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

New‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

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

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

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

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

Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

Microwave‐driven catalytic processes have attracted the attention of many researchers and industries as these processes are powerful strategies for use in industrial electrification. However, undetectable hot spots inside the catalyst bed can be misleading in terms of the deceptive effects of microwaves on acceleration of catalytic reactions because of the lack of physicochemical knowledge about these processes. In this paper, the hot spots temperatures were estimated based on catalytic ammonia (NH3) synthesis/decomposition equilibrium. The reaction temperature estimated from the chemical equilibrium was more than 100 °C higher than the catalyst bed surface temperature measured with an infrared thermometer under microwave irradiation. Assuming a formation of very steep local high temperature fields, rigorous kinetic analysis led to an estimate that the active site fraction which reaches the high temperature was only about 10 % of total sites. Compared with a resistivity heating furnace, the energy input was reduced by more than 85 % under microwave irradiation. The temperature estimated by the chemical equilibrium was almost corresponding to the temperature estimated by an oscillation of extended X‐ray absorption fine structure recorded under microwave irradiation. This paper presents a proposal for a method to estimate hot spots based on thermodynamic equilibrium, yielding a quantitative evaluation of energy consumption reduction caused by microwaves. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a wireline serial link transceiver for on‐package die‐to‐die links based on 12‐nm fin field‐effect transistor (FinFET) technology. The link is crucial for improving the computational performance of larger systems built from chiplets. To achieve high pin/energy efficiency and prevent traditional single‐ended signaling disadvantages, the transceiver adopts single‐ended ground‐reference signaling (GRS) to transfer information. To precompensate for the frequency‐dependent loss, retain the advantages of GRS, and avoid the disadvantages of traditional AC‐coupled equalizer, the transmitter in the transceiver adopts a DC‐coupled switched‐capacitor charge pump equalizer that can transmit GRS and its equilibrium value can be adjusted according to the channel attenuation. This paper proposes a method for timing de‐skew using digital control delay lines to eliminate the timing skew between data and the clock. In addition, this paper presents a duty cycle corrector to avoid duty cycle distortion, with an adjustment range of 44.5% to 52% and a power dissipation of only 30 μW. The transceiver has an area of 1.016 × 0.676 mm2, including one clock lane link and four data lane links. At a nominal 0.8‐V power supply voltage, the aggregate eye‐opening of the measured 25 Gb/s data on an organic substrate channel with an attenuation of −2.2 dB over 6 mm is 0.675 UI, with a bit error rate (BER) of less than 10−12 and an energy efficiency of 1.01 pJ/bit. [ABSTRACT FROM AUTHOR]

In this paper, we mainly revisit a weakly dissipative periodic two‐component b$$ b $$‐family system. Considering the dissipative effect, the local well‐posedness is first obtained for the system by applying the Kato's semigroup theory. We then utilize the characteristics line method to get one blow‐up criterion with the dispersive parameter k1∈(1,3]$$ {k}_1\in \left(1,3\right] $$. Finally, the other blow‐up criterion is derived with regard as considering the odevity of initial data by using energy method. When the parameters λ,ki(i=1,2,3)$$ \lambda, {k}_i\left(i=1,2,3\right) $$ belong to suitable range, our obtained results supplement the corresponding blow‐up criteria, which was derived by Liu and Yin (Nonlinear Analysis: Real World Applications 12 (2011) 3608–3620). [ABSTRACT FROM AUTHOR]

Summary: In this paper, an ultra‐low‐power and area‐efficient demodulator circuit for wireless downlink SCM/CWM modulation schemes is proposed. The proposed demodulator offers high performances and allows simultaneous power and downlink data transmission over a single 13.56‐MHz inductive link. The proposed method requires only two carrier cycles per data bit period, and also, a power index of 75% is obtained. Although the proposed demodulator can be applicable to other wireless systems, here, it is intended for implantable medical devices. To prove the performance of the proposed demodulator circuit, it has been designed and post‐layout simulations are provided using a standard 65‐nm CMOS technology and occupies 27 × 32 μm2 of active area. Post‐layout simulation results indicate that with a 1‐V power supply, the proposed circuit achieves an ultra‐low‐power consumption of 8.51 μW at a data rate of 6.78 Mb/s corresponding to an energy efficiency of 1.25 pJ/bit. [ABSTRACT FROM AUTHOR]

Summary: To enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system. [ABSTRACT FROM AUTHOR]

Summary Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

It is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real‐world network energy consumption. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

Grating couplers are widely used to couple waveguide modes with the far field. Their usefulness is determined not only by energy efficiency but also by additional supported functionality. In this paper, a plasmonic is demonstrated grating on a silicon nitride slab waveguide that couples both transverse electric (TE) and transverse magnetic (TM) waveguide modes with circularly polarized light in the far field. Specifically, it is experimentally confirmed that circularly polarized light excites TE and TM modes propagating in opposite directions, and the direction is controlled by the handedness. The routing efficiency for normally incident light reaches up to 95%. The same structure operates in the outcoupling regime as well, demonstrating up to 97% degree of circular polarization, where the handedness is determined by the polarization and propagation direction of outcoupled modes. The results pave the way for the realization of polarization‐division multiplexers and demultiplexers, integrated circular polarization emitters, as well as detectors of the polarization state of the incident optical field. [ABSTRACT FROM AUTHOR]

The rapid growth of global energy consumption and the increasing demand for sustainable and renewable energy sources have urged vast research into harnessing energy from various sources. Among them, the most promising approaches are nanogenerators (NGs) and solar cells (SCs), which independently offer innovative solutions for energy harvesting. This review paper presents a comprehensive analysis of the integration of NGs and SCs, exploring advanced hybrid structures and their diverse applications. First, an overview of the principles and working mechanisms of NGs and SCs is provided for seamless hybrid integrations. Then, various design strategies are discussed, such as piezoelectric and triboelectric NGs with different types of SCs. Finally, a wide range of applications are explored that benefit from the synergistic integration of NGs and SCs, including self‐powered electronics, wearable devices, environmental monitoring, and wireless sensor networks. The potential for these hybrid systems is highlighted to address real‐world energy needs and contribute to developing sustainable and self‐sufficient technologies. In conclusion, this review provides valuable insights into the state‐of‐the‐art developments in NGs and SCs integration, shedding light on advanced hybrid structures and their diverse applications. [ABSTRACT FROM AUTHOR]

Biomass‐derived carbon material has drawn significant attention recently due to its wide availability, environmentally free, and effective performance of the resulting porous carbons for supercapacitor (SC) applications. Carbon electrode material derived from biomass is used for energy storage (ES) because it has distinct qualities in porosity, a large specific surface area, and excellent conductivity. Furthermore, these materials' homogeneous, flawless biological structures can be used as models to create electrode materials with accurate geometries. The ES devices, known as SCs, also known as ultra‐capacitors, serve as a link between a capacitor and a battery. Due to their charge storage, SCs can produce a much higher density than batteries. Several factors, including the electrode's potential window, the electrode materials characteristics, and the electrolyte choice, have a major effect on SC performance. Therefore, all efforts have been made to develop SC electrode materials. This paper explains the different types of SCs and how they work. The various available biomass resources, as well as the methods for producing them, are outlined. In addition, the different types of electrode materials, activation methods, heteroatom functionalization, and electrolyte types are all thoroughly examined. The application and research advancement of biomass‐derived carbon used in SCs over the past 3 years are highlighted. Furthermore, this research outlines the benefits of SCs for the environment and the economy, as well as present challenges and future recommendations for advancing biomass‐derived carbon applications. This article aims to give an in‐depth knowledge of carbon‐based biomass materials that are used in SCs. [ABSTRACT FROM AUTHOR]

Harvesting the omnipresent mechanical energy emerges as an essential solution in combating environmental pollution and alleviating energy deficits. However, the ultra‐low frequencies and variable amplitudes of most ambient mechanical energy pose significant challenges to effective energy harvesting. To address this issue, this paper reports an innovative triboelectric nanogenerator based on pendulum‐rotation transmission mechanism (PR‐TENG) designed for efficacious harvesting of continuous low‐frequency mechanical energy. It comprises two synergistic units: a transmission unit and a power‐generating unit. The transmission unit is capable of converting external low‐frequency mechanical excitations into unidirectional rotation. Through an internal speed‐increasing mechanism, the rotational speed of the rotor can be further elevated, thereby enabling the power‐generating unit to produce a frequency‐multiplied output and markedly improving the energy transduction efficiency. The mechanism of triboelectrification employed by the power‐generating unit further reduces the total weight of the device, thus advancing its portability. In addition, the coherent coaxial configuration of the device's components significantly enhances compactness and optimizes spatial efficiency. As a practical power source, the PR‐TENG is shown to be capable of directly powering LEDs. Owing to its distinctive operational mechanism and structural innovation, the PR‐TENG is a promising method to efficiently harvest low‐frequency mechanical energy from the ambient environment. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

The use of additive manufacturing techniques in the development of aerospace components is gaining ground. These innovative methodologies facilitate the proposal of new designs for components with weight reduced features without compromising their mechanical properties. This results in lower fuel consumption and emissions. The present paper focuses on a metal replacement process in a UAV's vertical tail, using a Design for Additive Manufacturing (DfAM) strategy and making use of the lightweight, high‐strength engineering polymer known as carbon PA. By comparing the results achieved through numerical simulations conforming to certification standards between the metal and carbon PA vertical tail model, this work points out the possibility of decreasing the structural mass of the component by up to 48% while maintaining structural integrity. This reduction is achieved by matching materials, design concepts, and manufacturing capabilities. [ABSTRACT FROM AUTHOR]

In this paper, BiOBr nanosheets were synthesized through a novel hydrolysis precipitation method, which significantly reduced energy consumption and time cost compared to traditional hydrothermal synthesis methods. The subjected hydrothermal and high‐temperature calcination process promote the generation of surface oxygen vacancies, which inhibited the recombination of photogenerated electron hole pairs and improve the photocatalytic performance of BiOBr. Additionally, by adjusting the addition amount of KOH in the synthesis process, BiOBr can grow towards more complex crystal structure, which also provides a new path for the fabrication of 2D materials. [ABSTRACT FROM AUTHOR]

Summary Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary: Communicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%. [ABSTRACT FROM AUTHOR]

All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

An anisotropic, fluid‐saturated porous medium with local thermal nonequilibrium is the subject of this paper's analysis of the stability of the commencement of convection caused by internal heat generation. The classical Darcy's momentum equation is applied with the Brinkman model. To observe the thermal instabilities in the fluid layer, it is assumed that there is steady heat extraction through the bottom boundary and constant heat generation inside the fluid layer. The critical internal Darcy Rayleigh and associated wave numbers are computed using linear instability and energy approaches using the Chebyshev pseudospectral method to determine the existence of instability or otherwise. The fluid layer becomes unstratified as a result of heat generation inside the fluid, which encourages the occurrence of any subcritical instability. The critical Darcy–Rayleigh number has been found to be significantly influenced by a number of parameters, including the anisotropy parameter, internal heat parameter, porosity‐modified conductivity ratio, inter‐phase heat transfer parameter, and Darcy–Brinkman number. [ABSTRACT FROM AUTHOR]

In order to explore the impact of natural resource rent and corruption control on environmental quality (ecological footprint, carbon emissions), this paper uses the moment quantile regression method within the framework of the environmental Kuznets curve hypothesis. Based on the availability of data, the research object is positioned as the sample data of 152 countries from 2002 to 2018 to obtain more in‐depth and comprehensive research conclusions. The empirical results show that: (1) Corruption control has a significant effect on improving the ecological environment in countries and regions with poor environmental quality. The impact of natural resource rent on environmental quality remains inconclusive, when environmental quality is relatively high, natural resource rent tends to promote the increase of carbon emissions, but there is a reduction effect on the ecological footprint. For countries and regions with better environmental quality, there will be an effect of increasing resource consumption and reducing pollutant emissions. (2) Increasing the consumption intensity of energy will lead to the deterioration of environmental quality, and increasing the proportion of renewable resources will increase and weaken this impact, so it is particularly important to improve the energy consumption structure and improve environmental quality. (3) The impact of urbanization on environmental quality dose not form a unified opinion, and the difference in the selection of environmental quality indicators brings variability to the results, which also prompts the adoption of multiple indicators or the establishment of a composite index that covers multiple perspectives of environmental quality to conduct comprehensive and in‐depth research on environmental quality. [ABSTRACT FROM AUTHOR]

Energy, being one of the significant drivers of growth, has gained much focus in the last four decades. A connection between energy and growth exists in the economy: on the one hand, energy is a vital factor of production while growth improves living standards, which increases energy consumption on the other hand. Previous studies on the energy‐growth nexus paid less attention to sectorial differentials in the consumption of energy and growth. Also, the literature could not relate that to sustainable development. Thus, this study explores the sectorial differentials in energy consumption and how they affect the energy‐growth nexus within the context of sustainable development. The research employed secondary data for 46 sub‐Saharan Africa (SSA) countries for the period 2007 to 2020. Using the General Methods of Moments estimation technique, we found that all the measures of energy consumption positively affect economic growth. Also, CO2 intensity is positively related to growth in the economy while CO2 emissions from solid fuels negatively affect economic growth. Again, Foreign Direct Investment (FDI) is positively impacting economic growth and Gross Domestic Product (SSA) per energy use. Policy implications are discussed in the paper. [ABSTRACT FROM AUTHOR]

With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day. [ABSTRACT FROM AUTHOR]

Due to its high energy storage density, high instantaneous power, quick charging and discharging speeds, and high energy conversion efficiency, flywheel energy storage technology has emerged as a new player in the field of novel energy storage. With the wide application of flywheel energy storage system (FESS) in power systems, especially under changing grid conditions, the low‐voltage ride‐through (LVRT) problem has become an important challenge limiting their performance. In this paper, we propose a machine‐grid side coordinated control strategy based on model predictive current control (MPCC) for the insufficient LVRT capability of traditional FESS during grid faults. Excellent dynamic properties are demonstrated by the technique, which allows the grid‐side converter output current to swiftly follow the reference current instruction. The FESS's LVRT capability is increased when the grid‐side converter uses the MPCC current inner loop rather than the proportional–integral current inner loop during grid voltage dips. This greatly increases the reactive power response speed and efficiently supports the quick recovery of grid voltage. According to simulation verification carried out by Matlab/Simulink, the suggested control approach can assure the long‐term dependable operation of the FESS during voltage dips. This study can also be used as a reference for improving the FESS's LVRT capabilities in the future. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Summary: The non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS. [ABSTRACT FROM AUTHOR]

This paper presents a novel dynamic bias latch‐type comparator combined with a voltage‐controlled delay line (VCDL), designed specifically for low‐power and low‐noise applications in high‐speed analog‐to‐digital converters (ADCs). The incorporation of the VCDL precedes the dynamic bias amplifier in the proposed comparator, thereby achieving a balance between energy efficiency and high‐speed operation. This innovative design enhances the input common voltage of the dynamic bias amplifier through the utilization of the VCDL, surpassing that of a conventional dynamic bias comparator. Furthermore, it demonstrates increased adaptability in technology scaling and efficient operation at lower supply voltages, leveraging inverters and other simple cells. The proposed comparator in 28 nm process technology achieves a 309.7 µV input‐referred noise while consuming approximately 145.8 fJ and completing comparisons in 125 ps with a 0.9 V supply and a 0.45 V input common‐mode voltage. [ABSTRACT FROM AUTHOR]

Summary: Single‐phase high‐frequency resonant inverters (SPHFRIs) with high power density, fast dynamic response, and high energy conversion efficiency have been widely studied and used in academia and industry. With the development of the modularization concept, the operation of multiple inverter modules is desirable because it can remove the limitations of cost, heat dissipation, and component volume in single inverter operation. However, a critical challenge in multiple inverter systems is that the both the phase and magnitude of the output voltage of each inverter module must be controllable to eliminate circulating currents between inverter modules. Great efforts have been made to solve this problem from three aspects: topology, modulation, and control strategy. In this paper, modulation strategies and topologies of different inverters are presented and reviewed to provide guidance to researchers working in this field. Firstly, multiple inverter system based on the SPHFRI is described and its advantages and disadvantages are analyzed. An important issue in the study of multiple inverter systems is presented, that is, the suppression of circulating current. Secondly, the circulating currents in a multiple inverter system are analyzed and derived from two aspects: (1) magnitude and phase angle and (2) active and reactive currents. Thirdly, the inverters used in the multiple inverter system are introduced, and their operating principles, advantages, and disadvantages are reviewed. A key comparison of several inverters is presented for the benefit of the readers. Fourth, through PI closed‐loop simulation, the performance of different multiple inverter systems for suppressing the circulating current is compared in two situations: (1) with magnitude discrepancy only and (2) with both magnitude and phase discrepancy. Finally, the advantages and disadvantages of each inverter are summarized, and the state of the art and future trends are presented. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers' voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs' chargers. Here, a model for encouraging industrial loads to dynamically invest in implementing the conservation voltage reduction (CVR) strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to the fact that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non‐linearity, multi‐modality, and inseparability. On the other hand, meta‐heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator (Q‐MOSFO‐CA) to solve the constrained UAV path planning issues. The multi‐objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q‐learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

Near‐infrared (NIR) phosphors offer a novel luminescent solution for NIR spectroscopy, but their efficiency remains low. Herein, it is demonstrated that the high transparency of a phosphor improves its external quantum efficiency by developing a new inorganic–organic composite phosphor. The new bulk‐size and highly transparent composite phosphor is fabricated through layer‐by‐layer assembly of micron‐sized hexagonal LiSrAlF6:Cr3+ powder phosphor. The inline transparency of the final bulk phosphor can be controlled from 0% to 90.2% at a large solid concentration of ≈47 vol.%. With high transparency, the new bulk phosphor allows the phosphor‐conversion light‐emitting diode (pc‐LED) a 2.9 times improvement in efficiency. This paper inspires a feasible and low energy consumption method to facilitate next‐generation NIR pc‐LED phosphors. [ABSTRACT FROM AUTHOR]

This paper focuses on the stability problem of networked control systems (NCSs) under denial‐of‐service (DoS) attacks and bandwidth limitation. Firstly, the periodic dynamic quantization mechanism is extended to insecure systems, and a periodic‐like quantization strategy is proposed. Due to the designable period, such mechanism allows a trade‐off between better system performance and less energy consumption. With finite quantization levels, a reasonable parametric design is given to avoid quantizer saturation under general energy‐constrained DoS attacks. Secondly, a suitable quantized controller is designed for the system affected by DoS attacks. The relationship between the quantized controller and the DoS attacks is analyzed, and the criterion for NCSs achieving global asymptotic stability is derived. Finally, two numerical examples are given to verify the correctness of the theoretical result. [ABSTRACT FROM AUTHOR]

In space exploration activities, a large amount of materials needs to be carried, which limits the sustainable development of exploration activities. In‐situ resource utilization (ISRU) is an important means to realize resource recycling and continuous space exploration, which converts space resources into oxygen and hydrocarbon fuels. The traditional ISRU in outer space mainly uses high temperature and high pressure to electrolyze water or reduce CO2, having problems such as low conversion efficiency, high energy consumption, and excessive equipment volume. Here, an electrochemical catalytic synthesis technology based on a microfluidic device is proposed, which can convert H2O and CO2 into O2 and organic matter by electrocatalytic method at room temperature and achieve efficient energy and matter conversion. The gas‐liquid mixing and electrochemical reaction were analyzed. A mathematical model of gas‐liquid two‐phase mixing and microfluidic chemical reaction was established. The research results demonstrate the reliability and efficiency of the microfluidic reaction device designed in this paper for ISRU. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

Decanter centrifuges are widely used in solid–liquid separation, and saving their energy consumption is very important. In this paper, a bend pipe installed at the end of the decanter centrifuge was designed to save energy, and the effect of different structural parameters of the energy‐saving bend pipes on the outlet velocity and the reaction torque was investigated. The results revealed that the outlet velocity and the reaction torque were reduced with the increase in the number of pipes, outlet area, and pipe length. Increasing the radial position had little effect on the outlet velocity, but increased the reaction torque. Decreasing the outlet area and increasing the radial position both weaken the separation performance of the decanter centrifuge. The best separation performance and reaction torque effect were achieved in a suitable structure. Furthermore, the turbulent intensity was reduced from 143% to 135% in the new machine installed with bend pipes. The energy‐saving efficiency dropped with the increase of the rotational speed and the value was 13.1% under 2500 rpm. The separation performance of the new machine was similar to that of the original machine, and the dewatering speed was increased with the increase of the axial position. [ABSTRACT FROM AUTHOR]

This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]