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Summary: In the pulp and paper industry, black liquor, which is a biomass resource, is burned to produce electricity. Black liquor is concentrated to 21 wt% water through an evaporator before being burned in a boiler. For the evaporator, a multiple‐effect evaporator (MEE) is mainly used, but it requires a large amount of energy and cost. Therefore, it is crucial to reduce energy and cost of evaporation process. Hence, this study suggested a novel process model that integrated mechanical vapor recompression (MVR) with MEE to increase the energy efficiency. The suggested MVR‐assisted evaporation process was composed of preheating and evaporation processes to effectively concentrate black liquor. In addition, it reduced the steam consumption of MEE by using MVR, which uses relatively inexpensive electric energy in the pre‐evaporation process. In the simulation results, the steam, electricity consumption, and the latent heat recovered from the secondary vapor of the suggested process were quantitatively analyzed to verify the energy efficiency. The results indicate that the proposed process increases substantial energy efficiency compared to the conventional process. Then, the appropriateness of the suggested process was evaluated by the techno‐economic analysis. The total annualized cost (TAC) is determined for both current and potential future economic benefits. TAC of the MVR‐assisted MEE configuration can be reduced by up to 77.54%. [ABSTRACT FROM AUTHOR]

Proper management and effective conversion of biomass residues for biofuel production are crucial to reduce deforestation due to the cutting of trees for cooking and heating as a primary source of fuel and improving energy utilization of households. Thus, this study is aimed at investigating the effects of biomass residues of the coffee husk (CH), sawdust (SD), khat waste (KW), and dry grass (DG) and binding materials prepared from the waste paper pulp (PP) and clay soil (CS) under a low-pressure piston press densification machine. The biomass waste and binders were combined in a 3 : 1 ratio of CH: PP, CH: CS, SD : PP, SD : CS, KW: PP, KW: CS, DG: PP, and DG: CS. The briquettes were produced using a manually operated closed-end piston press machine compacted at an average pressure of 2 MPa. Briquette proximate and ultimate analysis of moisture content, volatile matter, fixed carbon, and ash content was determined using standard ASTM methods, while the calorific value was determined using a bomb calorimeter and data analysis was carried out using the R-program. Results revealed that the briquette produced from biomass residues has a mean value of fixed carbon and calorific value that ranged from 38:62 ± 1:53 to 41:75 ± 2:14 and 3979:21 ± 232:05 cal/g to 4577:34 ± 397:11 cal/g, respectively. Generally, briquettes produced from saw dust residue and the paper pulp binder had better quality of fuel and this could be used as an alternative source of energy and proper waste management option. [ABSTRACT FROM AUTHOR]

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP. [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]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Energy management for energy harvesting-based embedded systems (EHES) is an emerging field, which aims to collect renewable energy from the environment to power an embedded system. In this work, we use the systematic mapping method to study the relevant literature, with the objective of exploring and analysing the state of the art in energy management for EHES, as well as to provide assistance for subsequent literature reviews. To this end, we conducted extensive searches to find articles related to energy harvesting, embedded systems, energy consumption, and energy management. We searched for papers from January 2005 to July 2019 from three mainstream databases, ACM, IEEE Xplore, and Web of Science, and found more than 3000 papers about EHES. Finally, we selected 142 eligible papers. We have completed the system mapping research from five aspects, namely, (1) research type (validation research, evaluation research, solution proposal, philosophical paper, opinion, and experience), (2) research goals (application or theory), (3) application scenarios, (4) tools or methods, and (5) paper distribution, such as publication year and authors' nationality. The results showed that the major research type of the EHES papers is validation research, accounting for 65%, which indicated research is still in the theoretical stage and many researchers focus on how to improve the efficiency of harvesting energy, develop a reasonable energy supply plan, and adapt EHES for real-world requirements. Furthermore, this work reviews the tools used for EHES. As the future development direction, it is indispensable to provide tools to EHES for research, testing, development, and so on. The results of our analysis provide significant contributions to understanding the existing knowledge and highlighting potential future research opportunities in the EHES field. [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]

Low energy consumption and green transformation of automobile product portfolio is the trend of the times. Automotive manufacturers make product portfolio decisions by setting multiple criteria such as fuel consumption, sales, and volume. It is also important to take into account the symbiotic interaction effects between automotive products. In order to achieve the above research objectives, this paper constructs the Lotka–Volterra MCGP model to make multicriteria decisions on automobile product portfolio design from the perspective of energy sustainability with BMW Brilliance is taken as an example to illustrate the process of using the multicriteria model. The empirical analysis successively measures product growth using the logistic model, analyzes the symbiotic relationship of product portfolios using the Lotka–Volterra model, and finally performs multicriteria evaluation using the MCGP model. In order to verify the reliability of the model, this paper verifies the robustness of the model from the perspectives of parameter dynamics, system boundaries, and model scalability. The results of empirical analysis and robustness analysis show that the Lotka–Volterra MCGP model proposed in this paper is applicable to the multicriteria decision-making of automobile product portfolio design from the perspective of energy sustainability. [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]

Computer technology and related Internet of things technology have penetrated into people's daily life and industrial production; even in competitive sports training and competition, the Internet of things technology has also been a large number of applications. Traditional intelligent wearable devices are mainly used to calculate the steps of athletes or sports enthusiasts, corresponding physical data, and corresponding body indicators. The energy consumption calculated by these indexes is rough and the corresponding error is large. Based on this, this paper will design a wearable device which can accurately calculate and monitor sports energy consumption based on relevant sensors and Internet of things technology. The corresponding core algorithm is the step counting algorithm, which can accurately calculate the relationship between human motion and the corresponding energy consumption and feed back to the intelligent device. In the experiment, the wearable device designed in this paper is compared with the traditional intelligent device. The experiment shows that the wearable device proposed in this paper is more accurate in energy consumption estimation than the traditional device, and its corresponding energy consumption is relatively small. [ABSTRACT FROM AUTHOR]

In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT's communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field's sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm's accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application. [ABSTRACT FROM AUTHOR]

With the continuous development of society and economy, energy consumption and exhaust emissions of high-speed trains have also gained more and more attention from the industry. Hence, optimizing the operation of high-speed trains is of pivotal significance to achieve optimization as required. On the basis of the intelligent robot palletizer technology, a spatial construction method and intelligent robot palletizer capable of operating through the dispatching of high-speed trains are put forward in this paper on the basis of the situation of high-speed trains assigned. The method of changing the arrival time of the trains and the order of departure are adopted to control the operation of the high-speed trains in an efficient and intelligent manner. With regard to the intervals of departure time, the overtaking of trains and other issues, a three-dimensional spatial travel route is established and used for processing. The intelligent robot palletizer is used to implement the dispatching of trains in the aspect of height. Subsequently, the minimum value method and secure network technology are used to verify that the technology of the intelligent robot palletizer is mature and reliable. Finally, a practical case analysis is carried out in this paper on some sections of the Harbin–Dalian high-speed railway to verify the accuracy and effectiveness of the method proposed in this paper. Through the comparative analysis with other methods, the advantages of the algorithm put forward in this paper can be observed. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the national carbon emission trading market, new energy system participates in the carbon emission trading market. Analysing the potentiality of virtual power plant trading in carbon emission trading market, this paper designs a two-stage joint trading mechanism for electricity and carbon market with a weekly cycle according to their characteristics, which contain multiple transaction types for both markets. In addition, this paper introduces a carbon capture system (CCS) in gas turbine, which reduces the actual carbon emissions and increases the carbon market income of virtual power plant. Furthermore, it improves the comprehensive and flexible operation capability by adjusting the operation level of CCS, which is conducive to the timely consumption of renewable energy. Aiming at the uncertainty of renewable energy output and electricity price, the paper adopts a multiscenario analysis method to deal with it and establishes a stochastic optimization model to maximize joint earnings. Finally, through example analysis with GAMS, the effectiveness of the scheduling model is verified with simulation results. The overall income of the virtual power plant is improved, and the low carbon power is realized. [ABSTRACT FROM AUTHOR]

Compared with most dynamic tests, point load test is widely used in engineering practice because of its wide requirements for test conditions. In offshore mining engineering, the surrounding rock is affected by dynamic load impact and corrosion of various aqueous solutions for a long time. Therefore, it is of great significance to study the influence of hydrochemical corrosion on dynamic mechanical properties of rock to ensure the stability of surrounding rock. In this paper, a large number of dynamic point load and dynamic uniaxial compression tests were carried out on the granite mined from a large mine by using the Split Hopkinson pressure bar test device after the corrosion of pH values of 2, 5, and 7 solutions, respectively. Based on the energy theory, the crushing energy consumption in the test process is analyzed, and finally the fitting formula of the dynamic compressive strength and the crushing energy consumption in the dynamic point load test is obtained through fitting analysis. The results show that when the crushing energy consumption in the dynamic point load test is used to estimate the dynamic compressive strength, a certain crushing energy consumption (the final value of this test is 70 J in this paper) is more accurate, and it can estimate the dynamic compressive strength under various corrosion conditions under this crushing energy consumption. However, with the change of crushing energy consumption in the dynamic point load test, this transformation relationship is no longer accurate, and it is necessary to modify this transformation relationship with corrosion condition as the influencing factor. This conclusion can provide reference for obtaining the dynamic compressive strength of rocks under different corrosion conditions in engineering field in the future. [ABSTRACT FROM AUTHOR]

Wearable sensors offer great potential in sports, fitness, and medicine. However, their limited battery life poses a major obstacle to their widespread use. This paper explores various energy solutions to extend the battery life of wearable sensors. The first part of the paper focuses on hardware improvements for wearable sensors, such as employing low-power sensors, energy- efficient microcontrollers, and power management circuits. The second part discusses the utilization of solar, thermal, and kinetic energy harvesting techniques to power wearable sensors. These methods aim to harness energy from the environment to supplement or replace battery power. The third part examines wireless power transfer techniques like radio frequency and magnetic resonance, which enable continuous power supply to wearable sensors without the need for physical connections. Software optimization is also highlighted as a crucial aspect in reducing energy consumption. This involves developing efficient algorithms for data processing and transmission, as well as implementing sleep and wake-up modes to minimize power usage during idle periods. Furthermore, the paper emphasizes the importance of user interaction and behavior in improving energy efficiency. It suggests the development of user-friendly interfaces and providing users with feedback on their energy usage. [ABSTRACT FROM AUTHOR]

Researchers are always looking for the improvement of existing methods. Today, CMOS technology is widely used, which has some advantages and disadvantages. One of the alternative methods for CMOS technology is QCA technology which compared to CMOS, has the advantages of low energy consumption and small occupied area. In this paper, by using the concepts and methods of QCA technology, a digital code converter is presented. In this converter, a new gate is used, which can produce outputs such as 4-input AND, 4-input OR, 4-input NAND, and 4-input NOR. The proposed converter has 10 inputs and 12 outputs. The 10 inputs are decimal numbers from 0 to 9, producing the output equivalent to excess-3, BCD, and gray codes. One of the advantages of this circuit is providing three different codes per input in just one circuit. In addition, due to the use of the new 4-input gate, the occupied area and the number of used cells were minimized. Simulations were performed by using QCADesigner-E version 2.2, and outcomes illustrated that the occupied area is equal to 0.29 μm2 and 380 QCA cells with 7 clock phases are used. The energy dissipation of the presented circuit is 171 meV. Also, given the favorable performance exhibited by the 4-input gate across various measurement parameters, it possesses the capability to be efficiently employed within larger and intricately designed circuits. [ABSTRACT FROM AUTHOR]

This paper assesses the transport system of Addis Ababa, Ethiopia, taking factors such as the number of vehicles, roadway width, speed of vehicles, longitudinal grade, and proportion of both fuel and electrical vehicles by dividing vehicles into seven classes, namely, car, minibus, small bus, coach, small truck, heavy truck, and truck trailer, to determine CO2 emission, CO emission fuel consumption, and electric consumption in addition to the percent to replace ICE vehicles. After selecting eight representative road sections in Addis Ababa city, input data was collected from both primary and secondary sources. Simulation of urban mobility (SUMO) is used to model the existing road transport system and two other scenarios, cases being 20% and 40% replacement of internal combustion engine vehicles by electric vehicles. Among the vehicle types studied under this paper, the SUMO results show that coaches are with the highest CO2 emission, releasing an average amount of 28.442 grams of CO2 every time step, while cars are with the lowest CO2 emission value of 6.542 grams. Minibuses are the top CO emitters, releasing an average of 0.420 grams of CO every time step, and truck trailers emit the smallest CO emission, 0.025 grams. Regarding electric consumption, the truck trailer is the vehicle type with the highest electric consumption, with a value of 2.282 kwh (watthour) consumption every time step, and cars are the least electricity-consuming vehicles, with a value of 0.151 kwh. The fourth point is fuel consumption; besides the high CO2 emission, coaches' consumption of fuel is leading by 8.946 grams, and cars use 2.087 grams of fuel every time step. Totally, public transport vehicles are responsible for higher emissions and huge fuel consumption. Therefore, if our transport system encourages the penetration of electric vehicles into the road transport system, a healthy and energy-efficient environment is reserved. Again, from a financial and environmental standpoint, the replacement of 40% of ICE vehicles by EVs enhances us with reduced costs and a green environment. [ABSTRACT FROM AUTHOR]

The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and fuel consumption. In this paper, in order to reduce fuel consumption, the number of trucks used is considered in the objective function along with the waste load and the travelling time. With the current computational capabilities, finding an optimal solution is challenging. Thus, this study aims to investigate the effect of well-known metaheuristic methods on this problem's objective function and computational times. The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright's algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. The findings here can be applied to improve operations in Saudi Arabia's waste management sector. [ABSTRACT FROM AUTHOR]

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks. [ABSTRACT FROM AUTHOR]

This paper studies the aircraft air circulation system and proposes a new architecture of the aircraft air circulation system. Combined with the neural network algorithm, the numerical simulation results of turbine, compressor, and heat exchanger are fitted and predicted, and the accuracy of the simulation model is verified by comparing with the experimental results. Comparing and analyzing the booster, F-15, F-35, and the newly proposed air circulation system, the engine bleed air volume consumed by the system under different flight conditions is obtained. The results show that the newly proposed air circulation system can save up to 71.4% of the engine bleed air compared with the simple boost and 29.8% of the engine bleed air compared with the F-35. The new air circulation system proposed in this paper can effectively improve the energy utilization rate of the system and reduce the consumption of engine bleed air. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research. [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]

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method. [ABSTRACT FROM AUTHOR]

This paper considers the hybrid flow shop scheduling problem with two types of sequence-dependent setup times, one depending on the order of jobs and the other depending on both the order of jobs and the machines where the jobs are allocated. Three constructive heuristic algorithms based on the NEH algorithm are developed with the object of minimizing the total energy consumption cost. The first algorithm, called EPRA, obtains the order of jobs using the energy consumption cost based on the processing time. The second algorithm, called ESRA, obtains the order of jobs using the energy consumption cost based on the setup time. The third algorithm, called ESPRA, takes advantage of both EPRA and ESRA by constructing fictitious jobs with ESRA and acquiring the sequence of fictitious jobs with EPRA. Due to the drawback of NEH algorithm for the FFS problem proposed in this paper, all three heuristic algorithms use EST rules to allocate the jobs to the machines. Two lower bounds are developed to evaluate the heuristic algorithms proposed in this paper. A computational experiment based on the split-plot design is developed to carry out the factor analysis, algorithm analysis, lower bounds analysis, and CPU running time analysis. The analysis reveals that all main factor interactions except the algorithm are significant; the ESPRA algorithm is generally outstanding among the three algorithms; LB1 is better than LB2; the CPU running time of ESPRA algorithm is significantly reduced compared to those of EPRA and ESST algorithms. [ABSTRACT FROM AUTHOR]

This paper uses SketchUp and OpenStudio to design and optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. By establishing a basic model, the performance parameters such as insulation thickness, typical outer window, window-to-wall ratio, and their contribution rates within the threshold range are optimized for energy conservation; simulate the energy consumption of the determined multiperformance parameter combination scheme, select the most energy-saving scheme, and calculate its energy-saving contribution rate; calculate the life cycle cost of the above schemes, select the scheme with the lowest cost, and analyze its economic benefits; select energy-saving cost and economic benefit, and calculate energy-saving contribution rate and economic benefit. Finally, through the comparative analysis of the design and test data, this paper concludes that EnergyPlus will optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. At the same time, the multiobjective optimization method and process of the passive energy-saving technology of the near-zero energy consumption building proposed in this paper, as well as the technology combination template under different needs, provide a reference for the selection of the technology path. [ABSTRACT FROM AUTHOR]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices. [ABSTRACT FROM AUTHOR]

The high consumption of energy resources but low utilization, low factor allocation, and unreasonable economic structure affect the sustainability of economic development. Based on the energy factor theory from a low-carbon perspective, this paper constructs an impact model of green total point productivity and conducts a detailed analysis of the resource mismatch in the energy industry. Under the premise of efficient resource allocation, the factor input of an enterprise is positively correlated with total point productivity. Second, the model adds environmental constraints, considers the undesired output of carbon dioxide, and considers the input of energy factors, and reduces the undesired output as much as possible while increasing the expected output. The model realizes the update estimation of each component of the stationary part of a class of nonstationary stochastic processes and can use the information at a certain time and its previous time in a cycle to forecast all subsequent times. In the simulation process, in order to make the results more objective and accurate, this paper calculates the index and its decomposition results under two conditions, and uses MaxDEA 6.0 professional software to measure the regional industry total point productivity index. The experimental results show that the effective factor of low-carbon data is 0.91, and the relative error is less than 3.11%, which meets the test threshold and effectively improves the robustness of the model. [ABSTRACT FROM AUTHOR]

With the improvement of the living standards of the Chinese people, the construction of the economic system will change from one that meets the needs of the people to one that is perfect. Under the current environment of building a resource-saving socialist harmonious society, it is very necessary for architectural design practitioners to return to the original architectural design and pay attention to the design and use of building materials during construction. The purpose of this paper is to explore and analyze the application of energy saving and environmentally friendly materials in architectural design. This paper firstly analyzes the building's energy consumption and building's energy efficiency. Every major achievement in building energy efficiency will be an important means of mitigating the Earth's energy. Building energy saving has become a major trend of global development, and then the optimal design of reinforcement selection for reinforced concrete beams based on a multiobjective genetic algorithm is discussed. The area of reinforcement required for the member control section is obtained according to the structural analysis program, the main purpose of reinforcement selection is to select safe, economical, and reasonable reinforcement on the premise of meeting the provisions of the code and the requirements of reinforcement structure. Finally, this paper conducts experimental research on the application of phase change energy storage building material SP28 in building energy conservation. The experimental results in this paper show that when the mass ratio of the phase change energy storage material in the wall is less than 15%, there is no wetting and leakage phenomenon. However, when the mass ratio of the phase change energy storage material contained in the wall is greater than 15%, the phenomenon of wetting and leakage will occur, which will cause greater losses when added to the wall. Therefore, it is not suitable for application and building walls. Regardless of leakage, wetting, or economic considerations, the mass ratio of phase change material of 10% is the most suitable for use in walls. It not only has no wetting and no leakage but also has a good energy storage effect and can effectively adjust the ambient temperature. [ABSTRACT FROM AUTHOR]

With the popularity of 5G mobile communication services, the number of users has increased dramatically, as well as the nonuniformity of user density distribution; many users are in nonideal channel conditions, so unmanned aerial vehicle (UAV) as a passive relay equipment platform has gradually entered people's vision. Intelligent reflective surfaces (IRSs) capable of reconfiguring electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we start from the point of minimizing the energy consumption and nodes of UAV passive relay; the paper proposes to install intelligent reflecting surface (IRS) on UAV as a new passive relay and establish the communication coverage model of UAV-IRS. Then, the main relationship between the coverage radius and hover height of UAV-IRS is verified. In view of different distribution densities of target users, a CK++ (cyclic k -means++) is proposed to solve the spatial deployment problem of UAV-IRS, where the optimal solution of the system is obtained through cyclic clustering. And the algorithm is verified to effectively improve the performance of urban mobile communication and user communication service quality through numerical stimulation. [ABSTRACT FROM AUTHOR]

In order to explore the connotation and construction path of the principle of good faith from the perspective of civil and commercial law, this paper combines intelligent algorithms to analyze the connotation and construction path of the principle of good faith from the perspective of civil and commercial law. Moreover, this paper presents a topology network specially used to improve the energy absorption efficiency of the isolation resistor used in the power divider and theoretically analyzes the effectiveness of the isolation topology. In addition, this paper further optimizes the topology of the two-way broadband filter power divider and builds a simulation model based on the basic content of the principle of good faith. From the research results, the simulation model proposed in this paper can play a certain role in the connotation and construction path of the principle of good faith from the perspective of civil and commercial law. On this basis, some suggestions on the connotation and construction path of the principle of good faith are put forward. [ABSTRACT FROM AUTHOR]

Traditional historical buildings carry the culture and spirit of China for thousands of years and have a unique charm that modern buildings do not have. However, traditional historical buildings have gradually declined over time, and their restoration not only takes time and materials but also causes certain harm to the environment. Therefore, this paper has aimed to explore how traditional historical buildings can develop in a sustainable and ecologically adaptive image in modern times. For traditional historical buildings, this paper has taken traditional cave dwellings in northern Shaanxi as an example to analyze their ecological adaptability, and based on the analysis results, traditional cave dwellings have been transformed. Specifically, this paper has taken the comfort of residential houses as the evaluation standard and has selected the thermal stability of the building and the energy consumption of heat supply as indicators to compare and analyze the traditional cave dwellings and the renovated cave dwellings. The experimental results of this paper have found that the traditional historical buildings are the crystallization of the wisdom of the ancients for thousands of years. However, due to the limitation of technology, the lighting and thermal stability of traditional cave dwellings are not high. After scientific design, the lighting and thermal stability of the transformed cave are 100% and 30% higher than those of the traditional cave, respectively, and the heating energy consumption is reduced by 50%. [ABSTRACT FROM AUTHOR]

Energy-saving driving and regenerative braking energy utilization are two main ways to realize energy-saving optimization of urban rail transit train timetables. On the basis of the more mature energy-saving driving achievements of the predecessors, the absorption utilization rate of regenerative braking energy is improved by adjusting the dwell time of trains in the station so that while a train is braking, other trains in the same electric section are just under traction. In this paper, the overlap time between the traction and braking processes of different trains is used as a measure of the proportion of regenerative braking energy that is absorbed. In order to maximize this overlap time, an energy-saving optimization model of urban rail transit train timetable based on regenerative braking technology was established. To facilitate the solution, the nonlinear constraints are converted to linear at the time of model construction in this paper. In the solution, the spatio-temporal local rolling algorithm and the commercial optimization software ILOG CPLEX are used for the solution. The solution results show that the method in this paper can effectively improve the absorption and utilization of regenerative braking energy. [ABSTRACT FROM AUTHOR]

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission. [ABSTRACT FROM AUTHOR]

To increase the fuel efficiency of fuel cells, lengthen their useful lives, and fulfil the demands for high energy and high power during the operation of hybrid electric vehicles. This paper's goal is to thoroughly examine the distributed parameter model-based multienergy management technique of fuel cell hybrid electric vehicles. Firstly, the simplified model of a hybrid system is constructed according to the distributed parameter model, and the fuel cell model, unidirectional DC/DC converter, and battery are described in detail. The multienergy management of hybrid electric vehicles based on improved deep Q-learning is adopted, the multienergy management strategy based on deep Q-learning is designed, so as to reduce fuel consumption and improve the working efficiency of fuel cells, optimize the energy distribution of lithium batteries and fuel cells, and adopt the experience playback mechanism of summation tree structure in the process of strategy training to complete the multienergy management of hybrid electric vehicles. The strategy described in this study can successfully increase the overall power performance of fuel cell hybrid vehicles, extend the battery's service life, and increase fuel economy, according to simulation results, which has a certain practical value. [ABSTRACT FROM AUTHOR]

Summary: The internet of things (IoT) is a distributed heterogeneous network of lightweight nodes with very minimal power and storage. The IoT energy system for smart applications such as smart grid, smart building, and smart transportations depends on the IoT architecture, determining the high or low‐energy consumption levels. Most of the IoT objects are power‐driven by batteries with short life spans that require replacement. The replacement phase is tedious; hence this paper comprehensively discussed the IoT energy system, energy resources, and energy storage as these three elements are crucial to enable energy efficiency for the IoT applications. In comparison to the battery‐powered solutions, the scavenging of infinite quantities of energy makes the IoT systems robust. Therefore, this paper further elaborates on understanding the current situation in terms of renewable energy harvesting for these low‐energy systems. The IoT energy storage highlighted in this paper includes fuel cell, lithium battery, and supercapacitor technology. This paper also provides the findings for IoT energy system challenges and open issues in management and storage in terms of bidirectional, continuity, autonomy, fluctuation, conversion, consumption, integration, multifunction, and stability. This paper will assist researchers in more quality yet practical energy usage and savings, better IoT system architecture, and smart application sustainability. [ABSTRACT FROM AUTHOR]

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare. [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]