Fusion device and its structure may be designed by various means. Two of most popular are; weight basis (described in previous study by author) and energy basis. In present research, later method is explored and described. Energy basis is based on amount of energy released from device upon detonation followed by explosion. Its beneficial in a sense that device size can be kept as independent variable as compared to dependent in former case. Further, it shortens the design procedure. For example, heat transfer pattern in such approach directly helps in quantifying wall thickness which dictates material, fabrication, and manufacturing route. It may also eliminate anisotropy as wall thickness is direct function of amount of heat at which it will rupture and can be much thinner. Device geometry can also be flexibility controlled as it is no longer dependent on pay load bay capacity. This allows more freedom in designing subsystems (compartments, their locations, focusing, switches, and mixers). Such devices can be more compact and simpler. Few such design configurations are proposed.

Dark energy is the primordial energy of the universe. It was not created and will not be destroyed. Dark energy is the “stuff” out of which universal space is made off. Stellar objects exist in dark energy which is a kind of superfluid energy, that we do not know much about it. In interstellar areas, dark energy has a Planck energy density that diminishes in the center of stellar objects accordingly to the amount of their mass. Motion and rotation of stellar objects put in motion and rotation also surrounding dark energy around them. The rotation of supermassive black holes in the center of spiral galaxies rotates the surrounding dark matter and causes the so-called “dark matter effect”. The orbital velocity of stars that are rotating around the central black hole is not diminishing with the distance, as is the case in our solar system. There is no hidden dark matter that causes this effect. The cause is the rotating dark energy of the entire area of the galaxy around the central black hole.

The current manuscript presents a review on existing kiln burner technologies for the cement production process, in the context of the current climate of energy transition and environmental remediation. Environmental legislation has become ever stricter in response to global climate change, and cement plants need to adapt to this new reality in order to remain competitive in the market and ensure their longevity. The cement production process is a well-established technology with more than a century of existence. There are several plants in operation whose process is outdated by modern standards, particularly considering the current industry decarbonization needs. The cement process requires tremendous amounts of energy, mainly recovered from the combustion of solid, liquid or gaseous fuels, which yields massive emissions of greenhouse gases. Thus, an important onus is placed upon the minimization of pollutant emission in the combustion system, as well as a substitution of fossil fuels with more sustainable alternatives. One of the sus-tainable alternative fuels comes in the form of refuse derived fuels (RDF). These high caloric frac-tions of municipal solid waste present a double advantage by reducing the amount of fossil fuels used and reducing the landfilling fraction of waste. However, their use in rotary kiln burners comes with important limitations for burner operation, namely that a high degree of control over primary air supply is needed to ensure complete combustion with minimal pollutant emission.

In classical General Relativity, White Holes (WH) refer to the time reverse Schwarzschild (SW) Black Hole (BH) solution of a point mass $M$ and gravitational radius $r_S=2GM$. The SW metric is static so that a time reverse WH solution simply corresponds to a violation of causality. The WH solution does not make any physical sense here. For a more physical model, such as the Lemaitre-Tolman-Bondi (LTB) metric with finite mass inside $r_S$, we show that both the expanding and collapsing solutions correspond to a BH and not to a WH.

Blockchain technology has gotten much interest recently from academics, industry, and governments worldwide. It is regarded as a technical innovation that can disrupt various application fields affecting many aspects of our lives. Cryptocurrencies are a public blockchain application's success story that sparked extensive research and development. Scalability, energy consumption, and security, on the other hand, remain significant challenges. With low throughput, high transaction delay, and high energy consumption, most cryptocurrencies are experiencing low-efficiency difficulties. The scalability issue with public Blockchains is preventing organizations and sectors from receiving effective solutions. As a result, it is critical to bridge the gap and develop new frameworks that connect Blockchain with those goals. This paper examines the evolution of blockchain architecture and consensus protocols, provides a retrospective analysis, discusses the rationale for the various architectures and protocols' change, and captures the assumptions supporting their development and contributions to collaborative application development. However, existing research on consensus algorithms is insufficient. The features of the algorithms are discussed insufficiently in those papers, and some prominent blockchain consensus methods are not examined in terms of their scopes. The study’s findings are delivered in tabular formats, allowing for a clear representation of these algorithms. We discovered in our investigation that scalability is a result of many parameters such; transaction throughput, number of nodes, storage, block size, high communication, latency, cost. Furthermore, Due to its off-chain data storage and smaller block size, PoF is much more scalable than PoS and PoW, and Because PoA is a hybrid of PoW and PoC, it does not necessitate a large number of computational resources. As a result, it has a higher throughput than PoW. PoPF entails ranking all participating nodes and appointing n accountants to compete for adding new blocks. This article addresses the need by analyzing a wide range of consensus algorithms using a complete taxonomy of attributes and delving into the consequences of several still-present flaws in consensus algorithms.

Although impending urbanization is a well-acknowledged problem, there is a rising concern about how the urban forms will change and what can be the impacts on the global energy demand. As hubs of economic, social and cultural activities, cities are major energy consumers and GHG emissions. Energy consumption is a technical or a spatial problem? From Newman and Kenworthy to today, several studies have tried to shed light on this nexus. In this work, the controversial paradigm of urban density is discussed as a key component of the fight against climate change impacts. Concerning energy consumption, an in-depth bibliometric analysis is developed to identify the interdependencies of the terms. As a key ‘promise’ of an efficient urban configuration, density has been the core of diverse studies but with still under exploration arguments. This work provides a way forward for planners seeking to design strategies related to dense urban tissues exploring controversial paradigms as a key solution for energy-efficient problems.

In paper I, we showed that the observed (LCDM) Universe can be modeled as a local Black Hole (BH) Universe (BHU) of fixed mass M, without Dark Energy (DE): cosmic acceleration is caused by the BH event horizon rS=2GM. %The same BHU solution can be used to model the interior of a stellar or galactic BHs. Here, we propose that our BHU could form from the hierarchical free-fall collapse of regular matter. We argue that the singularity could be avoided with a Big Bounce explosion, caused by neutron degeneracy pressure (Pauli exclusion principle). This happens at GeV energies, like in core collapse supernova, well before the collapse reaches Planck energies (1E19 GeV). If our universe formed this way, there is no need for cosmic Inflation or a singular start (The Big Bang). Nucleosynthesis and recombination follow a hot expansion, like in LCDM, but cosmological measurements, which are free parameters in LCDM, could be estimated from first principles. Dark Matter could be made up of a wide mass range of primordial compact objects (BHs and Neutron stars), remnants of the collapse and bounce, which can provide a faster start for galaxy formation. We present a simple prediction to explain the observed value of Lambda and the coincidence problem.

The number of people with neurodegenerative disease continues to increase every year. A new perspective is needed in overcoming this disease. In this review, researchers collected information about epigenetics and energy factors of neurodegenerative disease driven by mitochondria. Mitochondrial epigenetic dysregulation can cause damage to the neuron system. The increase in the amount and interaction of alpha-synuclein with SAMM50 in the mitochondria is one of the factors causing neurodegenerative disease. As an energy provider in the body, the existence of harmonization in the regulation of mitochondria specifically the mitochondrial outer membrane is important. Hydrogen peroxide (H₂O₂) has neuroprotective abilities to overcome the impairment function of mitochondria in neurodegenerative patients. Based on the computational simulation of this case, it can be used as the basic concept for the development of the role of H₂O₂ in neurodegenerative disease.

We propose that the Big Bang does not have a singular start, but that it originates from gravitational collapse of a low density cloud that collapsed 25 Gyrs ago to form a Black Hole (BH) of mass M ≃ 6 × 1022M⊙. The collapse continued inside the BH for 11Gyrs until the density achieves neutron degeneracy and the collapse bounces into expansion like a core collapse supernova. From observations today, this model is very similar to the standard Big Bang cosmology but there is no need for Inflation or Dark Energy (DE). The observed cosmological constant Λ is not a new form of DE, but results from the dynamics of the Big Bang expansion inside the BH event horizon rS = 2GM = √3/Λ. Why our Universe has such a large mass M (or small Λ value)? If τO(≃ 10Gyr) is the astronomical timescale needed for observers like us to exist, we find a simple anthropic prediction, based only on gravitational collapse from Gaussian fluctuations, that the maximum probability for M is MO < M < 3MO where MO = τO/3G. This agrees well with the measured values for τO and M in our Universe.

The level of technological development of any civilization can be gaged in large part by the amount of energy they produce for their use, but also encompasses that civilization’s stewardship of their home world. Following the Kardashev definition, a Type I civilization is able to store and use all the energy available on its planet. In this study, we develop a model based on Carl Sagan’s K formula and use this model to analyze the consumption and energy supply of the three most important energy sources: fossil fuels (e.g., coal, oil, natural gas, crude, NGL and feedstocks), nuclear energy and renewable energy. We also consider environmental limitations suggested by United Nations Framework Convention on Climate Change, the International Energy Agency, and those specific to our calculations to predict when humanity will reach the level of a Kardashev scale Type I civilization.

Recently, most countries suffering from global warming due to the waste gases coming out of the combustion engines used to generate electric power by traditional methods. Therefore, many countries are currently trying to find alternative solutions to this problem by using renewable energies such as solar energy, wind energy, and energy generated from the waves of the ocean and sea to overcome pollution problems. The waves of the seas and oceans have enormous and largely untapped energy, so this work presents an efficient way to use the energy of sea waves to generate electricity. And also, trying to be a way to produce electricity from wave energy in the future. The best suitable places along the shores of Jizan city were inspected to install the buoy system to revenue advantage of the wave supreme height to achieve the height amount of electrical energy. A mathematical model was made to analyze the wave energy and convert it into energy extracted by mechanical force. The mathematical analyzes used the data collected from satellite maps of the numerous severe waves in the Red sea of the Jazan area, and the data published in previous research along that area. It was found that the beach of the Al Shuqaiq area is the greatest for installing the buoy and obtaining the highest electrical energy due to the presence of the highest wave intensity, followed by the beach of the Baysh and Al Morgan area. Also, one of the objectives of this research is to study the design of a device powered by a buoy to use the waves of the Red Sea to generate electric power. The influence of the buoy system design parameters such as the buoy diameter, length of the cylinder, and the length of the connecting rod on the electrical energy generation from wave energy was investigated. The current device is designed with a gearbox to produce continuous power with a single electric generator. A floating mooring device uses the rise and fall of bulges to convert sea wave energy into electrical energy. The device consists of a float, arm, two wheels of different diameters, a gear set, and an electric generator. The effect of the design of several factors on the performance of the device for converting the sea waves energy into electrical energy, including the length of the buoy arm, the wave height by changing the cam diameter, and the conversion ratios between the gear set, to optimize the output power of the wave energy.

The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to the excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed among the challenging problems. This study overcomes essential issues overlooked by the contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The proposed study encompasses two stages: (1) predictive optimization model which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan is devised to decide the role of nanogrids as prosumers or consumers by emphasizing the use of PV-produced energy. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. The outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

We consider the role information energy can play as a source of dark energy. Firstly, we note that, if stars and structure had not formed in the universe, elemental bits of information describing the attributes of particles would have exhibited properties similar to the cosmological constant. The Landauer equivalent energy of such elemental bits would be defined identical in form and value to the characteristic energy of the cosmological constant. But, with the formation of stars and structure, stellar heated gas and dust now provides the dominant contribution to information energy with the characteristics of a dynamic, transitional, dark energy. At low redshift, z<~1.35, this dark energy emulates the cosmological constant with a near constant energy density, w=−1.03±0.05, and an energy total similar to the mc² of the universe's ∼10⁵³ kg of baryons. At earlier times, z>~1.35, information energy was phantom, differing from the cosmological constant, Λ, by a CPL parameter difference of ∆w_o=−0.03±0.05 and ∆w_a=−0.79±0.08, values sufficient to account for the H₀ Tension. Information dark energy agrees with most phenomena as well as Λ, while exhibiting characteristics that resolve many tensions and problems of ΛCDM: the cosmological constant problem; the cosmological coincidence problem; the H₀ and σ₈ tensions. As this proposed dark energy source is not usually considered, we identify the expected signature in H(a) that will enable the role of information dark energy to be falsified by experimental observation.

The standard model of cosmology assumes that our Universe began 14 Gyrs (billion years) ago with a hot Big Bang expansion out of nothing. It can explain a vast range of different astrophysical data from a handful of free cosmological parameters. However successful this model is, we have no direct evidence or fundamental understanding of some key assumptions: low entropy start, Inflation, Dark Matter and Dark Energy. Here we present a simpler and more physical explanation for the same observations that do not require such assumptions or new laws of Physics. It is based on the evidence that we live inside a Black Hole (BH) of mass $ M \simeq 5 \times 10^{22} M_{\odot}$, which we observed as cosmic acceleration. How did the Big Bang end up inside such a BH? We propose that 25 Gyrs ago, a very low density dust cloud collapsed and form such BH. As there was no pressure support, the collapse continued inside until it reached neutron energy densities (GeV). Causal regions of solar mass size rebound by neutron degeneracy, like a supernovae, producing the Big Bang expansion and compact dark matter remnants. During collapse, perturbations exit the horizon to re-enter during expansion, given rise to the observed universe. We review the evidence for such BH Universe.

A supernova close enough to the Earth is a spectacular event: it can appear as a \new star" as luminous as Venus, or even more, visible for several days. The rate of Galactic supernovae is expected to be of about one in 30 years, with a fraction visible at naked eye; however in all the history of human civilization only seven supernovae in the Milky Way have been reported, the last two (1572 and 1604) during Galilei's life. The supernova of 1604, today called Kepler's Supernova, was observed by Galilei, Kepler and other astronomers in Europe, Korea, and China. Like the supernova SN1572, today called Tycho's supernova, it has been the subject of extensive studies, and inspired observational measurements and philosophical considerations on the nature of the heavens. The remnant of SN1604 has been indicated by recent X- and gamma-ray data to be a likely site of cosmic ray acceleration. The first recorded data of optical observations, together with new data, can still tell us a lot about the early evolution of this supernova.

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

With the increased use of laterite nickel ore, the impact of high Al2O3 slag on blast furnace smelting has gradually increased. In this paper, the effects of slag basicity and Al2O3 content on slag viscosity and enthalpy change under constant temperature conditions was investigated. The changes in slag structure were analyzed by activation energy and Fourier Transform Infrared (FT-IR) spectroscopy. The relationship between slag components and slag temperature and viscosity when slag heat is reduced was investigated. The results showed that the viscosity first slightly decreased and then significantly increased with increasing basicity at constant temperature. With the addition of Al2O3 content, the viscosity of the slag increases. The activation energy increases with increasing slag basicity and Al2O3. With increasing basicity, the [SiO4]4- tetrahedral unit trough depth becomes shallow, the [AlO4]5- asymmetric stretching band migrates to lower wave numbers, and the slag structure depolymerizes. With the increase of Al2O3 content, the trough of [SiO4]4- tetrahedra deepens and the center of the symmetric stretching band moves to a higher wave number. The [AlO4]5- asymmetric stretching band becomes obvious, indicating the complexity of the slag structure. When the heat decreases, the slag temperature increases as the basicity increases, and the slag thermal stability is better at the basicity of 0.95-1.05. As the Al2O3 content increases, the thermal stability of the slag becomes worse.

More than 110 countries including 500 cities worldwide have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, the heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision-makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution based Multi-Criteria Decision Making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the final ranking. The robustness of the proposed method is verified by 3 sensitive analyses from different aspects. Air to water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide the decision-makers apply this ranking guideline with their practical feasible situations.

The prevalence of overweight and obesity is increasing worldwide not only in Western countries but also in Asian countries. Among Asian countries, South Asian countries experience the rapid increase of overweight and obesity that co-exist with the rapid increase of obesity-related non communicable diseases such as diabetes, dyslipidemia and cardiovascular. The phenomena in South Asian countries are triggered by growth in population size, population aging, urbanization and changes in lifestyle including increases in energy intake and reductions in physical activity. The imbalance between energy intake and energy expenditure leads to the development of positive energy balance, which over the time acumulate in a higher body fat. South Asians were reported to have a more unfavorable body composition with a higher body fat percentage as compared to BMI-matched Caucasians. The differences in body composition between South Asians and Caucasians contribute to differences in resting energy expenditure, in which South Asians have a lower resting energy expenditure as compared to BMI-matched Caucasians. Resting energy expenditure is the largest component of daily total energy expenditure, and therefore play an important role in determining the energy balance between energy intake and expenditure.

The paper aims to illustrate the different roles that social sciences can play in the study of the energy transition. To this end, a scheme is developed that frames the relative position of the social sciences with respect to other disciplines (metaframe). Secondly, socialization is identified as a charismatic category capable of providing an original, typically sociological contribution to the complex and hesitant energy transition (masterframe)

The plate embedded with acoustic black hole (ABH) indentations is potential for structural vibration and noise control. This work focuses on the mid- and low-frequency performance of plates embedded with the array of ABH for energy focalization and vibration &amp; noise suppression. Plates embedded with two-dimensional ABHs are modelled with detailed Finite Element (FE) models, and the power flow method is introduced to analyze the energy propagation characteristics arising from ABH effect. Then the distribution of average vibration power density along ABH radius is studied. Next, the energy dissipation effects of the plate model embedded with ABH array with two types of damping layers are investigated. Finally, the sound pressure levels of the ABH structure are calculated and discussed. This work is helpful to understand the characteristics of plates embedded with ABH array in reducing vibration and noise radiation. Results show the tremendous potential of ABH array for vibration and noise control.

Nowadays, some coffee production centers are still classification manually, so it requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and performance of the coffee bean classifier that can accelerate the process of classification beans. The classifier used consists of three main parts, namely the frame, driving force, and sieves. Research parameters include classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was found at a rotational speed of 91.07 rpm and a 16° sieves angle with a classifier working capacity of 38.27 kg/h, the distribution of the seeds retained in the first sieve was 56.77 %, the second sieves was 28.12%, and the third sieves was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieves angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classification, namely small, medium, and large.

Binary switches, which are the primitive units of all digital computing and information processing hardware, are usually benchmarked on the basis of their ‘energy-delay product’ which is the product of the energy dissipated in completing the switching action and the time it takes to complete that action. The lower the energy-delay product, the better the switch (supposedly). This approach ignores the fact that lower energy dissipation and faster switching usually come at the cost of poorer reliability (i. e. higher switching error rate) and hence the energy-delay product alone cannot be a good metric for benchmarking switches. Here, we show the trade-off between energy dissipation, energy-delay product and error-probability, for both an electronic switch (a metal oxide semiconductor field effect transistor) and a magnetic switch (a magnetic tunnel junction switched with spin transfer torque). As expected, reducing energy dissipation and/or energy-delay-product generally results in increased switching error probability and reduced reliability.

The fact that quantum gravity does not admit a co-variant vacuum state has far-reaching consequences for all physics. It points out that space could not be empty, and we return to the notion of an ether . Such a concept requires a preferred reference frame for, e.g., universe expansion and black holes. Here, we intend to discuss vacuum and quantum gravity from three essential viewpoints: universe expansion, black holes existence, and quantum decoherence.

The basis of the discrete-impulse energy supply (DIES) concept is the efficient use of supplied energy. The references describe in detail the general principles of DIES, examine the energy and thermodynamic aspects and the main mechanisms of intensification that can be initiated on the basis of this principle. DIES mechanisms conveniently can be divided into hard and soft ones. The former should be used to stimulate hydromechanical processes, and the latter to accelerate the processes of phase heat and mass transfer, or for the purpose of intensive mixing of multicomponent media. The authors have studied the possibility of using DIES to intensify the hydromechanical processes, in particular emulsification of milk fat (homogenization of milk, preparation of spreads), processing of cream cheese masses. Objects of research were whole non-homogenized milk, fat emulsions, cream cheese mass. In order to evaluate the efficiency of milk homogenization the homogenization coefficient change was studied, which was determined by centrifugation method as the most affordable and accurate one. Emulsions were evaluated according to the degree of destabilization, resistance and dispersion of the fat phase. The rheological characteristics of cheese masses were evaluated by the effective viscosity change.

Little information is available regarding the impacts of dietary energy level on the gut microbiota and metabolites of donkeys. This studied aimed to explore the effects of dietary energy content on growth performance, intestinal microbiome and metabolome of Dezhou donkeys. Thirty-six 9-month-old male Dezhou donkeys were assigned to two groups fed low or high content energy diets (LE or HE). Results showed that donkeys fed HE had improved (P < 0.05) the average daily gain (ADG) and feed efficiency (G/F), compared with those receiving LE diet. Compared to the LE group, feeding HE specially increased the abundances of unidentified_Prevotellaceae (P = 0.02) while decreased the richness of unidentified_Ruminococcaceae (P = 0.05) of donkeys. Compared to LE group, feeding HE diet significantly (P < 0.05) affected the metabolic pathways involving the aspartate metabolism and urea cycle. In addition, the increased bacteria and metabolites in the HE-fed group exhibited a positive correlation with improved growth performance of donkeys. Taken together, feeding HE diet increased the richness of some specific bacteria and upregulated growth-related metabolic pathways, which contributed to the augmented growth performance of donkeys. Thus, it is a recommendable dietary strategy to feed HE diet to fattening donkey for superior production performance and feed efficiency.

Archaea are an essential class of gut microorganisms in humans and animals. Despite the substantial progress in gut microbiome research in the last decade, most studies have focused on bacteria, and little is known about archaea in mammals. In this study, we investigated the composition, diversity, and functional potential of gut archaeal communities in pigs by re-analyzing a published metagenomic dataset including a total of 276 fecal samples from three countries: China (n=76), Denmark (n=100), and France (n=100). For alpha diversity (Shannon Index) of the archaeal communities, Chinese pigs were less diverse than Danish and French pigs (P<0.001). Consistently, Chinese pigs also possessed different archaeal community structures from the other two groups based on the Bray-Curtis distance matrix. Methanobrevibacter was the most dominant archaeal genus in Chinese pigs (44.94%) and French pigs (15.41%), while Candidatus Methanomethylophilus was the most predominant in Danish pigs (15.71%). At the species level, the relative abundance of Candidatus Methanomethylophilus alvus, Natrialbaceae archaeon XQ INN 246, and Methanobrevibacter gottschalkii were greatest in Danish, French, and Chinese pigs with a relative abundance of 14.32%, 11.67%, and 16.28%, respectively. In terms of metabolic potential, the top three pathways in the archaeal communities included the MetaCyc pathway related to the biosynthesis of L-valine, L-isoleucine, and isobutanol. Interestingly, the pathway related to hydrogen consumption (METHANOGENESIS-PWY) was only observed in archaeal reads, while the pathways participating in hydrogen production (FERMENTATION-PWY and PWY4LZ-257) were only detected in bacterial reads. Archaeal communities also possessed CAZyme gene families, with the top five being: AA3, GH43, GT2, AA6, and CE9. In terms of antibiotic resistance genes (ARGs), the class of multidrug resistance was the most abundant ARG, accounting for 87.41% of archaeal ARG hits. Our study reveals the diverse composition and metabolic functions of archaea in pigs, suggesting that archaea might play important roles in swine nutrition and metabolism.

Motivated by the work of Saridakis (Phys. Rev. D 102, 123525 (2020)), the present study reports the cosmological consequences of Barrow holographic dark energy (HDE) and its thermodynamics. Literatures demonstrate that Dark Energy (DE) may result from electroweak symmetry breaking that triggers a phase transition from early inflation to late time acceleration. In the present study, we incorporated viscosity in the Barrow HDE. A reconstruction scheme is presented for the parameters associated with Barrow holographic dark energy under the purview of viscous cosmology. Equation of state (EoS) parameter is reconstructed in this scenario and quintessence behaviour is observed. Considering BarrowHDE as a specific case ofNojiri-Odintsov (NO) HDE, we have observed quintom behaviour of the EoS parameter and for some values of n the EoS has been observed to be very close to −1 for the current universe. The generalised second law of thermodynamics has come out to be valid in all the scenarios under consideration. Physical viability of considering Barrow HDE as a specific case of NO HDE is demonstrated in this study.

The limitedness of the nonrenewable local energy resources in Israel, even in background of the later gas fields’ findings, continues to force the state to devote various efforts for the ‘green’ energy development. These efforts include installations both in the solar and in the wind energy, with a purpose to improve the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique is widely applied in many studies for valuation of renewable energy investment projects. Hence, we apply in this study the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel.

A long standing problem in paleontology is the presence of giant arthropods during the so-called carboniferous era. The same difficulty occurs for the size of plants at the same period. We devise a possible answer to this question entirely different from the theories developed until now, based on the hypothesis that the size of atoms is decaying for very large times and the dimensions of polymers such as chitin and cellulose, containing expansible holes, are decreasing more slowly than rigid materials such as metals, alloys, composite materials and stone. Incidentally, this hypothesis might also give an alternative statement to the expansion of space.

The General equilibrium theory tries to show how and why all free markets tend toward equilibrium in the long run. However, what is meant by equilibrium in this paper is more from a thermodynamical point of view. In order to understand the actual situation, it is necessary to study open systems which are complex. In physics, such a behavior in a complex system can be explain by using Non Equilibrium Thermodynamics. A system is able to self-organize and sustain itself away from equilibrium. Economic systems may fluctuate around a particular point. To sustain it far from the equilibrium state, it needs to degrade more energy and materials. In this study, the energy consumption patterns of Sri Lanka and USA are discussed. The pattern concerning Sri Lanka is close to the model proposed here, whereas the energy consumption pattern of USA is more complicated due to external factors.

Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

A growing body of evidence indicates that aging of the brain is strictly related to the decline of energy metabolism. In particular, in older adults, the neuronal metabolism of glucose declines steadily resulting in a growing deficit of ATP production. The decline is evoked by deficient NAD recovery in the salvage pathway and subsequent impairment of the Krebs cycle. NAD deficit impairs also the activity of NAD-dependent enzymes. All these open vicious circles of neurodegeneration and neuronal death. Some brain structures are particularly prone to aging and neurodegeneration. These are pathological foci of neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. This review article summarizes the impacts and mutual relationships between metabolic processes both on neuronal and brain levels. It also provides directions on how to reduce the risk of neurodegeneration and protect the elderly against neurodegenerative diseases.

Two well-known drawbacks in fuzzy clustering are the requirement of assign in advance the number of clusters and random initialization of cluster centers.; the quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of FCM using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms generation is important both for the producers/traders to minimize the imbalance costs and for the Transmission System Operators to insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale PV plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome these undesired effects of the current dispatching regulations.

Using a joint statistical analysis, we test a four-dimensional FLWR model embedded in a five-dimensional bulk based on the Nash-Greene embedding theorem. Performing a Markov Chain Monte Carlo (MCMC) modelling, we combine observational data sets as those of the recent ``Gold 2018'' growth data, the best-fit Planck2018/$\Lambda$CDM parameters on the Cosmic Microwave Background (CMB), the Baryon Acoustic Oscillations (BAO) measurements, the Pantheon Supernovae type Ia and the Hubble parameter data with redshift ranging from $0.01 < z < 2.3$ to impose restrictions on the model. From linear Nash-Green fluctuations of the metric, we obtain the related perturbed equations in longitudinal Newtonian gauge to obtain the evolution of growth matter. We show that a mild alleviation is obtained from the $\sigma$ tension between the growth amplitude factor and the matter content $(\sigma_8$-$\Omega_m)$ of the observations from CMB and Large Scale Structure (LSS) probes with degeneracies on the parameters. We also apply the Om(z) diagnosis to distinguish the model from $\Lambda$CDM. The Akaike Information Criterion (AIC) is used and we find a relative statistical consistence of the present model with both $\Lambda$CDM and $w$CDM models up to 1$\%$ of percentage difference at early times.

The Hubble parameter H(z), as a function of redshift, is modified by the presence of a new term originated from the extrinsic curvature in an embedded space-time. Assuming an asymptotic expansion factor $a\sim 0$ or, equivalently $z\rightarrow \infty$, it is obtained a nearly resemblance of the present model ($\beta$-model) with quintessence $w$CDM model with $H(z)\sim H_{wCDM}(z)$ at background level. In conjunction with $\Lambda$CDM, we test the models using a pack of recent datasets like that of the ``Gold 2018'' growth data, the best-fit Planck2018/$\Lambda$CDM parameters on the Cosmic Microwave Background (CMB), the Baryon Acoustic Oscillations (BAO) measurements, the Pantheon Supernovae type Ia and the Hubble parameter data with redshift ranging from $0.01 < z < 2.3$. Performing the Akaike Information Criterion (AIC) to ascertain the statistical viability of the model from Jeffreys' scale, we apply a joint likelihood analysis to the data with the Markov Chain Monte Carlo (MCMC) method. We find that the present model is in very good agreement with observations with a close statistical equivalence with the $\Lambda$CDM and $w$CDM cosmologies at 1-$\sigma$ level. We also show that a mild alleviation of the $\sigma$ tension between the growth amplitude factor and the matter content $(\sigma_8$-$\Omega_m)$ of the observations from CMB and Large Scale Structure (LSS) probes. A comparison of the aforementioned full pack of data in MCMC chains is made with the resulting MCMC from Pantheon SNIa+$H(z)$ in order to analyse the sensitivity of the models and how they respond to a cosmography analysis on the evolution of $H(z)$ and the deceleration parameter $q(z)$. In this sense, we find that only the $\beta$-model can be unaffected to the variations of the previous datasets due to its several minima in the likelihood (degeneracies) of the related parameters with an overall percentage relative difference only up to 4$\%$.

From the linear Nash-Green fluctuations of background metric, we present the perturbation equations in an embedded four space-time. In the context of a five-dimensional bulk, we show that the perturbations are only propagated by the gravitational tensorial field equation. In a Newtonian conformal gauge, we study the matter density evolution in sub-horizon regime and on how such scale may be affected by the extrinsic curvature. We use the "extended Gold 2018'' growth dataset with 25 datapoints and the best fit Planck2018/LambdaCDM parameters. Hence, we determine the evolution equation for the density growth delta(a) as a result from the embedded equations of the background geometry. By using solar constraints, we analyse the evolution of the effective Newtonian constant G_eff and showing that applying Taylor expansion to G_eff (a) under the constraint of time-derivative of G_eff(0)=G at a= 1 in matter domination era, we get an agreement with Big Bang Nucleosynthesis (BBN) and also an alleviation of the 3-sigma tension to 1-sigma contour between (sigma8-Omega_m) of the observations from Cosmic Microwave Background (CMB) and Large Scale Structure (LSS) probes.

First, we propose a scale-invariant modified gravity interacting with a neutral scalar inflaton and a Higgs-like SU(2) × U(1) iso-doublet scalar field based on the formalism of non-Riemannian (metric-independent) spacetime volume-elements. This model describes in the physical Einstein frame a quintessential inflationary scenario driven by the “inflaton” together with gravity-inflaton assisted dynamical spontaneous SU(2) × U(1) symmetry breaking in the post-inflationary universe, whereas SU(2) × U(1) symmetry remains intact in the inflationary epoch. Next, we find the explicit representation of the latter quintessential inflationary model with a dynamical Higgs effect as an Eddington-type purely affine gravity.

Over abundance of magnetic monopoles predicted by the Grand Unification Theories is inconsistence with current astronomical observation. The inflationary hypotheses with a vacuum energy deriving the exponential expansion can explain the two long-standing problems, flatness and horizon of the universe; and somehow suppress the abundance of monopoles. However, the dynamical scalar field has considerable uncertainties, which even leads to inflationary models separating from particle physics. This paper makes a small change, the early universe undergoes free expansion rather than an adiabatic one widely adopted, In such a case, the annihilation of abundant magnetic monopoles is just required in driving the inflation, so that the three long-term problems are automatically solved. On the other hand, the relic mass of failed annihilation of monopoles is responsible for the dark matter at present epoch. And ongoing annihilation on stars and compact objects corresponds to the dark energy. As monopole relics may exist in the form of monopole anti-monopole pairs, a new strategy of direct search is proposed.

This paper presents a review of the electrical heating method for heavy oil recovery based on past, current, and future prospects of electrical heating. Heavy oil is one of the potential crude oil used as a link to reduce the crisis of light oil used today. The obstacle of heavy oil is a high viscosity and density in which thermal injection is a method for heavy oil recovery, but it results in economic and environmental issues. Electrical heating is one of the thermal methods by transferring heat into the reservoir. The basic process of electrical heating is to increase the mobility of the oil. Because the temperature rises, it can reduce oil viscosity and makes it easier for heavy oil to flow. The past and current developments have been carried out to fill up the gap of electrical heating projects. The future prospects must meet energy efficiency, and the excessive heat will damage formation that must be tackled in the future prospect. the works adopt several electrical heating projects and applications in the world where the works give a brief future prospect of electrical heating.

To understand microbial growth with mathematical models has a long tradition that dates back to the pioneering work of Jacques Monod in the 1940s. Growth laws are simple mathematical expressions that aim at describing growth rates of microbes as functions of external parameters, in particular nutrient concentrations. These laws are now widely applied to construct, e.g., dynamic ecosystem models. However, to explain the growth laws from underlying (first) principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified principle limitations of the growth process. Whereas these approaches considered a growing microbe as a black box, modern theories heavily rely on genomic resources to describe and model genome-scale networks to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modelling approaches only in a rather superficial fashion, and it appears that recent modelling approaches and classical theories are disconnected fields. In order to stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamic principles. We start with classical black-box models of cellular growth, and continue with genome-scale modelling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer classical black-box parameters, which are experimentally accessible in growth experiments. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

The ΛCDM model successfully models the expansion of matter in the universe with an expansion of the underlying metric. However, it does not address the physical origin of the big bang and dark energy. A model of cosmology is proposed, where the state of high energy density of the big bang is created by the collapse of an antineutrino star that has exceeded its Chandrasekhar limit. To allow the first neutrino stars and antineutrino stars to form naturally from an initial quantum vacuum state, it helps to assume that antimatter has negative gravitational mass. While it may prove incorrect, this assumption may also help identify dark energy. The degenerate remnant of an antineutrino star can today have an average mass density that is similar to the dark energy density of the ΛCDM model. When in hydrostatic equilibrium, this antineutrino star remnant can emit isothermal cosmic microwave background radiation and accelerate matter radially. This model and the ΛCDM model are in similar quantitative agreement with supernova distance measurements. Other observational tests of the above model are also discussed.

In order to meet the national energy saving goals set in the Vietnam National Energy Efficiency Program in the period of 2019 – 2030 (VNEEP), the Vietnamese government has adopted a series solutions and policies to improve energy efficiency. The Vietnam’s 63 provinces will be as main actor for the national achievement in energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems. In this study, provincial and national data from General Statistic Office are analyzed in terms of the energy efficiency levels. With the trends of economic development and energy consumption in both national and regional levels, the Lorenz curve between Vietnamese energy consumption and GDP is investigated. The Lorenz coefficient shows the energy allocation is nether reasonable nor balanced. By using clustering method, the 63 provinces of Vietnam clustered into 7 groups that the provinces in the cluster has the similar indexes of energy efficiency i.e. ability, responsibility, potential and difficulty. The energy consumption and GDP are predicted in the period of 2019 – 2025. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 are heavy contribution. Among them, the provinces in the cluster 2 and 3 need to focus on the industry sector in their energy saving policy. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development.

This paper is to summarize the involvement of the stress energy tensor in the study of fluid mechanics. In the first part we will see the implication that carries the stress energy tensor in the framework of general relativity. In the second part, we will study the stress energy tensor under the mechanics of perfect fluids, allowing us to lead third party in the case of Newtonian fluids, and in the last part we will see that it is possible to define space-time as a no-Newtonian fluids.

This brief communication considers and illustrates dark matter and dark energy within the Baryon Phase Transition (BPT) cosmological model as well as some experiments that may confirm (or deny) the validity of the model.

A major goal of neuroscience is understanding how neurons arrange themselves into neural networks that result in behavior. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns, or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations.Here, we propose a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of the found neuronal phenomena from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, contrasting with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue providing the necessary energy which, in the case of the brain, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations, with Gibb’s free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy, by far, is expended by action potentials and post-synaptic potentials; therefore, plasticity can be reinterpreted in terms of their energy context. Consequently, neurons, through their synapses, impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Subsequently, the energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management also emerges as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory inputs. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which properties arise in neural circuits and how. Essentially, the proposal of the Energy Homeostasis Principle is also readily testable for simple neuronal networks.

This paper presents a hydrodynamic numerical model for a pitching wave energy converter (WEC). The model uses potential wave theory and is based on Cummins' equation, with nonlinear hydrostatic restoring stiffness and excitation forces based on instantaneous body position and water surface elevation. The numerical model can include non-linear forces, like quadratic drag, power-take-off and other forces that may account for unknown viscous effects observed in experiments. The paper discusses the applicability and limitations of the code and presents the cases where assumptions and simplifications can be made. The goal is to conclude on the simplest, yet accurate, version of the model by evaluating its accuracy using experimental data. The case study for validation is Floating Power Plant's (FPP's) WEC [1]. In the full-scale commercial project, FPP's device consists of a semisubmersible platform hosting a wind turbine (5-8MW) and 4 WECs, each one connected to the platform by a rotation shaft. Due to the configuration of the platform, strong interactions occur between the WECs and the structure, as they are very closely spaced. In order to validate a numerical model able to simulate these hydrodynamic interactions, wave basin experiments with a similar but simplified setup were performed.

The establishment of low carbon assessment initiatives is a crucial task especially at the city level. The determination of which source of carbon contributed more require robust data set and strategic approach. Hence, by using the campus as a small city approach, the establishment of carbon assessment and its’ reduction initiatives was required to keep track of the hotspot of the carbon source. The substantial amount of carbon source from campus operations such as energy consumption in the building, waste generation, and water consumption were identified. Moreover, as institutions of higher education, the execution of low carbon campus was initiated structurally involves the triangulation of research activities, teaching & learning and as well as campus operations or known as campus living lab approach. The application of low carbon cities framework, LCCF and assessment system enables to strategize the low carbon campus initiatives through the use of carbon footprint concept and the LCCF carbon track.

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

In a world where conventional sources of energy are fast depleting, the quest for alternative energy sources may hold the key for the survival of humanity. In the present work, emphasis has been given to the idea of producing energy from perovskite based solar cells. In order to bring this idea into fruition, a unique and novel nano structured perovskite material n-propyl ammonium lead chloride (C₃H₇NH₃⁺PbCl₃⁻) was prepared through a unique co-precipitation route using n-propyl amine (n-C₃H₇NH₂) and hydrochloric acid as the starting precursors with aqueous solution of Pb(CH₃COO)₂3H₂O. Finally acetic acid was added to the solution and this solution was allowed to concentrate and then gradually cooled down to room temperature. After math, the synthesized material was spin-coated on TiO₂ film to fabricate the solar cell. The device was then undergone systematic analysis using XRD, SEM, UV and Photo Conversion to get a transparent idea regarding its structural, electrical and optical properties. When experimentally applied, this perovskite-based solar cell has shown energy conversion efficiency (η) of around 6.01 % which is noticeably good. Thus it can be concluded that this material is promising for fabrication of vastly efficient solar cells. This technology can be tried in large scale as an alternative of conventional energy in the near future.

Malaysia generates significant quantities of Oil Palm Wastes (OPW) which can be potentially valorised into sustainable bioenergy as envisaged by the National Biomass Strategy (NBS-2020). Despite significant investments, policy directives and government support, the valorisation of OPW into bioenergy has remained low exacerbating waste management challenges. Therefore, the strategies and impediments to the rapid bioenergy development and bioelectricity generation from OPW require practical assessment. Therefore, this paper examines the level of development and diffusion of the biomass innovation system in Malaysia based on the Functions of Innovations Systems (FIS) approach developed by Dutch and Swedish researchers. Furthermore, the key factors hindering biomass energy technologies implementation in Malaysia and potential solutions were identified, highlighted and examined. Based on the FIS analysis the functions; entrepreneurial activities, knowledge development, and resources mobilization functions are well established in the Malaysian biomass innovation system (BIS). However, the functions of guidance of search; creation of legitimacy; knowledge diffusion and market formation are underdeveloped resulting in the low penetration of bioenergy in Malaysia. Other factors include; fossil fuel subsidies, numerous or conflicting energy policies and weak collaboration between academia and the industry. The outlined challenges can be addressed by revising fuel subsidies, Feed-in tariffs, RETs implementation, roles of supervisory agencies, and bureaucratic procedures for access to funds for research and development of bioenergy in Malaysia.

The European Food Safety Authority (EFSA) has identified some risk factors for the occurrence of side effects linked to energy drinks (EDs) consumption by young people. Tachycardia, sleeplessness, caffeine addiction may be caused by excessive consumption of EDs during parties, sport matches, ect. EDs consumption has been evaluated in a sample of students in Italy together with some aspects of their lifestyle. The survey was performed in two high schools from September 2014 to June 2015. 583 students between 14 to 18 years were recruited and a standard questionnaire (EFSA checklist) was used to collect information on responders characteristics, beverages consumption, EDs with alcohol, and EDs and sports. 350 out of 583 responders (60%) consumed EDs and 146 out of 583 responders (25%) reported an occasional alcohol consumption. Despite 82 out of 146 alcoholic drinkers (56%) were EDs-alcohol consumers, only 70 out of 583 adolescents (12%) reported habitual EDs consumption. Moreover, 38 out of 379 (10%) of all physically active adolescents reported frequent EDs consumption before sportive trainings. Study results highlight the need for primary prevention measures in communication campaigns and training delivered by school to limit potential health threats related to excess of EDs consumption.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

On June 8 and June 9, 2017, we gave two invited lectures at the Zelinsky Institute of Organic Chemistry (ZIOC) of the Russian Academy of Sciences, Moscow. The first lecture, “Sol-Gel Catalysts: Making Green Chemistry Possible”, focused on the practical outcomes of 25 years of research on sol-gel entrapped catalysts. The second, “Chemistry for the Bioeconomy: From Discussion to Action”, offered a critical insight to the forthcoming bioeconomy. Both lectures aroused much interest in the audience and ended with a vigorous discussion lasting about one hour. An outlook is provided herein.

In the past 50 years, calorimeters have become the most important detectors in many particle physics experiments, especially experiments in colliding-beam accelerators at the energy frontier. In this paper, we describe and discuss a number of common misconceptions about these detectors, as well as the consequences of these misconceptions. We hope that it may serve as a useful source of information for young colleagues who want to familiarize themselves with these tricky instruments.

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.

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.

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.

The article discusses the formation of local electric energy systems (LEES) based on renewable energy sources (RSE) as balancing groups in the electric power system (EPS). These systems can function autonomously, providing electricity even in extreme conditions. LEES incorporate small hydroelectric, photovoltaic, and wind power plants, whose output is unstable due to varying natural conditions. To address this, energy storage systems are included to ensure stability and balance. LEES may vary significantly in terms of reliability, power losses, and quality, making it essential to choose the optimal configuration. Optimizing power supply by focusing on individual indicators is discouraged, as it may negatively affect others. To evaluate LEES performance, an integral indicator has been developed, combining the theory of Markov processes and similarity. This indicator compares actual performance with an "ideal" standard, simplifying the assessment of complex systems. It allows comparison of different transmission and distribution options without needing to evaluate all technical and economic details. The proposed integral indicator effectively reflects real operating conditions and assesses the efficiency, quality, and optimality of power supply, while remaining adaptable to partial indicators.

Great Himalayan range with regions namely, Jammu & Kashmir, Ladakh and high altitude parts of north eastern states inherently experience high solar exposure, cold, arid climate and significantly challenging geography. With a scarcity of affordable public housing projects in these regions a grid enabled low energy residential prototype is proposed having the capability to operate on nearly zero consumption of heating and cooling energy with the application of indigenous and contemporary energy efficient passive design techniques. Preliminary study, development of design and reading of simulation results were applied for the performance and benefit analysis of the low energy building prototype. Analysis of the climatic zone further classified and narrowed the design criterion by confirming upon beneficial architectural components proven to be energy efficient. These architectural components include, optimization of direct solar gain and orientation of building envelope, modified trombe wall system, traditional sunrooms, solar chimney with openable vents, efficient spatial planning and compliance of relevant parameters such as window-wall-ratio (WWR). The proposed low energy housing for the economically weaker section is estimated to save nearly 50% of active fuel based household heating energy. Site has been planned in such a way that about 100% of required electricity can be generated from on site renewable energy for the proposed infrastructure, it is proven more beneficial if additional electricity is generated to offset the power supply and result in lower or subsidized electricity bills. Design is compliant to different codes relevant to energy conservation and low-income housing with an embodied carbon of about 1.2 kgCO2/m2 per dwelling unit giving ‘A’ grade in cradle to gate impacts. The findings and performance of this prototype can serve as a guideline for energy efficient design in low income group housing situated in severe cold and arid regions of India such as Ladakh (U.T.).

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 analysis of the key factors which prevent or contribute to the promotion and development of the Power Purchase Agreements (PPAs) in order to meet the sustainability goals represents an important issue which worth being investigated, especially in the situation in which entering in bilateral relations inside this contract has a powerful meaning in achieving new climate targets, developing and improving the use of green energy, and promoting a sustainable green economy. The main goal of this research is to identify and analyze the main factors which help or pre-vent the promotion and development of a PPA, in order to meet the sustainability goals by promoting a green economy. For this research survey based on comparative analysis, questionnaires and interviews with energy experts, Spearman’s correlation matrix and IBM SPSS Statistics for Window are also used. The results suggest that although there are positive interest and an open attitude towards the PPA from both sides (sellers and buyers), but there is still place for improvement, the lack of knowledge or the insufficient measures taken so far being two of the reasons way until now in Romania we do not have a developed PPA market and the level of knowledge regarding this type of mechanism is quite low.

Hydrogen plays an important role in the implementation of Australia’s Net-Zero Strategy. It competes with lithium-ion rechargeable batteries in the electrification of Australian land transportation sector, and can provide an alternative energy storage medium for the greening of the country’s electrical grid system. This paper reviews the role hydrogen plays in the current government’s Net-Zero strategy, and investigates the potential role of other bioenergy sources. It is found that hydrogen and batteries play essentially complementary roles in reducing transport emissions, and that the former energy source is more suited to the operation of long-distance commercial service vehicles than batteries. It is suggested that bioenergy fuels such as biomethane, second generation biofuels and HVO can provide suitable bridging alternatives in the event that Australia’s EV strategy is unable to meet the 2030 target.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

In cosmology dark energy and dark matter are included in the ΛCDM model, but they are still completely unknown. Because in black holes Lorentz invariance seems not to be applicable for curved space-time, we introduce a model for a reduced speed of light in black holes due to quantum gravity effects and the Heisenberg uncertainty relation. Then black holes are a source for a scalar field with dark energy characteristics. This model has no information paradox for black holes because particles / radiation entering the black hole are redshifted in their wavelength that far that the wavelength has the size of the Schwarzschild radius and thus they are in some way "frozen" in the black hole. We show that the scalar field also has characteristics of dark matter shortly after Planck time, when we use a Big Bounce model. This model also presents an alternative to cosmological inflation with the possibility to solve the flatness and horizon problem and the problem of density fluctuations.

To address the problems of wind abandonment in the regional integrated energy system (RIES), which occurs when cogeneration unit operates in a heat-determined electricity operation mode, and the low efficiency of the energy storage system in a low-temperature environment, we propose an optimal scheduling method for RIES based on fine energy storage and wind power dissipation. First, a fine energy storage model is established on the source side and the conditional value at risk(CvaR) theory is used to quantify the uncertainty of wind power; then a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference between heat and power loads, and to promote the consumption of wind power. Finally, with the objective of minimizing the total cost of RIES optimal dispatch, the example is solved on the MATLAB platform. The simulation results show that compared with the traditional model, the proposed model is not only more adaptable to the low-temperature environment, but also can effectively improve the system economy and wind power consumption.

: There has been an ever-increasing demand for energy from the past decades. Renewable energy technologies play a major role in satisfying the energy demand as well as in a decreased CO2 emissions. Solar and Wind energies are the major upcoming technologies in renewable energies. Decentralized power production is a system where the energy production and consumption are very close to each other. Microgrids can be decentralized or grid connected and they, with renewable energy sources, encounter a problem of storage as the power production from solar and wind is intermittent. This paper discusses the comparison between using batteries and pumped storage hydropower (PSH) as an energy storage system and the integration of wind and solar PV energy sources. HOMER software simulations are used to obtain optimized renewable energy integration in microgrid and to understand its economic analysis. Two scenarios are run with the model where one considers battery and the other considers PSH to obtain the economic and technical best results of these microgrids. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results show that microgrid with storage of PSH is economical with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh in comparison with batteries solution which has an NPC of 95.2M€ and an LCOE of 0.786 €/kWh. The role of storage is understood by differentiating the data into different seasons using Python for data analysis. Furthermore, sensitivity analysis is made by varying the capital cost multiplier of Solar PV and Wind Turbine to obtain optimal solutions.

The global transport sector of the world economy contributes about 15% of the Greenhouse Gas (GHGs) emissions in the world today. The University of Saskatchewan has pursued the green energy transition over the years. They have spearheaded diverse sustainability projects and agendas, due to the importance of curbing climate change and advancing sustainability. The transport system in the university campus is one area of focus where the Sustainability Office plans to introduce some innovations, as a way of curbing GHG emissions while also advancing sustainability practice in the university campus. The study carried out an economic benefit analysis on the campus fleet (consisting of 91 ICE vehicles) to determine if it is economically or financially feasible to transition from Internal Combustion Engine (ICE) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert software for analyzing renewable energy technology projects. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), Benefit-Cost-ratio, GHG emission reduction potential, etc. were analyzed. The findings revealed that the GHG emission from the campus fleet will be reduced by 100% (this will result in the removal of about 298.1 tCO₂ from the environment). Also, the fleet manager will save approximately $129,049 (88.9%) in fuel costs. Apart from these, the return on investment will be achieved in year 5 (all things being equal), but can be reduced to year 2 if the vehicles are put into constant and active use (eliminating most idle times. Also, the Sustainability Office will be making a GHG reduction revenue of $14,906.

Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialisation. The design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimisations and the prospects as self-sustaining power source were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the progresses as well as the technical challenges requiring research efforts to solve to realise numerous potential applications of IPVFC systems such as in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications.

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

In this paper, the methodology using Insect Intelligent Building (I^2B) technology for establishing energy consumption monitoring system of public buildings is prevailed. The computing process node and distributed algorithm are utilized to implement the energy consumption collection and data transmission and data pre-processing. Taking a commercial building as a case study, CPNs are applied to set up the building energy consumption monitoring system, with the Spanning Tree Algorithm for generating network topology，and BPNN method for solving abnormal data and recovering missing data. The research results demonstrate the proposed method can effectively improve the performance of plug-and-play and self-identified and self-configuration of energy consumption monitoring system.

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context to which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico-chemical properties of the amino acids (native vs. mutant) can still have minimal effects of protein thermodynamics. However, if a mutation causes significant perturbation of either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize on the effects of mutations on two important biophysical characteristics, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical characteristics.

The application of thermoelectric generators (TEG) on the nozzle of an aviation jet engine was studied by finite element TEG-simulations. Against the background of system-level requirements of the reference aircraft this work reports the resulting requirements on the TEG design with respect to applied thermoelectric (TE) element lengths and fill factors (F) within the TE modules in order to maintain a positive effect on the specific fuel consumption. Assuming a virtual optimized TE material and varying the convective heat transfer coefficients at the nozzle surfaces this work reports the achievable power output. System-level requirement on the gravimetric power density (> 100 Wkg-1) can only be met for F ≤ 21%. Extrapolating TEG coverage to the full nozzle surface, the power output reaches 1.65 kW per engine. Assessment of further potential is demonstrated by a parametric study on the fill factor, convective heat transfer coefficients, and materials performance. This study confirms a feasible design range for TEG installation on the aircraft nozzle with a positive impact on the fuel consumption. This application translates into a reduction of operational costs, allowing for an economically efficient installation of TEG in consideration of the cost-specific power output of modern thermoelectric materials.

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

With the rising awareness of environmental issues, concerns about pollution and global warming have gained importance, and electricity generation costs continue to increase. As a result, research on energy recovery using thermoelectric modules has attracted attention in recent years. Given the lack of significant breakthroughs in thermoelectric materials, improving the structure of thermoelectric modules has become a key direction for enhancing efficiency. Common thermoelectric module types include flat plate (Flat Plate TEG), annular (Annular TEG), flexible (Flexibility TEG), segmented, and cascaded designs. Factors influencing thermoelectric performance include the number, shape, height, contact area of thermoelectric elements, temperature difference, copper plate length, contact resistance, and the geometric arrangement of the elements. This study is based on the commercially available TGM1-127-1.0-0.8 thermoelectric module. By simulating the output performance of both flat plate and annular thermoelectric module structures under identical conditions—such as total volume of thermoelectric material, shape, and height of the thermoelectric legs, the total contact area with the heat source, temperature differential, and total volume of the connecting copper plate—we observed that the number of thermoelectric elements not only determines the open-circuit voltage but also influences the output power. The trend of output power remains consistent under varying temperature differences, regardless of changes in the load resistance.

Recent progress in studies of Rydberg double-well electronic energy states of MeNg (Me=12-group atom, Ng=noble gas atom) van der Waals (vdW) molecules is presented and analysed. The presentation covers approaches in experimental studies as well as ab-initio-calculations of potential energy curves (PEC). The analysis is shown in a broader context of Rydberg states of hetero- and homo-diatomic molecules with PECs possessing complex ‘exotic’ structure. Laser induced fluorescence (LIF) excitation spectra and dispersed emission spectra employed in spectroscopical characterization of Rydberg states are presented on the background of diversity of spectroscopic methods of their investigations such as laser vaporization-optical resonance (LV-OR), pump-and-probe methods and polarization labelling spectroscopy. Importance and current state-of-the-art applications of Rydberg states with irregular potentials in photoassociation (PA), vibrational and rotational cooling, molecular clocks, frequency standards and molecular wave-packet interferometry is highlighted.

This study focuses on the design, installation, and performance evaluation of a photovoltaic farm situated in Tulcea County, Romania, connected to a 20 kV distribution network. The farm, with a peak capacity of approximately 2800 kWp, benefits from Romania’s favorable climate conditions and supportive renewable energy policies. The analysis encompasses both the photovoltaic panel array and the electrical energy evacuation system. The substation at the park is equipped with technology that adheres to European standards and incorporates advanced features typical of modern electricity distribution networks. The assessment includes an evaluation of the park’s operational efficiency, with detailed calculations of the performance ratio and various power losses attributed to factors like temperature fluctuations, fouling, inverter efficiency, grid-related challenges, and availability issues. The farm contributes around 3620 MWh of electricity to the grid annually, demonstrating the effectiveness of photovoltaic installations in promoting sustainable energy solutions within regional energy networks.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

India, the world’s most populous country, is the world's third-largest emitter of greenhouse gases (GHGs). Despite employing several energy sources, it still relies heavily on coal, its primary energy source. Given India’s swiftly rising energy demand, this challenges meeting emission reduction targets. India has significantly increased investments in renewables like solar and hydrogen in recent years. While commendable, these initiatives alone cannot meet the country's expanding energy demands. In the short term, India must rely on both domestic and imported fossil fuels, with natural gas being the most environmentally friendly option. In this context, this paper attempts to forecast energy consumption, natural gas production, and consumption in India till 2050 using both univariate and multivariate forecasting methods. For multivariate forecasting, we have assumed two alternative possibilities for GDP growth: the business-as-usual and the high-growth scenarios. Each of our forecasts indicates a notable shortfall in the projected production of natural gas compared to the expected demand, implying our results are robust. Our model predicts that nearly 30-50 percent of India’s natural gas consumption will be met by imports, mainly in the form of LNG. Based on these findings, this paper recommends that Indian government policies emphasize increasing domestic natural gas production, importing LNG, and expanding renewable energy resources.

The average sizes \ {\bar{L}}_{i\ } and their dispersion W_i along the i-th axis of crystallites in powders are used to determine the X-ray diffraction sizes, D_{i\ XRD}, averaged over crystallite columns within the BWA method. Numerical calculations have been carried out for an orthorhombic lattice of crystallites, such as LiFePO4, NMC, having Lamé's g-type superellipsoid shape. For Lognormal distributions, the analytical expression for the normalized coefficient Kn is found and includes two constants. The first K_{g,0} is a constant at W→0, the second K_g is a constant depending on the g -type shape. The dependences of D_{i\ XRD} are also calculated for Normal distribution. A fairly simple equation can be obtained as a result of analytical transformations in the framework of experimentally validated approximations. However, a simpler way is to carry out numerical computer calculations with subsequent approximation of the calculated curves. Using the obtained analytical expressions to control technologies from nuclear fuel to cathode materials will improve the efficiency of flexible energy network especially storage in autonomous and standby power plants.

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which can ultimately limit the amount of rooftop solar photovoltaics (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modelled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process and quasi dynamic simulation (QDSL) models were used to represent the dynamic volt-watt and volt-var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear regression was used to form trend equations that represent various network characteristics against the number of PV connections. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer owned rooftop solar PV without the need for detail modeling.

Abstract: The internationally accepted goals of eliminating climate gas emissions implies substantial investments in renewable energy (RE) facilities. This will inevitably lead to major impacts on landscapes. Landscape concerns linked to RE facilities are already leading to controversies in many local communities. In this article, we focus on the question of landscape democracy related to the establishment of RE facilities. Based on recommendations from the European Landscape Convention, an analytical framework is presented identifying three main dimensions of landscape democracy, followed by an overview of arrangements, procedures and methods that are or may be used to encourage democracy. The procedures and methods are analysed based on examples from Denmark and Norway. This is followed by an analysis of decision levels with a special focus on the principle of subsidiarity. Finally, recommendations are presented to strengthen landscape democracy in relation to the installation of RE-facilities.

Up to now, Schrödinger equation, Klein-Gordon equation (KGE) and Dirac equation are believed the fundamental equations of quantum mechanics. Schrödinger equation has a defect that there is no NKE solutions. Dirac equation has positive kinetic energy (PKE) and negative kinetic energy (NKE) branches. Both branches should have low momentum, or nonrelativistic, approximations: one is Schrödinger equation and the other is NKE Schrödinger equation. KGE has two problems: it is an equation of second time derivative, and calculated density is not definitely positive. To overcome the problems, it should be revised as PKE and NKE decoupled KGEs. The fundamental equations of quantum mechanics after the modification have at least two merits. They are of unitary in that everyone contains the first time derivative and are symmetric with respect to PKE and NKE. This reflects the symmetry of the PKE and NKE matters, as well as matter and dark matter, of our universe. The problems of one-dimensional step potentials are resolved by means of the modified fundamental equations for a nonrelativistic particle.

The mechanisms of natural oscillations and resonance are described, considering the peculiarities of the transformation of elastic and kinetic energy in the implementation of the law of conservation of energy in local and integral volumes of the body, using the concept of mechanics based on the concepts of space, time and energy. When describing the motion in the Lagrange form, the elastic deformation energy of the particles is determined by the quadratic invariant of the tensor, whose components are the partial derivatives of Euler variables with respect to Lagrange variables. The increment of the invariant due to elastic deformation is represented as the sum of two scalars, one of which depends on the average value of the relative lengths of the edges of the particles in the form of an infinitesimal parallelepiped, the second is equal to the standard deviation of these lengths from the average value. It is shown that each of the scalars can be represented in the form of two dimensionless kinematic parameters of elastic energy, which participate in different ways in the implementation of the law of conservation of energy. One part of the elastic energy passes into kinetic energy and participates in the implementation of the law of conservation of energy for the body as a whole, considering external forces. The second part is not converted into kinetic energy but changes the deformed state of the particles in accordance with the equations of motion while maintaining the same level of the part of the elastic energy of the particles used for this. The kinematic parameters differ from the volume density of the corresponding types of energy by a factor equal to the elastic modulus, which is directly proportional to the density and heat capacity of the material and inversely proportional to the volume compression coefficient. Transverse, torsional, and longitudinal vibrations are considered free and under resonance conditions. The mechanisms of transformation of forced vibrations into their own after the termination of external influences and resonance at the superposition of free and forced vibrations with the same or similar frequency are considered. The formation of a new free wave at each cycle with an increase in the amplitude, which occurs mainly due to internal energy sources, and not external forces, is justified.

We present a model of space that considers it to be a quantized dynamical entity which is a component of the universe along with matter and radiation. The theory is used together with thermodynamic data to provide a new view of cosmic evolution and an insight into the nature of dark energy and dark matter. Space is made up of energy quanta. The universe started from an atomic size volume at very high temperature and pressure near the Planck epoch. Upon expansion and cooling, phase transitions occurred resulting in the formation of radiation, fundamental particles, and matter. These nucleate and grow into stars, galaxies, and clusters. From a phase diagram of cosmic composition, we obtained a correlation between dark energy and the energy of space. Using the Friedmann equations, data from WMAP studies of the composition of the universe at 3.0 x 10⁵ (a=5.25 x 10^-2) years and at present (a=1), are well fitted by our model with an equation of state parameter, w= -0.7. The accelerated expansion of the universe, starting at about 7 billion years, determined by BOSS measurements, also correlates well with the dominance of dark energy at 7.25 x 10⁹ years ( a= 0.65). The expansion can be attributed to Quintessence with a space force arising from a quantum space field. From our phase diagram, we also find a correlation suggesting that dark matter is a plasma form of matter similar to that which existed during the photon epoch immediately prior to recombination. Our Quantum Space Model leads to a universe which is homogeneous and isotropic without the need for inflation. The thermodynamics of expansion is consistent with BOSS data that show the process to be adiabatic and the rate of expansion decelerating during the first 6 billion years after the Big Bang. However, it became non-adiabatic and accelerating thereafter. This implies an influx of energy from a source outside the universe; it warrants consideration as a possible factor in the accelerated expansion of the universe.

DC residential distribution system (RDS) consisted by DC living home will be a significant integral part in the future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between main grid and demand sides. The utilization of distributed generations (DGs) requires an economic operation, stability, environmentally friendly in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through centralized energy management system (CEMS), but a control approach to implement and ensure DG output voltages to various DC buses in DC RDS. Based on data collection, prediction and a certain objection, the expert system in CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of unit power schedule. In this work, a DC RDS is as a case study to demonstrate the process, the RDS is associated with unit economic models, cost minimization objective is proposed to achieve based on real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

The surface thermodynamic properties of polymers and copolymers modified by supramolecular structures are very used in several industrial processes, such as selective adsorption, paints, coatings, colloids, and adhesion applications. Background: Inverse gas chromatography at infinite dilution was proved to be the best technique to determine the surface properties of solid surfaces by studying the adsorption of some model polar and non-polar organic molecules adsorbed on solid surfaces by varying the temperature. Methods: The retention volume of adsorbed solvents is a precious parameter to obtain the London dispersive and polar free energies and the London dispersive surface energy of styrene–divinylbenzene copolymer modified by supramolecular structure of melamine using the Hamieh thermal model and our new methodology consisting in the separation of the two polar and dispersive free energy of interaction. This led to the determination of the polar acid and base surface energy, and the Lewis’s acid-base constants of the various solid materials. Results: all surface energetic properties of styrene–divinylbenzene copolymer modified by melamine at different percentages were determined as a function of temperature, following our new methodology. Conclusions: It was observed that the styrene–divinylbenzene copolymer exhibited the highest London dispersive surface energy which decreased when the melamine percentage increased. All materials presented higher Lewis’s basicity and this Lewis’s basicity increased with the percentage of melamine.

Textbooks tell us that the coupling constant of Dirac magnetic monopoles is 34.259. Here I show by using the quark hypothesis that at zero temperature the coupling constant is as high as 308.331. Moreover I show that it is a running coupling constant and is smaller than 0.5 at the Planck temperature.

This paper presents an effective Levenberg-Marquardt procedure for modeling accelerometers used in the energy industry. The proposed procedure applies the results of accelerometer calibration methods commonly used in engineering practice using stationary or portable calibrators. These methods are extended here to more advanced solutions, which will constitute the basis for further research (both practical and theoretical) into improving the accuracy of accelerometers, which play an extremely important diagnostic function in the operation of machines, devices and the wide range of components used in the energy industry.

Developed within the scope of a collaborative R&DT project, a 1 kW pilot unit aimed to perform the recovery of previously liquefied biomass from lignocellulosic forest resi-dues, later used as a carbon source in a co-electrolysis process, intended for the pro-duction of synthesis gas (H2, CO, CO2), also known as syngas. The syngas will be trans-formed into other value-added products, namely synthetic biofuels such as methane, methanol or biodiesel. This paper presents the results of the research currently being carried out at ISEL with the objective of further advancing development on the pilot unit by the execution of optimization tests with and without the addition of two dif-ferent biomass types.

The idea of varying constants of nature is very old, and has commanded a lot of attention 1 since first mooted. The variation of the gravitational parameter G and cosmological parameter Λ is still an active area of research. Since the idea of a varying G was introduced by Dirac almost a century ago, there are even theories that have variable G such as Brans-Dicke theory and the scale covariant theory. Both these theories also have a varying Λ in their full generalisations. A varying Λ was also introduced around the same time as that of varying G. It is interesting to note that a possible solution to the cosmological constant problem can be realised from a dynamic Λ. In this work, we focus on a varying Λ and G framework. In almost all studies in the simplest framework of variable Λ and G, it is found that one of them has to increase with time. However, observations and theoretical considerations indicate that both Λ and G should decrease with time. In this paper, we propose a solution to this problem, finding theories in which both Λ and G decrease with time.

Agricultural intensification through simplification and specialisation has homogenised diverse landscapes, reducing their heterogeneity and complexity. While the negative impact of large, simplified fields on biodiversity is well-documented, the role of landscape structure in mitigating and stabilising climate is becoming increasingly important. Despite considerable knowledge of landscape cover types, the understanding of how landscape structure influences climatic characteristics remains limited. To explore this further, we studied an area along the Czech-Austrian border, where socio-political factors have created stark contrasts in landscape structure, despite similar topography. Using Land Parcel Information System (LPIS) data, we analysed the landscape structure on both sides, and processed eight Landsat 8 and 9 OLI/TIRS scenes from the 2022 vegetation season to calculate vegetation indices (NDVI, NDMI) and microclimatic features (surface temperature, albedo, and energy fluxes). Our findings reveal significant differences between the two regions. Czech fields, with their larger, simpler structure and lower edge density, experience more extreme temperatures and fluctuating energy fluxes, while Austrian fields exhibit greater stability. These patterns are consistent across landscape classes, with Austria’s finer landscape structure providing higher stability throughout the vegetation season. In light of climate change and biodiversity conservation, these results emphasise the need to protect and restore landscape complexity to enhance resilience and environmental stability.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.

Wind energy is one of the main drivers of the renewable energy market. The wind turbine gearbox, used as a multiplier - is one of the main components directly related to wind turbine efficiency and lifespan. Therefore, the strict control of the gearbox project and its manufacturing processes or even minor improvements in this component heavily and positively impact energy production/generation over time. This work diagnoses a 1.6 MW gearbox fracture mechanics to identify its main reason and how the chain of events took place, leading to the catastrophic failure. The diagnoses underwent numerical simulation (Finite Element Analysis - FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses between the teeth, the region with the crack nucleated was the top land (tip of the tooth). The most likely factors that led to the premature failure were the missed lubrication for the planetary bearings, lack of gear raw material cleanliness, and excessive case depth at the tip of the sun shaft gear teeth. With the sun gear, planet bearings, and planet gears broken in pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, ending up as a premature gearbox failure.

The article is focused mainly on verifying the suitability of data from experimental milling heat-treated beech wood and on investigation of the effect of technical and technological parameters of milling on the energy consumption of this process. The independent parameters of machining process are cutting speed, feed speed, rake angle, and hydrothermal modification of experimental wood material. Based on analysis of variance it can be argued that the greatest statistically significant effect on energy consumption have cutting speed and rake angle of the tool while the feed speed had the least influence. The measured data of cutting power during milling were used to build a regression model and validate it, while the most suitable type of model with correlation of 87 % is Classification and Regression Tree followed by model created by Random Forests method.