Search

This paper examines the nature of the economic activity–energy use nexus for 82 countries and the period from 1971 to 2014. Compared to all other papers, the current study is based on a significantly larger number of observations and was carried out by using new and advanced non-stationary panel data econometric techniques that overcame most of the existing methodical failures. The findings of the Granger non-causality test quite robustly lead to the conclusion that there is bi-directional causality both in the short and the long run. Finally, the obtained estimation results suggest that the increase of energy consumption by 1% results in an increase of GDP per capita between 0.54 and 0.56%. Also, the increase of GDP by 1% results in an increase in energy consumption between 0.47 and 0.48%. Governments cannot simply apply energy conservation policy by reducing total consumption of fossil fuels without negatively affecting economic activity and generating of spiral of mutual effects with positive ecological and very negative economic consequences. It is therefore necessary to make significant efforts to modernize the energy sector and to stimulate companies to invest in new energy-efficient production technologies relying on alternative environmentally friendly energy sources. [ABSTRACT FROM AUTHOR]

With increase in the demand of better data collection from various IoT devices, researchers are showing more interests in providing the enhanced data collection methods to the users. Many data collection methods have proposed, but still more efficiency and better results are required. In this paper, a novel and secure data collection method from IoT devices has proposed. In this paper, SEED (Secure and energy efficient data-collection) method has discussed. It includes creation of aggregator nodes and path discovery algorithms. Integrity of data is analysed by using MD5 hashing technique. This hashing technique is one of the most usable techniques as compared to other. In the regard of implementation of this novel secure and energy data collection method, MD5 is used to just hash the processing data in the entire process. In earlier scenarios, there were various issues regarding fault tolerance, congestion, energy wastage, path discovery and load balancing in a network. To resolve these issues, updating of the aggregator node is done after each failure or transmission. Transmission of huge amount of data can create different challenges in network. This research is completely different from other existing researches of data collection with routing as it deals with sink node by using a unique path discovery algorithm. This proposed mechanism provides the better results for all of the nodes and network in terms of energy efficiency and throughput. [ABSTRACT FROM AUTHOR]

In the multi-axis motion control field, the point-to-point multi-objective learning algorithm, taking target point tracking accuracy as the major control objective, limits the optimization of other control objectives such as motion distance and energy consumption of the system. Therefore, this paper initially proposes multi-objective region iterative learning control algorithm, extending point-to-point tracking to region-to-region tracking and further optimizing other performance indexes via flexibly setting the region size on the grounds of the tracking requirements. In view that the fixed weight coefficient of each object in the multi-objective iterative learning algorithm is not suitable for occasions with dynamic control requirements, this paper further presents time-varying multi-objective region iterative learning control algorithm and unifies point-to-point tracking and region-to-region tracking under one algorithm framework. In addition, by means of designing time-varying weight matrix, strengthening major control objectives, weakening minor control objectives and satisfying the ever-changing control requirements, experimental results show that time-varying multi-objective region iterative learning algorithm is capable of flexibly adjusting the weight size of each objective in line with various control needs so that the control system is endowed with higher tracking accuracy in trajectory tracking segment as well as lower energy consumption and shorter motion distance in region tracking segment, thus having good flexibility. [ABSTRACT FROM AUTHOR]

It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market. [ABSTRACT FROM AUTHOR]

Cloud computing is seen as a more promising technology than any other traditional information technology computing paradigm in today's world. It essentially functions as an on-demand resource provisioning platform that requires no active user participation. The resource provisioning strategies necessitate proper load distribution management across the cloud network, without which the cloud would experience biased workload performance. Today virtualization is the cornerstone of cloud computing, allowing data dissemination and administration via deploying virtual machines. Modern applications contain data that need to be stored into a scheme called polyglot persistence (combining SQL and NoSQL data-stores). However, these services are tailored to specific storage requirements, necessitating aggregating them from several heterogeneous clouds or migrating data from one cloud to another. Data migration can be done offline where the database is independent of the application, or otherwise, the application has to be down for the migration period. This paper developed a middleware in.NET Core facilitating the live migration of persistent polyglot data in heterogeneous clouds. This paper presents the proof of concept for live migration of the database layer of an application hosted on any supported clouds to any implemented cloud's data-store. Our suggested technique performs better in migration time, energy usage, and throughput aspects as compared with the offline migration scenario. In our experimentation, we found that while migrating data in offline mode from SQL to mongo and vice versa there is a marginal increase of 29% and 11%, respectively, in latency time. This increase is acceptable and tolerable while considering the live data migration scenario. [ABSTRACT FROM AUTHOR]

The size of the wireless sensor network (WSN) is extending continually with use of IOT networks. The main difficulty for design wide area WSN is to maintain the higher stability period and energy efficiency (EE) for the routing protocols. The creation of clustering-based routing protocols was applied to the optimization of overall network energy. But, traditional clustering methods were unable to produce improved node heterogeneity, and extended network lifetime. Distributed clustering-based routing protocols are specially designed for enhancing the EE of the networks. In addition, EE can be improved by enhancing the heterogeneity of the node distribution. This paper aims to design the extended distributed clustering-based EE routing protocol. The heterogeneity of nodes is improved by introducing the additional intermediate advanced nodes layer in the network. Therefore, paper proposed to design the Multi-Level Heterogynous EDEEC rousing protocol called ML-HEDEEC by adapting optimum energy enhancing parameters. The notes are divided to normal, advance, advance-interdicted and supper nodes based on the energy allocated to them. The probability of nodes is modified for better clustering and cluster head election by introducing additional energy enhancement parameter. In addition, it is proposed to automatically adopt the network initial energy based on the scaling of network dimensions. This may lead to enhance EE of the network and may improve stability period. Finally, the results are evaluated for a case of WSN routing under the dynamic sink locations. Performance is compared for various distributed clustering protocols and other state of art protocols viz. LEACH, SEP, zonal- SEP, DEC considering the network scaling. Various performances of the network stability, packets sent to base station, and lifetime,.are defined for result evaluation. The network dimensions are scaled up to four times and proposed protocol is tested under scaling consideration. In addition, sink locations are also varied for dynamic sink locations performance evaluation. Overall paper efficiently designed and test heterogeneous improved routing protocol with extended lifetime and stability. [ABSTRACT FROM AUTHOR]

Clean, affordable, and efficient energy sources are inevitable for a sustainable world. Energy crisis, especially the poor access and affordability, demand–supply mismatches, energy inequality, and high dependence on non-renewable energy sources, are the challenges before the attainment of clean energy goals for sustainable development. The 5-year review from the adoption of sustainable development goals (SDGs) by using bibliometric and thematic analysis was conducted in this review. This review is a synthesis of 175 scientific papers on SDG 7. Policy reforms and better funding; technology innovation and inclusion; and economic growth, rapid promotion of renewable, and alternative fuels are the recommendations for the achievement of the energy goals. Future research on energy-related goals should focus on energy justice, policy reforms, energy poverty, poor affordability, off-grid transmissions, renewable energy sources, alternative fuels, reforms in the energy supply chain, and international cooperation for better implementation of projects and for attracting foreign capital and private funds. This paper invites the attention of practitioners, academicians, funding agencies, and international agencies to collaborate in the initiatives for a clean, green, and energized world. [ABSTRACT FROM AUTHOR]

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality. [ABSTRACT FROM AUTHOR]

Ensuring the safety of Arctic shipping and preserving the Arctic ecological environment are emerging as key challenges in the shipping sector. Ship collisions and getting trapped in ice are frequently occurring under dynamic ice conditions due to the Arctic environment, making research on ship navigation in Arctic routes significant. Leveraging ship networking technology, we developed an intelligent microscopic model which considered factors such as the future motion trends of multi-ships in front and the influence of pack ice, and carried out a stability analysis of the model utilizing linear and nonlinear methods. Additionally, the accuracy of the theoretical results was further validated through simulation experiments with diverse scenarios. The conclusions manifest that the model can magnify the anti-disturbance ability of traffic flow. Additionally, the problem of energy consumption due to ship speed is explored, and it is determined that the model has a positive intention in reducing speed fluctuations and ship energy consumption. This paper highlights the potential of intelligent microscopic models in studying the safety and sustainability of Arctic shipping routes, providing targeted initiatives to improve safety, efficiency, and sustainability in Arctic shipping. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition. [ABSTRACT FROM AUTHOR]

Background: Biodiesel is a renewable and ecofriendly fuel for internal combustion engines. However, fuel standards need to be adapted for efficiency and commercial use. This paper deals with a novel process of its production using a purification step that counters the high costs of production and experimental analysis using multiresponse optimization. Methods: Soybean oil was chosen as a biodiesel of 5%, 10%, and 15% blend with common diesel fuel and is experimentally tested in a variable compression ratio compression ignition engine. The biodiesel is blended with common diesel fuel to run the engine without any modification in its setup, which also solves most of the operational problems. The functional relationship between the input parameters and the performance characteristics of the engine is evaluated by statistical response surface methodology using the Box–Behnken design model, which generates a design of experiment resulting in an optimum experimental run that reduces the overall cost of the experimental investigation. Uncertainty analysis is done to minimize the gap between the results considering the errors of each piece of equipment. Validation of the results is also carried out. Results: The analysis of variance is used to measure the acceptability of the model and the competency of the model to predict output performance. The optimum value of input parameters which are obtained are 4.5 kg for the load, the compression ratio of 18, and B05 for the fuel blend, which results in maximum performance of brake power of 3 kW, minimum fuel consumption and emissions of CO and NOx, which are 0.39 kg/kWh, 0.01%, and 50 ppm. Conclusions: Cost analysis reveals that biodiesel produced from the novel process of transesterification is reasonable as compared with the conventional process. It is also environmentally more sustainable, which cannot be ignored. This technique can be used in future research for cost-effective production fields such as combustion parameters and biofuels produced from waste, which need to be explored. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

In the context of increasing global resource and environmental problems, it is of great practical significance to accurately test the impact of various factors on energy use efficiency for maintaining national energy security and formulating relevant policies. This paper measures firms' total factor energy efficiency (TFEE) using the two-stage stochastic frontier method within the data envelopment analysis (DEA) framework, leveraging data from listed firms in China spanning 2010 to 2022. Employing the establishment of free trade zones (FTZs) as a quasi-natural experiment, we apply the staggered differences-in-differences (DID) and stacked DID methods and analyze the impact of FTZs on firms' TFEE. The results show that the establishment of FTZs significantly promotes the improvement of firms' TFEE, and it has a greater promotion effect on heavily polluting, non-manufacturing, state-owned, private, and small-scale firms. The results of the mechanistic analysis showed that the promotion effect of FTZs on firms' TFEE is mainly realized through three channels: increasing government subsidies, reducing the financing constraint effect, and encouraging the technology innovation effect. Furthermore, industry-level decomposition results indicate that the surge in industry energy efficiency primarily results from improvements within firms rather than inter-industry variations. This paper's results propose that countries can enhance energy efficiency by progressively endorsing the implementation of FTZs. [ABSTRACT FROM AUTHOR]

This paper addresses a preliminary study on the emission control of pollutants from an industrial furnace. The stack sampling technique was implemented in this study, and the quantity of pollutants released from the furnace smoke was monitored. The transverse points required to collect the data were selected based on the upstream and downstream disturbances. At first, the investigation was made with the furnace oil. A 15.28% and 5.1% of CO2 and O2 were observed in the flue gas from of regular furnace oil. Hence, by considering the emission level observed, the analysis was repeated with the low sulphur-low stack and the coal tar fuel. The emission of SO2 and N2 was reduced with coal-tar fuel compared with the regular furnace fuel. Thus, the proposed operational adjustment attenuates the pollutant emission, which also helps to reduce the unfavorable impact on the environment. In the future, the authors intend to include a design change in the furnace burner, which would help to increase fuel efficiency and reduce pollution. [ABSTRACT FROM AUTHOR]

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

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

Flood-based communication protocols are attractive due to their easy and fast network startup, resiliency to communication or node loss, and node mobility. Concurrent transmissions are flooding-based protocols that enable low-latency, network-wide communication synchronously. They also provide energy-efficient data dissemination with higher delivery reliability over traditional flooding. This paper proposes the Good Flood Bed (GFB) method to reduce further the energy consumption of networks operating based on concurrent transmission by preventing flood in parts of the network where there is no need for flood data. GFB starts with a distributed leader-election process to choose a root node. Then, the root node builds a spanning tree among nodes. The tree establishes a parent-child relationship and forms a new controlled flood bed. We show the effectiveness of the proposed method by simulation and experimenting on a BLE5 small-scale test bed. Experiments prove that this method has the potential to cut network energy consumption by 50%. [ABSTRACT FROM AUTHOR]

In peer-to-peer(P2P) networks, content delivery is very critical, but there are not many relevant research results in content delivery technology. In this paper, in order to simulate content delivery in P2P mode, the number of online players is abstracted into the number of servers that can provide services, the peers write content to buffer is abstracted into the arrival of customers, and the players read content from buffer is abstracted into the service process of the servers. Based on the consideration of the effect of the number of P2P online players on energy consumption, an M/M/c queueing model with random variation in the number of servers is developed, and negative customers and preemptive priority policies are introduced. The matrix–geometric solution method and Gauss-Seidel iterative method are used to derive the performance measures of the system at steady state for two types of contents. And some numerical examples are given using Matlab for analyzing the trend of performance measures in P2P networks with parameters. The benefit function is established to obtain the parameter values that make the social benefit optimal by Nash equilibrium and social optimal strategy, and provide the theoretical basis for the scheduling of P2P peers. [ABSTRACT FROM AUTHOR]

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs. [ABSTRACT FROM AUTHOR]

Increasing the adoption of energy-efficiency measures (EEM) by households is one of the practical strategies to increase energy efficiency and reduce greenhouse gas emissions. For the 3.24 million low-income households in Australia, partly due to their potentially disproportionately large waste of usable energy, there is a gap between the potential for energy efficiency through existing EEM and the actual energy-efficiency level. Many measures increase energy efficiency in buildings; however, barriers persist for such households to adopt these measures, of which limited research has yet to be conducted. In response, this study examines the relationships involved by a questionnaire survey of 212 low-income households in Australia, showing that financial barriers have the greatest influence followed by split incentive barriers, with the provision of information having no significant role to play. This paper contributes to filling the gap of limited energy-efficiency research involving low-income households, revealing the effect of energy-efficiency barriers on their adoption of EEM. [ABSTRACT FROM AUTHOR]

Purpose: In addition to its considerable economic benefits, mining generally has a significant environmental impact on the ecosystem and human health, despite the efforts made. Mali, the 13th gold producer in the world and 3rd in Africa, deserves very special attention. This paper aims to identify and assess the potential environmental impacts and environmental hotspots of gold mining in Mali, using a life cycle assessment (LCA) approach. Methods: An industrial gold mine located west of Bamako in the Kéniéba circle in Mali exploiting two main underground deposits was considered in this paper. SimaPro software (v.9.0.0) is used for LCA by applying the ReCiPe 2016 Midpoint/Endpoint (H) V1.08 method to quantify the impacts generated. In addition, a sensitivity analysis of the scenarios proposed by Bjorklund is carried out to improve the reliability of the results obtained. Results and discussion: The results show that the main category of impact of gold mining that contributes most to the total environmental impact is human carcinogenic toxicity due to the production of electricity, the use of diesel, heavy fuel oil, hydrogen peroxide, sodium cyanide, aluminium chloride, and on-site emissions. In addition, the impacts of fossil resource depletion, terrestrial ecotoxicity, ozone formation in terrestrial ecosystems, mineral resource depletion, ozone formation in human health, terrestrial acidification, global warming, marine ecotoxicity, fine particle formation, and freshwater ecotoxicity also contribute considerably. Water consumption is identified as a positive impact of gold mining. Conclusion: Following the identification of environmental hotspots by this paper, further research is strongly recommended into the efficient production and rational consumption of energy and the choice of less energy-intensive, green technologies to promote more environmentally efficient gold mining. [ABSTRACT FROM AUTHOR]

Financial inclusion (FI) is an integral part of the financial system in any economy. It shapes the energy consumption patterns in the countries, but corruption may disturb these patterns. To explore this phenomenon, this paper aims at examining the impact of financial inclusion on energy consumption by considering the important role of corruption in 22 selected OECD nations. The study covers the period from 2004 to 2019 and uses second-generation tests for unit root and cross-sectional dependence. This study uses Driscoll-Kraay standard errors with random effect and other robustness measures for estimations. The results show that financial inclusion and income increase energy consumption. Here, it is required to encourage renewable energy sources and discourage non-renewable energy sources. Results also reflect that corruption control, globalization, and education decrease energy consumption in OECD countries. This is an indication that OECD consists of advanced countries where education ratio and international trade are of higher volume that cause reduction in energy consumption through modern technology. The empirical findings suggest that financial inclusion needs to be included in the policy-making for growth and energy consumption strategies. [ABSTRACT FROM AUTHOR]

The pursuit of economic growth has implications for carbon emissions and climate change. Achieving low carbon development is important for attaining the targets of the sustainable development goals. Africa is often described as a largely import-dependent continent. The continent also requires significant investment in information and communication technologies (ICT) and renewable energy to achieve low-carbon economic growth. However, empirical evidence on the joint impacts of imports of goods and services, clean energy use, ICT, and economic growth on carbon emissions in Africa is scanty and mixed. This paper investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions by using rich data on total per capita carbon dioxide (CO2) emissions, economic growth, import of goods and services, renewable energy consumption, fixed telephone subscriptions, mobile cellular subscriptions, and individuals using the internet in Africa (2001 ─ 2020) obtained from the World development indicators (WDI) database. Using the Panel autoregressive distributed lag model (PARDL), we found that mobile cellular subscriptions, and level of economic growth significantly increased per capita CO2 emissions in Africa in the long run while renewable energy consumption and technologies and import of goods and services significantly decreased per capita CO2 emissions in the long run. We conclude that information and communication technologies, level of economic growth, import of goods and services, and renewable energy consumption exert impacts on carbon emissions in Africa. Highlights: Achieving low-carbon development in Africa is possible. This research investigated the impacts of information and communication technologies, renewable energy consumption, import and economic growth on carbon emissions in Africa. This study utilized the Panel autoregressive distributed lag model and panel data from 31 African countries. The results shed new light on the emission reduction potential of renewable energy consumption and import of goods and services. The study also shows that ICTs increase carbon emissions in Africa. This article offers recommendations concerning carbon emissions reduction. [ABSTRACT FROM AUTHOR]

The rising global energy demand necessitates innovative solutions for harnessing renewable energy sources. Solar ponds have received attention as they present a viable means to address this challenge by absorbing and storing solar radiation. This article provides a comprehensive review of solar pond technology, including its principles, applications, heat extraction mechanisms, and approaches to optimize performance, with special attention to the salt-gradient solar pond. Additionally, the article identifies challenges that currently hinder the large-scale adoption of solar pond technology and offers recommendations for future research. By providing a detailed analysis of the current trends and future research directions, this paper seeks to contribute to the ongoing efforts to improve these systems, exploring various approaches to increase their efficiency and make them more economical and environmentally sustainable. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a parallel two-stage compression (PTC) cycle on the domestic refrigerator-freezer in a laboratory according to the IEC 62552–2015 standard is investigated. The relatively large temperature difference between the fresh food compartment (FFC) and the refrigerant in the fresh food compartment evaporator (FFCE) leads to high compression and irreversible losses in the refrigeration circuit. To avoid the mentioned problems and reduce energy consumption, a PTC cycle is used. The innovation of this article is the 4E analyses (energy, exergy, exergoeconomic, exergoenvironmental) for the PTC cycle using the results obtained from the laboratory. Ultimately, the multi-objective optimization of the PTC cycle is conducted. For optimizing the PTC cycle, the total cost rate and the exergy efficiency are set as targets and the evaporator pressure and condenser pressure as variables. MATLAB and REFPROP 9.1 are used to model the system. Results showed that the highest exergy destruction rate is associated with the FFCE (0.349 kW) and compressor (0.183 kW). The highest exergy efficiency among the equipment is evaporators. The maximum exergoeconomic factor is related to the freezer compartment evaporator (FZCE) (98.70%) and then the compressor (98.62%). FZCE has the highest environmental impact (99.9%). [ABSTRACT FROM AUTHOR]

Public institutions, emblematic of public infrastructure, exhibit extensive reach and operational scope, positioning them as vanguards in China's dual carbon initiatives and serving as exemplars. Electricity and natural gas predominantly fuel the operations of public institutions. Notably, the fixed commute routes and consistent procurement patterns of office personnel yield a standardized energy consumption profile within these entities. Researching carbon emissions related to commuting and evaluating procurement strategies for reducing carbon footprints in public institutions demonstrate a precision-tailored approach. This paper, through an analysis of the energy consumption characteristics, utilization structure of public institutions, and the commuting behaviors and procurement practices of office personnel, establishes a bespoke carbon accounting model specifically designed for public institutions, seamlessly embedded within a comprehensive platform. By providing fundamental methodological frameworks and advanced technological foundations for carbon accounting in public institutions across China, this work propels the nation's efforts towards carbon peak and ultimately carbon neutrality. [ABSTRACT FROM AUTHOR]

Background: The transition to energy-sustainable systems is a globally accepted concept, but it is implemented with various degrees of success around the world. The aim of this paper is to determine the status of green transition in five Central Asian countries (Tajikistan, Turkmenistan, Uzbekistan, Kazakhstan, and the Kyrgyz Republic) that are among the highest energy consumers globally and the most vulnerable to climate change. Despite a notable improvement in living conditions across all countries over the past two decades, the region remains underdeveloped. Economic growth is based on the export of natural resources and remittance inflows, but the structure of the economy is monolithic. Main findings: Upon analysing transition indicators for the period 2000–2020, no evidence of significant changes towards a sustainable energy transition is found. All countries in the region (except Uzbekistan) have recorded an increase in carbon emissions, while the carbon intensity of the energy mix is almost the same. While there has been visible progress in reducing energy intensity, the countries in the region remain among the most energy-intensive economies. Despite the region's substantial potential for renewable energy production, it remains underutilized. Conclusions: A multitude of varied problems accompanies the green transition in Central Asian countries. The energy market that has not yet been liberalised, along with significant subsidies and low energy tariffs, discourages investments in renewable energy sources and energy efficiency initiatives. A high level of corruption, rigid legislative and institutional infrastructure, and insufficient capital are additional factors that determine the speed, scope, and effectiveness of the green transition. Thus far, the primary focus in these countries has been on ensuring energy security. However, there has been a discernible surge in investments (particularly from China) in renewable energy projects. Although energy efficiency and climate change issues are formally defined, the energy transition goals are often declarative without an action plan that provides concrete steps in transition process. Strategic planing of economic development that includes all related sectors (not only the energy sector) and paying attention to socio-economic and environmental indicators of sustainable development are prerequisites for successfully implementating the energy transition in these countries. [ABSTRACT FROM AUTHOR]

Understanding nuclear behaviour is fundamental in nuclear physics. This paper introduces a data-driven approach, Continued Fraction Regression (cf-r), to analyze nuclear binding energy (B(A, Z)). Using a tailored loss function and analytic continued fractions, our method accurately approximates stable and experimentally confirmed unstable nuclides. We identify the best model for nuclides with A ≥ 200 , achieving precise predictions with residuals smaller than 0.15 MeV. Our model's extrapolation capabilities are demonstrated as it converges with upper and lower bounds at the nuclear mass limit, reinforcing its accuracy and robustness. The results offer valuable insights into the current limitations of state-of-the-art data-driven approaches in approximating the nuclear binding energy. This work provides an illustration on the use of analytical continued fraction regression for a wide range of other possible applications. [ABSTRACT FROM AUTHOR]

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

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

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

In this paper the photobiomodulation on isolated mitochondria of bovine liver is studied as a thermodynamic process of conversion of energy. This analysis is conducted by considering a particular set-up for the photobiomodulation experiments of interest. It allows, in particular, the computation of the electromagnetic field and the related energetic quantities in the stimulated organelles. The measurements of the excess of biochemical power density produced by the illuminated mitochondria are performed at regular time intervals after the experiments. The calculations and the measurements finally allow us to obtain the first results on the efficiency of the process of conversion of electromagnetic energy into excess of biochemical energy released by the isolated organelles. [ABSTRACT FROM AUTHOR]

Clustering is one of the most promising approaches to reducing energy usage in Wireless Sensor Networks, where power remains a bottleneck (WSNs). The vast majority of advances in energy efficiency result in exorbitant computing costs. Because of its superiority in simulating human decision making and its capacity to turn several inputs into a single output, the fuzzy-logic based clustering algorithm is preferred over others. This paper proposes a technique to energy-efficient clustering based on fuzzy logic (FLEE). Energy consumption is a significant barrier for geographically dispersed wireless sensor networks (WSNs). Because of the rapid improvement of energy harvesting technology, a novel approach known as simultaneous wireless information and power transmission (SWIPT) has emerged as a hot research topic due to its use in tackling the energy scarcity problem in WSNs. The bulk of existing SWIPT systems rely on time switching (TS) or power splitting due to their high complexity, lack of flexibility, and implementation issues (PS). In this paper, we provide a novel Fuzzy Logic-based Energy Efficient—SWIPT approach for adaptively dividing data packets into energy data packets and information data packets using fountain codes and real-time node decoding status information. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter, as well as the power splitting ratios at the SWIPT receiver, are jointly changed to maximise the spectrum efficiency of wireless information transfer in wireless sensor networks. The numerical demonstration of the FLEE-SWIPT performance of many fixed modulation schemes under different fading situations. The FLEE-SWIPT transceiver with adaptive modulation has been proved to be useful. The results show an increase in Network Lifetime of 12.342 s, an increase in Network Throughput of 1579.41 kbps, a decrease in Energy consumption of 23.789 J, a decrease in Packet Loss Ratio of 38.61%, a decrease in Packet Delivery Ratio of 96.33%, a decrease in Encryption Time of 2.987 s, a decrease in Decryption Time of 3.567 s, and a decrease in Execution. [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance. [ABSTRACT FROM AUTHOR]

Background: Nature-based solutions (NBS) in urban areas offer an opportunity to improve environmental conditions and to reduce CO2 emissions towards establishing climate-neutral cities in the next few decades. Furthermore, the implementation of NBSs—vertical or horizontal green infrastructures on public facilities—could in particular improve both climate, including outdoor thermal conditions on a micro-scale (especially during the summer season) and the energy demand of buildings as well as save heating energy during the winter period. Results: On both selected buildings, extensive green roofs were implemented as an NBS intervention. The analysed data were obtained using the monitoring systems (from 2019 to 2022) installed on two public buildings in Novi Sad (Republic of Serbia) and Osijek (Republic of Croatia), with a focus on climate/bioclimate characteristics and thermal transmission capacities. Four automatic weather stations (AWS) were used for microclimate monitoring, along with the heat flow meter (HFM) method, to measure the alterations in the thermal transmittance (U value) of a flat concrete roof before and after energy refurbishment and the installation of a green roof. The outcomes of this study show that the air temperatures (Ta) and globe temperatures (Tg) near the green roof are lower by 0–3 °C for Ta and by 0–16.5 °C for Tg than the values captured by the AWSs at other locations. An even more interesting fact is that the green roof has a constant cooling potential during tropical nights, and based upon this research, the cooling value is around 2 °C for Tg (the Ta value is not distinct). The thermal transmittance results show that more savings can be achieved by applying a green roof with an 8 cm thick substrate: U values decreased by 50–69%, as measured by two different heat flux sensors. Conclusions: Nature-based solutions, such as the implementation of an extensive green roof, have positive effects on diverse aspects of urban environments and building energy savings, which are particularly evident in extreme seasons, both summer and winter. Applying the proposed monitoring and assessment system could help local communities in their efforts to reduce carbon-based emissions. This paper provides a good example of the implementation of NBSs on a local- and a micro-scale. [ABSTRACT FROM AUTHOR]

Background: The main goal of the paper is to define the level of energy efficiency in the economies of selected countries in the Balkan region that have opted for the EU Green Deal, a circular economy, and a transition to carbon neutrality. Energy efficiency, as a determinant of carbon neutrality, was selected as an indicator for analysis because it records particularly unfavorable indicators in the region under observation. The research was carried out on a sample of seven Balkan countries and their surrounding areas. An initial qualitative analysis was followed by a quantitative analysis based on a combination of statistical methods and soft computing. Six indicators were selected for the analysis covering a period of 30 years (1990–2020). Results: A significant obstacle to the green transition and the region's transition to a circular economy and carbon neutrality is energy efficiency and energy related pollution—the reliance of most countries on coal-fired thermal power plants for electricity generation. The research results showed the following: (a) the degree of economic development and membership in the European Union are not significantly related to the level of energy efficiency; (b) most of the sampled countries are in the initial stages of introducing activities to achieve carbon neutrality; and (c) only Slovenia has documented consistent indicators and evident advancements in its efforts to achieve carbon neutrality. Based on the research findings, proposals for improvements were made in the direction of policymaking and in a methodological sense. Conclusions: The implementation of circularity and carbon neutrality as a long-term goal of the European Union is not necessarily related to the level of economic development, nor can its trajectory be exclusively ascertained by means of data processing and monitoring. A more precise understanding of a carbon-neutral future can be achieved through the incorporation of qualitative data to a greater extent, a realistic evaluation of historical facts and their repercussions, as well as projections of the effects that reality and global developments after 2022 will have on each country. [ABSTRACT FROM AUTHOR]

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

Functional circuits are a group of combinational logic circuits which may be utilized for certain tasks and with specific planning. Decoders, multiplexers, and demultiplexers are all functional circuits that come in useful when creating complex systems. Quantum-dot cellular automata (QCA) is a flourishing technology that would be so practical in the field of computational digital circuits in terms of its advantages such as low energy consumption. This paper proposes various quaternary 1:4 decoders (enabling decoder, active-high and active-low decoders). Then, 4:1 multiplexer and 1:4 demultiplexer were architectured using the proposed 1:4 decoder. In the following, a quaternary to the binary converter (a 4-valued digit to a 2-bits circuit) is designed regarding the validated proposed structures. All designs were simulated and verified by QCASim. The total area used for the decoder, multiplexer, demultiplexer, and quaternary to the binary converter are 0.01, 0.19, 0.03, 0.13 μm2. The complexity and delay are 30, 387, 88, 214 and 0.5, 3.25, 1, 2 respectively. This work gets compared to CMOS and carbon nanotube field-effect transistor articles. Furthermore, the proposed 4:1 quaternary QCA multiplexer got compared with the binary QCA multiplexers. The comparison results show that our proposed designs are efficient in terms of having a low delay, area, and complexity. [ABSTRACT FROM AUTHOR]

The bumper is an automotive part act as a protective shield in the vehicle. It bears and absorbs the load when the vehicle incurs a collision. If the bumper has got more weight it leads to more fuel consumption and decreases the fuel efficiency of the vehicle. This problem can be overcome by reducing the weight of the bumper without compromising its performance. The main objective in bumper design and fabrication is that it should possess less weight and at the same time, it should withstand the higher load during a collision. In this paper, the properties and performance of the developed bumper composite are compared with the existing bumper. The bumper performance is optimized using a finite element analysis with ANSYS R15.0. From the cost analysis, it has been observed that the weight and cost of the Basalt composite bumper is 49% and 56% lesser than the steel bumper. The Impact strength and Factor of Safety values of the Basalt fabric composite bumper is 47.6% and 32.5% higher than the steel bumper. The total deformation of the Steel bumper is 15.7 mm, and the Basalt composite bumper is 32 mm, which shows 51% higher deformation than steel. [ABSTRACT FROM AUTHOR]

This paper investigates whether carbon dioxide (CO2) intensity of gross domestic product (GDP) matters for environmental deregulations in Southern European countries over the period of 1990–2018 while controlling economic growth, globalization, and energy consumption. The present study uses the second generation panel based techniques, namely cross-sectional dependency test, cross-sectional unit-root test, Westerlund cointegration test, augmented mean group and common correlated effects mean group estimators, and Dumitrescu–Hurlin causality test to measure the effect of CO2 intensity of GDP, economic growth, globalization, and energy consumption on environmental degradation. The empirical finding of the present study reveals that the CO2 intensity of GDP is an important factor in determining environmental degradation in South European countries, as the outcomes show that a 1% boost in CO2 intensity of GDP is causing a 1.7728% increase in CO2 emissions. Moreover, a 1% increase in economic growth caused a 0.2568% boost in CO2 emissions. The result is crucial for policy decision-making and can perhaps be applied to take decisive policy actions to mitigate environmental issues. [ABSTRACT FROM AUTHOR]

The papers considers the problems relating to the use of separation processes in membrane apparatuses for the production of cold in mix refrigerating machines as an alternative to absorption refrigerating machines, which will ensure a more stable process for obtaining a refrigerant from the mixture, reduce material costs for the production of a refrigerating installation, and increase the energy efficiency of the refrigerating installation operating on mixture components. An analysis of the use of traditional absorption and membrane refrigerating machines was carried out from the point of view of energy efficiency and the number of working elements. The primary method for calculating the process of separation of an absorbent–refrigerant mixture based on reverse osmosis processes is presented. [ABSTRACT FROM AUTHOR]

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system. [ABSTRACT FROM AUTHOR]

In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE). In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method. [ABSTRACT FROM AUTHOR]

This paper investigates energy-efficient communication within an integrated sensing and communication system. The system employs a dual-function radar-communication base station. This base station concurrently serves multiple mobile users for communication purposes while also performing target sensing within a designated range cell. An active reconfigurable intelligent surface (RIS) is utilized to enhance communication efficiency. The focus of this work is to optimize the communication energy efficiency by jointly allocating the transmit power and configuring the RIS elements. This optimization is achieved while satisfying constraints on both the probing power required for target sensing and the quality-of-service demands of the communication users. Two novel optimization methods are proposed, combining techniques from alternating optimization, sequential programming, and fractional programming. Through numerical simulations, the effectiveness of the developed algorithms is validated. Additionally, the performance of the system utilizing an active RIS is compared to that of a system with a passive RIS, specifically in terms of their respective communication energy efficiencies. [ABSTRACT FROM AUTHOR]

Objective: Energy usage in has been increased due to the rising demand of cloud infrastructure. The government policy has been focused on building the green IT data center. The energy data need to be collected in order to monitor the energy usage. However, in an old typical data center, the building has been built with no support of such data collection. In this research, we aim to design the energy data collection system for our existing data center, a case study of data center in Thailand at the university. Based on the collected data, an energy usage monitoring system and prediction can be developed. Methods: In the case study of Kasetsart University data center, the building and electric layouts were predetermined. The building layout and existing IT hardware were investigated. We designed the meter types and the number of meters to be installed for the building the energy data collection system. The corresponding database system was also designed for data logging, data visualization and analysis purpose. Results: As a result, 25 installed meters along with the add-on network system were installed for logging data. A data usage example was demonstrated by building the data visualization and analysis. The presented 1 year dataset collected showed the changes of energy usages which can be used to compare with real activities happening in the campus. This encourages the integration of other related environment data such as outside temperature which may affect the electric billing cost. The dataset can be used for prediction of the electric usage; thus, the policy for reducing the electric billing cost could be established. In this paper, as a data note, we focus on the methodology of data collection required for data center. [ABSTRACT FROM AUTHOR]

In this paper, we improve networks' spectral efficiency (SE), extend networks' lifetime, and maximize networks' energy efficiency (EE) of two-way full-duplex (FD) relay networks. Firstly, to improve networks' SE and to extend networks' lifetime simultaneously, we design a two-way FD relay transmission strategy with simultaneous wireless information and power transfer and direct links (DLs). The designed transmission strategy can complete a bidirectional communication in only one time slot with the exists of DLs and the energy-constrained relay node. With the designed transmission strategy, we further give the characteristics of relay amplification factor, the analysis of the designed transmission strategy, and the EE analysis of traditional half-duplex two-way amplify-and-forward relaying. Secondly, to maximize networks' EE, we present both the EE maximization problems and analyses of the designed transmission strategy with equal power allocation and optimal power allocation. To solve the EE maximization problems, we further propose the alternating optimal algorithm and give complexity analysis of the algorithm. Simulations show that our designed transmission strategy can improve the SE and EE of the networks. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

In this paper, we prove the existence of a reservoir that has a finite-dimensional output and makes the reservoir computing model universal. Reservoir computing is a method for dynamical system approximation that trains the static part of a model but fixes the dynamical part called the reservoir. Hence, reservoir computing has the advantage of training models with a low computational cost. Moreover, fixed reservoirs can be implemented as physical systems. Such reservoirs have attracted attention in terms of computation speed and energy consumption. The universality of a reservoir computing model is its ability to approximate an arbitrary system with arbitrary accuracy. Two sufficient reservoir conditions to make the model universal have been proposed. The first is the combination of fading memory and the separation property. The second is the neighborhood separation property, which we proposed recently. To date, it has been unknown whether a reservoir with a finite-dimensional output can satisfy these conditions. In this study, we prove that no reservoir with a finite-dimensional output satisfies the former condition. By contrast, we propose a single output reservoir that satisfies the latter condition. This implies that, for any dimension, a reservoir making the model universal exists with the output of that specified dimension. These results clarify the practical importance of our proposed conditions. [ABSTRACT FROM AUTHOR]

This manuscript presents a scientometric review of recent advances in microwave pretreatment processes for sewage sludge, systematically identifying existing gaps and prospects. For this purpose, 1763 papers on the application of microwave technology to sludge pretreatment were retrieved from the Web of Science (WoS) using relevant keywords. These publications were then analyzed using diverse scientometric indices. The results show that research in this field encompasses applications based on the non-thermal effects of microwaves, enhanced effectiveness of anaerobic digestion (AD), and the energy balance of this pretreatment system. Overcoming existing technical challenges, such as the cleavage of extracellular polymers, reducing microwave energy consumption, understanding the non-thermal effects of microwaves, promoting AD of sludge in combination with other chemical and physical methods, and expanding the application of the technology, are the main scientific focuses. Additionally, this paper thoroughly examines both the constraints and potential of microwave pretreatment technology for wastewater treatment. [ABSTRACT FROM AUTHOR]