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Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

NiFe2O4 material is grown on carbon paper (CP) with the hydrothermal method for use as electrocatalysts in an alkaline electrolyzer. NiFe2O4 material is used as the anode and cathode catalysts (named NiFe(+)/NiFe(−) hereafter). The results are compared with those obtained using CP/NiFe as the anode and CP/Ru as the cathode (named NiFe)(+)/Ru(−) hereafter). During cell operation with NiFe(+)/Ru(−), the current density reaches 500 mA/cm2 at a cell voltage of 1.79 V, with a specific energy consumption of 4.9 kWh/m3 and an energy efficiency of 66.2%. In comparison, for NiFe(+)/NiFe(−), the current density reaches 500 mA/cm2 at a cell voltage of 2.23 V, with a specific energy consumption of 5.7 kWh/m3 and an energy efficiency of 56.6%. The Faradaic efficiency is 96–99%. With the current density fixed at 400 mA/cm2, after performing a test for 150 h, the cell voltage with NiFe(+)/Ru(−) increases by 0.167 V, whereas that with NiFe(+)/NiFe(−) decreases by only 0.010 V. Good, long-term stability is demonstrated. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

This document is a compilation of research papers that explore the topics of Smart Cities and Positive Energy Districts (PEDs). The papers cover a range of subjects including energy efficiency, renewable energy communities, smart urban management, and financial schemes for sustainable neighborhoods. The findings emphasize the importance of energy renovation, potential energy and financial savings, and the need for collaborative approaches and policy direction to achieve sustainable urban development. The papers also provide insights into innovative solutions and technological advancements that can contribute to the realization of carbon-neutral and sustainable cities. [Extracted from the article]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

The pulp and paper industry (PPI) has several opportunities to contribute to meeting prevailing climate targets. It can cut its own CO2 emissions, which currently account for 2% of global industrial fossil CO2 emissions, and it has an opportunity to produce renewable energy, fuels, and materials for other sectors. The purpose of this study is to improve understanding of the decarbonization prospects of the PPI. The study provides insights on the magnitude of needed annual renewal rates for several possible net-zero target years of industrial fossil CO2 emissions in the PPI and discusses decarbonization opportunities, namely, energy and material efficiency improvement, fuel switching, electrification, renewable energy production, carbon capture, and new products. The effects of climate policies on the decarbonization opportunities are critically evaluated to provide an overview of the current and future business environment of the European PPI. The focus is on Europe, but other regions are analyzed briefly to widen the view. The analysis shows that there are no major technical barriers to the fossil-free operation of the PPI, but the sector renovates slowly, and many new opportunities are not implemented on a large scale due to immature technology, poor economic feasibility, or unclear political environment. [ABSTRACT FROM AUTHOR]

In this review paper, information about bicycle lamp technical parameters is provided. The review is based on the searched Web of Science (WoS) and Scopus bibliographic databases and Transportation Research Information Services, which include references to books, technical reports, conference proceedings, and journal articles. This review showed that these papers about bicycle lighting legal rules in given countries are very easy to reach, but deep searching must be performed to find this research paper having the information about types of optical systems and light sources used in bicycle lamps. This research was conducted using PRISMA 2020 assumptions in the area of bicycle lighting technologies and innovations. The information about bicycle lamp technical parameters was obtained on the basis of the examination of technical documents and papers from high-impact journals. The author's review shows that contemporary bicycle lamps are becoming increasingly smarter and more energy efficient, while also enhancing safety and comfort for cyclists on their routes in and beyond urban environments. [ABSTRACT FROM AUTHOR]

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of scientific papers and market reports analyzing the economic competitiveness of fuel cell electric buses (FCEBs) with respect to their conventional alternatives via the total cost of ownership (TCO) methodology. We discussed the variables and data taken into account and compared the resulting outcomes by year and geographical areas. It emerged that FCBs are not currently cost competitive. The decreasing trend in acquisition and fuel costs, however, indicates potential for future competitiveness. We find that the current TCO literature on FCEBs presents several areas of uncertainty and weakness. Potential improvements can be achieved by: (i) extending the geographic coverage to Asian and African developing countries; (ii) making use of real-world data instead of simulated data, in particular, concerning acquisition costs, hydrogen costs under different pathways, fuel efficiency, and maintenance costs; (iii) clarifying the role of infrastructural costs; (iv) exploring the existence of economies of scale at fleet level; (v) distinguishing among different bus sizes. [ABSTRACT FROM AUTHOR]

Hybrid electric aircraft use traditional engine and electric propulsion combinations to optimize aircraft architecture, improve propulsion efficiency, and reduce fuel consumption. As a new technology, the fuel and energy consumption calculation of hybrid electric aircraft is more complicated than traditional aircraft due to the usage of different energy forms. The purpose of this paper is to develop the analytical method for fuel and energy consumption for hybrid electric aircraft. This paper summarizes the working principle of hybrid electric aircraft, including the system architecture and power conversion mechanism. The calculation of fuel and energy consumption for hybrid electric aircraft is carried out in detail. In order to evaluate large hybrid electric aircraft, the architecture, based on energy flow, is established, and turbofan engine, electrical system, electric duct fan, and aerodynamic model characteristics are established. With a single-aisle aircraft as an example, the fuel and energy consumption under the 800 nautical mile range is performed. It shows that fuel consumption can be reduced by 10% and energy consumption by 4.7% compared with a traditional aircraft. The effects of different range and battery ratios are analyzed. The payload range for the hybrid electric aircraft is analyzed. The results show that even though the hybrid electric aircraft reduces the payload and range, it can significantly reduce fuel and energy consumption. [ABSTRACT FROM AUTHOR]

The development of healthy building technology has become a major trend in the global construction industry, especially in China, owing to accelerating urbanization and increasing health awareness among residents. However, an effective evaluation framework to quantify and evaluate the maturity of healthy building technology is lacking. This paper proposes a novel maturity evaluation model for healthy building technology. After analyzing the Driver–Pressure–State–Impact–Response (DPSIR) framework for asserting the maturity of healthy building in China, it constructs an evaluation indicator system, comprising five and twenty-seven first- and second-class indicators, respectively. Subsequently, this paper constructs an improved projection pursuit model based on border collie optimization. The model obtains evaluation results by mining evaluation data, thus overcoming the limitations of traditional evaluation models in dealing with complex data. The empirical research results demonstrate that China is in the optimization stage in terms of the level of maturity of healthy building technology. The weight of impact is as high as 0.2743, which is the most important first-level indicator. Strict green energy utilization policy requirements are the most important secondary indicator, with a weight of 0.0513. Notably, the model is more advanced than other algorithms. In addition, this paper offers some countermeasures and suggestions to promote healthy building in China. Developing and applying this model can promote and popularize healthy building technology in China and even the globe and contribute to a healthier and more sustainable living environment. [ABSTRACT FROM AUTHOR]

The Avoid–Shift–Improve framework has been used since its conception in the 1990s to help decision-makers prioritize action towards environmental sustainability in the transport sector. The core of the framework establishes a clear priority of action among the three main strategies that give it its name, thus highlighting the prominent role transport demand reduction should have within policy discussions. However, although its general formulation allows for a fruitful application to other sectors, the approach and its definitions have rarely been extended beyond transport. In particular, the energy sector could significantly benefit from an application of its methodology since the prioritization of energy demand reduction over energy efficiency would be in line with an optimized path towards decarbonization. This paper outlines a theoretical application of the A-S-I framework to the energy sector that allows the categorization of energy policies in terms of Avoid, Shift, or Improve strategies. Moreover, the paper presents an analysis of several energy policies databases to evaluate to what extent policies are addressing energy demand reduction, shift to less-carbon-intensive energy sources or energy efficiency. The results of the study show that most energy-related policies seem to support improving efficiency in current technology (18–33% of policies, depending on the database that is considered) and shifting towards low-carbon sources (28–48% of policies) more than reducing or altogether avoiding energy demand in the first place (6–22% of policies). Further research is recommended to strengthen the results, especially by evaluating the significance of each policy in terms of committed investment, as well as to understand the main factors that contribute to Avoid-type policies being under-represented in the energy sector. [ABSTRACT FROM AUTHOR]

Carbon containing waste has a certain calorific value and utilization value due to the presence of carbon elements. However, the current treatment methods are mostly traditional landfill disposal, incineration, or expensive physical and chemical reaction methods, which clearly do not comply with the rules of the current clean and resource-saving society. In this paper, a new technology and system for carbon containing wastes is proposed, which comprehensively treats and recycles carbon containing wastes, including solid waste, wastewater, waste gas, etc., using pyrolysis–gasification two-stage-cycle technology. The calculation results indicate that the technical scheme proposed in this article is feasible and can achieve the recycling of intermediate and final products, the efficient and clean utilization of carbon containing waste, and the graded utilization of energy. The clean utilization rate of carbon containing waste can be effectively improved, the energy consumption and cost of separate treatments can be reduced, and zero waste emissions can be completely achieved by the comprehensive pyrolysis–gasification two-stage-cycle treatment and resource utilization technology, which is one of the preferred solutions for future resource-saving societies. [ABSTRACT FROM AUTHOR]

Black-liquor-thickening in an integrated multi-effect evaporation plant consumes substantial amounts of steam produced in pulp and paper mills, and its efficient operation is, thus, crucial. Industrial applications of heat pumps in the pulp and paper industry, especially in black liquor evaporation, show promise in terms of cutting energy consumption and in decarbonizing this industrial branch. Modelling of such a plant includes momentum, heat and mass transfer issues, enriched with black liquor material specification. An existing black liquor evaporation plant which thickens inlet black liquor from 17% to 75% wt. dry solids with a dry solid flow of 2500 tonnes per day is considered. It already includes a MVR (mechanical vapor recompression) pre-evaporator as well as water-condensate stripping columns. A mathematical model of this plant is created in a Matlab environment and, after verification of obtained results, it serves for analyses of possible plant modifications. Among the modification options, installation of a second MVR is modeled and its impact on the whole plant is examined. As a result, a differential (marginal) change in steam and electricity consumed in the plant is obtained. Model results indicate the possibility of a reduction of process steam consumption of around 10 tonnes per hour and an increase in electricity consumption of 600 kW. A favorable simple payback period of 2.5 years can be expected for the considered investment. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) power systems are one of the most promising technologies to address growing energy demand and ecological challenges. This paper proposes smart switching to mitigate inrush currents during the connection of single-phase transformers used in PV systems. An effective inrush current mitigation contributes to the reliability of PV systems. The inrush current severity is influenced by the pseudorandom residual flux at the transformer core and the energization point-on-wave. The most common approach to avoid inrush currents is controlled connection, which requires prior knowledge of the residual flux. However, the residual flux can differ in each case, and its measurement or estimation can be impractical. The proposed smart switching is based on a comprehensive analysis of the residual flux and the de-energization trajectories, and only requires two pieces of data (ϕRM and ϕ0, flux values of the static and dynamic loops when the respective currents are null), calculated from two simple no-load tests. It has a clear advantage over common approaches: no need to estimate or measure the residual flux before each connection, avoiding the need for expensive equipment or complex setups. Smart switching can be easily implemented in practical settings, as it considers different circuit breakers with distinctive aperture features, making it cost-effective for PV systems. [ABSTRACT FROM AUTHOR]

As part of its plan to transition to an energy secure and environmentally sustainable future, Canada has had a national carbon pricing system since 2019. When first introduced, the $20 ('$' refer to Canadian dollars (CAD) in this paper) per tonne price was widely accepted by most Canadians and seen as a way of helping Canada meet its emissions reduction pledges made at the 2015 United Nations Climate Change Conference (COP 21) in Paris. The Canadian system is novel in that it both charges consumers for their emissions and reimburses them for their expected emissions; this is intended to raise awareness of their emissions and encourage those who can afford to opt for lower-emissions energy services to do so. By 2023, the combination of the carbon price reaching $65 per tonne and the post-pandemic economic slowdown was seized on by numerous politicians as a way of pushing back against the carbon pricing system, with most demanding the entire system be scrapped. The debate intensified in late 2023 and into 2024, when the federal government removed the carbon tax on home heating oil because the reimbursement was insufficient to cover the cost of the tax. In this paper, we consider the recent actions of two Canadian provinces, Saskatchewan and Nova Scotia, embroiled in the federal carbon pricing system debate due to the removal of the carbon tax on fuel oil for space heating. The objective of this paper is to identify how some of the reasons, including global post-pandemic inflation and other challenges facing Canadians, such as those cited in third-party polls, have contributed to a rise in the system's unpopularity. Our method estimates and compares the impacts of the carbon tax on the household energy services for space and water heating, lighting and appliances, and private (i.e., household) transportation for different types of housing (apartment, single-attached, and single-detached) and number of occupants (two, three, and four) in Saskatchewan and Nova Scotia. The results of this work show that while Saskatchewan households have higher energy intensities than those in Nova Scotia, the impact of the carbon tax on Nova Scotians using fuel oil for heating was greater than in Saskatchewan. In Saskatchewan and Nova Scotia, natural gas and electricity, respectively, are used for heating. This paper concludes with a summary of our findings and potential options for improving perceptions of the system. [ABSTRACT FROM AUTHOR]

A variety of LED types can be employed for street and road lighting purposes. White phosphor-converted LEDs represent the most widely used option. However, amber LEDs are increasingly being used to reduce some negative effects associated with light pollution. These LEDs can be manufactured using both phosphor-converted and monochromatic direct chip technologies. This paper addresses the link between the reduction in short-wavelength light emissions which contribute to sky glow and the energy efficiency of LED-based road lighting. This paper focuses on an example illustrating the common misconception that reducing light pollution also means saving energy. Through the consideration of how spectral power distribution influences both mesopic vision and the amount of emitted blue light, it has been concluded that while monochromatic direct amber LEDs consume more energy than their white or amber phosphor-converted counterparts, their use in outdoor lighting is justifiable due to their potential effects of reducing sky brightness. [ABSTRACT FROM AUTHOR]

This article examines the transformative potential of integrating reconfigurable intelligent surfaces (RISs) into sixth-generation (6G) wireless non-terrestrial networks (NTNs). The focus is on the RIS's capability to address diverse user requirements, including secure data transmission, power efficiency, extended coverage, and enhanced data rates. The paper delves into the synergy between RISs and NTNs, emphasizing key components like multiple-input multiple-output (MIMO) systems and advanced radio communications. Additionally, it highlights the crucial role of artificial intelligence (AI) and machine learning (ML) in optimizing RIS-based beamforming to solve scientific and engineering challenges while ensuring energy efficiency and sustainability in NTN operations. By positioning RISs as a key enabler in shaping the future of wireless communication systems, this research underscores their significance in unlocking the full potential of NTNs and advancing next-generation wireless communications. This paper contributes valuable insights and projections for future research directions, highlighting RISs' potential to revolutionize NTNs for 6G technologies. [ABSTRACT FROM AUTHOR]

The coal mining industry in Pakistan faces recurring fatal accidents due to data scarcity, lack of research, and technology adoption. This paper reviews the current status of coal mining, the ongoing energy crisis, the utilization of indigenous coal resources, and the prevailing safety challenges. By comparing coal mining safety standards in Pakistan with global benchmarks, this study proposes advanced mining technologies to improve productivity and safety. This study emphasizes the importance of investigating the underlying factors causing mining accidents in order to devise effective strategies to mitigate them. The lack of relevant data and the reluctance to adopt technology in the industry are identified as major obstacles to improving safety conditions. The proposed strategies for overcoming safety issues include improving data collection and analysis, increasing research efforts, and promoting the adoption of advanced technology. Thus, this paper highlights the urgent need to address safety concerns in Pakistan's coal mining industry to avoid further loss of lives and resources. [ABSTRACT FROM AUTHOR]

Digestate is a product with valuable fertilizing properties, remaining after the anaerobic fermentation process. An essential feature of the substance in question is its high water content of up to 97%. To improve the fertilizer value of the digestate, it is necessary to dehydrate it to produce a concentrated product. This paper determined the possibility of dewatering the digestate using an innovative reactor design. The study, conducted on a laboratory scale, used digestate from a Polish biogas plant. The dewatering technique described in the paper is based on the evaporation and condensation of water vapor on the inner surface of the reactor dome. The condensate accumulated on the leach trough and was directed to a storage tank. During the weeks of testing, 11.5 kg of condensate was separated from the initial weight of the digestate (32 kg), with a dry weight of 6.11%. The resulting condensate from dehydration had an average pH value of 9.0 and an average ammonium nitrogen content of 2.07 g∙kg−1. The economic calculations made in the paper allowed for estimating the expected savings associated with the management of digestate in Poland. The research showed the proposed technology's high potential for dewatering digestate under laboratory conditions. [ABSTRACT FROM AUTHOR]

Urban building energy modelling (UBEM) has consistently been a pivotal tool to evaluate and control a building stock's energy consumption. There are two main approaches to build up UBEM: top-down and bottom-up. The latter is the most commonly used in engineering. The bottom-up approach includes three methods: the physical-based method, the data-driven method, and the grey-box method. The first two methods have previously received ample attention and research. The grey-box method is a modelling method that has emerged in recent years that combines the traditional physical method with the data-driven method while it aims to avoid their problems and merge their advantages. Nowadays, there are several approaches for modelling the grey-box model. However, the majority of existing reviews on grey-box methods concentrate on a specific technical approach and thus lack a comprehensive overview of modelling method perspectives. Accordingly, by conducting a comprehensive review of the literature on grey-box research in recent years, this paper classifies grey-box models into three categories from the perspective of modelling methods and provides a detailed summary of each, concluding with a synthesis of potential research opportunities in this area. The aim of this paper is to provide a foundational understanding of grey-box modelling methods for similar research, thereby removing potential barriers in the field of research methods. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform. [ABSTRACT FROM AUTHOR]

With the widespread use of fossil fuels, the Earth's environment is facing a severe threat of degradation. Traditional large-scale power grids have struggled to meet the ever-growing demands of modern society. The implementation and functioning of microgrids not only enhance the use of renewable energy sources but also considerably diminish the environmental damage resulting from fossil fuel consumption. However, the inherent instability of renewable energy presents a major challenge to the reliability of microgrids. To address the uncertainties of wind and photovoltaic power generation, it is urgent to adopt effective operational control methods to adjust power distribution, thereby achieving an economically efficient system operation and ensuring a reliable power supply. This paper utilizes a microgrid system consisting of wind power, photovoltaic power generation, thermal power units, and energy storage devices as the research object, establishing an economic dispatch model aimed at minimizing the total operating cost of the system. To solve this problem, the paper introduces second-order oscillatory particles and improves the Particle Swarm Optimization algorithm, proposing a second-order oscillatory chaotic mapping particle swarm optimization (SCMPSO). The simulation results show that this method can effectively optimize system operating costs while ensuring the stable operation of the microgrid. [ABSTRACT FROM AUTHOR]

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system's protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems. [ABSTRACT FROM AUTHOR]

This article presents an example of the joint use of a compressor heat pump that uses propane as a natural, ecological thermodynamic medium and a phase heat accumulator that uses paraffin as a medium. Special attention has been paid to the solidification process of the phase change material, and a simple theoretical model of the solidification of this material has been proposed. Thermodynamic balance calculations were carried out for the compressor heat pump and the phase heat accumulator. This paper presents a theoretical analysis of two examples of heating using a compressor heat pump, implementing the Linde cycle for the refrigerant R290 (propane): high-temperature heating at a temperature of 80 °C and low-temperature (surface) heating at a temperature of 60 °C, with the same unit heat output of 0.376 kW taken from the lower-temperature heat source of each evaporator. This heat is generated by the solidification of the PCM. The compressor power is 77 W in the first case and 40 W in the second. The energy efficiency coefficients of the compressor heat pump for the proposed combination of a phase heat accumulator and compressor heat pump are 5.98 and 10.40. The joint use of a heat accumulator and a heat pump presented in this paper can be used in applications for the heating of domestic water or water for space heating. [ABSTRACT FROM AUTHOR]

In large public facilities, such as airport terminals or open-plan office spaces, the HVAC system typically consumes substantial amounts of energy. However, individuals often gather at specific areas while other zones are occupied by transient or occasional users. To minimize operational energy usage, this paper aims to reduce thermal comfort demands in non-targeted areas. This paper introduces a method for regulating the thermal environment around occupants exclusively in the variable-air-volume (VAV) air conditioning running mode. The investigation utilizes Airpak modeling and experimental verification techniques. Additionally, an analysis of temperature field and velocity field distributions within the room under the "personal space" operation mode is presented. The results suggest that adjusting the numbers of air vents, openings, airflow velocities, and air supply orientations can establish a comfortable thermal environment for inhabitants and reduce the overall ADPI value. The combined air supply mode leads to a 16.7% reduction in power usage compared to traditional full-space operation. [ABSTRACT FROM AUTHOR]

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we present an optimization planning method for enhancing power quality in integrated energy systems in large-building microgrids by adjusting the sizing and deployment of hybrid energy storage systems. These integrated energy systems incorporate wind and solar power, natural gas supply, and interactions with electric vehicles and the main power grid. In the optimization planning method developed, the objectives of cost-effective and low-carbon operation, the lifecycle cost of hybrid energy storage, power quality improvements, and renewable energy utilization are targeted and coordinated by using utility fusion theory. Our planning method addresses multiple energy forms—cooling, heating, electricity, natural gas, and renewable energies—which are integrated through a combined cooling, heating, and power system and a natural gas turbine. The hybrid energy storage system incorporates batteries and compressed-air energy storage systems to handle fast and slow variations in power demand, respectively. A sensitivity matrix between the output power of the energy sources and the voltage is modeled by using the power flow method in DistFlow, reflecting the improvements in power quality and the respective constraints. The method proposed is validated by simulating various typical scenarios on the modified IEEE 13-node distribution network topology. The novelty of this paper lies in its focus on the application of integrated energy systems within large buildings and its approach to hybrid energy storage system planning in multiple dimensions, including making co-location and capacity sizing decisions. Other innovative aspects include the coordination of hybrid energy storage combinations, simultaneous siting and sizing decisions, lifecycle cost calculations, and optimization for power quality enhancement. As part of these design considerations, microgrid-related technologies are integrated with cutting-edge nearly zero-energy building designs, representing a pioneering attempt within this field. Our results indicate that this multi-objective, multi-dimensional, utility fusion-based optimization method for hybrid energy storage significantly enhances the economic efficiency and quality of the operation of integrated energy systems in large-building microgrids in building-level energy distribution planning. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV's flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions. [ABSTRACT FROM AUTHOR]

To study the influence of tip clearance on the working characteristics of high-altitude fans, this paper takes the MIX-140 high-altitude fan as the research object. Five different tip clearance (TC) models are selected. The CFD method is used to analyze the changes in the working characteristics of the fan under different flow rates and TCs in ground and high-altitude environments. The reliability of the numerical method is verified through a fan test bench. The results show that the tip leakage flow caused by the TC will continuously deteriorate the working characteristics of the fan. Under the rated flow rate in the ground environment, for every doubling of the blade's TC, the static pressure difference will decrease by 5%, the efficiency will decrease by 2%, and the power will decrease by 3%. In a high-altitude environment, the flow rate corresponding to the maximum shaft power point of the fan will continuously increase with an increase in the tip clearance, which will bring about additional energy consumption. For high-altitude fans, the deformation caused by the high-speed rotation of the impeller needs to be taken into account. Undoubtedly, it is advantageous to choose the smallest possible tip clearance value. The results of the analysis and test methods in this paper will provide a basis for designing the tip clearance of high-altitude fans. [ABSTRACT FROM AUTHOR]

Within high-precision indoor environments, such as semiconductor fabrication or textile plants, humidity control is paramount for preserving product integrity and reducing energy expenditure. The non-uniform indoor air environment poses a significant challenge in achieving humidity regulation that meets the distinct requirements of various locations. Traditional feedback control mechanisms may lead to instability, overshooting, and oscillation in indoor parameters. This paper proposes a comprehensive method to address humidity assurance issues in high-precision indoor environments by establishing analytical expressions that link the demand parameters at different locations with air supply parameters. Using a case study, this paper examines several typical operational scenarios with diverse control objectives, including minimizing dehumidification energy consumption, minimizing air supply humidity adjustment values, and constraints on adjustable air supply inlets. This method enables rapid calculation of air supply humidity and regulation of humidity parameters at multiple locations within the indoor environment. It considers various locations, requirements, optimization targets, and precision, demonstrating that it can quickly determine the optimal air supply parameters based on the objective function. This method facilitates rapid adjustment and high-precision assurance of different humidity requirements at multiple locations, making it suitable for high-precision design and control of indoor humidity environments. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of buildings is an important element of the effort to address global warming. The thermal performance of building envelopes is the most important thermal and physical property affecting energy performance. Therefore, identifying the thermal performance of a building envelope is essential to applying effective energy-saving measures. The U-value is a quantitative indicator of the thermal performance of the building envelope quantitatively. Methods for determining the U-value are largely classified into passive methods, which use building information without measurement campaigns, and active methods, which conduct in situ measurements. This paper reviews and evaluates the most commonly used methods and experimental results of previous studies to determine the actual U-value of a building envelope. Accordingly, this paper focuses solely on field measurement studies, excluding laboratory measurements. Comparing the existing methods used to determine the U-value can help researchers choose appropriate field measurement methods and future research directions. [ABSTRACT FROM AUTHOR]

In view of the increasing environmental challenges and the growing demand for sustainable energy solutions, the optimization of microgrid systems with regard to economic efficiency and environmental compatibility is becoming ever more important. This paper presents the Microgrid Performance and Investment Rating (MPIR) index, a novel assessment framework developed to link economic and environmental objectives within microgrid configurations. The MPIR index evaluates microgrid configurations based on five critical dimensions: financial viability, sustainability, regional renewable integration readiness, energy demand, and community engagement, facilitating comprehensive and balanced decision making. The current cases focus on the area of Greece; however, the model can have a wider application. Developed using a two-target optimization model, this index integrates various energy sources—including photovoltaics, micro-wind turbines, and different types of batteries—with advanced energy management strategies to assess and improve microgrid performance. This paper presents case studies in which the MPIR index is applied to different microgrid scenarios. It demonstrates its effectiveness in identifying optimal configurations that reduce the carbon footprint while maximizing economic returns. The MPIR index provides a quantifiable, scalable tool for stakeholders, not only advancing the field of microgrid optimization, but also aligning with global sustainability goals and promoting the transition to a more resilient and sustainable energy future. [ABSTRACT FROM AUTHOR]

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research. [ABSTRACT FROM AUTHOR]

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle's overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization. [ABSTRACT FROM AUTHOR]

The EU has set the ambitious target of raising the share of EU energy consumption produced from renewable resources to 32% by 2030, with a target of climate neutrality by 2050. The aim of this paper is to assess the role of biomass usage in the context of these targets. The paper identifies the progress made between 2013 and 2022 by focusing on a selection of EU countries. The largest bioenergy increments of 130, 77, and 60 PJ were reported for Poland, Sweden, and the Netherlands. This study evaluates the crucial role of co-generation and heat in EU regions, with biomass usage between 55 and 80% of the combined heat and power (CHP) energy in Nordic countries. The future perspectives for bioenergy based on EU policies, biomass resources, and technical issues were addressed. The EU possesses around 9% of the global biomass supply, ensuring a certain level of biomass resource dependence. Thus, the biomass usage demand in energy production, non-energy sectors, and transport is expected to rise, leading to increments of 13–76% on biomass imports. It appears that bioenergy development is mostly limited by economic issues and uneven support for bioenergy in different EU countries as well as environmental issues. The study shows a promising and sustainable potential of bioenergy in the EU as a renewable energy source while minimizing negative impacts on the environment and the economy. By 2050, liquid biofuels are likely to be increasingly used in the transport sector. Non-energy sector usage of biomass is still in an early stage of development, except for the pulp and paper industry, and significant use of biomass in non-energy sectors seems unlikely in the near future. [ABSTRACT FROM AUTHOR]

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability. [ABSTRACT FROM AUTHOR]

The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Transitioning to clean cooking fuels is not only part of achieving SDG7 but also makes a significant contribution to mitigating climate change by reducing carbon emissions. Research projects and pilots across a number of countries in Africa and South Asia have been exploring the suitability and energy performance of different cooking appliances and fuels. The paper presents the first statistical analysis across these multiple datasets to determine the range of energy required to cook dishes using different technologies and fuels. The paper draws out distinctions between African and Asian dishes, notably the impact of energy-intensive dishes prepared mostly in Africa. The paper demonstrates that the standard efficiency-based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices, so a novel alternative approach based on specific energy consumption and termed energy ratios is developed. Charcoal stoves are shown to use 15 times as much energy as electric pressure cookers (EPCs) to cook African dishes, and a detailed review of how the EPC works explains why this should be. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels and also for estimating household cooking costs. Fuel and electricity prices from studies show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy-efficient electric cooking devices to defy the conventional wisdom of the energy ladder. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures. [ABSTRACT FROM AUTHOR]

Although there are opportunities to reduce electrical energy demand in unit processes of mechanical pulp-based paper and paperboard production, this may not be financially beneficial. This is generally because energy optimization opportunities connected to reduced refiner electricity demand in mechanical pulping systems also results in less steam available for the drying of the paper. As modern high consistency refiner systems produce approximately one ton of steam for each MWh of electricity when producing one ton of pulp, a reduction in electric energy demand leads to increased fuel demand in steam boilers to compensate for the steam shortage. In this study, we investigated what the financial and environmental situation would look like if we were to expand the system border from a paper mill to a larger system consisting of a mechanical pulp-based paper or paperboard mill, a district heating system with an incineration boiler and a chemical pulp mill. Mechanical pulp production has a wood to product yield of >90%, a high electric energy demand to separate woodchips to pulp and is a net producer of heat and steam while chemical pulp-based production has a wood to product yield of 50%, a low electric energy demand and is a net heat and electricity producer due to the combustion of dissolved wood polymers. The aim of this research is to create useful and robust models of how to use excess heat from certain industry sites to cover the steam shortage in other industry sites by means of utilizing and optimizing the district heating systems connecting these sites. For this purpose, we used a simulation tool which dynamically allows us to evaluate different scenarios. Our results shows that there is great potential to reduce both carbon dioxide emissions and production costs for industry sites and society by means of these tools. [ABSTRACT FROM AUTHOR]

Recently, as the use of wearable devices and the demand for eco-friendly energy have increased, many studies have been conducted on triboelectric nanogenerators (TENGs), which can economically harvest energy. Paper is considered a promising substrate and frame material that can be used to manufacture self-powered TENGs, owing to its flexibility, low cost, and accessibility. Herein, we present a sandwich-structured foldable paper-based TENG (FP-TENG) that comprises flexible materials and uses paper as a substrate. The FP-TENG can generate up to 572 mW/m2 of power via contact–separation of the triboelectric electrified body at the top and bottom. With more folds of the FP-TENG, the triboelectric cross-sectional area increases, and, thus, the electrical output increases. In addition, the proposed TENG exhibits excellent durability without signal degradation under 5000 cycles of repeated pushing motions. To demonstrate its practicality, the FP-TENG was manufactured in the form of a wristwatch Velcro and connected to an electronic watch panel to supply power. Various deformations are possible with origami, and they can drive wristwatches through external forces. Therefore, the FP-TENG is expected to be utilized as a sustainable and promising eco-friendly energy source for small electronic devices. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI's potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into "Sustainability of AI" and "AI for Sustainability", revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field's maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations' Sustainable Development Goals (SDGs), and addressing stakeholders' influence. [ABSTRACT FROM AUTHOR]

This paper studies the performance of the communication link between a ground station and the satellites of a LEO constellation, employing code division multiplexing and a non-linear high-power amplifier. The analysis shows that the input power selection at the high-power amplifier of the ground station has a significant impact on overall system performance. The results concerning output power, the challenge of adjusting the back-off with a continuously changing number of satellites, and improved energy efficiency suggest operating in saturation. In this scenario, we can choose to transmit directly the sign of the sum of the signals directed to individual satellites. Analytical exact and simplified results are derived, enabling the estimation of performance as a function of the number of satellites being served when the amplifier operates at saturation. These analytic results are further validated through simulations. A formula to compute the loss across different numbers of satellites is also presented. The performance under saturated amplifier conditions is evaluated, compared, and discussed, providing valuable insights for simplifying the design and operation of satellite uplink communication systems under power amplifier constraints. [ABSTRACT FROM AUTHOR]

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a "building–floor–room" hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments. [ABSTRACT FROM AUTHOR]