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The internet of things (IoT) is an exponentially growing network of physical objects equipped with sensors, software and network connectivities to collect, process, transmit and receive data. Wireless sensor networks (WSNs) play an essential role in supporting the IoT. These networks, made up of nodes with the ability to monitor their environment, enable the collection and transmission of specific data in real time, offering enhanced applications and services within IoT networks. This symbiosis between WSN and IoT can be defined as WSN-based IoT. The complexity of WSN-based IoT lies in the effective management of these varied devices, each with its own distinct capabilities. Clustering is a popular technique for reducing the communication load, conserving energy, aggregating data and optimizing the performance of WSN-based IoT systems. Once the cluster heads (CHs) are chosen, conventional clustering algorithms typically use a single radius of clustering (RC) to group devices into multiple clusters. However, this approach may not be optimized for WSN-based IoT networks, as devices may have different features, for example, the residual energy ( R Enrg ) and the distance to the base station (DistBS). In a previous work, we proposed the DCOPA (a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications) protocol for clustering in WSN-based IoT networks. DCOPA applies the same clustering algorithm to the elected CHs, without considering their distinctions in terms of R Enrg and DistBS. The proposed new approach, called unequal-DCOPA (UDCOPA), allows us to define for each CH its adaptive radius of clustering (ARC) which will be sensitive to the local criteria of R Enrg and DistBS of the CH concerned. The ARC is modeled as a multicriteria system applied to each CH. Simulation results show that our new UDCOPA approach outperforms DCOPA and LEACH protocols for energy management, load balancing, scalability and network lifetime. UDCOPA increases lifetime by (62.61%) over LEACH and by (32.72%) over DCOPA. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) directly impacts the energy consumption, resource utilization, and service quality of cloud data centers (CDCs), and it has become an active research topic in cloud computing. Inspired by the ant colony system (ACS) which has been proven effective metaheuristic approach for solving NP-hard problems, this paper proposes an improved ACS-based energy efficiency strategy (EEACS) for VMP problems. Our approach considers each virtual machine (VM) as an energy-consuming block, taking into account its individual energy requirements. EEACS ranks the physical machines (PMs) in a CDC in descending order based on their energy efficiency and optimizes both server selection and pheromone updating rules within the ACS. By guiding artificial ants towards promising solutions that balance energy consumption and resource utilization, EEACS ensures that VMs are placed efficiently based on pheromone and heuristic information. Extensive simulations in both homogeneous and heterogeneous computing environments demonstrate the effectiveness of our proposed strategy. The experimental results show that the EEACS enhances the resource utilization and achieves a notable reduction in energy consumption in comparison to conventional heuristic and evolutionary-based algorithms. [ABSTRACT FROM AUTHOR]

Given the current trends, it seems obvious that temperatures and atmospheric carbon dioxide levels will rise over the coming years. To deal with climate change, a gradual transition to more sustainable viticulture operations is required. It seems necessary to carry out accurate local studies for future projections. The purpose of the current study was to choose the best grape production system in Jowzan Valley, Malayer, Iran. This region was registered by FAO as a Globally Important Agricultural Heritage System (GIAHS) in 2018. This study seeks to answer the question: if climate change has occurred in the Malayer region, which of the traditional and trellis systems is more sustainable from economic, energy, and environmental aspects? The sixth phase of the Coupled Model Intercomparison Project (CMIP6) was used to project the climate parameters of Malayer in the past and the future. The output of climate models based on rainfall, temperature, and wind (as factors influencing grape yield) in the observation period of 1992–2021 (the base period) and forecasting the future period of 2021–2100 under three scenarios (SSP1-26, SSP2-45, and SSP5-85) were obtained. The determination of hotspots in terms of cost, energy, and environment for two production systems was done with the approaches of material and energy flow cost accounting (MFCA) and life cycle assessment (LCA). The results of these two approaches help us know which of the two systems are currently more sustainable in terms of economic, energy, and environmental aspects. Grape production in 1 hectare was chosen as a functional unit (FU). The system boundary included the foreground processes that the farmer was directly involved in managing. Grape data were collected in the crop year of 2021–2022 from the vineyards of Jowzan region. According to results, under SSP1-26, SSP2-45, and SSP5-85 scenarios, the annual mean temperature is expected to rise at rates of 0.27, 0.477, and 0.82 °C 10a−1 in the future (2021–2100), respectively. From an economic point of view, labor was recognized as the main input. By changing the system from traditional to trellis, production costs can be expected to be halved. The main hotspot in terms of negative products was the loss of grapes during production or harvesting. Such conditions were also true in terms of energy. From the environmental aspect, on-farm emissions and electricity were identified as hotspots. These effects are greatly reduced in the trellis system compared to the traditional system. In the case of changing the system from traditional to trellis, these effects can be reduced to a reasonable extent by improving the efficiency of irrigation and fertilizer distribution through the irrigation system. According to the results obtained from the climate scenarios as well as the results of the economic, environmental, and energy evaluation of the two production systems, it can be concluded that the use of the trellis system can be more sustainable than the traditional system. [ABSTRACT FROM AUTHOR]

The building sector is a significant contributor to global energy consumption, necessitating the development of innovative materials to improve energy efficiency and sustainability. Phase change material (PCM)-enhanced concrete offers a promising solution by enhancing thermal energy storage (TES) and reducing energy demands for heating and cooling in buildings. However, challenges related to PCM leakage, mechanical strength reduction, and encapsulation durability hinder widespread adoption. This paper critically reviews the various PCM incorporation techniques and evaluates their impact on thermal performance and structural integrity. Our findings revealed that while PCM integration improves thermal efficiency, it reduces mechanical strength, especially at higher PCM content. To address these challenges, recent work emphasizes advancements in encapsulation technologies and thermal property optimization, focusing on mitigating leakage and enhancing durability. Organic PCM types are favored for concrete applications due to their compatibility, though issues with leakage control and strength retention remain unresolved. Hence, the choice of incorporation technique must balance application requirements, allowable mechanical property reductions, and cost constraints. Moreover, the lack of standardized methods to assess long-term PCM-composite durability highlights a need for further development of PCM encapsulation and integration techniques to optimize thermal and structural performance. [ABSTRACT FROM AUTHOR]

Due to the environmental impacts caused by increasing energy consumption, energy efficiency has become an important policy objective for various countries and governments. While many studies have explored the determinants of energy intensity, the question of how economic complexity and the shadow economy affect energy efficiency has received inadequate attention. This paper connects these issues by studying the direct and complementary effects of economic complexity and the shadow economy on energy efficiency in G7 countries. The empirical findings indicate that both economic sophistication and the shadow economy exert adverse impacts on energy intensity. However, each factor has a harmonizing effect on the other’s influence on energy intensity. While the expansion of the shadow economy reduces the harmful effect of economic complexity on energy intensity, economic sophistication diminishes the damaging impact of the shadow economy. Therefore, the effect of either economic complexity or the shadow economy on energy intensity depends on their interactions and their influence on total energy use. These findings provide governments with policy implications for handling the interrelations between these economic, social, and energy issues. [ABSTRACT FROM AUTHOR]

To accomplish net-zero carbon emissions in the built environment by 2050, we must equitably decarbonize commercial buildings, including reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not associated with major building end uses like lighting and HVAC. Research shows PPL energy reduction strategies and control technologies have the potential to save energy. But even when implemented, these savings have rarely been achieved and there has not been widespread uptake in U.S. commercial buildings. We investigate why these technologies and strategies have not seen widespread adoption and identify behavior and technology pathways to increase PPL reduction in U.S. commercial buildings. We examined behaviors of commercial building stakeholders through 44 interviews and cross-referenced qualitative analysis findings with in-depth technical knowledge of existing PPL control technologies and reduction strategies. PPL control implementation must be paired with management strategies, such as occupant engagement and training, to achieve optimal savings, and best practices should be disseminated across the industry. We found that increasing access to cost and energy savings data will promote uptake of PPL control technologies and allow designers to better incorporate PPLs into building design. Improving access to funding for PPL energy efficiency projects and addressing the split-incentive problem will increase adoption of PPL efficiency and control. Code bodies should continue to include PPL monitoring and reduction measures in energy codes. Key building stakeholders, including cybersecurity and information technology teams, should be involved in PPL monitoring and reduction strategy processes for successful implementation. [ABSTRACT FROM AUTHOR]

Numerous studies are currently focused on improving the performance and efficiency of electric vehicles (EVs). This research aims to evaluate the necessity for a practical testing methodology to simulate real-world driving scenarios by comparing the driving range measured on a chassis dynamometer with the ranges observed under various actual driving conditions. Tests were conducted on the chassis dynamometer using the multi-cycle test (MCT) mode, employing the urban dynamometer driving schedule (UDDS) and the highway fuel economy driving test (HWFET). Subsequently, we assessed the energy efficiency of three routes compliant with the real-driving emissions-light duty vehicles (RDE-LDV) regulations under real-world driving conditions. Our findings revealed disparities in energy efficiency ranging from 10.8 to 22.9% when driving on the same route and up to 29.3% when driving on different routes. This study highlights the importance of tailoring information provision, such as certification tests, to each country's environmental context. [ABSTRACT FROM AUTHOR]

Micro-Zonal Occupant-Centric Control (MZOCC) saves HVAC energy by creating comfortable virtual micro-zones around occupants and controlling diffusers using occupant centric control (OCC). This is needed in modern open-plan offices with flexible work schedules to avoid energy wasted in air-conditioning unoccupied regions. Despite this, there is no clarity yet on how micro-zones are to be planned within thermal zones. There exist knowledge gaps between established methods of thermal zoning for HVAC design and micro-zoning. Several temporal and spatial factors influence the transient thermal exchanges between virtual micro-zones which increases the complexity of micro-zoning. This study develops protocols for micro-zoning to simplify the process of MZOCC. The study first outlines factors critical in micro-zoning open-plan offices which are identified from literature and survey of Indian offices. Results indicate that Indian open-plan offices are diverse and caters to a variety of activities within each thermal zone. Occupancy survey shows that office spaces are partially occupied, most of the time. The second part delineates protocols for micro-zoning following assumptions derived from literature and survey data. In the third stage, these protocols are used to shortlist the best micro-zonal layouts for MZOCC from numerous possible layouts. The effectiveness of the protocols is confirmed using CFD simulations where it is observed that the shortlisted layouts perform better than others. These layouts act as a starting point, on which airflow control can be strategized to further improve MZOCC. Results also show that planned MZOCC saves 73% energy compared to zone level OCC. [ABSTRACT FROM AUTHOR]

Since heating-related energy consumption is one of the main sources of greenhouse gas emissions and accounts for a large part of domestic energy use in Europe, reducing heating-related energy consumption has great potential to reduce Europe's greenhouse gas emissions. This study examines which factors determine people's intention to decrease heating-related energy usage, specifically lowering the temperature in winter. It was part of a larger European project focused on promoting energy reduction. This study presents a smaller-scale model tested among 363 individuals from five pilot countries, i.e., Belgium (n = 58), Croatia (n = 82), Germany (n = 105), Greece (n = 33), and Portugal (n = 85). We applied three robust theoretical frameworks: the Theory of Planned Behaviour, the Value Belief Norm Theory, and the Prototype Willingness Model. We conducted a confirmatory factor analysis to ensure construct validity, followed by a structural equation model. Our findings suggest that willingness (from the Prototype Willingness Model) is the most important predictor in explaining someone's intention to reduce heating-related energy consumption. Additionally, perceived behavioural control, subjective norms, and attitudes (part of Theory of Planned Behaviour) also play an important role in predicting the intention to reduce energy consumption. Lastly, personal moral norms (from the Value Belief Norm Theory) have a significant impact, but their effect is moderate compared to the other predictors. [ABSTRACT FROM AUTHOR]

Thermographic inspection is particularly effective in identifying thermal bridges because it visualizes temperature differences on the building's surface. The focus of this work is on energy-efficient computing for deep learning-based thermal bridge (anomaly) detection models. In this study, we concentrate on object detection-based models such as Mask R-CNN_FPN_50, Swin-T Transformer, and FSAF. We do benchmark tests on TBRR dataset with varying input sizes. To overcome the energy-efficient design, we apply optimizations such as compression, latency reduction, and pruning to these models. After our proposed improvements, the inference of the anomaly detection model, Mask R-CNN_FPN_50 with compression technique, is approximately 7.5% faster than the original. Also, more acceleration is observed in all models with increasing input size. Another criterion we focus on is total energy gain for optimized models. Swin-T transformer has the most inference energy gains for all input sizes (≈ 27 J for 3000 x 4000 and ≈ 14 J for 2400 x 3400). In conclusion, our study presents an optimization of size, weight, and power for vision-based anomaly detection for buildings. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) is the process of mapping virtual machines to physical machines, which is very important for resource utilization in cloud data centres. As such, VM placement is an NP-class problem, and therefore, researchers have frequently applied meta-heuristics for this. In this study, we applied a hybrid meta-heuristic that combines ant colony optimisation (ACO) and grey wolf optimisation (GWO) to minimise resource wastage, energy consumption, and bandwidth usage. The performance study of the proposed work is conducted on variable number of virtual machines with different resource correlation coefficients. According to the observations, there is 2.85%, 7.61%, 15.78% and 19.41% improvement in power consumption, 26.44%, 57.83%, 77.90% and 83.89% improvement in resource wastage and 2.94%, 8.20%, 9.99% and 10.72% improvement in bandwidth utilisation as compared to multi-objective GA, ACO, FFD and random based algorithm respectively. To study the convergence of the proposed method, it is compared with few recent hybrid meta-heuristic algorithms, namely ACO–PSO, GA–PSO, GA–ACO and GA–GWO which exhibits that the proposed hybrid method converges faster. [ABSTRACT FROM AUTHOR]

The extensive deployment of large cloud data centers has led to substantial energy consumption. Energy conservation is a critical concern for cloud service providers seeking to lower their operating costs within their data centers. To address this energy consumption challenge, effective approaches such as VM consolidation and VM migration are essential. These approaches must carefully balance the trade-off between energy consumption and Service Level Agreement Violations (SLAV). In this paper, we propose an energy-efficient VM selection algorithm for VM consolidation and call it the Simultaneous CPU–Ram Utilization Balancer (SCRUB) policy. This algorithm takes into account CPU and RAM utilization while trying to maintain a balance between energy consumption and SLAV. To evaluate the performance of our proposed method, we implemented it using the Cloudsim simulation toolkit and conducted simulations using real-world workload traces from PlanetLab and Google over three different days. The results show that the SCRUB VM selection policy has led to improvements in various metrics, including reduced energy consumption and a decreased number of VM migrations compared to existing VM selection policies. Specifically, it achieved a 16.98% decrease in energy consumption and a 46.42% reduction in the number of migrations for the PlanetLab dataset, and a 10.95% decrease in energy consumption and a 43.96% decline in the number of migrations for the Google dataset compared to the baseline algorithm MMT. [ABSTRACT FROM AUTHOR]

Continuous growth of the manufacturing sector is resulting in to higher energy demand due to which the manufacturing costs and greenhouse gas emissions are also increasing. Beside reduction in energy consumption; improvement in energy efficiency, power factor and reduction in cutting temperatures are also vital to ensure better sustainability of the machining sector. This work evaluates the trade-offs between energy, heat generation and cutting quality during milling of medium carbon steel (EN8) alloy steel. The effects of input process parameters viz. spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle has been studied on the energy consumption, energy efficiency, power factor, cutting temperatures, surface roughness response parameters. The inclusion of helix angle as an input factor and, using energy efficiency and power factor as output parameters are the major highlights of this work. The machining experiments were conducted using response surface methodology for design of experiments. The multi objective optimization was carried out by using desirability approach, for three different groups of response variables considering the different importance of energy consumption, cutting temperatures and surface roughness, under different manufacturing circumstances. The predictability of the multiple regression approach was found to be more than 90% for all the responses which highlights model significance. The direct and interaction effect were studied and discussed in details for all the responses. The values of the composite desirability achieved in all the three types of optimization problems were on higher side (0.813, 1 and 0.794). The results of the optimization were confirmed by conducting the experiments the optimized settings. The percentage error between experimental and RSM predicted result was found to be within acceptable limits. This study can be helpful for reducing the energy consumption and cutting temperature without compromising on surface roughness, in the machining of medium carbon steel. [ABSTRACT FROM AUTHOR]

In recent years, the quality of the environment has declined dramatically as a result of human activities, which threaten the sustainability of our ecosystem. In this context, a number of earlier studies have investigated the environmental problems through the lens of different environmental indicators such as ecological footprint and carbon dioxide emissions. However, it is possible that by taking these factors into account, the supply side of environmental quality will be neglected. This research seeks to address this deficiency by investigating the impact of green growth, energy efficiency, green technology innovation, economic growth, trade openness, and human capital on the level of load capacity factor. This indicator places equal emphasis on the supply and demand sides of the ecosystem by taking into account both ecological footprint and bio capacity. In this setting, the present study makes use of CS-ARDL model for the top 10 manufacturing countries from the years 1990 to 2019. The short- and long-run findings of the CS-ARDL model unveil that green growth, energy efficiency, green technology innovation, and human capital all positively influence the load capacity factor, suggesting that these factors help the top 10 manufacturing countries to improve their sustainability limits. In contrast, trade openness and economic expansion in underlying countries increase environmental degradation by lowering the level of LCF. These outcomes are also validated by the AMG and CCEMG approaches and the granger causality test. On the basis of the findings, important policy recommendations are presented with the aim of assisting the top 10 manufacturing economies in enhancing the quality of their environment and fulfilling the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Turbid media such as fog and haze seriously affects the quality of imaging for systems such as urban surveillance and satellite remote sensing. Image dehazing has become a research hotspot in the field of computer vision. Neural-network-based image dehazing has the potential of high performance, but requires high computational power and storage space, making it costly to deploy in a system with limited hardware resources, especially for edge computing systems. In this paper, we propose an energy-efficient dehazing neural network named E 2 AOD-Net, which is pruned (generalization performance rises), quantized, pipelined, and parallelized over AOD-Net. We implement E 2 AOD-Net on FPGA platform, achieving a lightweight dehazing hardware accelerator that realizes real-time dehazing. The experimental results show that the frame rate of E 2 AOD-Net hardware accelerator inference reaches 38.3 FPS, while consuming the power of 2.491 watts. The VMAF index is improved by 89.14%. The energy efficiency is 42.61 GOPS/w. [ABSTRACT FROM AUTHOR]

The concentration on Mobile Wireless Sensor Networks (MWSN) has significantly increased due to its various applications such as traffic, automation, industrial infrastructure, military, health, and so on. Minimizing the energy expenditure of nodes in WSN is a challenging task for improving their life expectancy due to the limited battery source available. In this paper, the Energy aware Multiobjective Levy Flight Artificial Rabbits Optimization (E-MLFARO) is proposed to enhance the energy efficiency and data delivery of the WSN. The Levy Flight incorporated in the E-MLFARO offers the dynamics that improve the searching capacity in identifying the optimum CHs and path. At first, the E-MLFARO is used to choose optimal Cluster Heads (CHs) from the normal nodes based on their energy, distance, CH balancing, and node degree. Next, multi-hop routing is achieved by using the E-MLFARO to ensure consistent data broadcasting over the network. The developed E-MLFARO is used to increase data delivery while minimizing energy expenditure. The E-MLFARO method is analyzed by alive nodes, energy expenditure, received packets, Packet Delivery Ratio (PDR), and life expectancy. The existing researches, namely, ISSADE, DRE-LEACH, DE-ACO and EOR-iABC are used to compare with the E-MLFARO. The alive nodes of E-MLFARO for 1500 rounds is 100 which is higher when compared to the ISSADE. [ABSTRACT FROM AUTHOR]

Climate change and the ongoing energy transition can increase energy poverty rates. To date, the main tool employed to alleviate energy poverty has involved income transfers to vulnerable households. However, measures that seek to improve a home's energy efficiency have recently gained increasing relevance. In this study we assess the effectiveness of these two types of policy, assuming universal coverage and optimal behaviour. Results points that income transfers and energy efficiency measures have the potential to decrease the proportion of households in energy poverty; however, the magnitude of their respective effects differs greatly. The average impact of energy efficiency measures provides for a greater reduction in energy poverty rates than income transfer policies. Although the greatest reduction in energy poverty is obtained by combining both measures, this combination of tools leads to overlapping effects with income transfers making only a marginal contribution once total retrofit have been implemented. [ABSTRACT FROM AUTHOR]

Recent studies demonstrated that green innovation and environment-related technologies reduce energy intensity and improve energy efficiency, contributing to the reduction of carbon emissions. However, the existing studies employ linear estimation methods to examine the relationship between green innovation and energy intensity and do not consider the indirect implications of institutional quality for the effect of green technology on energy intensity. Institutional quality is found to be an essential driver of innovation, and countries may need to achieve at least a minimum level of institutional quality to promote green innovation and improve their energy intensity. To test this hypothesis, this paper examines the relationship between energy intensity and green innovation using a panel dataset from 72 countries between 1996 and 2017 and a panel threshold model when institutional quality is considered a threshold variable. The findings highlight that green innovation reduces the energy intensity if and only if countries surpass a certain threshold of institutional quality. Therefore, countries need to improve their institutional quality to promote green innovation and benefit from green technologies in improving their energy intensity. [ABSTRACT FROM AUTHOR]

In the last decade, metalworking firms around the world faced the challenge of embracing Industry 4.0. Amidst this, some machining companies battle with outdated, unconnected equipment on their shop floors. This work introduces a novel methodology linking machining events to electrical power behavior, enabling real-time monitoring of machine tool states. The prototype, OEE-Plus, tested on a legacy transfer line, extends Overall Equipment Effectiveness by incorporating an energy consumption indicator. The significant result is a practical solution empowering small to medium-sized enterprises to assess the efficiency and sustainability of their legacy machinery. [ABSTRACT FROM AUTHOR]

This study investigates the impact of renewable energy integration on energy efficiency within the China-Pakistan Economic Corridor (CPEC) from 2000 to 2022. Efficient renewable energy utilization is crucial for the CPEC’s sustainable development. We employed multiple linear regression analysis on data from national energy statistics databases to examine the relationship between energy efficiency defined as the ratio of total renewable energy output to input in (TWh) and four renewable energy sources: hydropower, biofuel, solar PV, and geothermal. Our model controlled for potential confounding factors and met assumptions of linearity, multivariate normality, and absence of multicollinearity. Hydropower exhibited a highly significant negative correlation with energy efficiency (-0.632, p < 0.001), with a regression coefficient of -7.642 × 10⁻⁴ (p < 0.001). Similarly, biofuel showed a significant negative correlation (-0.222, p < 0.001) and a coefficient of -9.580 × 10⁻⁴ (p < 0.001). These findings suggest that increased production from these sources is associated with decreased energy efficiency, potentially due to transmission losses or inefficiencies in conversion technologies. In contrast, solar PV (-0.027, p = 0.638) and geothermal (-0.014, p = 0.806) showed no statistically significant relationship with energy efficiency, indicating that at their current scales of deployment, they do not significantly impact overall efficiency. A moderate negative correlation (r=-0.114, p = 0.047) was observed between solar PV and geothermal production, possibly reflecting resource allocation choices within the renewable energy portfolio. These results highlight the need for targeted interventions to improve the efficiency of hydropower and biofuel production and emphasize the potential for future contributions from solar PV and geothermal as their deployment and grid integration expands. The findings contribute to a more nuanced understanding of the complex interplay between renewable energy generation and overall energy efficiency within large-scale development projects. [ABSTRACT FROM AUTHOR]

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. [ABSTRACT FROM AUTHOR]

China and the United States have the highest demand for fossil fuel energy for transportation and power generation, which promotes growth while also damaging the environment. Policymakers, and environmental scientists, are increasingly interested in understanding key strategies to improve energy efficiency to mitigate fossil fuel energy demand and sustain the environment and growth. In this context, this study uses augmented mean groups (AMG) and common correlated effects mean group (CCEMG) estimators to reveal the impact of China-US technological innovation, transportation infrastructure, and power generation on fossil fuel energy demand and fossil fuel energy intensity from 1995 to 2020. Besides, the study examines whether fossil fuel energy use and fossil fuel energy intensity are the main drivers of carbon dioxide emissions (CO2) and economic growth. The analysis results show that technological innovation has a strong adverse influence on fossil fuel energy use, and fossil fuel energy intensity. Electricity generation, road transport infrastructure investment and aviation infrastructure investment have considerably contributed to fossil fuel energy use, and fossil fuel energy intensity. The construction of rail transit infrastructure has significantly alleviated fossil fuel energy demand, and fossil fuel energy intensity. Fossil fuel energy use and fossil fuel energy intensity significantly contribute to carbon dioxide (CO2) emissions and economic growth. However, technological innovation and renewable energy use can substantially reduce carbon dioxide (CO2) emissions, while making a considerable contribution to economic growth. Based on empirical investigation, this study provides a series of useful insights into the sustainable development goals of the Chinese and American economies. [ABSTRACT FROM AUTHOR]

The indoor comfort of occupied buildings has become essential, as it significantly affects the productivity and health of occupants. This review provides a comprehensive overview of air and thermal comfort studies in residential buildings using alternative materials. This study aims to provide the trends and the state of the research in this field, to summarize the optimization methods, and to offer a perspective on future studies. The results demonstrate that in comparison to traditional air conditioning systems, natural ventilation systems, and air purifying plants have the potential to reduce energy consumption and carbon emissions in buildings by up to 60%. Also, cross-laminated timber (CLT) buildings can achieve energy savings of up to 30% compared to conventional buildings due to their superior insulation properties and reduced thermal bridging. This review has provided highlights to help the building owners to promote a healthier environment, reduce energy consumption and achieve more comfortable indoor air quality. The study promotes incorporating innovative materials and use of natural ventilation systems, smart air conditioning systems and air purifying plants. [ABSTRACT FROM AUTHOR]

Since the last two decades, carbon neutrality has become a primary target of all economies. Governmental environmental policies stand as the most potent tools in the arsenal when it comes to tempering the effects of climate change. Fostering the adoption of green energy sources and embracing energy efficiency principles may assume an essential role in upholding the standard of ecological integrity. The primary objective of this inquiry revolves around the meticulous analysis of the intricate interplay between economic growth, the trajectory of industrialization, the up-gradation of industrial sector structure, the integration of green energy paradigms, and the implementation of energy efficiency strategies and environmental policies in the frame spanning from 2000 to 2019. To tackle the matter of cross-sectional dependency and heterogeneity, second-generation cointegration estimators, Dynamic Seemingly Unrelated Regression (DSUR) and Augmented Mean Group (AMG), were employed to estimate long-run relationships. The consequences of DSUR and AMG indicate that while economic and industrial growth contributes to environmental degradation, renewable energy usage, and medium–high-tech industries mitigate the carbon emissions in selected countries. Further study results suggest that energy intensity positively impacts environmental degradation, which means energy efficiency helps mitigate CO2 emissions in these countries. This study also reveals that the degree of stringency in environmental policy negatively affects CO2 releases in the selected nations. Consequently, our study recommends the enhancement of the stringency of environmental policies, promoting environmentally friendly energy usage, the efficient use of energy, and the advancement of industries into medium–high-tech industries as effective ways to mitigate climate change in specific developing countries. [ABSTRACT FROM AUTHOR]

IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protocol compared to the Ride algorithm, highlighting its potential for optimizing network performance and improving resource utilization in IoUT environments. [ABSTRACT FROM AUTHOR]

Electronic components cooling (ECC) for manufacturing and technological uses has become one of the most interesting topics that researchers have focused in the modern era. Because of the continuous minimization in the electronic components size and substantial quantity of heat generation; the traditional cooling methods cannot be able to follow rapidly reduction in such high amount of heat flux. The use of nanofluid is an intriguing possibility for ECC. Such advancements may result in advancements in the field of electronic equipment in addition to improved efficiency of energy. The present article proposes is to study the use of various types of nanofluids over different shapes for preventing redundant heat, to effectively control the thermal stress as well as to maintain the electronic components temperature. Some fascinating features related to utilizing nanofluids to ECC are also addressed. In addition, further study prospects and directions in this area are put forward. It can be noticed that the addition of γ-Al2O3 NPs in water increases the heat transmission efficiency by 37% and 28%, while keeping Reynolds numbers (Re = 601.3 and 210) respectively. The cooling ability of the automobile radiator upsurges up to 17.46% with the addition of Al2O3-NPs. [ABSTRACT FROM AUTHOR]

This paper presents a thermodynamic and sustainability analysis for an experimentally developed solar water heater-with water treatment. The parabolic trough collector (PTC) is employed to capture thermal energy from the sun, which is subsequently utilized to increase the temperature of water. "Experimental and numerical" investigations are divided into two cases. Case 1: PTC with a glass cover and case 2: PTC without glass cover. Using an experimental analysis, data are collected, such as solar insolation and water outlet temperature. The collected data are utilized to analyse the thermodynamic performance of the proposed system. The first law of thermodynamics helps to quantify the system's performance, called energy analysis, whereas the second law of thermodynamics provides qualitative performance, called exergy analysis. The maximum energy and exergy efficiency achieved by the proposed system are 69.5% and 6.15%, respectively. Simultaneously, an exergy-based sustainability analysis is proposed, which shows how effectively the fuel exergy is utilized with the proposed system. The maximum sustainability index for case 1 is 1.07, and for case 2, it is 1.08. At the end of the experimental investigation, a slight decrease in total dissolved solid (TDS) was detected, indicating that if we enhance the performance of the PTC system, by changing the reflector material and installing evacuated tubes, the quality of the water may improve. [ABSTRACT FROM AUTHOR]

In order to explore the heat transfer properties of natural convection in the cavity, this article uses the three-dimensional CFD inverse method and the temperature measurement points based on the experimental results. In addition, different flow modes and mesh divisions are used. For the applicability of different turbulence models, the least squares method is then used to calculate Q value of the heat source in the cavity and compare that with the experimental measurement results and consistent with the overall trend. Finally, the temperature distribution diagram and velocity streamline diagram of the simulation results are provided to visualize the flow field and analyze its heat transfer characteristics. The results show that the flow pattern and the number of grid points have a great influence on the results. In terms of experiments, with the change of the cavity size and the influence of the opening, the air flow will be different. To achieve the effect of enhancing natural convection, due to the inflow of a large amount of cold air, although the overall temperature of the cavity is greatly reduced, the upper cold wall is surrounded by cold air, which reduces the heat transfer coefficient. In order to verify the reliability and usability of the results of the inverse algorithm in this article, the obtained results of heat transfer coefficient and heat dissipation are compared with previous results or empirical formulas in other related literature. When the height drops from h = 0.1 m to h = 0.05 m, the average increase in heat transfer coefficient is 2.79%; while for the height drops from h = 0.05 m to h = 0.02 m, the average increase in heat transfer coefficient is 74.8%. When side fin length is reduced form W2 = 0.1 m to W2 = 0.08 m, the average increase in heat transfer coefficient is 14.33%; when side fin length is reduced form W2 = 0.08 m to W2 = 0.06 m, the average increase in heat transfer coefficient is 41.28%. [ABSTRACT FROM AUTHOR]

The present research utilizes an inverse 3D CFD technique, coupled with the least squares approach and laboratory temperature readings, to predict the suitable turbulence flow model and unknown heat transfer rates within a cubic hot box. This enclosure features four cylindrical horizontal tubes arranged in a diamond configuration, functioning as heaters to generate heat within the space. Furthermore, the heat dissipation from each tube, flow field and temperature contours are also established. The energy equation for tubes and the governing equations for air are solved by coupling. The present estimates are obtained assuming a constant heat transfer rate. The range of the Rayleigh number is from 60,294 to 76,824. Testing various turbulence models revealed that the zero-equation turbulence model reduces the root mean square discrepancy between the simulated thermal behavior and laboratory outcomes compared to other methodologies. To verify the chosen flow model's accuracy, the Nusselt numbers and flow field distributions aress compared with existing correlations and interferometer photographs. The maximum errors between the estimates of heat loss and heat transfer rate and the corresponding values from previous studies are 44 and 28% for St = 0.036, 34 and 3% for St = 0.045, and 37 and 18% for St = 0.054. To assess the impact of tube surface and spacing on the flow field model, calculation results are compared with those from four square-arranged tubes, revealing a lack of existing studies on this specific investigation. [ABSTRACT FROM AUTHOR]

With mobile and embedded devices getting more integrated in our daily lives, the focus is increasingly shifting toward human-friendly interfaces, making automatic speech recognition (ASR) a central player as the ideal means of interaction with machines. ASR is essential for many cognitive computing applications, such as speech-based assistants, dictation systems and real-time language translation. Consequently, interest in speech technology has grown in the last few years, with more systems being proposed and higher accuracy levels being achieved, even surpassing human accuracy. However, highly accurate ASR systems are computationally expensive, requiring on the order of billions of arithmetic operations to decode each second of audio, which conflicts with a growing interest in deploying ASR on edge devices. On these devices, efficient hardware acceleration is key for achieving acceptable performance. In this paper, we propose a technique to improve the energy efficiency and performance of ASR systems, focusing on low-power hardware for edge devices. We focus on optimizing the DNN-based acoustic model evaluation, as we have observed it to be the main bottleneck in popular ASR systems, by leveraging run-time information from the beam search. By doing so, we reduce energy and execution time of the acoustic model evaluation by 25.6 and 25.9 %, respectively, with negligible accuracy loss. [ABSTRACT FROM AUTHOR]

The very large-scale integration implementation of a unique hardware-oriented image compression technique for wireless sensor networks (WSNs) is presented in this work. Networks of individually owned sensors spread out throughout an area that can detect, measure, and report changes in environmental variables are known as wireless sensor networks (WSNs). Color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, estimation, quantization, and enhanced Golomb-rice coding (EGRC) are all included in the proposed design. A unique improved BTC with an enhanced Golomb-rice coding (IBTC-EGRC) framework has been proposed in this paper. IBTC training framework has been developed using the fuzzy decision-based approach to achieve representative levels and satisfy WSN requirements to accomplish the cost-effective and power-efficient features. Two ideal reconstruction values and bitmap files have been obtained for every block. IBTC divides images into variable block sizes for mathematical translation and inter-pixel redundancy removal. The subsampling, estimation, and quantization stages have minimized redundant data. Finally, EGRC has been used to code the value with the highest likelihood. An EGRC module decreases memory use and computation complexity. The EGRC technique reduces hardware resource utilization by removing the need for the context module, a crucial part of lossless image compressor designs and its memory. Proposed method, Golomb-rice parameter forecasting and managment module is used to preserve pixel connection and improved compression ratio. A UMC 180 nm CMOS technology has been used to implement the suggested framework. This design has 5.8k synthesized gate counts and a core area of 56,000 µm2. 100 MHz and 3.01 mW were the operational frequency and energy consumption, respectively. The proposed method has a 9.37% reduction in gate count compared to the previous fuzzy BTC-based approach. [ABSTRACT FROM AUTHOR]

The success of future 5G/6G communication relies heavily on efficient and low-latency wireless cellular device-to-device communication, which enhances network spectrum, energy efficiency, and data transfer rate. To achieve this, advanced technologies such as deep learning and swarm optimization are utilized to improve energy-efficient communication while considering latency. The fifth and beyond-generation networks require intelligent and efficient technologies to control and transfer data. Unmanned Aerial Vehicles (UAVs) are employed to establish high-transfer-rate data communication between devices using minimal energy consumption. This study also focuses on integrating UAV-based D2D communication with other advanced technologies, highlighting the significance of integration with possible solutions to enhance network performance. The development of 5G/6G communication technologies aims to meet the increasing demands of wireless networks, such as faster data rates, lower latency, improved reliability, and more excellent device and application support. However, the current network advantages need to be enhanced to meet the needs of the upcoming digital environment. Thus, to address these challenges, we propose a novel approach that utilizes optimized deep learning models in three ways for UAV-based device-to-device communication: 1. Improved Hybrid Particle Swarm Optimization and Effective K-means clustering approach (IHPSO-K) 2. Hybrid Fuzzy C means (HFCM) approach, and 3—greedy algorithm to overcome the restrictions faced by UAVs in meeting the latest technological requirements. With the above considering features, we use two methods to execute the efficient performance of the proposed algorithm, namely the device-centric approach and the network-centric approach. Combining these techniques can help achieve a more accurate and efficient clustering of data points in UAV-based D2D communication. This can lead to improved performance in terms of throughput, latency, and energy consumption, which are critical factors in such communication scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The European Society of Intensive Care Medicine (ESICM) Green Paper aims to address the challenge of environmental sustainability in intensive care and proposes actionable strategies for integrating sustainability into intensive care unit (ICU) stakeholder actions. Methods: The ESICM Executive Committee appointed a task force of topic experts and ESICM committee representatives to develop the ESICM Green Paper. The task force convened biweekly from January to June 2024, identifying key domains for environmental sustainability and prioritizing actions. Drafts were iteratively refined and approved by the ESICM Executive Committee. Results: Climate change will impact activities in intensive care in many ways, but also the impact of ICU activities on the environment is considerable; drivers for this include extensive resource use and waste generation in ICUs from energy consumption, use of disposable items, and advanced therapies for critically ill patients. The ESICM Green Paper outlines a structured approach for ICUs to reduce their environmental impact, emphasizing energy efficiency, waste reduction, and sustainable procurement. Furthermore, it endorses the need for awareness and education among healthcare professionals, integration of sustainability into research, and sustainable policies within scientific societies. Conclusions: The ESICM Green Paper reviewed the relevance of climate change to intensive care and provided suggestions for clinical practice, research, education, and ESICM organizational domains. It underscores that reducing intensive care's ecological footprint can coexist with high-quality patient care. Promoting a resilient, responsible healthcare system is a joint responsibility of all ICU stakeholders. [ABSTRACT FROM AUTHOR]

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive methodology for improving Wireless Body Area Networks (WBANs) in healthcare systems using Enhanced Gray Wolf Optimization (GWO). The methodology begins with WBAN initialization and the configuration of critical network parameters. To improve network performance and trustworthiness, direct trust calculations, historical trust , and energy trust, as well as energy consumption models based on distance and transmission type, are integrated. The use of an Enhanced GWO approach makes it easier to select optimal cluster heads, guided by a customized fitness function that balances trust and energy efficiency. This work has been carried on a PC with 16 GB RAM using MATLAB R2022b tool for simulation purpose. The methodology outperforms existing methods in terms of throughput, computation time, and residual energy. This promising methodology provides improved data routing, energy efficiency, and trustworthiness, making it a valuable asset in WBAN-based healthcare systems. [ABSTRACT FROM AUTHOR]

With the increasing demand for resources from cloud computing users, enhancing the energy efficiency of data centers and ensuring the Quality of Service have become noteworthy research topics. Most Previous studies on Virtual Machine (VM) consolidation often neglect the delay and energy consumption associated with Power Mode Transitions (PMTs), leading to potential deviations in experimental results from real-world scenarios. In this paper, we propose a Two-Level Heuristics Virtual Machine Consolidation (TLHVMC) method that takes into account the overheads of PMTs. This method employs a Greedy Strategy-based hybrid heuristic Placement (GSP) algorithm for resource allocation. After incorporating underloaded host detection for host classification, GSP applies distinct greedy strategies on various host lists, which balances data center workload and minimizes active hosts. Moreover, Time Window-based Centralized power mode Control policies are implemented to centrally manage host power modes. These policies improve energy-saving by suspending idle hosts and reduce the Service Level Agreement violations by adaptively resuming hosts based on the data center's state within a time window. Experimental results on real workload traces show that TLHVMC significantly improves the energy efficiency of the data center and maintains the required cloud system performance compared to some other VM consolidation algorithms. [ABSTRACT FROM AUTHOR]

This work analyzes the effect of positioning small cells (SC) on an existing heterogeneously scattered macro cellular (MC) network. The impact of SC deployment based on the planned blend of K-means and user density (KUD) algorithms on an existing MC network is explored. With an application of the Cox process, heterogeneous and uneven distributions of 50 and 100 users were generated for investigation. The simulated outcomes and plotted graphs illustrate a gradual increment in the average user throughput, sum rate, and energy efficiency with the overlaying of 8 SCs using the proposed KUD algorithm in an existing MC. [ABSTRACT FROM AUTHOR]

In recent decades, many government administrations have tracked energy efficiency programs (EEPs) against environmental concerns. This was done so that it could potentially be useful as a supportive mechanism for smart technologies applied under the smart city concept. To facilitate this, tradable white certificates (TWCs) have been implemented as popular financial instruments used by energy-intensive sectors to boost cleaner production. In this study, we address an industrial EEP development with a TWC instrument as a multi-agent problem. We study this problem for the first time in the context of a supply chain that includes a manufacturer, an energy producer, and household energy consumers. Furthermore, we explore a new monopolistic pricing model for energy services and energy-efficient products, regarding the rebound effect, energy consumption, and social welfare. Additionally, we discuss two revenue-cost-sharing contracts and compare them as contracts using a comprehensive parametric and experimental analysis. The results show that the second proposed contract has some advantages over the first one. However, the second contract leads to less production than the first one does, while at the same time leading to less social welfare. Also, the findings suggest that the second investigated contract is a more appropriate instrument for the obligated parties than the first one when the aim is to improve the performance of TWC schemes. These findings can provide better circumstances for governance to optimize the critical parameters' level on TWC schemes with the lowest analytical cost. [ABSTRACT FROM AUTHOR]

Green nanotechnology offers several possibilities to bridge the nano and sustainability domains and help us achieve the Sustainable Development Goals (SDGs) and address major global concerns. In this review, we detail the fundamentals and applications of GNT toward SDGs goals. This includes energy production, pollution abatement, water treatment, and eco-friendly agricultural practices. The discussion includes challenges related to legal, ethical and safety aspects as well as limits of the technology. Prospects call for collaborative efforts across various disciplines and policy guidelines in bridging eco-friendly nanotechnology with UN SDGs. We hope that the insights provided in this review can help guide future research and policies toward more environmentally responsible practices, improving global environmental stewardship with a special emphasis on nanotechnology. [ABSTRACT FROM AUTHOR]

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a promising domain that is gaining more attention because of its applicability and suitability in modern applications that comprise health care, disaster management, and environment monitoring purposes. Energy efficiency is considered an important issue because of the restricted energy of non-rechargeable batteries in the sensor. Cluster based routing is significant in handling the issue of energy efficiency in the WSN. In this paper, the Energy-based Multi Objective Golden Jackal Optimization (EMOGJO) is proposed for enhancing energy efficiency. The EMOGJO is used to select the optimum Cluster Heads (CHs) by using the Mean Node Energy (MNE), Individual Node Neighborhood Count (INNC), the interspace between sensors, the interspace between CH and BS, and node degree. Subsequently, the route through CHs until Base Station (BS) is discovered using the EMOGJO which is optimized by energy, and interspace between CH and BS measures. Therefore, the EMOGJO is used to enhance life expectancy while increasing the throughput. The EMOGJO method is evaluated using alive nodes, energy consumption, data packets received in BS, throughput, packet loss ratio and life expectancy. The EMOGJO is compared with the existing approaches that are, Hybrid Improved Whale Artificial Ecosystem Optimization (HIWAEO), Butterfly Optimization Algorithm (BOA)-Ant Colony Optimization (ACO), and Energy Efficient Cluster Based Routing Protocol using Firefly Algorithm namely EECRAIFA. The life expectancy of EMOGJO for 350 nodes is 17,778 rounds, which is greater than the HIWAEO's 1590 rounds, for the same number of nodes. [ABSTRACT FROM AUTHOR]

In wireless communication, radio access is a key and important technology. Cognitive Radio (CR) and non-orthogonal multiple access (NOMA) are two promising technologies for next generation networks. The combination of CR and NOMA can improve spectral efficiency and outage probability. In this paper, we introduce an underlay CR inspired NOMA system, in which we use a coordinate direct and relay transmission scheme for data transmission. A relay is used to reduce the effect of co-channel interference in CR inspired NOMA system. The relay in this article functions as decode and forward. In this article, different from other existing literature, we use a new scheme for decoding primary and secondary users symbols in a way that increases system performance, improves outage probability and energy efficiency. In this method, in the first phase, the primary transmitter broadcasts the symbol of the primary user and the secondary transmitter broadcast a superimposed symbol. In the second phase, the relay makes a decision based on the detection result based on the proposed plan and transmits the symbols. The primary and secondary users combine the first and second phase signals to improve the outage probability and increase system performance. Based on this, we introduce a signal-to- interference plus noise ratio combined, the use of this method improves the probability of outage compared to the other designs in this article. In this article, the closed form for the outage probabilities of the primary and secondary users has been extracted, as well as the outage probability at high signal-to-noise ratio (SNR)s and the asymptotic expressions for the outage probabilities of the primary and secondary users. Also, energy efficiency and throughput have been extracted for and it has been shown that choosing the optimal power allocation coefficient is important for increasing energy efficiency and throughput. Also, the simulation results show the high performance of the method proposed in this article compared to other designs. [ABSTRACT FROM AUTHOR]

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers' infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO 2 Savings of 460.92 lbs CO 2 /month. [ABSTRACT FROM AUTHOR]

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network's lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor's operational mode and communication frequency according to the node's energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field. [ABSTRACT FROM AUTHOR]

Solar collector systems efficiently transform sunlight into energy that may be used to meet various needs. This research aimed to use the Taguchi method to determine the ideal operating parameters for a solar thermal collector with a rectangular spiral absorber. Controllable parameters including mass flow rate, solar radiation, and absorber design were manipulated during the energy recovery process, and features like PV temperature and outlet water temperature were used to assess the system's effectiveness. The findings indicate that certain criteria significantly affect response indicators. The observed percentage contribution of absorber design, solar radiation, and the mass flow rate was 69.19%, 27.99%, and 2.83% in PV surface temperature. In comparison, the individual percentage contributions were 73.63%, 13.51%, and 10.57% for absorber design, solar radiation and mass flow rate for water output temperatures. The present model's R2 values for PV and outlet water temperatures are 97.24% and 99.67%, respectively. The Predictive regression model was found in fine harmony and the maximum percentage error is limited to 0.68%. The maximum analytical electrical efficiency was observed with a spiral rectangular absorber of 14.57% at the lowest mass flow rate of 0.04 kg/s at the lowest radiation level of 600 W/m2. In comparison, maximum analytical thermal efficiency was observed with a spiral rectangular thermal absorber of 63.56% at the highest flow rate of 0.06 kg/s and the highest solar radiation level of 1000 W/m2. The analytical and experiment findings were in better agreement in this study, with the highest relative error of 7.52%. According to the study's findings, the rectangular absorber-based PVT system is at its best at a higher mass flow rate to lower PV temperature and boost thermal energy recovery via water. The present research work can be extended for exergy, environmental, and economic feasibility analysis. [ABSTRACT FROM AUTHOR]

The study aims to comprehend sustainable behaviors in high-income nations, where human–environment interactions are crucial. Increased transportation needs in industrialized countries highlight the importance of environmental challenges affecting human well-being. Railway passenger carrier, automobile energy efficiency, technology breakthroughs, financial incentives, and public–private partnerships (PPPs) affect congestion and sustainability, which the study analyses for sound policy inferences in a panel of 28 high-income nations from 2000 to 2022. The panel ARDL estimates reveal that railway passenger carrier increases carbon emissions in the short run while it improves them over time, highlighting the importance of urban planning. Environmental pollution, energy use, transportation behavior (including PPPs), and technical innovation have an inverse connection, demonstrating the efficacy of energy-efficient transport methods, research and development, and renewable energy sources. However, economic incentives highly correspond with carbon-intensive habits, emphasizing the need for high-income countries to phase them out. [ABSTRACT FROM AUTHOR]

The cognitive radio sensor network (CRSN) faces additional difficulties in developing topology maintenance methods. Spectrum-aware clustering techniques, which depend on channel availability, have existed in the past, satisfactory clustering may not be possible. Due to the sensor nodes' restricted ability to transmit data due to energy resources, CRSNs have an energy efficiency problem.As a result of the failure of the clusterheads (CH) the activity of clustered networks stopped. To extend the lifespan of the network, a reliable selection of CHs should be carried out in the network. Utilizing the weighted clustering parameter, an adaptive clustering approach is suggested. Queue length, spectrum availability, node coverage and residual energy are all factors that are taken into account while choosing a CH. Every node's cluster head weight (CHW) is calculated using the proposed technique, and this CHW is used to choose which CHs to use for each node. The suggested approach includes a quality relay node selection mechanism using fuzzy logic for inter- and intra-cluster communications after clustering and CH selection. As inputs for selecting a relay, the channel quality, traffic index and connection error rate are used by the fuzzy system. The proposed method is superior to the current clustering methods for CRSN when studying the simulation data produced in terms of residual energy, end-to-end latency, routing overhead, network throughput, and longevity. [ABSTRACT FROM AUTHOR]

In existing schemes, there has been no analysis or steps built for balancing data integrity and energy consumption of clusters in the network, and providing security in WSN is difficult. In this research work, to overcome these troubles, we have used a Query-based Location-Aware Secure Multicast Routing for Wireless Sensor Networks (QLAMSR) is proposed to attain energy efficiency and security. There are three modules involved here. The first module is about the network model and the system overview to obtain more network lifetime. In the second module, an advanced encryption standard and RC6 algorithms is used here to provide authentication and data integrity. In the third module, efficient energy routes are established and demonstrated to illustrate network reliability. Node trust and reliable route energy efficiency are calculated to produce efficient data integrity through the idea of a fuzzy decision model. The proposed protocol is simulated with the Network Simulator tool (NS 2.34) to analyze the network performance metrics and finally its shown that proposed QLAMSR attains route energy efficiency by 39–92% and data integrity rate by 18–89(packets/sec). [ABSTRACT FROM AUTHOR]

Today, air management in air-conditioning processes is characterized by low displacement preferences independent of energy loads. Particularly in low-carbon approaches where thermal control management is gaining value, energy sustainability is being shaped together with effective heat management. In the thermal control approaches of air-conditioning systems, low-temperature air distribution together with heat mobility can be seen as an approach for an energy-efficient solution. In this study, first of all, the technical characteristics of the low-temperature air distribution system and its effects on the air-conditioning systems were examined, and the efficiency effects of the distribution system on the air-conditioning potential were evaluated. In the study, the energy efficiency effect of refrigerant preferences was especially assessed. It has been observed that the low-temperature system preference in air handling units varies depending on the refrigerant preference. In fact, it represents an increase in efficiency of 61.95% for R410a fluid and 61.84% for R134a. In addition, the environmental performance index based on entropy production also indicates an advantage of 22.24% for R410A and 25.26% for R134a. At the end of the study, thermodynamic and environmental assessments were made depending on the performance effect of the system. [ABSTRACT FROM AUTHOR]

The sustainable renovation of existing buildings is currently at the top of the agenda of the European Union. Sustainability is typically defined as the result of the interaction of environmental, economic, and social aspects, and it is now considered a major target objective in all sectors of our economy, including the construction one. The concept of sustainable renovation has changed significantly over time, leading to the current interpretation that considers the need to simultaneously improve safety and resilience against natural hazards and minimise energy and resource consumption, as well as to reduce impacts along the life cycle of the building. This manuscript presents insights into combined/integrated environmental and seismic retrofitting techniques and assessment methods for the sustainable renovation of the existing building stock, specifically focussing on those conceived according to a Life Cycle Thinking (LCT) approach. This manuscript goes beyond the current available state of the art by highlighting the evolution of the concept of building sustainability throughout time, as well as defining a comprehensive taxonomy of available retrofitting strategies, while also identifying common clusters among available research papers. This research effort is part of the mission of the European Association of Earthquake Engineering (EAEE) Working Group 15 (WG15), which focusses on 'combined seismic and environmental upgrading of existing buildings". [ABSTRACT FROM AUTHOR]