Energy Estimates Research Articles

Assessment of Sustainable Transportation Model Using Energy-Efficient Algorithm

The proliferation and extensive utilization of vehicles have escalated energy use and increased environmental damage. Utilizing large amounts of information on metropolitan traffic patterns can enhance vehicle transportation's environmental and financial aspects. This can be achieved through an efficient decrease in fuel consumption and contaminants. This study aims to present a novel framework for evaluating the Sustainable Transportation Model with Energy-Efficient Algorithm (STM-EE) by utilizing Route Planning Algorithms (RPAs) in a simulated environment. The CARLA simulator was used to compare the widely used conventional Dijkstra technique and an Integrated Genetic Algorithm (IGA) that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) in different driving scenarios. This study aims to quantify the effects of RPA decisions on the energy consumption of vehicles. The efficiency of comparing the two RPAs has been enhanced by employing an offline energy estimate methodology. The proposed architecture is assessed through EE simulations to demonstrate its efficacy and adaptability. The results support the development of energy-efficient RPA solutions, which contribute to advancing the STM field.

Randomized semi-quantum matrix processing

We present a hybrid quantum-classical framework for simulating generic matrix functions more amenable to early fault-tolerant quantum hardware than standard quantum singular-value transformations. The method is based on randomization over the Chebyshev approximation of the target function while keeping the matrix oracle quantum, and is assisted by a variant of the Hadamard test that removes the need for post-selection. The resulting statistical overhead is similar to the fully quantum case and does not incur any circuit depth degradation. On the contrary, the average circuit depth is shown to get smaller, yielding equivalent reductions in noise sensitivity, as explicitly shown for depolarizing noise and coherent errors. We apply our technique to partition-function estimation, linear system solvers, and ground-state energy estimation. For these cases, we prove advantages on average depths, including quadratic speed-ups on costly parameters and even the removal of the approximation-error dependence.

Energy management scheme in microgrid with LVDC using smart energy estimation technique

Energy management scheme in microgrid with LVDC using smart energy estimation technique

Leveraging neural networks to correct FoldX free energy estimates.

Proteins play a pivotal role in many biological processes, and changes in their amino acid sequences can lead to dysfunction and disease. These changes can affect protein folding or interaction with other biomolecules, such as preventing antibodies from inhibiting a viral infection or causing proteins to misfold. The ability to predict the effects of mutations in proteins is crucial. Although experimental techniques can accurately quantify the effect of mutations on protein folding free energies and protein-protein binding free energies, they are often time-consuming and costly. By contrast, computational techniques offer fast and cost-effective alternatives for estimating free energies, but they typically suffer from lower accuracy. Enhancing the accuracy of computational predictions is therefore of high importance, with the potential to greatly impact fields ranging from drug design to understanding disease mechanisms. One such widely used computational method, FoldX, is capable of rapidly predicting the relative folding stability ( ) for a protein as well as the relative binding affinity ( ) between proteins using a single protein structure as input. However, it can suffer from low accuracy, especially for antibody-antigen systems. In this work, we trained a neural network on FoldX output to enhance its prediction accuracy. We first performed FoldX calculations on the largest datasets available for mutations that affect binding (SKEMPIv2) and folding (ProTherm4) with experimentally measured . Features were then extracted from the FoldX output files including its prediction for . We then developed and optimized a neural network framework to predict the difference between FoldX's estimated and the experimental data, creating a model capable of producing a correction factor. Our approach showed significant improvements in Pearson correlation performance. For single mutations affecting folding, the correlation improved from a baseline of 0.3 to 0.66. In terms of binding, performance increased from 0.37 to 0.61 for single mutations and from 0.52 to 0.81 for double mutations. For epistasis, the correlation for binding affinity (both singles and doubles) improved from 0.19 to 0.59. Our results also indicated that models trained on double mutations enhanced accuracy when predicting higher-order mutations (such as triple or quadruple mutations), whereas models trained on singles did not. This suggests that interaction energy and epistasis effects present in the FoldX output are not fully utilized by FoldX itself. Once trained, these models add minimal computational time but provide a substantial increase in performance, especially for higher-order mutations and epistasis. This makes them a valuable addition to any free energy prediction pipeline using FoldX. Furthermore, we believe this technique can be further optimized and tested for predicting antibody escape, aiding in the efficient development of watch lists.

Global existence and extinction for a fast diffusion p-Laplace equation with logarithmic nonlinearity and special medium void

Abstract This article is devoted to the global existence and extinction behavior of the weak solution to a fast diffusion p p -Laplace equation with logarithmic nonlinearity and special medium void. By applying energy estimates approach, Hardy-Littlewood-Sobolev inequality, and some ordinary differential inequalities, the global existence result is proved and the sufficient conditions on the occurrence of the extinction and nonextinction phenomena are given.

Effect of increasing supplementation of dietary glycine on growth performance, meat quality, liver characteristics, and intestinal health in broiler chickens raised under heat stress conditions

The current study aimed to investigate the effect of increasing supplementation of dietary glycine (Gly) on growth performance, meat quality, liver characteristics, and intestinal health in broiler chickens raised under heat stress (HS) conditions. A total of one thousand six hundred 25-d-old broiler chickens were randomly allotted to 1 of 5 dietary treatments with 8 replicates. Each replicate comprised 20 male and 20 female birds. A negative control (NC) diet was prepared to meet or exceed energy and nutrient requirement estimates, whereas a positive control (PC) diet was formulated to contain increasing concentrations of AMEn by 50 kcal/kg as well as those of digestible amino acids, total Ca, and available P by 10% compared with the respective concentrations in the NC diet. Three additional diets were prepared by supplementing the NC diet with 0.4, 0.8, or 1.6% Gly. All chickens were raised under cyclic HS conditions at 29°C ± 0.89°C for 10 h/d and 23°C ± 1.45°C for the remaining time over an 18-d feeding trial. Results indicated that broiler chickens fed the NC diet had a greater (P < 0.05) FCR than those fed the PC diet under HS conditions. Increasing supplementation of up to 1.6% Gly in diets decreased (linear, P < 0.001) FCR in broiler chickens. Increasing supplementation of dietary Gly tended to increase (linear, P = 0.070) water holding capacity in the breast meat. Increasing supplementation of dietary Gly decreased (linear, P < 0.05) serum aspartate aminotransferase concentrations and tended to decrease blood heterophil:lymphocyte (linear, P = 0.083) and liver malondialdehyde concentrations (quadratic, P = 0.084). A tendency for increased villus height (linear, P = 0.086) and a significant increase in villus height:crypt depth ratio and goblet cell numbers (linear, P < 0.05) were identified following increasing Gly supplementation. In conclusion, increasing supplementation of dietary Gly improved feed efficiency, meat quality, liver health, and intestinal morphology possibly by mitigating oxidative stress and stress response in broiler chickens raised under HS conditions.

Exploiting Non-Abelian Point-Group Symmetry to Estimate the Exact Ground-State Correlation Energy of Benzene in a Polarized Split-Valence Triple-Zeta Basis Set.

Local electronic-structure methods in quantum chemistry operate on the ability to compress electron correlations more efficiently in a basis of spatially localized molecular orbitals than in a parent set of canonical orbitals. However, many typical choices of localized orbitals tend to be related by selected, near-exact symmetry operations whenever a molecule belongs to a point group, a feature which remains largely unexploited in most local correlation methods. The present Letter demonstrates how to leverage a recent unitary protocol for enforcing symmetry properties among localized orbitals to yield a high-accuracy estimate of the exact ground-state correlation energy of benzene (D6h) in correlation-consistent polarized basis sets of both double- and triple-ζ quality. Through an initial application to many-body expanded full configuration interaction (MBE-FCI) theory, we show how molecular point-group symmetry can lead to computational savings that are inversely proportional to the order of the point group in a manner generally applicable to the acceleration of modern local correlation methods.

Analytical estimate of effective charge and ground-state energies of two to five electron sequences up to atomic number 20 utilizing the variational method

BackgroundThe variational method, a quantum mechanical approach, estimates effective charge distributions and ground-state energy by minimizing the Hamiltonian's expectation value using trial wave functions with adjustable parameters. This method provides valuable insights into system behavior and is widely used in theoretical chemistry and physics. This paper aims to investigate ground-state energies and isoelectronic sequences using the variational method, introducing a novel approach for analyzing multi-electron systems. This technique allows for determining effective charge values and ground-state energies for 2–5 electrons sequence up to Z ≤ 20. Hydrogenic wave functions are used as a trial wave function to calculate effective charge in 1 s, 2 s, and 2p states. Two varying parameters were used to calculate an approximate wave function for the system. These values are then used in non-relativistic Hamiltonian with electron–electron interaction terms to calculate the ground-state energy of an atom.ResultThe results align with the reported experimental values, showing a marginal 1% error.ConclusionA Python algorithm is established based on the variational principle. It was found that, based on a few selected parameters in scripting the program, a very promising result was obtained. Furthermore, adding more variational parameters can minimize the difference between experimental and theoretical values, and this technique can be extended to elements with higher atomic numbers.

Organocatalytic[3 + 2]Cycloaddition: Synthesis of Quinazoline Containing Sulfonyl 1,2,3‐Triazoles as Potent EGFR Targeting Anti‐Breast Cancer Agents

ABSTRACTA general strategy was developed for the synthesis of new fully decorated 1,2,3‐triazoles (4a–4m and 5a–5g) containing quinazolines from 1‐(4‐nitrophenyl)‐2‐(quinazolin‐8‐ylsulfonyl) ethan‐1‐one and several azides using Ramachary organocatalytic azide‐ketone cycloaddition method. This reaction is reported for the synthesis of fully substituted sulfonyl‐1,2,3‐triazolyl quinazolins at a temperature of 100°C and the yields of the products produced are satisfactory to excellent. In vitro anticancer activity of all these derivatives demonstrated that six compounds, 4d, 4f, 4i, 4j, 5d, and 5e, were effective against two human breast cancer cell lines, MCF‐7 and MDA‐MB‐231. Compounds 4f, 4j, and 5d had more action against both cell lines than Erlotinib. Later, the findings of inhibitory assays of potent compounds 4d, 4f, 4i, 4j, 5d, and 5e against the tyrosine kinase EGFR revealed that compound 5d proved more potent than the reference erlotinib, while 4f and 4j had comparable efficacy. In silico molecular docking studies were also performed on six strong medicines to identify interactions with the EGFR receptor, and the energy estimations were shown to be comparable with the observed IC50 values. Ultimately, using SWISS/ADME and pkCSM, the in silico pharmacokinetic profile of potent compounds 4d, 4f, 4i, 4j, 5d, and 5e was predicted. All of the compounds precisely followed the principles established by Lipinski, Veber, Egan, and Muegge.

وجود حل محلي لنظام الجذب الكيميائي والتنافر مع الانتشار غير الخطي والمصدر اللوجستي

In this paper, we are concerned with a model arising from biology, which is a coupled system of chemotaxis equations and viscous compressible fluid equations through transport and external forcing. The local existence of solutions to the Cauchy problem is investigated under certain conditions. Precisely, for an attraction-repulsion chemotaxis model system over three space dimensions, we obtain local existence and uniqueness of convergence on classical solutions near constant states. We prove local existence of unique solutions in three dimensions by using energy estimates.

Cheminformatics-enhanced discovery of therapeutic agents targeting isocitrate lyase in Mycobacterium tuberculosis infections

Tuberculosis (TB) is a global health challenge; therefore, there is an urgent requirement to develop a novel and more effective anti-TB therapeutic. This study targeted the isocitrate lyase (ICL) protein due to its pivotal role in the pathogenicity of Mycobacterium tuberculosis (Mtb). Virtual screening of 8752 bioactive compounds used an ML-based QSAR model and molecular docking. ADMET testing was performed on the top three hits to identify the compound most closely mimicking a drug molecule. The top hits, 648 and 2785758, showed high binding affinity towards ICL with −7.3 and −7 kcal/mol, comparable to the control. These molecules also showed strong binding with the residue Asp108, which plays a vital role in ICL activity. Molecular dynamics simulations showed stability for 648 and 2785758, comparable to the control compound used in this study. It was found that 648 bound to the protein maintained the RMSD constant and consistent at 0.3 nm for a complete 100 ns simulation. 2785758 showed a comparable RMSD trend to the control. Both 648 and 2785758 showed high RMSF for critical residue Asp108. Further, PCA and FEL confirmed the formation of a stable complex. MM/GBSA estimations of binding free energy indicated that compounds 648 had an elevated level of stability (ΔG TOTAL = −28.11 kcal/mol) and 2785758 (ΔG TOTAL = −21.05 kcal/mol). This study suggests that compounds 648 and 2785758 can potentially affect the activity of ICL, leading to its inactivation and ultimately preventing the progression of tuberculosis. Communicated by Ramaswamy H. Sarma

Physical-space estimates for axisymmetric waves on extremal Kerr spacetime

We study axisymmetric solutions to the wave equation on extremal Kerr backgrounds and obtain integrated local energy decay (or Morawetz estimates) through an analysis exclusively in physical-space. Boundedness of the energy and Morawetz estimates for axisymmetric waves in extremal Kerr were first obtained by Aretakis [13] through the construction of frequency-localized currents used in particular to express the trapping degeneracy. Here we extend to extremal Kerr a method introduced by Stogin [63] in the sub-extremal case, simplifying Aretakis' derivation of Morawetz estimates through purely classical currents.

Uniform regularity and vanishing dissipation limit for the 3D magnetic Bénard equations in half space

In this paper, we are concerned with the uniform regularity and zero dissipation limit of solutions to the initial boundary value problem of 3D incompressible magnetic Bénard equations in the half space, where the velocity field satisfies the no-slip boundary conditions, the magnetic field satisfies the perfect conducting boundary conditions, and the temperature satisfies either the zero Neumann or zero Dirichlet boundary condition. With the assumption that the magnetic field is transverse to the boundary, we establish the uniform regularity energy estimates of solutions as both viscosity and magnetic diffusion coefficients go to zero, which means there is no strong boundary layer under the no-slip boundary condition even the energy equation is included. Then the zero dissipation limit of solutions for this problem can be regarded as a direct consequence of these uniform regularity estimates by some compactness arguments.

Automated Adaptive Absolute Binding Free Energy Calculations.

Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a fully automated ABFE workflow based on the automated selection of λ windows, the ensemble-based detection of equilibration, and the adaptive allocation of sampling time based on inter-replicate statistics. We find that the automated selection of intermediate states with consistent overlap is rapid, robust, and simple to implement. Robust detection of equilibration is achieved with a paired t-test between the free energy estimates at initial and final portions of a an ensemble of runs. We determine reasonable default parameters for all algorithms and show that the full workflow produces equivalent results to a nonadaptive scheme over a variety of test systems, while often accelerating equilibration. Our complete workflow is implemented in the open-source package A3FE (https://github.com/michellab/a3fe).

Applicability of energy-theta mapping in resonant soft X-ray reflectometry to probe depth dependence of oxidation state and crystallographic environment in iron oxide multilayers

In the present paper we discuss applicability of the synchrotron method of resonant x-ray reflectometry to non-destructive depth profiling of multilayer heterostructures with low optical contrast between the sublayers. The two-dimensional mapping approach comprising evaluation and measurement of reflectance as a function of photon energy and grazing angle is shown to provide a convenient way to effectively utilize the enhancement of optical contrast between the sublayers that exists at the absorption edges of chemical elements due to particular spectral shape peculiarities related to oxidation state, crystallographic environment and magnetization. In the present study the evaluation of the resonant X-ray reflectivity maps is performed using a specially developed modeling and fitting software. Estimation of the photon energy / grazing angle combinations at which the reflectance is most sensitive to the minor changes in the physical properties of the sublayers is illustrated by the example of ferroic-on-semiconductor nanoscale heterostructures composed of nanoscale film of metallic iron oxidized from either top or bottom side. The subtle differences in resonant soft x-ray reflectance caused by variation of the oxide film thickness, oxidation state and crystallographic environment are discussed. The presented approach is applicable to a large class of heterostructures composed of sublayers that can be distinguished only by the differences in their near edge X-ray absorption fine structure.

Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R2 of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates.

Analyzing the FMN-heme interdomain docking interactions in neuronal and inducible NOS isoforms by pulsed EPR experiments and conformational distribution modeling.

Nitric oxide synthases (NOSs), a family offlavo-hemoproteins with relatively rigid domains linked by flexible regions, require optimal FMN domain docking to the heme domain for efficient interdomain electron transfer (IET). To probe the FMN-heme interdomain docking, the magnetic dipole interactions between the FMN semiquinone radical (FMNH•) and the low-spin ferric heme centers in oxygenase/FMN (oxyFMN) constructs of neuronal and inducible NOS (nNOS and iNOS, respectively) were measured using the relaxation-induced dipolar modulation enhancement (RIDME) technique. The FMNH•RIDME data were analyzed using the mesoscale Monte Carlo calculations of conformational distributions of NOS, which were improved to account for the native degrees of freedom of the amino acid residues constituting the flexible interdomain tethers. This combined computational and experimentalanalysis allowed for the estimation of the stabilization energies and populations of the docking complexes of calmodulin (CaM) and the FMN domain with the heme domain. Moreover, combining the five-pulse and scaled four-pulse RIDME data into a single trace has significantly reduced the uncertainty in the estimated docking probabilities. The obtained FMN-heme domain docking energies for nNOS and iNOS were similar (-3.8kcal/mol), in agreement with the high degree of conservation of the FMN-heme domain docking interface between the NOS isoforms. In spite of the similar energetics, the FMN-heme domain docking probabilities in nNOS and iNOS oxyFMN were noticeably different (~ 0.19 and 0.23, respectively), likely due to differences in the lengths of the FMN-heme interdomain tethers and the docking interface topographies. The analysis based on the IET theory and RIDME experiments indicates that the variations in conformational dynamics may account for half of the difference in the FMN-heme IET rates between the two NOS isoforms.

Estimation of solar energy based on solar angles: cities of Iraq case study

Estimation of solar energy based on solar angles: cities of Iraq case study

Prandtl boundary layer expansion with strong boundary layers for inhomogeneous incompressible magnetohydrodynamics equations in Sobolev framework

We consider the validity of Prandtl boundary layer expansion of solutions to the initial boundary value problem for inhomogeneous incompressible magnetohydrodynamics equations in the half-plane when both viscosity and resistivity coefficients tend to zero, where the no-slip boundary condition is imposed on velocity while the perfectly conducting condition is given on magnetic field. Since there exist strong boundary layers, the essential difficulty in establishing the uniform L∞ estimates of the error functions comes from the unboundedness of vorticity of strong boundary layers. Under the assumptions that the viscosity and resistivity coefficients take the same order of a small parameter and the initial tangential component of magnetic field has a positive lower bound near the boundary, we prove the validity of Prandtl boundary layer ansatz in L∞ sense in Sobolev framework. Compared with the homogeneous incompressible case considered in [33], there exists a strong boundary layer of density. Consequently, some suitable functionals should be designed and the elaborated co-normal energy estimates will be involved in analysis due to the variation of density and the interaction between the density and velocity.

A Lagrange Galerkin Scheme for the Numerical Stability of the Shallow Water Equations with the Transmission Boundary Condition through Energy Estimates

A Lagrange Galerkin Scheme for the Numerical Stability of the Shallow Water Equations with the Transmission Boundary Condition through Energy Estimates