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To deal with stagnated performance and energy improved by successive technology scaling, system-technology co-optimization (STCO) comes as a rescue which involves the co-optimization of the important system parameters from the high-level application all the way down to the low-level technology. This article addresses the interconnect dominance issue in advanced nodes as a bottleneck in energy-efficient static RAM (SRAM)-based last-level cache (LLC) and aims to mitigate it through an STCO mechanism. Our main approach in this work is the utilization of a workload-aware controlled dynamic segmented bus (DSB) as the intramacro (interbanks) interconnect. Based on our results, our approach can improve the energy efficiency of the SRAM-based LLC by an average of 35%. [ABSTRACT FROM AUTHOR]

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With scholars paying increasing attention to energy sustainability, integrating energy sustainable utilization into the energy‐related assessment framework is particularly useful for achieving SDG7. This study empirically assesses the energy‐related efficiency (EE) comprised of the energy utilize stage and energy sustainable stage in China using a dynamic two‐stage data envelopment analysis model, and its influencing factors including digital economy with fixed‐effect models. The findings reveal a slight downward trend of EE from 2011 to 2020, primarily due to the poor performance of energy utilize stage. The subregional analysis illustrates that increasing clean energy use (CEU) is more beneficial to energy efficiency than reducing carbon emissions (CER). Dagum Gini coefficients indicate a growing disparity between regions. Additionally, a "U‐shaped" relationship between digital economy and EE is found, but two‐thirds provinces of China have not yet surpassed the turning point. Moreover, different degrees of CEU and CER will influence the impact of digital economy on EE. Environmental regulations and green technology innovation play positive moderating roles in the "U‐shaped" relationship. This study recommends to stimulate the energy sustainable use through higher adoption of clean energy sources and carbon emissions reduction, as well as highlight the green value of the digital economy. [ABSTRACT FROM AUTHOR]

Green roofs represent a hallmark of sustainable design and provide opportunities for increased recognition and credits within sustainability assessment frameworks. This study examines the thermal performance of green roofs in Auckland, Christchurch, and Wellington, considering two scenarios: present conditions as a "baseline scenario" and future climate change projections for "scenario 2050." Firstly, we simulated green roofs to measure the Total Fuel Consumption (TFC) of a single residential building for heating and cooling purposes with a green roof compared to the same building with a traditional bare roof using Design Builder software. Then, we utilized the Climate Change World Weather File Generator tool to predict future climate change trends in 2050. The findings reveal substantial energy-saving potential in oceanic climates. In the baseline scenario, the green roof could reduce TFC by about 3% (Auckland), 2% (Christchurch), and 1% (Wellington). In scenario 2050, these reductions increase to 3.3%, 2.6%, and 1%, respectively. Notably, green roofs exhibit an impact during summertime, with TFC reductions of approximately 8.5% (2022) and 9% (2050) in Auckland, 4.5% (2022) and 5.6% (2050) in Christchurch, and 1.5% (2022) and 0.2% (2050) in Wellington. Thus, the findings of this research not only contribute to a deeper understanding of promising techniques to combat climate change but also provide valuable insights that can inform decision-making processes regarding sustainable urban development towards Sustainable Development Goal 13. [ABSTRACT FROM AUTHOR]

Surface characteristics can be improved using diamond burnishing. In this work, an ecological indicator (energy efficiency- EF) and tribological factors (the coefficient and friction- COF and specific wear rate- SWR) of a single diamond burnishing process were optimized. The process parameters were the burnishing speed (V), burnishing depth (D), feed rate (f), the diameter of the tip (DT), and initial average roughness (IR). An efficient cooling-lubrication device comprising the minimum quantity lubrication and Ranque–Hilsch vortex tubes was utilized to facilitate burnishing trials. The Kriging models of burnishing performances were developed regarding parameters. The CRITIC method, Crow Search Algorithm, and TOPSIS were employed to select weights and optimality. The optimizing outcomes indicated that optimal values of the V, D, f, DT, and IR were 160 m/min, 0.10 mm, 0.05 mm/rev., 7 mm, and 2.00 μm, respectively. The EF was improved by 34.1%, while the COF and SWR were decreased by 33.2% and 8.1%, respectively. The optimal data could be applied to practical diamond burnishing to improve energy efficiency and tribological parameters for burnished external surfaces. The optimizing technique comprising the Kriging models, CRITIC, Crow Search Algorithm, and TOPSIS could be efficiently utilized to solve complex issues for burnishing operations and other machining processes. A developed system could be applied to enhance cooling-lubrication efficiencies for different machining operations, such as turning, milling, and grinding processes. [ABSTRACT FROM AUTHOR]

Injecting oil inside the compressor chambers of the oil-flooded, twin-screw compressors has several advantages. Oil cools the compressing fluid upon mixing with it and hence the compression process is brought nearer to the ideal isothermal compression process. The oil also serves as a lubricant between the meshing rotors and other clearance gaps in the compressor. The thin film of oil formed in the clearance gaps prevents internal leakages too; enhancing the volumetric efficiency of the compressor. Among these desirable effects of injecting oil in screw compressors, there is an undesirable effect too. The interaction (friction) of oil films formed in various clearance gaps with the rotors leads to a drag power loss. Recent studies such as Abdan et al. have proposed more detailed and accurate methods to estimate the oil drag losses in screw compressors. These methods enable the modelling of the effect of even minor changes in rotor profile on the drag loss power. Predictions of this model were hence used to tweak the screw rotor profiles with an objective to reduce the oil drag losses. Such profiles were then retrofitted in the existing machines and tested. Comparing the differences in power consumption of these machines, the component of oil drag loss was deduced. The experimental results show close agreement with the oil drag loss prediction model. The reduction of oil drag losses through profile modifications led to an improvement in the specific power of oil-flooded screw compressor. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest final consumers of energy, especially for air conditioning, and research has shown that there is a great potential for energy savings in this sector. The performance of a chiller can vary significantly depending on system partial loads and evaporator and condenser operating temperatures. The condensers of water-only chillers take the heat of condensation from the refrigerant only through the sensible heat of the refrigerant, leading to significant water flows. The outside air temperature and the intensity of solar radiation directly influence the consumption of electricity on the chiller. Having available the values for the solar radiation intensity I [W/m2 ] and the outside air temperature te [⁰C], with the help of the proposed mathematical model, validated by experimental measurements, the optimal values for the minimum electricity consumption of the system are obtained, under the conditions which the chilled water temperature is 7/12⁰C and the indoor air temperature is 27⁰C [ABSTRACT FROM AUTHOR]

Continuous growth of the manufacturing sector is resulting in to higher energy demand due to which the manufacturing costs and greenhouse gas emissions are also increasing. Beside reduction in energy consumption; improvement in energy efficiency, power factor and reduction in cutting temperatures are also vital to ensure better sustainability of the machining sector. This work evaluates the trade-offs between energy, heat generation and cutting quality during milling of medium carbon steel (EN8) alloy steel. The effects of input process parameters viz. spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle has been studied on the energy consumption, energy efficiency, power factor, cutting temperatures, surface roughness response parameters. The inclusion of helix angle as an input factor and, using energy efficiency and power factor as output parameters are the major highlights of this work. The machining experiments were conducted using response surface methodology for design of experiments. The multi objective optimization was carried out by using desirability approach, for three different groups of response variables considering the different importance of energy consumption, cutting temperatures and surface roughness, under different manufacturing circumstances. The predictability of the multiple regression approach was found to be more than 90% for all the responses which highlights model significance. The direct and interaction effect were studied and discussed in details for all the responses. The values of the composite desirability achieved in all the three types of optimization problems were on higher side (0.813, 1 and 0.794). The results of the optimization were confirmed by conducting the experiments the optimized settings. The percentage error between experimental and RSM predicted result was found to be within acceptable limits. This study can be helpful for reducing the energy consumption and cutting temperature without compromising on surface roughness, in the machining of medium carbon steel. [ABSTRACT FROM AUTHOR]

In recent years, the quality of the environment has declined dramatically as a result of human activities, which threaten the sustainability of our ecosystem. In this context, a number of earlier studies have investigated the environmental problems through the lens of different environmental indicators such as ecological footprint and carbon dioxide emissions. However, it is possible that by taking these factors into account, the supply side of environmental quality will be neglected. This research seeks to address this deficiency by investigating the impact of green growth, energy efficiency, green technology innovation, economic growth, trade openness, and human capital on the level of load capacity factor. This indicator places equal emphasis on the supply and demand sides of the ecosystem by taking into account both ecological footprint and bio capacity. In this setting, the present study makes use of CS-ARDL model for the top 10 manufacturing countries from the years 1990 to 2019. The short- and long-run findings of the CS-ARDL model unveil that green growth, energy efficiency, green technology innovation, and human capital all positively influence the load capacity factor, suggesting that these factors help the top 10 manufacturing countries to improve their sustainability limits. In contrast, trade openness and economic expansion in underlying countries increase environmental degradation by lowering the level of LCF. These outcomes are also validated by the AMG and CCEMG approaches and the granger causality test. On the basis of the findings, important policy recommendations are presented with the aim of assisting the top 10 manufacturing economies in enhancing the quality of their environment and fulfilling the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

This work explores the elaboration of wood particle‐based composites incorporating bio‐based phase change materials, with epoxidized linseed oil or clay as a binder. Fire performance evaluation of the novel composites includes an assessment to determine the impact of the addition of boric acid as a fire retardant, as well as the incorporation of recycled paper fibres containing boric acid, and the application of trimethoxymethylsilane coating. The study employs thermogravimetric analysis and cone calorimetry under uniform external irradiance, with a T‐history method to analyse thermal behaviour. Results indicated that fire retardants do not compromise the energy functionality of bio‐based phase change material composites, exhibiting a latent heat of approximately 50 J/g. The density ranges from 750 to 875 kg/m3. The use of clay as a binder improves fire performance, leading to a 60% decrease in total heat release and 52% of the composite mass remaining after analysis. Although enhancing fire performance presents challenges, incorporating wood particles in clay demonstrates a promising potential approach for safe use in building applications, contributing to energy efficiency in indoor heating and cooling. The findings contribute valuable insights into these materials for creating safer and more efficient building solutions, particularly in terms of thermal regulation and fire safety. [ABSTRACT FROM AUTHOR]

Turbid media such as fog and haze seriously affects the quality of imaging for systems such as urban surveillance and satellite remote sensing. Image dehazing has become a research hotspot in the field of computer vision. Neural-network-based image dehazing has the potential of high performance, but requires high computational power and storage space, making it costly to deploy in a system with limited hardware resources, especially for edge computing systems. In this paper, we propose an energy-efficient dehazing neural network named E 2 AOD-Net, which is pruned (generalization performance rises), quantized, pipelined, and parallelized over AOD-Net. We implement E 2 AOD-Net on FPGA platform, achieving a lightweight dehazing hardware accelerator that realizes real-time dehazing. The experimental results show that the frame rate of E 2 AOD-Net hardware accelerator inference reaches 38.3 FPS, while consuming the power of 2.491 watts. The VMAF index is improved by 89.14%. The energy efficiency is 42.61 GOPS/w. [ABSTRACT FROM AUTHOR]

Reduced wear and friction are directly related to longer component service lives and increased energy efficiency. The mechanisms used to achieve this goal in mechanical systems include the study of lubricant chemistry, surface coating, and surface modification. Significant research has been conducted on friction reduction of valve trains of internal combustion engines using surface modifications such as coatings and surface texturing. Here, an experimental approach has been adopted to investigate the effect of micro surface texturing on Thermo-Elastohydrodynamically Lubricated cam/tappet contact in a direct-acting valve train. A commercial vehicle valve train has been instrumented to study the effect of varying surface texture area density on friction under realistic engine operating conditions. Fiber laser surface texturing has been used to microtexture three samples of tappet shims with texture densities of 5%, 8%, and 10%. The tests have been run at four engine speeds—300, 500, 700, and 900 RPM—and three temperatures—30 °C, 60 °C, and 90 °C. The experimental results show a significant friction reduction of up to 18.33% for textured shims at the temperature of 90 °C. The friction reduction performance of the 8% textured shim has been optimum for all RPMs at the higher temperatures. [ABSTRACT FROM AUTHOR]

The concentration on Mobile Wireless Sensor Networks (MWSN) has significantly increased due to its various applications such as traffic, automation, industrial infrastructure, military, health, and so on. Minimizing the energy expenditure of nodes in WSN is a challenging task for improving their life expectancy due to the limited battery source available. In this paper, the Energy aware Multiobjective Levy Flight Artificial Rabbits Optimization (E-MLFARO) is proposed to enhance the energy efficiency and data delivery of the WSN. The Levy Flight incorporated in the E-MLFARO offers the dynamics that improve the searching capacity in identifying the optimum CHs and path. At first, the E-MLFARO is used to choose optimal Cluster Heads (CHs) from the normal nodes based on their energy, distance, CH balancing, and node degree. Next, multi-hop routing is achieved by using the E-MLFARO to ensure consistent data broadcasting over the network. The developed E-MLFARO is used to increase data delivery while minimizing energy expenditure. The E-MLFARO method is analyzed by alive nodes, energy expenditure, received packets, Packet Delivery Ratio (PDR), and life expectancy. The existing researches, namely, ISSADE, DRE-LEACH, DE-ACO and EOR-iABC are used to compare with the E-MLFARO. The alive nodes of E-MLFARO for 1500 rounds is 100 which is higher when compared to the ISSADE. [ABSTRACT FROM AUTHOR]

Underwater data centers, with their low energy use, cost, and latency, are seen as the future of data centers. This paper designs a Total Auxiliary System (TAS) for underwater data centers, including temperature and humidity control unit, biofouling prevention, and seawater corrosion prevention. In this paper, a model for the temperature and humidity control unit was developed, and key parameters such as heat load, specific moisture extraction rate (SMER), coefficient of performance (COP), and dehumidification volume were simulated. The effectiveness of the cooling performance was verified using Computational Fluid Dynamics (CFD) simulations. The system design also includes biofouling prevention via electric pulse and seawater corrosion prevention using cathodic protection with plastic coating and applied current. Calculations and verifications show that this system reduces energy consumption and ensures the long-term stable operation of underwater data centers. [ABSTRACT FROM AUTHOR]

This article focuses on the challenges of modeling energy supply systems for buildings, encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings. Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material, such as for thermal upgrades, which consequently incurs additional economic costs. It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions, considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in buildings. In addition, it explores the methods and mechanisms for modeling the operating modes of electric boilers used to collectively improve energy efficiency and indoor climatic conditions. Using the developed mathematical models, the study examines the dynamic states of building energy supply systems and provides recommendations for improving their efficiency. These dynamic models are executed in software environments such as MATLAB/Simscape and Python, where the component detailing schemes for various types of controllers are demonstrated. Additionally, controllers based on reinforcement learning (RL) displayed more adaptive load level management. These RL-based controllers can lower instantaneous power usage by up to 35%, reduce absolute deviations from a comfortable temperature nearly by half, and cut down energy consumption by approximately 1% while maintaining comfort. When the energy source produces a constant energy amount, the RL-based heat controller more effectively maintains the temperature within the set range, preventing overheating. In conclusion, the introduced energy-dynamic building model and its software implementation offer a versatile tool for researchers, enabling the simulation of various energy supply systems to achieve optimal energy efficiency and indoor climate control in buildings. [ABSTRACT FROM AUTHOR]

Climate change and the ongoing energy transition can increase energy poverty rates. To date, the main tool employed to alleviate energy poverty has involved income transfers to vulnerable households. However, measures that seek to improve a home's energy efficiency have recently gained increasing relevance. In this study we assess the effectiveness of these two types of policy, assuming universal coverage and optimal behaviour. Results points that income transfers and energy efficiency measures have the potential to decrease the proportion of households in energy poverty; however, the magnitude of their respective effects differs greatly. The average impact of energy efficiency measures provides for a greater reduction in energy poverty rates than income transfer policies. Although the greatest reduction in energy poverty is obtained by combining both measures, this combination of tools leads to overlapping effects with income transfers making only a marginal contribution once total retrofit have been implemented. [ABSTRACT FROM AUTHOR]

Recent studies demonstrated that green innovation and environment-related technologies reduce energy intensity and improve energy efficiency, contributing to the reduction of carbon emissions. However, the existing studies employ linear estimation methods to examine the relationship between green innovation and energy intensity and do not consider the indirect implications of institutional quality for the effect of green technology on energy intensity. Institutional quality is found to be an essential driver of innovation, and countries may need to achieve at least a minimum level of institutional quality to promote green innovation and improve their energy intensity. To test this hypothesis, this paper examines the relationship between energy intensity and green innovation using a panel dataset from 72 countries between 1996 and 2017 and a panel threshold model when institutional quality is considered a threshold variable. The findings highlight that green innovation reduces the energy intensity if and only if countries surpass a certain threshold of institutional quality. Therefore, countries need to improve their institutional quality to promote green innovation and benefit from green technologies in improving their energy intensity. [ABSTRACT FROM AUTHOR]

The results of mathematical modeling of a building's thermal insulation influence on the efficiency of intermittent heating mode are presented. This work aim is to assess the influence of thermal insulation on the efficiency of using the intermittent heating mode for the educational building of the Odessa National Polytechnic University's Heat Engineering Laboratory. The paper analyzes the factors related to improving the efficiency of the programmed heat supply mode. Modeling of the operating modes for heat supply system operated in intermittent heating mode is carried out. There was performed the mathematical modeling of the heating system operation modes for the most unfavorable climatic conditions at low outdoor temperatures, with respective system efficiency indicators obtaining. The potential of energy saving level for administrative, educational, and office buildings depending on the thickness of thermal insulation of external and internal walls is studied. Recommendations have been elaborated for the use of buildings thermal insulation to improve the efficiency of intermittent heating. [ABSTRACT FROM AUTHOR]

The main purpose of many current studies regarding energy efficiency is the improvement of the thermal resistance of buildings. To fulfill this goal, the development of advanced insulating materials, to be incorporated in the building envelopes, is imperative. Aerogels are ultralight porous materials typically produced via the sol‐gel process followed by supercritical drying of the wet gel. They exhibit very high porosities and a mesoporous‐macroporous structure which endows aerogels with extremely low thermal conductivity. This makes them ideal candidates for ambient thermal insulation applications. However, the cost for aerogel insulation is considerably higher than the one of standard insulation products. In the present work, highly porous aerogel‐like materials based on silica and commercial novolac resin were developed and added to common mortars. The prepared materials were dried under ambient pressure to minimize the manufacturing cost. The bulk density of silica quasi‐aerogels was 0.03 g/cm3–0.09 g/cm3 and that of the novolac resin samples 0.09 g/cm3–0.21 g/cm3. The aerogels were incorporated in mortars and cured for 28 days before measurement of thermal conductivity. The values of the thermal conductivity coefficient of the measured samples were 0.047 W/m K–0.058 W/m K for the silica reinforced mortars and 0.036 W/m K–0.044 W/m K for the novolac reinforced ones. [ABSTRACT FROM AUTHOR]

This research focuses on the importance of maintaining generator output voltage stabilization using automatic voltage regulator (AVR), especially in dealing with load impacts and environmental changes. Through the implementation of internet of things (IoT), this system can be controlled and monitored remotely, enabling real-time monitoring without physical presence at the generator location. The main objective of this research is to increase energy efficiency by optimizing generator operation. System development methods include design, prototyping and testing stages. Test results show that the automatic voltage regulator is effective in maintaining a stable output voltage of around 220 V, even though the current varies according to the existing load. The power produced ranges from 23 to 670 W, with a power factor between 0.7 and 1. Despite a slight voltage drop to 217 V, the power factor increases to 0.93. The system uses NodeMCU to send data to Blynk and Google Spreadsheets servers, as well as servo motors and PZEM-004T sensors for control and monitoring. Overall, this research shows that the internet of things-based automatic voltage regulator system is effective in maintaining stability and increasing generator operational efficiency, with the ability to manage voltage, current, power, and power factor efficiently. [ABSTRACT FROM AUTHOR]

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region's weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%. [ABSTRACT FROM AUTHOR]

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%. [ABSTRACT FROM AUTHOR]

In the last decade, metalworking firms around the world faced the challenge of embracing Industry 4.0. Amidst this, some machining companies battle with outdated, unconnected equipment on their shop floors. This work introduces a novel methodology linking machining events to electrical power behavior, enabling real-time monitoring of machine tool states. The prototype, OEE-Plus, tested on a legacy transfer line, extends Overall Equipment Effectiveness by incorporating an energy consumption indicator. The significant result is a practical solution empowering small to medium-sized enterprises to assess the efficiency and sustainability of their legacy machinery. [ABSTRACT FROM AUTHOR]

Energy performance and heat release from buildings can differ with variations in envelope and operational parameters. This study presents an evidence-based assessment for different study locations in Japan including Sapporo, Tokyo, and Osaka. The analysis used a comparative approach using a reference energy performance simulation model for large office buildings using EnergyPlus to assess the impact of design variations on building performance. The analysis included 66 simulations with 10 design parameters and 2 construction types. The study coupled energy performance with heat release from the building towards outdoor environments since heat release can influence the local thermal environments. Desirable tradeoffs were observed for timber constructions by increasing the cooling setpoint by 1°C reduced cooling and heating energy use by 10.7% and 11.9% in Sapporo, 8.5% and 22.7% in Tokyo, and 6.8% and 24.4% in Osaka. Similarly, cooling and heating peak loads were reduced by 5.9% and 2.4% in Sapporo, 5.4% and 2.0% in Tokyo, and 6.5% and 5.2% in Osaka. Additionally, nighttime heat release also decreased by 12.4% and 13.6% in Sapporo and Tokyo, and 6.2% and 5.7% in Osaka for concrete and timber constructions. The study provides early-stage design support for energy-efficient buildings and improved local thermal environments. [ABSTRACT FROM AUTHOR]

The release of greenhouse gases (GHG) is acknowledged as a serious hazard that fuels climate change (CC). The Middle East and North Africa (MENA) countries face particular environmental challenges due to its contribution to carbon dioxide emissions (CO2). There is, however, a scarcity of literature about the region's environmental challenges, despite its vulnerability to the consequences of CC. Therefore, the purpose of this research is to look at various solutions to the CC issue in the MENA area, that is, energy efficiency (EE), economic development, urbanization, fossil fuel (FF) usage, and renewable energies (RE). Further, the study seeks to evaluate CO2 emissions in the region by establishing a RE index. By applying the fresh technique known as the cross-sectional autoregressive distributed lag estimator to panel data for 18 MENA countries from 1990 to 2019, this study quantifies the impact of these variables on CO2 emissions. The findings reveal that the explanatory variables' significance varies across regional panel clusters, with economic growth having a significantly positive effect on CO2 emissions in the long run. Moreover, the study rejects the Environmental Kuznets Curve hypothesis validation and highlights the importance of EE in boosting environmental quality in the long and short runs. Additionally, the outcomes suggest that RE contributes to reducing CO2 emissions, while the urbanization level and FF consumption adopted by MENA countries do not improve their environment. Thus, mitigating the negative consequences of CC in the MENA area necessitates encouraging EE and lobbying for a shift from FF to RE sources. [ABSTRACT FROM AUTHOR]

Meeting net zero emissions targets requires co‐operation of multiple actors across scales and sectors, including small and medium‐sized enterprises (SMEs); research on action by SMEs to improve energy efficiency of their buildings has however been limited. This article focuses on the work of local authorities in engaging SMEs in energy efficiency initiatives. Empirical findings are from systematic analysis of semi‐structured interviews with local authorities participating in the Energy Efficient Scotland pilot programme for national scale retrofit. The modes of governance conceptual framework (self‐governing, governing by partnership, governing through enabling, governing by provision and governing by authority) is combined with a local government capacities framework (responsibility, political authority, finance, personnel, knowledge and energy materialities) to explore the different strategies available to local authorities. Findings reveal that local authorities utilise various modes of governing for engaging SMEs; however some governing modes (self‐governing, governing through authority and governing through provision) proved more effective than others (governing through partnership and through enabling). The results demonstrate that the modes of governance used to engage SMEs in energy efficiency depend on local authority capacities. However, Scottish local authority capacities are varied, resulting in differential ability to utilise the different modes of governing. Government policy for heat in buildings therefore needs to be sensitive to the specificities of different local authorities in order to extend their capacities for engage SMEs through both direct and enabling modes of governance. [ABSTRACT FROM AUTHOR]

This study investigates the impact of renewable energy integration on energy efficiency within the China-Pakistan Economic Corridor (CPEC) from 2000 to 2022. Efficient renewable energy utilization is crucial for the CPEC’s sustainable development. We employed multiple linear regression analysis on data from national energy statistics databases to examine the relationship between energy efficiency defined as the ratio of total renewable energy output to input in (TWh) and four renewable energy sources: hydropower, biofuel, solar PV, and geothermal. Our model controlled for potential confounding factors and met assumptions of linearity, multivariate normality, and absence of multicollinearity. Hydropower exhibited a highly significant negative correlation with energy efficiency (-0.632, p < 0.001), with a regression coefficient of -7.642 × 10⁻⁴ (p < 0.001). Similarly, biofuel showed a significant negative correlation (-0.222, p < 0.001) and a coefficient of -9.580 × 10⁻⁴ (p < 0.001). These findings suggest that increased production from these sources is associated with decreased energy efficiency, potentially due to transmission losses or inefficiencies in conversion technologies. In contrast, solar PV (-0.027, p = 0.638) and geothermal (-0.014, p = 0.806) showed no statistically significant relationship with energy efficiency, indicating that at their current scales of deployment, they do not significantly impact overall efficiency. A moderate negative correlation (r=-0.114, p = 0.047) was observed between solar PV and geothermal production, possibly reflecting resource allocation choices within the renewable energy portfolio. These results highlight the need for targeted interventions to improve the efficiency of hydropower and biofuel production and emphasize the potential for future contributions from solar PV and geothermal as their deployment and grid integration expands. The findings contribute to a more nuanced understanding of the complex interplay between renewable energy generation and overall energy efficiency within large-scale development projects. [ABSTRACT FROM AUTHOR]

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. [ABSTRACT FROM AUTHOR]

China and the United States have the highest demand for fossil fuel energy for transportation and power generation, which promotes growth while also damaging the environment. Policymakers, and environmental scientists, are increasingly interested in understanding key strategies to improve energy efficiency to mitigate fossil fuel energy demand and sustain the environment and growth. In this context, this study uses augmented mean groups (AMG) and common correlated effects mean group (CCEMG) estimators to reveal the impact of China-US technological innovation, transportation infrastructure, and power generation on fossil fuel energy demand and fossil fuel energy intensity from 1995 to 2020. Besides, the study examines whether fossil fuel energy use and fossil fuel energy intensity are the main drivers of carbon dioxide emissions (CO2) and economic growth. The analysis results show that technological innovation has a strong adverse influence on fossil fuel energy use, and fossil fuel energy intensity. Electricity generation, road transport infrastructure investment and aviation infrastructure investment have considerably contributed to fossil fuel energy use, and fossil fuel energy intensity. The construction of rail transit infrastructure has significantly alleviated fossil fuel energy demand, and fossil fuel energy intensity. Fossil fuel energy use and fossil fuel energy intensity significantly contribute to carbon dioxide (CO2) emissions and economic growth. However, technological innovation and renewable energy use can substantially reduce carbon dioxide (CO2) emissions, while making a considerable contribution to economic growth. Based on empirical investigation, this study provides a series of useful insights into the sustainable development goals of the Chinese and American economies. [ABSTRACT FROM AUTHOR]

An experimental analysis of the performance of packed-bed latent thermal energy storage system using paraffin wax as the phase change material is carried out for plain and dimpled spherical capsules. Two different inlet temperatures (70°C and 35°C) of heat transfer fluid (HTF), the flow rates ranging from 1 L/min to 5 L/min. The study reports charging time, energy and exergy efficiency. The dimpled spherical capsules charge faster than plain capsules, the energy and exergy efficiency analysis showed that dimpled capsules yields higher energy efficiency of 90.86%, 97.12% and 89.06% respectively for 1 L/min, 3 L/min and 5 L/min. [ABSTRACT FROM AUTHOR]

The study aims to optimize the poly-generation of power and urea through the thermochemical conversion of biomass feedstock into hydrogen-rich syngas using steam gasification. This hydrogen-rich syngas serves as a crucial intermediate for producing valuable products, including power and urea. Biomass wastes such as date pits, manure, sludge, and food waste are utilized in this poly-generation process. Using Aspen Plus process modeling software, the system integrates biomass steam gasification with the cogeneration of urea and power, simulating the system's performance. A sensitivity analysis assesses the effects of carbon dioxide utilization and the power-to-urea splitting ratio on the system's overall energy and heat demands. The optimized results of the thermodynamic assessment of the integrated system demonstrate an overall energy efficiency of 52.30% and exergy efficiency of 56.40%, resulting in a power generation of 39.49 MW, a urea production rate of 2.8 kg/s, and a total steam production of 15.56 kg/s. These findings provide valuable insights for determining the optimal power ratio to urea production. • Optimal feedstock blending strategies are employed to enhance hydrogen yield. • Biomass steam gasification yields hydrogen-rich syngas for power and urea synthesis. • Aspen Plus simulation models integrated biomass gasification and cogeneration system. • Sensitivity analysis examines CO 2 utilization and power-to-urea ratio effects. • The system achieves 52.30% energy efficiency and 56.40% exergy efficiency. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (ESSs) (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the ESSs is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). In this paper, we investigated the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimizing the probability of service outage and energy wastage). [ABSTRACT FROM AUTHOR]

The indoor comfort of occupied buildings has become essential, as it significantly affects the productivity and health of occupants. This review provides a comprehensive overview of air and thermal comfort studies in residential buildings using alternative materials. This study aims to provide the trends and the state of the research in this field, to summarize the optimization methods, and to offer a perspective on future studies. The results demonstrate that in comparison to traditional air conditioning systems, natural ventilation systems, and air purifying plants have the potential to reduce energy consumption and carbon emissions in buildings by up to 60%. Also, cross-laminated timber (CLT) buildings can achieve energy savings of up to 30% compared to conventional buildings due to their superior insulation properties and reduced thermal bridging. This review has provided highlights to help the building owners to promote a healthier environment, reduce energy consumption and achieve more comfortable indoor air quality. The study promotes incorporating innovative materials and use of natural ventilation systems, smart air conditioning systems and air purifying plants. [ABSTRACT FROM AUTHOR]

This work presents a process for the requirement management and conceptual design phases of hydraulic systems. The aim is to provide a foundation for developing more energy efficient systems for the working hydraulics of construction machines. A framework is developed and applied in a case study of a loader crane. An important part of the framework is to evaluate and compare different designs based on customer usage of the machine. Emphasis has also been put on incorporating the knowledge and experience of both industry and academia. A large number of different designs are evaluated during a concept exploration phase. The design space is then narrowed down to the system design analysis phase. Losses from simultaneous operation are significant, systems with two and four pumps respectively are therefore selected for analysis by demonstrators. In order to reach equal energy performance, two-pump systems need to be more complex, i.e., they require a more sophisticated control and more components, than four-pump systems. When comparing demonstrator results, the conclusion is that for two-pump systems, it is more challenging to reach the theoretical performance, both in terms of energy efficiency and of drivability, than for the less complex systems made possible with four-pumps. [ABSTRACT FROM AUTHOR]

ABSTRACT This research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments. [ABSTRACT FROM AUTHOR]

Since the last two decades, carbon neutrality has become a primary target of all economies. Governmental environmental policies stand as the most potent tools in the arsenal when it comes to tempering the effects of climate change. Fostering the adoption of green energy sources and embracing energy efficiency principles may assume an essential role in upholding the standard of ecological integrity. The primary objective of this inquiry revolves around the meticulous analysis of the intricate interplay between economic growth, the trajectory of industrialization, the up-gradation of industrial sector structure, the integration of green energy paradigms, and the implementation of energy efficiency strategies and environmental policies in the frame spanning from 2000 to 2019. To tackle the matter of cross-sectional dependency and heterogeneity, second-generation cointegration estimators, Dynamic Seemingly Unrelated Regression (DSUR) and Augmented Mean Group (AMG), were employed to estimate long-run relationships. The consequences of DSUR and AMG indicate that while economic and industrial growth contributes to environmental degradation, renewable energy usage, and medium–high-tech industries mitigate the carbon emissions in selected countries. Further study results suggest that energy intensity positively impacts environmental degradation, which means energy efficiency helps mitigate CO2 emissions in these countries. This study also reveals that the degree of stringency in environmental policy negatively affects CO2 releases in the selected nations. Consequently, our study recommends the enhancement of the stringency of environmental policies, promoting environmentally friendly energy usage, the efficient use of energy, and the advancement of industries into medium–high-tech industries as effective ways to mitigate climate change in specific developing countries. [ABSTRACT FROM AUTHOR]

IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protocol compared to the Ride algorithm, highlighting its potential for optimizing network performance and improving resource utilization in IoUT environments. [ABSTRACT FROM AUTHOR]

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets. [ABSTRACT FROM AUTHOR]

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems. [ABSTRACT FROM AUTHOR]

Electronic components cooling (ECC) for manufacturing and technological uses has become one of the most interesting topics that researchers have focused in the modern era. Because of the continuous minimization in the electronic components size and substantial quantity of heat generation; the traditional cooling methods cannot be able to follow rapidly reduction in such high amount of heat flux. The use of nanofluid is an intriguing possibility for ECC. Such advancements may result in advancements in the field of electronic equipment in addition to improved efficiency of energy. The present article proposes is to study the use of various types of nanofluids over different shapes for preventing redundant heat, to effectively control the thermal stress as well as to maintain the electronic components temperature. Some fascinating features related to utilizing nanofluids to ECC are also addressed. In addition, further study prospects and directions in this area are put forward. It can be noticed that the addition of γ-Al2O3 NPs in water increases the heat transmission efficiency by 37% and 28%, while keeping Reynolds numbers (Re = 601.3 and 210) respectively. The cooling ability of the automobile radiator upsurges up to 17.46% with the addition of Al2O3-NPs. [ABSTRACT FROM AUTHOR]

This paper presents a thermodynamic and sustainability analysis for an experimentally developed solar water heater-with water treatment. The parabolic trough collector (PTC) is employed to capture thermal energy from the sun, which is subsequently utilized to increase the temperature of water. "Experimental and numerical" investigations are divided into two cases. Case 1: PTC with a glass cover and case 2: PTC without glass cover. Using an experimental analysis, data are collected, such as solar insolation and water outlet temperature. The collected data are utilized to analyse the thermodynamic performance of the proposed system. The first law of thermodynamics helps to quantify the system's performance, called energy analysis, whereas the second law of thermodynamics provides qualitative performance, called exergy analysis. The maximum energy and exergy efficiency achieved by the proposed system are 69.5% and 6.15%, respectively. Simultaneously, an exergy-based sustainability analysis is proposed, which shows how effectively the fuel exergy is utilized with the proposed system. The maximum sustainability index for case 1 is 1.07, and for case 2, it is 1.08. At the end of the experimental investigation, a slight decrease in total dissolved solid (TDS) was detected, indicating that if we enhance the performance of the PTC system, by changing the reflector material and installing evacuated tubes, the quality of the water may improve. [ABSTRACT FROM AUTHOR]

In order to explore the heat transfer properties of natural convection in the cavity, this article uses the three-dimensional CFD inverse method and the temperature measurement points based on the experimental results. In addition, different flow modes and mesh divisions are used. For the applicability of different turbulence models, the least squares method is then used to calculate Q value of the heat source in the cavity and compare that with the experimental measurement results and consistent with the overall trend. Finally, the temperature distribution diagram and velocity streamline diagram of the simulation results are provided to visualize the flow field and analyze its heat transfer characteristics. The results show that the flow pattern and the number of grid points have a great influence on the results. In terms of experiments, with the change of the cavity size and the influence of the opening, the air flow will be different. To achieve the effect of enhancing natural convection, due to the inflow of a large amount of cold air, although the overall temperature of the cavity is greatly reduced, the upper cold wall is surrounded by cold air, which reduces the heat transfer coefficient. In order to verify the reliability and usability of the results of the inverse algorithm in this article, the obtained results of heat transfer coefficient and heat dissipation are compared with previous results or empirical formulas in other related literature. When the height drops from h = 0.1 m to h = 0.05 m, the average increase in heat transfer coefficient is 2.79%; while for the height drops from h = 0.05 m to h = 0.02 m, the average increase in heat transfer coefficient is 74.8%. When side fin length is reduced form W2 = 0.1 m to W2 = 0.08 m, the average increase in heat transfer coefficient is 14.33%; when side fin length is reduced form W2 = 0.08 m to W2 = 0.06 m, the average increase in heat transfer coefficient is 41.28%. [ABSTRACT FROM AUTHOR]

The present research utilizes an inverse 3D CFD technique, coupled with the least squares approach and laboratory temperature readings, to predict the suitable turbulence flow model and unknown heat transfer rates within a cubic hot box. This enclosure features four cylindrical horizontal tubes arranged in a diamond configuration, functioning as heaters to generate heat within the space. Furthermore, the heat dissipation from each tube, flow field and temperature contours are also established. The energy equation for tubes and the governing equations for air are solved by coupling. The present estimates are obtained assuming a constant heat transfer rate. The range of the Rayleigh number is from 60,294 to 76,824. Testing various turbulence models revealed that the zero-equation turbulence model reduces the root mean square discrepancy between the simulated thermal behavior and laboratory outcomes compared to other methodologies. To verify the chosen flow model's accuracy, the Nusselt numbers and flow field distributions aress compared with existing correlations and interferometer photographs. The maximum errors between the estimates of heat loss and heat transfer rate and the corresponding values from previous studies are 44 and 28% for St = 0.036, 34 and 3% for St = 0.045, and 37 and 18% for St = 0.054. To assess the impact of tube surface and spacing on the flow field model, calculation results are compared with those from four square-arranged tubes, revealing a lack of existing studies on this specific investigation. [ABSTRACT FROM AUTHOR]

The thermal performance and energy efficiency of buildings are critical factors in achieving sustainable energy systems as energy needs for heating and cooling are expected to represent more than 50% of global final energy consumption. This study analyzes conventional renewable energy systems for heating and cooling in buildings, focusing on strategies for developing net-zero-energy buildings. This review covers the integration of renewable energy, the use of intelligent energy management systems, and the optimization of thermal processes. It also compares various systems based on their advantages and limitations and analyzes emerging trends in the thermal management of buildings in different climate zones. The synthesis of recent literature highlights practical recommendations for achieving high thermal performance in buildings, including the importance of selecting appropriate energy systems based on local climatic conditions, optimizing system efficiency, and taking advantage of new materials and advanced technologies. This review aims to contribute to promoting sustainable construction practices with the integration of renewable energy sources and improving the energy efficiency of buildings. [ABSTRACT FROM AUTHOR]

This paper studies the impacts of inverter clipping on bifacial PV modules under different weather and ground reflectivity. A 5 kW bifacial array was connected to a 3.8 kW grid-tied inverter, a 10 kWh Li-ion battery, and an EV charger. A PV output calculation model was developed to compare the estimated output of the modules with the actual measurements to evaluate the relation between ground reflectivity and clipping loss. The results showed that clipping potentially occurs on sunny days in summer from 10:00 to 15:00 during the period with the highest solar irradiance. Three colors of ground cover were also examined to compare the performance of bifacial modules under different albedo reflective properties. The results indicated that the white ground in winter leads to the highest bifacial gain (13.1%) and daily DC efficiency (22.2%) due to the combination of high reflectivity with low solar angle giving maximum upward reflection of direct sunlight. This same combination shows a minimal advantage in summer due to the clipping. The proposed model is evaluated, demonstrating 98.2% agreement between modeled and actual data for all conditions. Furthermore, simulation models based on the actual system with different system sizes and ground reflectivities have been studied to evaluate the impacts of the clipping in terms of technical losses and financial returns. The analysis shows that a high reflective ground condition can provide the best financial benefit, and the clipping loss does not have a great effect on the finance of the project since the loss is less than 4% of the annual production even in an extreme case. [ABSTRACT FROM AUTHOR]

The maritime industry, central to global trade, faces critical challenges related to energy efficiency and environmental sustainability due to significant energy loss from waste heat in marine engines. This review investigates the potential of waste heat recovery (WHR) technologies to enhance operational efficiency and reduce emissions in marine systems. By analyzing major WHR methods, such as heat exchangers, Organic Rankine Cycle (ORC) systems, thermoelectric generators, and combined heat and power (CHP) systems, this work highlights the specific advantages, limitations, and practical considerations of each approach. Unique to this review is an examination of WHR performance in confined marine spaces and compatibility with existing ship components, providing essential insights for practical implementation. Findings emphasize WHR as a viable strategy to reduce fuel consumption and meet environmental regulations, contributing to a more sustainable maritime industry. [ABSTRACT FROM AUTHOR]

This paper investigates the optimization of insulation thickness with respect to the integration of renewable energy systems in residential buildings in order to improve energy efficiency, maximize the contribution of renewables and reduce life cycle costs. Using the DesignBuilder and EnergyPlus software, this study models a representative two-story residential building located in Athens, Greece. The building envelope features extruded polystyrene thermal insulation and windows with unplasticized polyvinyl chloride frames and low-e glazing. Six scenarios with hybrid renewable energy systems are analyzed, including air- and ground-source heat pumps, solar thermal systems and a biomass fired boiler, so as to assess energy consumption, economic feasibility and internal air temperature conditions. A Pareto-fronts-based optimization algorithm is applied to determine the optimal combination of insulation thicknesses for the walls, the roof and the floor, focusing on minimizing the life cycle cost and maximizing the percentage of renewable energy utilized. The results demonstrate that scenarios involving biomass boilers and solar thermal systems, both for heating and cooling, when combined with reasonable thermal protection, can effectively meet the recent European Union's directive's goal, with renewable energy systems contributing more than 50% of the total energy requirements, whilst maintaining acceptable internal air temperature conditions and having a life cycle cost lower than contemporary conventional buildings. [ABSTRACT FROM AUTHOR]

This paper explores the development of positive energy communities using Eordaia, Greece, as a case study. The approach combines building and district-level energy analysis to achieve nearly zero energy performance through retrofitting, district-level storage systems, and renewable energy technologies. A parametric analysis utilizing RETSCREEN Expert and EnergyPlan software determines the optimal mix of technologies based on technical and financial parameters, with Eordaia, a region in energy transition and part of the RESPONSE Horizon project, illustrating the practical benefits. It includes a neighborhood of 105 mixed-use properties and two municipal buildings where a range of renewable energy sources and energy efficiency measures are applied. Insulation, photovoltaic systems, LED lighting, predictive thermostats, and windows coated with nanotechnology are some of the key interventions considered. The findings show considerable reductions in CO2 emissions and energy use, with payback periods ranging from 8.7 to 9.6 years. This study underscores the value of district-level strategies over individual building retrofits, highlighting cost savings and improved energy performance. These findings offer valuable insights for urban planners and policymakers aiming to transform urban areas into sustainable, positive energy districts, supporting the EU's 2050 net-zero emissions goals. [ABSTRACT FROM AUTHOR]

Today, most of the world's electric energy is generated by burning hydrocarbon fuels, which causes significant emissions of harmful substances into the atmosphere by thermal power plants. In world practice, flue gas cleaning systems for removing nitrogen oxides, sulfur, and ash are successfully used at power facilities but reducing carbon dioxide emissions at thermal power plants is still difficult for technical and economic reasons. Thus, the introduction of carbon dioxide capture systems at modern power plants is accompanied by a decrease in net efficiency by 8–12%, which determines the high relevance of developing methods for increasing the energy efficiency of modern environmentally friendly power units. This paper presents the results of the development and study of the process flow charts of binary and trinary combined-cycle gas turbines with minimal emissions of harmful substances into the atmosphere. This research revealed that the net efficiency rate of a binary CCGT with integrated post-combustion technology capture is 39.10%; for a binary CCGT with integrated pre-combustion technology capture it is 40.26%; a trinary CCGT with integrated post-combustion technology capture is 40.35%; and for a trinary combined-cycle gas turbine with integrated pre-combustion technology capture it is 41.62%. The highest efficiency of a trinary CCGT with integrated pre-combustion technology capture is due to a reduction in the energy costs for carbon dioxide capture by 5.67 MW—compared to combined-cycle plants with integrated post-combustion technology capture—as well as an increase in the efficiency of the steam–water circuit of the combined-cycle plant by 3.09% relative to binary cycles. [ABSTRACT FROM AUTHOR]

In this study, the practical application of self-healing asphalt mixtures incorporating steel wool fibers and induction heating was investigated, expanding upon previous research that primarily assessed the self-healing properties rather than optimizing the heating process. Specifically, the aim was to enhance the induction heating methodology for a semi-dense asphalt concrete mixture (AC 16 Surf 35/50 S). In this research, the induction heating parameters were refined to improve the self-healing capabilities, focusing on the following three key aspects: (i) energy consumption, (ii) heating rate, and (iii) heating homogeneity. The findings reveal that the current intensity, the percentage of ferromagnetic additives, and coil shape are critical for achieving optimal heating conditions. Higher current intensity and additive percentage correlate with improved heating speed and reduced energy consumption. Additionally, variations in coil shape significantly influence the heating uniformity. Although asphalt mixtures with steel slag coarse aggregates exhibit slightly higher specific heat, this aggregate type is preferable for sustainability, as it allows for the recycling of industrial waste. The optimized mixtures can rapidly reach high temperatures, facilitating effective crack repair. This innovation offers a durable, environmentally friendly, and cost-effective solution for road maintenance, thereby enhancing the longevity and performance of asphalt pavements. [ABSTRACT FROM AUTHOR]