Alcohol regulated phase change absorbent for efficient carbon dioxide capture: Mechanism and energy consumption.

Phase change absorbents based on amine chemical absorption for CO2 capture exhibit energy-saving potential, but generally suffer from difficulties in CO2 regeneration. Alcohol, characterized as a protic reagent with a low dielectric constant, can provide free protons to the rich phase of the absorbent, thereby facilitating CO2 regeneration. In this investigation, N-aminoethylpiperazine (AEP)/sulfolane/H2O was employed as the liquid-liquid phase change absorbent, with alcohol serving as the regulator. First, appropriate ion pair models were constructed to simulate the solvent effect of the CO2 products in different alcohol solutions. The results demonstrated that these ion pair products reached the maximum solvation-free energy (ΔEsolvation) in the rich phase containing ethanol (EtOH). Desorption experiment results validated that the inclusion of EtOH led to a maximum regeneration rate of 0.00763 mol/min, thus confirming EtOH's suitability as the preferred regulator. Quantum chemical calculations and 13C NMR characterization were performed, revealing that the addition of EtOH resulted in the partial conversion of AEP-carbamate (AEPCOO-) into a new product known as ethyl carbonate (C2H5OCOO-), which enhanced the regeneration reactivity. In addition, the decomposition paths of different CO2 products were simulated visually, and every reaction's activation energy (ΔEact) was calculated. Remarkably, the ΔEact for the decomposition of C2H5OCOO- (9.465 kJ/mol) was lower than that of the AEPCOO- (26.163 kJ/mol), implying that CO2 was more likely to be released. Finally, the regeneration energy consumption of the alcohol-regulated absorbent was estimated to be only 1.92 GJ/ton CO2, which had excellent energy-saving potential.

Effect of geopolitical risk on energy consumption policy: New empirical evidence from BRICS.

This study investigates the impact of geopolitical risk (GPR) on energy consumption. For empirical analysis, we utilize the dataset of BRICS nations spanning 25 years from 1998 to 2022. We employ three econometric models, namely fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and autoregressive distributed lag (ARDL), to analyze the relationships between GPR and energy consumption. Our empirical findings reveal several significant insights. Firstly, we observe a substantial negative influence of GPR on both fossil fuel energy consumption (FEC) and total energy consumption (TEC). This suggests that higher levels of GPR are associated with reduced utilization of fossil fuels and overall energy consumption within the BRICS countries. Conversely, we identify a significant positive effect of GPR on renewable energy consumption (REC). This implies that, as GPR rises, there is a corresponding increase in the adoption and usage of renewable energy sources. Furthermore, our analysis uncovers the presence of asymmetric effects pertaining to other key determinants of energy consumption, including FDI inflow, economic growth, banking sector development, and inflation rate. This study offers fresh empirical evidence on the intricate interplay between GPR and energy consumption in BRICS nations, shedding light on the significant impacts of GPR and the nuanced effects of various economic factors. These findings have important implications for policymakers and stakeholders seeking to navigate energy policy decisions in a geopolitically dynamic world.

Efficient removal of succinic acid by continuous hydrodynamic cavitation combined with ozone and side influent injection.

Micropollutants (MPs) encompass a range of human-made pollutants present in trace amounts in environmental systems. MPs include pharmaceuticals, personal care products, pesticides, persistent organic pollutants, micro- and nano-plastics, and artificial sweeteners, all posing ecological risks. Conventional municipal wastewater treatment methods often face challenges in completely removing MPs due to their chemical characteristics, stability, and resistance to biodegradation. In this research, an Advanced Oxidation Process, combining hydrodynamic cavitation (HC) with dissolved ozone (O3) and side injection, was employed to efficiently degrade succinic acid (SA), an ozone-resistant compound and common byproduct. The HC/O3 process was run to treat different synthetic effluents, focusing on evaluating the influence of O3-to-total organic carbon (TOC) ratio, cavitation number (Cv) and O3 dosage. Notably, the results from a series of 14 experiments highlighted the critical significance of a low O3-to-TOC ratio value of 0.08 mg/mg and Cv value of 0.056 in HC for achieving efficient SA removal of 41.2% from an initial SA solution (106.3 mg/L). Regarding a series of four proof-of-concept experiments and their replications, the average TOC removal reached 62% when treating wastewater treatment plant effluent spiked with SA. This significant removal rate was achieved under initial conditions: Cv of 0.02, O3-to-TOC ratio set at 0.77 mg/mg, TOC concentration of 47.7 mg/L, 106 mg/L of SA, and a temperature of 25 °C. Notably, the electrical energy per order required for the 62% reduction in TOC was a modest 12.5 kWh/m3/order, indicating the potential of the continuous HC/O3 process as a promising approach for degrading a wide range of MPs.

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology.

This study takes an old oceanarium in Jiangsu Province, China, as a case study and monitors the energy consumption values related to electricity, water and gas consumption for the whole year before the renovation of the oceanarium. Based on the energy consumption monitoring data, an in-depth analysis of the energy consumption defects of the oceanarium before the renovation is conducted using energy audit technology. We combine the characteristics of the oceanarium itself according to: (1) the results of the building energy audit; (2) the results of the envelope calculation; and (3) the latest energy-saving retrofit technology. We developed a more scientific building energy-saving retrofit programme. Using this method, we can accurately monitor the energy consumption problems in old oceanarium buildings and find building energy consumption defects. In this study, we monitor the real-time energy consumption of nine old government office buildings in Jiangsu Province, China using energy monitoring technology. We analyse and study the monitoring results and finally target the scientific energy-saving retrofits. This method can be used to carry out targeted energy-saving building renovation in the future and put forward new methods and ideas for studying the energy consumption of existing public buildings.

Improvement of thermal environment in the outdoor atrium by employing the spray system.

Outdoor atriums have recently been applied with increasing frequency for natural illumination, but they produce a harsh thermal environment easily in summer. Moreover, overheating of the outdoor atrium necessitates air-conditioning to moderate indoor thermal comfort. Simultaneously, the substantial heat emissions from air-conditioning outdoor units worsen the outdoor thermal environment, creating a vicious cycle. Traditional passive evaporative methods involving water and greenery, while capable of regulating the thermal environment, suffer from low evaporative efficiency and pose significant challenges. To improve thermal environment in outdoor atriums, the spray system was employed due to its high cooling efficiency, especially in open or semi-open spaces. In this study, a comparative experiment was conducted to evaluate the effectiveness of using a spray system for evaporative cooling in open outdoor spaces. Furthermore, employing high-efficiency evaporative cooling through spraying to disrupt the vicious cycle of indoor and outdoor thermal environments. The dual goals include regulating indoor and outdoor thermal conditions while also mitigating the local heat island effect. Temperature and humidity distribution within the atrium and adjacent hallways were monitored, along with the impact on air-conditioning operation consumption in neighboring offices. Results showed that the spray system significantly improved the thermal environment in the outdoor atrium, reducing the average and peak air temperatures by 0.94-2.83 °C and 2.92-5.21 °C, respectively. It also resulted in a drop in the average temperature by 0.56-1.62 °C and the peak temperature by 2.31-3.25 °C in adjacent hallways. This effectively eased the issue of overheating in these areas while raising the comfort level in adjacent office spaces. The predicted mean vote decreased from 1.46 to 0.87, indicating a significant improvement in thermal environment in neighboring offices. Furthermore, the daily energy consumption was reduced by 10.6-12.4% in neighboring offices. This study provided the valuable guidance for improving thermal environments within outdoor atrium.

Risk assessment of PM2.5 from fossil energy consumption on the respiratory health of the elderly.

Air pollution mainly comes from fossil energy consumption (FEC), and it brings great threat to public health. The respiratory system of the elderly is highly susceptible to the effects of air pollution due to the decline in body functions. PM2.5 is a major component of air pollution, so the study of the impact of PM2.5 generated by FEC on the respiratory health of the elderly is of great significance. The existing studies have focused more on the effect of PM2.5 on mortality, and this paper is a useful addition to the existing studies by examining the effect of PM2.5 from FEC on the health of the elderly from the perspective of prevalence. In this paper, the binary Logistic regression model was used to calculate the exposure-response relationship coefficient for respiratory health in older adults using the data in 2018 from the Chinese Longitudinal Healthy Longevity Survey. And referring to the Dynamic Projection model for Emissions in China, the changes in the number of older persons suffering from respiratory diseases due to PM2.5 from FEC in the baseline scenario, the clean air scenario, and the on-time peak-clean air scenario were predicted. The results indicated that: (1) PM2.5 from FEC mainly came from coal; (2) PM2.5 from FEC was detrimental to the respiratory health of the elderly, and older seniors were more affected as they age; (3) In the on-time peak-clean air scenario, the number of elderly people suffering from respiratory diseases due to PM2.5 from FEC was growing at the slowest rate. Based on the above results, this paper raised recommendations for reducing the effect of PM2.5 from FEC on the health of the elderly.

ICT and energy rebound effect: Empirical analysis based on data from Chinese cities.

Information and communication technology (ICT) is predicted to emerge as a new driver of economic growth in the future and has been identified as a significant strategic emerging industry. It is of great theoretical and practical significance to include ICT in the energy rebound measurement framework. Based on Chinese city-level data from 2006 to 2019, this paper incorporates ICT into an improved stochastic frontier (SFA) model of energy consumption to measure the energy rebound effect (ERE) in 252 prefecture-level cities, and further investigates the formation mechanism of ICT affecting the ERE. The results show that when ICT is included in the energy rebound measurement framework, the average value of ERE in each region of China ranges from 0.4627 to 0.6458, with an overall average value of 0.5532, indicating that China's actual reduction in energy consumption is only about 40% of that expected. In terms of distributional characteristics, the mean value of ERE increases from coastal to inland, with the center of gravity always deviating from mainland China's geometric center (103°50'E, 36°N), the degree of spatial imbalance in the east-west direction is much greater than in the north-south direction. It is worth noting that ICT has a significant dampening effect on ERE, and the conclusion still holds after a series of robustness tests. In addition, the mechanisms by which ICT affects energy rebound include breaking through geographical and administrative barriers and reducing the impact of market segmentation on factor mobility.

Energy Minimization for IRS-Assisted SWIPT-MEC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Impact of the circular economy on ecological footprint: evidence from Germany.

The circular economy practices contribute to sustainable development by maximising efficiency, utilising renewable resources, extending product lifespans, and implementing waste reduction strategies. This study investigates the individual impacts of four sources of the circular economy on the ecological footprint in Germany, a country that is among the pioneers in establishing a comprehensive roadmap for the circular economy. The four sources examined are renewable energy consumption (REC), recycling, reuse, and repair of materials. Using time series data from 1990 to 2021, the study employed the dynamic autoregressive distributed lag (ARDL) simulation technique and also applied kernel-based linear regression (KRLS) to test the robustness of the results. The findings revealed that reuse practices significantly reduce the ecological footprint in both the short and long run. REC and repair also substantially decrease the ecological footprint, as shown by the simulation analysis. Conversely, while recycling is generally considered crucial for minimising environmental impact, in this study, it was found to contribute to environmental degradation. This paradox may be attributed to the nascent state of the recycling industry and data limitations. The results from KRLS confirm the findings of the dynamic ARDL. It is recommended that policymakers develop measures that are appropriate, efficient, and targeted to enhance the role of each source of the circular economy in reducing the ecological footprint in Germany. The major limitation of the study is its reliance on the indirect measures of circular economy attributed to the non-availability of data on direct measures.

A hybrid deep learning and clonal selection algorithm-based model for commercial building energy consumption prediction.

In contemporary society, commercial buildings, as a crucial component of urban development, face increasingly prominent energy consumption issues, posing significant challenges to the environment and sustainable development. Traditional energy management methods rely on empirical models and rule-based approaches, which suffer from low prediction accuracy and limited applicability. To address these issues, this study proposes a commercial building energy consumption prediction and energy-saving strategy model based on hybrid deep learning and optimization algorithms. This model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and the clonal selection algorithm (CSA), aiming to enhance the accuracy and efficiency of energy consumption predictions. Experimental results demonstrate that the CNN-GRU-CSA Network (CGC-Net) model achieves mean absolute errors (MAE) of 17.12, 16.73, 16.62, and 15.94 on the Building Data Genome Project (BDGP), Commercial Building Energy Consumption Survey (CBECS), Nonresidential Building Energy Performance Benchmark (NEPB), and Building Energy Efficiency Benchmark (BEBDEE) datasets, respectively, significantly outperforming traditional methods and other models. Additionally, the model exhibits faster inference and training times. These results validate the stability and superiority of the CGC-Net model, providing an innovative solution and essential technical support for commercial building energy management.

Policy, innovation, market or behavior driven effects: Energy green transition and environmental management in Chinese heterogeneous cities.

Energy green transition (EGT) is currently one of the main measures for countries around the world to address the contradiction between economic growth and increasingly deteriorating environmental and climate issues. Cities are the center of energy consumption. The key to EGT lies in urban energy green transition. Therefore, the focus of this study is on the driving mechanism of urban EGT. Firstly, the spatial-temporal characteristic of EGT in Chinese heterogeneous cities is analyzed by using methods such as gravity model. Secondly, the possible paths includes policy driven, innovation driven, market driven, and behavior driven for urban EGT are discussed through theoretical analysis. Finally, combined with panel data of 236 Chinese cities in 2007-2022, this study empirically analyzes the complex driving mechanism of urban EGT. Results show that: (1) The EGT in Chinese cities is continuing. From the perspective of urban heterogeneous, EGT in 1-tier and 2-tier cities is significantly faster than that in 3-, 4-, and 5-tier cities. The EGT speed in eastern cities is the fastest, while that in northeastern cities is the slowest. The difficulty of EGT in energy resource-based cities is actually the greatest. From the perspective of spatial features, the spatial center of EGT in Chinese cities generally shows a changing trend from northwest to southeast. (2) Policy driven, innovation driven, market driven, and behavior driven constitute the complex driving mechanism of urban EGT, and policy driven is the primary driving force for this round of EGT. (3) Positive effect of economic development level and education level improvement on EGT in Chinese cities is significant while resource endowment and population agglomeration level exhibit significant inhibitory effects. (4) There are significant differences in the core driving force for EGT in heterogeneous cities. Both policy driven effect and market driven effect have the highest impact in 1- and 2-tier cities. Innovation driven effect, market driven effect, and behavior driven effect are only significant in eastern and central cities. In energy resource-based cities, innovation driven effect of green innovation is not significant. This study can assist government departments better in formulating relevant policies to support energy transition, promote technological innovation, design market mechanisms, and guide energy consumption behavior.

Based on the improved fuzzy analytic hierarchy and the TSE-MLR model energy consumption prediction of university: A case study.

The accurate prediction of building energy consumption on university campuses is a significant research area. Current studies often focus on predicting the energy consumption of specific building areas or individual equipment, and typically consider only one factor, limiting the accuracy and applicability of the predictions. This study introduces the Time Segmented Energy-Multiple Linear Regression (TSE-MLR) prediction model, which integrates the improved fuzzy analytic hierarchy and the multiple linear regression algorithm. The model is compared with traditional (MLR, BP) and advanced (RNN) models, and their various indexes are discussed and analyzed. By collecting meteorological and energy consumption data from the study site over the past 12 years, the key factors affecting energy consumption on the university campus were identified using the improved fuzzy analytic hierarchy. Subsequently, the TSE-MLR model was trained using energy consumption data from 2010 to 2016 and validated using data from 2017 to 2019. The prediction results of the TSE-MLR model were compared with those obtained through Multiple linear regression, BP neural networks, and RNN. The results demonstrated that the TSE-MLR model significantly reduced the prediction error by 13.8 % and exhibited higher accuracy compared to the other models. Therefore, the TSE-MLR model introduced in this study offers a new and effective approach to predicting university energy consumption and supporting energy management using existing data from university building operations across different periods.

An innovative analytical framework for energy consumption behavior: A study based on beijing households.

Residents' energy consumption behavior has significant impacts on the achievement of carbon reduction targets and the effectiveness of related policies. Up to now, there has not been a complete framework that can accommodate internal psychological factors and external environmental factors. Based on the Planned Behavior theory and Value-Belief-Norm theory, this paper has added economic factors, policy impact, and convenience of consumption as important external environmental factors into a proposed model; in addition, knowledge level, behavioral expectations, and consumption habits are incorporated as new internal psychological indexes to construct a expanded framework. The framework integrates both internal psychological and external environmental factors, enriching and deepening the psychological foundation of behavioral analysis. After performing outlier detection, confirmatory factor analysis, and other steps on samples obtained from a questionnaire survey, the results of the framework fitting data show that it has high explanatory power for residents' energy consumption behavior, which is significantly better than the existing models. Furthermore, the new critical path that determines Beijing residents' energy consumption behavior is obtained by using the framework. In summary, this paper presents theoretical and empirical foundations for designing and enhancing low-carbon policies.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks.

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The Archimedes Optimization Algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

Investigating the determinants of marine environmental degradation in Africa: What roles can economy, population, capture and energy play?

This study firstly examines the quality of marine eco-environment in Africa using Tapio decoupling model, and analyzes the sustainability level of the development of "population agglomeration - marine environment - economic growth". Secondly, a series of econometric tools, such as ARDL, FMOLS, AMG model and DH panel causality test, are used to investigate the long- and short-term impacts of economic growth, population agglomeration, marine capture and energy consumption on the African marine eco-environment, and to analyze the differences between the sub-regions in Africa. The results indicate that: Adebayo and Kirikkaleli (2021) (Adebayo and Kirikkaleli, 2021) the decoupling state of "population-environment" has shifted from expansive negative decoupling to more optimized strong decoupling, and "economy-environment" has gradually changed from strong negative decoupling and expansive negative decoupling to strong decoupling. Ali et al. (2017) (Ali et al., 2017) There existed a bi-directional causal relationship between the degree of marine environment degradation and economic growth, population agglomeration, marine capture and energy consumption. Al-Mulali and Sab (2012) (Al-Mulali and Sab, 2012) In the short term, the economic EKC hypothesis does not hold in North and West Africa, while Central, East and Southern Africa are consistent with the EKC hypothesis. In the long term, the EKC hypothesis is valid in Central, East and Southern Africa, while is not valid in North and West Africa. Overall, reducing population agglomeration levels, marine fishing and energy consumption might mitigate marine environmental degradation in Africa.

Dataset of an operating education modular building for simulation and artificial intelligence.

Improving energy efficiency in the building sector is a subject of significant interest, considering the environmental impact of buildings. Energy efficiency involves many aspects, such as occupant comfort, system monitoring and maintenance, data treatment, instrumentation… Physical modeling and calibration, or artificial intelligence, are often employed to explore these different subjects and, thus, to limit energy consumption in buildings. Even though these techniques are well-suited, they have one thing in common, i.e., the need for user cases. This is why we propose to share a part of the large volume of data collected on our modular education building. The building is located on Nanterre's CESI Engineering school campus and welcomes approximately 80 students daily. A network of more than 150 sensors and actuators allows monitoring of the physical behavior of the entire building, preserving optimal comfort and energy consumption. The dataset includes the indoor physical parameters and the operating conditions of each system to describe the physical behavior of the building during a year.

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences.

OBJECTIVES: The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system. MATERIALS AND METHODS: Energy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption. RESULTS: Our findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption. CONCLUSION: Implementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs. CLINICAL RELEVANCE STATEMENT: MRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact. KEY POINTS: Optimization of protocol settings reduced energy consumption by 31% and imaging time by 18%. DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol. During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.