The emergence of Distributed Ledger Technology (DLT) in the past decade has challenged our imagination to discover new, innovative and disruptive solutions to problems in domains ranging from finance and healthcare to supply chain and smart cities. However, the enormous energy consumption that has been observed in some of the most successful DLT applications raises the question of their long term sustainability. This article reviews the standardization efforts of the International Telecommunications Union (ITU) to provide guidelines to regulators and policy makers for making informed decisions on the applicability and sustainability of DLT architectures from the point of view of energy consumption.

Energy poverty in the Global South generally focuses on the lack of basic access to modern fuels and energy carriers. Impoverished people rely primarily on traditional biomass as a source of energy. This situation of basic resource deprivation, in which almost one third of the world's population lives, masks other relevant features of the problem. Current assessments of energy poverty in impoverished areas and the mitigation strategies being implemented derive from the development agenda and, with variations in detail and scope, highlight electricity connections and access to clean cooking fuels as guarantors of progress. However, a comprehensive understanding of energy poverty requires going beyond basic access, building on the interactions between the supply of energy sources and carriers, the provision of energy services and its impact on decent living conditions. Several studies in the last decade have attempted to construct an assessment framework centered on energy services to deal with these interactions. This work discusses the relevant dimensions in the framework (supply, services and impact in wellbeing), reviews the multidisciplinary work available in each aspect, presents a range of proposed taxonomies, and discusses the different issues. A detailed framework is proposed for the integrated assessment of the supply of energy carriers and energy equipment, the provision of relevant energy services, and the improvements obtained in living conditions.

Background: The control of energy metabolism is fundamental for cell growth and function, and anomalies are implicated in complex diseases and ageing. It is important for cells to carefully tune metabolic pathways to optimize their function in response to different nutrient or physiological conditions. Metabolism in yeast cells can be easily manipulated by supplying different carbon sources: on glucose yeast rapidly proliferates by fermentation, analogous to tumour cells growing by aerobic glycolysis, whereas on non-fermentable carbon sources metabolism shifts towards respiration. Results: We screened deletion libraries of fission yeast to identify over 200 genes required for respiratory growth. The growth medium and auxotrophic mutants strongly influenced respiratory metabolism. Most genes uncovered in the mutant screens have not been implicated in respiration in budding yeast. We applied gene expression profiling approaches to compare steady-state fermentative and respiratory growth and to analyse the dynamic adaptation to respiratory growth. The transcript levels of most genes functioning in key energy metabolism pathways were coherently tuned, reflecting anticipated differences in metabolic flows between fermenting and respiring cells. We show that the acetyl-CoA synthase, rather than the citrate lyase, is essential for acetyl-CoA synthesis in fission yeast. We also investigated the transcriptional response to mitochondrial damage by genetic or chemical perturbations, defining a retrograde response that involves the concerted regulation of distinct groups of nuclear genes that may avert harm from mitochondrial malfunction. Conclusions: These systematic and targeted analyses provide a rich framework of the genetic and regulatory basis of fundamental metabolic states to guide future studies on energy metabolism in fission yeast and beyond. Our study pinpoints weaknesses of commonly used auxotroph mutants for investigating energy metabolism. As a model for cellular energy regulation, fission yeast provides an attractive and complementary system to budding yeast.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle.

Technology is in the way to reach in its climax but the basic understanding of science in many phenomena is still awaited even though the nature witnesses it. Scientific research reveals strong analogy between photon and electron. When an atom deals neutral state, it levitates electron of outer ring from the back surface while placing the bit-energy at front surface. Gravitation behavior of that electron starts at the centre of relaxation point by including the force of side pole where the pulling force of nearby unfilled state of that atom from the front surface results into depict forcing energy shape like Gaussian distribution symbol with both ends turned called unit photon. The inertia is being involved at each stage of changing direction of that electron by introducing the disappearances of forces of two poles against the appearances of forces of two opposite poles during rest to motion and motion to rest in the first half-cycle. The same is the case in the second half-cycle of that electron during rest to motion and motion to rest but it is under different introduction of the disappearances of forces against appearances of forces. However, at stage of levitating or gravitating period of electron, only one force is being involved at one time where the opposite force is disappeared. The uninterrupted confined inter-state electron-dynamics of atom under the availability of several bits of bit-energy results into generate forcing energy shape like a wave. Two bits of bit-energy where shape of bit for first half-cycle is like integral symbol and second half-cycle is like opposite integral symbol which are being placed along the configuring trajectory of inter-state electron-dynamics during forward-direction cycle and two bits of bit-energy shape in opposite order are being placed along the trajectory of inter-state electron-dynamics during back-direction cycle. Generating forcing energy of unit photon in each cycle is pushing to the rear side by remaining connected till the electron is not restoring the state of rest. Silicon atom is considered as a model system under neutral state. Uninterrupted confined inter-state electron-dynamics result into generate forcing energy that can travel immeasurable length and unavailability of necessitating bit-energy at any interrupted stage result into generate an overt photon. Inter-state electron-dynamics for at least two cycles generate an overt photon –a photon length twice to unit photon. Under certain interaction of unit photon, it divides equally into two bits of bit-energy instead of dividing into tits and bits of heat. The mechanism of generating photon characteristic current by the electron of neutral state atom validates that atoms are four-dimensional discs at centre of dealing no force. An isolated electron is being grounded under directed forcing energy to impinge a neutral state atom where the gained instantaneous velocity under merged energy resulting into distort atom at that point. Matter changes the role of energy and force under various sorts of interactions. Here, heat energy and photon energy explore matter at atomic and electron levels, thus, devise basis of science to describe.

Along with the increasing industrialization of contries and population growth, the global. With the thougth of overcoming climate change and renducing greenhouse gas emissions in the atmosphere, interest in alternative sloar, wind, water, geothermal, tidal and ocean wave energy has significantly increased. Hydropower is one of the most used inexhaustible sources of energy on a global scale. The efficiency of water turbines can be increased by optimizing the blade cascade and the casing. For this purpose, research is conducted on specialized laboratory benches. The purpose is to determine the influencing parameters which affecting the nature of the power and cavitaion curves This paper presents the results of numerical and laboratoty studies of axial hydrokinetic turbines. A detailed analysis of the influence of the geometric parameters of the blades on the operating curves is made. A mathematica model for synthesis of the blade cascade is derived. An assessment of water energy soures has been made and sites of high energy potential have been pointed. The present material can be used for development of a methodology for calculation of efficient axial hydrokinetic turbines.

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

In this work we study the energy consumption by various modern secured hash functions (MD2, MD5, SHA-1, and SHA-2) and modern symmetric key encryption protocols (Blowfish, DES, 3DES, and AES) from the algorithmic perspective. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy et. al. on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the relevance and importance of cryptographic hashes and symmetric key protocols for modern ICT (Information and Communication Technology), and ICT enabled industry to keep them protected from dynamically changing threat scenarios. Hence the design of more energy efficient hashes and protocols will definitely contribute in reducing the ICT energy consumption that is continuously increasing.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

In this work, we report the electric energy storage properties of (1-x)Bi0.5(Na0.8K0.2)0.5TiO3-xBi0.2Sr0.7TiO3 (BNKT-BST; x = 0.15-0.50) relaxor ferroelectric ceramics enhanced via a domain engineering method. A rhombohedral–tetragonal phase, the formation of highly dynamic PNRs, and a dense microstructure are confirmed from XRD, Raman vibrational spectra, and microscopic investigations. The relative dielectric permittivity (2664 at 1 kHz) and loss factor (0.058) were gradually improved with BST (x=0.45). The incorporation of BST into BNKT can disturb the long-range ferroelectric order, lowering the dielectric maximum temperature Tm and inducing the formation of highly dynamic polar nano-regions. In addition, the Tm shifts toward high temperature with frequency and a diffuse phase transition, indicating relaxor ferroelectric characteristics of BNKT-BST ceramics, which is confirmed by the modified Curie Weiss law (γ=1.83). The rhombohedral–tetragonal phase, fine grain size, and lowered Tm with relaxor properties synergistically contribute to a high recoverable energy density Wrec of 0.81 J/cm3 and a high energy efficiency η of 86.95% at 90 kV/cm for x = 0.45.

The problem of energy poverty, when energy becomes unaffordable for some population group, is not only a problem for developing countries, but this phenomenon is appearing more and more often in European countries as well. In Europe, it is estimated that 50 to 125 million people are at risk of energy poverty. We hear more and more about energy poverty in connection with the current energy crisis and rising energy prices, but also as a result of insufficient use of renewable energy sources. Due to rising energy prices, we are increasingly hearing about deepening energy poverty in Slovakia as well. The aim of the contribution is to examine the development of energy poverty in Slovakia compared to other EU countries. The situation is studied from the view of the number of days of heating and cooling, the percentage of the population that cannot maintain adequate heat at home, the percentage of the population that has a lack of heat and the percentage of residents without enough heat. During the research, we used distribution analysis, trend analysis, analysis of variance and one-way analysis. The main results show that HDD index recorded decrease, CDD index recorded increase, and Energy poverty is most obvious in low-income group of inhabitants, having shortage of heat, when RES use contribute to mitigation of energy poverty. Solving the unfavorable situation with energy poverty is possible by increasing the share of Renewable energy sources in the gross final energy consumption for heating and cooling, primarily in residential buildings. The results provide information for policy makers in area of economic, social and environmental decision-making.

The term sustainable energy is very familiar to all of us yet its exact definition or meaning has so far been vague. To date the widely adopted definition of sustainable energy has been inspired by the definition of sustainable development formulated more than 30 years ago in the Our Common Future - the UN’s Report of the World Commission on Environment and Development. The current definition conceals the true spirit of the Report and this has some ramifications. It raises the question as to whether we have put too much emphasis on attaining the future dimension of sustainability in the middle of the situation where the energy survivability is in fact the real issue in many parts of the world. In this paper the role of energy in sustaining livelihoods of low income communities is discussed. Existing definition of sustainable energy that the whole world seems to have embraced is looked at critically and its downsides exposed. The identified pillars of sustainable energy and the associated issues is discussed and the need for an additional pillar, i.e. affordability is discussed and called for. This paper will argue that global definition of sustainable energy seems inappropriate and should be jettisoned and a more realistic definition should be formulated, adopted and embraced at local (i.e. national or regional) level taking into account local attributes and factors. The current push for a transition from the reliance on conventional energy sources to ‘cleaner’ energy sources (generally associated with renewable energy) has further exacerbated the issue of energy affordability that has made the conditions even more dire for so many groups of the world population. In the midst of this situation, those who push for a rushed energy transition seem to be those who also produce unsustainably high carbon footprint but who can easily offset their carbon foot-print through the ‘net-zero’ concept.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

A novel concept of a finned piston system is presented and analyzed where the compression heat is continuously extracted from the compression chamber. The resulting compression characteristic moves in direction of an isothermal process, reducing so the temperature of the compressed fluid in the compression chamber and reducing the necessary mechanical work for the process. The finned piston concept consists in an integrated heat exchanger inside of the chamber and being constituated of imbricated flat fins placed on the stator part and on the mobile piston. The internal heat exchange surface is strongly increased in comparison with a classical piston/cylinder. The energetic performance of the new system is evaluated with the help of simulation. Pressures, forces and temperature of the compressed gas are simulated as well as the mechanical work needed. The different curves are compared with adiabatic and isothermal characteristics.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Primary energy consumption is one of the key drivers of global CO₂ emissions that, in turn, heavily depend on the efficiency of involved technologies. Either the improvement in technology efficiency or the expansion of non-fossil fuel consumption require large investments. The planning and financing of such investments, by policy makers or global energy firms, require, in turn, reliable measures of associated global spreads and their evolution in time. In this paper, our main contribution is the introduction of index measures for accessing global spreads (that is, measures of inequality or inhomogeneity in the statistical distribution of a related quantity of interest) of technology efficiency and CO₂ emission in primary energy consumption. These indexes are based on the Gini index, as used in economical sciences, and generalised entropy measures. Regarding primary energy sources, we consider petroleum, coal, natural gas and non-fossil fuels. Between our findings, we attest some stable relations in the evolution of global spreads of technology efficiency and CO₂ emission, and a positive relation between changes in global spreads of technology efficiency and use of non-fossil fuel.

This scientific article outlines the Artificial Intelligence Integrated Energy Education Framework (AI-IEEF), a transformative model designed to revolutionize energy education and management practices. The framework is organized into five distinct layers: Organizational (AI-enhanced administration systems), Financial (dynamic AI financial models), Technology (advanced simulation and modeling), Methodology (AI in curriculum development and personalized learning), and Social (AI for community engagement and impact). An expert panel used the fuzzy Delphi method to achieve consensus on twenty key factors within these layers, establishing a solid foundation for analysis. Following this, the Fuzzy Analytic Hierarchy Process (AHP) was employed to calculate precise weights for each layer and their respective factors, providing a quantitative assessment of their relative importance. These weight calculations are crucial, as they guide resource allocation and strategic decision-making to ensure the framework is optimized for the evolving needs of energy management. Furthermore, the article introduces five tailored variants of the AI-IEEF, each addressing specific aspects of energy education and offering a comprehensive approach to navigating the challenges in the field. The five variants include the Smart Campus Energy Education, Global Energy Policy Analysis, Renewable Energy Research and Development, Energy Workforce Development, and Community Engagement and Outreach variants. Each provides a distinct approach to energy education, from transforming campuses into living labs for hands-on learning to fostering international collaborations that explore the global implications of energy policy. These variants emphasize practical skills, policy analysis, and community-focused solutions, ensuring that students are well-prepared to contribute effectively to the energy sector.

The thermoelectric generator (TEG) has much potential as a renewable energy technology that could be used in ocean thermal energy conversion (OTEC). This technique employs TEGs and heat exchangers as substitutes for the intricate elements found in conventional OTEC systems, including pressure valves, evaporators, condensers, and turbine generators. By harnessing the temperature difference between warm surface seawater and cold deep seawater, it facilitates the conversion of this thermal gradient into electrical energy. This study used computer simulations to look at and compare three different types of heat exchangers: a cavity-type heat exchanger, a heat exchanger with flat fins, and a heat exchanger with longitudinal vortex generators (LVGs). The objective was to evaluate the influence of these designs on the thermoelectric conversion performance when implemented in OTEC systems. The study's findings indicate that using heat exchanger configurations featuring flat fins, or LVGs, enhances the efficiency of thermal energy transfer from warm seawater to TEGs, resulting in a notable enhancement in the output power of TEGs. Furthermore, the efficiency of TEG thermoelectric conversion is influenced by varying seawater flow rates in relation to the performance of flat fins and LVGs. In contrast to the cavity-type heat exchanger, the utilization of a heat exchanger equipped with flat fins has been found to yield a notable enhancement in the output power of TEGs by approximately 22.92%. Furthermore, this configuration demonstrates a substantial improvement in thermoelectric conversion efficiency, exhibiting an increase of around 40.01%. These improvements are observed under certain conditions pertaining to flow rates and fin heights. Utilizing heat exchangers equipped with LVGs has been found to enhance the output power of TEGs by 13.02% and improve the overall thermoelectric conversion efficiency by 17.72%. These improvements are observed under certain conditions, including specific flow rates, LVG tilt angles, and locations.

Renewable active sludge is a smart material for wastewater treatment and the protection of surface water bodies. The generated pellets (dried and pelletised dehydrated anaerobically stabilised excess sludge) are produced in a quantity of 31.4 g ± 5.6 g dry matter (DM) per one Population equivalent (PE) calculated to COD (PE_COD) in one day. As pellets are combustible material, their energy utilisation must reach sustainable development goals (SDGs) - a bridge must be created between »treated sewage sludge as the tool to remove pollutants and nutrients from wastewater« and »preparation of the valuable material as a solid recovered fuel (SRF) that meets customer-specific requirements«. Technical Report CEN/TR 15508 and Technical Standard EN ISO 21640 set up methods for specifying and classifying pellets as an SRF. In the last eleven years (2010 – 2021), pellets' net calorific value (NCV) is 13.0 ± 0.7 MJ kg^-1 as received (ar). In 2021, the 80^th percentile of the Hg/NCV ratio was 0.079 mg Hg MJ^-1. In 2010 – 2021, the annual amount of Hg transferred to stakeholders reduced by 64.3 % m/m - from 10.1 kg to 3.67 kg. The halogen contents of the pellets do not threaten corrosion to the incineration facility. Stable pellets' energy potential and perspective ash composition for critical raw materials recovery qualify pellets as a specific waste stream and a renewable material for SRF production.

Jordan is classified as a low- to-middle-income, in which the primary energy sources are imported crude oil and natural gas. The energy expenses represent a high percentage of the country's GDP. Meanwhile, due to its geographical location and its climate, Jordan ranks very high among the world countries with respect to solar energy exploitation potential. Therefore, solar energy is a key resource that can be utilized to significantly improve both the country’s economy and energy security. It has to be highlighted that Jordan focus on a network dedicated to solar energy and it has to be mentioned the Erasmus Plus INNOMED Project based on virtual classrooms, which aims to promote innovative technologies and applications of solar energy in MENA Countries. This paper presents an overview of the solar energy resources in Jordan that includes: the current situation, the potential, and future expectations.

In the context of the coordinated development of digital economy and environmental protection, we take the strategy of "Broadband China" as a quasi-natural experiment, adopts the panel data of 284 cities from 2007 to 2020, and evaluates the policy effect of digital economy on total factor energy efficiency through the progressive DID model. The results show that: The construction of "Broadband China" demonstration city has significantly promoted the improvement of the total factor energy efficiency of the demonstration city, but there is also significant urban heterogeneity, which is manifested in the significant improvement of the total factor energy efficiency of the eastern city, the southern city, the big city and the non-resource-based city. It has no significant effect on the total factor energy efficiency of central and western cities, northern cities, small and medium-sized cities and resource-based cities. The construction of "Broadband China" demonstration cities can improve total factor energy efficiency by promoting technological progress and strengthening environmental regulations. Therefore, we should further strengthen the construction of digital infrastructure and give full play to its positive effects. Different cities should seek differentiated development paths based on their own resource endowments and development conditions, so that the digital economy can effectively improve the total factor energy efficiency.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

A comprehensive evaluation system of rural building energy consumption from a innovative composite perspective is established, which is suitable for southwest China. the brainstorming method and Delphi method were used to establish the indicator system, the ANP method was applied to calculate the weights of the comprehensive evaluation model, the scoring criteria of all evaluation indexes are constructed based on fuzzy evaluation theory, the applicability of the model is verified by an example of the countryside around Chengdu. The results showed that the score of some factors is low, whole or part area, just like the percentage of clean energy use(C24), the thermal performance of external walls(E21), the implementation of energy-saving measures(S22), were key factors affecting the target level, which has greater potential for improvement. The distribution of comprehensive indicators and evaluation factors have certain spatial distribution characteristics, the overall spatial distribution shows the characterwastic of "high in the southeast and low in the northwest". The key factors and spatial distribution characteristics found can be used as an important basis for the improvement of green energy-saving promotion measures, energy-saving retrofit programmes were proposed in terms of additional solar room, energy-saving of external walls and air-conditioning temperature setting guidance.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

A comprehensive evaluation system of rural building energy consumption from the innovative composite perspective was established, which was suitable for southwest of China. The index system was established by Brainstorming method and Delphi method, the weights of the comprehensive evaluation model were calculated by Network Process (ANP) method, the scoring criteria of all evaluation indexes were leveld based on fuzzy evaluation theory. The system model was verified by case analysis, at the countryside around Chengdu Second Circle. With the highest weight, lowest comprehensive score, and broadest range of comprehensive scores taken into consideration, three key factors affecting the target layer, namely "Percentage of Clean Energy Use", "Thermal Performance of Exterior Walls", and "Implementation Rate of Energy saving Measures". The distribution of comprehensive indicators and evaluation factors has certain spatial distribution characteristics, and the overall spatial distribution shows a characteristic of "high in the southeast and low in the northwest". Finally, Based on key factors and regional distribution characteristics, energy-saving measures have been proposed from three aspects: increasing sunrooms, adding wall insulation layers, and standardizing air conditioning temperature settings.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

The improvement of energy efficiency of hydraulic systems remains to be an essential challenge for the industry, and the demand for more sustainable solutions is increasing. A main focus in this endeavor is the ability to eliminate or strongly reduce the use of throttle control valves which have been the preferred control element in industrial hydraulic systems for decades. Components have been subject to continuous evolution, and current industrial grade hydraulic pumps and motors are both efficient and reliable. Even though few percentages of energy efficiency can still be achieved, the main achievements in terms of efficiency is associated with novel system designs rather than further development of components. An area subject to increasing attention is the field of variable-speed displacement control, allowing to avoid the main control valve throttle losses. Systems using this technology are, however, mainly developed as standalone drive systems, necessitating maximum force, speed and power installed in each axis, with limited hydraulic power distribution capability as compared to valve controlled systems. An emerging field addressing this challenge is so-called electro-hydraulic variable-speed drive networks which allow to completely eliminate the use of control valves and enables power sharing both electrically and hydraulically, potentially reducing the necessary installed power in many cases. The idea of such a technology was first proposed in 2022, and so far developments reported in literature have mainly been of theoretical nature. This article presents the first ever experimental results for a dual cylinder electro-hydraulic variable-speed drive network prototype. The prototype has been developed for an industrial application, but has initially been implemented in a laboratory testbench. Extensive data acquisition has been conducted while subject to the associated industrial motion cycle, under different load conditions. The data is further used in combination with models to predict the total efficiency of the drive network prototype under higher loads than what could be achieved in the laboratory, suggesting a total efficiency from the electric supply to the cylinder pistons of 68 %. Re-configuring the prototype to a known standalone drive system structure implies comparable efficiencies. Finally, the drive network is theoretically compared to a valve drive solution, generally suggesting the prototype drive network to provide efficiency improvements of at least 40 % in comparison.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

The paper addresses an analysis of the efficiency and profitability of the operation of a photovoltaic installation located in the geometric centre of Europe (near Białystok, Poland), where the intensity of solar irradiation is not too high compared to other European countries. It is calculated that in that place average solar irradiation being lower even by approx. 26 kWh than that for the whole Europe, which results in a 26% drop in the economic potential of the utilisation of solar energy for its conversion. A case study and an economic analysis show that without minimum funding amounting to 50% of the investment costs paid for the modernisation of a central heating system assisted by PV cells, the time of return of pecuniary expenditures exceeds 7 years. Apart from the Simple Pay-Back Time SPBT, discount indicators determined in the paper also include the net present value NPV and the internal rate of return IRR. Moreover, a direct ecological effect has been determined for such an investment.

The world is facing an energy crisis. Governments are seeking to provide universal energy access and guarantee energy security while trying to mitigate climate change. One possible solution is energy transitions towards low carbon energy systems. Among other things (physical infrastructure, public policy and regulatory enablers and knowledge and capacities) changes in the energy systems require a well informed and participative citizenship. Within this context the concept of energy literacy appears. Energy literacy is the understanding of how energy is generated, transported, stored, distributed and used, awareness about its environmental and social impacts and the knowledge to use it efficiently in the different sectors of the economy. This paper provides a systematic literature review in the Web of Science’s Core Collection. Most of the work done around energy literacy addresses its evaluation among different groups, particularly students at different levels, and the construction, application and evaluation of tools for improving energy literacy. Other frequently studied issues are the influence of energy literacy in decision making, its drivers and conceptual research about the topic. Energy enables citizens to effectively contribute to energy efficiency and sustainable development, nevertheless energy literacy is not strongly correlated to energy consumption habits.

The use of energy resources is essential to the survival and development of human civilization. However, inefficient energy use not only has an impact on the economy, but it also has a significant impact on climate change. Studying and analyzing the factors that increase energy consumption is crucial for improving energy management. The aim of this study is to analyze the factors influencing energy savings in a building taken as a study model under two different climatic conditions. Measurements of the building's energy performance were carried out using the EnergyPlus calculation engine, which enabled us to analyze the strengths of building parameters such as the number of lights, the equipment used, occupancy, temperature and the relationships between the factors influenced. EnergyPlus is an important software tool in the field of building energy efficiency, enabling the precise simulation of building energy consumption. Simulation scenarios based on actual consumption data carried out by researchers have made it possible to develop energy management strategies in buildings in order to optimize energy efficiency, and in particular to promote the thermal performance of these buildings. In the present work, EnergyPlus building energy simulation software is used to simulate the energy consumption of the building of the Faculty of Electrical Engineering at the "Gheorghe Asachi" Technical University in Iasi, in order to determine the factors influencing building energy consumption. It has been shown that occupancy and heating and cooling systems are the key factors influencing energy consumption in buildings. The advantage of this study is that it can be used to model the most influential factors in predicting future energy consumption in buildings.

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

This paper proposes how to reduce the consumption of active electrical energy (kwh) by 90.3% while maintaining the same mechanical work speed (RPM) at variable torque loads (fans or air-fluid blowers, centrifugal pumps are not included). of water and similar fluids), by using the “Fan Law” in an innovative way in PMSM-type synchronous motors (a comparative study never before carried out on the Fan Law). The work is carried out comparatively between brushless a-synchronous motors with starting loop (or motor with a short-circuited loop) versus brushed a-synchronous motors and PMSM-type synchronous motors without the need to use VDF (variable frequency drives), simplifying technology (electronics) and saving costs in an innovative way (R+D+i). The case study was developed on a design applied to a centrifugal air extractor/blower with PMSM/IPM type synchronous motor. Applying one of the fan affinity laws –with the impeller diameter 10.5 (mm) constant- the electrical power absorbed by the blower motor is proportional to the cube of the shaft speed: . Being “P” power (Watts) and “N” speed (RPM). Carrying out a comparative study between power (watts), active energy consumption (kwh) and rotational speed (RPM). This has a direct impact on the costs of residential and commercial single-phase active electrical energy consumption, measured in kilowatt-hours (kwh). Carrying out a comparative study between three (3) types of alternating current (AC) electrical machines, according to the NEMA (National Electrical Manufacturers Association); AC motors fall into three (3) categories. One (1), synchronous motors (Syncrhonous Motor) of three types: (1a) excitation by DC (DC Excited Motor), (1b) permanent magnet (Permanent Magnet Motor) and (1c) reluctance motor (Reluctance Motor) or motor Step by Step. Two (2), asynchronous induction motors of two types: (2a) Squirrel-Cage Induction Motor and (2b) Wound-Rotor Induction Motor. Three (3) series-wound motor (Series-Wound Motor) also called universal motor (they have carbons).

In this paper the attention is focused on the analysis and the optimization of energy flows in networked systems via a fluid-dynamic model. In particular, a cost functional that represents a term proportional to the kinetic energy of an energy system is studied. First, the functional is optimized for a simple network having a unique node, with an incoming arc and two outgoing ones. The optimization deals with distribution coefficients and explicit solutions are found. Then, the global optimization is obtained using the local optimal parameters at the various nodes of the system. Considering the case study of an energy hub, interesting results are obtained for the whole network, proving the correctness of the proposed approach.

The quickest and easiest way to avoid greenhouse gas (GHG) emissions is to purchase renewable electricity and offset the remaining emissions. However, the industrial sector’s electricity needs already exceed renewable electricity generation. Moreover, electricity accounts for only one third of the industry’s energy needs. Simultaneously, the advance of sectoral coupling and the decarbonisation of industrial processes, as well as the desire to rapidly decrease dependence on fossil fuels, are creating significant additional demand for renewable energy. Neither existing nor planned generation and transmission infrastructure will suffice to meet the expected short-term demand. Based on survey data from the German Industry Energy Efficiency Index, this article therefore examines the share of GHG savings that companies intend to achieve on- and off-site. Understanding how much additional generation and transmission capacity is needed by the industry to decarbonise and by when is crucial to identify and address the extent of excess demand. On average, companies plan to avoid 22 % of their 2019 emissions by 2025 and 27 % by 2030, primarily through on-site measures. In combination with the extrapolation of the entire industry’s needs for off-site capacity, the data calls for a rapid expansion of planning authority and green generation capacities.

Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it's vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital 'self-instruction' and 'self-inspection'. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, key for energy-efficient and Nearly Zero-Energy Buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.

Green and sustainable electrocatalytic conversion of nitrogen-containing compounds to ammonia are currently in high demand in order to replace the eco-unfriendly Haber-Bosch process. Model catalysts for the nitrate reduction reaction were obtained by electrodeposition of metal Co, Fe and bimetallic Fe/Co nanoparticles from aqueous solutions onto a graphite substrate. The samples were characterized by the following methods: SEM, XRD, XPS, UV-vis spectroscopy, cyclic (and linear) voltammetry, chronoamperometry and electrochemical impedance spectroscopy. Besides, the determination of electrochemically active surface was also performed for all electrocatalysts. The best electrocatalyst was a sample containing Fe-nanoparticles on the layer of Co-nanoparticles, which showed the Faradaic efficiency 58.2% (E=-0.785 V vs. RHE) at the ammonia yield rate of 14.6 μmol h-1 cm-2. An opinion was expressed on elucidation of the mechanism of coordinated electrocatalytic action of a bimetallic electrocatalyst. This work can serve primarily as a starting point for future investigations on electrocatalytic conversion reactions to ammonia using model catalysts of the proposed type.

The wood-based panel industry is experiencing an excessive accumulation of solid residues from the production of medium-density fiberboard (MDF) panels and moldings. It is possible to create new MDF products with acceptable physical and mechanical properties by revaluing MDF residues. Additionally, those products’ thermal properties can be improved by incorporating phase change materials (PCMs). This study aims to develop a wood-based fiberboard made of MDF residues, capable of storing thermal energy. Two types of PCMs, two PCM ratios, and two types of adhesives were used to produce eight different types of panels. The vertical density profile, thickness swelling, water absorption, internal bond (IB), and static bending properties – modulus of elasticity (MOE) and modulus of rupture (MOR) – were determined for each panel type. The specific heat of the panels was also determined. The results show the panels’ densities were greater than 700 kg/m3. Thickness swelling in water improved by 23% compared to the reference value of the control panel PCMs after PCM incorporation. The highest IB value was 1.30 MPa, which is almost three times the minimum required by regulation standards. The incorporation of PCMs reduced the panels’ bending properties compared to the properties of the control panels. Even though the values obtained are sufficient to comply with the minimum values set out in ANSI standard A208.2 with a MOE value of 2072.4 MPa and the values obtained are sufficient to comply with the minimum standards with a MOE value of 2072.4 MPa and a MOR value of 16.4 MPa. When microencapsulated PCM is used, the specific heat of the panels is increased by more than 100% over that of the control panels. This study develops a feasible alternative for using MDF residues: to create fiberboard that is capable of storing thermal energy and has adequate physical and mechanical properties.

A high step-up DC-to-DC converter that integrates an isolated transformer and a switched-clamp capacitor is presented in this study. The voltage stress of the main power switch should be clamped to 1/4 V by using the turn ratio and switched-clamp capacitor of an isolated transformer to achieve a high voltage gain. In addition, a passive clamp circuit is employed reduce voltage stress on the main power switch. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, thereby improving the power converter efficiency. The converter consists of one isolated transformer, one main switch, three capacitors, and four diodes. Operating principle and steady-state analyses are also discussed. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory. The maximum efficiency of the converter is 95.1 at 60 W.

Previously reported atomic data (spectral lines, wavelengths, energy levels, and transition probabilities) have been collected and systematically analyzed for the Cs VI. The present theoretical analysis is supported by extensive calculations made for Cs VI with a pseudo-relativistic Hartree--Fock (HFR) method together with the superposition of configuration interactions implemented in Cowan's codes. In this critical evaluation, we provided several possibly observable lines with Ritz-wavelengths, computed from the optimized energy levels, and theoretical transition probabilities with their estimated uncertainties. In addition, we provided the radiative transition parameters for several forbidden lines within the ground configuration $5s^25p^2$ of Cs VI.

The transition to sustainable energy systems in the Middle East and North Africa (MENA) region addresses critical challenges of environmental protection, energy security, and socio-economic resilience. Using neo-institutional theory, this paper examines the intricate relationship between formal and informal institutions and their significant impact on the region’s energy transition. Despite the MENA region’s abundant renewable energy resources, numerous institutional barriers, such as governance inefficiencies, regulatory inconsistencies, and entrenched socio-cultural norms, significantly hinder this transition. Through a review of existing literature, this study presents a comprehensive analytical framework designed to overcome these barriers, with a focus on the Just and Sustainable Energy Transition model. This strategic model emphasizes equity, sustainability, and inclusive governance as pillars for reform. The study offers detailed recommendations for institutional change and underscores the importance of collaborative efforts among governments, industry stakeholders, and international partners to unlock the MENA region’s renewable energy potential, advance global sustainability goals, and ensure a more secure and resilient energy future.

In this paper, we investigate the structural, microstructural, dielectric, and energy storage properties of Nd and Mn co-doped Ba0.7Sr0.3TiO3 [(Ba0.7Sr0.3)1-xNdxTi1-yMnyO3 (BSNTM) ceramics (x = 0, 0.005, and y = 0, 0.0025, 0.005, and 0.01)] via a defect dipole engineering method. The complex defect dipoles (MnTi"-VO∙∙)∙ and (MnTi"-VO∙∙) between acceptor ions and oxygen vacancies capture electrons, enhancing the breakdown electric field and energy storage performances. XRD, Raman spectroscopy, and microscopic investigations of BSNTM ceramics revealed the formation of a tetragonal phase, increased oxygen vacancies, and reduced grain size with Mn dopant, respectively. The BSNTM ceramics with x=0.005 and y=0 exhibit a high dielectric constant of 2058 and a dielectric loss of 0.026 at 1 kHz. These values gradually decreased to 1876 and 0.019 for x=0.005 and y=0.01 due to the Mn2+ ions at Ti4+-site, which facilitates the formation of oxygen vacancies, and prevents the decrease of Ti4+. In addition, the defect dipoles act as a driving force for depolarization to tailor the domain formation energy and domain wall energy, which provides a high difference between the maximum polarization of Pmax and remnant polarization of Pr (ΔP=10.39 µC/cm2). Moreover, the complex defect dipoles with optimum oxygen vacancies in BSNTM ceramics can provide not only a high ΔP but also reduce grain size, which together improve the breakdown strength from 60.4 to 110.6 kV/cm, giving rise to a high energy storage density of 0.41 J/cm3 and high efficiency of 84.6% for x=0.005 and y=0.01. These findings demonstrate that defect dipoles engineering is an effective method to enhance the energy storage performance of dielectrics for capacitor applications.

The research introduces the concept of Strong Energy (SE) as a force that encompasses all the different forms of energy in the universe. The study aims to create a mathematical framework that can describe the relationship between these different types of energy and how they can be converted from one form to another. The paper explores the theoretical implications of this model and investigates how it aligns with established physics principles like the conservation of energy and fundamental forces. The paper also discusses potential experiments and the impact this research could have on cosmology, particle physics, and our understanding of energy. This work sets the stage for future research in this area of study.

Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO₂. However, practical methods for estimating the energy consumption and CO₂ emissions of such operations during project planning are lacking. This paper presents a detailed model for estimating the energy consumption and CO₂ emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO₂ emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO₂ emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

Solar photovoltaic (PV) technology is a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. This paper explores the pivotal role of PV technology in re-ducing greenhouse gas emissions and combatting the pressing issue of climate change. At the heart of its efficacy lies the efficiency of PV materials, which dictates the extent to which sunlight is transformed into electricity. Over the last decade, substantial advancements in PV efficiency have propelled the widespread adoption of solar PV technology on a global scale. The efficiency of PV materials is a critical factor, determining how effectively sunlight is transformed into electricity. Enhanced efficiency, achieved through a decade of progress, has driven the global expansion of solar PV. Multi-junction photovoltaic materials have now exceeded 40% efficiency in lab tests. China leads the world in solar PV installations, boasting over 253 GW of installed capacity by the end of 2021. Other prominent countries in this sector are the United States, Japan, Germany, and India. Supportive policies like feed-in tariffs, net metering, tax incentives, and cost reductions in PV modules have made solar PV increasingly competitive against fossil fuel-based power generation. Solar PV technology holds immense potential for creating a cleaner, reliable, scalable, and cost-effective electricity system. To expedite its deployment and foster a more sustainable energy future, continued investment in research and development, along with supportive policies and market mechanisms, is essential. This paper underscores the pivotal role of solar PV technology in the global energy transition and advocates for a concerted effort to unlock its full potential in achieving a more sustainable and resilient energy future.

Owing to the large ratio of consumption in the building sector, energy saving strategies are required. Energy feedback is an energy-saving strategy that consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, realization of energy savings using only consumption information remains limited. In this paper, a building-energy three-dimensional (3D) visualization solution is thus proposed. This solution includes the process of diagnosing a building and providing prediction of energy requirements if a building improvement is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry (CRP) method without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The aim is to determine if the building manager will replace the facility after our recommendation is given to improve the building energy efficiency driven from the energy information. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.

Low energy consumption is a significant contribution of new and retrofitted buildings that place an emphasis on energy efficiency. Although the mandatory requirements of the U-factor and R-value are described in the Vietnamese National Technical Regulation on the energy efficiency of buildings—QCVN09:2017/BXD, these values are not widely publicized, leading to designers facing difficulties when choosing suitable materials at the design stage. This study focuses on the method used to declare the heat transfer values of building materials based on the energy labeling process in order to create an energy labeling program for building materials. To accomplish this objective, criteria and labeling procedures for windows, doors, walls, and building roofs have been developed. In addition, a case study of insulation product labeling was used to evaluate the applicability of the developed labeling program in the implementation phase.

Dark energy and dark matter are described as different manifestations of vacuum energy in a new cyclic universe framework of Extra Dimensional Symmetry (EDS) that doubles large spatial dimensions with microscopic partners. In this testable framework, vacuum energy, constrained by a Planck density constraint, exists in a gravitationally dark state where actual gravitational constant G_o = 0G. While a small repulsive component is constrained by an asymptotically evolving asymmetry to the active state where G_o = 2G, some components in the inert state are enabled to gravitationally attract by neutrino induced gravitational constant with light and shear activated heavy components. Observational and experimental tests are briefly discussed.

Low-energy cosmic rays (LECRs) play a crucial role in the formation of planetary systems, and detecting and reconstructing the properties of early LECRs is essential for understanding the mechanisms of planetary system formation. Given that LECRs interact with the surrounding medium to produce nuclear de-excitation line emissions with energy mainly within 0.1–10 MeV, which are unaffected by stellar wind modulation, these gamma-ray emissions can accurately reflect the properties of LECRs. This study introduces an innovative method for using gamma-ray emissions to infer LECR properties. We employed the Parker transport equation to simulate the propagation and spectral evolution of LECRs in a protoplanetary disk and calculated the characteristic gamma-ray emissions resulting from interactions between LECRs and disk material. These gamma-ray emissions encapsulate the spectral information of LECRs, providing a powerful tool to reconstruct the cosmic ray environment at that time. This method, supported by further theoretical developments and observations, will fundamentally enhance our understanding of the impact of CRs on the origin and evolution of planetary systems and address significant scientific questions regarding the cosmic ray environment at the origin of life.

Paper presents the energy policy of the Republic of Serbia with special attention to the energy situation on the government controlled territory. South Serbian autonomous province of Kosovo and Metohija is under UN jurisdiction since the 1999 according to UNSC Resolution 1244. Renewable energy sources are rarely used in Serbia with exception of energy from hydropower plants, but in this sector priorities in geothermal and energy coming from biomass recently increased. In natural gas sector, Serbia has the deal with Russia for construction of South Stream gas-line through Serbia and for construction of the first underground storage in depleted gas reservoir in Banatski Dvor. In 2008, Serbia also sold 51% of the government founded petroleum industry – NIS which has exclusive monopoly for exploitation of crude oil. Serbian government has complete monopoly in electric power sector. Electric power infrastructure became technologically obsolete, and operative efficiency is at very low level. Serbia has not yet decided whether Serbian Electric Power Industry – EPS will be privatized. District heating sector mostly natural gas fuelled is highly inefficient and it is in jurisdiction of local municipalities but also has social component dictated by central government.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

This paper develops a novel energy universal service bus system (EUSBS) based on emerging energy Internet (E-net) technologies. This EUSBS is a unified identification and plug-and-play interface platform to which high penetration distributed energy and equipment (DEE), including photovoltaic (PV), fans, electric vehicle charging stations (EVCSs), energy storage equipment (ESE), and commercial and residential users (CRUs), can access in a coordinated control and optimized utilization mode. First, the functions design, overall framework and topology architecture design of the EUSBS are expounded, among which the EUSBS is mainly composed of a hardware system and a software platform. Moreover, several future application scenarios are presented. Then, the hardware part of EUSBS is designed and developed, including the framework design of this hardware subsystem, and development of the hardware equipment for PV access, fans access, EVCS access, ESE access, and CRU access. The hardware subsystem consists of smart socket, and household/floor/building concentrators. Based on this, the prototypes development of EUSBS hardware equipment is completely demonstrated. Third, the software part of the EUSBS is developed as a cloud service platform for electricity use data analysis of DEE. This software subsystem contains the power quality & energy efficiency analysis module, optimization control module, information and service module, and data monitoring and electricity behavior analysis module. Based on this design, the software interfaces are developed. Finally, an application study on energy management and optimization of a smart commercial building is conducted to evaluate the functions and practicality of this EUSBS. The EUSBS developed in this paper is able to overcome difficulties in big data collection and utilization on sides of distribution network and electricity utilization, and eventually implement a deep information-energy fusion and a friendly supply-demand interaction between the grid and users. This contribution presents a detailed and systematic development scheme of the EUSBS, and moreover, the laboratory prototypes of the hardware and software subsystems have been developed based on E-net technologies. This paper can provide some thoughts and suggestions for the research of active distribution network and comprehensive energy management and optimization in power systems, as well as references and guidance for researchers to carry out research regarding energy management, optimization and coordinated control of the smart buildings.

This article aims to analyze household energy data to predict electricity self-sufficiency and identify the key features that impact it. For this purpose, we use SHAP (Shapley Additive Explanations), LightGBM (Light Gradient Boosting Machine), and a correlation heatmap-based framework to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan. We use the SHAP summary plot to identify the impact-wise order of key features influencing the electricity self-sufficiency rate (ESSR). Using SHAP, we demonstrated that key features are; housing types, average monthly electricity bill, floor heating, electric capacity, number of washing and drying machines, fee plans, occupation of household head, total floor area, average monthly gas bill, cooking equipment, etc. Furthermore, we adopted the LightGBM classifier with $\ell^1$ regularization to extract the most significant features and predict the electricity self-sufficiency rate of households. This LightGBM-based model can also predict the electricity self-sufficiency rate of households that did not participate in the questionnaire survey. A heat map is also used to analyze the correlation among household variables. The findings discussed in this article offer valuable insights for energy policymakers to achieve the targets of energy self-sufficient houses.

In 2021 an acute energy crisis inflicted severe damage on the global economy, leading to escalated prices for electricity, gas, and fuel. In order to shield individuals and businesses from the mounting energy expenses, governments have been compelled to implement policy measures, including tax reductions, price restrictions or discounts, and subsidies. This research paper examines these policy responses through the lens of energy sufficiency. Energy poverty poses a significant threat to social cohesion and support for climate-related initiatives. Therefore, it is imperative to employ compensatory measures. However, the design of such solutions must carefully consider the incentives to reduce energy consumption and associated carbon emissions. The findings of the analysis demonstrate that the escalation of energy costs holds promise for achieving energy sufficiency. Nevertheless, the government's response to the surge in energy prices and energy poverty falls short and lacks precision. Most of the policy changes primarily focus on regressive energy cost subsidies and nudging households away from fossil fuels, but they fail to generate the necessary impetus for achieving energy sufficiency, which involves the elimination of energy poverty and excessive energy consumption.

The paper presents a method to assess the effect of electrolyte additives on the energy capacity of Pb-acid batteries. The method applies to any chemically unreactive additive, including suspensions and gels. The approach is thermodynamically based and it leads to the definition of a region of admissible concentrations –the battery’s admissible range– where the battery can undergo an indefinite number of charge/discharge cycles without suffering permanent damage. An experimental procedure to determine this range is presented. The obtained results provide a way to assess the potential of electrolyte additives to improve the energy capacity of Pb-acid batteries. The same results also provide a means to determine the additive concentration that produces the maximum energy capacity increase of the battery. The paper closes with an example of application of the proposed approach to a practical case.

The increase in population and the challenges caused by it are always among the concerns of decision-makers and policymakers in large countries. One of the most serious concerns is in the field of energy consumption. Optimizing energy use is one of the most effective ways to manage limited resources and energy. Energy consumption in residential houses constitutes a significant percentage of the total energy consumption worldwide; therefore, current research focusing on mathematical models aims to provide a predictive multi-objective linear mathematical model for residential house consumption. For this purpose, the consumption of water, electricity, and gas for 80 residential houses, including 10 household appliances and equipment on a weekly basis, has been considered. The results showed that the mixed-integer multi-objective predictor model is able to optimize household consumption plans compared to the actual state of household energy consumption. Since the goals of household satisfaction and energy consumption costs in the model conflict with each other, the influence coefficient was calculated to demonstrate the simultaneous achievement of these goals. The optimal value achievable with the default model for the model's purposes is 69%.

Ecological Society is based on socioeconomic systems directed to the equilibrium of inter-relationships between environment, climate, production systems, technologies and human communities to promote the overall well-being of people and the Earth planet. It has a main goal for supporting sustainable development and equitable societies and achieving optimal health outcomes recognizing the interconnection between people, technologies, animals, plants, and their shared environment for the one health. Using data of scientific publications and patents until the year 2024, this study shows new technologies that can support the energy transition with the shift from fossil fuels to renewable energy sources in an effort to reduce CO₂ emissions for an ecological society having, whenever possible, zero-carbon economies to reduce environmental pollution and climate change and foster health and wellbeing of people. Results reveal that sustainable technologies supporting the transformation of the energy sector, economies and societies, and having a rapid growth, are blue hydrogen, floating photovoltaic systems, carbon capture and utilization, green hydrogen and liquid metal batteries. These findings can guide decisions of policymakers and stakeholders towards fruitful investments driving the transition from fossil-based to zero-carbon socioeconomic systems to have a long-run sustainable development of overall world system that has to direct towards an ecological society associated with one health.

Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This article studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia and other neighboring energy exporters, plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy doesn’t threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries towards the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporters of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects.

As a result of increased energy consumption, global trade has surged, and serious environmental problems have arisen. The problem is made more complicated by the fact that either environmental damage or economic advancement cannot be compromised. The elements contributing to environmental contamination must be identified and dealt with accordingly if we are to have a healthier future. In this study, what causes CO₂ emissions in Malaysia are examined using the Autoregressive distributed lag (ARDL) model using time series data from 1995 to 2020. The paper also examines the impact of energy use and economic complexity on CO2 emissions. Firstly, it is noted that the Environmental Kuznets Curve (EKC) theory holds in Malaysia. Furthermore, energy consumption has a detrimental impact on reducing CO2 emissions. Furthermore, it has been noted that a greater degree of economic complexity leads to an elevated amount of CO2 emissions over an extended period. The findings suggest significant environmental policy implications for reducing CO2 emissions in Malaysia.

Carbon Quantum Dots (CQDs) have emerged as promising nanomaterials for enhancing the performance of supercapacitors, offering unique advantages such as high surface area, excellent electrical conductivity, and tunable surface chemistry. This review provides a comprehensive overview of recent advancements in the synthesis, characterization, and integration of CQDs into supercapacitor devices. Various synthesis methods, including chemical oxidation, hydrothermal/solvothermal methods, microwave-assisted synthesis, and pyrolysis, are discussed, highlighting their impact on the structural and electrochemical properties of CQDs. Characterization techniques such as transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, and photoluminescence (PL) spectroscopy are elucidated for assessing the morphology, crystallinity, and optical properties of CQDs. The integration of CQDs into supercapacitors, including their utilization in composite electrodes, binder-free electrodes, and doping strategies, is explored to enhance specific capacitance, energy density, power density, and cycle stability of the devices. Challenges and future perspectives in the field are outlined, emphasizing the need for scalable synthesis methods, enhanced electrochemical performance, and sustainable device engineering to realize the full potential of CQD-based supercapacitors for various energy storage applications. This review aims to provide insights into the current state of research and guide future endeavors towards developing high-performance and environmentally sustainable energy storage technologies.

Reducing and time-shifting household energy demand is important for a sustainable transition of energy systems, and dynamic electricity pricing, like real-time pricing (RTP), is a popular instrument to incite households to change energy demand. However, the effect of such products relies on the assumption that dynamic price signals incorporate into everyday routines and rhythms. This paper investigates the flexibility of households’ everyday routines and rhythms. The study provides three insights based on 11 Danish household visits and interviews with electricity customers who are new to RTP. First, the interviews indicated confusion over terminology for dynamic pricing products and low interest in flexible energy demand. Second, everyday practices (already) appeared flexible but for other reasons than energy demand flexibility. Third, energy habits were important for providing meaning to everyday practices. In addition, the study shows that energy flexibility is not just about energy demand, but rather about all the other activities that form routines and rhythms in everyday life. We suggest 1) designing products that reflect ‘natural’ or ‘societal’ daily rhythms, 2) focusing on the engaged households, and 3) being aware that for some households, changes in pricing, including new payment schemes, might put extra pressure on already harried everyday routines.

Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

Energy management, especially in communities, is attracting growing interest, but is often marred by confusion between the terms "Energy Information System" (EIS) and "Energy Man-agement System" (EMS). This ambiguity hinders understanding of their use and role in improv-ing energy performance. Our objective is to clarify these terms and their contribution to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, role, as well as correlated elements such as or-ganizational structure, responsibilities, planning and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study reveals differences in the roles of the EIS and the SME, with a complementarity of 40% between the two systems, depend-ing on the positioning of the EIS in relation to the elements of the SME process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarifica-tion contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner.

This research paper aims to evaluate and model the performance of rice husk gasifier cook stoves for household energy use, a byproduct of rice milling. The study aims to address environmental issues and promote the use of rice husk as a renewable energy source and pest repellent in stoves. The study compared insulated and non-insulated gasifier stoves using rice husk gasification. The study compared the start-up time of insulated and non-insulated gasifier stoves, where the insulated stove showed a lower start-up time, but there was no significant difference at a 5% probability level. The temperature change also was not significant, as heat transfer and temperature change started when the husk was ignited. The burner stoves had different size and volume. The maximum operated or flame temperature was 1,190.30oC at room or minimum temperature of 27.33 oC, whereas the average temperature was 758.916oC for the non-insulated type of gasifier stove. The insulated gasifier stove also observed maximum and average temperatures of 518.30 oC and 289.014oC respectively at the same room temperature. The operating time of stoves directly influences the fuel consumption rate, specific gasification rate, and combustion zone rate of stoves. The fuel consumption rate has a significant effect on the type of gasifier stove, with the maximum rates being 3.820 and 2.099 kg/hr. The time to consume rice husks to gasify them depends on the density of the rice husk, reactor volume, and fuel consumption rate. The average specific gasification rate for insulated and non-insulated stoves is 106.45 and 120.63 kg/m2h, respectively. The fuel or rice husk holding capacity of each type of stove is significant at a 5% level, with the amount of rice husk and biochar produced being 1.18kg and 0.27kg for insulated stoves, and 2.511kg and 0.688kg for non-insulated stoves, respectively. The performance of gasifier stoves varies based on the bio char conversion rate, with non-insulated stoves producing a higher bio char mass per rice husk. Multiple regression analysis was used to model the relationship between time elapse with temperature and heat energy. The Squirrel-2020 data logger was used to measure the temperature produced by burning rice husks in the reactor of the stove as well as related generated and losses of heat. A relationship between the heat energy produced and the operating time of gasifier stoves was conducted to estimate the model. The cubic polynomial model was found to be the best predictive model fit for all variables of this study.

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works including Feedback-based Optimized Fuzzy Scheduling Approach (FOFSA) algorithm, Adaptive Task Allocation Technique (ATAT) and Osmosis Load Balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision-making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based Fuzzy Healthcare System (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for efficient Industrial Internet of Healthcare Things (IIoHT) of the future.

The evolution of transportation has been inextricably tied to the progress of civilization. Through innovation, the automobile business has been working to improve safety, quality, and compliance with environmental regulations. Electric vehicles have made significant strides in this area, but optimizing their efficiency requires a special focus because they expend energy that can be recovered in a variety of ways. Energy harvesting, a cutting-edge technology that captures wasted energy from vehicles, has recently received a lot of attention as it constitutes a means to improve the efficiency of electric vehicles. Dissipated energy can be converted into electricity using regenerative energy recovery systems and put to various uses. This study tenders a thorough examination into energy recovery technologies which could be applied to the various types of energy dissipated in electric vehicles. Firstly, the paper investigates the possible sources of energy recoverable from an electric vehicle, as well as the various types of energy dissipated. Secondly, the article examines the energy recovery technologies most frequently used in vehicles, categorizing them according to the type of energy and application. Finally, it determines that with further research and development, energy harvesting holds considerable potential for improving the energy efficiency of electric vehicles. New and innovative methods for capturing and utilizing wasted energy in electric vehicles can be established. The potential benefits of applying energy recovery systems in electric vehicles is a vital issue for the automobile industry to focus on due to the potential benefits involved. The ongoing progress currently being made in this field is expected to play a significant role in shaping the future of transportation.

Climate change has become a global nightmare, and the awareness of the causes of carbon emissions have resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon growth plan or emission reduction strategy. The world is transitioning towards a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. The study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All suggested that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.

This paper investigates the energy dissipation level of a nonlinear vehicle suspension system. The suspension system is modelled as a mass-spring-damper system, which is designed using the output frequency response function (OFRF) method. This method enables the derivation of an explicit relationship, between the system output spectrum (or Performance metric) and the system nonlinear parameters of interest. The OFRF polynomial, of the energy dissipation level, was derived in terms of the nonlinear parameters of interest. The results obtained show that the OFRF model, of the energy dissipation level, could be used to estimate the amount of heat dissipated, for a set of design parameters. This has potentials of providing some insights, in the design of vibration-based energy harvesters, as some of the wasted heat energy could be converted to electrical energy.

This study aims to analyze various types of pellet fuels used as energy sources in Bulgaria, focusing on their energy characteristics and the consumer preferences. Both standard and specialized methods such as thermogravimetry (TG) and differential scanning calorimetry (DSC) are used for the analyses. The results from TG and DSC indicate that during the drying of the samples, moisture is evaporated from their surface, along with the release of volatile substances. The processes during the analysis are accompanied by energy absorption, with the main stage being combustion, where the primary amount of heat is generated. Standard analysis methods show that wood pellets usually contain about 7.31% moisture, 0.72% ash and have a calorific value of 18.33 kJ/kg, while sunflower pellets contain about 7.62% moisture, 2.42% ash and have a calorific value of 19.63 kJ/kg. Mixed pellets contain about 7.07% moisture, up to 0.69% ash and have a calorific value of 18.05 kJ/kg. A specialized marketing research was conducted to investigate consumer attitudes and preferences toward purchasing different types of solid biomass fuels on Bulgarian market. This study provides information on the quality of pellets as fuel by offering a detailed analysis of their fuel properties and ensuring their effective utilization as an energy source.

The global energy transition is motivated largely by the need to combat climate change. Other reasons include limits to available fossil fuels, energy-related environmental impacts of many kinds, and societal goals such as equity and providing sustainable energy for the poor. This transition will significantly change the world both technically, economically and socially. It is taking shape with characteristics which prioritise certain types of policy, interventions and solutions, as well as certain actors and institutional forces. More fundamentally, it embodies underlying assumptions about what constitutes a desirable type of economy and society. Briefly stated, the energy transition is being geared to a conventional economic growth-focused type of development; including the not unchallenged concept of green growth. Not everyone, however, would agree on those priorities or the underlying assumptions. We point out various constraints and limitations which this approach places on the energy transition. Whilst much of the change being initiated is positive, we argue that different views, which lie closer to discourses such as strong sustainability (1), deep ecology (2) and ecological economics (3, 4) might prioritise differently and offer both necessary and desirable outcomes. Given the predominant focus on energy and climate, equally essential goals such as equity and eradication of poverty appear to be at particular risk of being missed. Building on examples and recent experience we argue that achieving global sustainable energy, not least the societal goals, remains unlikely within socio-political frameworks permeated by the imperatives of market-driven growth, the commodification of resources, and intense competitive dynamics. Technology and economics, the themes of this issue, have far-reaching societal implications and outcomes. We argue that it is essential that they be considered within the broad context discussed here.

This research was devoted to study the effect of the tacticity of PMMA on the surface physicochemical properties such as the London dispersive component of the surface energy of the polymer, the polar and dispersive free energies, the Lewis’s acid-base surface parameters, and the Lewis acid and base surface energies of atactic, isotactic and syndiotactic PMMAs as a function of the temperature. Inverse gas chromatography (IGC) at infinite dilution was used to determine the surface thermodynamic properties of PMMA at different tacticities. The experimental values of the net retention time and volume of model organic molecules adsorbed on the atactic, isotactic and syndiotactic PMMAs, were obtained by IGC technique at infinite dilution. The London dispersion interaction energy was used for the separation of the polar and dispersive energy of the different n-alkanes and polar solvents adsorbed on the different PMMA surfaces. The London dispersive surface energy γ_s^d (T) of PMMAs was determined by applying the Hamieh thermal model that took into account the effect of the temperature on the surface area of adsorbed molecules. The obtained results showed non-linear variations of γ_s^d (T) of atactic, isotactic, and syndiotactic PMMAs with three maxima characterizing the three transition temperatures of PMMAs, T_g, T_g and T_(liq-liq) with a shift of these temperatures with the isotactic and syndiotactic PMMAs. It was showed that the polar interaction free energy ∆G_a^p (T) also showed the presence of the three above transition temperatures of the polymers. The study showed an important non-linearity variation of the different acid-base parameters in the various polymer surfaces proving the highest effect of the temperature and the tacticity on these parameters. The determination of the enthalpic and entropic Lewis’s acid-base parameters showed an amphoteric character for the different PMMAs. The basicity of the atactic PMMA was about four times larger than its acidity outside the neighborhood of the transition temperatures of the polymer. Whereas, the isotactic PMMA was about 2 to 2.5 times more basic than acid, while the syndiotactic PMMA exhibited the highest basicity by showing a surface 5 to 8 times more basic than acid. However, the Lewis acid-base parameters strongly depended on the temperature and sudden variations of these surface parameters were observed around the transition temperatures largely depending on the PMMA tacticity. The determination of the polar acid and base surface energies of the three PMMAs led to the same previous transition temperatures with an important effect of the tacticity of PMMA on the values of these acid-base surface energies.

To control the volatility and reduce the electricity costs for indoor farming, the agrivoltaics agrotunnel introduced here uses: 1) high insulation for a building dedicated to vertical growing, 2) high-efficiency LED lighting, 3) heat pumps (HPs), and 4), solar photovoltaics (PV) provide known electric costs for 25 years. In order to size the PV array, this study develops a thermal model for agrotunnel load calculations and validates it using the Hourly Analysis Program and measured data so the effect of plants’ evapotranspiration can be included. HPs are sized, plug loads (i.e., water pump energy needed to provide for the hybrid aeroponics/hydroponics system, DC power running the LEDs hung on grow walls, and dehumidifier assisting in moisture condensation in summer) are measured/modeled. Ultimately, all models are combined to establish an annual load profile for an agrotunnel that is then used to model the necessary PV to power the system throughout the year. The results find that agrivoltaics to power an agrotunnel range from 40 to 50kW and make up an areas from 3.2 to 10.48 m²/m² of an agrotunnel footprint. Net zero agrotunnels are technically viable although future work is needed to deeply explore the economics of localized vertical food growing systems.

Previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness of offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the Central Mediterranean Sea. The first part compares the feasibility of offshore-based solar photovoltaic prices with those onshore, taking into consideration Malta’s land prices. The second part considers the use of a novel floating breakwater design that integrates energy storage. The breakwater is used to create a sheltered water area in deep waters for a multi-use marine park. Different revenue streams for the floating break water are considered, including renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, and the provision of energy storage services. It is found that the combined income from the multiple revenue streams is still insufficient to justify the investment and that financially support from Government is essential to render the floating breakwaters viable.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-adverse situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances the network’s lifetime. Many hybrid combination algorithms are proposed for clustering and the routing protocol to improve the network lifetime in disaster scenarios. However, the performance of these protocols is widely varied based on the underlying network configuration and the optimisation parameters considered. In this research, we have used the clustering parameters that are most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. Then, we proposed a bio-inspired hybrid (BAO-PSO) algorithm where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm is compared with the benchmark protocols LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime are considered performance matrices. The simulation result showed that the proposed algorithm increases network lifetime by 10%-20% by comparison with the benchmark algorithm.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

The integration of solar energy systems with agricultural practices, known as agrivoltaic systems, presents a promising solution for advancing sustainable food and energy production. This approach involves installing photovoltaic panels on agricultural lands, which allows for simultaneous crop cultivation and energy generation. Agrivoltaic systems offer a dual benefit: they enhance land use efficiency by utilizing the same area for both solar power generation and crop production, and they contribute to sustainable energy transitions. This abstract explores the benefits and challenges associated with agrivoltaic systems. Key advantages include increased land productivity, reduction in water usage due to shading effects, and potential economic gains from dual income streams. Furthermore, these systems can mitigate land use conflicts by combining food production with renewable energy generation. However, there are challenges to address, including the potential for decreased crop yields under solar panels, the need for appropriate system design to balance energy generation and agricultural needs, and the economic feasibility of initial investment and maintenance. The integration of agrivoltaic systems represents a critical step toward achieving sustainable development goals by promoting environmental stewardship and resource efficiency. Future research and advancements in technology are essential to optimize system performance and maximize benefits for both agriculture and energy sectors.

The Internet of Things (IoT) is experiencing exponential growth, revolutionizing various industries with its ability to connect devices and enable advanced applications. In the realm of energy production, IoT offers significant potential for optimizing processes and improving efficiency. This study presents a comprehensive monitoring and energy management strategy for the University of Cuenca's microgrid photovoltaic (PV) system, utilizing IoT and ThingSpeak as key technologies. The proposed strategy leverages intelligent environments and utilizes ThingSpeak as a robust platform for data presentation and analysis. By integrating IoT devices and sensors, real-time monitoring of PV system parameters such as solar radiation and temperature is achieved. The collected data is then analyzed using advanced algorithms and visualized through ThingSpeak, enabling effective energy management and decision-making. The developed monitoring system was rigorously tested in a laboratory microgrid setting, where the PV system is interconnected with other generation and storage systems, as well as the electrical grid. Through the seamless integration of these components, the monitoring system provides enhanced visibility and control over the microgrid's energy production. The results demonstrate the successful implementation of the monitoring system, showcasing its ability to improve the supervision, automation, and analysis of daily energy production. By leveraging IoT technologies and ThingSpeak, stakeholders can access real-time data, analyze performance trends, and optimize energy resources.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

A high-performance Lead-free Piezoelectric Energy Harvester (LPEH) based on a Ba0.85Ca0.15Ti0.90Zr0.10O3 + CuO 0.3 wt% (BCTZC0.3) composite was fabricated by sintering at 1450℃. The BCTZC0.3 composite, which has an enhanced high-energy-conversion constant (〖d_33×g〗_33), shows improved piezoelectric power-generation performance when compared with conventional piezoelectric energy harvesters. The BCTZC0.3-based LPEH produces instantaneous maximum power of 8.2 mW and an energy density of 107.9 mW/cm3 in a weak magnetic field of 250 μT. This energy harvester can be used to charge a capacitor and operate a wireless sensor network (WSN) system to provide temperature sensing and radio-frequency (RF) transmission in a 250 μT magnetic field. The proposed LPEH is a promising green-energy device for potentially self-powering WSN systems when applied.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

In the ground-based infrared optical remote sensing system, the target signal is very weak due to the dynamic strong light background and the movement of small and small targets. In order to improve the limit detection capability, the background suppression and target enhancement methods are required to be more suitable for this scene. To solve this problem, we first analyze the image features in the current scene, and propose a more perfect point target and noise model. Then, we propose a new WY distribution function based on the Fermi Dirac distribution function, and propose an anisotropic filtering method based on this function, and further suppress the back-ground through the difference results of two steps. Then, based on the distribution function, we further designed an energy concentration accumulation strategy in 9 scale directions, through which the SNR of the target is effectively improved and the suppression ability of the background is enhanced. Through quantitative and qualitative experimental analysis, compared with the same type of algorithms, the proposed method has better robustness against extremely weak targets and dynamic backgrounds.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

Relevance of the subject. Keyword selection is critical to finding relevant literature and requires justification for querying abstract databases and other sources to ensure accuracy and completeness. The topic of Energy Systems is of great importance due to the introduction of renewable energy sources, modern control methods, and complex energy systems during the energy transition. The aim of this study. The goal of this paper was to identify keywords that would be useful to subject matter experts when collecting literature on a particular topic, based on entries in the IEEE Terms field. Data. Bibliometric data were exported from the IEEE Xplore platform in the following order: for 2019-2023, 2000 records sorted by relevance, for 2024, 1680 records current as of April 11, 2024. Analytical methods applied and software used. VOSviewer, Scimago Graphica implementing the Clauset-Newman-Moore algorithm and agglomerative hierarchical clustering method implemented in Multidendrograms. Results. The main issues of the Energy Systems topic are presented in tabular and graphical formats. The fpgrowth utility offers flexible data preparation options, which makes it worthwhile to conduct a separate study to analyze the score of co-occurrence terms given by its algorithm.

Transitioning to clean cooking fuels is not only part of achieving SDG7, but also makes a significant contribution to mitigating climate change by reducing carbon emissions. The paper contradicts the conventional wisdom that electricity is too expensive to cook with. The paper explores the energy required to cook dishes using different technologies and fuels by analysing data from Cooking Diaries and Controlled Cooking tests conducted under the MECS programme. Electric pressure cookers (EPCs) use least energy when compared with electric hotplates, and a detailed description of how the EPC works explains why this should be. The paper draws out distinctions between African and Asian dishes, notably the impact of energy intensive dishes prepared mostly in Africa. Standard efficiency based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices so an alternative approach based on specific energy consumptions is proposed e.g. charcoal stoves use 15 times as much energy as EPCs to cook African dishes. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels. Historical costs show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy efficient electric cooking devices to defy the conventional wisdom of the energy ladder.

In order to study the strength changes of concrete specimens under different loading conditions, based on the principle of minimum energy dissipation and from the Angle of concrete failure energy per unit area, the finite element numerical simulation software was applied to concrete specimens with different failure modes of tension, compression, bending and torsion, and the double-broken line damage constitutive model was adopted to simulate and analyze the failure forms of concrete specimens under different loading conditions. The failure area and failure energy of each specimen during loading were quantitatively calculated under each failure mode, and the relationship between the failure area and failure energy consumed under different failure modes was analyzed. The results show that under different failure modes, the failure area of concrete specimens is different, the energy consumed during failure is different, and the strength is different. However, in the process of failure, no matter how the failure mode changes, the failure energy W per unit area remains unchanged and is a constant, which is related to the physical properties of concrete itself.

Energy drink (ED) consumption among Israeli-Arab adolescents is widespread. This study aimed to investigate the acute glycemic and insulin effects of EDs in healthy adolescents. Seventy-one Israeli-Arab adolescents (56% girls, average age 16.04±1.03 years) participated in a non-randomized, case-controlled, open-label study. Participants consumed ED (n=36) or a volume- and carbohydrate-matched non-caffeinated soft drink (SD, n=35), followed by a 2 h glucose tolerance test. Blood glucose was measured at baseline and 15, 30, 60, and 120 minutes post-consumption (T0, T15, T30, T60 and T120, respectively). Serum insulin concentration and caffeine relative intensity were determined 45 minutes post-consumption (T45). Blood glucose levels peaked significantly at T15 and remained significantly higher at T30 in the ED group compared to the SD group (p=0.005, p=0.017, respectively). Insulin concentrations were substantially higher at T45 in the ED group (t[64]=2.794, p=0.001). This pattern was especially prominent in heavy ED consumers. A positive correlation emerged between the amount of caffeine consumed (mg/kg), blood glucose levels at T15 and T30, and insulin concentration at T45. This study is the first to demonstrate the glycemic and insulin responses to ED consumption in adolescents, suggesting that regulatory measures limiting ED sales to adolescents could improve their health.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

In the current complex and uncertain macroeconomic situation, improving the inherent resilience of the new energy vehicle industry chain is the key to effectively addressing various risks and challenges, and achieving dynamic balance between high-quality industrial development and industrial chain security. Based on the basic concept of resilience and the actual situation of new energy vehicle industry chain development, this study constructs a comprehensive evaluation index system for the resilience of the new energy vehicle industry chain, which includes four dimensional first-level indicators of coping resistance, adaptive resilience, autonomous control and leading competitiveness. In addition, it is measured by combining the data related to the new energy vehicle industry chain in Changzhou City. Our study shows that the situation on the new energy vehicle industry chain is complicated, and the comprehensive evaluation index system is more favorable for evaluating its resilience level.

Observation of the distribution of the velocity of galactic rotation curves differed from their expected centripetal form and lead to the notion of Dark Matter or modifications to Newtonian and General Relativity, such as MOND, TeVeS and the like and even Quantised Inertia. We aim to show that General Relativity with Dark Energy/the Cosmological Constant is all that is needed, with the proviso that the Cosmological Constant can increase in the presence of a gravitational field and become gravitating to account for the hypothesis of Dark Matter Haloes.

Puerto Rico's geographic and climatic conditions render it highly susceptible to natural disasters, particularly hurricanes. The devastation caused by Hurricane Maria in 2017 highlighted the fragility of the island's centralized energy infrastructure, which led to prolonged power outages and severe disruptions in essential services. This review explores the potential of biomass energy to enhance resilience in post-disaster scenarios, focusing on Puerto Rico as a case study. Biomass energy, derived from organic materials like agricultural residues, woody biomass, and municipal solid waste, offers a renewable and locally sourced power solution that can be rapidly deployed. Key biomass conversion technologies, including direct combustion, gasification, anaerobic digestion, and pyrolysis, are examined for their applicability and benefits in emergency energy provision. The implementation of biomass-powered microgrids in Puerto Rico, such as those by Casa Pueblo and Arensis, demonstrates the feasibility and effectiveness of decentralized energy systems in disaster recovery. These microgrids provide reliable power to critical infrastructure, reduce dependence on fossil fuels, and support waste management efforts. The review also discusses ongoing research and development in advanced biomass technologies, such as second-generation biofuels, algae-based biomass, and biomass-to-hydrogen conversion, which can further enhance energy efficiency and sustainability. Additionally, the economic, environmental, and social impacts of biomass energy are evaluated, highlighting its role in job creation, waste reduction, emission control, and community resilience. Policy recommendations include streamlining permitting processes, enhancing financial incentives, developing feedstock supply chains, and incorporating biomass into disaster recovery plans. The findings suggest that integrating biomass energy into Puerto Rico’s energy strategy can significantly improve its capacity to respond to and recover from natural disasters, promoting long-term sustainability and resilience.

The accumulation of waste polymer in ecosystems is a problem that affects the environment on a global scale. This paper presents the design of Pwave+, a microwave pyrolysis system that has been optimized to recycle waste polymer into valuable hydrocarbon fuels and chemicals. Heating in a microwave oven is both volumetric and rapid, which improves the effectiveness of pyrolysis and the quality of the product. The production of waste polymer oil and gas products was boosted to its full potential by a continuous microwave vortex reactor. The product gases allowed for complete independence in terms of energy production. Modeling, simulation, and experimentation with advanced technology are used to optimize the pyrolysis parameters and the performance of the reactor. As a result, simulation and programming are solutions to problems. The results of this study demonstrated both the technical viability and the long-term viability of continuous microwave pyrolysis as a method for recycling mixed polymer waste streams. The Pwave+ continuous microwave vortex reactor is described in this study. It is capable of converting various polymer waste streams into hydrocarbon products as well as energy. The use of microwaves to assist in the pyrolysis process has the potential to cut down on plastic pollution and the amount of waste that is dumped in landfills. This could provide valuable insights for the design and commercialization of similar systems.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

Despite its growing importance as a dry beneficiation method, there is a lack of studies exploring the fundamentals aspects of dry jigging. In this sense, this study focuses on explore some essential aspects of density-based separation in dry jigs, like pulse profile, energy consumption and, especially, stratification over time, also exploring an innovative operational strategy here called “transient pulsation”. The tests were carried out using density tracers distributed across 11 density ranges (0.4-2.4 g/cm³) and employing a base bed (gravel) to analyze their segregation in a pilot-scale dry jig. Pressure drop and active power data were acquired to measure the pulse characteristics and energy consumption. The findings suggest that establishing the dry jigging cycle is more complex than in hydraulic jigs due to the compressibility of air. Stationary pulsation tests revealed an unstable behavior of stratification over time, contrasting with traditional hydraulic jigs, while transient pulsation tests unveiled varied trends in separation quality. Energy consumption analysis underscored the significant role of the blower drive. These insights have the potential to inform operational enhancements in dry jigging.

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

A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of cortical neurodevelopment, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth, explaining the spatial organization and functional significance of neuron response preferences, and compatible with features of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations provide a preliminary anatomically realistic model of cortical information processing.