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The rise in lightweight materials directly connects to efforts to boost fuel efficiency, cut emissions, and meet strict environmental standards. Over the past decade, automakers have significantly increased the use [...]

This study examines the potential impacts of energy efficiency and renewable energy on economic growth proxies by gross domestic product and environmental quality proxies by carbon dioxide emissions across eight selected Asian countries from 2000 to 2020. This study contributes by calculating green total factor productivity and carbon total factor productivity based on the famous Solow's residual via employing a modified extensive growth accounting model that internalized ignored factors such as energy efficiency and renewable energy. The employed panel cointegration techniques confirm that all variables are co-integrated with carbon dioxide emissions and economic growth. The pooled mean group/autoregressive distributed lag model analysis results indicate that energy efficiency is positively associated with both environmental quality and economic growth. Renewable energy hurts economic growth but has a positive effect on environmental quality which suggests the necessity of implementing an effective strategy for renewable energy alongside energy efficiency measures to enhance economic growth and environmental quality in the selected Asian countries. The findings from the fully modified ordinary least squares estimator are consistent with the environmental quality model. The average growth rate of green total factor productivity is positive despite negative contributions from energy efficiency and renewable energy. Similarly, the average growth rate of carbon total factor productivity is negative despite positive contributions from labor and capital. This discrepancy may be attributed to the beneficial effects of labor and capital as input productivity-driven. Embracing renewable energy sources can take significant steps toward improving environmental quality for future generations. Focusing on green technologies that enhance energy efficiency can substantially promote environmental quality and stimulate sustainable economic growth through innovation and climate change integration to achieve Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) technology is a vital solution for managing fluctuations in renewable energy, but conventional systems face challenges like low energy density and geographical constraints. This study explores an innovative approach utilizing deep aquifer compressed carbon dioxide (CO2) energy storage to overcome these limitations. To identify the factors affecting compressed CO2 energy storage system in deep aquifers, numerical simulations using T2well/ECO2N investigate hydrodynamic and thermodynamic behaviors, focusing on the impact of aquifer properties (depth, thickness, porosity, and permeability) and operational parameters (wellbore penetration depth through the aquifer and energy storage scale) on system performance. The findings reveal notable pressure variations in both the wellbore and aquifer during system operation and the injected supercritical CO2, input by geothermal energy from the surrounding formations, contributes to high energy storage efficiency across the entire system. The impact factor analysis suggests medium aquifer depth and permeability, a storage space with high porosity, increased aquifer thickness, greater wellbore penetration depth, and larger energy storage scales contribute to the safe and efficient operation of the system. [ABSTRACT FROM AUTHOR]

Thrust generation through pulse-laser ablation has been proposed for use in propulsion systems in space. However, to achieve a high thrust performance, the energy efficiency must be improved and it is important to determine the behavior of the ablation plume to identify where energy losses occur. In this study, the ion-current distribution of a plume with aluminum ablation was measured. A neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with a wavelength and pulse width of 1064 nm and 9 ± 2 ns, respectively, was used for the ablation. Experiments were conducted at three different fluences: 10, 15, and 20 J/cm2. The fluence was varied in two ways: changing the beam-spot diameter while maintaining the single-pulse energy and changing the single-pulse energy while maintaining the beam-spot diameter. The ion current was measured at various angles using Faraday probes. The generated impulse and reduction mass were also measured. By combining and analyzing the results, the divergence angle of momentum, mean valence of ablated aluminum, and energy efficiencies of several processes were estimated. • Aluminum pulse-laser ablation plume was investigated aiming use as space propulsion. • Nd:YAG laser with 1064 nm wavelength and 9 ± 2 ns pulse width used. • The ion current was measured at various angles using Faraday probes. • The generated impulse and reduction mass were also measured. • Divergence angle, mean ion valence, and energy efficiencies were estimated. [ABSTRACT FROM AUTHOR]

The utilization of perovskite quantum dots (PQDs) in photoelectric devices is indeed an area of intense research due to their advantageous properties such as tunability of visible light and efficient light emission. In this study, mixed CsPbI 3 and FAPbI 3 PQD dispersions were successfully employed for cation exchange to prepare red-color Cs 1−x FA x PbI 3 PQD with the photoluminescence (PL) wavelength from 689 nm (x = 0) to 778 nm (x = 1). It is also found that the aggregation of Cs 1−x FA x PbI 3 PQD films were strongly dependent on x. When x from 0 to 0.6, the holes in PQD thin films were gradually improved. On the opposite, when x > 0.6, the PQDs thin films returned more and more holes due to an excess of FA + cations. The resulting Cs 1-x FA x PbI 3 PQD based light-emitting diodes (LEDs) also demonstrated tunable emission wavelengths, and it is worth mentioning that the maximum external quantum efficiency (EQE) of 11.22 % was achieved with an emission wavelength of 758 nm when x = 0.6. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy in urban areas, there is a pressing need to manage energy consumption more effectively. Buildings, especially commercial and industrial ones, are major consumers of energy. Implementing AI-powered solutions can help monitor, predict, and reduce energy usage, leading to substantial cost savings and more efficient energy use. Integrating Industry 4.0 and the Internet of Things (IoT) into smart buildings presents a significant opportunity for enhancing energy management through advanced technologies. The increasing demand for energy in urban areas necessitates the development of more efficient energy management strategies, particularly within smart buildings, which are significant energy consumers. This study proposes an AI-powered deep learning framework utilizing Convolutional Neural Networks (CNNs) to enhance energy management in smart buildings, leveraging the ASHRAE - Great Energy Predictor III dataset. The proposed framework leverages deep learning techniques to analyze historical energy data, weather conditions, and building characteristics to forecast future energy usage accurately. Additionally, the framework includes anomaly detection mechanisms to identify inefficiencies and faults in the energy management system. By optimizing energy consumption in real-time and implementing demand response strategies, the framework aims to reduce energy costs and enhance the overall efficiency of smart buildings. The proposed CNN-IoT-based energy management framework in smart buildings demonstrates significant advancements over existing methods, achieving an accuracy of 88 %. These performance metrics indicate a substantial improvement in prediction accuracy and efficiency compared to existing approaches such as SVM, ELM, and LSTM. [ABSTRACT FROM AUTHOR]

In the context of a low-carbon economy, it is crucial to integrate environmentally friendly and intelligent designs into fitness facilities to ensure sustainable economic, environmental, and energy development. However, traditional fitness facilities face challenges in effectively monitoring user movements, conserving energy, and optimizing efficiency. The need to conserve energy and reduce resource costs in a low-carbon economy calls for the development of efficient algorithms that can maintain accuracy while minimizing computation and energy consumption. This study proposes a method that combines machine learning and computer vision techniques to enhance monitoring accuracy while minimizing computation and energy consumption. The ResNet-50 model is utilized to extract image features associated with human movements, while real-time object detection and tracking are performed using the YOLOv5 model. Experimental evaluations are conducted on a dataset comprising multiple action categories, and the results demonstrate the excellent performance and model efficiency of the proposed method. Specifically, on the KU-HAR dataset, the proposed algorithm achieves a reduction of over 46.8% in inference time and more than 45.9% in FLOPs, while improving the MAPE by more than 42.8%. These advancements significantly enhance the accuracy and robustness of human motion recognition, highlighting the importance of this approach in the green transformation and intelligent design of fitness facilities. • The proposed approach combines various deep learning algorithms, for better recognition of action. • Focuses on effectively monitoring user movements, conserving energy, and improving equipment efficiency. • Experimental evaluations demonstrate the exceptional performance and model efficiency. [ABSTRACT FROM AUTHOR]

Thermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning in the last decade, generating and processing real-time images have become one of critical methods in intelligent driving systems for new energy vehicles. However, the real-time images captured by sensors are susceptible to variations in various environments, including different weather and lighting conditions. To enhance the real-time image generation performance for new energy vehicles in complex environments, and improve real-time visual image processing capabilities, this study proposes an energy-efficient real-time visual image adversarial generation and processing algorithm, called as ENV-GAN. It hypothesizes a shared latent domain among mixed image domains after analyzing driving situations under various weather and lighting conditions. Mappings are established between different image domains. Besides, a multi-encoder weight-sharing technique is utilized to enhances the generative adversarial network model. Additionally, the algorithm integrates an attention module to enhance the model’s image generation. Experimental results and analysis demonstrate that the new algorithm outperforms existing algorithms in tasks such as defogging, rain removal, and lighting enhancement, offering high energy efficiency and low energy consumption. [ABSTRACT FROM AUTHOR]

The assessment of the thermal energy performance of earth-based buildings with respect to the semi-arid Mediterranean climate of Morocco is scarce, even if the country is historically known for its earthen dwellings. According to this need, this work aims to understand and evaluate the indoor thermal comfort and energy performances of passive building in two different locations. We have used EnergyPlus modeling tool and in addition, the monitored ten-day indoor temperatures in two different thermal zones in the test prototype to justify the empirical validation of Building Energy Model. The findings demonstrate, the use of compressed earth blocks coupled with passive design strategies provides better comfort and great sustainability. Thus, the summer discomfort hours are reduced about 12% for both cities compared to conventional building. However, the combination of semi-arid climate-responsive passive design has allowed to reach a thermal energy intensity reduction difference from 20 to 65 kWh.m-2.y-1. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is made of several sensor nodes (SN) that monitor various applications and collect environmental data. WSNs are essential for a wide range application, including healthcare, industrial automation, and environmental monitoring. However, these networks are susceptible to several security threats, underscoring the need for robust attack detection systems. Therefore, in this study, a multi-objective-trust aware improved grey wolf optimization (M-TAIGWO) is implemented to mitigate various attacks types. This implemented M-TAIGWO method is used to select secure cluster heads (CH) and routes to obtain secure communication through the network. The implemented M-TAIGWO provides improved security against malicious attacks by increasing the energy efficiency. The important aim of M-TAIGWO is to attain secured data transmission and maximize the WSN network lifetime. The M-TAIGWO method's performance is evaluated through energy consumption and delay. The implemented method obtains a high PDR of 98% for 500 nodes, which is superior to the quantum behavior and gaussian mutation Archimedes optimization algorithm (QGAOA), with a delay of 15 ms for 100 nodes which is lesser than fuzzy and secured clustering algorithms. In comparison to the trust-based routing protocol for WSNs utilizing an adaptive genetic algorithm (TAGA), this implemented method achieves defense hello fold, black hole, sinkhole, and selective forwarding attacks effectively. [ABSTRACT FROM AUTHOR]

Sustainable construction practices rely on carefully selecting building materials and balancing environmental and economic considerations. This study examines the complex link between local climate, market dynamics, and building material selection. Market data analysis, parametric modeling, and brute‐force optimization are used to provide insights into construction decision‐making. Across 5540 simulations, a thorough assessment of the financial and energy performance of various materials for walls, roofs, windows, and floors is conducted. Incorporating Pareto ranking, parallel simulation, and sensitivity analysis, the comprehensive evaluation reveals the intricate tradeoffs between cost, thermal properties, and energy savings. The findings highlight the potential for optimal external wall solutions to reduce U‐values by up to 30% and achieve source energy savings of up to 25% source energy savings across diverse climates. By emphasizing the importance of local context in material selection, this study highlights how energy consumption patterns and transmission losses influence financial and energy performance, thus advancing sustainable construction practices. [ABSTRACT FROM AUTHOR]

Energy-inefficient buildings are a major driver of climate change. The aging, energy inefficient housing occupied by low-income households in the US and other high-income countries is a driver of notable environmental and health disparities as well. Public policies for alleviating the financial and health burdens of energy inefficient housing have existed for decades, but fallen short of reaching most households in need. This paper proposes a promising new approach to filling gaps left by public programs -- one that relies on mobilizing the tools of private governance, carbon offsetting and the voluntary carbon market (VCM) to finance energy upgrades for low-income households. We develop a new and readily applicable methodology for calculating energy and carbon savings from efficiency upgrades based on readily accessible publicly available data. Tailored to the needs of estimating energy and carbon savings from efficiency upgrades to low-income units, rentals in particular, this methodology can be fruitfully deployed in monetizing the carbon savings from efficiency driven reductions in household energy use. Specifically, we propose packaging the emission reductions generated through energy savings as carbon offsets, then selling these offsets on the voluntary carbon market to generate financing for energy upgrades to low-income homes not served by public energy efficiency programs. Given the multiple economic and health co-benefits from low-income energy upgrades, we expect that carbon offsets generated through such upgrades will be attractive to many corporate and institutional offset buyers, particularly those who seek to fulfill climate commitments while also advancing economic and human development in their host communities. [ABSTRACT FROM AUTHOR]

Introduction: Construction of agricultural greenhouses can be considered as one of the appropriate solutions to meet the growing food demands. However, high energy use in greenhouse productions on the one hand and energy limitation on the other hand are fundamental challenges facing mankind. The present study aims to measure and compare energy efficiency based on the components of energy use sustainability (Environmental Norms, Environmental Beliefs, Environmental Values, Technical Management, Technical Knowledge, Education Level, Greenhouse's Work Experience, Cost-Effectiveness and Educational-Extension Service) among greenhouse cucumber growers. Methods: The statistical population included cucumber production greenhouse owners in Kerman Province, Iran. Out of the total population, 356 cases were selected as a sample using two-stage cluster sampling method. The data collection tool in this study was a researcher-made questionnaire. The questionnaire validity was confirmed via the content validity method and its reliability was confirmed through the pilot test. The data obtained from the questionnaire was recorded, calculated, and analyzed by SPSS24, Excel2019, and Deap software. Results and discussion: The results showed that the average energy efficiency in the studied units was 0.72 (out of 1), so that 21 and 335 greenhouses used energy efficient and inefficient, respectively. According to the components of energy use sustainability, a significant difference was observed between efficient and inefficient greenhouses, so that the energy efficient greenhouses have a high level of related components. It is suggested that the decision-makers, stakeholders, and active policy makers in the field of greenhouse crops should consider all the components of energy use sustainability, so that the developed policies and programs can cover all dimensions and take into account different aspects of energy use sustainability. As the results of this study can serve as a reference for other similar areas. [ABSTRACT FROM AUTHOR]

Under the guidance of energy-saving and emission reduction goals, a low-carbon economic operation method for integrated energy systems (IES) has been proposed. This strategy aims to enhance energy utilization efficiency, bolster equipment operational flexibility, and significantly cut down on carbon emissions from the IES. Firstly, a thorough exploration of the two-stage operational framework of Power-to-Gas (P2G) technology is conducted. Electrolyzers, methane reactors, and hydrogen fuel cells (HFCs) are introduced as replacements for traditional P2G equipment, with the objective of harnessing the multiple benefits of hydrogen energy. Secondly, a cogeneration and HFC operational strategy with adjustable heat-to-electricity ratio is introduced to further enhance the IES's low-carbon and economic performance. Finally, a step-by-step carbon trading mechanism is introduced to effectively steer the IES towards carbon emission control. [ABSTRACT FROM AUTHOR]

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase. [ABSTRACT FROM AUTHOR]

Hydrogen mobility embodies a promising solution to address the challenges posed by traditional fossil fuel-based vehicles. The use of hydrogen in small heavy-duty road vehicles based on internal combustion engines (ICEs) may be appealing for two fundamental reasons: Direct electrification seems less promising in heavy-duty transport systems, and fuel cell-based hydrogen vehicle implementation may not proceed at the expected pace due to higher investment costs compared to ICEs. On the other hand, hydrogen combustion is gaining attractiveness and relies on robust and cheap technologies, but it is not the only renewable solution. In this framework, this work presents a methodology to assess the Well-to-Wheel primary energy consumption and CO2 emissions of small heavy-duty vehicles. The methodology is applied in a real case study, namely a passenger coach traveling on a 100 km mission in non-optimized conditions. Therefore, the suitability of hydrogen is compared with standard diesel and other alternative diesel-type fuels (biodiesel and synthetic diesel types). Hydrogen shows competitivity with standard diesel from the point of view of CO2 emission reduction (−29%) while it hides a higher primary energy consumption (+40%) based on the current power-to-hydrogen efficiency declared by electrolyzer manufacturers. Nonetheless, HVO brings the highest benefits both from the point of view of primary energy consumption and emission reduction, namely −35% and 464–634 kgCO2/100km avoided compared to hydrogen. Moreover, the availability of HVO—like other biofuels—does not depend on carbon from CO2 capture and sequestration systems. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs' battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. [ABSTRACT FROM AUTHOR]

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver's temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact. [ABSTRACT FROM AUTHOR]

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV's five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles. [ABSTRACT FROM AUTHOR]

This article aimed to identify actions to improve energy efficiency in households. A household's energy efficiency is aimed at obtaining the same or more services with lower energy input. The article presents energy consumption in households in Poland according to Statistics Poland and then discusses the results of the survey, where respondents were asked how they improve their energy efficiency. Improving households' energy efficiency has gained importance due to increased energy prices in recent years. The most common methods of improving energy efficiency in a household include energy-saving devices and LED lighting, thermal modernization of the building, replacement of the heating system, and changing habits. The results were presented using the documentation and comparative methods. The article uses data from Statistics Poland and surveys conducted among 1112 representatives of households in Poland. [ABSTRACT FROM AUTHOR]

This study investigates the enhancement of vehicle warm-up performance using phase-change materials (PCMs) and various thermal storage methods. The primary objective is to utilize the thermal energy lost during engine cooling to improve the cold-start performance, thereby reducing fuel consumption and emissions. Thermal storage devices incorporating PCMs were developed and tested by measuring temperature changes and energy transfer over soaking periods of 4, 8, 16, and 24 h. The results show energy transfers of 591, 489, 446, and 315 kJ at 4, 8, 16, and 24 h, respectively. In terms of the warm-up time, the use of thermal storage devices reduced the time required to reach 70 °C by up to 24.45%, with significant reductions observed across all soaking periods. This reduction in the warm-up time directly contributes to faster engine stabilization, leading to proportional improvements in fuel efficiency and a corresponding decrease in exhaust emissions, including CO2. The findings highlight the effectiveness of PCMs in improving the engine warm-up performance and emphasize the importance of optimizing thermal storage systems to balance energy efficiency and practical application considerations. Additionally, the experimental data provide useful benchmark information for computational simulation validation, enabling the further optimization of automotive thermal management systems. Integrating a PCM-based thermal storage device can significantly enhance a vehicle's warm-up performance, leading to reduced fuel consumption and lower emissions. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

Automated Storage and Retrieval System (AS/RS) is driven by multiple motors for loading and unloading the items (z-axis) onto the fork or stacker, then moving the items horizontally (x-axis) and vertically (y-axis) at a time. Thus, it is practical to determine the speed configuration for each movement. To be responsive, it is reasonable to set the speed as fast as possible. However, high speed leads to high energy consumption which is undesirable in the context of green warehousing. Given that the speed changes dynamically, it is important to have an advanced optimization model for balancing both objectives and providing accurate estimation. This study proposed metamodel-based simulation–optimization (MSO) allowing to jointly optimize four speed-related variables namely horizontal speed (x), vertical speed (y), fork or depth speed (z), and acceleration/deceleration under the dynamicity of AS/RS. A case study was given in a warehouse comprising five cranes and ten racks. Using Desirability Function Analysis, the optimal speed configuration is obtained efficiently for minimizing travel time and energy consumption of AS/RS. The result also shows that row-based storage provides better responsiveness and energy efficiency than random-based storage. Further, rack design also indicates a significant impact on the AS/RS speed configuration. [ABSTRACT FROM AUTHOR]

This review offers an extensive examination of the evolving landscape of Long Range (LoRa) Microstrip Patch Antennas (MPAs), highlighting their crucial role in optimizing LoRa systems within the Internet of Things (IoT). As the demand for energy-efficient standards like LoRa grows with the expanding IoT market, this research becomes increasingly relevant. This comprehensive review, the first of its kind, serves as a foundational resource for researchers seeking to optimize LoRa systems within the IoT. The study has categorized these LoRa MPAs – including monopole, Planar Inverted F Antenna (PIFA), dipole, yagi-uda, and array – into single band, dual-band, multiband, and wearable antennas, thus providing substantial viewpoints on their diverse design architectures and performance characteristics. Through systematic tabulation, the review facilitates a thorough comparison of antenna advancements. Notably, the review addresses inherent challenges in LoRa MPAs, emphasizing critical aspects that necessitate attention, including the need for miniaturization and integration, advancements in substrate materials and fabrication techniques, and the imperative for reconfigurable and adaptive antennas. Various approaches to enhance antenna performance are explored, including the metamaterial incorporation, slot-based enhancements, Electromagnetic Band Gap (EBG), dielectric resonator antennas (DRAs), substrate material considerations, and corrugation techniques. Looking ahead, the paper explores the future trends and subtle considerations that are poised to shape the trajectory of LoRa MPAs. To the best of our knowledge, this paper represents the first comprehensive review on the multifaceted topic of LoRa MPAs, serving as a foundational resource for researchers in the field. [ABSTRACT FROM AUTHOR]

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions. [ABSTRACT FROM AUTHOR]

The next decade will see severe environmental and technological risks, pushing our adaptive capacity to its limits. The EPBD Case Green directive, to counter this phenomenon, emphasizes accelerating building renovations, reducing GHG emissions and energy consumption, and promoting renewable energy installations. Additionally, it calls for deadlines to phase out fossil fuels and mandates solar system installations. This research provides a comprehensive perspective on the opportunities for and challenges of incorporating renewable energy into the built environment. It focuses on the 2961 residential buildings on Procida, a small island located south of Italy, to efficiently utilize energy resources and lay the groundwork for sustainability. Beginning with an analysis of the territorial, urban, historical–conservation, structural, and geological context, in addition to environmental assessments, the research develops a classification and archetypalization system using in-house software. This system aggregates data on the island's residential buildings, analyzes their current state, and formulates various intervention scenarios. These scenarios demonstrate how integrating technological–environmental design interventions, such as upgrading the building envelope and enhancing bioclimatic behavior, with energy retrofitting measures, such as replacing mechanical systems and installing solar panels, can improve the overall performance of the existing building stock and achieve energy self-sufficiency. [ABSTRACT FROM AUTHOR]

The US manufacturing sector faces critical challenges: improving sustainability, reducing energy consumption, and reducing greenhouse gas emissions. Energy Treasure Hunt (ETH) training, a service provided by the US Department of Energy's Better Plants program, offers a compelling solution. Although ETHs have traditionally focused on energy and cost savings, data indicate that ETHs can be used to identify opportunities to reduce emissions and water use and to support a sustainable and circular operation. These 3-day on-site events engage employees in a collaborative search for operational and maintenance efficiency improvement opportunities. The success of ETHs lies in a comprehensive methodology. Each phase in an ETH uses various tools and resources to empower employees to identify practical solutions. This study presents data from 13 ETHs conducted between 2023 and 2024 across diverse manufacturing subsectors in the United States and demonstrates that the events can help create a pragmatic decarbonization pathway. Through the events, a total of 234 energy and emissions reduction opportunities were identified, and the potential impact is significant. Implementing the recommendations could translate to annual savings of 497,299 MMBtu of energy, 64,374 kgal of water, and 4.85 million tCO2e of emissions. The fiscal savings from the proposed recommendations translate into nearly $5 million annually. This study identifies the opportunities by energy system type and by the specific actions recommended, while also analyzing the identified opportunities, presenting the most established sustainability recommendations. Case studies from participating partners are presented to further demonstrate that ETHs provide a practical and impactful approach to reducing energy consumption, emissions, and operating costs and promote a more sustainable future for the industrial sector. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR. [ABSTRACT FROM AUTHOR]

Sustainable technologies to replace current fossil solutions are essential to meet future CO2 emission reduction targets. Therefore, this paper compares key performance indicators of a hydrogen- and a methane-flame-fired crucible furnace with computational fluid dynamics simulations at identical firing powers, aiming to fully decarbonize the process. Validated numerical models from the literature were used to compare temperatures, radiation fields, radiation parameters and heat transfer characteristics. As a result, we observed higher combustion temperatures and a 19.0% higher fuel utilization rate in the hydrogen case, indicating more efficient operating modes, which could be related to the increased radiant heat flux and temperature ranges above 1750 K. Furthermore, higher scattering of the heat flux distribution on the crucible surface could be determined indicating more uneven melt bath temperatures. Further research could focus on quantifying the total fuel consumption required for the heating up of the furnace, for which a transient numerical model could be developed. [ABSTRACT FROM AUTHOR]

This review research aims to enhance the sustainability and functionality of shape-memory polymer composites (SMPCs) by integrating advanced 4D printing technologies and sustainable manufacturing practices. The primary objectives are to reduce environmental impact, improve material efficiency, and expand the design capabilities of SMPCs. The methodology involved incorporating recycled materials, bio-based additives, and smart materials into 4D printing processes, and conducting a comprehensive environmental impact and performance metrics analysis. Significant findings include a 30% reduction in material waste, a 25% decrease in energy consumption during production, and a 20% improvement in shape-memory recovery with a margin of error of ±3%. Notably, the study highlights the potential use of these SMPCs as biomimetic structural biomaterials and scaffolds, particularly in tissue engineering and regenerative medicine. The ability of SMPCs to undergo shape transformations in response to external stimuli makes them ideal for creating dynamic scaffolds that mimic the mechanical properties of natural tissues. This increased design flexibility, enabled by 4D printing, opens new avenues for developing complex, adaptive structures that support cell growth and tissue regeneration. In conclusion, the research demonstrates the potential of combining sustainable practices with 4D printing to achieve significant environmental, performance, and biomedical advancements in SMPC manufacturing. [ABSTRACT FROM AUTHOR]

Biomimicry applications in different domains, from material science to technology, have proven to be promising in inspiring innovative solutions for present-day challenges. However, biomimetic applications in the built environment face several barriers including the absence of biological knowledge of architects and planners and the lack of an adequate common means to transfer biomimetic concepts into strategies applicable in the urban context. This review aims to create a multidimensional relational database of biomimetic strategies from successful precedent case studies in the built environment across different city systems and on different application scales. To achieve this, a thorough systematic search of the literature was implemented to map relevant biomimetic case studies, which are analyzed to extract biomimetic strategies that proved to be applicable and successful in an urban context. These strategies are then classified and documented in a relational database. This will provide a guide for architects and planners on how to transfer biomimetic strategies to strategies applicable in the urban context, thus bridging the gap of their lack of biological knowledge. The resulting matrix of strategies provides potential strategies across most of the different city systems and scales with few exceptions. This gap will be covered in a future work, currently in progress, to expand the database to include all city systems and scales. [ABSTRACT FROM AUTHOR]

A multi-objective optimization is performed to obtain fueling conditions in hydrogen stations leading to improved filling times and thermodynamic efficiency (entropy production) of the de facto standard of operation, which is defined by the protocol SAE J2601. After finding the Pareto frontier between filling time and total entropy production, it was found that SAE J2601 is suboptimal in terms of these process variables. Specifically, reductions of filling time from 47 to 77% are possible in the analyzed range of ambient temperatures (from 10 to 40 °C) with higher saving potential the hotter the weather conditions. Maximum entropy production savings with respect to SAE J2601 (7% for 10 °C, 1% for 40 °C) demand a longer filling time that increases with ambient temperature (264% for 10 °C, 350% for 40 °C). Considering average electricity prices in California, USA, the operating cost of the filling process can be reduced between 8 and 28% without increasing the expected filling time. [ABSTRACT FROM AUTHOR]

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5–3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits. [ABSTRACT FROM AUTHOR]

A Carbon Footprint (CF) is defined as the total emissions of greenhouse gases, primarily carbon dioxide, methane, and nitrous oxide, and is a specific type of Environmental Footprint that measures human impact on the environment. Carbon dioxide emissions are a major contributor to anthropogenic greenhouse gases driving climate change. Wood, as a renewable and ecological material, has relatively low carbon emissions. The study aimed to review and analyze the criteria influencing the feasibility of constructing modern zero-carbon wooden buildings. The review was conducted in two phases: (i) a literature review and (ii) an assessment of existing buildings. The preliminary research led to (i) narrowing the focus to the years 2020–2024 and (ii) identifying key criteria for analysis: sustainable material sourcing, carbon sequestration, energy efficiency, life cycle assessment (LCA), and innovative construction practices. The study's findings indicate that all these criteria play a vital role in the design and construction of new zero-carbon wooden buildings. They highlight the significant potential of wood as a renewable material in achieving zero-carbon buildings (ZCBs), positioning it as a compelling alternative to traditional construction materials. However, the research also underscores that despite wood's numerous potential benefits, its implementation in ZCBs faces several challenges, including social, regulatory, and financial barriers. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices. [ABSTRACT FROM AUTHOR]

The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range. [ABSTRACT FROM AUTHOR]

Energy efficiency is widely recognized as the foundational and most critical strategy for decarbonizing the manufacturing sector. Misconceptions surrounding energy efficiency measures often hinder their widespread adoption. This article aims to debunk five common myths and provides data and resources to help implement efficiency projects faster and more effectively to achieve greater decarbonization. First, the article challenges the myth that organizations have exhausted all possible energy efficiency opportunities by achieving voluntary energy intensity goals or energy performance certification. Second, it also addresses the misconceptions that efficiency projects are capital-intensive, require many qualified specialists, and have long investment return periods. By presenting real-world case studies and referencing commonly found efficiency opportunities, the article illustrates that energy-savings opportunities are ubiquitous. Organizations can use various contracting mechanisms as well as financial and technical resources from utility companies and government programs to lessen their burden. The notion that efficiency measures can be implemented solely in proprietorship facilities is dispelled. This article emphasizes the importance of green leases and explains that aligning decarbonization goals between the lessor and lessee can help drive savings for both parties. Finally, using unbundled renewable energy certificates as the sole pathway to decarbonization is strongly discouraged. By debunking these prevalent myths, this article aims to foster a deeper understanding of energy efficiency's potential as a cornerstone of decarbonization efforts and to embrace it as a critical pathway toward a sustainable future. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs' performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters' effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 °C, the number of points served by EVs is 40–64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions. [ABSTRACT FROM AUTHOR]

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace 6.2.R4 bibliometric software to visualize, analyze, and interpret the literature related to the field of "Smart Building" in the WoS database from 2014 to 2023; Results: As a cross-sectoral and multidisciplinary field, smart building has received significant attention in recent years, with a rapid growth in the number of publications. International cooperation is strong, with China, the United States, and South Korea leading in the number of publications, but there is still room for enhanced collaboration among institutions. Keyword analysis shows that technology and humanized design are both crucial, and emerging technology has become the current research hotspot. Conclusions: The field of smart building has gained global attention, and more breakthroughs will be made in improving building efficiency, reducing energy consumption, and enhancing the user experience. This development is moving towards a smarter and more sustainable direction that will bring greater benefits to human life and the environment. [ABSTRACT FROM AUTHOR]

This study examines the thermal performance of building walls under full sunlight conditions using various insulation strategies. Specifically, it evaluates: (1) the effects of heat on building walls and indoor spaces; (2) the impact of groundwater cooling systems on thermal environments; (3) the influence of phenolic aldehyde insulation layers on heat transfer; and (4) the combined effects of groundwater cooling and phenolic aldehyde thermal insulation. Fluent–CFD (Computational Fluid Dynamics) was used in the study to simulate temperature transmission between the sun, the groundwater cooling system, and both indoor and outdoor spaces. Experimental analysis and simulations reveal that both the phenolic aldehyde insulation layer and the groundwater cooling system effectively reduce heat transfer, with the groundwater cooling system demonstrating the most significant impact. The phenolic aldehyde layer decreases the temperature difference between inner and outer walls by approximately 8 °C. The groundwater cooling system further reduces both inner and outer wall temperatures, helping to maintain cooler indoor environments. Simulation results indicate that, while the phenolic aldehyde layer effectively prevents external heat from penetrating into the room, it does not eliminate heat accumulation. In contrast, the groundwater cooling system efficiently dissipates heat, mitigating this issue. Groundwater analysis shows that maximum temperature differences occur at specific times of the day, with water flow effectively cooling the space. The combined use of the phenolic aldehyde insulation layer and the groundwater cooling system offers superior thermal performance. The phenolic layer provides effective heat blocking, while the groundwater system facilitates heat dissipation, optimizing indoor temperature and reducing air conditioning loads. This combination enhances overall comfort and energy efficiency, with the groundwater cooling system benefiting from reduced flow velocity and lower energy consumption. [ABSTRACT FROM AUTHOR]

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method's development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

This study addresses energy poverty, a situation where households are unable to afford the minimum energy services required to meet their basic needs, a problem with profound implications for the well-being of vulnerable populations. To identify the most affected areas, a geospatial analysis of energy poverty distribution in the Valencian Community (Spain) was conducted using a correlation study between the global vulnerability index and the energy poverty index, calculated from residential energy certificates. The results highlight areas of significant social, economic, demographic, and residential vulnerability, as well as regions with very high or very low energy consumption. Specifically, regions with high energy poverty and vulnerability indices were identified, enabling targeted interventions. The study concludes that interventions in these identified areas are essential to mitigate the adverse effects of energy poverty and improve living conditions for affected populations. This research offers a novel methodology for mapping regional energy poverty, surpassing previous studies in precision for identifying and addressing vulnerable areas. [ABSTRACT FROM AUTHOR]

Background: Milkrun-based material supply plays an important role in the automotive industry, as it is a material supply concept where high efficiency can be achieved. When implementing milkrun-based material supply, the milkrun supply of the production plant often has to be integrated with an existing warehouse material handling system, which frequently leads to a less efficient solution. Methods: In this paper, the author investigates the impact of a hub-based milkrun supply, where the collection processes in the component's warehouse and the distribution processes in the assembly plant are connected to a hub, which is responsible for the sequencing of component demands. After a systematic literature review, the paper introduces a novel mathematical model, which makes it possible to describe the conventional milkrun-based solutions, the hub-based milkrun solutions, and to compare them in terms of the length of transportation routes, transportation time, total service time, and virtual emission points of view. Results: The scenario analysis demonstrates that the hub-based solution can lead to an efficiency improvement of about 13% in total service time, 23% savings in transportation time, and 45% savings in transportation time in the component's warehouse. Conclusions: The article's findings suggest that implementing a hub-based milkrun system in automotive material supply can significantly enhance efficiency. The described approach could lead to more streamlined operations in production plants by optimizing the integration of milkrun systems. [ABSTRACT FROM AUTHOR]

The Yangtze River's substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle's operational range by 7.45%. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks (DNNs) have addressed new applications with intelligent autonomy, often achieving higher accuracy than human experts. This capability comes at the expense of the ever-increasing complexity of emerging DNNs, causing enormous challenges while deploying on resource-limited edge devices. Improving the efficiency of DNN hardware accelerators by compression has been explored previously. Existing state-of-the-art studies applied approximate computing to enhance energy efficiency even at the expense of a little accuracy loss. In contrast, bit-serial processing has been used for improving the computational efficiency of neural processing without accuracy loss, exploiting a simple design, dynamic precision adjustment, and computation pruning. This research presents Serial/Parallel Systolic Array (SPSA) and Octet Serial/Parallel Systolic Array (OSPSA) processing elements for edge DNN acceleration, which exploit bit-serial processing on systolic array architecture for improving computational efficiency. For evaluation, all designs were described at the RTL level and synthesized in 28 nm technology. Post-synthesis cycle-accurate simulations of image classification over DNNs illustrated that, on average, a sample 16 × 16 systolic array indicated remarkable improvements of 17.6% and 50.6% in energy efficiency compared to the baseline, with no loss of accuracy. [ABSTRACT FROM AUTHOR]

Industrial buildings have numerous kinds of energy-losing equipment, such as engines, ovens, boilers and heat exchangers. Energy losses are related to inefficient energy use and lousy work conditions for the people inside the buildings. This work is devoted to the recovery of lost energy from industrial buildings. Firstly, the residual energy of the building is extracted to be used to warm water. Consequently, the work conditions of the people inside industrial buildings can be improved by maintaining the adequate temperature. The recovery of the energy is performed by a multipurpose heat pump system (HP system). The working fluid used in the HP system is R134a, which is a traditional and cheap working fluid. The thermophysical properties of R134a are obtained through the PC-SAFT equation of state. This work presents a performance mapping based on the intercepted areas framework to evaluate which working conditions are the optimal operating variables. The latter depends on several key parameters, such as compressor work, heat delivery, heat absorbed and exergetic efficiency. The results show that the optimal work conditions are found at different condenser and evaporator temperatures, and these may be limited by what the designer considers a sound performance of the heat pump system. [ABSTRACT FROM AUTHOR]