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Lithium iron phosphate battery recycling is enhanced by an eco-friendly N 2 H 4 ·H 2 O method, restoring Li+ ions and reducing defects. Regenerated LiFePO 4 matches commercial quality, a cost-effective and eco-friendly solution. [Display omitted] • Low-temperature liquid-phase direct regeneration of LiFePO 4 with high efficiency. • Li+ ions anti-site defects being repaired Using N 2 H 4 ·H 2 O. • Regenerated LiFePO 4 showing good electrochemical performance. Lithium iron phosphate batteries, known for their durability, safety, and cost-efficiency, have become essential in new energy applications. However, their widespread use has highlighted the urgency of battery recycling. Inadequate management could lead to resource waste and environmental harm. Traditional recycling methods, like hydrometallurgy and pyrometallurgy, are complex and energy-intensive, resulting in high costs. To address these challenges, this study introduces a novel low-temperature liquid-phase method for regenerating lithium iron phosphate positive electrode materials. By using N 2 H 4 ·H 2 O as a reducing agent, missing Li+ ions are replenished, and anti-site defects are reduced through annealing. This process restores nearly all missing Li+ ions at 80 °C/6h. After high-temperature sintering at 700 °C/2h, the regenerated LiFePO 4 matches commercial LiFePO 4 in terms of anti-site defects and exhibits excellent performance with a 97 % capacity retention rate after 100 cycles at 1C. Compared to high-temperature techniques, this low-temperature liquid-phase method is simpler, safer, and more energy-efficient, offering a blueprint for reclaiming discarded LiFePO 4 and similar materials. [ABSTRACT FROM AUTHOR]

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

The current study reviews recent developments in hydrogen production methods. Also, the study seeks to take a step forward by comparing the methodology, energy entering each cycle, hydrogen production rate, production cost rate, environmental impacts, and energy and exergy efficiency in recent years. Hydrogen production methods are categorized based on initial energy resources. The resources are categorized into fossil fuel and renewable groups. Among fossil fuel-based techniques, steam reforming is the most frequently used approach, with a hydrogen production rate above 80%. Renewable resources for hydrogen production methods are grouped into water and biomass. For the water group, water splitting is performed by electrolysis technology, and for the biomass group, gasification is widely used due to higher efficiency and positive environmental effects. When the hydrogen production methods were introduced, 50 recent studies on hydrogen production methods were comprehensively compared. The studies were evaluated according to their different hydrogen production methods, including liquefaction, pyrolysis, gasification, combustion, solid oxide, alkaline, and polymer electrolyte membrane, all of which are renewable source-based technologies, needless of large and costly infrastructure. However, thermochemical methods, such as steam methane, pyrolysis reforming, etc., are justifiable for evaluation due to different challenges, including production efficiency, type of raw materials, and production purification, etc. The hydrogen green index (HGI) was introduced to better compare the sustainability and environmental impacts of the recent literature. As a dimensionless parameter, HGI is the rate of hydrogen production to CO 2 reduction per 1 USD. Although common comparison methods consider PEM as the best hydrogen production method, HGI suggests that SMR is the best option (HGI = 4.19). • The recent developments in hydrogen production methods is evaluated in this study. • Introducing HGI for eco-sustainability evaluation. • Steam reforming prevails among fossil fuel methods, yielding 80% hydrogen. • PEM and SMR have been used more than other methods in recent years. • The maximum CO2 reduction per one USD was obtained by PEM (equal to 8.58 kg/USD). [ABSTRACT FROM AUTHOR]

Two-step thermochemical cycle (TSTC) is a promising approach for solar-driven H 2 O splitting. However, extremely high reduction temperature (T red) is a key challenge limiting the development of TSTC. Two-step electro-thermochemical cycle (TSEC) has recently emerged to overcome this problem. However, the pros and cons of TSEC and TSTC are under debate. In this paper, a thermodynamic model was developed for studying the thermodynamic fundamentals and the variation rules of energy efficiency of TSEC and TSTC. The thermodynamic mechanisms of TSTC and TSEC were compared using the Gibbs free energy method and the Exergy & energy level analysis method. In comparison to TSTC, TSEC tends to operate at a lower T red , enabling isothermal operation at moderate temperatures (<1473 K) while sustaining a high H 2 O conversion rate. In addition, energy efficiency analysis reveals that the introduction of 0.2 V auxiliary voltage can increase the energy efficiency from 0.25 in TSTC to 0.38 in TSEC, showing the advantage of TSEC for H 2 O splitting in terms of energy efficiency. This study analyses the advantages and disadvantages of TSTC and TSEC for H 2 O splitting, demonstrating a reference for further development of solar-driven H 2 O splitting. • The thermodynamic mechanisms of TSTC and TSEC are compared. • TSEC tends to operate at a lower reduction temperature with a high H 2 O conversion rate. • A thermodynamic model is developed for studying the energy efficiency of TSEC for H 2 O splitting. • 0.2 V auxiliary voltage can increase the energy efficiency from 0.25 to 0.38. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel, sustainable, highly efficient, and affordable salt processing technology tailored to local salt producers. Novel technology converts raw solar sea salt into the finest dried salt, which is widely accepted by various industries. Experiments were conducted at a drying temperature of 80°C, with an air velocity of 30 m/s, and a feed rate of 125 kg per hour to evaluate the proposed novel salt processing technology. Performance evaluation was based on product quality parameters (e.g. wet moisture content, sodium chloride (NaCl) percentage, and whiteness) and specific energy consumption. The economic assessment relied on financial analysis and payback period parameters. The results indicated a reduction in salt sample moisture content from 10.37% ± 0.4% to 0.47% ± 0.02%, with NaCl percentage and whiteness of the salt produced at 98.5% ± 0.3% and 81 ± 0.7, respectively. The specific energy consumption for the novel salt processing technology was 0.119 ± 0.07 kWh/kg of the final salt product. Initial investment requirements totalled IDR 275,435,000, with a payback period of 1.66 years. This proposed novel salt processing technology has the potential to generate high-quality dried salt with a production efficiency of 89.5%. Consequently, it could enhance local producer’s businesses, seize opportunities in the national salt market, support rural industrialisation, and be best configured for Indonesia’s local salt producers. [ABSTRACT FROM AUTHOR]

The field of production engineering is constantly attempting to be distinguished for promoting sustainability, energy efficiency, cost-effectiveness, and prudent material consumption. In this study, three control parameters (3D printing settings), namely nozzle temperature, travel speed, and layer height (LH) are being investigated on polyamide 6/carbon fiber (15 wt%) tensile specimens. The aim is the optimum combination of energy efficiency and mechanical performance of the specimens. For the analysis of the results, the Box-Behnken design-of-experiment was applied along with the analysis of variance. The statistical analysis conducted based on the experimental results, indicated the importance of the LH control setting, as to affecting the mechanical strength. In particular, the best tensile strength value (σB = 83.52 MPa) came from the 0.1 mm LH. The same LH, whereas caused the highest energy consumption in 3D printing (EPC = 0.252 MJ) and printing time (PT = 2272 s). The lowest energy consumption (EPC = 0.036 MJ) and printing time (PT = 330 s) were found at 0.3 mm LH. Scanning electron microscopy was employed as a part of the manufactured specimens' 3D printing quality evaluation, while Thermogravimetric analysis was also conducted. The modeling approach led to the formation of equations for the prediction of critical metrics related to energy consumption and the mechanical performance of composite parts built with the MEX 3D printing method. These equations proved their reliability through a confirmation run, which showed that they can safely be applied, within specific boundaries, in real-life applications. [ABSTRACT FROM AUTHOR]

The growing prevalence of deep neural networks (DNNs) across various fields raises concerns about their increasing energy consumption, especially in large data center applications. Identifying the best combination of optimization techniques to achieve maximum energy efficiency while maintaining system performance is challenging due to the vast number of techniques available, their complex interplay, and the rigorous evaluation required to assess their impact on the model. To address this gap, we propose an open-source methodological framework for the systematic study of the influence of various optimization techniques on diverse tasks and datasets. The goal is to automate experimentation, addressing common pitfalls and inefficiencies of trial and error, saving time, and allowing fair and reliable comparisons. The methodology includes model training, automatic application of optimizations, export of the model to a production-ready format, and pre- and post-optimization energy consumption and performance evaluation at inference time using various batch sizes. As a novelty, the framework provides pre-configured "optimization strategies" for combining state-of-the-art optimization techniques that can be systematically evaluated to determine the most effective strategy based on real-time energy consumption and performance feedback throughout the model life cycle. As an additional novelty, "optimization profiles" allow the selection of the optimal strategy for a specific application, considering user preferences regarding the trade-off between energy efficiency and performance. Validated through an empirical study on a DNN-based cyber threat detector, the framework demonstrates up to 82% reduction in energy consumption during inference with minimal accuracy loss. [ABSTRACT FROM AUTHOR]

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

The electrodeposition process and the subsequent assessment of a novel cuprous oxide photocathode are conducted for solar light-based hydrogen production. The mesh dome photocathode helps facilitate the optimal light harvesting, enabling the efficient collection of the maximum accessible light from the solar simulator. This particular design also ensures that light reaches the remote surfaces of the photocathode through penetrating the mesh structure. The electrodeposition method is then employed to coat cuprous oxide onto the stainless-steel electrode. The subsequent examination of this photocathode occurs within an alkaline solution of potassium dioxide to assess its performance in hydrogen production. The study investigates the impact of the new design of the mesh photocathode on hydrogen generation rates, as well as related efficiencies of energy and exergy, under varying light angles during daylight hours. The present experimental findings reveal that the maximum rates of hydrogen production, achieved at a 45° tilt light and 0.25 M KOH, are 15.38 μg/s and it is 15.83 μg/s for vertical light positions. Furthermore, the highest recorded system exergy and energy efficiencies under vertical light conditions are 2.19% and 3.20%, respectively, compared to 2.14% and 3.12% at applying tilted light. The new photocathode design contributes to a notable 80.3% improvement in hydrogen generation rate at tilted light angles in comparison to vertical light conditions. • A novel PEC Cell Design with a unique photocathode is studied experimentally. • Hydrogen generation rates are estimated for the light of 45° and 90° orientation. • Exergy and energy efficiencies of the photoelectrochemical cell are assessed. • The current hydrogen apparatus accomplishes promising hydrogen generation results. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

In biological visual systems, retina with bidirectional photoresponse can adapt to variable environments and modulate their photosensitivity dependent on illumination conditions in an energy‐efficient way. However, to emulate the visual functionality, current systems still require a sustained power supply and manipulate the electrical stimulation manually according to light intensity, thus incurring high energy consumption and the lack of autonomous adaptive function. Herein, an organic ferroelectric neuristor emulating the visual self‐adaptation function of retina is introduced. Such autonomous adaptive behavior of device originates from the trap‐assisted ferroelectric polarization reversal under light illumination. It allows to realize bidirectional photoresponse in a single device according to light intensity, thus breaking through the limitation of commonly adaptive devices with single photoresponse mechanisms. In particular, only a single gate voltage pulse with an ultralow energy consumption of ≈34 pJ is required to accomplish the adaptation behavior, endowing the visual perception with high autonomy and power efficiency. Finally, the application potential of the neuristor in image pre‐processing and recognition under bright and dim light conditions is demonstrated. Therefore, this work opens up a path for the development of retina‐like visual systems with highly energy efficient. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the 'warm start' policy for each equipment, manifesting as a 51.32 % increase in initial reward compared to a random policy approach. [ABSTRACT FROM AUTHOR]

In recent years, the distribution network in Chile has undergone various modifications to meet new demands and integrate new technologies. However, these improvements often do not last as long as expected due to inaccurate forecasting, resulting in frequent equipment changes and service interruptions. These issues affect project investment, unsold energy, and penalties for poor quality of supply. Understanding the electricity market, especially in distribution, is crucial and requires linking technical quality standards with service quality factors, such as the frequency and duration of interruptions, to understand their impact on regulated distribution to customers. In this context, a comparative study will be carried out between Long Short-Term Memory (LSTM) and transformer architectures, with the aim of improving the sizing of distribution transformers and preventing failures when determining the nominal power of the transformer to be installed. Variables such as voltages and operating currents of transformers installed between 2020 and 2021 in the Valparaíso region, Chile, along with the type and number of connected customers, maximum and minimum temperatures of the sectors of interest, and seasonality considerations will be used. The compilation of previous studies and the identification of key variables will help to propose solutions based on error percentages to optimise the accuracy of transformer sizing. [ABSTRACT FROM AUTHOR]

Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded. [ABSTRACT FROM AUTHOR]

The benefits of energy efficiency are recognized in multiple socio-economic spheres. Still, the quantitative impact on macroeconomic performance is not fully understood, as modeling tools are not thermodynamically consistent—failing to explicitly include the useful stage of energy flows and/or thermodynamic efficiencies in primary–final–useful energy transformations. Misspecification in the link between energy use and the economy underplays the role of energy use and efficiency in economic growth. In this work, we develop and implement the Macroeconometric Resource Consumption model for Portugal (MARCO-PT), 1960–2014. Based on the post-Keynesian framework developed for the United Kingdom (MARCO-UK), our model explicitly includes thermodynamic energy efficiency, extending the analysis to the useful stage of energy flows. The model's stochastic equations are econometrically estimated. The historical influence of key variables—namely thermodynamic energy efficiency—on economic output is assessed through counterfactual simulations and computation of year-by-year output elasticities. The MARCO-PT model adequately describes the historical behavior of endogenous variables. Although its influence has decreased over time, thermodynamic efficiency has consistently been the major contributor to economic growth between 1960–2014, with an average output elasticity of 0.46. Total useful exergy is also a major contributing factor, with an average output elasticity of 0.29. Both have a higher influence than capital, labor, or other energy variables (final energy, prices). An adequate integration of thermodynamic efficiency is thus crucial for macroeconomic models. [ABSTRACT FROM AUTHOR]

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU. [ABSTRACT FROM AUTHOR]

Aiming at the situation of imperfect spectrum sensing of cognitive radio in actual cognitive ultra-dense network sce- narios, this paper proposed a resource allocation scheme based on imperfect spectrum sensing. The target was to maximize the energy efficiency of secondary network in cognitive ultra-dense network with imperfect spectrum sensing while taking into ac- count cross-tier/co-tier interference constraints and ensuring users quality of service. And this paper constructed the energy efficiency optimization problem based on the network model, which was a mixed integer non-convex programming problem, then transformed it into an equivalent convex optimization problem by the time-sharing relaxation method and Dinkelbach me- thod, and finally used the Lagrangian dual method to get its optimal solution, thereby obtained the sub-channel and power allo- cation strategy for optimal energy efficiency. Based on this, this paper proposed an iterative sub-channel and power allocation algorithm. In order to trade off the computational complexity, this paper also proposed a practical sub-channel and power allo- cation algorithm. Simulation results show that the proposed algorithms effectively improve energy efficiency. [ABSTRACT FROM AUTHOR]

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables. [ABSTRACT FROM AUTHOR]

This document addresses some inherent problems in Machine Learning (ML), such as the high computational and energy costs associated with their implementation on IoT devices. It aims to study and analyze the performance and efficiency of quantization as an optimization method, as well as the possibility of training ML models directly on an IoT device. Quantization involves reducing the precision of model weights and activations while still maintaining acceptable levels of accuracy. Using representative networks for facial recognition developed with TensorFlow and TensorRT, Post-Training Quantization and Quantization-Aware Training are employed to reduce computational load and improve energy efficiency. The computational experience was conducted on a general-purpose computer featuring an Intel i7-1260P processor and an NVIDIA RTX 3080 graphics card used as an accelerator. Additionally, a NVIDIA Jetson AGX Orin was used as an example of an IoT device. We analyze the feasibility of training on an IoT device, the impact of quantization optimization on knowledge transfer-trained models and evaluate the differences between Post-Training Quantization and Quantization-Aware Training in such networks on different devices. Furthermore, the performance and efficiency of NVIDIA's inference accelerator (Deep Learning Accelerator - DLA, in its 2.0 version) available at the Jetson Orin architecture are studied. We concluded that the Jetson device is capable of performing training on its own. The IoT device can achieve inference performance similar to that of the more powerful processor, thanks to the optimization process, with better energy efficiency. Post-Training Quantization has shown better performance, while Quantization-Aware Training has demonstrated higher energy efficiency. However, since the accelerator cannot execute certain layers of the models, the use of DLA worsens both the performance and efficiency results. [ABSTRACT FROM AUTHOR]

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

This study delves into the dynamic evolution of solids content in a pilot-scale through air drying tissue machine, aiming to enhance comprehension and refine optimization strategies for tissue manufacturing. It focuses on development of solids content throughout the process. Analyzing the interaction between process parameters and solids content provides deeper insights into water removal dynamics. The research employs solid contents monitoring techniques in pilot scale, offering a detailed view of solids content evolution from the wet web's inception to the final tissue product. It significantly highlights the impact of key parameters, particularly pulp refining, on solids content across all positions of the pilot machine. Additionally, the study emphasizes the influence of vacuum system configurations, identifying the relationship between energy expended in the vacuum system and evaporation energy. Furthermore, the results indicate that compression and air displacement dewatering in vacuum boxes are less speed-sensitive compared to heat-driven evaporation in the TAD- and Yankee cylinders. Solid contents at all positions in the tissue machine correlates between various dewatering elements. Optimizing site-specific dewatering and evaporation strategies can potentially save drying energy in the Yankee drying phase which hold promise for enhanced energy and resource utilization, aligning with demands for sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

The gap in demand and supply of energy across Sub-Saharan African (SSA) countries has increased energy insecurity in the region. Therefore, power outages have become pervasive, causing unemployment and a decline in production output. Among the several energy-saving factors identified in the literature across developing economies, technological spillover driven by trade openness appears to be a prominent factor in improving energy efficiency. Thus, this study evaluates the impact of machinery imported from OECD and non-OECD countries and its corresponding research and development (R&D) spillover on energy efficiency performance in 18 SSA countries from 1995 to 2017. Using a stochastic energy distance function, we discover that aggregated data from OECD and non-OECD countries have no significant effect on energy efficiency performance across SSA countries. However, results from disaggregated data for OECD and non-OECD machinery imports show that OECD machinery imports improve energy efficiency contrary to non-OECD imports. Thus, technology spillover from OECD countries is advantageous for SSA countries to reduce long-term energy-based emissions. Furthermore, our results show that human capital has no significant effect on SSA energy efficiency. Consequently, the results possess some policy implications; for instance, policymakers responsible for promoting science and technology could increase investment in human capital development by developing technology and engineering expertise and increasing GDP allocation to R&D activities. For energy efficiency scores, we observe substantial differences in efficiency across SSA countries – implying potential improvements in energy efficiency across SSA countries. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

In this work, ultraviolet (UV)-/ultrasound (US)-based advanced oxidation processes were used to removal of Reactive Red 120 (RR120). The optimum conditions were determined as initial dye concentration = 100mg L−1, hydrogen peroxide concentration (H2O2) = 100 mg L−1, pH = 3 for the UV/H2O2 process, iron dosage (Fe2+) = 50 mg L−1, [H2O2] = 250mg L−1, pH = 3, ultrasound frequency = 53 kHz, and ultrasound power = 90 W for the sono-Fenton (Fe/H2O2/US) process. Besides, in the mineralization study, total organic carbon (TOC) reductions were 37.5%, 43.7%, 50.5%, and 58.9% for Fe/H2O2/US, UV/H2O2, foto-Fenton (Fe/H2O2/UV), and sono-photo-Fenton (Fe/H2O2/UV/US) processes, respectively. The sole and other processes removal percentages were below 27%. These results help us to understand how these different advanced oxidation processes removal performances by means of decolorization efficiencies are similar like as TOC reduction. A significant reduction in removal performance (27.8% because tert-butyl alcohol) of was found compared with no addition of radical scavenger (89.2%) in UV/H2O2. For sono-Fenton, the same situation was occurred, and the removal efficiency was decreased from 70.2 to 42.5% because of sodium azide radical scavenger. In the water matrix study, regardless of this process in the area of removal performance, the major issue of some ions persistence. (The performance declined 89.2–40.7% due to the carbonate ions for UV/H2O2; increased 70.2–82.1% due to the carbonate ions for sono-Fenton.) These findings may shed light on these processes' behavior of azo dyes in some specific scenarios such as laboratory or pilot scale utilization. [ABSTRACT FROM AUTHOR]

Whether the energy-consuming right transaction system (ECRTS) can achieve a win–win development in economic growth and energy saving is a practical issue that needs to be tested urgently. The existing studies lack results that test the policy effect of ECRTS from the enterprise perspective. This paper uses the ECRTS implemented in 2016 as a natural experiment drawing on the relevant data of Chinese listed enterprises from 2009 to 2020. It adopts a difference-in-difference model to assess the effect of ECRTS on energy efficiency. The research finds that ECRTS improves energy efficiency of enterprises, and ECRTS can improve energy efficiency by promoting technological innovation and optimizing the efficiency of capital allocation. Further, ECRTS is more conducive to improving the energy efficiency of state-owned enterprises, enterprises with executives financial background, high pollution enterprises, and enterprises in regions with low resource endowments. Moreover, the improvement of energy efficiency can further enhance the value of enterprises after enterprises participate in ECRTS. The study provides a direct answer to the key question of whether the ECRTS policy can actually support the high-quality economic development. [ABSTRACT FROM AUTHOR]

The fast growth of building constructions industry and of the universal population is the main cause of increase in global energy consumption. Therefore, the improvement of the building envelope encourages the scientific community to investigate the use of alternative materials that assure indoor comfort conditions and at the same time promote the reduction of energy consumption by adopting energy saving strategies in the manufacturing process. The present research contribution aims to focus on the potential use of innovative building materials developed with waste paper, classified as urban non-hazardous discard component. The use of paper pulp originated from recycled paper and cardboard with the addition of textile fibers from industrial by-products was explored. Here, an effort has been made to realize sustainable insulating panel for internal use, considering different mix design and recycled components in various percentages. The research focuses on hygric, thermal, and physical performances of recycled waste based panels, measuring physical aspects as bulk porosity and bulk density and testing hygrothermal performances as the dry-state thermal conductivity, thermal diffusivity, volumetric heat capacity, and water vapour permeability. Furthermore, in order to assess the hygrothermal behaviour of the building envelope, a numerical simulation, carried out by WUFI® Plus in a Mediterranean context, was developed using as inputs the results collected from the measurement campaign in laboratory. [ABSTRACT FROM AUTHOR]

Decision-makers want to be reliably advised on the implications of the decisions they make. Very sophisticated models, which decision-makers are often unfamiliar with, are typically used to provide such assessments for large and complex systems. However, even having access to these models, decision-makers can rarely handle them. A model is best known to its developers, who, therefore, need to be contracted to estimate the effects of the proposed policies. This takes time and money, yet leaves the credibility of the results questionable in countries with a limited culture of cooperation between decision-makers and a modeling community. One possible, yet partial, solution is to use an ensemble of models. Another option is to use a set of compact meta-models to address specific policies and measures; the parameters of such compact models can be assessed using other, large and complex, models. Decision-makers can run these simple compact models on their own to make policy dialogue more operational and to have more confidence in the results. This paper presents one such model, which consists of 95 compact sub-models designed to outline comprehensive energy efficiency programs, along with the results of its pilot application for an illustrative set of policies. This application has shown, that such models may serve as an effective tool for a prompt policy dialogue with all stakeholders in compiling the policy package to untap the most of the available energy efficiency potential to meet sector-specific or economy-wide goals in terms of energy savings or energy intensity reduction. [ABSTRACT FROM AUTHOR]

At the end of 2019, the EC presented the European Green Deal – a set of policy initiatives with the ultimate goal of making Europe climate-neutral by 2050. This means that all EU member states must reach climate neutrality. Emissions in all sectors shall be substantially reduced to achieve this goal. Most of the energy is produced using fossil fuels, which contributes to climate change, water and air pollution, as well as the depletion of natural resources. Individual and district heating systems, which supply residential buildings with heat and hot water, are responsible for a part of global emissions. Electricity generation also produces emissions. The EU recognises the mitigation of climate change as one of the main challenges that must be overcome to ensure sustainable development of the European Union, which is why it has defined ambitious goals to promote decarbonisation and is deliberately moving towards the implementation of renewable energy. This article aims at analysing the opportunity of using renewable energy resources in multi-apartment residential buildings in the context of sustainability. [ABSTRACT FROM AUTHOR]

As the global economy is increasingly influenced by energy policy and efficiency, the opportunities of energy data classification are broadening. Performance metrics, especially for Deep Learning (DL), have motivated the accelerated development of computing hardware. In particular, High-Performance Edge Computing (HPEC) has an important role in complementing cloud computing in the collection, pre-processing, and post-processing of big data in a more privacy-preserving manner. Yet, such opportunities bring challenges concerning the selection of hardware platforms and classification algorithms. Therefore, in this article, we aim to carry out a comparative study that examines the merits, performance and efficiency metrics, and limitations of notable HPEC platforms centered around a DL classification framework. In this implementation, a 2D Gramian Angular Field (GAF) energy data classifier is presented and run on a publicly available dataset. Following, a DL classifier is trained and exported as a lightweight counterpart for validation on several commonly used HPEC platforms, namely the Jetson Nano, ODROID-XU4, Coral Dev Board, and the BeagleBone AI. Current results show competitive computational performance among the reviewed platforms, as fast as ~ 8 ms per classified image. [ABSTRACT FROM AUTHOR]

Through energy and exergy analysis, the present work revisits the combustion processes generated using different fuels (CH4, C3H8, H2, LPG, Natural gas and Biogas) under the same operating conditions. First, numerical computations, in which the turbulent dynamic k-ϵ model coupled with a probability density function (PDF) approach together with different sub-models that ensure the reliability of the results, are used. Then, the First and Second Laws of thermodynamics are applied to all fuel combustion cases considered previously to predict the energy loss, exergy destruction and their corresponding efficiencies. Next, variations in the air factor, the air inlet temperature and the fuel inlet temperatures are considered to analyse the response of combustion system performance as well as energy and exergy losses for the different fuels under investigation. The results show an amelioration of combustion parameters for the mixture of fuel and air using values of λ greater than 1. Also, the preheating of the inlet air temperature has a positive effect on energetic parameters. However, preheating the inlet fuel does not take a considerable role in the efficiency amelioration and loss reduction of the combustion system. It further turns out that biogas features higher energy efficiency compared to other fuels, at approximately 89.64%, while hydrogen fuel exhibits higher exergy efficiency at around 78.70% followed by biogas with 74.96%. [ABSTRACT FROM AUTHOR]

Summary: The advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency. [ABSTRACT FROM AUTHOR]

Energy efficiency is a challenging and relevant research field in modern manufacturing industries, where robotic systems play an essential role in the automation of several industrial operations. In this paper, we present an approach for the energy-efficiency optimization of a 3-DOF parallel robot. The proposed strategy leverages the task placement, the execution time, and the length of the robot lower arms to minimize the energy consumption for the execution of a predefined high-speed pick-and-place operation. To evaluate the actuators energy consumption, the kinematic, dynamic and electro-mechanic mathematical models, as well as an equivalent multibody model, of the parallel robot are implemented. The results of extensive numerical simulations show that the proposed strategy provides notable improvements in the energy efficiency of the parallel robot, with respect to alternative approaches. Starting from a pick-and-place task with optimal task placement with a consumption of 38.2 J (with a cycle time of 0.4 s), the energy expenditure can be reduced to 3.75 J (with a cycle time of 1.86 s), with a reduction percentage of 90.2%, by additionally optimizing the execution time, and the length of the robot lower arms. These results lead to a reduction from 5733 J/min (for 150 cycles/min) to 121 J/min (for 32 cycles/min), allowing to choose the best trade-off between robot productivity and consumed energy. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

This article describes a method for determining the geometric working volume of satellite positive displacement machines (pump and motor). The working mechanism of these machines is satellite mechanism consisting of two non-circular gears (rotor and curvature) and circular gears (satellites). Two variants of the satellite mechanism are presented. In the first mechanism, the rolling line of the rotor is a sinusoid "wrapped" around a circle. In the second mechanism, the rolling line of the rotor is a double sinusoid "wrapped" around a circle. A method for calculating the area of the working chamber as a function of the rotor rotation angle is presented, based on mathematical formulae of the rotor, the curvature and the satellite rolling lines. It has been shown that the second variant of the satellite mechanism is advantageously characterised by a larger difference between the maximum area of the working chamber and the minimum area of this chamber. New mathematical formulas have been proposed to calculate the area of the working chamber for any angle of rotation of the shaft (rotor) based on the maximum and minimum values of the area of this chamber. It was thus confirmed that the geometric working volume depends on the maximum and minimum area of a working chamber and on the height of the satellite mechanism. The analyses of the area of the working chamber were carried out both for the mechanism without gears (the area delimited by the rolling lines of the elements of the mechanism) and for the real mechanism with gears. Differences in the values of these fields were also detected. [ABSTRACT FROM AUTHOR]

Materials offering high energy density are currently desired to meet the increasing demand for energy storage applications, such as pulsed power devices, electric vehicles, high-frequency inverters, and so on. Particularly, ceramic-based dielectric materials have received significant attention for energy storage capacitor applications due to their outstanding properties of high power density, fast charge–discharge capabilities, and excellent temperature stability relative to batteries, electrochemical capacitors, and dielectric polymers. In this paper, we present fundamental concepts for energy storage in dielectrics, key parameters, and influence factors to enhance the energy storage performance, and we also summarize the recent progress of dielectrics, such as bulk ceramics (linear dielectrics, ferroelectrics, relaxor ferroelectrics, and anti-ferroelectrics), ceramic films, and multilayer ceramic capacitors. In addition, various strategies, such as chemical modification, grain refinement/microstructure, defect engineering, phase, local structure, domain evolution, layer thickness, stability, and electrical homogeneity, are focused on the structure–property relationship on the multiscale, which has been thoroughly addressed. Moreover, this review addresses the challenges and opportunities for future dielectric materials in energy storage capacitor applications. Overall, this review provides readers with a deeper understanding of the chemical composition, physical properties, and energy storage performance in this field of energy storage ceramic materials. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency. [ABSTRACT FROM AUTHOR]

The issue of enhancing energy recovery efficiency is a key concern within the European Union's climate protection efforts. In particular, it applies to all processes and plants for the harvesting, gathering, and conversion of energy. The abandonment of fossil fuels in favour of alternative energy sources, and the increasing of energy efficiency and its recovery, is now a widely accepted direction of energy development. This study focuses on facilities that recover and process energy from municipal waste left after recycling processes, known as waste-to-energy (WtE) plants. These plants' energy recovery efficiency is governed by the R1 Formula in EU countries. This report is based on an analysis of four years of operational data from selected Polish municipal waste incinerators, supplemented by a discussion of various studies on energy recovery efficiency. The primary objective of this report is to evaluate the effectiveness of these plants in contributing to sustainable waste management and energy recovery. The main effect of the developed report is the set of results of the energy recovery efficiency factor values, determined based on the R1 formula valid in the EU legislation, tabulated and graphically illustrated, and calculated for five selected Polish waste-to-energy plants. The presented results, with their graphical interpretation, discussion, and conclusions, provide insights into several factors influencing the value of the R1 efficiency factor. They can be a valuable contribution to operators of waste-to-energy plants, especially those operating in countries outside the EU. [ABSTRACT FROM AUTHOR]

Small nuclear power plants are a promising direction of research for the development of carbon-free energy in isolated power systems and in remote regions with undeveloped infrastructure. Improving the efficiency of power units integrated with small modular reactors will improve the prospects for the commercialization of such projects. Power cycles based on supercritical carbon dioxide are an effective solution for nuclear power plants that use reactor facilities with an initial coolant temperature above 550 °C. However, the presence of low temperature rejected heat sources in closed Bryton cycles indicates a potential for energy saving. This paper presents a comprehensive thermodynamic analysis of the integration of an additional low-temperature organic Rankine cycle for heat recovery to supercritical carbon dioxide cycles. A scheme for sequential heat recovery from several sources in S-CO2 cycles is proposed. It was found that the use of R134a improved the power of the low-temperature circuit. It was revealed that in the S-CO2 Brayton cycle with a recuperator, the ORC add-on increased the net efficiency by an average of 2.98%, and in the recompression cycle by 1.7–2.2%. With sequential heat recovery in the recuperative cycle from the intercooling of the compressor and the main cooler, the increase in efficiency from the ORC superstructure will be 1.8%. [ABSTRACT FROM AUTHOR]

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents' well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. [ABSTRACT FROM AUTHOR]

Summary In the last decades, wireless sensor networks (WSNs) have appeared as an active research area owing to their high applicability in different application areas like healthcare, automation, and the military. Several issues have been encountered in the WSNs, including energy consumption, deployment of sensor nodes, routing algorithms, energy efficiency, cluster head (CH) selection, and robustness. Cluster‐based routing is one of the energy‐efficient solutions in the WSN. At the same time, the hot spot problem remains a challenging problem, which needs to be addressed in the design of energy‐efficient solutions for WSNs. Motivated by the abovementioned discussion, c The comprehensive comparison study pointed out the betterment of the FLHBA‐CMHR technique in terms of different measures. The FLHBA‐CMHR technique obtains a maximum half‐node die (HND) of 1629 nodes, while the LEACH, FUCA, URBD, DEFL, and E‐FUCA techniques attain decreased HND of 793, 1085, 1092, 1139, and 1344 nodes correspondingly. [ABSTRACT FROM AUTHOR]

Summary Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

This study validates the mechanism of ultrasonic dehydration without transition of water into vapor. The process can be realized by cavitation dispersion of liquid initiated by collapse of cavitation bubbles of cylindrical shape formed in capillaries. The developed mathematical model considers the complete life cycle of cavitation bubbles, encompassing three stages: slow growth, rapid expansion with deformation, and collapse. The optimum range of sound pressure level from 150 dB (critical level) to 170 (limit level) at which cavitation dispersion of liquid from capillaries takes place has been determined. The optimum sample size corresponding to the ultrasonic wavelength in gas, at which the dehydrated productivity is maximized, has been determined. The mechanism of ultrasonic dispersion of liquid during drying was confirmed by experiments. Practical implementation of ultrasonic drying of food products (carrots) confirmed effectiveness of implementation of dehydration mechanism and provided a reduction of drying time by 1.9 times with a reduction of power consumption by 1.7 times. [ABSTRACT FROM AUTHOR]

Iron redox flow batteries (IRFBs) are cost-efficient RFBs that have the potential to develop low-cost grid energy storage. Electrode kinetics are pivotal in defining the cycle life and energy efficiency of the battery. In this study, graphite felt (GF) is heat-treated at 400, 500 and 600 °C, and its physicochemical and electrochemical properties are studied using XPS, FESEM, Raman and cyclic voltammetry. Surface morphology and structural changes suggest that GF heat-treated at 500 °C for 6 h exhibits acceptable thermal stability while accessing the benefits of heat treatment. Specific capacitance was calculated for assessing the wettability and electrochemical properties of pristine and treated electrodes. The 600 °C GF has the highest specific capacitance of 34.8 Fg−1 at 100 mV s−1, but the 500 °C GF showed the best battery performance. The good battery performance of the 500 °C GF is attributed to the presence of oxygen functionalities and the absence of thermal degradation during heat treatment. The battery consisting of 500 °C GF electrodes offered the highest voltage efficiency of ~74%, Coulombic efficiency of ~94%, and energy efficiency of ~70% at 20 mA cm−2. Energy efficiency increased by 7% in a battery consisting of heat-treated GF in comparison to pristine GF. The battery is capable of operating for 100 charge–discharge cycles with an average energy efficiency of ~ 67% for over 100 cycles. [ABSTRACT FROM AUTHOR]

Thermoelectric power can convert heat and electricity directly and reversibly. Low-dimensional thermoelectric materials, particularly thin films, have been considered a breakthrough for separating electronic and thermal transport relationships. In this study, a series of Bi0.5Sb1.5Te3 thin films with thicknesses of 0.125, 0.25, 0.5, and 1 μm have been fabricated by RF sputtering for the study of thickness effects on thermoelectric properties. We demonstrated that microstructure (texture) changes highly correlate with the growth thickness in the films, and equilibrium annealing significantly improves the thermoelectric performance, resulting in a remarkable enhancement in the thermoelectric performance. Consequently, the 0.5 μm thin films achieve an exceptional power factor of 18.1 μWcm−1K−2 at 400 K. Furthermore, we utilize a novel method that involves exfoliating a nanosized film and cutting with a focused ion beam, enabling precise in-plane thermal conductivity measurements through the 3ω method. We obtain the in-plane thermal conductivity as low as 0.3 Wm−1K−1, leading to a maximum ZT of 1.86, nearing room temperature. Our results provide significant insights into advanced thin-film thermoelectric design and fabrication, boosting high-performance systems. [ABSTRACT FROM AUTHOR]

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time. [ABSTRACT FROM AUTHOR]

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes. [ABSTRACT FROM AUTHOR]

In Germany, there are over 32,000 schools, representing great potential for climate protection. On the one hand, this applies to educational work, as understanding the effects of climate change and measures to reduce GHG emissions is an important step to empower students with knowledge and skills. On the other hand, school buildings are often in bad condition, energy is wasted, and the possibilities for using renewable energies are hardly used. In our "Schools4Future" project, we enabled students and teachers to draw up their own CO2 balances, identify weaknesses in the building, detect wasted electricity, and determine the potential for using renewable energies. Emissions from the school cafeteria, school trips, and paper consumption could also be identified. The fact that the data can be collected by the students themselves provides increased awareness of the contribution made to the climate balance by the various school areas. The most climate-friendly school emits 297 kg whilst the school with the highest emissions emits over one ton CO2 per student and year. Our approach is suitable to qualify students in the sense of citizen science, carry out a scientific investigation, experience self-efficacy through one's own actions, and engage politically regarding their concerns. [ABSTRACT FROM AUTHOR]