Ditching-rotary tiller was widely used in tillage and ditching furrow during rapeseed sowing. As its ditching furrow system (consist of four identical ditching plows) would bear great drag resistance from the soil during operation, the ditching plow was optimized based on differential geometry analysis and EDEM simulation. Resistance-velocity simulation indicated the plow with parabolic type guide curve had better reduce-drag characteristics. Differential geometry analysis indicated decay rate of E and growth rate of G could represent the variable regularity of drag resistance with the increase of velocity. The L value could represent the magnitude of drag resistance. The smaller L value, decay rate of E and growth rate of G, the better drag-reduce characteristics. Quadratic rotation orthogonal combination design shown the optimal structural parameter values were: Δθ=15°, θmin=45°, h=250mm, l=100mm. The minimum average drag resistance was 230.1N. The differential geometry analysis of optimal ditching plow shown |dE/dκ| and |dG/dκ| could obtain minimum value, indicating growth rate of drag resistance for optimal plow was the lowest within the experimental level. Field experiment indicated the operation effect of optimized ditching furrow system could met the requirements of rapeseed sowing. The research provided in-depth theoretical guidance for Drag-reduce design of plow.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

With the popularization of natural gas and the requirements for environmental protection, the development and utilization of natural gas is particularly important. The status of natural gas in China's oil and gas exploration and development is constantly improving, and the country is paying more and more attention to the exploitation and utilization of natural gas. The Upper Paleozoic tight sandstone in the Ordos Basin is characterized by low porosity, low permeability and large area of concealed gas reservoirs. By injecting CO2 into the formation, the recovery of natural gas can be improved, and at the same time, the stable storage of CO2 can be achieved to achieve a win-win situation of CO2 emission reduction and utilization. Injecting greenhouse gas CO2 into gas reservoirs for storage and improving recovery has also become a hot research issue. In order to improve the recovery efficiency of tight sandstone gas reservoir, this paper takes the complex tight sandstone of Upper Paleozoic in Ordos Basin as the research object, through indoor physical simulation experiments, carried out the influence of displacement rate, fracture dip angle, core permeability, core dryness and wetness on CO2 gas displacement efficiency and storage efficiency, and analyzed the influence of different factors on CO2 gas displacement efficiency and storage efficiency to improve the recovery and storage efficiency. The research results show that under different conditions, when the CO2 injection pore volume is less than 1PV, the relationship between the CH4 recovery rate and the CO2 injection pore volume is linear, and the tilt angle is 45 °. When the CO2 injection pore volume exceeds 1PV, the CH4 recovery rate increases slightly with the increase of displacement speed, the recovery rate of CO2 displacement CH4 is between 87% - 97%, and the CO2 breakthrough time is 0.7PV-0.9PV. In low-permeability and low-speed displacement cores, the diffusion of CO2 molecules is more significant. The lower the displacement speed is, the earlier the breakthrough time of CO2 is, and the final recovery of CH4 slightly decreases. Gravity has a great impact on CO2 storage and enhanced recovery. The breakthrough of high injection and low recovery of CO2 is earlier, and the recovery of CH4 is about 3.3% lower than that of low injection and high recovery. The bound water makes the displacement phase CO2 partially dissolved in the formation water, and the CO2 breakthrough lags about 0.1PV. Ultimately, CH4 recovery factor and CO2 storage rate are higher than those of dry core displacement. The research results provide theoretical data support for CO2 injection to improve recovery and storage efficiency in complex tight sandstone gas reservoirs.

The use of carbon nanotubes to improve the mechanical properties of polymers is one of the promising directions in materials science. The addition of single-walled carbon nanotubes (SWCNT) to a polymer results in significant improvement of mechanical, electrical, optical, and structural properties. However, the addition of SWCNTs does not always improve the polymer properties. Also, when a certain content of SWCNTs is exceeded, the mechanical properties of the nanocomposite are getting worse. This article reports the results of computer simulations for predicting the mechanical properties of polymer/single-walled carbon nanotube nanocomposites. The efficiency of reinforcing polymer composites is considered depending on the concentration of carbon nanotubes in the polymer matrix, their size and structure. The elastic moduli of the nanocomposites were predicted using computer simulations for unit cell tension (0.1%). General trends in the mechanical properties of composites with polypropylene, poly (ethyl methacrylate), polystyrene matrices and SWCNTs are shown.

The fluorinated organic compounds have superior physicochemical properties than general organic compounds due to the strong C-F single bond, and widely used in medicine, biology, pesticides, and materials science. In order to gain a deeper understanding of the physicochemical properties of fluorinated organic compounds, fluorinated aromatic compounds have been investigated by various spectroscopic techniques. 2-Fluorobenzonitrile and 3-fluorobenzonitrile are important fine chemical intermediates, and their excited state S1 and cationic ground state D0 vibrational features remain unknown. In this paper, we used two-color resonance two photon ionization (2-color REMPI) and mass analyzed threshold ionization (MATI) spectroscopy to study S1 and D0 states vibrational features of 2-fluorobenzonitrile and 3-fluorobenzonitrile. The precise excitation energy (band origin) and adiabatic ionization energy were determined to be 36028 2 cm-1 and 78650 5 cm-1 for 2-fluorobenzonitrile; and 35989 2 cm-1 and 78873 5 cm-1 for 3-fluorobenzonitrile, respectively. The density functional theory (DFT) at the levels of RB3LYP/aug-cc-pvtz, TD-B3LYP/aug-cc-pvtz, and UB3LYP/aug-cc-pvtz were used to calculate the stable structures and vibrational frequencies for the ground states S0, excited state S1, and cationic ground state D0, respectively. Franck-Condon spectral simulations for transitions of S1 S0 and D0 S1 were performed based on above DFT calculations. The theoretical and experimental results are in good agreement. The observed vibrational features in S1 and D0 states are assigned according to the simulated spectra and the comparison with structurally similar molecules. Several experimental findings and molecular features were discussed in detail.

On the aero-engine combustor liner, a phenomenon of overheating resembling a hot spot exists, accompanied by a preferential peeling of the YSZ thermal barrier coating that will negatively affect the service life of the component. The temperature gradient of the hot spot may affect the YSZ fracture pattern.In this paper, the morphological and property changes of YSZ sintering is investigated experimentally, and a coupled analysis of the YSZ Crack Propagation Mode under the hot spot is performed using ABAQUS. The results show that the pore fractal size D of YSZ increases by 15%. Inside the hot spot region, the coating has a shear stress of 75MPa. By inserting Cohesive units globally in the model to simulate the random crack initiation and propagation, unlike the thinning of YSZ layered peeling caused by uniform superheating, hot spot will cause the crack initiation at the tip of the pores inside the coating and the oblique propagation, eventually forming an oblique crack connection through the pores. When the temperature gradient reaches 30K/mm, the crack propagation is 40% greater than in a uniform temperature field; consequently, the YSZ in the hot spot boundary region with a greater temperature gradient results in more severe bulk peeling.

Wearable thermoelectric generators (w-TEGs) convert thermal energy into electrical energy to realize self-powering of intelligent electronic devices, thus reducing the burden of battery replacement and charging, and improving the usage time and efficiency of electronic devices. Through finite element simulation, this study successfully designed high-performance thermoelectric generator and made it into wearable thermoelectric module by adopting “rigid device - flexible connection” method. It was found that higher convective heat transfer coefficient on cold-end leads to larger effective temperature difference and better power generation performance of device in typical wearable scenario. Meanwhile, at same convective heat transfer coefficient on the cold-end, longer TE leg length leads to larger temperature difference established at both ends of device, larger device output power and open-circuit voltage. However, when the convective heat transfer coefficient increases to a certain level, optimization effect of increasing TE leg length on device power generation performance will gradually diminish. For devices with fixed temperature difference between two ends, longer TE leg length leads to higher resistance of TEG, resulting in lower device output power but slight increase in open-circuit voltage. Finally, sixteen 16×4×2 mm2 TEGs (L=1.38 mm, W=0.6 mm) and two modules were fabricated and tested. At hot end temperature Th=33 ℃ and cold end temperature Tc=30 ℃, the actual maximum output power Pout of TEG is about 0.2 mW, and the actual maximum output power Pout of TEG module is about 1.602 mW, which is highly consistent with the simulated value. This work brings great convenience to research and development of wearable thermoelectric modules and provides new, environmentally friendly and efficient power solution for wearable devices.

Fracture of viscoplastic materials is a complex process due to its time-dependent and plastic responses. Numerical simulation for fracture plays a significant role in crack prediction and failure analysis. In recent years, the phase field model is becoming a competitive approach to predict crack growth and has been extended to inelastic materials such as elasto-plastic, viscoelastic and viscoplastic materials, etc. However, the contribution of inelastic energy to crack growth is seldom studied. For this reason, we implement the phase field model coupled with a viscoplastic constitutive in finite element framework, in which the elastic energy and inelastic energy are used as crack driving energy. The implicit algorithm for a viscoplastic constitutive is presented, this procedure is suitable for other viscoplastic constitutive relations. The strain rate effect, creep effect, stress relaxation effect and cyclic loading responses are tested using a single element model with different inelastic energy contributions; A titanium alloy plate specimen and a stainless-steel plate specimen under tension are studied compared with the experimental observations in existing literature. The results show that the above typical damage phenomenon and fracture process can be well reproduced; The inelastic energy significantly affects the force-displacement responses but has slight effect on the crack paths.

Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

In this paper a combined Laplace transform (LT) and boundary element method (BEM) is used to find numerical solutions to problems of anisotropic functionally graded media which are governed by the transient diffusion-convection-reaction equation. First, the variable coefficients governing equation is reduced to a constant coefficients equation. Then, the Laplace-transformed constant coefficients equation is transformed a boundary-only integral equation. Using a BEM, the numerical solutions in the frame of Laplace transform may then be obtained from this integral equation. Then the solutions are inversely transformed numerically using the Stehfest formula. Some problems considered are those of compressible or incompressible flow, and of media which are quadratically, exponentially and trigonometrically graded materials. The results obtained show that the analysis used to transform the variable coefficients equation into the constant coefficients equation is valid, and the mixed LT-BEM is easy to implement for obtaining the numerical solutions. The numerical solutions are verified by showing their accuracy and steady state. For symmetric problems, the symmetry of solutions is also justified. Moreover, the effect of the anisotropy and inhomogeneity of the material on the solutions are also shown, as to suggest that it is important to take the anisotropy and inhomogeneity into account when doing experimental studies.

Breast and Cervical cancer are significant causes of morbidity and mortality among women worldwide. Usually, conventional therapies and early detection has shown success in metastasis and relapse rates and improved overall survival of the patients. The elimination of the efficacy of chemotherapies and target therapies because cancer stem cells create resistance against chemotherapy, metastasis, and relapse. Studies reveal that cancer stem cell markers are important for improving efficiencies of targeted therapies. Our study aims to analyze the stem cell markers and target the most significant stem cell marker by phytochemicals and their derivates. Through the analysis, it was observed that CXCR4 and SOX-2, mir-1, mir-125, and mir-429 may have a crucial role in stemness of breast and cervical cancer respectively, while BPMD & MD simulations revealed Di-valinoyl curcumin as a potential compound for CXCR4 targeting.

Hydrogel microneedles are a promising technology for the delivery of different types of medicines locally and painlessly, as well as ISF extraction. As the hydrogel microneedles are inserted into the tissue, they swell and release drugs. To improve the effectiveness of this technology in delivering medicine at controlled and desirable doses and intervals, a deep understanding of the mechanism of drug delivery inside the microneedles is required. In this work, drug diffusion inside a tapered microneedle is investigated using numerical simulation. The microneedle is divided into many small elements, and the mass transfer equation of meloxicam is solved in each element over time. The skin is simulated as the sink in the microneedle surface for drug absorption. Simulations are performed for different sizes of microneedles. For a microneedle with a height of 500 µm and a base diameter of 250 µm, the drug completely penetrates the skin within 3.2 seconds. The rate of drug diffusion from the tip of the microneedle is higher than diffusion from the side area near the microneedle base. The obtained data demonstrate that in addition to the height and the base diameter, the microneedle’s aspect ratio, h/d, also affects the time of drug diffusion. We present a nonlinear equation to predict the time of complete drug diffusion as a function of the microneedle geometrical parameter, including the height and base diameter. The proposed equation calculates the total drug diffusion time with an error of less than 7% for all studied cases. Predicting drug diffusion patterns inside microneedles can be helpful in the biomedical field, especially in the drug-controlled release system for the optimization of drug delivery.

Dynamic Realistic Failure Process Analysis (DRFPA) is a numerical software based on FEM it is firstly validated by comparing with the experimental test. Such validation is carried out under two consistent concrete specimens under uniaxial compression. After validation, DRFPA is used to study the influence of two parallel pre-existing crack inclination angles on the fracture of concrete under dynamic loads. The evolution of wing cracks and secondary cracks is investigated by observing the initiation, coalescence, and propagation of them. Meanwhile, the relationship between failure mode and inclination angles is discussed via analyzing the acoustic emission events. Finally, it is concluded the relationship between inclination angles of pre-cracks and performance/stability of concrete structure.

For the numerical simulation of components made of short fiber-reinforced composites the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study numerical simulations of tensile test specimens are conducted utilizing a finite deformation elastic-ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach tensile tests until failure are carried out experimentally, that confirm the assumption of spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations are performed for different values of the correlation length. The numerical simulations endorse the influence of the correlation length on the overall behavior. For a correlation length of 5mm a good conformity with the experimental results is obtained. Therefore, it is concluded, that the presented modeling approach is suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.

Emergence of multi drug resistant (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis poses a serious threat to TB control as the available treatment options are less effective in these cases. Therefore, new strategies are required for identification of new drugs and drug targets. The M. tuberculosis dihydrodipicolinate synthase is a putative drug target with no potent inhibitor. Here in this study, we have used a comprehensive computational approach to identify small molecule inhibitors and validated them in vitro. As similar structures tend to have similar functions, therefore 3 approaches, namely, (a) known inhibitor-based approach, (b) substrate analogues, (c) product analogues were used for compound screening from various chemical libraries. The fourth approach we have taken is fragment library screening.In vitro studies revealed that β-hydroxypyruvic acid tartronic acid are inhibitors of Mtb-rDapA with maximum inhibition 48% and 51% at 500µM concentrations respectively. We have also observed that β-hydroxypyruvic acid is a time dependent inhibitor of Mtb-rDapA. Further, thermal shift assays showed that β-hydroxypyruvic acid interacts with Mtb-rDapA and the thermal stability of Mtb-rDapA shifted by 3˚C. Among the product analogues of Mtb-DapA, dipicolinic acid showed to bind to Mtb-rDapB shifting thermal stability of Mtb-rDapB by 0.5˚C in binding. Additionally, we have designed a new approach by combining substrate and product analogues to achieve better inhibitions. In vitro assays validated that this combination approach increased maximum inhibition up to 100%. We envision that these compounds would serve as potent leads for tuberculosis drug discovery.

It is well established that the interacting effects of temperature and precipitation will alter agroecological systems on a global scale. These shifts will influence the fitness of specialty crops, specifically strawberries (Fragaria x ananassa), an important crop in the Northeastern United States. In this study, four precipitation scenarios were developed that are representative of current and probable-future growing season precipitation patterns. Using a precipitation simulator, we tested these scenarios on potted day neutral strawberries. This study generated four primary results: (1) though treatments received different amounts of precipitation, little difference was observed in soil volumetric water content or temperature. However, treatments designed to simulate future conditions were more likely those designed to simulate current conditions to have higher nitrate-in-leachate (N-leachate) concentrations; (2) neither total precipitation nor seasonable distribution were associated with foliar or root disease pressure; (3) while there was a slightly higher chance that photosynthetic potential and capacity would be higher in drier conditions, little difference was observed in the effects on chlorophyll concentration, and no water stress was detected in any treatment; and (4) leaf biomass was likely more affected by total rather than seasonal distribution of precipitation, but interaction between changing rainfall distribution and seasonal totals is likely to be an important driver of root biomass development in the future.

As use of composite materials increases, the search for suitable automated processes gains relevance to guarantee production quality by ensuring uniformity of the process, minimizing the amount of generated scrap and reducing time and energy consumption. Limitations on production by traditional means such as hand lay-up, vacuum bagging and in-autoclave methods, tend not to be as efficient when the size and shape complexity of the part being produced increases, motivating the search for alternative processes such as the Automated Tape Laying (ATL). This work aims to describe the process of modelling and simulating a composite ATL with in situ consolidation by characterizing the machine elements, using the finite differences method in conjunction with energy balances, in order to create a digital twin of the process for further control design. The modelling approach implemented is able to follow the process dynamics when changes to the heating element are imposed as well as to predict the composite material temperature response, making it suitable to work as a digital twin of a production process using an ATL machine.

Background Augmented reality (AR) in surgery can offer an enhanced view of reality through the superimposition of computer-generated digital images on the real environment. It allows surgeons to integrate image visualisation, improving operative efficiency, surgical outcomes, surgical training and patient education. This review aims to evaluate the current status of augmented reality in surgery, surgical training and potential future applications. Methods We performed a non-systematic review of available literature from January 2005 to August 2021 by searching PubMed, EMBASE and the Cochrane library using a combination of terms “augmented reality”, “virtual reality”, “surgery”, “simulation” and “training”. Articles considered for this review were identified by relevant search criteria including title, keywords, abstract, and full-text. Conclusions AR technologies present an exciting new trend with multiple potential applications in surgery. Intraoperative AR systems have shown promise in specialties involving fine movement of organs during surgical procedures, including Neurosurgery, Ears, Nose and Throat and Orthopaedic Surgery. AR has also exhibited the potential to enhance surgical training and improve knowledge acquisition; it can foster international collaborations via telesurgery and telepresence. In the near future, AR will likely work in symbiosis with surgeons, serving as a complex computer-human coalition which can improve patient outcomes, patient education and surgical training.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

Background: Simulations have historically aided training programs by providing a realistic and holistic replication of professional scenarios and procedures. Simulations have developed over the past 40 years to include varying fidelities and modalities of simulation. Learning in a simulation-centered environment has benefits, ranging from improved patient care to specific skills acquisition while catering to students’ numerous and varied learning approaches. Simulation is a multifaceted field that benefits all parties, the teachers, the learners, and the patients. The application of simulation to medical education and its amalgamation with other modes and substitutes allows for a more integrated learning and testing curriculum that advances the current trajectory of medical education. Such developments, however, are limited to resource rich areas, leaving behind low-middle income countries to use traditional, less evolved methodologies and practices. This review aimed to explore different aspects of simulation and focus specifically on low-cost task trainers and their accessibility. Method: The purpose of the study was to assess the accessibility of low-cost task trainers in terms of cost-effectiveness, distribution, validation, and frequency within specific specialties. To do so, 84 PubMed publications have been screened, and 39 filtered research studies have collected the necessary data. After analyzing the papers, we classified the following information – process, specialization, validation (y/n), costs, development location, and year of publication. Results: After carefully analyzing the accumulated data from the selected 39 publications, we found that most studies (i.e., 6 out of 39) were published in 2020. Emergency Medicine was the most common specialty for which low-cost trainers were developed (9 out of 39 procedural simulators); Otolaryngology followed this with 8 out of 39 trainers and general surgery with 7/39 of the task-trainers. The price ranges fluctuated and fell within the price bracket of USD 0 to USD 400 collectively. Our review also uncovered the concentration of development of such innovations solely in high income countries (HICs). Conclusion: Simulation is an invaluable tool applicable to a diverse range of phases of medical education. Future conjunction of simulation with low-cost substitutes along with increased encouragement and enthusiasm towards developing cost effective simulation-based learning environments (SBLEs) with the reserves and requirements of these areas in mind may prove to be a reliable option for low and middle resource settings

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

Overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma . Predominately, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS) -induced apoptosis. Here, we showed a novel link between BRF2 and DNA damage response. Due to the lack of BRF2 specific inhibitors, through virtual and molecular dynamics screening, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested Bexarotene as a potential BRF2 inhibitor. We found that Bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress (Tert-butylhydroquinone (tBHQ))-induced levels of BRF2 and consequently, lead to a decrease in cellular proliferation of cancer cells which may in part be due to drug pretreatment induced reduction of ROS generated by the oxidizing agent. Our data thus, provide the first experimental evidence that BRF2 is a novel player in DNA damage response pathway and Bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Methods based on simulation pedagogy are widely used to practice hands on skills in safety environment. The usability of an EEG-simulator on clinical neurophysiology course was evaluated. Second year biomedical laboratory science students (N=35) on this course was included in the study. They were divided into three groups. Two groups used the EEG simulator with different feedback modes and one group without use of the simulator. Results was expected to reveal a correlation between user experience and learning outcomes. This study made used of a mixed method study design. During the study students were asked to keep a learning diary throughout the course on their experience. Diaries were analyzed qualitatively based on content analyses. Quantitative analyses based on an UX questionnaire that measures classical usability aspects (efficiency, perspicuity, dependability) and user experience aspects (novelty, stimulation) and the students’ feelings to use simulator. The quantitative data was analyzed using SPSSTM software. The quantitative and qualitative analyses showed that the use of EEG-simulator which was evaluating teaching-learning process have an extra benefit in clinical neurophysiology education and students felt that simulator is useful in learning. The simulation debriefing session should be followed by a full theoretical and practical session. Students compare their learning from the simulator with that of the actual placement which fosters the reflective practice of learning again deepening the understanding of the EEG electrode placement and different wave patterns.

The conformational state of the activation loop (A-loop) is pivotal for the activation of most protein kinases. Hence, the characterization of the conformational dynamics of the A-loop is important to increase our understanding of the molecular processes related to diseases and to support the discovery of small molecule kinase inhibitors. Here, we carry out a combination of Molecular Dynamics (MD) and Essential Dynamics (ED) analyses to fully map the effects of phosphorylation, ADP, and conformation disrupting (CD) inhibitors (i.e., CD532 and MLN8054) on the dynamics of the A-loop of Aurora-A. MD revealed that the stability of the A-loop in an open conformation is enhanced by single phospho-Thr-288, while paradoxically, the presence of second phosphorylation at Thr-287 decreases such stability and renders the A-loop more fluctuant in time and space. Moreover, we found that such post-translational modification has a significant effect on the direction of A-loop motions. ED analysis suggests that the presence of the phosphate moiety induces the dynamics of Aurora-A to sample two distinct energy minima, instead of a single large minimum, as in unphosphorylated Aurora-A states. This suggests that the conformational distributions of Aurora-A with both single and double phospho-threonine modifications are remarkably different from the unphosphorylated state. In the closed states, binding of CD532 and MLN8054 inhibitors has the effect of increasing the distance of the N- and C-lobes of the kinase domain of Aurora-A, and the angle analysis between those two lobes during MD simulations showed that the N- and C-lobes are kept more open in presence of CD532, compared to MLN8054. As the A-loop is a common feature of Aurora protein kinases, our studies provide a general description of the conformational dynamics of this structure upon phosphorylation and different ligands binding.

Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though a lot and research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from working with district heating and cooling projects, along with relevant research, corresponding research gaps and proposed research directions.

This research evaluates the inhibitory effect of L-amino acids (AAs) with different side chain lengths on Fe surface implementing Monte Carlo (MC) simulation. A quantitative and qualitative description of the adsorption behavior of AAs on the iron surface has been carried out. Calculations have shown that the absolute values of the adsorption energy of L-amino acids increase with side chain prolongation; they are also determined by the presence of heteroatoms. AAs from nonpolar and basic groups have the best adsorption ability to the iron surface, which indicates their highest inhibitory efficiency according to the results of MC simulation.

This paper examines the influence of physical parameters on the collapse dynamics of a spherical bubble filled with diatomic gas ($\kappa=7/5$). The present numerical investigation shows that each physical parameter affects the bubble collapse dynamics differently. After comparing the contribution of each physical parameter, it appears that, of all the parameters, the surrounding liquid environment affects the bubble collapse dynamics the most. Meanwhile, surface tension has the weakest influence and can be ignored in the bubble collapse dynamics. However, surface tension must be retained in the initial analysis since this, as well as the pressure difference jointly control initial bubble formation. As an essential part of this study, a general Maple code is provided.

In the process of metal cutting, the anti-wear performance of the tool determines the life of the tool and affects the surface quality of the workpiece. The finite element simulation method can directly show the tool wear state and morphology, but due to the limitations of the simulation time and complex boundary conditions, it has not been commonly used in tool life prediction. Based on this, a tool wear model was established on the platform of a finite element simulation software for the cutting process of titanium alloy TC4 by end milling. The key technique is to embed different types of tool wear models into the finite element model in combination with the consequent development technology. The effectiveness of the tool wear model is validated by comparing the experimental results with the simulation results. At the same time, in order to quickly predict the tool life, an empirical prediction formula of tool wear was established, and the change course of tool wear under time change was obtained.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

The purpose of this paper is to simulate a two-dimensional Rayleigh-Taylor instability problem using the classical method of Finite Element analysis of a multiphase model using ANSYS FLUENT 19.2. The governing equations consist of a system of coupled nonlinear partial differential equations for conservation of mass, momentum and phase transport equations. The study focuses on the transient state simulation of Rayleigh Taylor waves and subsequent turbulent mixing in the two phases incorporated in the model. The Rayleigh Taylor instability is an instability of an interface between two fluids of different densities which occurs when the lighter fluid is pushing the heavier fluid in a gravitational field. The problem was governed by the Navier-Stokes and Cahn-Hilliard equations in a primitive variable formulation. The Cahn- Hilliard equations were used to capture the interface between two fluids systems. The objective of this article is to perform grid dependency test on Rayleigh Taylor Instability for 2 different mesh size and compare the results for the variation in Atwood Number. The results were validated with the observations from previous published literatures.

In order to reveal the mechanism and influencing factors of high pressure jet cavitation of nozzle in submerged environment, this study focused on the evolutionary process of cavitation bubbles and combined finite volume method and mixed multi-phase flow model to analyze the cavitation, velocity distribution and experimental cavitation intensity of fishing net cleaning equipment. Results show that the cavitation inception, growth and collapse mainly occurred in the peripheral region of the flow field. Ring-shaped cavitation erosion zone appeared on the test sample target. A lot of small dense erosion pits were densely distributed in the ring-shaped erosion zone, erosion marks were observed in the center. The cavitation erosion intensity was greatly affected by the nozzle structure. As the diameter of nozzle increased from 0.6 mm to 1 mm, the maximum gas volume fraction increased by 8.53%. The nozzle outlet enlargement angle greatly increased the cavitation intensity. The nozzle with an outlet angle of 30° exhibited the optimal cavitation erosion performance . The cavitation volume fraction of the nozzle with short necking structure was slightly larger than that of the nozzle with long necking structure at the same level in the necking length rang of 3mm to 7mm. In terms of the influence of nozzle structure on the cavitation erosion effect, the nozzle diameter D ranked the first, followed by the outlet angle α, and the necking length L was at the last.

Binder jet printed components typically have low overall density in the green state and high shrinkage and deformation after heat treatment. It has previously been demonstrated that, by including nanoparticles of the same material in the binder, these properties can be improved as the nanoparticles can fill the interstices and pore throats between the bed particles. The beneficial effects from using these additive binder particles can be improved by maximising the binder particle size, enabling the space within the powder bed to be filled with a higher packing efficiency. The selection of maximum particle size for a binder requires detailed knowledge of the pores and pore throats between the powder bed particles. In this paper, a raindrop model is developed to determine the critical radius at which binder particles can pass between pores and penetrate the bed. The model is validated against helium pycnometry measurements and binder particle drop tests. It is found that the critical radius can be predicted, with acceptable accuracy, using a linear function of the mean and standard deviation of the particle radii. Percolation theory concepts have been employed in order to generalise the results for powder beds that have different mean particle sizes and size distributions. The results of this work can be employed to inform the selection of particle sizes required for binder formulations, to optimise density and reduce shrinkage in printed binder jet components.

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

Two-dimensional arrays of hollow nanotubes made of TiO2 are a promising platform for sensing, spectroscopy and light harvesting applications. Their straightforward fabrication via electrochemical anodization, growing nanotube pillars of finite length from a Ti foil, allows precise tailoring of geometry and, thus, material properties. We investigate these photonic crystal structures with respect to reduction of front surface reflection, achievable field enhancement, and photonic bands. With the Rigorous Coupled Wave Analysis (RCWA), we study the optical response of photonic crystals made of thin-walled nanotubes relative to the bare Ti foil substrate, including under additional charge carrier doping.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

Micro-pollutants such as 17β-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.

Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

At 100% penetration, intermittent renewable energy causes outages. To flatten the leaping supply and demand curves of renewable energy, an energy storage medium and carrier are required. The Hydrogen energy stotrage system (HydESS), which combines a proton exchange membrane (PEM)-based water electrolysis (WE) system and a fuel cell (FC), has the quickest response time, the longest duration, and the greatest storage capacity. HOMER tools for techno-economic analysis are used to evaluate the HydESS hybrid system. Kyiv, Ukraine, is appealing due to its spatial-temporal wind speed, solar irradiance, and energy security. With an LCOE of $0.245 kWh−1 and 9.3% fuel cell power output, the most feasible solution is onshore wind turbine-coupled PEMFC HydESS. As a result 1,000-kW wind power, 250-kW PEMFC, 250-kW PEMWE, and 441-kW BESS are optimized to serve a 3,900 kWh day academic residence load. When compared to a diesel generator and a BESS, the hybrid HydESS reduces CO2 emissions by 4,132 tons over a 25-year period. The LCOE of a hybrid HydESS system is determined by the SOFC lifetime rather than the PEMFC's 2% difference between 40,000 and 50,000 h with the same rated power. This study recommends accelerating PEMFC development with fluctuation-endurable materials and design.

Unmanned aerial vehicles serving as air base stations (UAV-BSs) are used in telecommunications, when the network is overloaded or unavailable, providing access to users on the ground. Due to its dynamics of providing access to networks in different altitudes, the electromagnetic signal of UAVs suffers losses along the path, thus facing several problems related to transmission, such as: attenuation, fading and distortion. In this work is proposed a 3D positioning strategy for UAVs, taking into account height, coverage radius and flight energy efficiency. Results show that the proposed algorithm is able to position the UAVs in suboptimal locations, providing network access for mobile users, maximizing the number of connected devices in the UAV-BS and saving energy, thus obtaining better performance than the approaches conventional.

Although the gravitational force is well known for its extremely weak value in the framework of ordinary macroscopic objects at our scale, the generally discredited framework of pushing gravity might lead us to discover a huge source of energy which is mostly lost during the action of the gravity force due to the large distances between nucleons compared with their diameters. In case this energy is identified and partially diverted from its role in gravity, we would benefit from a gigantic reservoir of energy reminiscent of Nikola Tesla's dream of a free unlimited energy source

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

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

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

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

Purpose of publication: - Preparation of bibliometric data exported from the Scilit platform on energy efficiency and conservation for further analysis to identify relevant research topics. - To identify potential issues in the processing of data exported from the Scilit platform. - Providing colleagues with the opportunity to use the prepared data and examples of their analysis for independent research on topical issues of energy efficiency and energy conservation using materials provided by the Scilit platform. Research Materials: Files in CSV and RIS formats exported from Scilit for the query "energy conservation OR efficiency" in Common Fields [Title, Abstract, Keyword], using filters: Content Type → Journal Article; Year→2021-2023; Subject → Industrial Engineering (29.8K), Energy and Fuel Technology (9.8K), Manufacturing Engineering (9.2K). A total of 30K records sorted by their relevance (10K for each year) were exported. Data are current as of 14-03-2024. Methods: Preprocessing of title, annotation, and keyword field texts using lemmatization dictionaries collected on GitHub, removal of keywords taken from GATE and spaCy, and "manual" editing. Using VOSviewer to analyze publication topics by clustering keywords based on their co-occurrence. Using Scimago Graphica to build bubble diagrams.Application of the GSDMM algorithm for clustering bibliometric records by title and annotation texts. Creation of a dictionary for this algorithm using the keyword field.Use of the Carrot2 demo version and the NMF algorithm for a more detailed analysis of the topics of the record clusters obtained from GSDMM. Results: are presented in the form of initial and interim tables and graphs obtained in the course of this study. The full tables are provided as references to the attached materials. Supplementary material for this preprint on figshare: Chigarev, Boris (2024). Supplementary material for preprint "Analyzing the Possibilities of Using the Scilit Platform to Identify Current Energy Efficiency and Conservation Issues". figshare. Dataset. https://doi.org/10.6084/m9.figshare.25574058.v1

Wind turbines are a solution for sustainable energy, significantly reducing carbon emissions and fostering a circular economy for more cost-effective and cleaner power generation, in line with worldwide environmental aspirations. In this context, this research aims to explore a novel Two-Stage Twisted Blade micro-Vertical Axis Wind Turbine ($\mu$-VAWT) alternative inspired by the Savonius Rotor (SR). This investigation utilizes the $\kappa-\omega$ SST turbulence model to explore the power coefficient ($C_P$) and torque coefficient ($C_T$), finding $C_P$ values ranging from 0.02 to 0.04 across the turbine by altering the free stream velocity ($V$). $C_T$ analysis further delves into four specific sections, highlighting areas of particular interest. These results are validated by examining velocity contours, pressure contours, and streamlines in four horizontal sections, demonstrating that the proposed turbine model exhibits minimal torque fluctuation. Moreover, the analysis of vertical wind streamlines illustrates very low interference with various wind turbine proposals, underscoring the turbine's efficiency and potential for integration into diverse wind energy projects.

This paper focuses on the paradigm that explains the link between energy transitions and financial transformations. In particular, it attempts to shed some light on the following question: How did the transition from wood to coal affect financial development in late 18^th and early 19^th century Britain? The results demonstrate that the energy transition in Britain during the 1^st Industrial Revolution constituted an important moment in the country's financial development and can be used as an example of how an energy innovation can foster the financial development of particular countries or regions.

Different severe energy crises episodes occurred in the world in the last five decades. Energy crises lead to deterioration of international relations, economic crises, changes in monetary systems and social problems in countries. This paper aims to show the essential determinants of energy crises by developing a binary logit model which estimates the predictive ability of thirteen indicators in a sample that covers the period from January 1973 to December 2022. The empirical results show that the energy crises are mainly due to energy supply-demand imbalances (petroleum stocks, fossil energy production-consumption imbalance and changes in energy imports by country groups), energy investments (oil and natural gas drilling activities), economic and financial disruptions (inflation, dollar index, index of global real economic activity) and geopolitical risks. Additionally, the model is capable of accurately predicting the world energy crisis event months with 99% probability.

Our analysis of previous studies shows that solar energy and wind can consume negligible amount of water to generate one megawatt of electricity.

In the context of the energy transition, the integration of land use considerations into energy planning is increasingly vital, especially in scenarios where land availability is a constraint. This study addresses the challenge of incorporating land-use aspects into Energy System Optimization Models (ESOMs), with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. Findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

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

Higher concentrations of greenhouse gases resulting from anthropogenic actions associated with energy generation are one of the causes of climate change. In view of this, several efforts have been undertaken in the search for more sustainable alternatives, and photovoltaic (PV) technology has stood out among the different possibilities. However, PV generation is highly sensitive to future climate variability, which is a source of uncertainty that can complicate energy planning and compromise the viability of systems. This theme has received attention from the academic community, but some challenges to map and identify relevant literature have been encountered. Therefore, this study was conducted to analyze and identify relevant aspects of international scientific production on the impacts of climate change on the potential of photovoltaic production, CC-PVP, through bibliometric techniques. For this, 3900 articles from the Web of Science and Scopus databases, published between 1960 and 2021, were retrieved and analyzed through a bibliometric approach, using the SciMAT tool. Among the results obtained, it is worth pointing out that the CC-PVP research field (i) has moderate maturity, (ii) is concentrated in the areas of energy, fuels and technology, as well as environmental sciences and meteorology, (iii) has the most studied themes currently related to energy and the forecasting of photovoltaic energy production and electric energy consumption in the world, especially when considering climate change, and (iv) is more researched by Chinese, North Americans and Australians.

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

The paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system, and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. Also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

Solar pavement technology has numerous benefits for advanced life and environmental protection, including reduced fossil fuel usage, reduction harmful emissions from conventional electricity generation and handy reliable electricity via solar energy. This paper aims to find an alternative for cost effective renewable energy sources is in conjunction with the principles of sustainability. This research studies whether the application of thermal energy harvesting road pavement using sustainable conductive piping such as stainless steel, copper, and aluminium are feasible in Malaysia. The scope of the paper is to optimize the suitable parameters for different materials of thermal energy harvester such as pipe material, pipe depth inserted into pavement, types of pipe arrangement, pipe spacing, and flow rate for maximum thermal energy extraction using numerical simulation. The simulation is testing under engineering software ANSYS Workbench 19.2 (Fluent) and Solidworks 2020 to develop project prototype from optimize thermal energy harvester. Thus, serpentine copper pipe shows the highest heat efficiency which is 32.22% with the temperature outlet of 327.35K (54.21°C) at the pipe located at 50 mm from the surface pavement with the pipe spacing of 80 mm centre-to-centre.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

The objective of the present work is to investigate the performance of flat-plate solar panels in Greece that continuously follow the daily motion of the Sun. To that end, the annual energy sums are estimated on such surfaces from hourly solar horizontal radiation values at 43 locations covering all of Greece. The solar horizontal radiation values are embedded in the typical meteorological years of the sites obtained from the PV-GIS tool. All calculations use a near-real surface albedo; an isotropic and an anisotropic model are used to estimate the diffuse-inclined radiation. The analysis provides regression equations for the energy sums as a function of time (month, season). The annual energy sums are found to vary between 2247 kWhm−2 and 2878 kWhm−2 under all-sky conditions with the anisotropic transposition model. Finally, maps of Greece showing the distribution of the annual and seasonal solar energy sums under all- and clear-sky conditions are derived for the first time.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.

As Alberta increases solar power generation, land use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases increased) agricultural output is the co-locating of solar photovoltaics and agriculture: agrivoltaics. This study reviews policies that impact the growth of agri-voltaics in Alberta. Solar PV-based electricity generation is governed by three regula-tions based on system capacity. In addition, agrivoltaics falls under various legisla-tions, frameworks, and guidelines for land utilization. These include Land Use Frame-work, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Dis-position, Bill 22 and other laws/policies all of which are reviewed in the context of agrivoltaics. Several policies are recommended to support rapid diffusion of agrivolta-ics. First, open access research into agrivoltaics, which not only will help optimize agrivoltaic systems for the region, but also coupled with public education is expected to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaic technology, developing agrivoltaic standards, making agri-voltaic technology-friendly regulations/frameworks and developing programs and pol-icies to incentivize agrivoltaic deployment over conventional PV will all accelerate dif-fusion. Through these measures, Alberta can achieve conservation and sustainability in food and energy sector while simultaneously addressing the renewable energy and climate-related goals.

In Europe, the recent application of regulations oriented to zero-energy buildings and climate neutrality in 2050 has led to a reduction in energy consumption for heating and cooling in the construction sector. The thermal insulation of the building envelope plays a key role in this process and the requirements about the maximum allowable thermal transmittance are defined by country-specific guidelines. Typically, high insulation values provide low energy consumption for heating, however, they may paradoxically imply the risk of overheating in summer period and thus negatively affect the overall performance of the building. In addition, the embodied energy and related emissions caused by the manufacturing and transportation processes of thermal insulation cannot be further neglected in the evaluation of the best optimal solution. Therefore, this paper aims to evaluate the influence in terms of embodied and operational energy of various walls’ thermal insulation thicknesses on residential buildings in Europe. To this end, the EnergyPlus engine was used for the energy simulation within Ladybug & Honeybee tools, by parametrically conducting multiple iterations; 53 variations of external wall U-value, considering high and low thermal mass scenarios, were simulated for 100 reference cities of the European context, using a representative multifamily building as a reference. The results demonstrate that massive walls generally perform better than lightweight structures and, of course, the best solution in terms of energy varies according to each climate. The optimal values are graphically reported on the map of Europe according to specific climatic features, providing a guidance for new constructions and building retrofit.

This study reports an empirical analysis of an all-electric, Net Zero Energy Housing (NZEH) development located in a mixed-humid climate zone (4A, Virginia, USA). Circuit-level energy monitors were used to measure energy consumption and energy production data (solar photovoltaic) at 1-hr intervals in six identical apartments over 24 months. The study employs a multi-step case study methodology to a) empirically evaluate energy consumption and production data, b) identify the temporal variability of energy consumption and production data at different time scales, c) understand the impact(s) of weather and human-building interaction on energy consumption and production, and d) synthesize the study’s “lessons learned” toward data-driven recommendations for future NZEH researchers and practitioners. The study found that the development’s net zero energy goal was achieved in three of six case units and that NZEH housing performance was more influenced by human-building interaction than weather variability. The analysis also found the solar photovoltaic (PV) performance to be reliable across the sampled units over the periods of measurement, suggesting that solar PV could be oversized as an approach to overcome verifiability in HBI and achieve NZEH performance goals.

Indonesia is an interesting case study for students of sustainable development and sustainable energy due to its ability to connect the multiple “worlds” it has become part of. Indonesia is an important bridge to Muslim countries, the voice of the Global South in the G20 and a main pillar of the 134-country-strong G77. Indonesia’s development trajectory is also key to the achievement of the Paris Climate Agreement as well as of the 2030 Agenda. Students can learn from how Indo-nesia address contradictions that would have been unsurmountable for other countries. Indone-sia’s energy transition offers helpful lessons, because of its aspiration to become a developed country by 2045. This goal is only possible when a country is able to effectively address barriers and caveats to sustainable energy. It is interesting how Indonesia focuses on silver linings and come up with pragmatic solutions to energy-related issues. This is followed by the “teaching guide,” which provides recommendations how the lessons from Indonesia can be embedded into a learning experience. The “learning activation approach” is introduced, which encourages stu-dents to systematically reflect on the complexity of selected contexts and understand this com-plexity by looking at the technical issues and processes that allow decision-making.

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m² (3,531 ft²) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour intervals by 56 microinverters. The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modelling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings.

A reliable and affordable energy supply is a fundamental prerequisite for reducing poverty, promoting investment, and boosting economic growth in the developing world. Among the different challenges that developing countries face, chronic energy crises are harrowing. The crises result from the unsatisfactory state of the central grid, a misguided energy mix, and ill-informed policies, among other things. The possibility of solving energy crises through a variety of alternative solutions is worth exploring. This review discusses two paths of energy development side by side: a traditional “hard” path of energy development (i.e., central grid extension powered by fossil fuels and nuclear energy expansion) and a relatively recent “soft” path of energy development, which is based on energy conservation and the deployment of renewable energy resources. This paper focuses on one central axis of the discussion: centralization vs. decentralization. This discussion, in turn, has technological, economic/business, and political dimensions. Finally, the paper discusses the significance of the debate from meeting the developing world’s energy demands. The paper intends not to prefer one or another path of energy development, nor it gives recommendations on diffusing or adopting those development paths. Instead, it explores the literature’s central arguments that might help frame the questions for further research. While this debate could be used to ask interesting questions that might help solve the energy crisis in the developing world, the discussion informs countries to advance policies specific to their circumstances under the umbrella of a sound and thoughtful energy productivity policy framework.

The measures for tackling the COVID-19 may shrink the global GDP by approximately 6% in 2020, the deepest post-war recession. As a result, the global energy demand declined by 3.8% in the first quarter of 2020. Concerning fossil fuels, this conjuncture reduced the demand drastically and collapsed the prices to historic levels. Despite the general market disruptions, renewable energy sources (RES) seem to be more resilient to the crisis because they are the only sources that will grow in demand in 2020, driven by priority dispatch. The RES´s significant growth in cumulative installed capacity in the last two decades and the significant cost reductions of RES and energy storage technologies are positive signs towards better market conditions for the global energy transition. Currently, the crisis is seen by international agencies and transition scholars as an opportunity to advance a renewable-based energy transformation. Nevertheless, this article aims at caution about another possibility: if societal changes are not urgently implemented, the crisis may weaken the global energy transition. This article examines this last possibility from a three-level perspective: 1) post-COVID economic recovery, 2) low oil and natural gas prices and competitiveness of alternative sources and, 3) reorganization of the world energy market and the OPEC+. This paper exists to stimulate debate.

For a hierarchy of large scale structures, like galaxies, galaxy clusters, super-clusters, etc. the requirement that their gravitational (binding) self-energy density must at least equal or exceed the background repulsive dark energy density implies a universal mass-radius relation given by M/R² ~ 1g/cm². Here we point out that this mass-radius relation also holds for important individual biological structures, and even various organisms as a whole such as humans, trees, and to behemoths like whales, even though the physics is different. Here we try to understand this universality from two different physical considerations. We also point out the coincidence that the densities of most biological entities are close to that of water, and that the average density of main-sequence stars and giant planets are close to this density. We give a physical basis for the same.

The threats of climate change and the sustainable supply of clean energy are global challenges that require an international approach to the energy supply. Utilizing the wind and solar energy potential of regions where these renewable sources are especially viable to produce hydrogen by means of water electrolysis represents an attractive option to counter the above-mentioned challenges. Within the scope of this techno economic analysis of a worldwide hydrogen supply infrastructure based on renewable energy, selected regions are assessed on the basis of their wind or solar energy potential. In contrast to established analyses of hydrogen infrastructures, this paper introduces a worldwide allocation approach to the supply hydrogen from strong wind and solar regions to different demand regions on the premise of a global supply cost minimum. The allocation results show a significant dependence of hydrogen export volumes and the oversea transport distances of potential trading partners. Hence, the transnational trading flows of hydrogen derived from wind and solar energy are concentrated in continental regions.

The transition towards a renewable energy system is essential in order to reduce greenhouse gas emissions. The increase in the share of variable renewable energy sources (VRES), which mainly comprise wind and solar energy, necessitates storage technologies by which the intermittency of VRES can be compensated for. Although hydrogen has been envisioned to play a significant role as a promising alternative energy carrier in a future European VRES-based energy concept, the optimal design of this system remains uncertain. In this analysis, a hydrogen infrastructure is posited that would meet the electricity and hydrogen demand for a 100% renewable energy-based European energy system in the context of 2050. The overall system design is optimized by minimizing the total annual cost. Onshore and offshore wind energy, open-field photovoltaics (PV), rooftop PV and hydro energy, as well as biomass, are the technologies employed for electricity generation. The electricity generated is then either transmitted through the electrical grid or converted into hydrogen by means of electrolyzers and then distributed through hydrogen pipelines. Battery, hydrogen vessels and salt caverns are considered as potential storage technologies. In the case of a lull, stored hydrogen can be re-electrified to generate electricity to meet demand during that time period. For each location, eligible technologies are introduced, as well as their maximum capacity and hourly demand profiles, in order to build the optimization model. In addition, a generation time series for VRES has been exogenously derived for the model. The generation profiles of wind energy have been investigated in detail by considering future turbine designs with high spatial resolution. In terms of salt cavern storage, the technical potential for hydrogen storage is defined in the system as the maximum allowable capacity per region. Whether or not a technology is installed in a region, the hourly operation of these technologies, as well as the cost of each technology, are obtained within the optimization results. It is revealed that a 100 percent renewable energy system is feasible and would meet both electricity demand and hydrogen demand in Europe.

As the energy efficiency demands for future buildings become increasingly stringent, preliminary assessments of energy consumption are mandatory. These are possible only through numerical simulations, whose reliability crucially depends on boundary conditions. We therefore investigate their role in numerical estimates for the usage of geothermal energy, performing annual simulations of transient heat transfer for a building employing a geothermal heat pump plant and energy piles. Starting from actual measurements, we solve the heat equations in 2D and 3D using COMSOL Multiphysics and IDA-ICE, and discover a negligible impact of the multiregional ground surface boundary conditions. Moreover, we verify that the thermal mass of the soil medium induces a small vertical temperature gradient on the piles surface. We also find a roughly constant temperature on each horizontal cross-section, with nearly identical values if the average temperature is integrated over the full plane or evaluated at one single point. Calculating the yearly heating need for an entire building we then show that the chosen upper boundary condition affects the energy balance dramatically. Using directly the pipes’ outlet temperature induces a 54% overestimation of the heat flux, while the exact ground surface temperature above the piles reduces the error to 0.03%.

Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence time, duration and level of damage can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

The There are many factors influencing the performance of photovoltaic (PV) systems. Among these factors, temperature and solar radiation are two major parameters that have a large effect on the efficiency of PV systems. The cell temperature of PV panels is related to the ambient temperature while the solar radiation incident on the surface of the PV modules depends on the slope and azimuth of these modules. Furthermore, ground reflectance (albedo) affects the irradiance incident on the PV panel surface, which in turn affects the output of a PV system. Nevertheless, the effects of these factors on the economic performance of the solar PV systems are scarcely reported. This paper presents a complete design of a stand-alone PV/battery system to supply electric power for a mobile base station in Choman, Erbil, Iraq. The effects of different factors on the total electricity produced by PV arrays and its economic performance are simultaneously investigated. HOMER software has been used as a tool for the techno-economic and environmental analysis. As indicated from the simulation results, the PV array capacity and its economic performance are highly affected by the variation of the slope and azimuth. With a base case (albedo of 20% and average annual ambient temperature of 11°C), the best feasible system which is achieved by facing PV due to south with a tilt angle of 40° or 45°, is found to have net present cost (NPC) of 70595 $ and cost of energy (COE) of 0.54 $/kWh. Moreover, the results indicate that increasing the ground reflectance from 10% to 90% results in a 7.2% decrease in the PV array capacity and about 3% decrease in the NPC and COE. On the other hand, increasing the ambient temperature from 0°C to 40°C results in a 19.7% increase in the PV array capacity and an 8.2% increase in the NPC and COE. Furthermore, according to the ambient temperature of Choman, using PV modules with high sensitivity to temperature is found to be an attractive option. Provided simulation performance analysis proves that the studied parameters must be treated well to establish an enabling environment for solar energy development in Iraq.

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

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

It's a method to achieve energy savings in single-phase active power greater than that obtained with the "Fan Law" in electric machines applied to ventilation. Quantitative analysis methods were based on electrotechnical techniques, practiced with the corresponding laboratory equipment on the working materials (three prototypes of electric machines). The results obtained from experimentation on the test bench were recorded in tables that collect data of formulas, values, and physical units. The discussion provides a comprehensive comparative study, mainly between power (watts), active energy consumption (kWh), and rotational speed (revolutions per minute). The permanent magnet synchronous motor (PMSM) with the coupling of an RL circuit design does mechanical work at its maximum speed of 3000 (RPM) with only 6.3 (Watts), which is equivalent to 25.2% of the active power required by the single-phase induction asynchronous motor (or shaded pole motor), which required 25 (Watts) to rotate at 1690 (RPM). This translates to 75% lower active power with a 44% superiority in speed, resulting in a 75% savings in single-phase active energy (measured in kWh). The same occurs when comparing it to the universal AC motor (with brushes and wound rotor) to maintain a speed of 3000 (RPM); since it consumes 64.8 (Watts), which is 90.3% more single-phase active energy than that required to achieve the same rotational speed as the permanent magnet synchronous motor (PMSM). The study was conducted on all motors with the same impeller blade diameter and under identical air temperature and atmospheric pressure conditions.

In the traditional power system, customers respond to their primary electricity consumption pattern based on price or incentive to take additional advantages. By developing energy hubs (EHs) where electricity, heat, natural gas and other forms of energy are coupled together, all types of energy customers even the inelastic loads can participate in demand response (DR) program. This novel vision has led to the concept of "integrated demand response (IDR)". IDR programs (IDRPs) in EHs involve coordinating multiple DR activities across different energy systems, such as buildings, industrial complexes, and transportation networks. The main purpose of IDR is that multi-energy users can respond not only by shifting or reducing energy consumption from the demand side, but also by changing the type of energy consumed, in response to the dispatching center. The integration of IDRPs in EHs can help to reduce energy costs, improve grid stability, and increase the penetration of renewable energy sources (RES) in the power system. Moreover, by synchronizing DR activities across different energy systems, IDRPs can provide additional benefits, such as improved energy efficiency, reduced greenhouse gas emissions, and increased resilience to power outages and other disruptions. In this paper, we provide a review, assessment, and classification of fundamental principles, modeling techniques and optimization methods for IDR programs in EHs.

This paper is devoted to an accurate determination of the London dispersive and polar free energy of adsorption, the two Lewis acid γs+ and base γs- components of polar surface energy γsAB of 2D single-crystalline and polycrystalline covalent organic frameworks such as TAPPy-TPA-COFs. Inverse gas chromatography (IGC) at infinite dilution was used to quantify the different surface parameters of the different materials. From IGC measurements, one determined the net retention time of the adsorption of n-alkanes and several polar solvents on single-crystalline and polycrystalline covalent organic frameworks. The free surface Gibbs energy of adsorption was obtained for the various organic molecules at different temperatures from their net retention volume values. The separation between the London dispersive energy and the polar energy of adsorbed molecules was carried out by using a new thermodynamic parameter PSX chosen as new indicator variable and taken into account the deformation polarizability and the harmonic mean of the ionization energies of solvents and solid materials, derived from London dispersion equation. The obtained results are very promising for the accurate determination of the surface thermodynamic parameters of adsorption of organic solvents on solid surfaces.

To regulate the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply-demand balance, the government can adjust BEV regulations and offer subsidies. The Jawa-Madura-Bali (Jamali) electrical system, being the largest in Indonesia [1], faces the challenge of accommodating the increasing number of vehicles [2]. Subsequent to analyzing Jamali's electricity supply using data from the National Electricity Company (RUPTL) [3], simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, internal combustion engine (ICE) and electric vehicle populations, initial charging time (ICT), slow- and fast-charging ratios, and BEV charge load curves are simulated. Scenario variables like supply capacity growth speed, vehicle population growth speed, subsidy impact on EV attractiveness, ICT, and fast-charging ratio are subsequently simulated for 2024-2029. Four key simulation outcomes are identified: the best-case scenario (Scenario 1776), achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst outcome scenario leads to a 23.12% EV percentage, necessitating 47.566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on literature research are evaluated. This simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029 [4], aiding the government in planning regulations and subsidies effectively. Improving the simulation's accuracy depends on refining input variables over time.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

Sustainable energy development (SED) is a crucial component of the Sustainable Development Goals (SDG), aiming to maintain economic and social progress while protecting the environment and mitigating climate change’s effects. SED serves as a transition paradigm for sustainable development, providing a blueprint for energy peace and prosperity for people and all uses. This article presents the history of SED and then uses a critical discourse approach to summarize existing review studies in SED. Ten interlinked themes of SED are identified, with two of them considered to be among the least studied in existing SED reviews and in the current global discussion around climate change. This study explores these two themes, which include energy financing and the need for 100% renewable energy (RE), a sub-theme of decarbonization strategy working towards the 1.5–2.0 °C scenario. The study suggests that the current G20 countries’ contributions, if maintained continuously per annum, in addition to 80% more funding from private investment compared to the amount in the 1.5 °C scenario financial requirements for clean energy, are sufficient to limit global warming. In addition to the present drive for 100% RE, the article also discusses emerging issues, such as energy storage options with an indication of hydrogen as the most promising, other energy-related development agendas, and the need for regional security stability to prevent energy wars. Selected SED decarbonization strategies are presented across the power, transport, building, and industrial sectors. The study concludes with progress and directions for future research, mainly the need for re-defining nationally determined contribution (NDC) through an emissions budgeting and centralized global or regional emissions stock-taking strategy working towards the 1.5 °C scenario.

Conventional methods of climate change (CC) mitigation have not ‘bent the curve’ of steadily rising annual anthropic CO2 emissions or atmospheric concentrations of greenhouse gases. This study reviews the present position and likely future of such methods, using recently published literature with a global context. It particularly looks at how fast they could be implemented, given that the limited time available for avoiding catastrophic CC (CCC). The study then examines solar geoengineering, an approach often viewed as complementary to conventional mitigation. The review next introduces equity considerations, and shows how this will shorten even further the time available for effective action for CC mitigation. The main findings are as follows. Conventional mitigation approaches will be implemented too slowly to be of much help in avoiding CCC, partly because some suggested technologies are infeasible, while others are either of limited technical potential, or, like wind and solar energy, cannot be introduced fast enough. Because of these problems, solar geoengineering is increasingly advocated as a quick-acting and effective solution. However, it could have serious side effects, and given that there will be winners and losers at the international as well as the more regional level, political opposition may make it difficult to implement. The conclusion is that global energy consumption itself must be rapidly reduced to avoid catastrophic climate change.

Background: Mitigating climate change requires fundamentally redesigned energy systems in which renewable energy sources ultimately replace fossil fuels such as natural gas. In this context, the question how and to which extent the gas sector can contribute to an increasingly climate-neutral future EU energy system is heavily debated among scholars, energy industry experts and policy makers. Methods: We take a two-step approach: We begin with a review of studies from energy industry and academia to discuss potential gas sector contributions from a holistic energy system design point of view; this is followed by a comprehensive discussion of technical potentials, micro-economic conditions and societal implications of renewable gas. We then enrich our findings with the results of an empirical focus group process. Results: The gas sector can not only contribute to balancing volatile renewable energy production but also enable the supply of renewable energy to end-users in gaseous form; based on existing infrastructure. This could reduce costs for society, increase public acceptance and ultimately speed up the energy system transformation. There is the theoretical technical potential to substitute major parts of natural gas with renewable gas of biogenic and synthetic nature. This, however, crucially requires a supportive policy framework like the one established for renewable electricity. Conclusion: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.

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

The International Renewable Energy Agency (IRENA) was established in 2009 and in 2024, its membership increased to 168 members plus the European Union countries. The purpose of IRENA is to promote the adoption of renewable energy across all industries in the world. Despite the utilization of renewable energy in sectors like power generation and construction, its application in other industries remains limited. Therefore, a literature review study was conducted to investigate the cost and other spatial constraints associated with renewable energy resources, including modeling and optimization techniques that could offer viable solutions to the future challenges faced by the renewable energy production systems. This paper has summarized the result of this study with a focus on the significance of modeling and optimization techniques in energy conservation to highlight the untapped potential for leveraging renewable energy resources in industrial applications. This paper also examined a few case studies from various regions of the world, demonstrating the promising opportunities for research in the modeling and optimization of renewable energy management systems.

Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namelly the urban heat island concept, is introduced and related with the existence of PV systems. It is concluded that urban building energy modelling (UBEM) can be effective to study the performance of PV systems in the urban environment. It allows to simultaneously predict the building energy performance and the microclimate effect. However, there is a need to develop new methodologies to overcome the challenges associated to UBEM, especially in what concerns non-geometric data, which leads to a major source of errors. and to find an effective method to predict the effect of PV systems in the urban environment.

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Geoenvironmental engineering involves defining solutions for complex problems, such as containment systems management, contaminant transport control, wastewater management, remediation of contaminated sites and valorization of geomaterials and wastes. In the last years, energy harvesting (EH) - or energy scavenging - methods and technologies have been developed to reduce the dependence on traditional energy sources, namely fossil fuels, and nuclear power, also responding to the increase of energy demands for human activities and to fulfill sustainable development goals. EH in geoenvironmental works and surrounding soil and water environment include a set of processes for capturing and accumulating energy from several sources considered deemed wasted or unusable associated to soil dynamics, stress and strain of geomaterials, hydraulic, vibrations, biochemical, light, heating and wind sources can be potential EH systems. Therefore, this work presents a review of the literature and critical analysis on the main opportunities for EH capturing, accumulating and use in geoenvironmental works, among basic electric concepts and mechanisms, analyzing those works in complex conditions involving biological, chemical, mechanical, hydraulic, and thermal coupled actions, concluding with the main investigation and challenges within geoenvironmental aspects for EH purpose.

In this paper, spacetime is defined as the absolutely invariant homogeneous "coordinate spacetime" frame and its modulation by the relatively variable non-homogeneous "metric spacetime" under its constraints. This modulation is caused by matter and motion, and carries the function of transmitting interactions, reflecting the diffeomorphism invariance and Lorentz invariance. This inhomogeneous spacetime concept makes the medium nature of spacetime more obvious and avoids the problem of "path dependence" caused by the concept of curved spacetime. In this paper, it is argued that all energy-momentum has internal spatio-temporal properties, and all interactions must be transmitted through spacetime metrics. On this basis, a converging and dispersing cyclic multiverse model is proposed. This model of the universe is inevitable as long as there are two macroscopic manifestations of the matter of the universe, namely, gravitation induced convergence and "explosive" dispersion caused by different factors. The new cosmic model matches the observed accelerated "expansion" of the universe and no longer requires the dark energy hypothesis.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

Using a wide range of organic substrates in the methane fermentation process enables efficient biogas production. Nonetheless, in many cases, the efficiency of electricity generation in biogas plant cogeneration systems is much lower than expected, close to the calorific value of the applied feedstock. This paper analyses energy conversion efficiency in a 1 MWel agricultural biogas plant fed with corn silage or vegetable waste and pig slurry as a feedstock dilution agent, depending on the season and availability. Biomass conversion studies were carried out for 12 months, during which substrate samples were taken once a month. The total primary energy in substrates was estimated in laboratory conditions by measuring the heat of combustion in a ballistic bomb calorimeter (17,760 MWh·year-1), and in the case of pig slurry, biochemical methane potential (BMP, (201.88±3.21 m3·Mg VS-1). Further, the substrates were analysed in terms of their chemical composition — from protein, sugar and fat content to mineral matter determination, among other things. The results obtained during the study were averaged. Based on such things as the amount of biogas produced at the plant, the amount of chemical (secondary) energy contained in methane as a product of biomass conversion (10,633 MWh·year-1) was calculated. Considering the results obtained from the analyses, as well as the calculated values of the relevant parameters, biomass conversion efficiency was determined as a ratio of chemical energy in methane to (primary) energy in substrates, which was 59.87%, as well as electricity production efficiency, as a ratio of electricity produced (4,913 MWh·year-1) to primary energy, with a 35% cogeneration system efficiency. Full energy conversion efficiency, related to electricity production, reached a low value of 27.66%. This article provides an insightful, unique analysis of energy conversion in an active biogas plant as an open thermodynamic system.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Increasing of renewable energy share in total energy production is a direction that leads towards European Union’s aims of carbon neutrality by 2050, as well as increases energy self-sufficiency and independence. Some of the main challenges to increase renewable energy share while providing efficient and secure energy supply are related to optimization and profitability of de-centralized energy production systems. Integration of energy storage systems in addition to decentralized renewable energy production, for example, by solar panels, leads to more effective electricity supply and smart energy solutions. The modelling of such complex dynamic system can be performed using system dynamics method. The aim of the article is to forecast the practice of electricity storage in Latvia in the coming decades. A system dynamics model predicting the implementation of battery storage in private households was created for the case study of Latvia. Modelling results reveal that under the right conditions for electricity price, investment costs and with the right policy interventions battery storage technologies combined with PV panels have high potential for utilization in household sector. Model results show that in baseline scenario with no additional policies up 21 422 households or 10.8 % of Latvian households could have combined PV and battery system installed in 2050. Moderate subsidy policy can help to increase this number up to 25 118.

The optimization of the energy grid is a critical task for ensuring a sustainable and efficient energy future. Deep machine learning techniques have the potential to improve energy grid optimization by predicting energy demands and supplies, optimizing energy production and distribution, and detecting and preventing fraud. However, there are also several challenges associated with the use of deep machine learning in energy grid optimization. These include the lack of standardized datasets and data quality issues, interpretability and explain-ability of machine learning models, ethical and social implications of using machine learning, and integration with existing energy infrastructure and regulatory frameworks. Having a clear understanding that continued research and developments in deep learning applications to energy field are crucial for achieving a sustainable and efficient energy future. This paper therefore reviewed existing literature for challenges and opportunities associated with deep machine learning in energy grid optimization; and highlights the importance of continued research and development in the field. The paper found out that opportunities for future research in deep machine learning for energy grid optimization include advancements in machine learning algorithms and techniques, development of new datasets and data collection methods, integration of machine learning with other emerging technologies. It also established needs for collaborative research and public-private partnerships.

We use a recently proposed formulation of quantum mechanics based, not on potential functions but rather, on orthogonal energy polynomials. In this context, the most important building block of a quantum mechanical system, which is the wavefunction at a given energy, is expressed as pointwise convergent series of square integrable functions in configuration space. The expansion coefficients of the series are orthogonal polynomials in the energy; they contain all physical information about the system. No reference is made at all to the usual potential function. We consider, in this new formulation, few representative problems at the level of undergraduate students who took at least two courses in quantum mechanics and are familiar with the basics of orthogonal polynomials. The objective is to demonstrate the viability of this formulation of quantum mechanics and its power in generating rich energy spectra illustrating the physical significance of these energy polynomials in the description of a quantum system. To assist students, partial solutions are given in an appendix as tables and figures.

Considering the actual world economical trends, one of the most important questions is now and in the future: how to reduce power consumption of electronic systems. Since the invention of computers, the electrical energy consumption step by step increased. Now when not only computers, but electric vehicles, robots, automation, and unmanned aerial vehicles play a very important role of our life, the main problem of system designers is how to reduce energy consumption in these systems. But also the existing already working systems must be revised in order to decrease their electric power consumption. The importance of this subject (energy control) shows that a huge number of research publications and survey papers deal with it. Just focusing on the last one or two years (2021 and 2022) the search hit 221000 titles (103000 hits only in 2022). Analyzing all the research areas is almost impossible, but focusing on some important research subjects, where one of the main topic is “energy saving methods” can give an overview about the subject. The paper focuses on the area of industrial robotic systems, electric vehicles, and embedded systems.

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. But recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder’s priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be broadly defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making.

Law of conservation of energy is the most well-established law of modern physics. But, inflationary cosmological model which assumes the universe to be created from a point of singularity has not been able to identify the source of energy for creation of this universe. As per uncertainty principle, a quantum vacuum fluctuation can at most create energy of ~10^-8 kg in a Planck time of ~5.4 ×10^-44 s whereas total positive energy due to matter/vacuum existing in the universe is more than ~10⁵⁴ kg. To solve this problem, we have recently published a new cosmological model (Biswaranjan Dikshit, Canadian Journal of Physics, 100, 218-225, [2022]) in which we have mathematically proved that the total energy of the universe can be zero right from the beginning of the universe to till date due to negative gravitational potential energy and thus law of conservation of energy can be satisfied during creation of the universe from nothing. In this article, starting from a simple argument, we explain why net energy of the universe can be zero only in our model, not in standard cosmological model. An interesting fact pointed out in this paper is that the factor ~10⁶² by which radius of universe has increased from its starting value of ~10^-36 m to present value of ~10²⁶ m is same as the factor by which positive energy has increased from Planck energy ~10^-8 kg to present value ~10⁵⁴ kg. This is in agreement with our new model that predicts the positive energy of universe to be proportional to the radius of universe. We also bring out to the notice that zero energy universe is possible only if smallest possible length in nature is, l_min=l_p(π/2)^1/2 = 2.0×10^-35 m. Minimum possible length is important in theory of quantum gravity and string theory.