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

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

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

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

• Combining hydrodynamic and acoustic cavitation significantly enhances the energy-efficient processing of cellulose fibers. • Achieving maximum efficiency requires extensive optimization through multiphysical simulations and experimental investigations to understand the interaction of physical phenomena. • Effective sonicator design requires tuning of sonotrodes, signals, structural geometry, fluid volumes, flow conditions, and nozzle design to achieve synchronized resonance amplification at high power. • Cavitation treatment gives a notably improved fiber properties at specific concentrations, temperatures when combined with proper acoustic and hydrodynamic cavitation intensities. The cellulose pulp refining process is crucial for achieving high-quality paper characteristics. This research aims to enhance energy efficiency while maintaining good fiber quality using hydrodynamic and acoustic cavitation (HAC). Experiments were conducted with an in-house developed flow-through sonicator combined with a novel Venturi nozzle for hydrodynamic cavitation. The Venturi design was determined by analytical modeling and verified by CFD simulation with multi-phase turbulence models to balance cavitation intensity and turbulence against the acoustic cavitation effect. Experimental evaluation of two batches of CTMP fibers, pre-processed in different ways, showed significant improvements in paper strength and fiber properties. The best results for Batch 1 (HC and LC) were obtained with 386 kWh/bdt for AC and 350 kWh/bdt for HC (60 °C, 2 % concentration). The tensile strength index increased by 26 %, and the TEA-index, related to freeness, increased by 55 %. HAC treatment (750 kWh/bdt, 70 °C, 1.5 % concentration) of the less refined Batch2 (HC) yielded results better than the Batch 1 reference. These findings confirm the energy-efficient potential of the sonicator concept compared to traditional industrial processes. The conclusion is that HAC-refining of softwood pulp requires a proper balance between hydrodynamic and acoustic cavitation intensities. Both fiber concentration by weight and temperature are critical for an energy-efficient process. [Display omitted] [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has emerged as a promising approach to address the trade-off between energy efficiency and indoor comfort in buildings, potentially outperforming conventional Rule-Based Controllers (RBC). This paper explores the real-world application of a Soft-Actor Critic (SAC) DRL controller in a building's Thermally Activated Building System (TABS), focusing on optimising energy consumption and maintaining comfortable indoor temperatures. Our approach involves pre-training the DRL agent using a simplified Resistance-Capacitance (RC) model calibrated with real building data. The study first benchmarks the DRL controller against three RBCs, two Proportional-Integral (PI) controllers and a Model Predictive Controller (MPC) in a simulated environment. In the simulation study, DRL reduces energy consumption by 15% to 50% and decreases temperature violations by 25% compared to RBCs, reducing also energy consumption and temperature violations compared to PI controllers by respectively 23% and 5%. Moreover, DRL achieves comparable performance in terms of temperature control but consuming 29% more energy than an ideal MPC. When implemented in a real building during a two-month cooling season, the DRL controller performances were compared with those of the best-performing RBC, enhancing indoor temperature control by 68% without increasing energy consumption. This research demonstrates an effective strategy for training and deploying DRL controllers in real building energy systems, highlighting the potential of DRL in practical energy management applications. [Display omitted] • Implementation of SAC as a continuous action thermostatic controller. • SAC pre-trained in simulation before the deployment in a real-world office. • Development of a benchmarking strategy to compare DRL and RBC in real deployment. • Reduced temperature violations by 68% maintaining the same energy consumption. • Discussing practical DRL controller deployment challenges. [ABSTRACT FROM AUTHOR]

Clustering in the Internet of Things (IoT) involves organizing devices into groups to streamline network management and optimize resource utilization, including Internet connections, energy usage, coverage, quality of service, and connectivity. DCOPA (A Distributed Clustering Based on Objects Performances Aggregation for Hierarchical Communications in IoT Applications) is a recent distributed clustering protocol based on a timer for cluster formation where the election of Cluster Heads (CHs) is modeled as a multicriteria problem. In this paper, three contributions are presented. Firstly, the DCOPA protocol is analyzed with a focus on its multi-criteria aggregation function T(i) which directly contributes to the election of the CHs and the formation of the network's clusters. This is then followed by an in-depth analysis of the impact and the variation of the weights assigned to the two aggregated criteria which are the energy and the distance from the base station. A verification of the scalability, load balancing and distribution of the clusters and CHs will follow. Secondly, a new formal notation for the performance analysis, specifically focusing on the mortality and lifetime based on the Vector of Performance Indicators (VPI), will be introduced for IoT. As a third contribution, a revised version of DCOPA is introduced called ADCOPA (Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks). ADCOPA is based on a new property which is the dynamicity or the variability of the weights of the criteria used in the election function of CHs. The simulation results show that the ADCOPA algorithm, which dynamically adjusts the weights of the criteria during the network's lifetime, outperforms the DCOPA algorithm. The latter uses static weights for the criteria that remain unchanged for the entire lifetime of the network. This confirms that the ability to dynamically adjust the weighting of the criteria is an important factor in achieving better performance. • In-depth performance analysis of the DCOPA protocol. • The impact of varying the criteria weights on the DCOPA lifetime parameters. • A novel formal notation for analyzing the performance of an IoT network. • A novel Adaptive DCOPA protocol that uses dynamic weighting is proposed. • Simulation results show that the ADCOPA protocol performs better. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) in large-scale high-density ad hoc networks is an NP-hard problem of fractional function optimization. Most traditional algorithms in the literature focus only on power control, mainly optimizing the solution of power control with the objective of sum-rate maximization or weighted sum-rate maximization, etc., and thus are unsuitable for solving fractional function EE optimization problems. In this paper, the architectural design of the neural network algorithm is directly guided with the objective of maximizing EE. Specifically, we first prove that the EE problem can be described as a graph optimization problem with permutation equivariance. Then, we propose an energy-efficient optimized graph convolutional neural network (EEOGCN) algorithm that satisfies permutation equivariance. Experiments show that the algorithm has better energy efficiency performance compared to other algorithms, can be directly applied to weighted energy efficiency optimization and beamforming energy efficiency optimization problems, and exhibits good generalization performance and scalability to large-scale high-density problems. In particular, the proposed method is computationally efficient and can solve the weighted energy efficiency optimization problem for 3000 transceiver pairs on a single GPU within 2 ms. [ABSTRACT FROM AUTHOR]

Energy efficiency is a complicated process in designing Wireless Sensor Networks (WSNs). Effective routing is a precise solution for the energy efficiency constraint in WSN owing to the higher sharing of energy consumption during communication. Thus, routing is an alternative way out to this issue along with hierarchy-based clustering techniques. The clustering in WSN is carried out by picking the Cluster Head (CH), which is assumed as the essential or leader node. They perform more tasks, and thus, more energy is consumed. The clustering classifies the nodes into groups to avoid the huge communication range that is deputized to the CH. Hence, this paper establishes a routing algorithm for saving energy using a new optimization strategy via solving the multi-function formulation of the developed protocol in WSN. Here, the cluster head selection is performed with the Adaptive Remora Optimization Algorithm (AROA) to derive the multi-function derivation. The multi-function formulation undergoes with known factors such as energy consumption, distance, throughput, Packet Delivery Ratio (PDR), and path loss. The experimental result indicates an enhanced network lifetime and saving the energy of the developed heuristic-based routing protocol in WSN. [ABSTRACT FROM AUTHOR]

This paper reviews the recent evolution of unmanned aerial vehicles (UAVs) through the lens of thermodynamic design. Emphasis is placed on using constructal theory in order to understand the relationship between different degrees of freedom within these complex dynamic systems. Size scale, the way in which UAVs are categorized based on weight and dimensions and one of the most significant factors in the design of any UAV, is analyzed in terms of its relationship to energy consumption. Specifically, constructal theory is used to predict the minimum size at which any UAV will need to be tethered in order to support its energy requirements. [ABSTRACT FROM AUTHOR]

Currently, most existing research on non-orthogonal multiple access (NOMA) systems converts power allocation into two independent procedures, i.e., inter-subchannel power allocation and intra-subchannel power allocation. However, improper inter-subchannel power allocation will adversely affect the power allocation process of intra-subchannel. If the power allocations of inter-subchannel and intra-subchannel are optimized simultaneously, it usually needs high computational complexity, and is hard to obtain an optimal solution. To tackle this issue, this paper studies the joint subchannel power allocation for NOMA systems and proposes a new intelligent algorithm called quantum carnivorous plant algorithm (QCPA) for simultaneously optimizing inter-subchannel and intra-subchannel power allocations. Analysis of the convergence of QCPA verified its superior performance on the test functions. By utilizing the optimal solution obtained by QCPA as the power allocation scheme, energy efficiency and sum transmission rate in NOMA systems are significantly increased compared to existing traditional power allocation methods. The results above demonstrate that QCPA effectively addresses test functions and the power allocation problem for NOMA. QCPA possesses favorable convergence in comparison to other comparison algorithms and methods. [ABSTRACT FROM AUTHOR]

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration. [ABSTRACT FROM AUTHOR]

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

Investing in energy efficiency measures is a major challenge for SMEs, both for environmental and economic reasons. However, certain barriers often make it difficult to invest in such measures. Although public financial support helps to overcome economic barriers, public bodies face the challenge of identifying which SMEs display the greatest potential to invest in energy efficiency measures. By applying a random forest technique and by using sampling balancing techniques, this paper identifies the profile of industrial SMEs that might be potential beneficiaries of public aid, thereby helping public institutions to target their calls and direct their efforts towards this group of SMEs. Specifically, liquidity and indebtedness are found to be the most useful predictors for SMEs in the industrial sector. The results are robust and reveal that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than applying traditional estimation techniques. By identifying potentially benefiting firms, this work helps to boost the effectiveness of public subsidies and to improve the channeling of public funds, which ultimately favors investment in energy efficiency. • Public subsidies favor energy efficiency measures by reducing the up-front cost and making the investment more profitable. • Subsidy effectiveness depends on the capacity to identify which SMEs are potential beneficiaries of energy subsidies. • We evidence that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than traditional techniques. • The most useful predictors for SMEs in the industrial sector are related to liquidity and indebtedness. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs often face limitations in terms of their energy resources, making energy efficiency a critical concern when designing WSNs. Clustering and multihop routing are effective methods for maximizing the lifetime of WSNs. The Graph Neural Network (GNN) is an emerging architecture in neural networks that has recently gained attention for solving problems in various domains, including wireless networks. In this paper, we propose a method based on GNN to create static clusters of equal size. This approach aims to balance energy consumption among the nodes, which is an important sub-goal in designing an energy-efficient WSN clustering protocol. Additionally, we introduce a distributed cluster head selection scheme that operates independently within each cluster. Another crucial aspect of energy efficiency in WSNs is multi-hop routing, which helps avoid long-distance communication that consumes significant amounts of energy. Our proposed centralized routing protocol establishes exclusive routes to the base station for each cluster. This ensures even energy consumption for relay nodes and prevents the hotspot problem. To evaluate our proposed protocol, we conducted simulations comparing it with several state-of-the-art counterparts. The empirical results demonstrate that our approach outperforms other protocols in terms of lifetime and coverage metrics. [ABSTRACT FROM AUTHOR]

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico's state electricity company, which are provoked by the enterprise's growing past-due portfolio and losses, and the possible infringement of electricity consumers' rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users' rights. [ABSTRACT FROM AUTHOR]

Intermittent embedded devices and systems are widely used in various scenarios, but they often experience power failures due to unstable power supplies. Non-volatile memory (NVM) is gaining popularity in embedded systems due to its byte-addressability, low access latency, and high density. As a result, backups can be stored in NVM, and global data are directly manipulated in NVM in this architecture. However, NVM has limited write endurance and modifications to global data frequently occur which results in a significant number of writes. As a result, the extensive NVM accesses may lead to poor performance for intermittent programs. This paper presents an energy-efficient approach, called shadow-stack to minimize NVM write overhead while ensuring consistency. Furthermore, we propose a series of principles to assist programmers in rewriting the programs. And we prove the correctness of these principles using formal methods. Experimental results demonstrate that shadow-stack achieves an approximately 95.1%, 81.3%, and 76.6% improvement in NVM writes overhead compared with log-based, Coach, and Clank, respectively on average. Simultaneously, shadow-stack also significantly reduces energy and time consumption compared with log-based approach by up to 19.4 × and 6.87 × , Coach by up to 4.2 × and 2.46 × , and Clank by up to 3.4 × and 2.26 × respectively. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

This paper introduces and investigates the performance of a system where an unmanned aerial vehicle (UAV) is utilized to assist terrestrial backscattering devices (BDs) in wireless energy charging and data transmission. The UAV task is data collection through the backscatter communication with finite block length. To assess performance, the study not only considers energy efficiency (EE) but also derives closed-form expressions for age-of-information (AoI). The block error rate (BLER) expression is approximated using Gaussian-Chebyshev quadrature and the first-order Riemann integral. Furthermore, the optimization problem is solved to maximize EE and minimize transmit power through a search algorithm. Both EE and spectral efficiency (SE) are investigate via the number of transmission bits. The analytical and simulation results show that the optimized EE and minimum transmit power of UAV can be obtained by selecting an appropriate packet length and UAV altitude. Besides, the Rician factor K also has a significant impact on BLER of the considered system. Finally, the study points out that the system performance is strongly influenced by factors such as the utilized bandwidth, the specific operating environments, and the elevation angle of UAV. [ABSTRACT FROM AUTHOR]