This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The ba...This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.展开更多
To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat...To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat-maize cropping systems,a three-year field experiment was designed to quantify the carbon footprint(CF)and energy efficiency of the cropping systems in the North China Plain.The study parameters included four tillage practices(no tillage(NT),conventional tillage(CT),rotary tillage(RT),and subsoiling rotary tillage(SRT))and two fertilizer regimes(inorganic fertilizer(IF)and hybrid fertilizer with organic and inorganic components(HF)).The results indicated that the most prominent energy inputs and greenhouse gas(GHG)emissions could be ascribed to the use of fertilizers and fuel consumption.Under the same fertilization regime,ranking the tillage patterns with respect to the value of the crop yield,profit,CF,energy use efficiency(EUE)or energy productivity(EP)for either wheat or maize always gave the same sequence of SRT>RT>CT>NT.For the same tillage,the energy consumption associated with HF was higher than IF,but its GHG emissions and CF were lower while the yield and profit were higher.In terms of overall performance,tilling is more beneficial than NT,and reduced tillage practices(RT and SRT)are more beneficial than CT.The fertilization regime with the best overall performance was HF.Combining SRT with HF has significant potential for reducing CF and increasing EUE,thereby improving sustainability.Adopting measures that promote these optimizations can help to overcome the challenges posed by a lack of food security,energy crises and ecological stress.展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
This study analyzes the energy impact of applying green roofs on flat roofs of existing buildings,assessing their potential to reduce the demand for non-renewable primary energy for heating and cooling.Through dynamic...This study analyzes the energy impact of applying green roofs on flat roofs of existing buildings,assessing their potential to reduce the demand for non-renewable primary energy for heating and cooling.Through dynamic numerical simulations conducted on two real buildings located near Florence,Italy,and modeled in 130 different European locations,with a particular focus on the Mediterranean climate,it was possible to quantify the energy benefits derived from the application of green roofs on existing structures.The results show that,while the effect on heating is limited,with an average reduction in energy demand of only a few percentage points,the impact on cooling is significantly more pronounced,with average savings of 20%in non-renewable primary energy,particularly in Mediterranean climates with high CDD(cooling degree days)values.The study confirms that green roofs can be an effective solution to improve the energy efficiency of existing buildings with flat roofs in the Mediterranean climate,in line with European goals for reducing CO_(2) emissions and promoting renewable energy.展开更多
Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitat...Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters.Efficient network traffic management remains a challenge,with existing approaches often resulting in increased delay and reduced throughput.Additionally,insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios.Several routing strategies and procedures have been developed to effectively reduce communication-related energy consumption based on the selection of relay nodes.The relay node selection is essential for data transmission in WBAN.This paper introduces an Adaptive Relay-Assisted Protocol(ARAP)for WBAN,a hybrid routing protocol designed to optimize energy use and Quality of Service(QoS)metrics such as network longevity,latency,throughput,and residual energy.ARAP employs neutrosophic relay node selection techniques,including the Analytic Hierarchy Process(AHP)and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)to optimally resolve data and decision-making uncertainties.The protocol was compared with existing protocols such as Low-Energy Adaptive Clustering Hierarchy(LEACH),Modified-Adaptive Threshold Testing and Evaluation Methodology for Performance Testing(M-ATTEMPT),Wireless Adaptive Sampling Protocol(WASP),and Tree-Based Multicast Quality of Service(TMQoS).The comparative results show that the ARAP significantly outperformed these protocols in terms of network longevity and energy efficiency.ARAP has lower communication cost,better throughput,reduced delay,increased network lifetime,and enhanced residual energy.The simulation results indicate that the proposed approach performed better than the conventional methods,with 68%,62%,25%,and 50%improvements in network longevity,residual energy,throughput,and latency,respectively.This significantly improves the functional lifespan of WBAN and makes them promising candidates for sophisticated health monitoring systems.展开更多
The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhan...The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment(PME)is inactive or served by a Primary Base Station(PrBS).Secondary Mobile Equipment(SME)accesses this spectrum through a Secondary Base Station(SrBS)using opportunistic access,i.e.,spectrum sensing.Hybrid Multiple Access(HMA),combining Orthogonal Multiple Access(OMA)and Non-Orthogonal Multiple Access(NOMA),can enhance Energy Efficiency(EE).Additionally,SME Clustering(SMEC)reduces inter-cluster interference,enhancing EE further.Despite these advancements,the integration of CR technology,HMA,and SMEC in CRN for better bandwidth utilization and EE remains unexplored.This paper introduces a new CRassisted SMEC-based Downlink HMA(CR-SMEC-DHMA)method for 6G CRN,aimed at jointly optimizing SME admission,SME association,sum rate,and EE subject to imperfect sensing,collision,and Quality of Service(QoS).A novel optimization problem,formulated as a non-linear fractional programming problem,is solved using the Charnes-Cooper Transformation(CCT)to convert into a concave optimization problem,and an ε-optimal Outer Approximation Algorithm(OAA)is employed to solve the concave optimization problem.Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA,surpassing the performance of current OMAenabled CRN,NOMA-enabled CRN,SMEC-OMA enabled CRN,and SMEC-NOMA enabled CRN methods,with ε-optimal results obtained at ε=10^(−3),while satisfying Performance Measures(PMs)including SME admission in SMEC,SME association with SrBS,SME-channel opportunistic allocation through spectrum sensing,sum rate and overall EE within the 6G CRN.展开更多
The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serv...The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.展开更多
The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-ran...The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
Refrigeration systems are essential across various sectors,including food preservation,medical storage,and climate control.However,their high energy consumption and environmental impact necessitate innovative solution...Refrigeration systems are essential across various sectors,including food preservation,medical storage,and climate control.However,their high energy consumption and environmental impact necessitate innovative solutions to enhance efficiency while minimizing energy usage.This paper investigates the integration of Phase Change Materials(PCMs)into a vapor compression refrigeration system to enhance energy efficiency and temperature regulation for food preservation.A multifunctional prototype was tested under two configurations:(1)a standard thermally insulated room,and(2)the same room augmented with eutectic plates filled with either Glaceol(-10℃ melting point)or distilled water(0℃ melting point).Thermocouples were calibrated and deployed to record air and PCM temperatures during freeze–thaw cycles at thermostat setpoints of and Additionally,a-30℃ -35℃ .defrosting resistor and timer were added to mitigate frost buildup,a known cause of efficiency loss.The experimental results show that PCM-enhanced rooms achieved up to 10.98℃ greater temperature stability during defrost cycles and reduced energy consumption by as much as 7.76%(from 0.4584 to 0.4231 kWh/h).Moreover,the effectiveness of PCMs depended strongly on thermostat settings and PCM type,with distilled water demonstrating broader solidification across plates under higher ambient loads.These findings highlight the potential of PCM integration to improve cold-chain performance,offering rapid cooling,moisture retention,and extended product conservation during power interruptions.展开更多
In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for peo...In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for people’s production and life,require high attention from industry insiders in terms of their energy efficiency testing.Relying on energy efficiency testing can achieve the goal of energy conservation and emission reduction,and related quality control technologies will also inject new momentum into the green development of the industry.This article will discuss the practical strategies of quality control technology for energy efficiency testing of electronic and electrical products based on the significance of such testing,hoping to provide some help.展开更多
The utilization of ironsand for preparing oxidized pellets poses challenges,including slow oxidation and low consolidation strength.The effects and function mechanisms of high-pressure grinding roll(HPGR)pretreatment ...The utilization of ironsand for preparing oxidized pellets poses challenges,including slow oxidation and low consolidation strength.The effects and function mechanisms of high-pressure grinding roll(HPGR)pretreatment on the oxidation and consolidation of ironsand pellets were investigated,and the energy utilization efficiency of HPGR with different roller pressure intensities was evaluated.The results indicate that HPGR pretreatment at 8 MPa improves the ironsand properties,with the specific surface area increasing by 740 cm^(2) g^(-1) and mechanical energy storage increasing by 2.5 kJ mol^(-1),which is conducive to oxidation and crystalline connection of particles.As roller pressure intensity increases to 16 MPa,more mechanical energy of HPGR is applied for crystal activation,with mechanical energy storage further rising by 18.1 kJ mol^(-1).The apparent activation energy for pellet oxidation initially decreases and then increases,reaching a minimum at 12 MPa.Simultaneously,the roasted pellets porosity decreases by 2.8%,while the compressive strength increases by 789 N.At higher roller pressure intensity,the densely connected structure between particles impedes gas diffusion within the pellets,diminishing the beneficial effects of HPGR on pellet oxidation.Moreover,excessive roller pressure intensity decreases the HPGR energy utilization efficiency.The optimal HPGR roller pressure intensity for ironsand is 12 MPa,at which the specific surface area increases by 790 cm^(2) g^(-1),mechanical energy storage increases by 10.6 kJ mol^(-1),the compressive strength of roasted pellets rises to 2816 N,and the appropriate preheating and roasting temperatures decrease by 250 and 125°C,respectively.展开更多
Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an in...Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.展开更多
Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV...Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV-aided BC system,where the power beacons(PBs)are deployed as dedicated radio frequency(RF)sources to supply power for backscatter devices(BDs).After harvesting enough energy,the BDs transmit data to the UAV.We use stochastic geometry to model the large-scale BC sys-tem.Specifically,the PBs are modeled as a type II Mat´ern hard-core point process(MHCPP II)and the BDs are modeled as a homogeneous Poisson point process(HPPP).Firstly,the BDs’activation proba-bility and average coverage probability are derived.Then,to maximize the energy efficiency(EE),we opti-mize the RF power of the PBs under different PB den-sities.Furthermore,we compare the coverage proba-bility and EE performance of our system with a bench-mark scheme,in which the distribution of PBs is mod-eled as a HPPP.Simulation results show that the PBs modeled as MHCPP II has better performance,and we found that the higher the density of PBs,the smaller the RF power required,and the EE is also higher.展开更多
In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories an...In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various workshops.Therefore,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA method.Firstly,an in-depth analysis of the relationships between different energy efficiency indicators was conducted.Based on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator system.Secondly,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation methods.Subsequently,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each workshop.Using a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each workshop.Furthermore,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation model.This model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.展开更多
Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniqu...Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.展开更多
In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assiste...In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.展开更多
Ultra-low emission of nitrogen oxide(NO_(x))is an irreversible trend for the development of waste-to-energy industry.But traditional approaches to remove NO_(x) face significant challenge s,such as low denitration eff...Ultra-low emission of nitrogen oxide(NO_(x))is an irreversible trend for the development of waste-to-energy industry.But traditional approaches to remove NO_(x) face significant challenge s,such as low denitration efficiency,complex denitration system,and high investment and operating cost.Here we put forward a novel polymer non-catalytic reduction(PNCR)technology that utilized a new type of polymer agent to remove NO_(x),and the proposed PNCR technology was applied to the existing waste-to-energy plant to test the denitration performance.The PNCR technology demonstrated excellent denitration performance with a NO_(x) emission concentration of<100 mg/Nm^(3) and high denitration efficiency of>75%at the temperature range of 800-900℃,which showed the application feasibility even on the complex and unstable industrial operating conditions.In addition,PNCR and hybrid polymer/selective non-catalytic reduction(PNCR/SNCR)technology possessed remarkable economic advantages including low investment fee and low operating cost of<10 CNY per ton of municipal solid waste(MSW)compared with selective catalytic reduction(SCR)technology.The excellent denitration performance of PNCR technology forebodes a broad industrial application prospect in the field of flue gas cleaning for waste-to-energy plants.展开更多
A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable ci...A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable cities.This is particularly true in Africa,where there aren’t many studies on the topic.The current study suggests a 90 m^(2) model of a sustainable building in a dry climate that is movable to address the issue of housing in remote areas,ensures comfort in harsh weather conditions,uses solar renewable resources—which are plentiful in Africa—uses biosourced materials,and examines how these materials relate to temperature and humidity control while emitting minimal carbon emissions.In order to solve the topic under consideration,the work is split into two sections:numerical and experimental approaches.Using TRNSYS and Revit,the suggested prototype building is examined numerically to examine the impact of orientation,envelope composition made of bio-sourced materials,and carbon emissions.Through a hygrothermal investigation,experiments are conducted to evaluate this prototype’s effectiveness.Furthermore,an examination of the photovoltaic system’s production,consumption,and several scenarios used tomaximize battery life is included in the paper.Because the biosourcedmaterial achieves a thermal transmittance of 0.15(W.m^(-2).K^(-1)),the results demonstrate an intriguing finding in terms of comfort.This value satisfies the requirements of passive building,energy autonomy of the dwelling,and injection in-network with an annual value of 15,757 kWh.Additionally,compared to the literature,the heating needs ratio is 6.38(kWh/m^(2).an)and the cooling needs ratio is 49(kWh/m^(2).an),both of which are good values.According to international norms,the inside temperature doesn’t go above 26℃,and the humidity level is within a comfortable range.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.U23A20279,62271094)the National Key R&D Program of China(No.SQ2023YFB2500024)+2 种基金the Science Foundation for Youths of Natural Science Foundation of Sichuan Provincial,China(No.2022NSFSC0936)the China Postdoctoral Science Foundation(No.2022M720666)the Open Fund of Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,China(No.BDIC-2023-B-002).
文摘This work focuses on maximizing the minimum user’s security energy efficiency(SEE)in an unmanned aerial vehicle-mounted reconfigurable intelligent surface(UAV-RIS)enhanced short-packet communication(SPC)system.The base station(BS)provides short packet services to ground users using the non-orthogonal multiple access(NOMA)protocol through UAV-RIS,while preventing eavesdropper attacks.To optimize SEE,a joint optimization is performed concerning power allocation,UAV position,decoding order,and RIS phase shifts.An iterative algorithm based on block coordinate descent is proposed for mixed-integer non-convex SEE optimization problem.The original problem is decomposed into three sub-problems,solved alternately using successive convex approximation(SCA),quadratic transformation,penalty function,and semi-definite programming(SDP).Simulation results demonstrate the performance of the UAV-RIS-enhanced short-packet system under different parameters and verify the algorithm’s convergence.Compared to benchmark schemes such as orthogonal multiple access,long packet communication,and sum SEE,the proposed UAV-RIS-enhanced short-packet scheme achieves the higher minimum user’s SEE.
基金supported by research grants from the Natural Science Foundation of Shandong Province,China(ZR2020MC092)the Key Research and Development Project of Shandong Province,China(2019TSCYCX-33)the Key Research and Development Project of Shandong Province,China(LJNY202025).
文摘To make agricultural systems sustainable in terms of their greenness and efficiency,optimizing the tillage and fertilization practices is essential.To assess the effects of tilling and fertilization practices in wheat-maize cropping systems,a three-year field experiment was designed to quantify the carbon footprint(CF)and energy efficiency of the cropping systems in the North China Plain.The study parameters included four tillage practices(no tillage(NT),conventional tillage(CT),rotary tillage(RT),and subsoiling rotary tillage(SRT))and two fertilizer regimes(inorganic fertilizer(IF)and hybrid fertilizer with organic and inorganic components(HF)).The results indicated that the most prominent energy inputs and greenhouse gas(GHG)emissions could be ascribed to the use of fertilizers and fuel consumption.Under the same fertilization regime,ranking the tillage patterns with respect to the value of the crop yield,profit,CF,energy use efficiency(EUE)or energy productivity(EP)for either wheat or maize always gave the same sequence of SRT>RT>CT>NT.For the same tillage,the energy consumption associated with HF was higher than IF,but its GHG emissions and CF were lower while the yield and profit were higher.In terms of overall performance,tilling is more beneficial than NT,and reduced tillage practices(RT and SRT)are more beneficial than CT.The fertilization regime with the best overall performance was HF.Combining SRT with HF has significant potential for reducing CF and increasing EUE,thereby improving sustainability.Adopting measures that promote these optimizations can help to overcome the challenges posed by a lack of food security,energy crises and ecological stress.
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
文摘This study analyzes the energy impact of applying green roofs on flat roofs of existing buildings,assessing their potential to reduce the demand for non-renewable primary energy for heating and cooling.Through dynamic numerical simulations conducted on two real buildings located near Florence,Italy,and modeled in 130 different European locations,with a particular focus on the Mediterranean climate,it was possible to quantify the energy benefits derived from the application of green roofs on existing structures.The results show that,while the effect on heating is limited,with an average reduction in energy demand of only a few percentage points,the impact on cooling is significantly more pronounced,with average savings of 20%in non-renewable primary energy,particularly in Mediterranean climates with high CDD(cooling degree days)values.The study confirms that green roofs can be an effective solution to improve the energy efficiency of existing buildings with flat roofs in the Mediterranean climate,in line with European goals for reducing CO_(2) emissions and promoting renewable energy.
文摘Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters.Efficient network traffic management remains a challenge,with existing approaches often resulting in increased delay and reduced throughput.Additionally,insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios.Several routing strategies and procedures have been developed to effectively reduce communication-related energy consumption based on the selection of relay nodes.The relay node selection is essential for data transmission in WBAN.This paper introduces an Adaptive Relay-Assisted Protocol(ARAP)for WBAN,a hybrid routing protocol designed to optimize energy use and Quality of Service(QoS)metrics such as network longevity,latency,throughput,and residual energy.ARAP employs neutrosophic relay node selection techniques,including the Analytic Hierarchy Process(AHP)and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)to optimally resolve data and decision-making uncertainties.The protocol was compared with existing protocols such as Low-Energy Adaptive Clustering Hierarchy(LEACH),Modified-Adaptive Threshold Testing and Evaluation Methodology for Performance Testing(M-ATTEMPT),Wireless Adaptive Sampling Protocol(WASP),and Tree-Based Multicast Quality of Service(TMQoS).The comparative results show that the ARAP significantly outperformed these protocols in terms of network longevity and energy efficiency.ARAP has lower communication cost,better throughput,reduced delay,increased network lifetime,and enhanced residual energy.The simulation results indicate that the proposed approach performed better than the conventional methods,with 68%,62%,25%,and 50%improvements in network longevity,residual energy,throughput,and latency,respectively.This significantly improves the functional lifespan of WBAN and makes them promising candidates for sophisticated health monitoring systems.
文摘The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation.Sixth-Generation(6G)networks,utilizing Cognitive Radio(CR)technology within CR Network(CRN),can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment(PME)is inactive or served by a Primary Base Station(PrBS).Secondary Mobile Equipment(SME)accesses this spectrum through a Secondary Base Station(SrBS)using opportunistic access,i.e.,spectrum sensing.Hybrid Multiple Access(HMA),combining Orthogonal Multiple Access(OMA)and Non-Orthogonal Multiple Access(NOMA),can enhance Energy Efficiency(EE).Additionally,SME Clustering(SMEC)reduces inter-cluster interference,enhancing EE further.Despite these advancements,the integration of CR technology,HMA,and SMEC in CRN for better bandwidth utilization and EE remains unexplored.This paper introduces a new CRassisted SMEC-based Downlink HMA(CR-SMEC-DHMA)method for 6G CRN,aimed at jointly optimizing SME admission,SME association,sum rate,and EE subject to imperfect sensing,collision,and Quality of Service(QoS).A novel optimization problem,formulated as a non-linear fractional programming problem,is solved using the Charnes-Cooper Transformation(CCT)to convert into a concave optimization problem,and an ε-optimal Outer Approximation Algorithm(OAA)is employed to solve the concave optimization problem.Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA,surpassing the performance of current OMAenabled CRN,NOMA-enabled CRN,SMEC-OMA enabled CRN,and SMEC-NOMA enabled CRN methods,with ε-optimal results obtained at ε=10^(−3),while satisfying Performance Measures(PMs)including SME admission in SMEC,SME association with SrBS,SME-channel opportunistic allocation through spectrum sensing,sum rate and overall EE within the 6G CRN.
基金Supported by the Self-funded Research Project of Beijing FibrLink Communications Co.Ltd.“Research on Key Technologies forUnifiedManagement of Air-to-Earth Integrated CommunicationNetworks(546826230034).”。
文摘The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.
基金funded by King Saud University Researchers Supporting Project Number(RSPD2025R1007),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
基金supported in entire part by the Biomaterials and Transport Phenomena Laboratory Agreement No.30303-12-2003,at the University of Medea.
文摘Refrigeration systems are essential across various sectors,including food preservation,medical storage,and climate control.However,their high energy consumption and environmental impact necessitate innovative solutions to enhance efficiency while minimizing energy usage.This paper investigates the integration of Phase Change Materials(PCMs)into a vapor compression refrigeration system to enhance energy efficiency and temperature regulation for food preservation.A multifunctional prototype was tested under two configurations:(1)a standard thermally insulated room,and(2)the same room augmented with eutectic plates filled with either Glaceol(-10℃ melting point)or distilled water(0℃ melting point).Thermocouples were calibrated and deployed to record air and PCM temperatures during freeze–thaw cycles at thermostat setpoints of and Additionally,a-30℃ -35℃ .defrosting resistor and timer were added to mitigate frost buildup,a known cause of efficiency loss.The experimental results show that PCM-enhanced rooms achieved up to 10.98℃ greater temperature stability during defrost cycles and reduced energy consumption by as much as 7.76%(from 0.4584 to 0.4231 kWh/h).Moreover,the effectiveness of PCMs depended strongly on thermostat settings and PCM type,with distilled water demonstrating broader solidification across plates under higher ambient loads.These findings highlight the potential of PCM integration to improve cold-chain performance,offering rapid cooling,moisture retention,and extended product conservation during power interruptions.
文摘In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for people’s production and life,require high attention from industry insiders in terms of their energy efficiency testing.Relying on energy efficiency testing can achieve the goal of energy conservation and emission reduction,and related quality control technologies will also inject new momentum into the green development of the industry.This article will discuss the practical strategies of quality control technology for energy efficiency testing of electronic and electrical products based on the significance of such testing,hoping to provide some help.
基金financially supported by the General Program of National Natural Science Foundation of China(No.52174330)Hunan Provincial Innovation Foundation for Postgraduate(No.QL20220069)Postgraduate Innovative Project of Central South University(No.1053320214756).
文摘The utilization of ironsand for preparing oxidized pellets poses challenges,including slow oxidation and low consolidation strength.The effects and function mechanisms of high-pressure grinding roll(HPGR)pretreatment on the oxidation and consolidation of ironsand pellets were investigated,and the energy utilization efficiency of HPGR with different roller pressure intensities was evaluated.The results indicate that HPGR pretreatment at 8 MPa improves the ironsand properties,with the specific surface area increasing by 740 cm^(2) g^(-1) and mechanical energy storage increasing by 2.5 kJ mol^(-1),which is conducive to oxidation and crystalline connection of particles.As roller pressure intensity increases to 16 MPa,more mechanical energy of HPGR is applied for crystal activation,with mechanical energy storage further rising by 18.1 kJ mol^(-1).The apparent activation energy for pellet oxidation initially decreases and then increases,reaching a minimum at 12 MPa.Simultaneously,the roasted pellets porosity decreases by 2.8%,while the compressive strength increases by 789 N.At higher roller pressure intensity,the densely connected structure between particles impedes gas diffusion within the pellets,diminishing the beneficial effects of HPGR on pellet oxidation.Moreover,excessive roller pressure intensity decreases the HPGR energy utilization efficiency.The optimal HPGR roller pressure intensity for ironsand is 12 MPa,at which the specific surface area increases by 790 cm^(2) g^(-1),mechanical energy storage increases by 10.6 kJ mol^(-1),the compressive strength of roasted pellets rises to 2816 N,and the appropriate preheating and roasting temperatures decrease by 250 and 125°C,respectively.
基金Funding Statement:This research was conducted as part of the Tech4You Project“Technologies for climate change adaptation and quality of life improvement”,n.ECS0000009,CUP H23C22000370006,Italian PNRR,Mission 4,Component 2,Investment 1.5 funded by the European Union-NextGenerationEU.
文摘Indoor air quality(IAQ)is often overlooked,yet a poorly maintained environment can lead to significant health issues and reduced concentration and productivity in work or educational settings.This study presents an innovative control system for mechanical ventilation specifically designed for university classrooms,with the dual goal of enhancing IAQ and increasing energy efficiency.Two classrooms with distinct construction characteristics were analyzed:one with exterior walls and windows,and the other completely underground.For each classroom,a model was developed using DesignBuilder software,which was calibrated with experimental data regarding CO_(2) concentration,temperature,and relative humidity levels.The proposed ventilation system operates based on CO_(2) concentration,relative humidity,and potential for free heating and cooling.In addition,the analysis was conducted for other locations,demonstrating consistent energy savings across different climates and environments,always showing an annual reduction in energy consumption.Results demonstrate that mechanical ventilation,when integrated with heat recovery and free cooling strategies,significantly reduces energy consumption by up to 25%,while also maintaining optimal CO_(2) levels to enhance comfort and air quality.These findings emphasize the essential need for well-designed mechanical ventilation systems to ensure both psychophysical well-being and IAQ in enclosed spaces,particularly in environments intended for extended occupancy,such as classrooms.Furthermore,this approach has broad applicability,as it could be adapted to various building types,thereby contributing to sustainable energy management practices and promoting healthier indoor spaces.This study serves as a model for future designs aiming to balance energy efficiency with indoor air quality,especially relevant in the post-COVID era,where the importance of indoor air quality has become more widely recognized.
文摘Backscatter communication(BC)is con-sidered a key technology in self-sustainable commu-nications,and the unmanned aerial vehicle(UAV)as a data collector can improve the efficiency of data col-lection.We consider a UAV-aided BC system,where the power beacons(PBs)are deployed as dedicated radio frequency(RF)sources to supply power for backscatter devices(BDs).After harvesting enough energy,the BDs transmit data to the UAV.We use stochastic geometry to model the large-scale BC sys-tem.Specifically,the PBs are modeled as a type II Mat´ern hard-core point process(MHCPP II)and the BDs are modeled as a homogeneous Poisson point process(HPPP).Firstly,the BDs’activation proba-bility and average coverage probability are derived.Then,to maximize the energy efficiency(EE),we opti-mize the RF power of the PBs under different PB den-sities.Furthermore,we compare the coverage proba-bility and EE performance of our system with a bench-mark scheme,in which the distribution of PBs is mod-eled as a HPPP.Simulation results show that the PBs modeled as MHCPP II has better performance,and we found that the higher the density of PBs,the smaller the RF power required,and the EE is also higher.
基金funded by the National Social Science Fund of China(Grant No.23BGL234).
文摘In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various workshops.Therefore,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA method.Firstly,an in-depth analysis of the relationships between different energy efficiency indicators was conducted.Based on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator system.Secondly,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation methods.Subsequently,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each workshop.Using a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each workshop.Furthermore,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation model.This model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.
文摘Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.
基金supported in part by the National Natural Science Foundation of China under Grant 62071253,Grant 62371252 and Grant 62271268in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022800in part by the Jiangsu Provincial 333 Talent Project.
文摘In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.
基金supported by the National Natural Science Foundation of China(No.92367107)。
文摘Ultra-low emission of nitrogen oxide(NO_(x))is an irreversible trend for the development of waste-to-energy industry.But traditional approaches to remove NO_(x) face significant challenge s,such as low denitration efficiency,complex denitration system,and high investment and operating cost.Here we put forward a novel polymer non-catalytic reduction(PNCR)technology that utilized a new type of polymer agent to remove NO_(x),and the proposed PNCR technology was applied to the existing waste-to-energy plant to test the denitration performance.The PNCR technology demonstrated excellent denitration performance with a NO_(x) emission concentration of<100 mg/Nm^(3) and high denitration efficiency of>75%at the temperature range of 800-900℃,which showed the application feasibility even on the complex and unstable industrial operating conditions.In addition,PNCR and hybrid polymer/selective non-catalytic reduction(PNCR/SNCR)technology possessed remarkable economic advantages including low investment fee and low operating cost of<10 CNY per ton of municipal solid waste(MSW)compared with selective catalytic reduction(SCR)technology.The excellent denitration performance of PNCR technology forebodes a broad industrial application prospect in the field of flue gas cleaning for waste-to-energy plants.
文摘A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable cities.This is particularly true in Africa,where there aren’t many studies on the topic.The current study suggests a 90 m^(2) model of a sustainable building in a dry climate that is movable to address the issue of housing in remote areas,ensures comfort in harsh weather conditions,uses solar renewable resources—which are plentiful in Africa—uses biosourced materials,and examines how these materials relate to temperature and humidity control while emitting minimal carbon emissions.In order to solve the topic under consideration,the work is split into two sections:numerical and experimental approaches.Using TRNSYS and Revit,the suggested prototype building is examined numerically to examine the impact of orientation,envelope composition made of bio-sourced materials,and carbon emissions.Through a hygrothermal investigation,experiments are conducted to evaluate this prototype’s effectiveness.Furthermore,an examination of the photovoltaic system’s production,consumption,and several scenarios used tomaximize battery life is included in the paper.Because the biosourcedmaterial achieves a thermal transmittance of 0.15(W.m^(-2).K^(-1)),the results demonstrate an intriguing finding in terms of comfort.This value satisfies the requirements of passive building,energy autonomy of the dwelling,and injection in-network with an annual value of 15,757 kWh.Additionally,compared to the literature,the heating needs ratio is 6.38(kWh/m^(2).an)and the cooling needs ratio is 49(kWh/m^(2).an),both of which are good values.According to international norms,the inside temperature doesn’t go above 26℃,and the humidity level is within a comfortable range.
