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The residential sector has a large potential to reduce its energy use. Improving the energy performance of buildings is one way to realise this potential. For single-family buildings, improving the energy efficiency by energy renovations can produce a net financial gain. However, there are unaccounted barriers that act as impediments for house-owners to undertake energy efficiency measures. This study postulates that transaction costs are such a barrier. Transaction costs are defined as the cost of making an economic exchange on a market in addition to the market price. The purpose is to empirically estimate the magnitude of the transactions costs and its determinants for energy efficiency measures in the residential sector. Specifically, the transaction costs for heat insulation and energy-saving windows in Swedish single-family buildings are assessed. The analysis is based on a unique dataset, constructed from a web-based survey. The results indicate that transaction costs for energy efficiency measures are considerable. The average transaction cost to make additional heat insulation is SEK 18,046 (EUR 1,510) and SEK 21,106 (EUR 1,766) to install energy-saving windows. The determinants of the transaction costs are cognitive limitations, social connectedness, asset specificity and previous experiences. Therefore, to reduce the transaction costs, the complexity of energy efficiency measures must be addressed, potential opportunistic behaviour must be reduced, and financial options expanded. [ABSTRACT FROM AUTHOR]

Fuel poverty is a pressing global issue for households, particularly low-income ones. This study employs a subjective approach to examine fuel poverty in Jordan. Analyzing data from a survey of 490 participants in Zarqa Governorate, it evaluates the main determinants of subjective fuel poverty. The survey was conducted over 1 month, from March 10 to April 10, 2022. Findings reveal that the sample households experience challenges related to fuel poverty. The modeling of subjective indicators indicates an association between income level and the probability of being in arrears with utility bills. Moreover, the results highlight issues such as an inability to maintain adequate summer and winter temperatures and the presence of leaks, damp, and rot. In light of these findings, policy interventions should focus on enhancing energy efficiency and supporting vulnerable low-income households, particularly those in rural areas. [ABSTRACT FROM AUTHOR]

This review outlines the potential of phycoremediation as a natural, cost-effective solution for domestic wastewater treatment in Africa, particularly focusing on its application in less densely populated and rural areas. The urgency of improving sanitation access, a key objective in both the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030), is underscored by the fact that half of Africa's population suffers from diseases linked to inadequate water and sanitation facilities. South Africa, a focal point of this study, faces significant challenges in wastewater management. These include the limited capacity of wastewater treatment plants to handle the burgeoning wastewater volumes due to population growth, unregulated discharges causing fluctuating pollution levels, and high operational costs leading to improper sludge disposal and odor issues. Compounding these problems are frequent power outages, financial constraints impacting wastewater treatment plant operations and maintenance across Africa, and a lack of skilled personnel to manage these facilities. [ABSTRACT FROM AUTHOR]

In this paper, we outline a transdisciplinary approach and design thinking methodology (TADTM) to tackle complex problems. Our premise is that these problems need a fundamental understanding of technological solutions and those for human interactions, business operations, financing, socioeconomic governance, legislation, and regulations. They must be approached by different decisionmakers from different disciplines to establish seamless interactions and structured teamwork. In this regard, we emphasize the need for a transdisciplinary framework that accounts for personal preferences based on human behavior as well as the traditional interdisciplinary frameworks. To test and prove our hypothesis, three case studies are discussed. Case Study 1 is based on our studies at a major medical establishment, and Case Study 2 is about the integrated engineering and architecture approach we used at our university campus. Case Study 3 is based on an ongoing project to lead industrial corporations to change their energy policies with practical energy efficiency measures and by adapting renewable/alternative energy adaptations for their operations. Developing creative solutions and strategies to decrease atmospheric greenhouse gas emissions requires such an energy transition framework and should involve every person, company, entity, and all governments. It can only be achieved with efforts on both local and global levels, which needs to convince (a) industries to change their traditional operation modalities, (b) people to alter their consumption behaviors, and (c) governments to change their rules, regulations, and incentives. The complexity and magnitude of this enormous task demand the coordination and collaboration of all stakeholders, as well as the need for technological innovations. [ABSTRACT FROM AUTHOR]

The energy efficiency optimization of Florida's residential buildings is essential for the reduction of fossil fuel consumption and greenhouse gas emissions. This optimization requires designers to accurately calculate the building energy loads at the design stage to efficiently design the cooling and heating systems. However, due to time and cost constraints, designers usually explore only a few design alternatives and simulate their energy use. This study developed an artificial-neural-network (ANN)-based generative design (GD) framework to automate the design process of detached residences while optimizing their energy performance. The ANN model was developed using the machine learning platform TensorFlow and some Python-based Keras libraries based on a big data set of about 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and the Autodesk Revit GD add-on that uses the nondominated sorting genetic algorithm (NSGA-II), in which multiple design parameters mainly relating to the house geometry and its energy performance were incorporated. Considering 10 independent variables, including the total wall area, roof area, floor area, and the windows' U-value, the ANN model predicted the required capacities of the cooling and heating systems in detached houses, with R2 values of 0.955 and 0.904, respectively. The ANN then was integrated within the GD framework for performance evaluation purposes. The findings of this study resulted in a fully automated 3-min design process of an energy-efficient detached house envelope, maximizing the productivity of designers and developers and greatly reducing the financial strain and time consumed using traditional techniques. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital baseband precoding techniques are limited in their ability to mitigate inter-user interference within identical beam directions, representing a fundamental constraint in mmWave downlink transmissions. This study introduces an innovative analog beamforming-based interference mitigation strategy for downlink transmissions in reconfigurable intelligent surface (RIS)-assisted hybrid analog–digital (HAD) mmWave systems. This is achieved through the joint design of analog beamformers and the corresponding coefficients at both the RIS and the BS. We first present derived closed-form approximation expressions for the achievable rate performance in the proposed scenario and establish a stringent upper bound on this performance in a large number of RIS elements regimes. The exclusive use of analog beamforming in the downlink phase allows our proposed transmission algorithm to function efficiently when equipped with low-resolution analog-to-digital/digital-to-analog converters (A/Ds) at the BS. The energy efficiency of the downlink transmission is evaluated through the deployment of six-bit A/Ds and six-bit pulse-amplitude modulation (PAM) signals across varying numbers of activated RIS elements. Numerical simulation results validate the effectiveness of our proposed algorithms in comparison to various benchmark schemes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have a wide variety of application areas and one of these areas is border crossing security. Unauthorized crossing of border areas, unauthorized arms and drug trafficking can be avoided at a lower cost and easier than conventional methods by monitoring the borders with the help of a WSN. In this study, we offer a mathematical model that guarantees the detection of possible intruders by scheduling the activities of the sensors whatever the route the intruder follows throughout the border zone or whatever the time the intruder enters to the route. To achieve the highest possible WSN management efficiency, we integrate coverage, routing, data routing, and sensor scheduling WSN design issues into the mathematical model. We first demonstrate the effectiveness of scheduling the sensors by the help of the offered mathematical model by comparing it against a random activity schedule of the sensors with respect to network lifetime and intruder detection ratio performance measures. We also develop a Lagrangean heuristic strategy to solve realistic sized instances of the proposed problem. We produce several random border zone instances with varying sizes and test the proposed solution strategy to illustrate the effectiveness of the offered solution strategy by comparing its performance against the performance of a commercial mixed-integer linear programming (MILP) solver. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has rapidly emerged as an essential technology, enabling seamless communication between billions of interconnected devices. These devices generate a massive amount of data that requires efficient management to ensure optimum performance in IoT environments. Dynamic load balancing (DLB) is a crucial technique employed to distribute workloads evenly across multiple computing resources, thereby reducing latency and increasing the overall efficiency of IoT networks. This paper presents a novel DLB approach based on type-2 fuzzy logic systems (T2FLS) to enhance the performance and reliability of IoT environments. The proposed T2FLS-based DLB technique addresses the inherent uncertainties and imprecisions in IoT networks by considering various parameters, such as workload, processing capability, and communication latency. A comprehensive performance evaluation is carried out to compare the proposed method with traditional DLB approaches. Simulation results demonstrate that the T2FLS-based DLB technique significantly improves the network’s response time, throughput, and energy efficiency, while also providing better adaptability and robustness to dynamic changes in IoT environments. This study contributes to the advancement of DLB techniques in IoT networks and lays the groundwork for further research in this field. [ABSTRACT FROM AUTHOR]

Electric vehicles are one of the most innovative and promising areas of the automotive industry. The efficiency of traction equipment is an important factor in the operation of an electric vehicle. In electric vehicles, the energy stored in the battery is converted into mechanical energy to drive the vehicle. The higher the efficiency of the battery, the less energy is lost in the conversion process, which improves the overall energy efficiency of the electric vehicle. Determining the performance characteristics of the traction battery of an electric vehicle plays an important role in the selection of the vehicle and its future operation. Using mathematical modelling, it is shown that battery capacity, charging rate, durability and efficiency are essential to ensure the comfortable and efficient operation of an electric vehicle throughout its lifetime. A mathematical model of an electric truck including a traction battery has been developed. It is shown that, with the help of the developed mathematical model, it is possible to calculate the load parameters of the battery in standardised driving cycles. The data verification is carried out by comparing the data obtained during standardised driving with the results of mathematical modelling. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

Due to its environmental benefits, CO2 shows great potential in refrigeration systems. However, a basic CO2 transcritical (BCT) refrigeration system used for airconditioning in buildings might generate massive indirect carbon emissions for its low COP. In this study, a novel CO2 transcritical/two-stage absorption (CTTA) hybrid refrigeration system is broadly investigated, and both energy efficiency and life cycle climate performance (LCCP) are specifically engaged. The theoretical model shows that optimal parameters for the generator inlet temperature (TG2), intermediate temperature (Tm), and discharge pressure (Pc), exist to achieve maximum COPtol. Using the LCCP method, the carbon emissions of the CTTA system are compared to six typical refrigeration systems by using refrigerants, including R134a, R1234yf and R1234ze(E) etc. The LCCP value of the CTTA system is 3768 kg CO2e/kW, which is 53.6% less than the BCT system and equivalent to the R134a system. Moreover, its LCCP value could be 3.4% less than the R1234ze(E) system if the COP of the CO2 subsystem is further improved. In summary, the CTTA system achieves ultra-low carbon emissions, which provides a potential alternative to air conditioning systems in buildings that can be considered alongside R1234yf and R1234ze(E) systems. [ABSTRACT FROM AUTHOR]

The energy transition is one of humanity's most significant challenges. All micro, small, medium, and large enterprises will have to face it, and some are already making progress. This paper focuses on the involvement of the smallest actors in the energy transition. Micro and small enterprises comprise the largest part of the economy and significantly impact its development. Concerning the broad scope of their activities, these entities are characterized by a specific owner–manager relationship. This means that the final decision is influenced by business objectives, the manager's approach, their awareness of change, and personal and other factors. Based on the analysis of data from 400 companies, it was found that the involvement of micro and small enterprises in the energy transition is related to their market and financial success. [ABSTRACT FROM AUTHOR]

Implementation of a new European Union directive on energy renovation in Denmark can be expected to have a strong impact on the country's energy renovation requirements. Accordingly, an indicator framework has been developed with the aim of identifying and quantifying the energy renovation potential of a specific Danish municipality. We identified and selected four indicators—energy consumption, CO2 emissions, heating costs and current energy labels—specifically for detached dwellings. The physical renovation potential was then quantified based on the indicator results, and an average score for the four indicators was calculated for each of the 10,228 detached dwellings in the municipality. All four indicators were weighted equally in the calculations of the total renovation potential score. The methods used in this study can be applied more widely as they rank detached dwellings according to their renovation potential. However, we also found that more detailed data are necessary to determine the specific renovation potential of detached dwellings. A greater effort to collect more data on the part of the municipalities would improve the accuracy of analyses. [ABSTRACT FROM AUTHOR]

In a scenario characterised by mitigation concerns and calls for greater resilience in the energy sector, energy audits (EAs) emerge as an essential mean for enhancing end-use energy consumption awareness and efficiency. Such a tool allows us to assess the different energy carriers consumed in a productive sector, offering insight into existing energy efficiency improvement opportunities. This opens avenues for research to devise an econometrics-based methodology that encapsulate production sites and their environmental essentials. This paper contributes to the literature by exploiting the EAs received by the Italian National agency for New technologies, Energy, and Sustainable Economic Development (ENEA) in 2019 from the Italian plastics manufacturing sector, matched with Italian firm-based data extracted from the Analisi Informatizzata delle Aziende Italiane (Italian company information and business intelligence) (AIDA) database. In particular, we investigate how the implementation of energy efficiency measures (EEMs) is influenced by a set of contextual factors, as well as features relating to the companies and EEMs themselves. The empirical investigation focuses on the EAs submitted to ENEA in 2019, which was strategically chosen due to its unique data availability and adequacy for extensive analysis. The selection of 2019 is justified as it constitutes the second mandatory reporting period for energy audits, in contrast to the 2022 data, which are currently undergoing detailed refinement. In line with the literature, the adopted empirical approach involves the use of both the OLS and logistic regression models. Empirical results confirm the relevance of economic and financial factors in guiding the decisions surrounding the sector's energy performance, alongside the analogous influence of the technical characteristics of the measures themselves and of the firms' strategies. In particular, the OLS model with no fixed effects shows that a one-percent variation in investments is associated with an increase in savings performance equal to 0.63%. As for the OLS model, including fixed effects, the elasticity among the two variables concerned reaches 0.87%, while in the logistic regression, if the investment carried out by the production sites increases, the expected percentage change in the probability that the energy-saving performance is above its average is about 187.77%. Contextual factors that prove to be equally influential include the incentive mechanism considered and the traits of the geographical area in which the companies are located. Relevant policy implications derived from this analysis include the importance of reducing informational barriers about EEMs and increasing technical assistance, which can be crucial for identifying and implementing effective energy solutions. [ABSTRACT FROM AUTHOR]

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach. [ABSTRACT FROM AUTHOR]

The greenhouse gas emissions and global energy consumption associated with construction materials can be greatly reduced by incorporating phase change materials into buildings. These materials can absorb and release energy during a phase transition, thus reducing the energy consumption while achieving human thermal comfort. Molecular dynamics simulations were used to study the miscibility of both linear and branched polyethylene with two types of paraffin (octadecane and hexatriacontane) at two different temperatures. The results showed that blending was better at high temperatures for both polymers, and branched polyethylene was more miscible with both paraffins than linear polyethylene was. Hexatriacontane exhibited better blending characteristics. From the molecular dynamics results, we concluded that linear polyethylene/paraffin blends were more suitable as phase change materials in buildings than branched polyethylene/paraffin blends, as they were miscible at high temperatures but separated into two phases at ambient temperature. The optimal blends were subsequently incorporated into a cement model to study their influence on the mechanical properties of this construction material. The introduction of the blends into the cement model deteriorated its mechanical properties. [ABSTRACT FROM AUTHOR]

Green manufacturing and remanufacturing revolve around recycling end-of-life (EOL) products. One crucial aspect of this process is the disassembly line balancing problem (DLBP), which involves assigning disassembly tasks to workstations in a logical order. However, current DLBP models often overlook operator-related factors and energy efficiency. This paper introduces a multi-objective DLBP model that considers both operator and energy factors. The model aims to minimise disassembly line idle rate, improve line smoothness, reduce energy consumption, and balance operator workload under multiple constraints. Since this problem is NP-hard, we also develop a hybrid intelligent algorithm that combines simulated annealing and the water cycle algorithm (SWCA) to solve DLBP effectively using real-world case disassembly. For multi-objective optimisation problems, choosing multiple non-inferior solutions can be challenging. In this work, we embrace an ‘optimisation before decision-making' approach, integrating multi-criteria decision-making techniques with neural networks optimised by a particle swarm optimisation (PSO) algorithm to identify the most satisfactory solution. [ABSTRACT FROM AUTHOR]

This study develops a stylized theoretical model to analyze how digital economy agglomeration affects energy efficiency. Then, we employ a two-way fixed effect model to identify the impact of digital economy agglomeration on energy efficiency. We find that a higher degree of digital economy agglomeration significantly increases a city’s energy efficiency. This result is robust when we address endogeneity concerns by exploiting a policy shock, geographic distance, and historical variation as instruments. We explore several explanations, including advancements in green technology, upgrading of industrial structures, and enhancements in public and governmental environmental concerns. Our findings provide evidence that substantiates each of these mechanisms. Our heterogeneous analysis indicates that the impact of digital economy agglomeration on energy efficiency is only significant in cities with higher attention to digitization, stricter environmental regulations, greater energy consumption, and better industrial development. This study provides the first city-level evidence of the relationship between digital economy agglomeration and energy efficiency, which has important implications for the energy transition in developing countries. [ABSTRACT FROM AUTHOR]

The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well. [ABSTRACT FROM AUTHOR]

A water-processable and low-cost nanocomposite material, based on gelatin and graphene, has been used to fabricate an environmentally friendly temperature sensor. Demonstrating a temperature-dependent open-circuit voltage between 260 and 310 K, the sensor effectively detects subzero ice formation. Notably, it maintains a constant temperature sensitivity of approximately −19 mV/K over two years, showcasing long-term stability. Experimental evidence demonstrates the efficient regeneration of aged sensors by injecting a few drops of water at a temperature higher than the gelation point of the hydrogel nanocomposite. The real-time monitoring of the electrical characteristics during the hydration reveals the initiation of the regeneration process at the gelation point (~306 K), resulting in a more conductive nanocomposite. These findings, together with a fast response and low power consumption in the range of microwatts, underscore the potential of the eco-friendly sensor for diverse practical applications in temperature monitoring and environmental sensing. Furthermore, the successful regeneration process significantly enhances its sustainability and reusability, making a valuable contribution to environmentally conscious technologies. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to examine the difficulties encountered in underwater acoustic Sensor Networks (UASN) due to empty spaces in acoustic route routing. The presence of empty areas inside the network often results in the unsuccessful transmission of data packets in sparsely populated underwater acoustic sensor networks (UASNs). The current techniques used to identify and circumvent empty spaces are characterized by a significant expenditure of time and a reliance on location data. To tackle this issue, the study introduces a new protocol named Secure Opportunistic Vacant Hole Avoidance Routing (SOVHAR), which aims to enhance routing pathways by disregarding vacant spaces. The proposed approach guarantees the continuous transmission of packets to the intended destination by using a local neighbourhood search strategy on sensor nodes. This strategy considers several criteria, such as node depth, signal amplitude, and the number of intermediate hops. The protocol further considers variables such as salinity, losses, and noise level to enhance the precision of the localized search for energy-efficient packet transmission. A fitness function is established to assess the efficacy of a sensor node in transmitting data packets, therefore aiding in avoiding areas with limited coverage while minimizing additional costs. The efficacy of the proposed SOVHAR is assessed via simulations, demonstrating its energy efficiency and capability to avoid empty spaces while considering different communication factors. This evaluation highlights its enhanced resilience compared to current methodologies. [ABSTRACT FROM AUTHOR]

Rice straw is a promising feedstock for gasification, and using it in combined heat and power systems allows us to generate renewable electricity and heat while reducing reliance on fossil fuels and combatting climate change. As the demand for clean energy grows, machine learning will become even more crucial for managing our energy systems. A combined heat and power system was integrated based on rice straw gasification and the applications of linear and quadratic regression machine learning algorithms were evaluated using the residual analysis and the analysis of variance. The results show that quadratic models outperform linear models in all cases. Specifically, for efficiency modeling, the quadratic model scores 98.2 %, surpassing the linear model's 89.6 %. In emission modeling, the quadratic model outperforms the linear model with an R-sq score of 97 % versus 88.1 %. The results show that equivalence ratio of gasifier has the greatest impact, accounting for 65 % of the observed variation on emission linear model. In the quadratic emission model, linear factors contribute 58 % and quadratic factors contribute 42 %. Among the linear factors, pressure ratio has a significant influence with a contribution rate of 89 %. In terms of quadratic factors, pressure ratio has the highest influence, with a remarkable contribution rate of 92 %. This research emphasizes the significance of enhancing the precision of regression machine learning algorithms by conducting residual analysis and variance analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Optimization of train operation is crucial in rail transport networks where large amounts of energy are used. The energy-efficient operation of trains is seen to be a good approach to saving operating costs in metro systems. There are two main techniques for efficient train operation. These are decreasing traction energy and increasing the usage of regeneration energy. In order to make maximum use of the regenerative energy of trains, an integrated optimization method that calculates the optimum train speed trajectory and timetable is proposed in this study. The suggested model aimed to increase the overlapping time of braking and accelerating train groups by improving the time-table. In addition, it is intended that two trains in the same electric zone do not accelerate or brake simultaneously so that the catenary voltage does not rise and decrease too much. The Istanbul M3 subway system with its real operating data is modeled and simulated. The genetic algorithm and simulated annealing algorithm were employed in the optimization process. The outputs of the study show that the consumption of traction energy can be substantially decreased by employing the best train speed profile and timetable. [ABSTRACT FROM AUTHOR]

The object of the research of the article is transport system of a city. The paper reviews the necessity to assess the greenhouse emissions in the city and proposes the methods for greenhouse emissions inventory of the urban transport system. The proposed approach is aligned with guidelines for the development of Sustainable Energy and Climate Action Plan (SECAP) of Mayor Covenants of European Union (EU). The methodologies outlined in the paper allow to estimate annual greenhouse emissions from transport sector. The SECAP defines transport sectors based on ownership and functioning as following: municipal transport fleet, public transport, and private and commercial transport. The paper proposes the methodology to estimate direct and indirect emissions in each of the described sector based on the information that is typicaly available to municipalities in Ukraine. The assessment is conducted on disaggregated level for different fuel types (diesel, petroleum, natural gas, biofuel, electricity, etc.) and separately for each fleet type (buses, trucks, passenger vehicles, specialized machinery). Total CO2 emissions are then estimated by multiplying the amount of fuel consumed by the emission factor for each fuel type and vehicle type. Information of fuel consumption is estimated based on annual milage and estimated based on available data and number of assumptions proposed in the paper. The proposed methodology for greenhouse emissions inventory for the transport sector allows to analyze available data, recommendations for data collection and a methodology for determining CO2 emissions from the operation of the transport system with sufficient accuracy of calculations. Based on these results, it is possible to forecast changes in energy consumption and emissions in the transport sector as a result of various interventions. For example, in Zhytomyr city the results of inventory were used to develop a set of measures, which include updating the rolling stock of electric transport; increasing the energy efficiency of the power grids of the transport system, developing cycling infrastructure. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. [ABSTRACT FROM AUTHOR]

Cloud computing is an internet-based infrastructure for services such as computations, storage, etc., hosted on physical machines. The machines on cloud infrastructure scales between a few tens to thousands of machines that are linked in an unstructured way. In cloud computing, minimizing energy consumption and its associated costs is the primary goal while preserving efficiency and performance. It progresses the system’s overall productivity, reliability, and availability. Furthermore, reducing energy use not only lowers energy expenses but also helps to safeguard our natural environment by lowering carbon emissions. The objective of our proposed work is to reduce energy usage in the cloud environment and enhance its performance. We propose a hybrid approach that incorporates an energy-aware self-governing task scheduler, namely, Artificial Neural Network (ANN), and a metaheuristic Black Widow Optimization (BWO) algorithm to solve the optimization issues. Our suggested task scheduler focuses on minimizing energy consumption, improving the makespan, and reducing the operating cost while keeping a low number of active cloud racks. The cloud environment is highly scalable in this scenario since we adopt a metaheuristic BWO algorithm. CloudSim simulation framework is used for implementation and experimental analysis. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements. [ABSTRACT FROM AUTHOR]

This work analyzes the changes introduced by Royal Decree 905/2022, on aid to the Spanish wine sector within the framework of the Strategic Plan of the Common Agricultural Policy (CAP) and the response of the Autonomous Community of Catalonia expressed in the Order ACC/68/2023, on aid for the restructuring and/or reconversion of vineyards; highlighting the importance of the participation of the Autonomous Communities in the implementation of policies aimed at mitigating climate change and the necessary coordination and cooperation relations with the Spanish State and municipalities to achieve the objectives set out in the 2015 Paris Agreement. [ABSTRACT FROM AUTHOR]

Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber‐physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI‐based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real‐world industrial solutions has not so far developed at the same pace. Currently, 90% of AI‐based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI‐based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non‐supervised, human‐understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real‐world case study from the automotive sector, comparing it to an extensive benchmark comprising state‐of‐the‐art unsupervised and semi‐supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI‐based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection. [ABSTRACT FROM AUTHOR]

The positive energy district (PED) is a rather recent concept that aims to contribute to the main objectives of the Energy Union strategy. It is based on an integrated multi-sectoral approach in response to Europe's most complex challenges. But to what extent have its development and implementation been supported by research and innovation programs? And what is the state of the art of its implementation and effective penetration into the current energy systems of European cities, according to the evidence provided by the scientific literature? This study aims to investigate these issues, providing a critical overview of the PED situation by means of a systematic literature review based on the use of open-access bibliometric software supplemented with content analysis. The results show that less than half of the documents analyzed refer to actual case studies, 80% of which were funded as part of research projects. This seems to lead to the conclusion that although PEDs have been strongly encouraged by the scientific community and policy initiatives at the European level, their implementation in cities is still limited. Moreover, an uneven distribution among countries can be observed. To overcome the existing barriers to PED diffusion and implementation, it would be useful to provide more ad hoc funding and, above all, facilitate its accessibility also by municipalities not yet well integrated into European projects, initiatives, and networks. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models' output is related to input data. This is called "interpretability", and it is focused on explaining what feature influences a model's output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are "black boxes" that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments. [ABSTRACT FROM AUTHOR]

Reducing the heat demand of existing buildings is an essential prerequisite for achieving a greenhouse gas-neutral energy supply. Numerous studies and open-source tools deal with heat demand mapping. It is not uncommon that estimated heat demands deviate from real heat consumption, so existing approaches should be improved by including in-depth building information. Some tools have recognised this problem and offer built-in functions for factoring various parameters into their assessments. Nevertheless, the necessary information is usually missing and should be obtained first. In this paper, we analyse the impact of thermal refurbishment and climate on building heat demand; hence, generate public datasets with corresponding key figures for each building type in different efficiency states and years. Accounting for already performed refurbishments in methodologies for assessing the actual state heat demand for cities will result in a reduction of at least 8% up to more than 21%, depending on whether conventional or passive house components were installed. As a result of climatic differences within Germany, a building's heat demand can be up to 39% higher or up to 21% lower than the heat demand of an identical building in the reference climate of Germany. By further developing the approaches of the tools Hotmaps and Heat Cadastre Hamburg, we could improve the estimated heat demand of Hamburg to a value approximating the real consumption. [ABSTRACT FROM AUTHOR]

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model's loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. [ABSTRACT FROM AUTHOR]

Today's energy challenges are multifaceted. Over the past 30–40 years, energy issues have been discussed and published on an extensive scale. The green transition involves concrete actions related to increasing energy efficiency, replacing fossil fuels with alternative fuels, producing energy using renewable resources, creating various means of transport that use electric motors, identifying technical solutions that generate an increased energy yield in the case of buildings, and waste reduction, reuse and recycling. In order to attain a climate-neutral environment, it is mandatory to impose regulations, measures and actions to help decarbonize the energy sector. The analysis of published articles on these issues is the subject of this large and information-dense review. Concretely, the transition to climate neutrality will generate obvious advantages at an economic, social and technological level, for example, the opportunity for economic growth, new business models and new markets, and the generation of new jobs or technological development. At the same time, this paper underscores the need for a multifaceted approach, integrating technological innovation, policy intervention and global cooperation for an effective energy transformation. The review suggests future issues and research directions, focusing on viable strategies for energy transition and its socio-economic environmental impacts. [ABSTRACT FROM AUTHOR]

This research addresses the increasing importance of understanding how Artificial Intelligence can facilitate the transition of companies to a Circular Economy model. This study focuses on energy management, examining its impact on efficiency and emissions across a multi-case analysis of 18 projects in diverse sectors. The findings indicate that Artificial Intelligence positively influences both variables, with variations across applications and sectors. Notably, Artificial Intelligence significantly enhances energy efficiency in four out of six sectors, achieving over 5% improvement in half of the projects. Regarding emissions, positive effects are observed in 15 out of 18 projects, resulting in over 5% reductions in seven cases. Artificial Intelligence plays a pivotal role in emissions reduction in the Design and Energy sectors, with some projects achieving over 20% reductions. Additionally, this study explores how improved energy efficiency positively affects strategic business variables, such as cost, quality, and delivery time. The impact on emissions contributes to reducing occupational risks, particularly those associated with chemical and biological agents. Although managers are satisfied, measures need to be taken to overcome the lack of employee acceptance. These findings are of great interest to the stakeholders involved in the integration of Artificial Intelligence into companies. [ABSTRACT FROM AUTHOR]

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company's profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed. [ABSTRACT FROM AUTHOR]

The water and sewage sector is responsible for approximately 3.5% of energy consumption in the European Union (EU). Leaks causing water losses in water distribution systems (WDSs) are responsible for approximately 24% of water consumption in the EU, which contributes to additional energy losses and emissions of greenhouse gases (GHGs). The implementation of the Directive of the European Parliament and the EU Council on the quality of drinking water (Directive (EU) 2020/2184) introduces the obligation to report water losses by large water utilities in EU Member States. The reported indicator will be the infrastructure leakage index (ILI) which is the ratio between current annual real loss (CARL) and unavoidable annual real loss (UARL). The paper presents a comparative analysis of selected water loss performance indicators calculated for 12 Polish WDSs. Results show that values of calculated indicators were diverse. The overestimation of both the reported value of operating pressure and total length of service connections may lead to the overestimation of UARL and thus to the underestimation of ILI. Obtaining a satisfactory, but incorrect, value of ILI may result in the abandonment of activities aimed at water loss reduction. Water losses in water distribution systems (WDSs) contribute to a significant increase in both energy consumption and GHG emissions. Total approximated electrical energy related to CARL consumed in 2021 by eleven utilities (except for one company) amounted to 3.276 GWh and total approximated carbon emissions amounted to 2807.84 MgCO2eq. In the case of four WDSs, reduction of ILI to the value of 1.5 may reduce GHG emissions by 31–54%. It can be concluded that the implementation of Directive (EU) 2020/2184 will require unification of methodology for calculation of parameters used in ILI evaluation in all EU Member States. [ABSTRACT FROM AUTHOR]

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives. [ABSTRACT FROM AUTHOR]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology. [ABSTRACT FROM AUTHOR]

Research in the field of Wireless Sensor Networks aims to develop protocols ensuring minimal energy dissipation. In this work, we propose a lightweight congestion control based sink mobility solution to improve data gathering that considers jointly energy and memory constraints. Thus, we propose a mechanism to avoid and resolve situations when nodes may not receive new messages because of buffer saturation. This can causes data loss and energy waste while retransmitting non-received data by the source node. Indeed, our model moves the sink close to the congested nodes to alleviate their queues once a congestion risk was detected. The performances of our approach were approved by intensive simulations. Obtained results proved that the network lifetime achieved by our strategy is two to five times better than that reached by comparative approaches. We note also a considerable improvement in packet delivery ratio and a high adaptability to increased data traffic. [ABSTRACT FROM AUTHOR]

Modern automotive propulsion technologies must achieve the highest CO2 reduction potential quickly to abide by the requirements of the Paris Climate Agreement. A collective utilization of renewable fuels, e‐fuels, hydrogen, and electrical energy will be able to meet different mobility and transport requirements in an optimal and CO2‐neutral approach. The well‐to‐wheel greenhouse gas emissions of a propulsion system are determined by two factors, that is, the energy efficiency of the system and the carbon intensity of the energy source. Regardless of the CO2 emission generated during the battery manufacturing and recycling process, the carbon intensity of the battery electric vehicles during operation is mainly decided by the carbon intensity of the electricity being consumed. The relatively low fleet ratios of battery electric and hydrogen‐powered vehicles and the massive remaining useful life of current internal combustion engine vehicle stock limit their impact on decarbonization in the near term. The expansion of charging infrastructure requires significant acceleration for the success of large‐scale and rapid electric vehicle adoption. For internal combustion engines, the focus is to further improve energy efficiency and the adoption of low‐to‐zero carbon renewable fuels. Hybrid and plug‐in hybrid vehicles are demonstrating the advantages of combining state‐of‐the‐art technologies to reduce both energy consumption and carbon emissions. In this review, the present status of propulsion systems is reviewed in detail, considering both the market penetration and well‐to‐wheel carbon emissions. The decarbonization potentials of various propulsion systems are then discussed. [ABSTRACT FROM AUTHOR]

Summary: Artificial intelligence (AI)‐based wireless sensor network technology is the future of advancement for real‐time applications. With AI wireless sensor network technology, it is possible to collect data from any environment, analyze in real time, and use it to optimize processes and operations. AI wireless sensor network technology provides an unprecedented level of accuracy as well as the ability to detect even the slightest changes in a given environment. The AI‐based approach uses clustering‐based techniques with self‐organizing map (SOM) for energy conservation in resource‐constrained networks. By clustering the network, it becomes more energy efficient, as data can be shared among members of a cluster without needing to be transmitted across multiple nodes. The proposed AI cluster‐based routing approach outperforms in terms of energy consumption and computational challenges of the network. The results obtained demonstrate the proposed approach to achieve lower energy consumption than the existing algorithms while providing the same level of performance in terms of throughput and latency, as well as a comparison with traditional justification techniques. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters. [ABSTRACT FROM AUTHOR]

The energy cost and harmful effect of residual chlorine produced as a by-product in the electrochemical processes are the main obstacles in the extensive use of electrochemical recirculating aquaculture systems (ERAS). In this study, a pilot electrochemical system was used in a shrimp cultivation experiment to investigate the effects of current density, geometric feature, timing, and other parameters on the effective control of inorganic nitrogen, pathogens, and residual chlorine in aquaculture water. A 50-L electrochemical batch reactor (BR) equipped with Ti-RuO2/Ti electrodes could effectively remove inorganic nitrogen (the initial concentration was 4.0 mg/L) and inactivate Vibrio in the aquaculture seawater in 5 min, when a current density of 66 mA/cm2 and electrodes with a surface area of 154 cm2 were used. Air stripping was found to be effective in resolving residual chlorine produced from electrochemical process. In the experiment of shrimp cultivation, the ERAS equipped with a 50-L batch reactor and 500-L shrimp tank effectively kept the inorganic nitrogen concentration in the rearing water to a desirable concentration (0.2 mg/L) when a nitrogen load of 4.3 g (transformed from a daily quota of commercial prawn feeds) was used. By precisely controlling the time at which electrooxidation and air stripping alternated, an electrochemical recirculating aquaculture system could effectively remove inorganic nitrogen and residual chlorine, carry out disinfection, and reduce energy cost. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Considering the escalating global warming and its severe consequences, the exploitation of hydrogen, an eco-friendly energy carrier, is gaining great interest. This paper comprehensively reviews the most common hydrogen production, storage, transportation and utilization processes, while their effectiveness is explored from energy, water, economic and environmental perspectives. Their main characteristics, advantages, shortcomings and challenges are discussed as well. The research reveals that the hydrogen's water footprint, energy consumption, carbon emissions, and cost vary significantly depending on the specific technologies used throughout its life cycle, from production to end use. For instance, fossil fuel-based hydrogen production methods, such as steam methane reforming, offer elevated energy performance and cost-effectiveness, but have a significant carbon footprint. In contrast, water electrolysis is environmentally friendly, but requires more water and has lower energy efficiency. Besides, high-pressure containers are the preferred method for hydrogen storage, while liquid and solid storages are better suited for high-energy-density applications. Regarding hydrogen transportation, pipelines are effective over long distances, whereas tankers are convenient for short distances. Furthermore, fuel cells are more energy, carbon and water efficient when using hydrogen as fuel, but they are generally more expensive than internal combustion engines. [Display omitted] • Hydrogen production, storage, transportation and utilization methods are reviewed. • Their energy efficiency, water use, cost, and environmental impact are explored. • Key recommendations to boost the utilization of hydrogen systems are outlined. • Fossil fuel-based technologies are more energy-efficient and cost-effective. • Renewable-based processes are more environmentally friendly but water-intensive. [ABSTRACT FROM AUTHOR]

With the large-scale application of the proton exchange membrane electrolysis cell (PEMEC), the power allocation of the multi-stack PEMEC system has attracted ever-growing attention. However, fluctuating power significantly affects the lifetime and energy efficiency of the system. In this work, a power allocation strategy of the system is proposed based on detailed modeling and characteristics analysis. A complete model of PEMEC is developed considering bubble coverage for research of operating characteristics. To analyze the energy efficiency characteristics of the system, a power model of the PEMEC stack is established. A mathematical method to real-time obtain the frontier of optimal energy efficiency of stacks is also proposed. The degradation model of the proton exchange membrane is developed to determine the safe operation boundary. And then considering the operating status, voltage degradation is introduced to characterize the degradation of PEMEC dynamically. Finally, a power allocation strategy is proposed considering the system's energy efficiency and degradation. Furthermore, a composite revenue function is presented to compare the performance of different power allocation strategies. The results show that the system has the highest consolidated revenue under the proposed strategy. Current work is conducive to the popularization and generalization of the multi-stack PEMEC system. • A comprehensive PEMEC model considering the bubble coverage effect is developed. • A method to improve the performance of the multi-stack PEMEC system is proposed. • The safe operational boundary of PEMEC is studied based on the PEM degradation model. • The voltage degradation to define the dynamical degradation of PEMEC is introduced. • A power allocation strategy is suggested considering energy efficiency and degradation. [ABSTRACT FROM AUTHOR]