's design and operation. The proposed system exhibits impressive energy and exergy efficiencies, calculated at 52.65% and 51.10%, respectively. Moreover, the waste heat recovery combined cycles contribute an additional 1,759.73 kW, equivalent to 31.65% of the total system output. The innovative models are validated against experimental data from the literature, demonstrating strong agreement. Furthermore, a comprehensive parametric study investigates the influence of varying the current density from 900 to 1,950 A/m2, leading to a total energy efficiency variation of 41.59%, ranging from 82.45% to 40.86%. The organic Rankine cycle (ORC) performs exceptionally well, capitalizing on both cold energy and high-temperature waste heat to achieve high energy recovery efficiency. The WHB is capable of providing 8,200 kg/h of superheated vapor stream at 151°C and 499 kPa for seafarer accommodation and heating purposes. The economic viability analysis is conducted to assess the potential for investment, maintenance costs, and the payback period associated with the proposed system. [ABSTRACT FROM AUTHOR]

The choice of mechanical and electrical topologies can affect piezoelectric energy harvesting efficiency, but the problem of achieving high-efficiency energy conversion in energy harvesters stacked as cantilevers has not been perfectly solved. This study focuses on the topology of piezoelectric elements in a stacked vehicle suspension vibration energy harvesting device. Through theoretical analysis, the stress expressions of the excited and driven elements are derived. The stress of the piezoelectric elements is affected by the positions of the connection and excitation points. A stress model was established for a four-piece piezoelectric element connected by a thin light rod in ANASYS. The simulation results show that the average stress in the piezoelectric bending element model is maximum when the excitation and connection points are located at both ends of the free end. Compared with the middle position of the free end of the piezoelectric element, the average stress of the model is increased by 92.904%. Considering the difference in voltage generated by piezoelectric elements, four kinds of electrical topology are designed and analyzed experimentally. When the driven elements are connected in parallel and then connected in series with the excited element, the output power varied the least with the change of load resistance. The system produces a high power and offers a wide selection of load resistors. When the load is 26 kΩ, a single set of four piezoelectric elements produces 86.407 mW of output power. [ABSTRACT FROM AUTHOR]

The growing emphasis on indoor air quality and public health is fuelling the need for efficient yet affordable air purification techniques. In this study, the influence of biochar particle size on its adsorption efficiency toward airborne pollutants was examined. Bark-derived biochar particles were treated by grinding or ball milling, and then, seven samples with different particle size groups were separated. Biochar particles were characterized by particle size, proximate, SEM, XRD, and physisorption analyses. For adsorption efficiency, two different pollutants were tested at variable initial concentrations. The physical composition and XRD patterns of the biochar with different particle sizes were comparable. The ball-milled sample was an exception in that it had higher ash content and additional XRD peaks signifying contamination of the sample. The porosity of biochar was greater in smaller particles. Ball milling increased the specific surface area and total pore volume by 102% and 48%, respectively. Biochar with finer particle size exhibited the highest adsorption potential towards formaldehyde and methanol among other samples. It should be emphasized that simple mechanical grinding is preferred for reducing biochar size to avoid the risk of eventual contamination, greater energy consumption, and slower processing related to ball milling. When a low concentration of pollutant was tested (1 ppm formaldehyde), the effect of particle size on the adsorption efficiency was more noticeable. However, the effect of particle size was less dominant when higher concentrations of pollutants were tested. Smaller biochar particles (<100 μm) are more favourable for indoor air remediation given their superior adsorption efficiency of volatile organic compounds occurring at low concentrations in the buildings. [ABSTRACT FROM AUTHOR]

A well-ventilated room is essential to reduce the risk of airborne transmission. As such, the scientific community sets minimum limits on ventilation with the idea that increased ventilation reduces pathogen concentration and thus reduces the risk of transmission. In contrast, the upper limit on ventilation is usually determined by human comfort and the need to reduce energy consumption. While average pathogen concentration decreases with increased ventilation, local concentration depends on multiple factors and may not follow the same trend, especially within short exposure times over large separation distances. Here, we show through experiments and high-fidelity simulations the existence of a worst-case ventilation where local pathogen concentration increases near the receiving host. This occurs during the type of meetings that were recommended during the pandemic (and in some cases solely authorized) with reduced occupancy adhering to social distancing and short exposure times below 20 minutes. We maintain that for cases of high occupancy and long exposure time, increased ventilation remains necessary. [ABSTRACT FROM AUTHOR]

An experimental investigation is carried out in this study to investigate the effect of wheel alignment, particularly the front total toe-in angle, on tire wear and emissions for a light-duty vehicle. Such investigations reveal that there is a substantial correlation among rolling resistance, energy consumption, tire wear, tire travel life, and the total toe-in angle of the front wheel. It is observed that the rate of loss in tire travel life with regard to a condition without misalignment is up to 98.33% when the front total toe-in angle is out of alignment (ranging from 0.00° to 4.20°). It is found that rolling resistance increases by about 128.86%, while CO2, CO, and NOx emissions rise by nearly 36.67%, 26.83%, and 31.25%, respectively, as the front total toe-in angle increases from 0.00° to 4.20°. The experimental results also reveal that tire circumferential groove wear is observed at 0.04 mm after the vehicle's travelling distance of 500 km, where the front total toe-in angle is 0.00°, and the tire travelling life is 92250 km. In addition, the tire circumferential groove wear is investigated as 2.40 mm after the vehicle's travelling distance and tire travel life are recorded to be 3,500 km and 1537.50 km, respectively, due to the occurrence of misalignment (the front total toe-in angle is 4.20°). Finally, a regression model is proposed using the test data. Such a model would be useful to explain the relationship between the related factors and determine the rate of tire wear and emissions. It is noteworthy that the wheels should always remain aligned in accordance with the manufacturer's specifications in order to ensure optimal performance and longevity of the tires [ABSTRACT FROM AUTHOR]

Inhaling airborne droplets exhaled from an infected person is the principal mode of COVID-19 transmission. When residential energy efficiency workers conduct blower door tests in occupied residences with a COVID-19-infected occupant potentially present, there is a concern that it could put the workers at risk of infection with massive flows of air being generated by the tests. To minimize this risk, computational fluid dynamics (CFD) simulations were conducted for four prototype houses to develop guidelines for workers to follow during their service visits. The CFD simulations visualized the movements and evaluated the residence time of small particles released at certain locations under a series of scenarios representing situations that are likely to be encountered during in-home energy efficiency services. Guidelines were derived from the simulated tracks of droplets to help to increase the safety of the worker(s). [ABSTRACT FROM AUTHOR]

Facing the method's limitations of the existing drone inspection on offshore wind farms, we adopt a new comprehensive-assisted drone automated inspection scheme under the comprehensive assistance. Our objectives are saving energy and high-efficient inspection. The such inspection is used to formulating the two mixed-integer nonlinear programming problems based on two new drone basic models: the mobile edge computing driven drone computation system model and the drone flight model. To solve the problems, we split them into four subproblems, and a new improved heuristic algorithm is created to address. In turns, the waypoints, total inspection time, inspection energy consumption, and traveling distance of unmanned aircraft vehicle (UAV) and the traveling distance of boat are obtained by K -means algorithm and the smallest enclosing circle (SEC) algorithm, the Lin-Kernighan Heuristic 3 (LKH-3) algorithm, and the LKH. Finally, conducting the comprehensive optimization and simulation, the simulation numeric results are gotten. The simulation results demonstrate that as for the two aspects of the total energy consumption and inspection efficiency under different data amount and average wind speed, the scheme improves at most 44% and 23.5% than the current other three; the scheme achieves the objective of saving energy and high-efficient inspection. [ABSTRACT FROM AUTHOR]

Acquiring a vehicle or financing its purchase is often considered a luxury, particularly for middle-class households. In addition to their primary concern, consumers prioritize distinguishing elements such as vehicle type, size, capacity, engine power, fuel efficiency, safety features, and life-cycle costs. Furthermore, the choice of vehicle has multiple factors and is contingent upon the financial prosperity of the household, allowing for precise expression. The decision-making process is complex and entails selecting the most appropriate alternatives. This study proposes a novel fuzzy technique for order of preference by similarity to the ideal solution (FTOPSIS) method to solve vehicle selection multi-criteria decision-making (MCDM) problems. Decision-makers express their opinions on each alternative and criterion in linguistic terms, in terms of generalized interval type-2 trapezoidal fuzzy numbers (GIT2TrFNs). The FTOPSIS process involved utilizing the defuzzification of GIT2TrFNs to acquire the normalization matrix. Finally, the proposed approach prioritizes the options and chooses the best vehicle when faced with conflicting criteria, as illustrated through a numerical illustration. [ABSTRACT FROM AUTHOR]