Search

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

Energy-efficient computation is an increasingly important target in modern-day computing. Scientific computation is conducted using scientific workflows that are executed on highly scalable compute clusters. The execution of these workilows is generally geared towards optimizing run-time performance with the energy footprint of the execution being ignored. Evidently. minimizing both execution time as well as energy consumption does not have to be mutually exclusive. The aim of the research presented in this paper is to highlight the benefits of energy-aware scientific workflow execution. In this paper. a set of requirements for an energy-aware scheduler are outlined and a conceptual architecture for the scheduler is presented. The evaluation of the conceptual architecture was performed by developing a proof of concept scheduler which was able to achieve around 49.97% reduction in the energy consumption of the computation. [ABSTRACT FROM AUTHOR]

Sustainability continues to be a key field of study, encapsulating three principal dimensions: social, economic and environmental, which are also found within the context of climate change. However, there appears to be limited literature drawing upon the between sustainability and climate change. relationship, particularly in connection to carbon emissions and energy management. These issues have already been the subject of legislation in different countries, though still, predominantly individually rather than from an integrated perspective. The Climate Change Act (2008) provides a platform through which the relationship between sustainability and climate change can be considered. This paper establishes this relationship aspects of this relationship by employing the increased use of insulation within the UK housing stock to contribute to achieving the carbon reduction set by the Act. Taking a retrospective view through theoretical numerical modelling, this paper demonstrates that CO2 reductions were achievable. The results demonstrate that links can be drawn between sustainability and climate change and identifies that significant CO2 savings, through robust energy management of the UK housing stock, these results can be achieved. It is also suggested that the theoretical mole developed can be reproduced to consider climate change targets and provide benchmarks, not only in the UK but in other countries. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic grid integration brings many negative effects to the distribution network, and carrying out the research on its consumption capacity (CC) in the distribution network is the key to guarantee the safe and stable operation of the distribution network. This paper takes the maximum power access mode and minimum network loss as the optimization objectives, and takes voltage deviation, voltage fluctuation and short circuit capacity as the constraints to construct the evaluation model of distributed PV power generation CC. In view of the above problems, this paper uses edge computing to construct the evaluation of new energy (NE) CC of the grid, and applies it to the IEEE33 node network and the actual network. Finally, the established mathematical model and algorithm are validated (relative to Monte Carlo, the evaluation time of edge computing and time series production simulation is shorter, 13.11s and 23s, respectively). [ABSTRACT FROM AUTHOR]

This paper examines the pervasive barriers hindering the widespread adoption of energy efficiency measures across various sectors. It categorizes these barriers into economic barriers, high upfront costs, organizational challenges, such as technical expertise and behavioral barriers, namely risk aversion and framing. To address these barriers, a comprehensive review of energy efficiency policies is conducted. These policies include incentives, coercive instruments, award systems, university industry collaboration, and technical support. To review energy efficiency barriers and policies, a methodological approach integrating non-structured snowball sampling with targeted literature review techniques was devised. Finally, the paper underlines that the success of these policies is contingent upon the active involvement, the collaboration, and the feedback of businesses, ensuring the feasibility of these policies. Their proactive engagement is indispensable in tackling energy efficiency barriers and driving the implementation of energy-efficient technologies, thus paving the way for a more sustainable and competitive businesses. [ABSTRACT FROM AUTHOR]

This paper examines the role of energy efficiency in enhancing corporate sustainability and competitiveness. Employing a systematic literature review methodology, we analyze the financial advantages, corporate sustainability alignment, and crisis resilience associated with energy efficiency measures in business operations. The results highlight the significant cost savings, improved profitability, and enhanced competitiveness resulting from energy-efficient practices. Furthermore, we elucidate how energy efficiency initiatives align with broader corporate sustainability objectives, reduce environmental impact, and promote corporate social responsibility. Additionally, the paper discusses how energy efficiency enhances organizational resilience by bolstering preparedness for crises and promoting long-term sustainability and adaptability. The main implications of our findings underscore the importance of prioritizing energy efficiency as a strategic imperative for businesses seeking to achieve sustainability goals while maintaining competitiveness in a rapidly evolving global landscape. [ABSTRACT FROM AUTHOR]

This paper examines the pervasive barriers hindering the widespread adoption of energy efficiency measures across various sectors. It categorizes these barriers into economic barriers, high upfront costs, organizational challenges, such as technical expertise and behavioral barriers, namely risk aversion and framing. To address these barriers, a comprehensive review of energy efficiency policies is conducted. These policies include incentives, coercive instruments, award systems, university industry collaboration, and technical support. To review energy efficiency barriers and policies, a methodological approach integrating non-structured snowball sampling with targeted literature review techniques was devised. Finally, the paper underlines that the success of these policies is contingent upon the active involvement, the collaboration, and the feedback of businesses, ensuring the feasibility of these policies. Their proactive engagement is indispensable in tackling energy efficiency barriers and driving the implementation of energy-efficient technologies, thus paving the way for a more sustainable and competitive businesses. [ABSTRACT FROM AUTHOR]

This paper examines the role of energy efficiency in enhancing corporate sustainability and competitiveness. Employing a systematic literature review methodology, we analyze the financial advantages, corporate sustainability alignment, and crisis resilience associated with energy efficiency measures in business operations. The results highlight the significant cost savings, improved profitability, and enhanced competitiveness resulting from energy-efficient practices. Furthermore, we elucidate how energy efficiency initiatives align with broader corporate sustainability objectives, reduce environmental impact, and promote corporate social responsibility. Additionally, the paper discusses how energy efficiency enhances organizational resilience by bolstering preparedness for crises and promoting long-term sustainability and adaptability. The main implications of our findings underscore the importance of prioritizing energy efficiency as a strategic imperative for businesses seeking to achieve sustainability goals while maintaining competitiveness in a rapidly evolving global landscape. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. [ABSTRACT FROM AUTHOR]

The paper presents the historical review of a series of indicators of energy poverty in Bulgaria, including the recently adopted official definition, and the existing and planned policy instruments to reduce energy poverty in the field of energy efficiency, heating appliances and systems, and renewable energy sources. Targeted heating support and temporary direct support in the form of targeted heating grants and compensation under Operation Support for Vulnerable Households -- SAFE are not analysed. The official definition of energy poverty is used to assess both the level and the effectiveness of the definition and its ability to reflect national specifics and trends, its potential shortcomings in the programme scope, targeted groups and results. [ABSTRACT FROM AUTHOR]

Renewable energy aspires to boost decarbonisation and power the circular economy in accordance with the Green Deal objectives. The transition, however, faces many challenges and understanding the drivers behind it is essential. This paper analyses 27 European Union countries for the period 2008 - 2020 to identify economic and social determinants influencing renewable energy consumption. The results indicate that the population density, education, economic growth, digitalisation and research and development have a considerable positive impact. Urbanisation, on the other hand, appears to exert a negative influence. In addition, the empirical findings reveal a unidirectional causal relationship running from education, economic growth and energy poverty towards renewable energy consumption. This research provides useful insights into important drivers of renewable energy adoption which can facilitate or hinder the energy transition and achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Existing unconventional intersection designs can enhance the capacity of intersections to deal with repaid pressure on urban commuting, whereas they force vehicles to multi-stop and reorganize, resulting in safety risks and high fuel consumption. This paper proposes the Intermittent Lane Intersection (ILI) that combines the advantages of variable lane intersection and CTE (Combining the tandem control and exit lanes for left-turn control). An integrated optimization model is established to obtain the optimal geometric and signal control parameters, and a homothermic genetic algorithm is used to solve the model. Then, the application of ILI in different traffic demands of the Tianfu Third Street intersection is analyzed. Compared with the benchmark design (TI, CTE, and CTE with signal coordinated control), ILI can effectively reduce intersection delay, fuel consumption, and parking times. Additionally, ILI has great application superiority when the traffic arrival rate is [1500,1700] and the left-turn ratio is [0.40,0.60]. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) has emerged as a pivotal technology to address the escalating communication demands of Internet of Things (IoT). To meet the data transmission needs in IoT systems, we propose an antenna selection method of massive MIMO systems and joint power allocation strategy considering IoT user devices grounded in quantum energy valley optimization (QEVO) in this paper. The derivation of a maximum energy efficiency equation has been established to optimize system resources and provide high quality of service meeting the IoT user devices requirements. To tackle the nonlinear, multi-constrained hybrid optimization challenge proposed for massive MIMO resource allocation in IoT systems, we introduce a quantum energy valley optimization algorithm. This algorithm harnesses the strengths of quantum computation and energy valley optimization (EVO) mechanisms. Simulations indicate that our proposed method can efficiently meet real-time user transmission requirements while markedly enhancing system energy efficiency. When compared with existing power allocation strategies and optimization algorithms applied in massive MIMO communication systems, our approach demonstrates superior performance. The proposed method demonstrates the highest performance across various simulation scenarios when applied to both allocation strategies and system energy efficiency. Our proposed method with highest performance can be properly used on massive IoT devices. [ABSTRACT FROM AUTHOR]

As the number of subscribers for Long Term Evolution (LTE) technology and associated resources increase, it has become mandatory for the LTE mobile operators to evaluate how efficiently the existing LTE cell network will perform in real-time scenarios. Controlling the transmitting services in the LTE cell network is an important factor as it assists in increasing the entire network execution. The best way to attain this is to attain the integrity between the users or achieve the efficient values of spectral efficiency (SE) and energy efficiency (EE). Spectral efficiency and energy efficiency are important parameters for a cellular network to exhibit its network performance level. In this paper, the real-time information records of approximately 50000 BTS sites, which were comprised of Key Performance Indicators (KPIs) such as Radio Resource Control (RRC), E-UTRAN Radio Access Bearer (ERAB), Packet drop, etc. have been considered. From the data set, throughput has been predicted using the Random Forest Algorithm. Further Spectral and Energy efficiency has been predicted. Spectral efficiency (SE) helps in controlling the transmitting power of the cell network and Energy Efficiency helps in enhancing the network quality of service (QoS). The maximum value of the spectral and energy efficiency at the uplink throughput and downlink throughput had been taken from the Nokia Drive Test sheet. If the predicted values lie beyond the limits of the network, it will indicate that the network performance has deteriorated and has scope of improvement. The resulting variables were chosen on the basis of the real field assessment by the Nokia Network Pvt. Ltd in India. [ABSTRACT FROM AUTHOR]

Faced with rapidly rising energy demand in industrialised societies and widespread global concern, countries are actively promoting the transition from conventional to renewable energy systems. The goal is to invest in renewable energy in the most efficient way to meet rising energy demand and reduce the challenges posed by climate change. However, decision makers must carefully weigh various factors when selecting the most appropriate renewable energy investment projects. This paper presents a novel method for Multi-Attribute Decision Making(MADM) that uses the Bipolar Complex Fuzzy(BCF) to convey the vagueness and uncertainty of decision makers, so that the result obtained better reflects the actual scenario and the subjective biases of decision makers. We defined BCF Einstein Weighted Averaging (BCFEWA) operator and BCF Einstein Ordered Weighted Averaging (BCFEOWA) operator to aggregate evaluation information. Then we discussed some properties of the proposed aggregation operators. Additionally, we present an integrated MADM technique grounded in the BCF framework that combines the CRiteria Importance Through Intercriteria Correlation (CRITIC) and ELECTRE III methods. Specifically, the CRITIC method determines attribute weights, and the ELECTRE III method ranking the alternatives to determine the best renewable energy investment projects. After analysing the results and comparisons, it can be inferred that the suggested methodology offers an effective evaluation process. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) play a crucial role in healthcare and various other domains by enabling continuous monitoring of vital signs and activities. However, challenges such as low-powered sensors, unstable links, and routing issues hinder their efficiency and reliability. In this paper, we propose the Thermal Aware High Throughput (TAHT) routing protocol to address these challenges. TAHT utilizes a multi-hop topology and prioritizes stable links for efficient routing, considering parameters like energy, link quality, and temperature. Through experimental evaluation, TAHT demonstrates a 10% to 20% improvement in throughput, a 15% reduction in temperature overheads, and enhanced overall performance compared to benchmark protocols. By effectively managing energy consumption, minimizing duplicate data transmissions, and optimizing routing paths, TAHT offers a novel solution to improve the reliability and sustainability of WBANs, thus advancing the field beyond previous efforts in the literature. [ABSTRACT FROM AUTHOR]

Aerogels, with their exceptional properties such as high insulation performance, lightness, and eco-friendliness, present immense potential for use in the construction industry. This paper reviews the progress in research and application of various types of aerogels in construction, highlighting notable case studies demonstrating energy efficiency gains and sustainability impacts. It delves into the lifecycle of aerogels, discussing environmental implications at each stage. Despite their potential, aerogels face certain limitations including production complexity, brittleness, and high cost which hinder their widespread adoption. The paper further discusses these challenges and highlights the need for continuous research and development efforts to overcome them, with a view to achieving more scalable production, enhanced mechanical properties, and cost reduction. Looking forward, the paper discusses the expansive horizon of aerogel research, emphasizing the potential of smart aerogels, their integration with renewable energy systems, and exploring the possibility of recycling or creating bio-based aerogels to enhance their overall sustainability. While still in early stages, aerogels promise to be a major contributor to the future of construction, leading to more sustainable and efficient built environments. [ABSTRACT FROM AUTHOR]

The hydraulic turbine has been used extensively in the field of energy conservation. For turbines that have low heads and large discharge, improving recovery efficiency and stability is crucial due to their significant hydraulic impact. This paper provides a detailed analysis of the correlation between the influence of radial guide vanes on the stability of low-head, large-discharge turbines focusing on hydraulic performance and energy dissipation before and after the implementation of guide vanes. Moreover, in this paper, two types of turbines, with and without guide vanes, were designed considering the desulfurization scenario. Hydraulic efficiency, radial force, and internal flow field mechanics were numerically studied, and validated through experiments. The results reveal that the working range of the hydraulic turbine could be widened and the energy recovery efficiency improved by a maximum of 3.11% in the small flow rate under the action of guide vanes. Furthermore, it results in a substantial reduction in the radial force of the impeller. Subsequently, the variation in entropy production of different components under full flow rate conditions was compared between the models with and without guide vanes. The total energy consumption decreases sharply under overall working conditions due to the flow control ability of guide vanes affecting the flow state. The entropy production rate of the impeller remains the largest regardless of the presence of a guide vane in the turbine. The vortices inside the guide vanes increase obviously with the flow rate increase. [ABSTRACT FROM AUTHOR]

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays a pivotal role in modern resource management and sustainable development. This paper presents a concise overview of state-of-the-art techniques and methodologies employed in the field of energy consumption forecasting, with a particular emphasis on the application of Machine Learning (ML) models. The paper surveys recent advancements, addresses key challenges, and identifies promising directions for future research in this critical domain. By examining the current landscape of energy consumption forecasting through the lens of machine learning, this review aims to offer researchers and practitioners valuable insights and guidance for enhancing the accuracy and efficiency of energy consumption pattern prediction. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are challenged by the need for optimized Energy Consumption (EC), efficient Data Aggregation (DA), and reliable routing due to their dynamic topologies and limited resources. Existing solutions like TEAMR and DDQNDA address these concerns but face significant drawbacks--TEAMR lacks adaptability to rapidly changing topologies, while DDQNDA suffers from high computational overhead and delayed convergence, hindering its effectiveness in real-time scenarios. To overcome these limitations, this paper introduces the Adaptive Reinforcement Learning (RL)-Based DA and Routing Optimization (ARL-DARO) algorithm. The proposed methodology follows a systematic approach, beginning with cluster formation and Cluster Head (CH) selection (CHS) using the Grey Wolf Optimizer (GWO), which ensures Energy-Efficient (EE) clustering and optimal CH selection. In the next step, trust factors such as Node Connectivity (NC), Residual Trust (RT), and Cooperation Rate (CR) are integrated into Quality of Service (QoS) metrics as part of the Fitness Function(FF) to enhance route reliability and security. Finally, the ARL-DARO algorithm is employed to dynamically optimize both data aggregation and routing. It leverages Q-learning to select optimal routes based on energy efficiency, security, and link reliability, further reducing data redundancy and improving adaptability to realtime network changes. Performance is assessed using parameters such EC, packet delivery ratio (PDR), end-to-end latency (E2E delay), throughput, and network lifetime (NL) across networks with 100, 200, 300, 400, and 500 nodes. Results show that ARL-DARO significantly reduces energy consumption by up to 45%, increases throughput by 30%, and extends network lifetime, proving its effectiveness over existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Studies in Conservation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Studies in Conservation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the impact of digital financial inclusion (DFI) on household energy poverty. We develop a simple theoretical model to explain the energy poverty alleviation effect of DFI. Using multidimensional household energy consumption survey data, we find that DFI reduces the incidence of household energy poverty by about 33.5 % and the intensity of energy poverty by 24.6 %, driven mainly by improvements in energy efficiency and an increase in household off-farm work, resulting in a stronger role in rural and low-income groups. However, the threshold model demonstrates that the Internet and digital tools are a prerequisite for this pro-poor energy effect to be realized. Notably, DFI, while exhibiting spatial spillover effects, exacerbate energy poverty in neighboring areas due to the siphoning effect. [ABSTRACT FROM AUTHOR]

Mixed-mode ventilation system (MMVS) can significantly decrease the energy consumption of Egyptian office buildings while meeting indoor air quality requirements and thermal comfort of occupancy. This paper studies the effect of using mixed-mode ventilation system (MMVS) in achieving both energy efficiency and thermal comfort in office buildings. The research specifically focuses on investigating the energy-saving potential and thermal performance of existing office buildings in Egypt by merging natural ventilation (NV) with mechanical ventilation (MV) to reach an effective mixed-mode ventilation system (MMVS). Additionally, an energy performance simulation was used to study how to achieve thermal comfort and the building's indoor temperature. Firstly, a selected office building in Cairo was used as a case study, and its thermal performance was studied by simulation. Secondly, site measurements were conducted to record the real thermal performance of the tested office building. These measurements were used for the validation phase. The validation demonstrated a satisfactory agreement between the building simulation and measurement data for air temperature (T) and relative humidity (RH). Thirdly, building energy simulations and analysis were used for testing the energy performance and thermal comfort of the tested scenarios that have different alternatives in the operation settings. The results revealed that it is possible to increase the thermal comfort hours by merging natural ventilation and applying the mixed-mode ventilation system only by changing the operation settings on the MMVS performance. The building's annual energy consumption was reduced by approximately 27 % compared with the use of HVAC in the building. Natural Ventilation hours increased by approximately 27 % to reach 427 hours (h) and the HVAC hours decreased by 17 % to reach 898 hours (h). [ABSTRACT FROM AUTHOR]

The underwater Internet of Things (IoT) has received more and more attention from society and has been developing rapidly. The underwater wireless sensor network(UWSN) is an important part of the underwater Internet of Things and has become the focus of research and application. Due to the limitations of the underwater environment, UWSN has uneven energy consumption and high data transmission delay, which greatly reduces the survival cycle of UWSN. In this regard, this paper proposes an energy-saving routing scheme based on the ant colony algorithm, which continuously optimizes the routing paths through the ant colony algorithm in order to make the best routing path selection, balance the energy consumption of the UWSN, and improve the survival cycle of the network as a whole. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) in recent times offers a greater amount of flexible design during the deployment of it in the form of network. The interfacing of IoT with a wireless sensor network (WSN) requires an optimal solution to preserve the consumption of energy while the data has been transmitted via nodes. In this paper, we develop machine learning (ML)-assisted routing to route the data packets of IoT sensors from the real-time environment over WSN. In this study, we use three different planes for optimal routing of packets from the source node to the destination node. The source node is the IoT sensors that involve data collection, and the intermediate nodes are the WSN sensor nodes that route the packets. Optimal routing decisions based on the pre-trained data in an artificial neural network (ANN) stabilize the routing of data packets without congestion. The simulation is conducted to test the efficacy of the ANN routing in the integrated network, and the results show that the ANN-based routing achieves higher energy efficiency and throughput than other models. [ABSTRACT FROM AUTHOR]

Resource allocation, scheduling, and provisioning are indeed critical and complex issues in cloud computing environments, as they directly impact both the user experience and the profitability of cloud service providers. Also, the excessive use of resources and the fact that the required resources may exceed those available have raised additional challenges. The effective and optimal allocation of resources in this dynamic cloud environment poses a significant challenge due to the substantial increase in cloud utilization. Then, an energy-efficient task scheduling algorithm is required to improve the efficiency of the resource allocation process. This paper proposed an efficient and autonomous dynamic resource allocation in cloud computing with optimized task scheduling. We aim to minimize the cost of virtual machines and the makespan. Furthermore, its impact on the best response time and energy consumption has been studied. The simulation shows that our method gave better results than the former ones. [ABSTRACT FROM AUTHOR]

This paper analyzes the current situation of interval train control systems (railway interlocking) on the railway network of Kazakhstan, geared towards enhancing the effectiveness of railway transportation, the objective is to augment the capacity of rail lines, diminish operational expenses, minimize energy consumption, and mitigate wear on both tracks and rolling stock. To evaluate the data transmission channel of the TETRA standard, a test model with a long ping and a load test has been developed, and a two–stage test methodology has been proposed for the Zhetygen – Altynkol section. According to the results of the conducted experiments, it is proved that if the connection is lost for more than 60 seconds, the train automatically stops with an emergency braking command and, subject to the restoration of data transmission between the OBU (onboard unit) and the modem, the radio-blocking system works normally for cases with loss of communication and the board permits to move. For evaluating the influence of deploying radio communication on radio-blocking and signal strength, the Hata model within the COST231 framework was chosen. This model incorporates diverse factors contingent upon the environment, such as urban or suburban settings. The model of path loss «Loss of suburban macro-path» was chosen as a model. [ABSTRACT FROM AUTHOR]

The use of renewable energy presents a viable alternative to fossil fuels. However, their intermittent nature does not allow for an immediate response to energy demand. Thus, it is necessary and beneficial to harness various renewable sources and integrate a storage system as an auxiliary source to mitigate this intermittency. The hybridization of energy sources requires efficient management of power flows to ensure the proper functioning of the overall system, regardless of changing weather conditions. In this paper, we propose an intelligent power management control for hybrid wind-solar-battery systems connected to micro-grids based on fuzzy logic. The proposed control approach addresses several specific challenges compared to conventional methods in the intelligent energy management of renewable hybrid systems. It effectively manages the uncertainties and nonlinearities inherent in weather variations, optimizes performance by dynamically adjusting the operations of energy sources and storage systems, and ensures efficient realtime utilization of available energy resources, thus providing greater flexibility and adaptability. Additionally, it enhances the stability and reliability of micro-grids by integrating more flexible and adaptive decision-making mechanisms. The simulation results using MatLab/Simulink demonstrate the significant advantage of this intelligent management lies in its ability to precisely control the state of charge of the battery across five distinct levels, which is not achievable using traditional management practices that rely solely on the maximum and minimum levels of the state of charge. [ABSTRACT FROM AUTHOR]

The current sea monitoring equipment's are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean. [ABSTRACT FROM AUTHOR]

The paper looks into natural gas as a factor influencing the dynamics of the energy market in Eastern Europe. The energy market as such is in fact the correlation between various interrelated markets which have different effects on the final consumers' prices and on the inflation. The Eastern European energy market is currently one of the fastest-growing energy markets. It consists of resource-rich and transit countries (Belarus, Bulgaria, Czech Republic, Hungary, Poland, Moldova, Romania, Russia, Slovakia and Ukraine) with a population of roughly 291 million people, that contributes significantly to the world's energy consumption and security. The dynamics of this market depend on the new infrastructure for transiting and storing natural gas as well as the big undergoing investments in renewable energy infrastructure. [ABSTRACT FROM AUTHOR]

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H ∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism. [Display omitted] • A multi-event driven mechanism (MEDM) based joint non-fragile control scheme is derived. • The wind-integrated power system model based on MEDM is established. • The MEDM is integrated into the joint non-fragile controller design under DoS attacks. • The MEDM based joint non-fragile H ∞ automatic generation control strategy is proposed. • It provides stronger robustness and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the essential but tricky project of indoor area lighting graph, underscoring its importance in reaching energy efficiency, aesthetic attraction, and useful illumination. traditional lights design methods regularly fall short in adaptability and optimization, main to suboptimal illumination and electricity inefficiency. To triumph over these limitations, we suggest a unique approach employing a variable granularity co-evolutionary set of rules for indoor area lighting plan diagram. Our method initiates with a mathematical modeling of the indoor space, setting more than one optimization targets like lighting fixtures efficiency, cost-effectiveness, and aesthetic issues. The core of our method lies within the integration of a variable granularity method with a co-evolutionary set of rules. This amalgamation enables dynamic granularity modifications based on evolutionary progress, enhancing the search efficacy throughout multiple goals. Next steps include actual-time environmental tracking and comments integration, permitting adaptive lighting fixtures diagram modifications. Experimental results reveal the superiority of our approach over conventional techniques, showcasing huge upgrades in lighting fixtures best and energy intake. The proposed technique's adaptability and efficiency mark a widespread advancement in wise lighting fixtures diagram. [ABSTRACT FROM AUTHOR]

Energy efficient permutation flowshop scheduling problem (PFSP) is widely studied to reduce the energy consumption in manufacturing systems. Most traditional studies make the premise that setup time is a component of processing time. However, this assumption does not satisfy the small batches, more varieties manufacturing models criterion. Therefore, energy efficient PFSP with setup times thus becomes a pressing problem that needs to be resolved. The contribution of this paper includes: (1) a permutation flowshop scheduling problem (PFSP) mathematical model by considering energy consumed and setup time by each machine in the system; (2) a grey-wolf algorithm optimization with variable neighborhood search (VNS) method (GWO_VNS) is proposed for the multi-objective PFSP model; (3) solved 9 benchmarks problems of Taillard (1993) [8] for the minimization of flowtime (FT) and energy consumption (EC). The performance of the proposed GWO_VNS algorithm is evaluated on the benchmark problems selected from the published literature [15]. It is noted that the proposed algorithm performed better on both the objectives i.e., FT and EC minimization. Overall, the proposed algorithm achieved 33.25% and 30.56% average improvement in FT and EC minimization respectively on benchmark problems. [ABSTRACT FROM AUTHOR]

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape. [ABSTRACT FROM AUTHOR]

The environmental impact of radiology and radiotherapy activities is influenced by the energy consumption of equipment, the life cycle of consumables, waste generation, and CO 2 emissions caused by staff travel. This study aims to investigate radiographers' perception and knowledge of environmental sustainability issues. An online survey was created and distributed to European radiographers and therapeutic radiographers. The survey questions (n = 43) include demographic data; questions on their perceptions and actions regarding environmental sustainability in healthcare, energy consumption, emissions from staff travel, waste generation from radiological procedures; the role of radiographers in addressing sustainability issues within their departments. A total of 253 responses were collected from 27 European countries. About their perception on sustainability issues, most participants considered environmental sustainability in healthcare as very important. According to 63.6% (n = 161) of respondents, the energy consumption of radiological equipment is the major source of environmental footprints from radiology activities. Additionally, 44.7% (n = 113) believe that conducting diagnostic examinations remotely could reduce environmental footprints from staff commuting About their actions at workplace, over 70% (n = 192) reported turning off devices after use. Attention to waste recycling is high, but limited to paper, plastic and glass. Contrast agents recycling procedures are implemented by 13% (n = 33). The absence or unawareness of environmental sustainability procedures in the workplace was reported by 66% (n = 167). Radiographers could play an active role in environmental sustainability programs for 243 (96.1%) participants. This study provides a comprehensive overview of European radiographers' knowledge and perceptions concerning environmental sustainability issues. While radiographers recognize the importance of a green radiology department, significant gaps remain in their understanding of eco-friendly initiatives in radiology units' activities. Enhancing radiographers' skills with sustainability expertise could promote a greener culture within radiology departments. [ABSTRACT FROM AUTHOR]

This paper demonstrates improved throughput and energy efficiency of wireless communications by exploiting nonorthogonal multiple access (NOMA), multiple input–multiple output (MIMO), and radio frequency energy harvesting (EH) technologies. To assess the performance of NOMA MIMO communications with EH (MMe), we consider the nonlinear characteristics of EH devices and propose explicit expressions for throughput and outage probability. Based on our results, the system performance is significantly mitigated by EH nonlinearity and is considerably improved by increasing the number of antennas. Additionally, by appropriately adjusting the system parameters, our NOMA MMe innovation can avert complete outages while optimizing system performance. Moreover, the results demonstrate the superiority of the NOMA MMe over its orthogonal multiple access MMe counterparts. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

This paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all. [ABSTRACT FROM AUTHOR]

The influence of green finance (GF) on renewable energy development efficiency (REDE) has garnered considerable attention from both academia and society. To study the changes in China's REDE in recent years and GF's effect on it, this research first evaluates 30 provinces' REDE from 2010 to 2019 using a non-radial directional distance function (NDDF) based on global technical assumptions. Second, we evaluate the direct impact of GF on REDE. Finally, we explore the internal mechanism for how GF influences REDE. The results of this study are as follows. (1) GF has a significant promoting effect on REDE. (2) The improvement of renewable energy (RE) technological innovation level and the increase of foreign investment strengthen GE's promoting effect on REDE. (3) The positive effect of GF on REDE is prominent in regions with lower environmental regulations and carbon intensities and higher financial development levels. This paper offers relevant policy suggestions to improve REDE and to enhance GF development. [ABSTRACT FROM AUTHOR]

Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980–2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security. [ABSTRACT FROM AUTHOR]

Industrial digitization (ID) has become a new driving force for high-quality development of industries. Existing studies have mainly examined the global value chains position (GVCP) in terms of openness and technological progress, and there are a few studies from the perspective of industrial digitization. This paper constructs an input-output model to measure the GVCP of China's industry sectors by use the world input-output database (WIOD), and empirically tests the impact of ID on the GVCP of China's industry sectors by constructing ID indicators. The conclusions of the study are shown below. First, ID significantly enhances the GVCP of China's industry sectors, which can drive the industry sectors to realize value chain climbing. Second, mechanism analyses shows that ID can enhance the GVCP by increasing export technology complexity and revealed comparative advantage of China's industry sectors. Third, ID mainly influences the forward production lengths, and ID boosts GVCP more significantly for technology-intensive and pollution-intensive sectors. Fourth, the economic consequences show that ID decreases energy intensity and labor employment by improving the GVCP of China's industry sectors. Based on the findings, we believe that upgrading the digitization of industry sectors is an effective measure to drive up the value chains. [ABSTRACT FROM AUTHOR]

Conflicts, whether internal or external, have the ability to increase energy security risks. Given this background, this paper investigates the impact of conflicts on energy security risks in the panel data of 40 major energy-consuming economies from Asia, America, Europe, and Africa by employing the Cross-sectionally augmented Autoregressive Distributed Lag model from 1996 to 2021. The results show that the risks to long-term energy security fall with the rise in internal and external conflicts globally and in Asian, American, European, and African economies. However, all regions see decreased long-term energy security risk due to rising financial development. At the same time, national income mitigates energy security risks globally and in American countries. Technological innovation reduces long-term energy security risks globally in Asian, American, and European economies. Conversely, only gross domestic product and technological innovation cause the energy security risks to fall globally and in American economies. [ABSTRACT FROM AUTHOR]