Terms Of Energy Research Articles

Special Relativity in Terms of Hyperbolic Functions with Coupled Parameters in 3+1 Dimensions

This paper presents a method for parameterizing new Lorentz spacetime coordinates based on coupled parameters. The role of symmetry in rapidity in special relativity is explored, and invariance is obtained for new spacetime intervals with respect to the Lorentz transformation. Using the Euler–Hamilton equations, an additional angular rapidity and perpendicular rapidity are obtained, and the Hamiltonian and Lagrangian of a relativistic particle are expanded into rapidity spectra. A so-called passage to the limit is introduced that makes it possible to decompose physical quantities into spectra in terms of elementary functions when explicit decomposition is difficult. New rapidity-dependent Lorentz spacetime coordinates are obtained. The descriptions of particle motion using the old and new Lorentz spacetime coordinates as applied to plane laser pulses are compared in terms of the particle kinetic energy. Based on a classical model of particle motion in the field of a plane monochromatic electromagnetic wave and that of a plane laser pulse, rapidity-dependent spectral decompositions into elementary functions are presented, and the Euler–Hamilton equations are derived as rapidity functions in 3+1 dimensions. The new and old Lorentz spacetime coordinates are compared with the Fermi spacetime coordinates. The proper Lorentz groups SO(1,3) with coupled parameters using the old and new Lorentz spacetime coordinates are also compared. As a special case, the application of Lorentz spacetime coordinates to a relativistic hydrodynamic system with coupled parameters in 1+1 dimensions is demonstrated.

A minimal physical model for curvotaxis driven by curved protein complexes at the cell’s leading edge

Cells often migrate on curved surfaces inside the body, such as curved tissues, blood vessels, or highly curved protrusions of other cells. Recent in vitro experiments provide clear evidence that motile cells are affected by the curvature of the substrate on which they migrate, preferring certain curvatures to others, termed "curvotaxis." The origin and underlying mechanism that gives rise to this curvature sensitivity are not well understood. Here, we employ a "minimal cell" model which is composed of a vesicle that contains curved membrane protein complexes, that exert protrusive forces on the membrane (representing the pressure due to actin polymerization). This minimal-cell model gives rise to spontaneous emergence of a motile phenotype, driven by a lamellipodia-like leading edge. By systematically screening the behavior of this model on different types of curved substrates (sinusoidal, cylinder, and tube), we show that minimal ingredients and energy terms capture the experimental data. The model recovers the observed migration on the sinusoidal substrate, where cells move along the grooves (minima), while avoiding motion along the ridges. In addition, the model predicts the tendency of cells to migrate circumferentially on convex substrates and axially on concave ones. Both of these predictions are verified experimentally, on several cell types. Altogether, our results identify the minimization of membrane-substrate adhesion energy and binding energy between the membrane protein complexes as key players of curvotaxis in cell migration.

Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies.

Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.

Three-Center Tight-Binding Together with Multipolar Auxiliary Functions.

We present an ab initio tight-binding method that allows to improve on the effective potential and minimal basis approximations employed in semiempirical calculations. Three-center expansions are used to evaluate the zeroth-order Hamiltonian matrix elements and repulsive energy terms in the spirit of the Horsfield method. Self-consistency is handled by expanding atomic orbital products in an auxiliary basis following the work of Giese and York, combined with a two-center expansion of the exchange-correlation kernels. Together with nonminimal main basis sets (double-ζ plus polarization), we show that the resulting method trades a modest amount of accuracy for a significant gain in speed, compared to that of numerical atomic orbital density functional theory, in calculations on small molecules, bulk compounds, and metal nanoclusters.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations.

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Chaos and localized phases in a two-body linear kicked rotor system.

Despite the periodic kicks, a linear kicked rotor (LKR) is an integrable and exactly solvable model in which the kinetic energy term is linear in momentum. It was recently shown that spatially interacting LKRs are also integrable, and results in dynamical localization in the corresponding quantum regime. Similar localized phases exist in other nonintegrable models such as the coupled relativistic kicked rotors. This work, using a two-body LKR, demonstrates two main results; first, it is shown that chaos can be induced in the integrable linear kicked rotor through interactions between the momenta of rotors. An analytical estimate of its Lyapunov exponent is obtained. Second, the quantum dynamics of this chaotic model, upon variation of kicking and interaction strengths, is shown to exhibit a variety of phases: classically induced localization, dynamical localization, subdiffusive, and diffusive phases. We point out the signatures of these phases from the perspective of entanglement production in this system. By defining an effective Hilbert space dimension, the entanglement growth rate can be understood using appropriate random matrix averages.

Exploring the neurotoxicity of chiral dinotefuran towards nicotinic acetylcholine receptors: Enantioselective insights into species selectivity

Dinotefuran is a chiral neonicotinoid that is widely distributed in environmental matrices, but its health risks to different organisms are poorly understood. This study investigated the neurotoxic responses of honeybee/cotton aphid nicotinic acetylcholine receptors (nAChRs) to chiral dinotefuran at the enantiomeric scale and demonstrated the microscopic mechanism of species selectivity in nAChR-mediated enantioselective neurotoxicity. The findings indicated that (S)-dinotefuran had a higher affinity for honeybee nAChR than (R)-dinotefuran whereas both enantiomers exhibited similar bioactivity toward cotton aphid nAChR. The results of dynamic neurotoxic processes indicated the association of conformational changes induced by chiral dinotefuran with its macroscopic neurotoxicity, and (R)-dinotefuran, which exhibit low toxicity to honeybee, was found to induce significant conformational changes in the enantioselective neurotoxic reaction, as supported by the average root-mean-square fluctuation (0.35 nm). Energy decomposition results indicated that electrostatic contribution (ΔGele) is the critical energy term that leads to substantial enantioselectivity, and both Trp-51 (－2.57 kcal mol−1) and Arg-75 (－4.86 kcal mol−1), which form a hydrogen-bond network, are crucial residues in mediating the species selectivity for enantioselective neurotoxic responses. Clearly, this study provides experimental evidence for a comprehensive assessment of the health hazards of chiral dinotefuran.

Multiple Heat Recovery System for an Industrial Thermal Peeling Press Machine—Experimental Study with Energy and Economic Analyses

The enhancement of energy systems in industrial zones is attracting the attention of researchers from all over the world. At the same time, optimization and advancement in heat recovery systems are now generating major interest in the energy sector. In this context, the present study suggests a new multiple heat recovery system should be applied to an industrial thermal peeling press machine. The new system consists of multiple sources of energy: the heat excess in the chimney, the exhaust gas of the chimney, and the exhaust gas of the boiler. To proceed with testing the potential of the suggested system, a hydraulic thermal peel press machine in the wood industry undergoes different tests to achieve the best configuration that will enable this machine to reach its operational temperature when heating. Five test configurations are proposed, designed, and applied experimentally on this machine. Many parameters were effective during the experimental tests, such as water flow rate, ambient air temperature, and initial water temperature. It was found that the application of the multiple heat recovery system increases the rate of heating from around 7 °C/min to around 13 °C/min. In terms of energy and economy, the “chimney + boiler only” configuration proved to be the best system to apply during the fall and winter seasons.

Building energy characteristic evaluation in terms of energy efficiency and ecology

The assessment of energy efficiency and the environmental impact of building operation, in energy terms, determine to a large extent the energy intensity of the housing sector as a whole and the planning of energy transition measures. In order for these measures to be properly targeted and the building assessment to be reliable, it is necessary to accurately determine the energy demand of the building. The calculation model presented in the article allows for a multivariate assessment of a building's energy demand using static methods. The methodology presented in the article for the determination of energy performance certificates for buildings is complemented by an interpretation regarding the efficiency of the energy systems in the building and the environmental emissions. A model of the existing single-family building was evaluated for this purpose, and subjected to a multivariate analysis depending on the configuration of the heating system. Final and primary energy values, fuel consumption and emissions of pollutants to the environment were determined for each of total twenty-two variants. The analysis carried out and the results obtained show that there is no logical relationship between the primary energy indicator, used for the energy assessment of buildings, and the energy intensity of the building and its environmental impact.

Multi-objective optimization of the organic Rankine cycle cascade refrigeration cycle driven by sugar mills waste heat

The research on the recovery of low-grade thermal energy carried away by boiler flue gas is significant for sugar mills. This paper designs a waste heat recovery system based on sugar plant flue gas, integrating absorption refrigeration cycle and the organic Rankine cycle, and the effects of nine working fluids on the system are investigated. The aim is to realize the multi-form conversion of energy. The performance of the system is evaluated in terms of energy, exergy, and economic metrics. Multi-objective optimization is performed with the method of the NSGA-II genetic algorithm. The results show that Butane is the most suitable working fluid for ORC. The exergy efficiency of the system is 32.125% before optimisation, with an increased space cooling capacity of 15820.56 MW per year for the sugar mill. The exergy destruction analysis of the system reveals that the generator accounts for the highest proportion of exergy destruction (50.8%). The entire system shows the LCOE is as low as 0.0406$/kWh under the optimized condition. The optimized system can obtain an estimated annual electricity sales revenue of $136,300, and the sugar mill can save $308,600 in cooling costs. In addition, the payback period can be shortened to 5.79 years.

Charged AdS black holes in presence of string cloud and Cardy-Verlinde formula

In this study, charged AdS black holes with a string cloud are taken into consideration. We identify three distinct black hole solutions within a specific temperature range, which subsequently merge into a single black hole beyond this range in the case of spherical space with low chemical potential and string density. However, in the case of large chemical potential and string density, only one black hole solution exists for all temperatures. Notably, we observe that for flat and hyperbolic space, there is only one black hole solution irrespective of the chemical potential and string density. By subtracting the contribution of the extremal black hole from the on-shell Euclidean action, the thermodynamical quantities associated with these black holes are calculated. We also investigate the stability of these black holes and find that medium-sized black hole is unstable. In the dual boundary theory side we verify the existence of bound state of quark-antiquark pair. Finally, we establish the Cardy-Verlinde formula for the boundary theory and we observed an additional energy term arising because of the presence of string cloud.

Multi-bit MRAM based high performance neuromorphic accelerator for image classification

Binary neural networks (BNNs) are the most efficient solution to bridge the design gap of the hardware implementation of neural networks in a resource-constrained environment. Spintronics is a prominent technology among emerging fields for next-generation on-chip non-volatile memory. Spin transfer torque (STT) and spin-orbit torque (SOT) based magnetic random-access memory (MRAM) offer non-volatility and negligible static power. Over the last few years, STT and SOT-based multilevel spintronic memories have emerged as a promising solution to attain high storage density. This paper presents the operation principle and performance evaluation of spintronics-based single-bit STT and SOT MRAM, dual-level cells, three-level cells (TLCs), and four-level cells. Further, multi-layer perceptron architectures have been utilized to perform MNIST image classification with these multilevel devices. The performance of the complete system level consisting of crossbar arrays with various MRAM bit cells in terms of area, energy, and latency is evaluated. The throughput efficiency of the BNN accelerator using TLCs is 26.6X, and 3.61X higher than conventional single-bit STT-MRAM, and SOT-MRAM respectively.

Gas-phase oxidation of benzene using Cu-loaded MFI binderless zeolite compacts

Phenols are essential raw materials for various fields, and several studies have focused on their synthesis. The development of an efficient one-step synthetic method for the industrial production of phenols is highly important in terms of cost, energy, and environmental impact. In this study, we investigated the formation of phenols via the gas-phase oxidation of benzene using copper-loaded MFI zeolite compacts prepared via a binderless zeolite synthetic method. The catalytic activity of the zeolite in the gas-phase phenol one-step synthetic reaction was investigated, in addition to the effect of benzene diffusion in the molded zeolite and the reaction due to water vapor addition to the reaction gas. The results showed that the activity of the copper-loaded MFI zeolite compacts was almost equivalent to that of powdered copper-loaded MFI zeolites. An effect of diffusion was observed on the external boundary film diffusion where the linear velocity was low. However, no effect of diffusion or internal diffusion was observed in the area where the linear velocity was high. Furthermore, the addition of water vapor to the reaction gas suppressed the decrease in phenol formation activity due to the formation of coke on the catalyst surface.

Multi-pursuer pursuit differential game for an infinite system of second order differential equations

We study a pursuit differential game of many pursuers and one evader. The game is described by the infinite systems of $m$ inertial equations. By definition, pursuit in the game is completed if the state and its derivative of one of the systems are equal to zero at some time. In the literature, such a condition of completion of pursuit is also called soft landing. We obtain a condition in terms of energies of players which is sufficient for completion of pursuit in the game. The pursuit strategies are also constructed.

Kinetic energy correction coefficient for rectangular drainage channels

In urban water supply and drainage systems, rainwater channels or pipes are rectangular in design to help control the flow rate and adapt well to limited space. When the Bernoulli equation in fluid mechanics is used to solve the head loss of rectangular pipelines, the velocity parameter used in the kinetic energy term is usually the instantaneous or average velocity of the section at a certain point. Given that this velocity parameter is in exponential form, the smaller the error is, the greater the impact on the result will be. Thus, the kinetic energy term must be corrected. This study focuses on establishing a cross section velocity distribution model in a rectangular pipe and deriving the kinetic energy correction coefficient through the velocity distribution. Based on the Navier–Stokes equation, the partial differential equation describing the velocity distribution is further refined and simplified. Combined with the boundary conditions of the pipeline, the method of separating variables and Fourier transform are used to solve the equation. An example shows how to establish the velocity distribution model and find the analytical solution. Finally, the analytical formula of the kinetic energy correction factor of different cross section parameters and fluid properties is derived. To verify the accuracy of the analytical formula, the Fluent numerical simulation software is used for empirical verification, and then the Deming regression method is used to analyze the error between the theoretical and experimental values. The regression results of the kinetic energy correction coefficient prediction model established in this study are consistent with the actual values, and the confidence interval reaches 95%. This work provides strong guidance for the prediction of the kinetic energy correction coefficient in fluid mechanics and has an important theoretical and practical value.

Recent advances of membrane-based hybrid membrane bioreactors for wastewater reclamation

Membrane bioreactor (MBR) is an advanced wastewater treatment technology, which has been established for more than 3 decades. In MBRs, membrane separation allows not only rejecting microorganisms/greater-sized molecules but decoupling hydraulic retention time (HRT) and solid retention time (SRT). Low-pressure driven, porous membranes have been widely used in MBRs, but their performances are mainly limited for wastewater reuse applications. Recently, many attempts have been made to combine desalination technologies to advance hybrid MBR processes for wastewater reclamation. Nanofiltration (NF) and reverse osmosis (RO) have been applied with the MBRs to improve effluent quality, and their advantages and challenges have been well reported in terms of rejection efficiency, operational energy, fouling control and recovery of retentate stream. Alternatively, the direct introduction of non-pressurized desalination technologies such as forward osmosis (FO) and membrane distillation (MD) into MBR processes for wastewater reclamation or probably for microbial activity have been considered substantially due to their low energy consumption and excellent rejection efficiency of solid materials. However, several technical limitations still need to be resolved to commercialize hybrid FO- or MD-MBR processes. This paper reviews recent advances of MBR technology integrated with desalination technologies for wastewater reclamation and suggests perspectives to optimize membrane-based hybrid MBR process.

A heterogeneous 3-D stacked PIM accelerator for GCN-based recommender systems

AbstractModern recommendation systems integrate graph convolution neural networks (GCN) for enhancing embedding representation. Compared with widely deployed neural network-based models, the extra message propagation layer of GCN-based recommendation is featured with extensive computations and irregular memory access. However, architecture designs for prevailing deep neural network recommendation models assume simple pooling in the embedding layer. ReRAM-based GCN accelerators are specialized for graph-related operations. However, they are designed for general graphs, while GCN-based recommendation models mainly operate on the user-item graph. In this paper, we proposed a resistive random accessed memory (ReRAM) based processing-in-memory (PIM) accelerator, ReGCNR, for GCN-based recommendation. ReGCNR is featured with three key innovations. First, we exploit the 3-dimensional (3-D) stacked heterogeneous ReRAM to fit with the large-size embedding table and user-item graph. Then, we propose a joint degree mapping schema that maximizes the efficiency of the execution pipeline. After that, ReGCNR assembles a well-coordinated pipeline and hardware scheduling design to boost overall system performance. Results show that ReGCNR outperforms GPU by 69.83$$\times$$ × and 56.67$$\times$$ × in terms of average speedup and energy saving, respectively. In addition, ReGCNR outperforms state-of-the-art ReRAM-based solutions by 11.13$$\times$$ × speedups and 7.22$$\times$$ × energy savings on average.

Sistem Pemantauan Daya Listrik Berbasis Website

Human life today is very dependent on electrical energy. In almost everyday life, people need electricity, whether in offices, households, education, industry, and others. Excessive use of electricity can cause waste both in terms of costs and energy itself. Careful monitoring is needed to find out how much electrical power is used in electronic equipment. The important thing for users to pay attention to is monitoring electrical power consumption so that the use of electronic equipment remains within the time and cost limits that have been previously set. Therefore, the aim of this research is to make it easier for users to carry out this monitoring through a website-based system. Data from the PZEM-004T sensor is processed by the ESP32 microcontroller, then sent to the database via the internet network. In this way, this information can be accessed by users anytime and anywhere via websites connected to the internet. The level of data accuracy on the website reaches 100%, according to the reading results from ESP32 and Power Quality Analyzer as a comparison.

Cyanobacterial pigment adsorbed on TiO2 thin films

The rise in toxicity related to cyanobacterial bloom in freshwater is a current problem that perturbs the trophic chain and risks the ecosystems and human health. Currently, the use of biomass as a potential source of value-added bio-products is an important goal to be achieved in the scope of a sustainable bio-economy. Thus, taking advantage of such bacteria is needed. In the present work, we studied the use of cyanobacterial biomass coming from the Malambo swamp in Colombia as a source of Phycocyanobilin (C-PC) and Chlorophyll-a (Chla) which were used as natural pigments for TiO2 thin films. The concentration obtained of C-PC and Chla extracted were 215 μg/mL and 0.417 μg/mL, respectively. We modeled the natural dye adsorption kinetics on TiO2 thin films through three different models. The Langmuir model showed the best fitting, indicating that the pigment extracted from cyanobacterial biomass can sensitize thin TiO2 film through the formation of a monolayer. Furthermore, the TiO2 films present higher adsorption of C-PC (25.8 mg/g) than Chla (23.3 mg/g). Finally, the adsorption modes were assessed using periodic DFT approximations, which is a remarkable method for studying the structure and properties of solid materials. In terms of binding energies, it was found that the dye shows the strongest interaction with TiO2 through the titanium atom. Thus, the main contribution of this work is directed to explore in deep the natural dye adsorption on TiO2 from both experimental and computational point of view.

Systematic study of two proton radioactivity within the effective liquid drop model

In this study, we investigated two-proton radioactivity using the effective liquid drop model with varying mass asymmetry shapes and effective inertia coefficients (VMAS-EFF). Gaudin’s expression term for Coulomb potential energy is replaced in the current study’s calculation of potential energy. The VMAS-EFF combination predicts the half-life with the lowest standard deviation ( σ =1.03) compared to other models and previous constant mass asymmetry with Werner-inertial Wheeler’s coefficient combinations. The lowest standard deviation value indicates that the VMAS-EFF combination is suitable for reproducing the experimental data. We extended the half-life predictions to another 54 nuclei and compared them with six theoretical model predictions and two empirical formula predictions. The present work also investigated the decay possibilities using quantum tunnelling by computing the driving potential (V-Q) for various states of angular momentum l = 0, 2, 3, and 4. Found that 6Be, 6B, 8C, 12O,15,16Ne, 19Mg, 22Si, 26S, 30Ar 38,39Ti, 40V, 42Cr, 45Fe, 48,49Ni, 54Zn, 58,59,60Ge and 64Se nuclei have a high chance of emission for two protons, while 7B, 10N, 14F, 17Na, 24P, 28Cl,32K, 34Ca, 36,37Sc, 44Mn, 47Co, 52Cu, 56,57,58Ga, 61,62As nuclei have a lower chance for two proton than one proton decay. The computation of separation energy findings agree well with the driving potential findings.