The article is devoted to a bibliometric analysis of scientific publications reflecting trends in scientific publications related to the energy transition of leading universities in Saudi Arabia. The universities were selected based on the presence of joint publications with the Saudi Arabian Oil Company, they are: King Fahd University of Petroleum and Minerals, King Abdullah University of Science and Technology, and King Saud University. The importance of studying the experience of Saudi Arabia for Russia in the context of the energy transition was highlighted. The analysis was based on Scopus platform data related to Subject Areas: Physical Sciences. Scientists from Saudi Arabian universities are increasingly publishing with co-authors from China, India, Egypt, Pakistan, and Malaysia, while maintaining high interaction with co-authors from the United States, South Korea, and the United Kingdom. Authors from the Russian Federation publish poorly with co-authors from Saudi Arabia. The trends in the topics of scientific publications were investigated, and their connection with the renewable energy topic was shown. The VOSviewer software was used to determine the main research areas using the cluster analysis method based on the co-occurrence of key terms. The analysis was done for both Author's keywords and Index keywords of the Scopus system. Graphical representation of the final choice of terms to describe research trends was proposed. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

The gastrointestinal (GI) tract is a continuous channel through the body that consists of the esophagus, the stomach, the small intestine, the large intestine, and the rectum. Its primary functions are to move the intake of food for digestion before storing and ultimately expulsion of feces from the rectum through the anal sphincter. The mechanical behavior of GI tissues thus plays a crucial role for GI function in health and disease. The mechanical properties are typically characterized by a constitutive biomechanical model, which is a mathematical representation of the relation between load and deformation in a tissue. Hence, validated biomechanical constitutive models are essential to characterize and simulate the mechanical behavior of the GI tract under physiological and pathological conditions. Numerous constitutive models have consequently been proposed over the past three decades, mainly inspired by work done in cardiovascular tissues. Here, a comprehensive review of these constitutive models is provided. This review is limited to studies where a model of the strain energy function is proposed to characterize the stress-strain relation of a GI tissue. Several needs are identified for more advanced modeling of GI biomechanics including: 1) Microstructural models that provide actual structure-function relations; 2) Validation of coupled electro-mechanical models accounting for active muscle contractions; 3) Human data under physiological and pathological conditions to develop and validate models. The findings from this review provide guidelines for using existing constitutive models as well as perspective and directions for future studies aimed at establishing new constitutive models for GI tissues.

Hubble expansion may be considered as a velocity per photon travel time rather than as velocity or redshift per distance. Dimensionally, this is an acceleration and will have an associated curvature of space under general relativity. This paper explores this theoretical curvature as an extension to the spacetime manifold of general relativity, generating a modified solution with three additional non-zero Christoffel symbols, and a reformulated Ricci tensor and curvature. The observational consequences of this reformulation were compared with the ΛCDM model for luminosity distance using the extensive type Ia supernovae (SNe Ia) data with redshift corrected to the CMB, and for angular diameter distance with the recent baryonic acoustic oscillation (BAO) data. For the SNe Ia data, the modified GR and ΛCDM models differed by −0.15+0.11μB mag. over zcmb=0.01−1.3, with overall weighted RMS errors of ±0.136μB mag for modified GR and ±0.151μB mag for ΛCDM espectively. The BAO measures spanned a range z=0.106−2.36, with weighted RMS errors of ±0.034 Mpc with H0=67.6±0.25 for the modified GR model, and ±0.085 Mpc with H0=70.0±0.25 for the ΛCDM model. The derived GR metric for this new solution describes both the SNe Ia and the BAO observations with comparable accuracy to ΛCDM without requiring the inclusion of dark matter or w’-corrected dark energy.

Obesity represents a major public health problem with a prevalence increasing at an alarming rate worldwide. Continuous intensive efforts to elucidate the complex pathophysiology and improve clinical management have led to a better understanding of biomolecules like gut hormones, an-tagonists of orexigenic signals, stimulants of fat utilization, and/or inhibitors of fat absorption. In this article, we will review the pathophysiology and pharmacotherapy of obesity including inter-section points to the new generation of antidiabetic drugs. We provide insight into the effective-ness of currently approved anti-obesity drugs and other therapeutic avenues that can be explored.

One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

The high-resolution bistatic lidar developed at the Physikalisch-Technische Bundesanstalt (PTB) aims to overcome the limitations of conventional monostatic lidar technology which is widely used for wind velocity measurements in wind energy and meteorology applications. Due to the large measurement volume of a combined optical transmitter and receiver tilting in multiple directions, monostatic lidar generally has poor spatial and temporal resolution. It also exhibits large measurement uncertainty when operated in inhomogeneous flow, for instance, over complex terrain. In contrast, PTB’s bistatic lidar uses three dedicated receivers arranged around a central transmitter, resulting in an exceptionally small measurement volume. The coherent detection and modulation schemes used allow the detection of backscattered, Doppler shifted light down to the scale of single aerosols, realising the simultaneous measurement of all three wind velocity components. This paper outlines design details and the theory of operation of PTB’s bistatic lidar and provides an overview of selected comparative measurements. The results of these measurements have shown that the measurement uncertainty of PTB’s bistatic lidar is well within the measurement uncertainty of traditional cup anemometers, while being fully independent of its site and traceable to the SI units. This allows its use as a transfer standard for the calibration of other remote sensing devices. Overall, PTB’s bistatic lidar shows great potential to universally improve the capability and accuracy of wind velocity measurements, such as for the investigation of highly dynamic flow processes upstream and in the wake of wind turbines.

Vehicle driveability is one of the important vehicle attributes in range-extender electric vehicles due to the electric motor torque characteristics at low-speed events. The process of validating and rectifying vehicle driveability attributes is typically utilised by a physical vehicle prototype that can be expensive and required several design iterations. In this paper, a model-based energy method to assess vehicle driveability is presented based on a high-fidelity 49 degree-of-freedom powertrain and vehicle systems. Multibody dynamics components were built according to their true centre of gravity relative to the vehicle datum for providing an accurate system interaction. The work covered a frequency at less than 20 Hz. The results that consisted of the component frequency domination are structured and examined to identify the low-frequency sensitivity based on different operating parameters such as a road surface coefficient. An energy path technique was also implemented on the dominant component by decoupling its compliances to study the effect on the vehicle driveability and low-frequency response. The outcomes of the research provided a good understanding of the interaction across the sub-systems levels. The powertrain rubber mounts were the dominant components that controlled the low-frequency contents (< 15.33 Hz) and can change the vehicle driveability quality.

In this work, an energy-saving smart light controlling system has been proposed that can main-tain the desired intensity of light in a room automatically. Unlike the conventional light control system, the proposed system splits a large room into several zones and analyzes the light inten-sity of each zone; hence, the controlling unit adjusts the light intensity to the desired level. The main controlling unit consists of a light sensor, a motion sensor, a relay with a driver unit, an LCD display, etc. for controlling light efficiently to reduce the power waste. The sensors meas-ure the intensity of light, based on the standard light intensity data chart the controller units make a decision how many light bulbs are needed to be switched ON/OFF in a particular zone. Moreover, the system automatically switched-OFF all light bulbs when there is nobody in the room. Proteus design suite 8.0 is used to design and simulation of the proposed system. Moreo-ver, the PCB layout is designed using ExpressPCB version 7.5.0. The proposed system is capable of minimizing the power loss by up to 44% in comparison to the conventional light manage-ment system.

Smart air filters are beneficial to provide highly efficient particle removal, treat multiple contaminants simultaneously and conserve energy during air filtration processes. Herein, a type of self-supporting smart air filter (SSSAF) was fabricated by sandwiching the VOC-responsive PZT/PVDF electrospun membrane with two metal mesh electrodes. Besides the high filtration efficiency for sub-micron particles, the SSSAF showed good responses to pressure drop in the range of 0 to 500 Pa via the electroactivity of PZT/PVDF membrane. In addition, the SSSAF achieved VOC sensing function via the swelling properties of PZT/PVDF membrane in organic vapors, demonstrated by its signal to 50 to 200 ppm ethanol gases. The SSSAF based nanogenerator was employed to harvest wind energy, which was further applied to inhibit bacterial growth without the need of additional power input. The results provide new insight into development of all-in-one smart air filters.

Peer-to-peer (P2P) energy trading is a new data-driven business model currently being trialed within the energy sector. Introducing P2P transactions to an essential service such as energy could have far-reaching implications for individuals and the grid. This paper raises considerations and questions from social, economic/markets and regulatory points of view, that should be understood and addressed by societies and policymakers. It does this by considering under what circumstances it is reasonable to conceptualize P2P energy trading as part of the sharing economy, and drawing parallels to the sharing economy experience in other sectors. In order to reap the full societal benefits, while avoiding considerable risks to infrastructure and individuals, a policy approach promoting dialogue and innovation is necessary. We suggest the regulatory sandbox is the most appropriate tool to achieve this and would help avoid the breakdown of trust between policymakers and platform companies observed in other sectors.

Silk fibroin is a well-known biopolymer used in several applications in which the interaction with biological tissue is required. In fact, fibroin is extremely versatile and can be shaped to form several constructs useful in tissue engineering applications. Confocal imaging is usually per-formed to test the cells behaviour on the construct and in this context the fibroin autofluorescence is regarded as a problem. In addition, the autofluorescence is not intense enough to provide useful morphological images. In fact, to control study the constructs morphology other techniques are used (i.e. SEM, Micro-CT). In this work we propose a method based on the fluorescence energy transfer (FRET) to suppress the fibroin autofluorescence moving it to higher wavelength accessible to the confocal microscopy for a direct imaging.

The field of power harvesting has experienced significant growth over the past few years due to the ever-increasing desire to produce portable and wireless electronics with extended lifespans. The present work aims to introduce an approach to harvesting electrical energy from a mechanically excited piezoelectric element and investigates a power analytical model generated by a smart structure of type polyvinylidene fluoride(PVDF) that can be stuck onto fabrics and flexible substrates, although we report the effects of various substrates and investigates the sticking of these substrates on the characterization of the piezoelectric material.

This paper presents the values of several mechanical characteristics for two blends: H (60% PP + 12% PA6 + 8% EPDM + 20% Polybond 3200) and G (20% PP + 42% PA6 + 28% EPDM + 10% Polybond 3200) (%wt), comparing them to those of PP (polypropylene) and PA6 (polyamide 6). The adding of EPDM (ethylene propylene diene monomer rubber) and Polybond 3200 help reducing the disadvantages of simple blends made of PP+PA6, when the PA6 concentration allows for having a PA6 matrix with droplets of PP. SEM analysis helps for understanding the performance of material G as compared to the neat polymers. EDX analysis proved that there was a matrix inversion, material H having a PP matrix and material G a matrix made of PA6 with droplets of PP. Strain at break for PP and material H were proved to be insensitive to test speed, but materials G and PA6 had large value for strain at break and energy at break for v = 10 mm/min. Taking at basis the values for polyamide 6 (PA6), material G has greater values for energy at break: with 97.8% for v = 10 mm/min, with 29.5% for v= 250 mm/min and with 98% for 1000 mm/min, without exhibiting the micro and macro cavitation of PA6. This means that the recipe, the technology and the mechanical characteristics make material G a potential candidate for applications where a low and moderate impact resistant material is required.

In a previous series of papers relating to the Combined Gravitational Action (CGA), we have exclusively studied orbital motion without spin. In the present paper, we apply CGA to any self-rotating material body, i.e., an axially spinning massive object, which itself may be locally seen as a gravito-rotational source because it is capable of generating the gravito-rotational acceleration, which seems to be unknown to previously existing theories of gravity. The consequences of such an acceleration are very interesting, particularly for Compact Stellar Objects. Independently of the equation of state, it is found that the minimum radius of a stable neutron star is three times its gravitational radius, R_min = 3GM_NS/c², and its critical and maximum internal magnetic field strength cannot exceed the value of 3×10¹⁸ G.

We discuss the topic of the transparency of the Universe in gamma-rays due to the Extragalactic Background Light, and its cosmological and physical implications. Rather than a review, this is a personal account on the development of 30 years of this branch of physical science. Extensive analysis of the currently available information appears to us as revealing a global coherence among the astrophysical, cosmological, and fundamental physics data, or, at least, no evident need so far of substantial modification of our present understanding. Deeper data from future experiments will verify to what extent and in which directions this conclusion should be modified.

Following the concept of strong interaction, theoretically, fusion of proton seems to increase the binding energy of final atom by 8.8 MeV. Due to Coulombic repulsion, asymmetry effect, pairing effect and other nuclear effects, final atom is forced to choose a little bit of binding energy less than 8.8 MeV and thus it is able to release left over binding energy in the form of internal kinetic energy or external thermal energy. Thus, in cold fusion, heat release to occur, binding energy difference of final atom and base atom seems to be less than 8.8 MeV. Qualitatively, energy released during cold fusion seems to be approximately equal to 8.8 MeV minus the difference of binding energy of final and base atoms. Based on this idea, under normal conditions, for the case of ₂He⁴, fusion of four protons can liberate (35.2-28.3)=6.9 MeV and it is 3.5 times less than the current estimates. Point to be understood is that, lesser the binding energy of final atom, higher the liberated thermal energy and vice versa. With a suitable catalyst and sufficient hydrogen under suitable pressure, if reactor’s temperature is maintained at (1000 to 1500) ⁰C, there seems a lot of scope for a chain reaction of cold fusion in which light isotopes transform to their next stage with increased proton number or mass number and liberate safe and clean heat energy continuously. By arranging 4 to 6 reactors and charging them periodically in tandem, required thermal energy can be produced continuously. In this new direction, by carefully selecting the base isotope and its corresponding catalyst, experiments can be conducted and ground reality of cold fusion can be understood at various temperature and pressure conditions.

Long-chain branched metallocene-catalyzed high-density polyethylenes (LCB-mHDPE) were solution blended to obtain blends with varying degrees of branching. A high molecular LCB-mHDPE was mixed with low molecular LCB-mHDPE are varying concentrations, whose rheological behavior is similar but whose molar mass and molar mass distribution is significantly different. Those blends were characterized rheologically to study the effects of concentration, molar mass distribution, and long-chain branching level of the low molecular LCB-mHDPE. Owing to the ultra-long relaxation times of the high molecular LCB-mHDPE, the blends started behaving clearly more long-chain branched than the base materials. The thermorheological complexity showed an apparent increase in the activation energies Ea determined from G’, G”, and especially δ. Ea(δ), which for LCB-mHDPE is a peak function, turned out to produce even more pronounced peaks than observed for regular LCB-mPE and also LCB-mPE with broader molar mass distribution. Thus, it is possible to estimate the molar mass distribution from the details of the thermorheological complexity.

The use of different sources to energize a load is convenient in many applications, particularly those where two or more renewable energy sources are employed as: energy harvesting, hybrid vehicles, and off-grid systems. In these cases, a multi-input converter able to admit sources with different characteristics and, if necessary, select the output power of each source. Several topologies of multi-input converters have been proposed to this aim, however, most of them are based on multi-stage designs, which decreases efficiency and increases control complexity, particularly when more than two sources are used. In this work, a three-input step-up converter easy to control in open loop condition is analyzed. A designed procedure is described, and experimental results are presented for a 1 kW power converter. The implemented converter results in a higher voltage gain, less storage element keeping high efficiency compared to similar topologies. Using the procedure here proposed, this converter that was initially proposed for photovoltaic applications is enabled to be used in medium and high-power applications, for example when renewable energy sources are used.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

The grain-to-grain stress vertically in sediments is given by the overburden less the pore fluid pressure, σ, divided by the fraction of the horizontal area which is the supporting matrix , (1 − φ), where φ is the porosity. It is proposed that the fractional reduction of this ratio, Λ, with time is given by the product of φ 4m/3 , (1 − φ) 4n/3 , and one or more Arrhenius functions A exp(−E/RT ) with m and n close to 1. This proposal is tested for shale sections in six wells from around the world for which porosity-depth data are available. Good agreement is obtained above 30-40 C and porosities less than 0.5. Single activation energies for each well are obtained in the range 15-33 kJ/mole, close to pressure solution of quartz, 24 kJ/mol. Values of m and n are in the range 1 to 0.8, indicating nearly fractal pore-matrix spaces and water-wet interfaces. Results are independent of over- or under-pressure of pore water. This model explains shale compaction quantitatively. Given porosity-depth data and accurate activation energy, E, one can infer paleo-geothermal-gradient and from that organic maturity, thus avoiding unnecessary drilling.

The environmental safety of a car is currently one of the most important indicators of the vehicle competitiveness and quality in the consumer market. Currently, the assessment of the ecological properties of vehicles can be made based on various criteria. In the case of combustion-powered cars, the most attention is usually paid to the values characterizing their use, and in the environmental assessment, first of all to pollutant emissions and operational fuel consumption. The proposed article considers the possibility of using the life cycle assessment to analyze the ecological properties of a passenger car during its operation. A simplified LCA method of the vehicle was presented, which in strictly defined cases can be used for the analysis of environmental impacts and the assessment of the energy analysis related to its operation. For this purpose, a vehicle life cycle model was developed. Data on the operation of 33 passenger cars of different manufacturers with similar operational characteristics, coming from different production periods, were analyzed in detail. The obtained results were found to be highly sensitive to the assumptions made in the article.

Active galactic nuclei (AGNs) are throwing in the interstellar space huge jets of energy in the form of elementary particles. The calculation of the energy density of space in the centre of the black hole with the mass of the Sun shows that in the space-time singularity of such a black hole energy density of space there is so low that atoms become unstable and fall apart into elementary particles. In this sense, AGN is a rejuvenating system of the universe. It transforms its own old matter into fresh energy in the form of jets.

In recent decades, greenhouse gas (GHG) emissions have been a critical priority of global environmental policy. The leading cause of the increase in GHG triggering global warming in the atmosphere is the continuously growing demand for universal energy due to population and economic growth. Energy efficiency and reduction of CO2 emissions in highly-energy consuming sectors of Turkey are critical in deciding a low-carbon transition. In this study, the change of energy-related CO2 emissions in Turkey’s energy-intensive four sectors from 1998 to 2017 is analyzed based on the Logarithmic Mean Divisia Index (LMDI) method. It is used to decompose CO2 equivalent emissions changes in these sectors into five driving forces; changes in economic activity, activity mix, energy intensity, energy mix, and emission factors. Analytical results indicate that economic activity is a vital decisive factor in determining the change in CO2 emissions as well as sectoral energy intensity. The activity effect has raised CO2 emissions, while energy intensity has decreased. This method indicates that the impact of the energy intensity could be the first key determinant of GHG emissions. Turkey's efforts to be taken in these sectors in adopting low carbon growth policies and reducing energy-related emissions to tackle climate change are clarified in detail.

A run-of-the-mill remote sensor arrange comprises numerous vitality compelled sensor hubs that are arbitrarily sent is the sensor field. Vitality is one of the most significant parts of planning an information dispersal convention for applications, for example, war zone checking, living space observing, and so on. We present EGDD, an Energy-effective Grid-based Data Dissemination plot for arbitrarily conveyed remote sensor systems. The proposed conspire is vitality effective for dealing with both sink and source portability. EGDD depends on a virtual framework-based foundation for information scattering from various versatile sources to different portable sinks. The virtual lattice of square size is built by the main source showing up in the sensor field dependent on the remaining vitality of the sensor hubs. Moreover, we proposed an askew sending calculation for inquiry and information sending which guarantees that just a single spread hub advances the question and information at once. In EGDD, substitute scattering hubs are chosen ahead of time during the lattice development procedure and the least limit vitality level is characterized for spread hubs. When the vitality of a dispersal hub arrives at the base edge esteem, it is supplanted by another spread hub. The reenactment results show that the proposed EGDD conspire is more vitality proficient when contrasted with other information scattering conventions.

The residential sector in the United States is in need of comprehensive policy-making reforms that concurrently address housing affordability and environmental sustainability. This study investigates the feasibility of state-wide zero-energy affordable housing by analyzing historical data on climate, energy use, and solar system costs in the Commonwealth of Virginia. The hypothesis examined is that the net present cost of implementation of rooftop residential solar systems to achieve zero-energy Low-Income Housing Tax Credit (LIHTC) buildings is lower than the discounted present cost of energy of otherwise identical conventional buildings that run without renewable energy generation systems. The authors propose a generalizable framework for analyzing the feasibility of achieving region- or state-wide zero-energy LIHTC developments. To validate the framework, the authors employ a longitudinal sample of monthly energy use data from 2013-2016 obtained from 310 residential units of 15 LIHTC developments across the state. Based on statistical regression analysis, energy simulation, and simulation-based risk analysis, the authors find that the net present value of zero-energy LIHTC investments can be positive with a low risk. The investment value varies often depending on the zero-energy building definition, weather characteristics, retail price of electricity, and incentive rate. This study can help housing policymakers and industry professionals analyze and benchmark the feasibility of innovative zero-energy housing policies and projects.

Dietary approach is essential to obesity control, but the effectiveness of changes in meal frequency (MF) as strategies for loss and maintenance of body mass remain unclear. This study aimed to evaluate the influence of MF on a hypocaloric diet on weight loss, active ghrelin levels and metabolic indicators of women with obesity. This is a randomized, parallel clinical trial, including forty women, randomized in two groups, both following a hypocaloric diet, according to MF (G1 – six meals/day; G2 – three meals/day). Dietary, laboratory, anthropometric and body composition indicators were assessed, as well as energy expenditure (EE), before and after the 90 days of intervention. After intervention, both groups decreased body weight, body mass index (BMI), waist circumference, fat mass (FM), insulin and HOMA-IR. G1 increased insulin sensitivity and G2 reduced triglyceride and FM and increased fat-free mass (FFM). MF increased ghrelin levels. There were no differences in EE variables. Hypocaloric diet with different MF promoted a reduction in total weight, BMI, WC and FM and an improvement in glycidic metabolism. However, the accomplishment of the three meals/day increased the FFM and active ghrelin and reduced triglyceride, while six meals/day was more beneficial in increasing insulin sensitivity.

This paper investigates the use of deep learning techniques to perform energy demand forecasting. Specifically, the authors have adapted a deep neural network originally thought for image classification and composed of a convolutional neural network (CNN) followed by a multilayered fully connected artificial neural network (ANN). The convolutional part of the network was fed with a grid of temperature forecasting data distributed in the area of interest in order to extract a featured temperature. The subsequent ANN is then fed with this calculated temperature along with other data related to the timing of the forecast. The proposed structure was first trained and then used in a real setting aimed to provide the French energy demand forecast using ARPEGE forecasting weather data. The results show that the performance of this approach is in the line of the performance provided by the reference RTE subscription-based service, which opens the possibility to obtain high accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

The principles of causality and unitarity are studied within rational nonlinear electrodynamics proposed earlier. We investigate dyonic and magnetized black holes and show that in the self-dual case, when the electric charge equals the magnetic charge, corrections to Coulomb's law and Reissner-Nordstrom solutions are absent. In the case of the magnetic black hole, the Hawking temperature, the heat capacity and the Helmholtz free energy are calculated. It is shown that there are second-order phase transitions and it was demonstrated that at some range of parameters the black holes are stable.

The purpose of this case study was to evaluate the benefits that evidence-based nutritional and training recommendations could have on the time course of reconditioning following hip arthroplasty in a competitive master triathlete. Methods: During 38 weeks (from 6 weeks prior to surgery through to the return to competition), the athlete was provided with detailed training and nutritional recommendations based on the latest research evidence. Dietary intake (via the remote food photographic method), body composition (via DXA), peak oxygen uptake (VO2peak), peak power output (PPO) and cycling efficiency (GE) were assessed 6 weeks pre- and 8, 12, 18, 21 and 25-weeks post-surgery. Training load was quantified (TRIMP score) daily during the retraining. Results: Total body mass increased by 8.2 kg (attributable to a 3.5 and 4.6 kg increase in fat mass and lean mass, respectively) between week -6 and week 8 despite a reduction in carbohydrate (CHO) intake post-surgery (<3.0g/kg/day). This was accompanied with a decrease in VO2peak, PPO, and GE due to a drop in training load. From week 7, the athlete resumed training and was advised to gradually increase CHO intake according to the demands of training. Conclusions: Eventually the athlete was able to return to competition in week 32 with a higher PPO, improved VO2peak and GE. Throughout retraining, energy availability was maintained around 30 kcal/kg LBM/day, protein intake was high while CHO intake was periodised. Such dietary conditions allowed the athlete to maintain and even increase lean mass, which represents a major challenge with ageing.

An all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. The product is based on clear-sky LST retrieved from MSG/SEVIRI infrared (IR) measurements, complemented by LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a Microwave (MW) based LST product derived from AMSR-E measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

In this study, to reduce the use of cement and sand, porous feldspar with excellent economic efficiency was used as a substitute in heat storage concrete layer. When cement was replaced with porous feldspar, the compressive strength was approximately 16%–63% higher compared with when cement was replaced with silicate minerals. To compensate for the reduction in strength owing to the decreased cement content, mechanical and chemical activation methods were employed. When the specific surface area of porous feldspar was increased, the unit weight was reduced by approximately 30% and the compressive strength was increased by up to 90%. When chemical activation was applied using a solidifying agent, the strength was increased by approximately 30% even though 70% of the cement content was replaced with porous feldspar. When cement and sand were replaced with porous feldspar, the compressive strength was approximately two times that of ordinary cement mortar. When the heat storage concrete layer material was replaced with porous feldspar in a pilot scale experiment, the thermal conductivity and heat storage characteristics were better than those of ordinary cement mortar, and an energy-saving effect of approximately 57% was observed, confirming the excellent applicability of porous feldspar as a building material.

We have used the varying physical constant approach to resolve the flatness problem in cosmology. Friedmann equations are modified to include variability of speed of light, gravitational constant, cosmological constant and the curvature constant. The continuity equation obtained with such modifications includes scale factor dependent cosmological term as well as the curvature term along with the standard energy-momentum term. The result is that as the scale factor tends to zero (i.e. as the big-bang is approached) the universe becomes strongly curved rather than flatter and flatter in the standard cosmology. We have used the supernovae 1a redshift versus distance modulus data to determine the curvature variation parameter of the new model, which yields a better fit to the data than the standard ΛCDM model. The universe is found to be open type with radius of curvature R_c = 1.64 (1+z)^-3.3 c₀/H₀, where z is the redshift, c₀ is the current speed of light and H₀ is the Hubble constant.

The purpose of this paper is to find a quantum formulation of entropic gravity, incorporating the bijective theory. In a first part, we will describe the basis of the bijective theory. In a second part, we will incorporate the stress energy quantum tensor defined in the linear approximation of the Einstein’s equation in its quantum version to entropy. In the third part, we will integrate the bijective theory with the newly created relativistic quantum entropy. This will lead us four part where we will formalize quantum entropic gravity.

Biowaste generated in the process of Oxytree cultivation and logging represents a potential source of energy. Torrefaction (a.k.a. low-temperature pyrolysis) is one of the methods proposed for the valorization of woody biomass. Still, energy is required for the torrefaction process during which the raw biomass becomes biochar with fuel properties similar to lignite coal. In this work, models describing the influence of torrefaction temperature and residence time on the resulting fuel properties (mass and energy yields, energy densification ratio, organic matter and ash content, combustible parts, lower and higher heating values, CHONS content, H:C and O:C ratios) were proposed according to the Akaike criterion. The degree of the models’ parameters matching the raw data expressed as the determination coefficient (R²) ranged from 0.52 to 0.92. Each model parameter was statistically significant (p<0.05). Estimations of the value and quantity of the produced biochar from 1 Mg of biomass residues were made based on two models and a set of simple assumptions. The value of torrefied biochar (€123.4·Mg^-1) was estimated based on the price of commercially available coal fuel and its lower heating value (LHV) for biomass moisture content of 50%, torrefaction for 20 min at 200 °C. This research could be useful to inform techno-economic analyses and decision-making process pertaining to the valorization of pruned biomass residues.

Global waste generation keeps increasing over the year and requires innovative solutions to minimize their impacts on environmental quality and public health. Predicted 2.2 billion tonnes per year of global municipal waste generation in the year 2025 which 1.6 fold is higher than in 2012. Hence, a strategic plan must be ascertained to overcome the future challenges of MSW locally and globally. Universiti Putra Malaysia (UPM) coined an initiative to demonstrate a showcase pilot plant for green energy production from MSW. Therefore, the data was obtained from the survey and actual sampling within the UPM compound to estimate the MSW generated and it's potentially used for green energy production. It is estimated that 5.0 – 7.0 tonne per day of MSW generated which about 30 - 35% is an organic fraction. Upon separation, the organic fractions were digested into biogas through anaerobic. At the maximum conversion of organic fraction, about 775 kWh of electricity may able to generate from the waste. In this study, the complete biorefinery setup and utilize organic components from the MSW generated in UPM was proposed that the biogas subsequently will be used to produce green energy in the form of electricity or cooking fuel.

This paper aims to propose a new approach of territorial competitiveness assessment revisited from the resource-based view, as the combination of location-specific resources and capabilities can improve the territorial socio-economic development. A territorial competitiveness index is calculated in order to assess the potential of renewable energy sources to improve the sustainable development in islands. Different sources of information and methodologies have been employed to measure the variables included in the model, thus ensuring a rigorous process in the index calculation. In order to quantify the basic resources, for example, a methodology based on a multicriteria analysis (MCA) with geographic information system (GIS) is suggested, with the objective of obtaining an indicator called index of available territorial resources. This index synthesizes the map information through a numerical value that allows integrating the territorial resource with other indicators of the model. The results of the study show that capability development is a key factor to better exploit the territorial resource endowment in order to achieve a competitive advantage.

Phenomenon of vacuum fluctuations of virtual particles in high energy physics has been mathematically modeled by a third order differential equation. Aim of present theory is to unravel underlying mathematical equation capable of explaining microscopic phenomenon of vacuum fluctuations. Solution of such a differential equation after applying appropriate boundary conditions gives an acceptable wave function i.e. . Operation of energy and time operator on this wave function proves that these operators do not hold commutative property. Time energy uncertainty principle has been derived by calculating variance of energy and time for the same wave function.

Globally, public acceptance of waste-to-energy (WtE) incinerators is a crucial factor in implementing national waste-to-energy policies. This study adds to the literature on anti-incinerator sentiment by drawing upon an extended psychological-emotional model that integrates place-, trust-, and fairness-based pathways. A total of 338 residents in the Asuwei area in North Beijing completed a survey on a proposal to construct a WtE incinerator in the vicinity. Hierarchical regression analyses indicate that place attachment positively enhances anti-incinerator sentiment through direct effects, as well as through moderation and mediation effects between risk perception and opposing willingness. Further, institutional trust negatively moderates the impact of perceived risk on anti-incinerator sentiment, in addition to directly reducing perceived risk. Trust also influences anti-incinerator sentiment via risk perception, attesting to the effectiveness of a casual model of trust. Likewise, fairness perception acts as another determinant of opposing sentiment, similar to trust. We further reveal that procedural fairness plays a more significant role in predicting anti-incinerator sentiment than does distributional fairness. These findings demonstrate the importance of using a range of instrumental and more affective strategies to promote social acceptance of renewable energy infrastructure.

Bio-based glass-forming materials are now considered for thermal energy storage in building applications. Among them, Xylitol appears as a biosourced seasonal thermal energy storage material with high potential. It has a high energy density, a high and stable undercooling allowing storing solar energy at ambient temperature thus, reducing thermal losses and the risk of spontaneous nucleation (i.e., the risk of losing the stored energy). Generally when the energy is needed, the discharge triggering of the storage system is very difficult as well as reaching a sufficient power delivery. Both are indeed the mains locks for the use of pure Xylitol in seasonal energy storage. Different techniques have been hence considered to crystallize highly undercooled Xylitol. Nucleation triggering of highly undercooled pure Xylitol by using an air lift reactor has been proven here. This method should allow reaching performances matching with building applications (i.e., at medium temperatures, below 100 °C). The advantages of this technique compared to other existing techniques to activate the crystallization are discussed. The mechanisms triggering the nucleation are investigated. The air bubble generation, transportation of nucleation sites and subsequent crystallization are discussed to improve the air injection operating conditions.

In the paper, a fully discrete compact difference scheme with $O(\tau^{2}+h^{4})$ precision is established by considering the numerical approximation of the one-dimensional Allen-Cahn equation. The numerical solutions satisfy discrete maximum principle under reasonable step ratio and time step constraint is proved. And the energy stability for the fully discrete scheme is investigated. An example is finally presented to show the effectiveness of scheme.

Bianchi type III string cosmological models (massive and geometric) in presence of magnetic field with vacuum energy density following the techniques used by Letelier (1979) and Stachel (1980) are obtained. We find that the energy conditions (dominant as well as weak) given by Hawking and Ellis (1974) are satisfied for the models. Also the solutions obtained satisfy conservation equation for massive string with magnetic field and vacuum energy density. The models in general, represent anisotropic phase of space-time due to the presence of string. However, the models isotropize at late time and in special case. It has also been shown that the magnetic field is directly linked with the matter. For string dust model, the state finder parameters {r,s} are in excellent agreement with Planck results (Sahni et al. (2003)). The physical and geometrical aspects of the models with special cases are also discussed.

In this article it is shown how Milgrom’s acceleration constant is related with the Ω_Λ, Ω_m and Ω_B values of the matter distribution within observable universe as defined in the Standard Model of Cosmology, established by the Planck Collaboration Group. The result is a two-parameter model for the observable universe. The two parameters are the age of the universe and Milgrom’s acceleration constant. It is shown that these are sufficient to calculate the amounts of matter and dark energy in the universe, as well as the contributions of dark matter and baryonic matter in the matter part. The numerical results are in agreement with those of the Lamda-CDM model.

Wind energy powered pumps could be an alternative to conventional fuel powered pumps for water abstraction because they rely on a free energy and they are environmentally friendly. The objective of this study was to assess the potential of wind energy to operate water abstraction systems in Teso sub-region of Uganda for livestock watering Daily mean wind speeds recorded at a height of 10 m for a period of ten years (2005–2015) were collected from Amuria and Soroti Meteorological stations in the study area. Data were analyzed using Weibull distribution to evaluate the annual wind speed frequency distributions and consequently assess their potential for water abstraction. The results indicated that warmer months (January, February and March) have higher mean wind speeds than the cold months (August, September and October). High wind speeds in the dry seasons corresponded to the periods of high water demand. The highest shape parameter (k) of 3.07 was registered in 2009 and scale parameter (c) of 3.78 in 2012. The highest wind power density of 43 W/m² was obtained the year 2012 while the lowest wind power density of 15.47 W/m² was obtained for Soroti district in the year 2009. The maximum power extractable in Amuria in 2012 was 324 W/m² which is potentially enough for water abstraction. Maximum discharges of 1.86 m³/s and 1.52 m³/s were obtained for Amuria and Soroti districts respectively at mean wind speeds of 5 m/s. Therefore, Teso sub region winds have potential for water abstraction and Amuria district better sites for livestock watering using wind energy.

The use of Smart Antenna Systems (SAS) in pervasive environments such the Mobile Ad hoc Networks (MANET) has been promoted as the best choice to improve Spatial Division Multiple Access (SDMA) and throughput. Although directional communications are expected to provide great advantages in terms of network performance, directional MAC (Medium Access Control) protocols introduce several issues. One of the most known problems in this context is represented by the fact that, attempting to solve or at least mitigate the problems introduced by these kinds of antennas especially at MAC layer, a large amount of energy consumption is achieved ; for example, due to excessive retransmissions introduced by very frequently issue such as deafness and handoff. The expedients proposed in order to reduce these drawbacks attempting to limit beamforming time of nodes in cooperation with a Round-Robin scheduling, can grant high performance in terms of fairness and throughput. However the overall energy consumption in the network is not efficent due to the static approach. In view of this, we propose an Adaptive Beamforming Time with Round-Robin MAC providing for a dynamic assignment of the beamforming time with the purpose to limit the waste of energy of nodes.

The present paper aims to elucidate the impact of climate change on the availability and security of water and energy in the Middle East and North Africa Region (MENA Region). The region is particularly challenged by a number of factors including a large variability of bio-geographical characteristics, extreme population growth over the last few decades and substantial societal and economical transitions as well as armed conflicts in some of the countries of the region. Anticipated changes in climate conditions will exacerbate the challenges with regard to providing sufficient amounts of water and energy to the communities in the region. Impacts of climate change will materialize as an increasing number of heat waves, primarily in urban structures and the decline in water availability as a result of enhanced droughts and a growing numbers of dry spells. The interrelationships between energy and water and their mutual dependencies are addressed by the Water-Energy-Nexus concept. With regard to the challenges addressed here, Cyprus and the Eastern Mediterranean are a particular point in case. Mitigation and adaptation strategies include enhanced efficiency of energy and water use, integrated technology assessments regarding electricity generation and the production of potable water and electricity through concentrated solar power.

Diverse factors may have an impact in Carbon dioxide (CO₂) emissions; thus, three main contributors, energy consumption, exergy indicator and gross domestic product (GDP) are examined in this work. This study explores the relationship between economic growth and energy consumption by means of the hypothesis postulated for the Environmental Kuznets Curve (EKC). Panel data for 10 countries, from 1971 to 2014 have been studied. Despite all this wide gamma of research, the role of an exergy variable has not been tested to find the EKC; then exergy analysis is proposed. Exergy analyses were developed to propose an exergetic indicator as a control variable and a comparative empirical study is developed to study a multivariable framework with the aim to detect correlations between them. High correlation between CO₂, GDP, energy consumption, energy intensity and trade openness are observed, conversely not statistically significant values for trade openness and energy intensity. The results do not support the EKC hypothesis, however exergy intensity opens the door for future research once it proves to be a useful control variable. Exergy provides opportunities to analyze and implement energy and environmental policies in these countries, with the possibility to link exergy efficiencies and the use of renewables.

This paper deals with the techno-economic study of the hybrid renewable energy system based on energy storage aspect under the form of hydrogen and methane. Indeed, with the intermittency of the renewable energy sources such as photovoltaic and wind energy, several problems of produced energy injection to the power system network can be encountered due to the shortage or the excess of these sources. This situation appeals the use of systems that ensure the stability of network based on the storage of energy surplus into gas using electrolyzer systems, which will be used afterward to cover the eventual shortage. In the present paper, the study of performance of each pathway of methane and hydrogen storage has been performed by the treatment of multiple scenarios via different architecture case studies in an Algerian location. Whereas, the energy produced by the photovoltaic system, the wind energy and the gas micro turbine sources are considered similar in each case. The modeling and simulation of the studied system operation under optimization criteria has been performed in this work, where the main aim is to define the appropriate configuration taking into account the different with low costs of investment, maintenance operation and immediate reactivity with a big storage capacity.

The system design for Sustainable manufacturing is to consider both ecological and financial constraints. Manufacturing industry demands to advance in sustainable manufacturing by accounting in environment factors in it. All the factors that affect the environment need to be analyzed so that proper amendments or suggestions can be provided. To favour this, Computer aided life cycle inventory model has been presented for CNC milling and turning processes. Based on utilization of resources and stages, whole machining operation time can be divided into three phases named as process (milling or turning), idle and basic times. As parameters are different for evaluating the process times i.e. depth and width of cut in case of milling and initial and final diameters for turning, two different case studies has been presented one for each milling and turning process. Effect of material choice has been studied for different processes. Highly dense and hard materials takes more time in finishing the job due to low cutting speed and feed rates as compared to that of sot materials. In addition, face milling takes more time and consumes more power as compared to peripheral milling due to more retraction time caused by over travel distance and lower vertical transverse speeds than the horizontal transverse speed used in peripheral retraction process.

While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to detach and attach the air-caps. In this study, a window-mounted air-cap roller module using Velcro tape that may be easily attached, detached, and rolled up or down was developed and performance tested in a full-scale test bed. It was found that as the area of the air-caps attached on a window increased, the required indoor lighting energy increased. However, the window insulation improved, thus reducing the cooling and heating energy needed. Attaching the air-caps to the entire window surface effectively reduced the building’s energy consumption, but views through the window may be disturbed. Thus, the developed window-mounted air-caps enable an occupant to reduce the building energy consumption and maintain their view according to their need. The findings of this study may contribute to a reduction in building energy consumption without sacrificing a pleasant indoor environment. Further studies may be needed to verify their efficacy under varying indoor and outdoor conditions.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

This paper presents a study on the thermal efficiency improvement of the user equipment room in the district heating system based on reinforcement learning [1], and suggests a general method of constructing a learning network(DQN)[2] using deep Q learning[3], which is a reinforcement learning algorithm that does not specify a model. In addition, we introduce the big data platform system and the integrated heat management system for the energy field in the massive data processing from the IoT sensor installed in large number of thermal energy control facilities.

We present a new method for assessing the strength of fuzzy rules with respect to a dataset, based on the measures of the greatest energy and smallest entropy of a fuzzy relation. Considering a fuzzy automaton (relation) in which A is the input fuzzy set and B the output fuzzy set, the fuzzy relation R1 with greatest energy provides information about the greatest strength of the input-output and the fuzzy relation R2 with the smallest entropy provides information about uncertainty of the relationship input-output. We consider a new index of the fuzziness of the input-output based over R1 and R2. In our method this index is calculated for each pair of input and output fuzzy sets in a fuzzy rule. A threshold value is set for choosing the most relevant fuzzy rules with respect to the data.

The aim of this paper is to analyze and evaluate Renewable Energy Sources (RES) usage and their contribution to citizens’ life quality. For this purpose, a survey was conducted, using a sample of 400 residents in an urban area of Attica region in Greece. The methods of Principal Components Analysis and Logit Regression were used on a dataset containing respondents’ views on various aspects of RES. Two statistical models were constructed for the identification of the main variables that are associated with RES’ usage and respondents’ opinion on their contribution to life quality. The conclusions that can be drawn show that the respondents are adequately informed about some of the RES’ types while most of them use at least one of the examined types of RES. The benefits that RES offer, were the most crucial variable in determining both respondents’ perceptions on their usage and on their contribution to life quality.

In this paper, we study the fractional pseudo-parabolic equations u_t + (−△)^s u + (−△)^s u_t = u log |u|. Firstly, we recall the relationship between the fractional Laplace operator (−△)^s and the fractional Sobolev space H^s and discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence of global weak solution: for the low initial energy J(u₀) < d, the solution is global in time with I(u₀) > 0 or ∥u₀∥X_0(Ω) = 0 and blows up +∞ with I(u₀) < 0; for the critical initial energy J(u₀) = d, the solution is global in time with I(u₀) ≥ 0 and blows up at +∞ with I(u₀) < 0. The decay estimate of the energy functional for the global solution is also given.

Direct sizing criteria represent useful tools in the design of dissipative bracing systems for the advanced seismic protection of existing frame structures, especially when incorporated dampers feature a markedly non-linear behaviour. An energy-based procedure is proposed herein to this aim, focusing attention on systems including fluid viscous devices. The procedure starts by assuming prefixed reduction factors of the most critical response parameters in current conditions, which are evaluated by means of a conventional elastic finite element analysis. Simple formulas relating the reduction factors to the equivalent viscous damping ratio of the dissipaters, ξ_eq, are proposed. These formulas allow calculating the ξ_eq values that guarantee the achievement of target factors. Finally, the energy dissipation capacity of the devices is deduced from ξ_eq, finalizing their sizing process. A detailed description of the procedure is presented in the article, by distinguishing the cases where the prevailing structural deficiencies are represented by poor strength of the constituting members, from the cases having excessive horizontal displacements. A demonstrative application to the retrofit design of a reinforced concrete gym building is then offered to explicate the steps of the sizing criterion in practice, as well as to evaluate the enhancement of seismic response capacities generated by the installation of the dissipative system.

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

Energy dissipation is one of the most important factors in choosing stepped spillways. However, few studies are about energy dissipation with different horizontal face angles. In this paper, the realizable k-ε turbulence model was used to study the flow field and energy dissipation rates of different stepped spillways with five horizontal face angles in the skimming flow regions. It shows that the free water surfaces and energy dissipation rates change with horizontal face angles and the energy dissipation rates initially decrease and then increase as the horizontal face angle increases. And the horizontal face angles cause the great changes of flow field.

Solar energy is derived from photons of light coming from the sun in a form called radiation. Solar energy finds extensive application in air and water heating, solar cooking, as well as electrical power generation, depending on the way of capturing, converting and distribution. To enable such application, it is necessary to analyze the horizontal tilt angle of horizontal surfaces – in order that when the solar energy reaches the earth surface to be completely absorbed. This paper tends to describe the availability of solar radiation for south-facing flat surfaces. The optimal monthly, seasonal, and annual tilt angles have been estimated for Pristina. The solar radiation received by the incident plane is estimated based on isotropic sky analysis models, namely Liu and Jordan model. The annual optimum tilt angle for Pristina was found to be 34.7°. The determination of annual solar energy gains is done by applying the optimal monthly, seasonal and annual tilt angles for an inclined surface compared to a horizontal surface. Monthly, seasonal and annual percentages of solar energy gains have been estimated to be 21.35%, 19.98%, and 14.43%. Losses of solar energy were estimated by 1.13 % when a surface was fixed at a seasonal optimum tilt angle, and when it was fixed at an annual optimum tilt angle, those losses were 5.7%.

Most of the structures in flowing water are a challenge to their stability and sustainable with different flow conditions. Recent, renewable energy research and development covers ocean and river energy platform in which flow of water drag considered in various conversion devices towards the offshore and onshore establishment. Various energy platforms have been suggested for offshore development. However, the stability of these platforms in water is a serious concern. To study the water interaction over circular and square cross-section cascade system under the water has been carried out. Water flow around the pillars or column of the energy platform are analyzed through simulation software. Very low velocity 0.5 m/s has been considered to analyze the system. Total fifteen numbers of cascade pillars having circular and square cross-section area were considered. K-ε turbulence model is adopted to calculate the flow interaction to the column. A velocity, pressure, and energy fields are found around the column.

As the largest energy consumer and carbon emitter, China has made substantial efforts to improve energy efficiency for decrease energy consumption, while the energy rebound effect determines its effectiveness. The embodied energy consumption of construction projects accounted for nearly one-sixth of the total economy's energy consumption in China. This paper is based on the logical relationship among capital input, technological progress, economic growth, and energy consumption, adapting an alternative estimation model to estimate the energy rebound effect for the construction industry in China for the first time. Empirical results in our paper reveal that the energy rebound effect for the construction industry in China is about 59.5% for the period of 1990–2014. The results indicate that the energy rebound effect does exist in China’s construction industry and it presented a fluctuating declining trend. This implies that half of the energy savings by technological progress is achieved. In addition, China’s government should implement proper energy pricing reforms and energy taxes to promote the sustainable development of China’s construction industry.

In this work, zinc oxide (ZnO) thin film has been fabricated on glass substrate using pulse laser ablation (PLA) technique. The effect of laser pulses of 1000, 1500, 2000 pulses at laser energy 700 mJ as well as, laser energy of 600, 700, and 800 mJ at fixed laser pulses of 1500 pulse, with methanol as a solvent on the structural properties of prepared films using XRD, SEM and EDX. XRD results revealed that the ZnO thin films have hexagonal structure with polycrystalline in nature with preferred orientation of (002). Crystalline size was increased with the increasing of the pulses and at energy of 700 mJ and pulse of 1500 pulse seemed nanostructure like tree leaf. In addition, narrow FWHM and no phase change have been observed in all cases. SEM images showed that for all cases the films were homogenous with some island and cluster then cracking started to obtain with the increasing of increase the pulse number. EDX analysis showed that the prepared films were free of defects and contaminations.

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

Recently, there has been a growing interest in the production of electricity from renewable energy sources (RES). The RES investment is characterized by uncertainty, which is long-term, costly, depend on feed-in-tariff and support schemes. In this paper, we address the real option valuation (ROV) of a solar power plant investment. The real option framework is investigated. This framework considers the renewable certificate price, furthermore the cost of delay between establishing and operating the solar power plant. The optimal time of launching the project and assess the value of deferred option are discussed. The new three stage numerical methods are constructed, the Lobatto3C-Milstein (L3CM) methods. The numerical methods are integrated with concept of Black-Scholes option pricing theory, and applied in option valuation for solar energy investment with uncertainty. The numerical results of L3CM, finite difference and Monte Carlo methods are compared to show the efficiency of our methods. Our data set refers to the Arab Republic of Egypt.

The expansion of renewable energies is progressing strongly. The influence on the power supply networks by the volatility of the infeed must be met with new concepts. In this paper we investigate the possibilities of integrating microgrids as a cooperating unit in the power supply network to support further expansion of RES power plants. In this paper a differentiation of microgrids from similar network structures is established, a classification of proposed groups is made. Then, after the description of simulation of components in a microgrid, with practical advice, an example model is shown, which aids the dimensioning of the components within a microgrid to achieve a specified goal.

The building envelope has the most significant contribution in the reduction of the building energy consumption. Application of new, alternative and improved materials and systems has an important impact on the buildings performances. This paper is focused on the thermal transmittance, as an indicator of the thermal conductance of the construction element. It includes comparisons of the U-values, calculated by software, with those measured in situ on three representative façade walls. The walls have been constructed with the new wall system Fragmat NZ-1, a new product in Macedonian buildings. This research provides basic information on the thermal transmittance of the system. The results of the analysis show that the in situ measuring is a useful tool in validation of the precision of analytically calculated values, since it was difficult to obtain precise results from the analytical calculations only, especially when the layers are with non-uniform thickness.

The urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

This paper constitutes an original and new methodology for the determination of the surface properties of carbon fibers in the two oxidized and untreated forms by inverse gas chromatography technique at infinite dilution based on the effect of the temperature on the surface area of the various organic molecules adsorbed on the carbon fibers. The studied thermal effect proved a large deviation of the classical methods or models relative to the new determination of the surface properties of carbon fibers, such as the dispersive component of their surface energy, the free surface energy, the free specific energy, the enthalpy and entropy of adsorption of molecules on the carbon fibers. The obtained specific enthalpy and entropy of adsorption for the polar solvents led to the determination of the Lewis’s acid-base constants of carbon fibers. Different molecular models and chromatographic methods were used to quantify the surface thermodynamic properties of carbon fibers and compared to the thermal model. The obtained results proved that the oxidized carbon fibers gave more specific interaction energy and greater acid-base constants than that of the untreated carbon fibers and thus highlighting the important role of the oxidization on the acid-base of fibers. The determination of the specific acid-base surface energy of the two carbon fibers showed greater values of the oxidized carbon fibers relative to the untreated carbon fibers. An important basic character was highlighted for the two studied carbon fibers, larger than the acidic character. It was observed that the Lewis’s acid-base constants of the oxidized carbon fibers are larger than that of the untreated fibers with more accentuated basic charterer. This result was confirmed by all molecular models and chromatographic methods.

Hybrid reverse osmosis electrodialysis systems make it possible to obtain solutions with a higher concentration of components compared to single electrodialysis treatment, i.e. more efficient separation of brine and pure water. The article proposes experimental and pilot-industrial studies of a hybrid membrane system operation using industrial wastewater for the disposal of liquid waste from an ion-exchange chemical-desalting water treatment plant of a thermal power plant, followed by a calculation of economic efficiency and an analysis of the environmental feasibility of their use. The developed technological scheme offers separate processing of acidic and alkaline waste regeneration solutions using calcium carbonate reagent and desalination on baromembrane and electromembrane units to obtain clean water and dry residue. The hybrid system includes a booster filter press and an evaporator. The hybrid system makes it possible to provide a thermal power plant with a "zero discharge" with a minimum consumption of reagents and electricity, as well as return all water from wastewater to the power plant cycle.

Space-time evolution of our universe is explained by using the 3-dimensional quantized space model (TQSM) based on the 4-dimensional (4-D) Euclidean space. The energy (E = cDtDV), charges and energy density (|q| = r = cDt) and absolute time (ct) are newly defined based on the 4-D Euclidean space. The photon flat space with the constant energy density of r = cDt_q is proposed as the dark energy (DE). The dark energy is separated into the n DE and photon DE which create the new photon spaces with the constant energy density of r = cDt_q. The v DE is from the n pair production by the CPT symmetry and the photon DE is from the photon space pair production by the T symmetry. The vacuum energy crisis and Hubble’s constant puzzle are explained by the photon space with the n DE and photon DE. The big bang and inflation of the primary black hole is connected to the accelerated space expansion and big collapse of the photon space through the universe evolution. The big bang from the nothing is the pair production of the matter universe with the positive energy and the partner anti-matter universe with the negative energy from the CPT symmetry. Our universe is the matter universe with the negative charges of electric charge (EC), lepton charge (LC) and color charge (CC). This first universe is made of dark matter -, lepton -, and quark - primary black holes with the huge negative charges which cause the Coulomb repulsive forces much bigger than the gravitational forces. The huge Coulomb forces induce the inflation of the primary black holes, that decay to the super-massive black holes and particles.

We present a thermodynamic approach in modeling the evolution of the universe based on a theory that space consists of energy quanta, the spaceons. From wave-particle duality, they can be treated as an ideal gas. The model is similar to the Big Bang but without Inflation. It provides an insight into the nature of dark energy and dark matter, and an explanation for the accelerated expansion of the universe. The universe started from an atomic size volume of spaceons at very high temperature and pressure. Upon expansion and cooling, phase transitions occurred resulting in the formation of fundamental particles, and matter. These nucleate and grow into stars, galaxies, and clusters due to the action of gravity. From the cooling curve of the universe we constructed a thermodynamic phase diagram of cosmic composition, from which we obtained the correlation between dark energy and the energy of space. Using Friedmann’s equations, our model fits well the WMAP data on cosmic composition with an equation of state parameter, w= -0.7. The dominance of dark energy started at 7.25 x 10⁹ years, in good agreement with BOSS measurements. The expansion of space is attributed to a scalar quantum space field. Dark Matter is identified as a plasma form of matter similar to that which existed during the photon epoch, prior to recombination. The thermodynamics of expansion of the universe was adiabatic and decelerating during the first 7 billion years after the Big Bang; it accelerated thereafter. A negative pressure for Dark Energy is required to sustain the latter. This is consistent with the theory of General Relativity and the law of conservation of energy. We propose a mechanism for the acceleration as due to consolidation of matter forming Dark Energy Stars (DES) and other compact objects. The resulting reduction in gravitational potential energy feeds back energy for the expansion. Space will continue to expand and dark energy will undergo phase transition to a Bose-Einstein condensate, a superfluid form of matter. Self-gravitation can cause a bounce and start a new Big Bang. We show how the interplay of gravitational and space forces leads to a cyclic, maybe eternal, universe.

Societies around the world face serious energy problems related to the consumption of fossil fuels and the emission of dangerous radiation. To solve these problems, a new superconductor exhibiting a critical temperature higher than room temperature has been pursued but not achieved. This paper proposes a new energy generation system based on a circuit approach. Secondary to this process, a new type of superconductivity without refrigeration is demonstrated. In our previous paper [1], this system was proposed, but it did not describe the underlying theory in detail and did not mention an actual method to generate energy from the system. The present paper describes the theory of the existence of divergent current density and new superconductivity with no refrigeration. Moreover, the present paper proposes a method for extracting energy from the system by employing a voltage-controlled current source (i.e., a voltage–current converting method).The principle of the system is based on a circuit of two loops and independent current sources. First, the two electric loops are prepared, each with 4 diodes, where the diodes are oriented in the same direction within each loop, but their direction is opposite from loop to loop; four independent current sources connect the loops. In this circuit system, current is added iteratively as it flows along the loop according to Kirchhoff’s circuit law. As a result, a large current and electric potential are present along the loop. To confirm that this system works properly, it is necessary to demonstrate the presence of divergent currents in the transient state, and to do this, the present paper employs the Dirac equation and Lorentz conservation. Electric circuit software is employed to demonstrate that the presented method generates energy actually from our system.Our results confirm the presence of divergent current at a connected point of an independent current source in the transient state. Moreover, in the steady state, the theory demonstrates the Meissner effect (i.e., a London equation) and a new type of macroscopic wave function and condensation. For an initial small input current of 0.1 μA, the simulation reveals a large generating current of 7 kA and electric power of 10¹¹ W, which is much larger than unit of power from an average thermal power station; moreover, the system presents with superconducting electrical transport conditions.The present study is significant because it demonstrates theoretically the existence of divergent current density and a new type of superconductivity requiring no refrigeration. Secondly, the simulations show the generation of a large energy density that can be obtained in a small laboratory room with minimal cost.

We herein described an investigation of a theory, which describes the energies of neutrinos and the source of neutrino oscillations. A series of experiments were conducted to show evidences of the existence a neutrino mass. We also applied theories to explain the reason for the extremely small energy of a neutrino, mainly by employing a vacuum-derived superconducting energy gap from the Bardeen–Cooper–Schrieffer ground state. Moreover, we succeeded in obtaining the transition probabilities of neutrinos’ flavors (i.e., in terms of neutrino oscillation). We focused on the fact that up- and down-quantized space pairs combine by the Lorentz forces, undertake Bose-Einstein condensation, and then create a superconducting energy gap at the energy level of the vacuum with quantum mechanics fluctuation. Eventually, the superconducting energy gap vanishes to form a real body of the neutrino. Furthermore, assuming that the speed of the neutrino is near the speed of light and exhibits Planck’s blackbody emissions, we derived many-body interactions of neutrinos and applied them in Fermi’s golden rule. As a result, the neutrino energy we calculated agreed well within the realms of the experimental results. The calculated transition probabilities of neutrino’s flavor also explain the experiment results very well.

Electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialization, population growth, urbanization and of course the evolution of modern technologies in this 4th industrial revolution era. Such rapid increase in energy demand introduces a huge challenge in power system. Such has paved way for network operators to seek for alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a TNEP model that minimises the network reinforcements, operational costs and costs of renewable energy penetrations, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model has been tested in several IEEE test systems in multi-period scenarios. The paper also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variables’ decomposition techniques. Moreover, this paper tends to provide additional recommendation in terms of which particular year (within 20 years of planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. Such is achieved by running incremental periods simulations from base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time even for large power grid system.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

Multicropping can solve the energy and GHG balance problems, but the emergence, development and productivity of such mixed crops are at risk due to uneven distribution of precipitation. For this reason, investigations were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Single maize crop was compared with Crimson/red clover, Persian clover, and alfalfa intercropped maize cultivations. Results showed that under arid conditions, the intercrops biomass was about 4 times less than under humid. Humid conditions were less suitable for maize and when growing with intercrops they produced about 3-5 t ha-1 less dried biomass, and when growing sole - about 6 t ha-1 less biomass than in arid conditions. Due to the higher yield of maize biomass in arid season, better energy indicators of the crop were obtained in arid conditions than in humid. The difference between net energy was about 122-123 MJ ha-1 in all treatments, except for the maize crop with intercropped alfalfa, where the difference was 62 MJ ha-1. All tested technologies were environmentally friendly, CO2 equivalent varied between treatments from 804 to 884 kg ha-1. Uneven distribution of precipitation during vegetative season makes us think about the improvement of intercropping technologies. Sowing intercrops at the same time with maize could improve its germination but increase the problem of weed spread.

The effect of wave forces on the structural safety of fixed oscillating water-column (OWC) devices is critical. In this study, a three-dimensional numerical model of an oscillating water column (OWC) device is constructed using the computational fluid dynamics software OpenFOAM. Subsequently, the hydrodynamic performance of the numerical model is comprehensively validated. Finally, the hydrodynamic performance data are analyzed in detail to obtain meaningful conclusions. The results indicate that the horizontal wave force on the OWC device is approximately 6.6 to 7.9 times greater than the vertical wave force, whereas the lateral wave force is relatively small. Both the horizontal and vertical wave forces decrease as the relative water depth increases under a constant wave period and height. The horizontal wave force increases linearly with the wave height, whereas the vertical wave force increases with the square of the wave height. In addition, the highest dynamic water pressure is observed at the interface between the water surface and device, both within and outside the front wall of the gas chamber. The dynamic water pressure at different locations on the front chamber increases and subsequently decreases as the wave frequency increases.

A quantum theory of gravity is among the most pursued goals in physics. I present a definitive and direct experimental proof that refutes the widely believed hypothesis of quantum gravity. The detections of astrophysical gravitational waves (GW) are inferred from the differential oscillations of suspended mirrors of optical interferometers like the aLIGO detectors. If gravity is indeed quantized, then the average energy $\bar{E}$ in the minute oscillations of the mirrors, at a frequency $\nu$, corresponds to the absorption of an integer number $N$ gravitational quanta with total energy $E_{gw}=Nh\nu$. The coherent and coincident detections across large separations of detectors, and also the constraint of the equivalence principle, dictate that the average number of quanta $\bar{N}$ forcing the oscillations obeys $\bar{N}\gg 1$, or $1/\sqrt{\bar{N}}\ll 1$. However, the average energy $\bar{E}$ in the differential oscillations is smaller than the energy of a single quantum of GW radiation at the detected astrophysical GW frequencies. This startling finding implies that $\bar{N} <1$, which is physically impossible if gravity is quantized. This singular contradiction refutes the long-held hypothesis that gravity is quantized.

The purpose of this article is to present an IoT architecture implemented in an intelligent environment, focusing on the optimization of initial conditions and device management. A general control system was developed using an open-source platform, integrating aWi-Fi network and YAML coding to enable communication and control of the IoT architecture devices. The implementation includes actuators, sensors, and control devices that, through a graphical interface, allow the visualization of the historical record of activities and real-time measurements of connected devices. The design includes specific mechanisms and the analysis of materials through tensile tests on specimens, following the ASTM E8 standard, and deformation test simulations, in which PLA demonstrated better performance compared to materials like ABS and resin. The significance of the implemented IoT system lies in an energy saving of 58% compared to conventional structures. Additionally, the study of response times for network connections showed that using an Ethernet cable resulted in a response time of 1.21 seconds, representing a reduction of 1.65 seconds compared to the second lowest value obtained.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

Integrated energy systems (IES) seek to minimize power generating costs in future power grids through the dynamic coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, i.e., even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear of components. If a unit is flexibly operated without respecting limits on materials and components, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the limits on power variations (explicit constraints), the solution of the unit dispatch problem needs to meet the limits on temperature, pressure and flow rate variations (implicit constraints). The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher-validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., Balance of Plant, Gas Turbine, High-Temperature Steam Electrolysis. Finally, FARM module provides dedicated tools for visualizing the response of the process variables of interest during operation transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an Ecological Interface Design (EID) for IES units.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

Environmental preservation presents numerous challenges, particularly in capturing carbon di-oxide. Despite multiple studies on this topic, there remains a need for additional capture plants, particularly in the energy sector. The power industry emits flue gas during its post-combustion process, and capturing carbon dioxide from this gas would make power plants more environ-mentally friendly while aiding the preservation of the planet. The work conducted in This study aimed to investigate the gap in knowledge to contribute to the development of a better world without harming it. The identified research gap reveals no established optimal monoethanola-mine (MEA) solvent concentration for the process. A simulation was performed using Aspen Hysys to analyze the absorption efficiency at higher solvent concentrations and address this is-sue. The input data was obtained from a flue gas capture plant in the UK. The results obtained from discrete increments indicate an optimal solid result for an MEA concentration of 36 wt.%. Further analysis of temperature and pressure revealed that other solvents, such as diethanola-mine (DEA) and triethanolamine (TEA), are more stable at higher concentrations and that DEA and TEA at the same concentration as MEA consumed significantly less energy to capture carbon dioxide.

Retention of placenta (RP) in crossbred cattle poses health, reproductive, and economic challenges linked to oxidative stress, hormonal imbalances, and complex causative factors. Emphasizing the key role of mitochondria in oxidative phosphorylation (OXPHOS) and hormone synthesis, our study is the first to explore differences in OXPHOS activity, mitochondrial content, and mtDNA-encoded gene expression between normal and RP. In addition, gross morphological examination of placental tissues from both groups revealed noticeable disparities in cotyledons of RP as compared to normal placenta. Moreover, OXPHOS complex I specific activity was decreased in RP, and RT-qPCR analysis unveiled a significant reduction in transcript levels of 10 mtDNA-encoded protein genes. While COX3 and ATP8 genes showed no significant changes, ND6 increased in RP. Whereas, mtDNA copy number varied between animals but showed no statistically significant differences among groups. Retained placenta manifests alterations in the transcript levels of various mitochondrial genes, complex activity, and mtDNA content, potentially linked to heightened energy demands or mitochondrial insufficiency, posing an elevated risk. Overall, our study suggests a link between mitochondrial abnormalities, gene expression, and RP. This connection could influence the removal of fetal membranes by affecting energy supply, hormonal balance, and oxidative stress. This study also offers novel insights and serves as a valuable animal model for studying placental insufficiency in humans. Moving forward, comprehensive investigations are imperative to unravel underlying mechanisms and intricate roles of mitochondria in the context of RP, paving the way for targeted interventions to mitigate health, reproductive, and economic challenges in crossbred cattle.

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

Observation of the distribution of the velocity of galactic rotation curves differed from their expected centripetal form and lead to the notion of Dark Matter or modifications to Newtonian and General Relativity, such as MOND, TeVeS and the like and even Quantised Inertia. We aim to show that General Relativity with Dark Energy/the Cosmological Constant is all that is needed, with the proviso that the Cosmological Constant can increase in the presence of a light flux or some other factor from galaxies and become gravitating; the need for Dark Matter may be abated.

The scope of this work is to empirically check and prove the practical applicability of the Primordially Hydridic Earth (hereinafter PHE) concept for early exploration of the resources of naturally occurring hydrogen. With the PHE concept postulates interpreted within the local geological, tectonic, petrological and geophysical context, the reconnaissance plan, as well as the field exploration and data acquisition programs, were put together and implemented in the field. The results obtained from the surface (

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

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

In steel structures, oblique thin steel plates serve as panel zones in structures spanning large spaces (e.g., warehouses and gymnasiums). Considerable research has been conducted on the shear buckling of panels due to seismic loads acting on a structure. Conversely, under snow or wind loads, the panel zone may experience compressive and tensile stresses simultaneously from two directions. Considering the economic preference for thin steel plates, evaluating the elastic critical local buckling stresses in the panel zone under biaxial normal stress may provide essential information to structural engineers. In this study, an elastic buckling analysis based on the energy method is performed to clarify the impact of panel geometry and boundary conditions on the elastic local buckling stresses of oblique panel zones. As confirmed, the buckling stresses derived from the energy method could simulate the local buckling stresses with accuracy comparable to that of finite element analysis, and an engineer-friendly design formula was proposed. Finally, we determined the correlation of the buckling stresses under biaxial stresses and presented a method for evaluating this correlation. Engineers can utilize the provided design equations to more efficiently and accurately calculate buckling loads, facilitating safer and more economical design of structures with oblique plates.

Real assets in the energy market are subject to ecological uncertainty due to the penetration of renewables. We illustrate this point by analysing electrolysers, a class of assets which recently became the subject of a large interest, as they lead to the production of the desirable green hydrogen and green ammonia. The latter has the advantage of being easily stored and has huge potential in decarbonising both the fertilizer and shipping industries. We consider the optimization of green ammonia production with different types of electricity procurement in the context of stochastic power and ammonia markets, a necessary assumption to translate the features of renewable, hence intermittent, electricity. We emphasise the importance of using stochastic prices to effectively model the volatile nature of the price dynamics, illustrating the project risks that can be mitigated by hedging activities. This study shows the pivotal role of flexibility when dealing with fluctuating renewable production and volatile electricity prices to maximise profits and better manage risks.

Multi-cropping can solve the energy and GHG balance problems, but the emergence, development and productivity of such mixed crops are at risk due to increasing drought periods. For this reason, single maize crops with inter-row mellowing and weed-mulching practices were compared with faba bean, crimson and Persian clover, and alfalfa intercropped maize cultivations. Tests were performed at the Experimental Station of Vytautas Magnus University, Lithuania. Results showed that by the 30% higher energy inputs were calculated in technologies with intercropped crimson, Persian clover, and alfalfa than in monocropped maize. Single maize crops produced by the 23-40% higher biomass yields compare to clover and alfalfa intercropped maize because, due to the drought periods, intercrops weakly sprouted and developed. Lower harvests reduced the energy output by up to 38%, energy efficiency ratio - by up to 2.5 times and net energy - by up to 42%. All tested technologies were environmentally friendly and similar according to the CO2 equivalent. Long-term drought periods during vegetative season due to climate change make us think about the improvement of intercropping technologies. Sowing intercrops at the same time with maize could solve the problem of its germination but highlight the problem of abundant weed spread. Therefore, this takes us to another sphere of agrotechnologies - the effective non-chemical weed control.

Heterometallic rare-earth clusters have garnered much attention because of their interest-ing solid-state structures and versatile applications in catalysis and material and polymer chemistry over the last four decades. Particular interest is rare earth–transition metal co-ordination compounds that behave as single-molecule magnets (SMMs) because of their potential use in information storage, spintronic devices, and magnetic refrigeration sys-tems. Another implementation involves using lanthanide clusters in luminescent-based sensing for environmental protection and security screening or in photocatalytic energy conversion reactions. Heterometallic rare earth complexes offer the promise of catalytic performance of many organic reactions due to their high oxygenophilicity, Lewis acidity, high coordination numbers, and ability to change the coordination environment quickly. Therefore, they are commonly applied as catalysts in asymmetric synthesis, oxidation re-actions, and CO2 conversions or as initiators in ring-opening polymerization and copoly-merization of cyclic monomers. They are also attractive single-source molecular precur-sors for functional inorganic materials. This review focused on the unique features and properties of the heterometallic rare earth complexes, which are closely correlated with their molecular structure. Our summary is a valuable resource, providing researchers with an insightful exploration of the heterometallic rare-earth complexes that demonstrated multimetallic synergy and cooperativity effects in various applications.

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.

The paper proposes a methodological way to compensate the imbalance between energy generation and consumption using the battery block of electric vehicles as energy reservoir through the well-known Vehicle to Grid system (V2G). The method is based on a simulation process developed by the authors that take into consideration the daily fluctuations of energy consumption as well as the power level generated by an energy source, either conventional, renewable or hybrid. The study shows that for very large electric vehicle fleet the system renders non-viable, since the remaining energy in the battery block to let the electric vehicle usable for running during daytime avoids to compensating for the energy grid imbalance, only allowing to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. The method allows predicting the optimum size of electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance and size of the electric vehicle fleet.