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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]

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]

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]

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]

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. This document serves as a valuable resource for library patrons conducting research on energy efficiency and sustainable building practices. [Extracted from the article]

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 addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. [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]

Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods. [ABSTRACT FROM AUTHOR]

A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research about SPN geometry optimization has centered on geometric dilution of precision (GDOP). However, previous research rarely dealt with the topic of how SPN geometry configuration not only affects its GDOP, but also affects its energy balance. To obtain an optimal integrated performance, this paper employs the proportion of energy consumption in energy production as an indicator to assess SPN energy status and designs a composite indicator including GDOP and energy status to assess SPN geometry performance. Then, this paper proposes an SPN geometry optimization algorithm based on gray wolf optimization. Furthermore, this paper implements a series of simulations with an SPN composed of six SPs in a specific service area. Simulations show that the proposed algorithm can obtain SPN geometry solutions with good GDOP and energy balance performance. Also, simulations show that in the supposed scenarios and the specific area, a higher SP altitude can improve both GDOP and energy balance, while a lower SP latitude can improve SPN energy status. [ABSTRACT FROM AUTHOR]

In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

Fluidic actuation systems are optimizable as to energy consumption by reducing the friction in the hydraulic cylinders. Polymeric materials with special antifriction properties and good resistance to hydraulic fluids can be deployed to enhance the performance of hydraulic cylinders. Small friction forces can also be ensured by facilitating the hydrodynamic separation of the elements of the friction tribosystem, namely the seal and sealed-off surface, respectively. The study presented in this paper analyzed the hydrodynamic separation phenomenon in hydraulic cylinders with coaxial sealing systems of the pistons. The process underlying the forming of the fluid film between the seal and its contact surface was considered and the formula for calculating film thickness was deduced. This paper presents graphs that describe the variation of the fluid film thickness versus the dimensional and material characteristics of the sealing systems. The study yielded recommendations as to the most adequate polymeric material to be used and the optimum dimensional characteristics of the seal. [ABSTRACT FROM AUTHOR]

This paper provides a detailed literature review of the environmental implications of vehicle platooning, a topic gaining significant attention in transportation. While previous reviews have focused on the safety, planning, fuel economy, and microsimulation aspects of platooning, this paper delves into environmental aspects. It identifies a lack of research adopting a holistic approach to transport and environmental benefits and emphasizes the need for further research to enhance vehicle efficiency and improve air quality and health conditions. This study traces the historical evolution of platooning, highlighting the shift in research focus over the decades. It advocates for more research on platooning's environmental aspects, particularly pollutant emissions and air quality. The primary contributions of this work are threefold and include the following: firstly, it delineates simulation methodologies for platooning and the associated pollutant emissions; secondly, it offers a critical assessment of the existing literature on vehicle emissions, fuel consumption, and energy savings; and thirdly, it illuminates the prospective research challenges within the specialized domain of vehicle platooning. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm's robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm's stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes. [ABSTRACT FROM AUTHOR]

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

A cut-to-length (CTL) forest harvester is a purpose-built, hydraulically actuated mobile work machine used for felling, delimbing, and cross-cutting trees into dimensions and assortments. In such a machine, the diesel engine load is known to fluctuate greatly over the work cycle. To manage these changing power demands, a hydraulic hybrid system is implemented into a full-scale proof-of-concept machine to discover its functionality and to collect experimental results in real operating conditions. The hydraulic hybrid system installed on a CTL harvester machine was tuned to enable the collection of two actual datasets of timber harvesting, and data collection over a period of production use was carried out. In addition to updating the state-of-the-art research into heavy non-road mobile machinery regarding hybrid systems, this paper discusses the two tests carried out in actual production conditions with the studied hydraulic hybrid system. The adaptations and modifications to the studied and tested system since earlier papers are discussed—especially the improvements in the system dynamics and response as well as the control of the hybrid system. In conclusion, the tested system can be said to operate satisfactorily and shows clear advantages over the conventional system, even though it still has some limitations. The results show that the system has the potential to increase the productivity of the machine through the demonstrated higher peak power, as well as showing improvement in fuel efficiency under highly fluctuating loads, especially with heavy or large-volume tree stems. [ABSTRACT FROM AUTHOR]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

Solar energy is not only the most abundant energy on earth but it is also renewable. The use of this energy is expanding very rapidly mainly through photovoltaic technology. However, electricity storage remains a bottleneck in tackling solar resource variability. Thus, solar thermal energy becomes of particular interest when energy storage is required, as thermal energy storage is much cheaper than electricity storage. The objective of this paper is to make a short update on the CSP (Concentrated Solar Power) market as of the year 2023. It is based on the CSP-GURU database, which lists information on CSP power plants all over the world. Although this database is open, it is not easy to find UpToDate analysis. An overview of this expanding technology is presented and offers readable figures with the most important information. This includes the evolution of installed capacities worldwide along with upcoming projects (under construction) and technological trends. The evolution of storage capacities and operating temperatures is discussed. Investment costs and levelized cost of electricity are also provided to obtain reliable data for comparison with other energy technologies. Specific land requirements are highlighted, along with overall efficiency. Relevant examples are discussed in this paper. Eventually, it outlines the evolution of the CSP landscape with useful information for scientific and educational purposes. [ABSTRACT FROM AUTHOR]

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet plateau, with an average altitude of over 4000 m, has low annual average temperatures and a high demand for building heating. This region's abundant solar energy resources hold substantial practical significance for improving the indoor heat environment and reducing building energy consumption. This paper investigates the impact of orientation and skylight area ratio on building heat load and indoor temperature, using both actual measurement and simulation methods, with a case study of the comprehensive building at Beiluhe Observation and Research Station of Frozen Soil Engineering and Environment (Beiluhe Station), located in the Qinghai–Tibet Plateau region. Initially, a model was established using the EnergyPlus 9.4 software, with orientation variables set from east to west in 15° increments, to simulate the variations in building heat load resulting from orientation changes; simulations were then conducted for three different skylight area ratios under the optimal orientation to evaluate their influence on heat load and indoor temperature. The results show that for the architectural style examined in this paper, the optimal building orientation within the region is 30° south by east, with the optimal orientation range spanning from 45° south by east to due south. Heating load is negatively correlated with the skylight area ratio, and beyond a certain threshold, the rate of decrease in heat load diminishes or even stabilizes. The conclusions of this paper offer guidance for the orientation and skylight design of new buildings on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

The Europevan Union is highly dependent on energy. This paper analyses energy consumption in the transport sector, representing approximately 30% of total energy consumption. A particular focus will be placed on road freight transport, which accounts for 40% of total transport energy consumption, trying to contribute to its rationalization. The road freight transport volume (TKM: tonne-kilometres) vs. gross domestic product (GDP) ratio fell in 2010–2022 by an average of 17.6% in EU-27, meaning that there was decoupled growth. Germany is the country with the highest decoupling, nearly 36%. On the contrary, in Spain there is a coupling because the evolution of TKM and GDP was almost identical. The paper proposes possible relevant factors in road freight transport activity that may justify the discrepancies in values within EU member countries, showing the positive and negative impacts of the different variables studied. In addition, the SARIMA model is applied to forecast the evolution of relevant indicators of road transport in different countries. [ABSTRACT FROM AUTHOR]

Renewable energy has emerged as a promising solution to address the challenges of climate change, energy security, and socio-economic development. South Africa, with its abundant renewable energy resources, has made significant strides in the utilization of renewable energy over the past decade. This paper provides a comprehensive review of the progress of renewable energy advancement in South Africa, examining the policies, initiatives, and achievements in various renewable energy sectors. This study explores the country's transition from a heavily coal-dependent energy system to a diversified and sustainable energy mix. It analyses the growth of renewable energy technologies, such as wind power and solar photovoltaic (PV), highlighting the key milestones, challenges, and opportunities. Furthermore, this paper discusses the role of government support, regulatory frameworks, and private sector investments in driving renewable energy deployment in South Africa. Finally, it identifies the prospects and potential areas for further advancement in the renewable energy sector. This review aims to contribute to the understanding of South Africa's renewable energy journey and provides valuable insights for policy-makers, researchers, and stakeholders involved in the sustainable energy transition. [ABSTRACT FROM AUTHOR]

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region's abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season. [ABSTRACT FROM AUTHOR]

South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid's ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines. [ABSTRACT FROM AUTHOR]

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings. [ABSTRACT FROM AUTHOR]

This paper focuses on the implementation of a neural network accelerator optimized for speed and energy efficiency, for use in embedded machine learning. Specifically, we explore power reduction at the hardware level through systolic array and low-precision data systems, including quantized approaches. We present a comprehensive analysis comparing a full precision (FP16) accelerator with a quantized (INT16) version on an FPGA. We upgraded the FP16 modules to handle INT16 values, employing data shifts to enhance value density while maintaining accuracy. Through single convolution experiments, we assess the energy consumption and error minimization. The paper's structure includes a detailed description of the FP16 accelerator, the transition to quantization, mathematical and implementation insights, instrumentation for power measurement, and a comparative analysis of power consumption and convolution error. Our results attempt to identify a pattern in 16-bit quantization to achieve significant power savings with minimal loss of accuracy. [ABSTRACT FROM AUTHOR]

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing. [ABSTRACT FROM AUTHOR]

Daylighting design is not only dimensioning glazed surfaces to provide sufficient natural light to an occupied space but also a method of analyzing how this can be achieved without unwanted effects, such as gains and losses of heat, glare, and variations in daylighting intensity at various indoor distances and levels. The case study presented in this paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of window dimensions for minimum energy consumption. The results of the case study highlight the fact that the evolution of the total energy consumption, evaluated as the sum of the energy consumption due to additional heating/cooling and the artificial lighting required to compensate for the reduction in natural light, is strongly influenced by the dimensions of the glazed surfaces, as well as the minimum level of lighting imposed by the regime of activities carried out in the building. Thus, the outcomes obtained in the research show that at lighting values below 500 lx, the total energy consumption is directly proportional to the glazed surface. From values of 500 lx for the illuminance level, the total energy consumption drops from 2730 kWh/year for a window height of 230 cm to 2399 kWh/year for a height of 110 cm, after which it starts to rise again, reaching a value of 2786 kWh/year for a height of 30 cm. This phenomenon is also found at values higher than 500 lx; accordingly, for an imposed lighting of 1000 lx, the minimum total consumption is identified at a window height of 150 cm. The case study presented in this paper clearly highlights a complex relationship between the height of the glazed surface and the energy consumption required to compensate for heating or cooling and the reduction in natural lighting. Lower window heights reduce heat loss or gain but also correspondingly increase the energy consumption of artificial lighting. [ABSTRACT FROM AUTHOR]

In the context of sustainability, the integration of renewable energy into industrial processes not only minimizes dependence on fossil fuels but also contributes to the efficient management of industrial waste. By transforming organic waste, including agri-food and urban waste, into biogas, green energy can be generated, thus reducing the impact on the environment and closing the loop of material used in the economic circuit. Thus, a sustainable system can be promoted, where resources are continuously reused and exploited. Statistical methods and a decision tree with the Classification and Regression Trees (CRT) algorithm were employed to analyze data. The paper focuses on the importance of industrial waste and biogas for the generation, transformation, and consumption of energy in the EU (European Union)-27 countries. To provide a thorough analysis, we have divided these countries based on real gross domestic product (GDP) per capita, grouping them above/below the annual average for the period 2012–2021/2022. Descriptive statistics revealed observable differences between the two groups, but the paper aimed to provide evidence regarding the existence of these differences as statistically significant. Using the Kolmogorov–Smirnov test, the non-normal distribution of the data was confirmed, requiring non-parametric inferential methods. The Mann–Whitney U test revealed statistically significant differences between the two groups for all the studied variables. This comprehensive approach highlights the distinct energy-related characteristics influenced by economic development in the EU-27. [ABSTRACT FROM AUTHOR]

To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of omnidirectional mobile robot for land and air, which has three motion modes, combines the motion characteristics of land motion and air flight, has the ability to climb walls, and can be actively deformed to adapt to the working conditions according to the current working environment. The robot incorporates an innovative "rotor blade–single row omnidirectional wheel" composite structure, which is mainly characterized by a single row of continuous switching wheels covering the outside of each rotor blade, and does not need to provide additional power when moving on the ground and walls, relying on the driving force generated by the rotor blades to drive the continuous switching wheels driven by the rotor blades. This structure can effectively combine the land movement mode, wall crawling mode, and air flight mode, which reduces the energy consumption of the robot without increasing the weight, and we design a deformation device that can realize the transformation of the three modes into each other. This paper mainly focuses on the design of the robot structure and the analysis of the movement method, and the land omnidirectional movement experiments, wall crawling experiments, and air flight experiments were, respectively, carried out, and the results show that the proposed land and air omnidirectional mobile robot has the ability to adapt to the movement of each scene, and improves the upper limit of the robot's operation. [ABSTRACT FROM AUTHOR]

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A "sandwich" capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays' benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. [ABSTRACT FROM AUTHOR]

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less. [ABSTRACT FROM AUTHOR]

This paper explores the significant role of Wastewater Treatment Plants (WWTPs) in achieving environmental sustainability, with a particular focus on enhancing energy efficiency, resource recovery, and water reuse. WWTPs are crucial for removing pollutants and recovering resources from wastewater, thereby protecting public health and biodiversity. However, they are also associated with high operational costs, substantial carbon footprints, and energy-intensive processes. This article delves into various strategies and technologies to overcome these challenges, aiming to transform WWTPs from energy consumers to energy-efficient resource recovery hubs. Techniques such as anaerobic digestion and the use of advanced oxidation processes and microbial fuel cells are investigated for their potential in energy recovery and efficiency enhancement. Success stories from around the globe are highlighted to demonstrate the feasibility of transitioning to energy-positive WWTP operations. The integration of water reuse systems is also discussed, highlighting recent advancements that enable treated wastewater to be repurposed for agricultural, industrial, and potable uses, thereby promoting sustainability and water conservation. This paper emphasizes the importance of integrating cutting-edge energy management practices to minimize environmental impacts, reduce operational costs, and contribute to a more sustainable water sector. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

Waste ceramic is produced from different sources and, if not reused, is often disposed of in landfills, contributing to the pressure on landfill capacity and potentially releasing toxins into the surrounding environment as ceramics break down over time. The waste can easily be crushed to the required sizes, which has interested many researchers. It has been used as a replacement for concrete constituents, including cement and aggregates. This offers a promising avenue for reducing the environmental impact and promoting sustainable construction practices by reducing the amount of energy consumed and resources required for concrete production, as well as the amount of waste being sent to landfills. This paper aims to provide a review of the use of waste ceramic in concrete. In essence, the paper is divided into several sections. The first section highlights the current environmental issues associated with concrete production and waste disposal. The second section deals with ceramics, its raw materials, production steps, and the different types of waste ceramic used in concrete. A literature review of the use of waste ceramic in concrete was included in the third section. The study reports different research on the use of waste ceramic and its impacts on the mechanical and durability properties of concrete. [ABSTRACT FROM AUTHOR]

In Europe, it is estimated that 14% of existing buildings were built before 1919, whereas 26% were built before 1945. In Romania, about 31% of the buildings date from before 1961, contributing to the current stock of old buildings with historic and architectural value in the country. This paper illustrates the current state of buildings with historic and architectural value in Romania, alongside a case study of a representative administrative building in Câmpulung, Romania. The analysis of the Town Hall building in Câmpulung, Romania, demonstrates that potential energy savings of up to 47.53% can be achieved by implementing interventions such as upgrading windows, insulating the attic, and installing photovoltaic panels. The highest energy reduction is obtained by replacing the window glass with a value of 18.16% with attic insulation with a value of 16.1%. This paper also presents indoor measurements of temperature and humidity in different offices positioned in the north and the south. The study conducted on the south façade office revealed consistent temperatures ranging from 21.7 °C to 24.4 °C, with an average of 23.31 °C. However, the humidity levels fluctuated considerably, ranging from 17.1% to 39.1%, with an average of 26.89%. The sun-exposed section of the building saw relatively stable temperature conditions, but the varying humidity levels could have a detrimental impact on the quality of the indoor atmosphere and potentially decrease the effectiveness of the workforce. By contrast, the north façade office exhibited lower and more fluctuating temperatures, ranging from 19.8 °C to 23.6 °C, with an average of 21.74 °C. Additionally, it had higher and more stable humidity levels, ranging between 19.5% and 41.7%, with an average of 29.83%. A thermographic analysis was performed on the north façade of the Câmpulung Town Hall, utilizing thermal imaging technology to detect areas of heat loss, and thus identifying the energy inefficiency problems of the building's exterior. The investigation found notable variations in temperature, especially around the windows, where temperatures could be as high as 14.1 °C, highlighting the insufficiency of the building's antiquated timber-framed windows in preventing energy loss. [ABSTRACT FROM AUTHOR]

This paper presents the initial steps taken in analysing the benefits of connected autonomous vehicles (CAVs), especially Modular Autonomous Vehicles (MAVs), in search of sustainable solutions for reducing energy consumption per passenger in air transport. For this particular case, a Mobility-as-a-Service (MaaS) solution is proposed, correlating airside transport with landside transport, as an urban mobility alternative. To better understand the benefits of the proposal, this paper studies the impact on energy consumption conceptual differences between a conventional public transport fleet using Battery Electric Buses (BEBs) and an MAV fleet. Simulations for simple public transport tasks are performed to highlight the advantages of the modular vehicle concept, in which routes are assigned dynamically based on the requested passenger carrying capacity and travel distance, aiming to optimize the efficiency of the entire system. With a proven reduction in energy consumption due to better use of available passenger capacity and reduced travel times in which the vehicle is driving with a number of passengers less than half of its capacity, the concept can be addressed further in developing a predictive system that processes public transport data and delivers an optimized schedule for the entire fleet. The main goal being to improve overall operational efficiency and total cost of ownership, the second part of the paper studies the impact of weight distribution on efficiency parameters such as energy consumption, range, and overall performance of an electric bus. The impact on dynamic elements such as acceleration, braking, and cornering performance is analyzed, to assess the viability and safety of all types of electric bus operations. [ABSTRACT FROM AUTHOR]

Cultural institutions hold a unique position of influence, serving as hubs of education and knowledge dissemination for the people they serve. Embracing sustainable energy use in buildings in zoos and botanical gardens is a commitment to environmental responsibility that reinforces these institutions as trusted sources of information and community leaders on climate change, one of the most vital issues of our time. Sustainable energy solutions can synergize operations with educational missions, allowing zoos and botanical gardens to lead by example and inspire visitors to adopt eco-friendly practices in their own lives. In this opinion paper, Phipps Conservatory and Botanical Gardens President and CEO Richard Piacentini discusses key elements in developing a sustainable building energy plan, reviews potential barriers to implementation, and makes a case for adopting regenerative thinking and new metrics for measuring success—citing example cases from Phipps' Center for Sustainable Landscapes (CSL) project. Additionally, the paper demonstrates how green building certification systems and peer-based resource networks like The Climate Toolkit can help guide institutions in the process. [ABSTRACT FROM AUTHOR]

Smart grids are a cornerstone of the transition to a decentralised, low-carbon energy system, which offer significant benefits, including increased reliability, improved energy efficiency, and seamless integration of renewable energy sources. However, ensuring the security and resilience of smart grids is paramount. Cyber attacks, physical disruptions, and other unforeseen threats pose a significant risk to the stability and functionality of the grid. This paper identifies the research gaps and technical hurdles that hinder the development of a robust and secure smart grid infrastructure. This paper addresses the critical gaps in smart grid security research, outlining the technical challenges and promising avenues for exploration by both the industry and academia. A novel framework designed to enhance the reliability and security of smart grids was proposed against cyber attacks, considering the interconnectedness of the physical and cyber components. The paper further explores future research trends and identifies the key open issues in the ongoing effort to strengthen the security and resilience of smart grids. [ABSTRACT FROM AUTHOR]

The air pollution control in China has gradually transitioned from single pollutant control to multi-pollutant collaborative governance. Among the relevant factors, the control of ozone (O3) pollution has received widespread attention. Among the numerous air pollution regulations, environmental protection tax (EPT) is highly valued, as it can effectively control the emissions of O3 precursors. The objective of this study is to investigate whether the implementation of an EPT can reduce urban O3 pollution in China. Based on this, we used the difference-in-differences (DID) method, combined with the panel data from 221 cities in China from 2015 to 2020, to conduct an empirical analysis of the impacts of EPT reform policy on O3 pollution. The econometrics results indicate that the EPT reform policy can effectively inhibit urban near-surface O3 pollution by approximately 2.1%, and this result was confirmed to be accurate by multiple robustness tests. Additionally, significant spatial heterogeneities of this control effect are captured in this paper. Due to urban development levels and geographical factors, the better pollution reduction effects were national-model environmental protection, and efforts based on inland cities and high-level financial cities. Furthermore, three potential mechanisms, including cutting energy consumption, industrial structure optimization, and technological advance, were verified as being relevant to the impact path of EPT reform policy as to O3 pollution. This paper can provide empirical evidence for O3 pollution control in China, and also contribute to the further improvement of environmental protection policies. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) aim to improve safety and comfort of road users while contributing to the reduction of traffic congestion, air pollution, fuel consumption, and enabling mobility and accessibility of disabled and older people. As AV technology is rapidly advancing, there is an urgent need to explore how those new mobility services will impact urban transport systems, including the users, the infrastructure, and the design of future urban areas. This paper applies a systematic review to assess the role of AVs in urban areas. It reviews 41 articles published between 2003 and 2023, and uses inductive and deductive coding approaches to identify seven themes and thirty sub-themes within the literature. The seven include: benefits, attitudes, and behaviours and user perception, climate adaptation, climate mitigation, legislation and regulations, sustainability, and infrastructure. Studies related to benefits accounted for 25% of the sample, followed by behaviours and user perception (24%) and sustainability (22%). The least amount of research has been undertaken on the role of AVs to support climate adaptation. Geographically, almost half (#22) of the papers originate within Europe, followed by America (#10) and Asia (#7). There is only limited research originating from the Global South. This systematic review sets the scene for considering how AVs in public transport can be implemented in urban areas by establishing the current state of knowledge on user attitudes, perceptions, and behaviour, the benefits of AVs, the infrastructure and legislation and regulations required for AVs, and the role AVs have in climate mitigation, adaptation, and sustainability. [ABSTRACT FROM AUTHOR]

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel Continuous Variable Transmission (CVT) design. CVT is highly beneficial for actuators with rotary output as it can improve the energy efficiency of the actuators by providing an optimum transmission ratio. This property of CVT is highly beneficial for fossil-fuel-based vehicles, electric vehicles, wind turbines, industrial robots, etc. With the exception of Spherical CVT and DH CVT, all known CVTs like push belt CVTs, toroidal CVTs, Milner CVTs, etc., require additional gear sets and clutches for direction reversal and neutral gear ratio. However, Spherical CVT and DH CVT have low torque capacity due to a single traction point constraint. Foregoing in view, a new CVT named CAD CVT has been developed. The paper presents the design conception, the operating principle, the transmission ratio, the torque capacity, frictional losses, and experimental verification of the basic functionality by manufacturing a Proof of Concept (PoC). The proposed CVT is the only CVT capable of independent direction reversal and high torque capacity as it can transmit torque through multiple traction points. The new CVT will significantly impact high-torque applications in different engineering applications, especially land transport consisting of heavy vehicles like trucks, buses, and trailers. [ABSTRACT FROM AUTHOR]