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This paper analysed the causal link between education, employment, renewable energy consumption and carbon emissions in Africa, where there is scant evidence. Relying on panel data obtained from the World Development Indicators for thirty-two African countries covering a period of 19 years, and five panel rigorous regression models, we found that renewable energy, investment in education, school enrolment, net national income per capita, and employment had negative and significant effects on carbon emission, thus increasing these predictors would result in significant reduction in carbon emission in Africa. We identified a bidirectional causality between carbon emissions and net national income per capita, education expenditure and renewable energy consumption, and carbon emissions and employment. Our findings suggest that investment in education, renewable energy, and employment are relevant in mitigating carbon emissions in Africa. We recommend African governments to invest heavily in education, improve school enrolment, environmental education, renewable energy and employment provision to mitigate carbon emissions. Highlights: • Achieving carbon neutrality in Africa is important. • This research investigated the impact of education, employment, and renewable energy consumption on carbon emissions in Africa. • This study utilized five panel regression models – fixed effect with Driscoll-Kraay standard errors, panel fixed effect model, random effect model, panel fully modified ordinary least square model, and panel canonical correlation analysis and panel data from 32 African countries. • The results shed new light on the emission reduction potential of renewable energy consumption, education expenditure, school enrolment, per capita income, and employment. • This article offers recommendations to attain carbon neutrality and environmental sustainability. [ABSTRACT FROM AUTHOR]

The energy consumption from the building sector accounts for more than 21% of the total in China, where the energy-use intensity of public buildings could reach 3 times that of residential buildings. Energy efficiency improvement (EEI) is considered to be one of the most rapid and cost-effective means for the building sector to achieve carbon neutrality. The Chinese government has carried out a series of policy tool exploration to promote the energy efficiency improvement efforts by public building owners since over a decade ago, but encountered various problems and obstacles. This paper investigated and compared the current status challenges, and problems in the implementation of EEI policy tools in China and developed countries through literature review, field investigations and interviews with experts and stakeholders, and finally also put forward some possible coping strategies for Chinese government. Results indicated that energy grading and labeling, energy benchmarking, carbon-emission trading and energy-consumption quota management were the main 4 EEI policy tools designed based on measured energy consumption data, where the first 3 ones had been widely used over the developed countries but the last one was unique to China. Difficulty in large-scale collection of building energy-consumption data, lack of scientific methods on building energy-use level evaluation, limited data application and feedback, undesirable association between stakeholders, etc., were the main obstacles for achieving the long-term effectiveness of the EEI policy tools. Cooperating with energy supply enterprises to collect data, establishing an effective energy grading system and a closed loop of policy implementation, exploring a new model of building carbon-emission trading and a differential pricing mechanism, combined with innovative financial supports, might be the solutions. The main contribution of this study is that a comprehensive investigation and analysis on the current development status, obstacles, and possible solutions of the public building EEI policy tools in China was carried out, which is expected to provide a reference for the government of China and even the world. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet. Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution. [ABSTRACT FROM AUTHOR]

This article explores the domain of accurate Electric Vehicle (EV) charge prediction, a crucial aspect of the energy consumption system. Predicting EV energy consumption is challenging due to the dynamic dependence and heterogeneity. Despite various approaches proposed in previous studies for intelligent charging, many models rely on limited inputs and ignore the non-linear interactivity between different time series. Moreover, to our knowledge, previous research has not considered the number of connected EVs during the charging procedure. This paper develops an attention-based recurrent convolutional neural network model (LA-RCNN) designed to forecast EV charging load using multivariate time series inputs, including meteorological data and the number of connected users. The proposed model incorporates multiplicative Luong Attention to identify temporal dependencies and correlations. Our objective is to predict the national charging load by considering the charging state and the number of plug-in EVs connected to various charging stations. Using real-world EV charging data from three Chinese cities, we demonstrate that the LA-RCNN model significantly enhances forecast accuracy compared to benchmark methods, reducing MAPE by 21.33% and RMSE by 18.73% as compared to LSTM models. These results highlight the importance of nonlinear attention-based architectures and diverse contextual data sources for effective EV load prediction. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the viscoelastic wave equation with variable coefficients and a delay, which is subject to nonlinear and nonlocal boundary dissipation. The existence of strong and weak solutions is obtained by means of Faedo-Galerkin approximation and denseness argument. By introducing an equivalent energy functional and using the Riemannian geometry method, we show the general decay rate of energy when kernel function satisfies some sufficient small conditions that we provide the range accurately. [ABSTRACT FROM AUTHOR]

It has been shown that consumers often underinvest in energy efficiency despite net benefits over the longer term. One possible explanation is that they do not properly understand energy information when provided in physical units, as in most energy labels. Prior studies have investigated the effect of reframing energy information into monetary units. Outcomes are mixed, and it is unclear whether this is due to the use of different products, methodologies or to studies being conducted in countries with different energy prices and labelling standards. This paper overcomes this ambiguity by testing the effect of providing monetary energy information using the same experiment in a multi-country setting. Results show that the intervention's effectiveness varies considerably across countries. Moreover, they highlight the presence of within-country heterogeneities based on demographic characteristics, with monetary information being more effective for high-usage households but seemingly crowding out the motivation of those whose purchasing decisions depend on environmental considerations. [ABSTRACT FROM AUTHOR]

Energy has certainly played a robust role in economic development. However, the damages it has caused to the environment and climate change are innumerable and difficult to identify. Therefore, the new challenge facing humanity is no longer the rise in energy prices or resources scarcity, but rather the climate crisis. Against this background, our paper aims to analyzing the long-run relationship between the energy sector and climate change phenomena for a panel of 14 Mediterranean countries spanning the period 1990–2019. The analysis was based on different econometric techniques, containing panel unit root tests, panel cointegration tests, and panel regression estimators, i.e., Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators. The different findings revealed that energy consumption and various air quality indicators such as carbon dioxide emissions (CO2) and nitrous oxide emissions (N2O) have contributed to increasing greenhouse gas emissions (GHGs). This empirical investigation suggests that to minimize the spillovers of global warming and climate shift, the energy path in the Mediterranean should be adjusted by the implementation of energy efficiency measures and the advancement of renewable energy sources for reducing carbon emissions in order to preserve the environment for future generations. [ABSTRACT FROM AUTHOR]

Existing research has focused mainly on the technological effects of rigid constraints such as low-carbon policies, but there has been little exploration of flexible constraints such as the government's low-carbon concerns. To explore the role of flexible low-carbon constraints and effective paths for achieving low-carbon development, this paper theoretically and empirically investigates the influence of the government's low-carbon concerns on local firms' innovation in renewable energy technology and the underlying mechanism. Benchmark analysis shows that the government's low-carbon concerns can improve renewable energy technology among firms in an area. Mechanism analysis reveals that low-carbon concerns promote firms' renewable energy technology adoption by increasing the amount of research and development (R&D) investment and government subsidies and energy use costs of firms in the area. Heterogeneity analysis reveals that heterogeneity exists across technologies, firms, industries and locations in terms of the incentive impact of the government's low-carbon concerns on the level of renewable energy technology innovation. Extensive analysis indicates that the government's low-carbon concerns have a long-term incentive impact on the renewable energy technology of local firms and a positive demonstration impact on the renewable energy technology of neighboring firms. This study is helpful in that it explores the major driving factors behind the creation of a low-carbon economy and the achievement of climate targets. [ABSTRACT FROM AUTHOR]

This paper proposes a refrigerant cooling method using an inner tube in a storage tank to improve the cooling performance and thermal uniformity during the storing of edible oil. With a prototype of an oil tank in Central Grain Reserve of Zhenjiang, the experimental oil tank was built in a scale of 50:1. Both natural and manual cooling experiments were carried out for the experimental tank. The manual cooling process involved two supplying modes for the refrigerant tube (top and bottom) and four different refrigerant temperatures (10 ℃, 12 ℃, 14 ℃, 16 ℃). The experimental results show that, compared with natural cooling, manual cooling can effectively reduce the temperature difference and thermal stratification between upper and lower layers. The temperature difference is 6.79 ℃, 1.93 ℃, and 3.67 ℃ for the natural cooling, manual top supplying, and manual bottom supplying mode, respectively. Furthermore, for the two manual modes, the cooling efficiency of bottom supplying is 21.4% higher than that of the top supplying, and the average oil temperature drops by 0.8–1 ℃. Based on experimental results, different working conditions (20, 40, and 60 ml/s) were simulated to determine the optimal flow rate for bottom supplying mode. The simulation results indicate that the low flow rate (20 ml/s) corresponds to the best thermal uniformity, and the maximum temperature has no obvious change under different flow rate conditions. Therefore, it is not necessary to increase the flow rate to improve cooling efficiency considering the rising energy consumption. [ABSTRACT FROM AUTHOR]

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error. [ABSTRACT FROM AUTHOR]

This paper comprehensively examines passive and active energy retrofit strategies as a highly effective approach for reducing building energy consumption and mitigating CO2 emissions while enhancing comfort and sustainability. The paper further examines energy simulation software and assesses the integration of renewable energy systems in building to improve energy efficiency. The review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, ensuring a rigorous and comprehensive analysis. In addition, the study utilized bibliometric analysis with VOSviewer to provide valuable insights into the research trends and influential publications in building energy retrofits. Bibliometric analysis reveals strong collaboration among 17 authors, emphasizing their significant contributions. Keywords like energy retrofitting and efficiency are prominent, indicating their importance in academic literature. Findings show passive strategies are more effective in reducing energy consumption, though a combined approach with active strategies can yield optimal results. Retrofitting presents challenges, such as substantial initial costs and regulatory barriers. User acceptance is crucial, considering potential disruptions. The review highlights the importance of energy simulation software, with tools like EnergyPlus, eQUEST, and IES VE identified for evaluating and identifying cost-effective retrofit measures in building performance. By providing comprehensive insights into the various strategies and tools available for retrofitting buildings to achieve energy efficiency and sustainability goals, this review serves as an authoritative resource for building owners, managers, and professionals in the building industry. It offers invaluable guidance for informed decision-making and facilitates implementing effective, energy-efficient, and sustainable building retrofitting practices. [ABSTRACT FROM AUTHOR]

Stochastic resonance and energy consumption are significant for information processing and transmission in the neural system. In this paper, we constructed an excitatory–inhibitory cortical neuronal network to investigate the response of the system to weak signals and the corresponding energy consumption. The findings indicate that the excitability of neurons modulates the performance of signal response. Furthermore, the performance of signal response exhibits a bell-shaped dependence on ion channel noise, which is a typical manifestation of the stochastic resonance phenomenon. Stochastic resonance also exists in the network with increasing noise at different excitatory coupling strengths and inhibitory coupling strengths. Furthermore, it is found that the neuronal system obtains optimal transmission of the weak signal at a lower energy consumption. It illustrates that there is a certain economy and efficiency in the signal transmission. At weak inhibitory coupling strength, an optimal excitatory coupling strength exists to allow the neuronal network to make the optimal transmission of the weak signal. However, the phenomenon of double resonant peaks occurs at strong inhibitory coupling strength, which is due to the balance of excitatory and inhibitory synaptic currents. Finally, we demonstrated the robustness of the results to network topology and initial conditions. The results of this paper may contribute to the understanding of signal transmission and its energy consumption in cortical networks. [ABSTRACT FROM AUTHOR]

The research on constructed wetland (CW) and microbial fuel cell (MFC) has been separate studies worldwide with crucial achievements being made in both fields. Due to environmentally friendly feature (of CW) and rich microbial population and excellent electrode catalytic activity (of MFC), CW and MFC have their own anticipated application prospect in wastewater purification and biological electricity generation. More significantly, the idea of embedding MFC into CW to form CW-MFC expands the scope for both of them and this has received much interest in recent years due to its striking features of sewage treatment efficiency, electricity generation, sustainability, and environmental friendliness. The increasing interest and the lack of soul of CW-MFC emerging to the new researchers reflect the need to recall the idea and summarize its development with regard to achieving its reality via some key issues This forms the basis of the paper. The paper also includes how to enhance the efficiency of electricity generation and supplement energy consumption, the degradation of emerging pollutants, and the degradation mechanism as well as the potential joint application of CW-MFC with other treatment technique. A mass of CW-MFC design parameters has been synthesized from the literature. Challenges and potential directions of CW-MFC in the future are prospected. It is expected that the paper can serve as a linkage for bridging knowledge gaps for further studies of CW-MFC. [ABSTRACT FROM AUTHOR]

This paper introduces an energy management strategy for a hybrid renewable micro-grid system. The efficient operation of a hybrid renewable micro-grid system requires an advanced energy management strategy able to coordinate the complex interactions between different energy sources and loads. This strategy must consider some factors such as weather fluctuations and demand variations. Its significance lies in achieving the overarching objectives of these systems, including optimizing renewable energy utilization, reducing greenhouse gas emissions, promoting energy independence, and ensuring grid resilience. The intermittent nature of renewable sources necessitates a predictive approach that anticipates the energy availability and adjusts the system operation. The aim of this study was to develop an energy management system for a hybrid renewable micro-grid system to optimize the deployment of renewable energy resources and increase their integration in the power system. Therefore, the main objective of this work was to develop an energy management strategy that controls the flow of energy between the hybrid micro-grid system and the load connected directly as well as the load connected to the utility grid using MATLAB/Simulink software. The second objective was to control the charging and discharging of the battery. The results show that the developed algorithm was able to control the energy flow between the hybrid micro-grid system and the utility grid and also to ensure a proper relation between the charging /discharging rate of the battery based on their operating conditions. In this application, the battery was charged at higher power. It was seen that a higher charging power enables to fully recharge the battery in a shorter amount of time than usual. The results have shown that it is possible to maximize the charging time by using a greater power and this algorithm ensures the state of charge (SOC) of battery to remain in the admissible limits (between 20 and 100%). [ABSTRACT FROM AUTHOR]

Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization. The paper presents the universal QM-based scoring function that accurately and rapidly predicts protein-ligand binding affinities, outperforming current computational tools. This is demonstrated on the PL-REX experimental benchmark dataset. [ABSTRACT FROM AUTHOR]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [ABSTRACT FROM AUTHOR]

This paper presents a three-port isolated hybrid converter (3PIHC) with extended phase-shift modulation (EPM) to reduce voltage and current stress in the converter for DC microgrid applications. A high-frequency transformer with three independent windings is employed for isolating three ports in the converter. A wide variation in voltage transformation ratio is one of the main concern in DC microgrid, which need to be addressed for effective power conversion and utilization of the energy generation systems. Traditional phase-shift modulation (TPM) in 3PIHC leads to high conduction and core losses in the converter under wide variation in voltage transformation ratio. This paper proposes an extended phase-shift modulation in 3PIHC for different sequence of voltage transformation ratios between port 1 and port 2 (m 12) and between port 1 and port 3 (m 13) , which is a cost-effective solution for reducing losses in the converter. The peak current at all ports and power equations in 3PIHC are deduced, and the computational method for obtaining the phase-shift values with minimum current stress is explained in detail. A comparative evaluation study proves that the proposed three-port converter with EPM outperforms to TPM in the aspects like low current stress, reduced semiconductor switching losses and transformer losses for wide variation in voltage transformation ratios. A hardware prototype of the proposed converter is built, and the results are evaluated and presented. [ABSTRACT FROM AUTHOR]

Combine harvesters (CHs) use large amounts of diesel fuel and they contribute significantly to greenhouse gas (GHG) emissions and air pollutants. A reduction in fuel consumption of agricultural machines has immediate economic benefits but can also results in longer term environmental benefits. Savickas et al. (Precis Agric 2023. https://doi.org/10.1007/s11119-023-10060-6) introduced extensive telematics data analysis and field tests for identifying the potential sustainable use of CHs. This paper follows on from that research and discusses the need for an IT tool for the management of economic and environmental indicators of a CH. The innovative tool allows the comparison of the data of specific agricultural machines with the multi-year telematics data stored in the database. It offers opportunities to reduce environmental impact and evaluate the results of field trials, presenting them in terms of environmental impact, expressed as CO2 equivalents. [ABSTRACT FROM AUTHOR]

Numerical optimization models are used to develop scenarios of the future energy system. Usually, they optimize the energy mix subject to engineering costs such as equipment and fuel. For onshore wind energy, some of these models use cost-potential curves that indicate how much electricity can be generated at what cost. These curves are upward sloping mainly because windy sites are occupied first and further expanding wind energy means deploying less favorable resources. Meanwhile, real-world wind energy expansion is curbed by local resistance, regulatory constraints, and legal challenges. This presumably reflects the perceived adverse effect that onshore wind energy has on the local human population, as well as other negative external effects. These disamenity costs are at the core of this paper. We provide a comprehensive and consistent set of cost-potential curves of wind energy for all European countries that include disamenity costs, and which can be used in energy system modeling. We combine existing valuation of disamenity costs from the literature that describe the costs as a function of the distance between turbine and households with gridded population data, granular geospatial data of wind speeds, and additional land-use constraints to calculate such curves. We find that disamenity costs are not a game changer: for most countries and assumptions, the marginal levelized cost of onshore wind energy increase by 0.2–12.5 €/MWh. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Designing a cost-effective, energy-efficient and highly scalable network for data centers that can deliver sufficient bandwidth has drawn tremendous attentions recently. The data center networks constructed by using multi-port servers can provide sufficient bandwidth, such as BCube, DCell and GBC3. As the volume of data keeps growing rapidly, more and more servers are continuously added into data centers. In order to reduce the cost and energy consumption, data center networks can be expanded gradually by adding a small number of servers from time to time instead of adding a huge number of servers at a time. This paper proposes a new type of data center network structure called GHB, which is constructed by using commercial switches and multi-port servers. Two types of incomplete GHB structures are also proposed. A small number of servers can be gradually added into the incomplete structures without changing their topological properties. As shown in the experimental results, the throughput of GHB is comparable to that of BCube, and is larger than that of GBC3 and DCell. The analysis results indicate that GHB strikes a good balance among diameter, bisection width, incremental scalability, cost, and energy consumption in contrast to BCube, DCell and GBC3. Compared with the BCube and GBC3, GHB reduces the cost and energy consumption by about 6% and 18%, respectively. The highest throughput of GHB is higher than that of DCell and GBC3 by about 3.4% and 8.35%, respectively. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic and causal relationship among energy intensity, economic growth, urbanization, energy consumption, FDI, and PM2.5 in 1995–2019 period for China, India, Germany, Canada, USA and United Kingdom countries through Panel Fourier Bootstrapping ARDL (PFBARDL) model and Panel Fourier causality test. According to PFBARDL results, the evidence of cointegration between the variables was detected. In the PFBARDL model, the error correction coefficient is determined as -0.169. Results showed that there is a bidirectional causality between economic growth and PM2.5 release, between fdi and PM2.5 release, between energy intensity and PM2.5 release. Rising economic growth, energy consumption structure, and urbanization will have an adverse effect on PM2.5 concentrations. Policy makers must carefully balance the connection between mitigation of PM2.5 and diminishing energy consumption, and increasing economic growth, urbanization, and energy intensity for determining policies to control PM2.5 concentrations. Thus, in order to reduce PM2.5, the governments must explore a new economic pattern to decouple the linkage between PM2.5 and economic growth. [ABSTRACT FROM AUTHOR]

A comparative "cradle to grave" Life Cycle Analysis between the production processes of ceramic stoneware products and geopolymeric materials obtained by valorizing ceramic wastes is reported. This study presents an effective eco-design approach to obtain sustainable materials through a low energy consumption manufacturing process, a feature that is essential in a historical period of high geopolitical instability which makes the supplying of fossil fuels difficult and particularly expensive. In particular, the possibility of lowering production costs (saving on the cost of waste disposal, using a raw-second material, and a low-temperature production process) could represent a strong contribution to the environmental and economic sustainability of the Italian ceramic industry, which is going through a time of severe financial crisis which due to the unprecedent high cost of raw materials and energy. Finally, the new geopolymeric systems proposed in this paper could be profitably used in the field of green building, art, eco-design, and technical-artistic value-added applications, such as restoration, conservation, and/or rehabilitation of historic monuments, or simply as materials for building revetments. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE. [ABSTRACT FROM AUTHOR]

Energy consumption remains an open challenge in aerial systems such as CubeSats and therefore optimization of its allocation is a top priority for maximizing operational capacity. Our research review reveals a plethora of approaches for optimization of energy allocation and all achieving varying degrees of success and not without any compromises. In this paper, we exploit the use of serious gaming in a novel energy allocation algorithm that aims at minimizing energy consumption to maximize the utilities of both CubeSats and terrestrial sensors. To demonstrate this, we use Stackelberg for serious gaming and standalone topology for CubeSat configuration. The experimental results show that the use of a Stackelberg game approach for optimization has led to reduction in the required transmission energy in sensors, an improved link performance between the CubeSat and ground sensors, and an increase in network lifetime and performance without resorting into sensor power enhancements or other external power sources. The overall average operational capacity improvement predictions range between 22 to 27% across all performance indicators of energy efficiency across RF chains of link budgets. [ABSTRACT FROM AUTHOR]

One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis. [ABSTRACT FROM AUTHOR]

A holistic performance analysis and classification of multiport antennas (MPAs) is conducted in this paper. We focus on 5.9 GHz vehicle-to-vehicle communications suited to the emerging technology of intelligent transportation systems. Three-dimensional (3-D) uniform/isotropic, directional, and omnidirectional propagation scenarios are considered to account for any wireless environment. The presented analysis can be adapted to any MPA with an arbitrary number of ports, operating in any frequency band, or used in other emerging technologies such as in 5G and beyond 5G communications. On top of the classical key performance metrics (KPMs) in communication theory, i.e., the diversity antenna gain (DAG) and channel capacity (CC), we employ for the first time the energy efficiency as one more KPM capable to characterize performance and classify MPAs, particularly when DAG and CC fail to do so. Computation of the aforementioned KPMs departs from a covariance matrix formulation incorporating all intrinsic features that affect MPA performance, namely, MPA radiation characteristics, MPA termination conditions, and wireless propagation channel attributes. Accordingly, we derive the ideal form of the covariance matrix under the standardized and widely adopted 3-D uniform/isotropic wireless propagation scenario. A good MPA design should be one with a covariance matrix as close as possible to this ideal one. The adopted performance analysis methodology can thus inform the design of optimum MPAs and accordingly, we designed a proof-of-concept box-shaped MPA which shows outstanding performance across all propagation scenarios. It would be wise to conduct similar performance analyses as in this paper before releasing an MPA design. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and edge computing technology, deep learning-based automatic modulation classification (AI-based AMC) deployed at edge devices using centralised or distributed learning methods for optimisation has emerged in recent years, and has made great progress in the recognition accuracy and recognisable range of wireless signals. However, the lack of sufficient explanation of these models leads to low accuracy and training efficiency of model training, and their applications and further improvements are limited. Researchers have started to propose interpretable methods for technical analysis of deep learning-based AMC. In this paper, based on the research and application development of interpretable methods in recent years, we review the applicable methods and existing research challenges of interpretable automatic modulation classification. And an interpretable AI-based automatic modulation classification framework is proposed to map the interpretability of automatic modulation classification results by obtaining the contribution of wireless signal features to deep learning network training. Experimental results show that the proposed method possesses the ability to explore the classification mechanism of non-transparent auto-modulated classification networks and has the potential to help edge devices train networks with lower energy consumption and higher accuracy. [ABSTRACT FROM AUTHOR]

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS (The Code of TConNS is available at https://github.com/zhengLabs/NSO) . Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures. [ABSTRACT FROM AUTHOR]

This paper presents a low-power multi-rate clock and data recovery (CDR) for receivers of serial links. Its basic structure includes a current-mode logic bang–bang phase detector sampled by low-mismatch half-rate quadrature clocks, which are generated by voltage-controlled delay line (VCDL) and two-stage time-average circuits. The total delay of VCDL can be adjusted to accommodate a wide frequency range by its bias voltage, which is generated by a delay-locked-loop-based bias generator. The quadrature clocks are 64-phase adjustable with high linearity, which is realized by phase interpolator with a compensating structure. The parameters of phase detection loop are well designed to satisfy both high jitter tolerance and low clock jitter. Fabricated in a 40 nm CMOS technology, the CDR occupies an active area of 0.036 mm 2 only. With a wide operating range of 2–20 Gb/s, the chip consumes 62.5 mW, corresponding to an energy efficiency of 3.1 pJ/bit. The measured root-mean-square jitter and peak-to-peak jitter for the recovered clock at 9 GHz are 1.9 and 10.8 ps, respectively. [ABSTRACT FROM AUTHOR]

This paper aims at exploring the economy-wide impacts of achieving net-zero greenhouse gas (GHG) emissions by 2050 in Thailand. This study developed a recursive dynamic Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model of Thailand for the assessment. The macroeconomic impacts of Thailand's net-zero GHG emission targets by 2050 are analyzed relative to its 2-degree pathway. Results indicate that Thailand should put more effort in GHG mitigation actions to achieve the emissions peak by 2025 and net-zero GHG emissions by 2050. Improvement in energy efficiency; increasing electrification; expanding renewable energy utilization; deploying green hydrogen; bioenergy; carbon capture, utilization, and storage (CCUS); and behavioral changes are the key identified pillars of decarbonization to drive Thailand towards the pathways of net-zero emissions by 2050. Results show that there is a possibility of attaining net-zero GHG emissions by 2050 at the expense of an economic loss for Thailand. The gross domestic product (GDP) loss would be as high as 8.5% in 2050 to attain net-zero emissions. Lower productivity from the energy intensive industries such as petroleum refineries, coal and lignite mining, manufacturing industries, and transport are the key contributing sectors to the GDP losses. The price of carbon mitigation would shoot up to reach USD 734 per tCO2eq in 2050 from USD 14 per tCO2eq in 2025 to attain net-zero emissions in 2050. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper is to reconsider the necessity for the green transition and the key preconditions for the implementation of a circular economy in Western Balkan countries. With the objective of the research in mind, the method of analysis and synthesis was applied to determine (1) regulatory and institutional prerequisites for the green transition; (2) the need for the Western Balkan countries to redefine the model of sustainable economic growth towards the green transition; (3) the development opportunities for recovery defined in the Green Agenda for the Western Balkans; and (4) the possibility of implementing the circular economy in the Western Balkans. Main text: The main findings of the research indicate that: (1) the countries of the Western Balkan region, following the example of the EU, should define a national strategic approach to the green transition with an accompanying action plan and regulatory framework; (2) the biggest challenge of the green transition is the reform of the energy sector and the restructuring of the energy-intensive economy; (3) the countries have untapped potential in renewable energy sources and report the improvement of energy efficiency; (4) the circular economy can boost the green transition, because the countries of the region have a five-time lower value of resource productivity than the average of the EU, while the generation of waste (excluding major mineral wastes) per GDP unit is lower compared to the EU; (5) cross-sectoral governance should be more coordinated. Conclusions: The green transition might be a development opportunity for the Western Balkans, which should enable sustainable economic growth as well as energy security and environmental protection. However, the implementation of the Green Agenda is not easy, because the region faces the problem of underdeveloped regulatory and institutional capacities that might provide not only the base for long-term planning but also financial resources for the efficient implementation of projects. In addition, it is essential to understand the principles of the Green Agenda and the interaction of all activities that should enable the achievement of defined goals. [ABSTRACT FROM AUTHOR]

This study addresses the energy efficiency challenge in cloud data centers by examining the Virtual Machine Placement (VMP) problem. VMP involves mapping virtual machines (VMs) to physical machines (PMs) under capacity constraints. The paper focuses on the bin packing with linear usage cost (BPLUC) variant of bin packing, which includes fixed and variable costs in the calculation of the cost of a used bin. We prove that every approximation algorithm for the bin and vector bin packing can be used for BPLUC and VBPLUC, respectively. We propose a more power-efficient approach to VMP by applying a vector bin packing algorithm to minimize power consumption in data centers. We test the proposed algorithm on various synthetic and real workloads, and the experimental results demonstrate that it is more power-efficient than existing algorithms for VMP. The findings suggest that the proposed algorithm has significant implications for energy-efficient strategies in cloud data centers. Generally, this study makes contributes to the development of energy-efficient approaches to VMP that can help reduce power consumption and improve the sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes' scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations. [ABSTRACT FROM AUTHOR]

In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms. [ABSTRACT FROM AUTHOR]

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

Energy consumption reduces Wireless Sensor Network's (WSN's) lifetime. Hence, this paper addresses energy saving problematic in cooperative Orthogonal Frequency Division Multiplexing Access (OFDMA) systems for WSNs (COFDMA-WSN). Analytical handlings are implemented. The performance improvement of COFDMA-WSNs is executed by considering two different COFDMA-WSNs schemes. These schemes represent classical COFDMA-WSNs and Relay Supported (R-S) COFDMA-WSNs. Additionally, there are different pondered configurations due to these schemes. These configurations denote classical OFDMA network with classical WSN, R-S OFDMA network with classical WSN, classical OFDMA network with R-S WSN, and R-S OFDMA network with R-S WSN. Moreover, each configuration is applied with four different fractional frequency reuse (FrFR) techniques. These techniques represent strict (St) FrFR technique with Frequency Reuse Factor (FReF) = 3, St FrFR technique with FReF = 4, sectored (Sc) FrFR technique and soft frequency reuse (SoFR) technique. Consequently, there are sixteen different patterns of COFDMA-WSNs are considered. Moreover, closed-form terms (CFTs) for cluster-head's (C-H's) signal to interference ratio (SIR) and sensor node's (SN's) SIR are presented. Additionally, different metrics are evaluated to contrast the performance of altered patterns using the obtained CFTs. The outcomes demonstrate, that St FrRF4 system outperforms other systems in prime and ensuing links. The cause of this outcome can be credited, to the frequency reuse process decrement due to FReF increment. As a result, the interference sources decrease. Hence, the interfering signals drop. Consequently, St FrRF4 system achieves the highest values of SIR. Moreover, St FrRF3 technique has the second usage priority in the prime link. But, it losses this preference in the ensuing link and SoFR technique that applied in the fourth configuration takes this significance. The work outcomes attain much higher C-H's and SN's SIR improvements. Accordingly, the packet transmission and protocol behaviour are enhanced. So, the energy consumption is reduced. Consequently, WSN's lifetime is maximized. [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

Exploring underwater acoustic sensing networks to detect and monitor ocean characteristics provides a novel approach for marine environment monitoring, disaster warning and resource exploration. However, factors such as underwater acoustic communication, ocean currents and sensor power prevent the direct adoption of terrestrial wireless sensing networks. Moreover, most previous studies have simply increased the number of nodes while ignoring the interference caused by high node density. In this paper, data collection in an offshore tidal scenario is investigated by table forwarding based election (TFBE) scheme. To balance node energy and reduce energy consumption of autonomous underwater vehicles (AUVs), the improved algorithm (TFBE-A) is proposed. Meanwhile, in this scheme, we adopt AUVs to assist in collecting and put forward a 3D precision adjustable trajectory planning (3DPATP) algorithm. To better perform 3DPATP, the maximum minimum error estimation algorithm is presented to simplify the computer operations. Additionally, we study the probability distribution function of nodes in space, providing a foundation for arranging nodes and optimizing topology. Simulation results indicate that our algorithm outperforms compared strategies, particularly in latency and energy consumption. [ABSTRACT FROM AUTHOR]

With the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme. [ABSTRACT FROM AUTHOR]

In this paper, a low-energy adaptive clustering hierarchy (LEACH)-based routing protocol for wireless sensor networks (WSNs) is presented. The proposed protocol is labeled (ASTEEN) referring to an adaptive soft-thresholding-based energy-efficient sensor network. Its objective is to allow less energy consumption for data transition that affects the lifetime of the network. So, the node lifetime increases, and also the network lifetime increases. In contrary to other fixed soft-thresholding-based protocols, the adaptive soft threshold dynamically gets lower as the significance of the value of the sensed physical parameter increases. This leads to less-frequent transmission of less-critical sensed values, and more frequent transmission of more critical sensed values. This paper examines the correlated and uncorrelated sensed data values with the proposed protocol, and the performance is compared with those of other protocols. The proposed protocol succeeds in the sensing of samples with high correlation. [ABSTRACT FROM AUTHOR]

High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It's a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body's route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes' physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper's theories and methods can effectively help design the marathon route and determine the race time. [ABSTRACT FROM AUTHOR]

Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper. [ABSTRACT FROM AUTHOR]

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. It is of great significance to study the convergence of manufacturing energy carbon emission efficiency for realizing high-quality development of manufacturing in China. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses the coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China's manufacturing energy carbon emissions and its efficiency demonstrate an increasing trend. Coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not have σ convergence, but has β convergence, and its convergence has industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emission efficiency and promote high-quality economic development. [ABSTRACT FROM AUTHOR]

To assess the characteristics of household carbon emissions per capita (HCPC), this paper divided China's provinces into 4 groups based on the decoupling relationship between household consumption and related emissions. This classification helped to analyze the correlation and reflected the decoupling status between carbon emissions and household consumption and explored the effect of consumption growth on carbon emissions. Then, according to logarithmic mean divisia index (LMDI) model, HCPC in China's provinces was decomposed into four drivers including carbon coefficient, energy structure, energy consumption, and population structure effect. Through multi-regional (M-R) analysis, temporal evolution and spatial differences of these four drivers in both national and provincial level were studied. This comparison method introduced temporal and spatial decomposition results into the same framework, which may provide a new perspective for analyzing carbon emission trends. The results showed that (a) the HCPC in all 30 provinces increased significantly especially in Inner Mongolia, Tianjin, Xinjiang, Heilongjiang, and Beijing. Energy consumption effect was the leading factor promoting HCPC growth. Energy structure and population structure also promoted HCPC growth slightly, and carbon coefficient was the effect which had inhibitory effect on HCPC growth at regional level. (b) Spatial differences of HCPC between regions narrowed during this period. This is mainly due to the rapid growth of HCPC in region IV. Energy consumption effect was the dominant factor for the spatial differences. Based on the results, this paper proposed to adopt more effective measures to improve energy efficiency, develop clean energy, and optimize energy structure, especially in the provinces with faster growth in carbon emissions. [ABSTRACT FROM AUTHOR]

In this project, an architecture is implemented that can monitor and assist users in calculating their energy consumption by means of IoT technologies. It is necessary to create a system to improve and help the user in their consumption-based planning due to advances in technology. This implementation can be achieved with the use of IoT (Internet of Things) and thus ensure user convenience and comfort, which is the main goal of a smart home. These technologies involve the interoperability of the data obtained from each appliance and the location of users between the communication hardware layers specified for monitoring. This paper also provides information about cloud services, which are made available by middleware and may also include new external applications. The architecture offers details about consumption and recommends means to improve it by creating profiles which can help end users in making decisions for better optimization of the consumption rate that matches the demands of the customers or the electric power utility. This paper concludes that this architecture is very promising and new and viable technologies can be implemented to make significant improvements for the residents and electric power utilities. [ABSTRACT FROM AUTHOR]

In the wireless sensor networks (WSN), the cluster head node receives and aggregates the information collected by the sensors and forwards it to the base station (BS). Appropriate cluster head nodes help reduce the energy consumed for information transmission and prolong the network's lifetime. The fish migration optimization (FMO) algorithm is an emerging meta-heuristic algorithm, that imitates the grayling foraging and breeding in nature. This paper proposes a modified FMO (modFMO) algorithm, which uses an opposition learning based on the elimination principle and Cauchy-based mutation to enhance the FMO algorithm. The improved modFMO is compared with some excellent algorithms on CEC 2014 function sets, and the Friedman ranks test demonstrates the effectiveness of the improvement. In this paper, the FMO algorithm and the modFMO algorithms with the centralized control algorithm are applied to select the optimal cluster head respectively. This paper takes the average energy consumption of the cluster head nodes as the objective function. Compared with other classic algorithms, the simulation results demonstrate that the modFMO algorithm can extend the lifetime of wireless networks, reduce energy consumption, improve information transmission efficiency and improved the ability of FMO algorithm. [ABSTRACT FROM AUTHOR]