Values Of Parameters Research Articles

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

A hybrid stacking classifier with feature selection for handling imbalanced data

Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Convolution neural network hyperparameter optimization using modified particle swarm optimization

Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.

A new hybrid parallel genetic algorithm for multi-destination path planning problem

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.

Ultrasonic evaluation MAFLD in screening severety matabolic disorders

Metabolic dysfunction associated fatty liver disease (MAFLD) is a new proposed term for non-alcoholic fatty liver disease (NAFLD). a liver steatosis associated with Type 2 diabetes mellitus or increased physical weight/thickness, or with two or more metabolic disorders. This indicates metabolic abnormalities that result in fat accumulation in hepatocytes without the presence of alcoholic liver disease or other causes that would lead to hepatocyte abnormalities. Our goal was determine the correlation between certain stages of fatty liver disease determined by ultrasonography with increased body weight/obesity, diabetes mellitus and other metabolic disorders and with the values of biochemical parameters of liver enzymes. A prospective study was conducted at the public health institution Zivinice Health Center from August 2022 until January 2024 on a random sample of 101 patients. Observing the entire examined group, a difference in the gender distribution of hepatic steatosis is noticed, and graphically it is shown that men have hepatic steatosis more often than women. In fatty liver disease (MAFLD), stage G2 was most prevalent in 32% of patients. Ultrasonography, along with clinical and biochemical indicators, should be an integral part of screening and monitoring of metabolic disorders as it is a non-invasive and easily accessible imaging method and allows timely therapeutic action and prevention of hepatic and extrahepatic complications.

Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making

Clean energy has become a hot topic worldwide because of the continuous rise in the climate's temperature. Reduced air pollution is only one of the many economic and environmental advantages of clean energy. An effective contractor with experience and knowledge in clean energy projects is always required to handle the complicated process of planning, implementing, and maintaining the new clean energy infrastructure. Contractor selection for renewable energy projects (REPs) is a multi-attribute group decision-making (MAGDM) problem. Linguistic T-spherical fuzzy set (LT-SFS) can be compatible with fuzzier information, and the contractor selection problem also has a lot of fuzzy information, which makes LT-SFS very suitable for contractor selection for REPs. Hence, the present work proposes a MAGDM methodology utilizing a combinative distance-based assessment (CODAS) method within the LT-SF context. For that, the Einstein and Frank t-norm and t-conorm has been used to define new and generalized operational laws for linguistic T-spherical fuzzy numbers. Then, the linguistic T-spherical fuzzy Einstein and linguistic T-spherical fuzzy Frank aggregation operators are proposed to integrate the information data provided by experts. Moreover, the proposed aggregation operators based-CODAS approach is designed to evaluate and rank the available alternatives. A real-life decision problem of selecting the best suitable contractor for a renewable energy project is solved to verify our suggested technique. Moreover, the sensitivity analysis is also carried out by changing the parameter's value to check the consistency of the rank order. Finally, the new model is compared with existing approaches to demonstrate its strength. The comparative analysis shows that the results of the suggested technique are more feasible and beneficial than those of existing approaches.

Analysis of Current-Voltage Properties of Al/p-si Schottky Diode with Aluminium Oxide Layer

In our study, the effects of the metal oxide (aluminum oxide, Al2O3) thin film placed between the metal and the semiconductor on the diode's characteristics were investigated. The Al2O3 thin film was suitable for its growth on a p-type silicon substrate by the atomic layer deposition (ALD) technique. In this study, a diode structure with an oxide interlayer was fabricated. To investigate the electrical parameters of the fabricated Schottky diode, measurements of current-voltage (I-V) were carried out at room temperature and in the 5 V voltage range. Using the I-V measurements, diode parameters such as the barrier height (Φb), the ideality factor (n), and the current density (I0) were evaluated using the theory of thermionic emission (TE) and Cheung's method. Using the TE method and Cheung’s method, the approximate values of Φb, n parameters were calculated as 0.77 eV, 5.43, and 0.77 Ev, 5.97, respectively. According to calculations, the developed Schottky diode is a rectifier diode and has been determined to have photodiode properties. This research offers an understanding of the production and electrical characteristics of Schottky devices based on Al2O3.

Hybrid uncertainty approach for management of energy storage-embedded soft open points in distribution grids

Energy storage-embedded soft open point (ESOP) is an innovative electronic device that incorporates energy storage capabilities and is designed to interconnect two or three neighboring distribution feeders. Its primary functions include active power control, reactive power compensation, and grid voltage regulation. On the other hand, distribution grids have been equipped with other distributed energy resources (DERs) such as smart PV inverters, renewable energy sources (solar and wind), and so on. Therefore, this article proposes a novel management scheme for the mentioned devices in distribution grids based on a convex formulation to take powerful solvers like CPLEX into account. Since there are a set of uncertainties associated with the model, including renewable resources, loads, and market price, a hybrid framework is developed based on the unscented transformation (UT) method and information gap decision theory (IGDT). In other words, the uncertainties of renewable resources and loads are captured by the UT method which could provide a correlation among uncertain parameters, while the IGDT method is employed to model the uncertainty associated with the market price. The hybridization of these methods assists in reducing the discrepancy between the actual and predicted values of uncertain parameters. The model is applied to a 33-bus grid, considering various case studies. The proposed method results in a substantial operational cost reduction of approximately 26.82 %. Additionally, it ensures a flatter voltage profile across the network, with the minimum voltage reaching 0.985 p.u.

The Allen–Cahn model with a time-dependent parameter for motion by mean curvature up to the singularity

In this article, we investigate the temporal evolution of arbitrary, simple, closed two-dimensional (2D) and three-dimensional (3D) interfaces under motion driven by mean curvature up to a singularity. To facilitate this investigation, we propose a novel Allen–Cahn (AC) model with a time-dependent interfacial thickness parameter. The original AC equation was developed to model the phase separation of a binary mixture. It is well known that a level set or interface of the solution of the AC equation obeys the dynamics of motion by curvature as the interfacial thickness parameter approaches zero. Generally, it is difficult to find a closed-form analytic solution of the AC equation with any initial condition. Therefore, we need to estimate the solution of the AC equation through computational approaches such as finite difference method (FDM), finite element method (FEM), Fourier-spectral method (FSM), and finite volume method (FVM). Any simple, closed curves and convex surfaces eventually shrink to a point due to motion by mean curvature. Therefore, it becomes necessary to use adaptive mesh techniques to resolve this small size problem. However, even though we use adaptive mesh techniques, we still confront the relatively thick interfacial transition layer when the curves or interfaces become very small. To avoid this problem, we can start with a very small mesh size for a small value of the interfacial parameter, which results in an extremely high computational cost even when using adaptive mesh techniques. To resolve these issues, we present the AC equation with a time-dependent interfacial parameter and develop an adaptive mesh refinement system. To show the superior performance of the proposed mathematical equation and its computational algorithm, we present various numerical experiments and investigate the motion by mean curvature up to the singularity of curves in 2D space and interfaces in 3D space.

The effect of increased listening effort on the balance performance of patients with compensated vestibular lesion

ObjectivesThis study investigated the effects of listening effort (LE) on balance in patients with compensated vestibular deficits compared to healthy peers. MethodsThe subjects included two main groups: a control group of 15 healthy subjects and a study group of 19 patients with compensated vestibular pathology. The computerized dynamic posturography test (CDP) was conducted without the speech-in-noise task as a baseline, then the participant was subjected to a dual task in which the auditory task (speech-in-noise sentences) was given as the primary task, and the balance function test was the secondary task. Results: within-group analysisThe study group showed statistically significantly worse values of all body balance parameters under dual-task than the baseline in all conditions. These differences were much higher under the compliant platform conditions. However, these findings were not statistically significant in the control group. Between-group analysisThe study group showed a statistically significant decline in body balance reactions compared to the control group under dual-task with increased listening effort and the compliant platform. Study subgroup analysis revealed statistically significant differences between patients with unilateral vestibular loss (UVL) and those with bilateral vestibular loss (BVL) in the unstable platform condition. ConclusionOur study regarding implementing a dual-tasking paradigm as a measure of LE during the evaluation of chronic vestibular patients with CDP demonstrated how dual-tasking with increased LE affects postural stability. Because of this, patients will probably be more prone to tripping and falling in multitasking situations, as found in real-world settings. This fact should be taken into consideration while testing patients with chronic vertigo and compensated states at VNG. A dual-task paradigm helps uncover the unrevealed pathology.

Soret and Dufour effects on two dimensional steady MHD flow past a semi‐infinite vertical porous plate in the presence of thermal radiation and radiation heat absorption with constant heat and mass flux

AbstractThe problem of a two‐dimensional steady, MHD convective, incompressible, viscous flow past a uniformly moving semi‐infinite vertical porous plate embedded in a porous medium in the presence of radiation heat absorption, thermal radiation, thermal diffusion, and diffusion thermo effects with constant heat and mass flux are studied. In the fluid region, a transversely oriented, homogeneous magnetic field of intensity is applied. The non‐dimensional governing equations are solved analytically adopting asymptotic series expansion method. The impacts of several physical quantities including Grashof number, Schmidt number, Prandtl number, Soret number, radiation absorption, radiation parameter, magnetic field within the flow domain and transport characteristic have been explored graphically. It has been found that fluid velocity and skin friction fall with enhancing the values of magnetic parameter. Further, temperature and concentration fields rise with an increase in the Soret effect.

Uncertainty assessment of aquifer hydraulic parameters from pumping test data

Data from pumping tests is a noisy process, and therefore, performing the pumping test numerous times will not get the same drawdown values. As a consequence, various pumping experiments lead to different values for aquifer parameter estimates. The data of pumping tests are usually analyzed using traditional methods (aquifer tests and AQtesolv software), which depend on trial and error technique. During these methods, non-unique values of hydraulic parameters are selected, which usually have a high level of uncertainty. Uncertainty must be taken into account in determining aquifer parameters, especially when using groundwater models for decision makers. The main goal of this study is to build a comprehensive tool for quantifying uncertainty associated with hydraulic parameter estimation from different pumping test conditions for fully penetrating wells in confined and semi-confined aquifers. To achieve the previous objective, a FORTRAN code was developed to apply the Markov Chain Monte Carlo (MCMC) algorithm using different likelihood functions (exponential, inverse, and log). This developed tool can be used to detect the most probable range of aquifer parameters that are consistent with pumping test data with a high degree of confidence. The tool was successfully used to several hypothetical cases to demonstrate the uncertainty in the quantification of aquifer parameters and compare the findings to the standard method's results. Also, the concept was verified numerically (using Modflow program) with satisfactory results using a hypothetical case with well-known aquifer parameters. Finally, the tool was applied for actual pumping test data with good results.

Research on NURBS Curve Interpolation Algorithm Based on Mline-Gear

Aiming at the problem of speed fluctuation caused by the large error between the ideal interpolation step and the actual interpolation step in the traditional non-uniform rational B-spline ( NURBS ) curve interpolation, a NURBS curve interpolation algorithm based on Mline-Gear is proposed. The differential and derivative definitions are derived to transform the Mline and Gear formulas respectively, and the prediction and correction formulas of the interpolation parameters are obtained. Then, it is judged whether the interpolation parameter values obtained by the correction formula meet the speed fluctuation rate requirements, and the interpolation can be continued. Otherwise, the Steffensen descent formula is used to iteratively correct the interpolation parameter values. In order to ensure the calculation accuracy and avoid solving high-order derivatives, the fourth-order Runge-Kutta method is used to solve the initial two interpolation parameters. Finally, the algorithm is simulated. The simulation results show that the algorithm reduces the feed speed fluctuation rate while ensuring a small number of iterations.

Mathematical Formulation of a Co-infection Model and its Analyses for Monkeypox and HIV/AIDS Infections

The dynamics of the co-infection of monkeypox and HIV/AIDS is examined from a mathematical perspective via a deterministic 13-compartment model. This consists of the investigation of the equilibrium points, the basic reproduction numbers and the equilibrium points’ stability. The mathematical analysis reveals that the model is epidemiologically well-posed, and that the basic reproduction number for the monkeypox sub-model is a function of the likelihood of getting infected, the rate of effective contact, the infection coefficient of the monkeypox-infectious class, the monkeypox prevention measure, the progression rate from monkeypox-exposed class to monkeypox-infectious class, the natural death rate, the vaccination rate and the waning rate of the vaccine. It also depends on the recovery rate for the monkeypox-exposed class and the monkeypox-induced death rate. The analysis also reveals that the basic reproduction number for the HIV/AIDS sub-model is a function of the likelihood of getting infected, the rate of effective contact, the HIV/AIDS prevention measure, the infection coefficient of the monkeypox-infectious class, the HIV/AIDS-induced death rate, the natural death rate, the infection coefficient of AIDS-only class, and the progression rate of HIV-only class to AIDS-only. The stability analysis reveals that the disease-free equilibrium of the sub-models are globally asymptomatically stable, when the basic reproduction number is less than unity. Numerical simulations of the model reveal the effect of changes in certain parameter values on the population sizes. Increasing or lowering the values of certain parameters can significantly affect the sizes of some classes. The Maple 18 programming software was used to carry out all calculations and numerical simulations.

A non‐similar solution to dissipative Eyring‐Powell nanofluid flow over a nonlinear stretching surface with an inclined magnetic field and active/passive nanoparticles flux

AbstractAmongst numerous non‐Newtonian fluid models, Eyring‐Powell fluid is prominent owing to its shear thinning, pseudoplastic, and yield stress unique characteristics. It has varied industrial applications including the modeling of polymer melts, food products, and blood flow. This study discusses the Darcy Forchheimer flow of Eyring‐Powell nanoliquid along a nonlinear stretched surface influenced by an inclined magnetic flux. The mass and heat transmissions are supported by active and passive control of nanoparticles respectively. The unique effect of viscous dissipation is introduced to influence the liquid velocity and temperature. Unlike the majority of the research being published that espouses similar solutions, we adopted the non‐similar analysis of the proposed problem to eradicate the chance of the variable in the parameters. This was a challenging task and was taken as a mathematical art rather than a science. The established Buongiorno model is followed to compute the thermophoretic and Brownian motion effects. The numerical computation of this mathematical system is performed using the bvp4c algorithm. The results are described through graphs and in numerically calculated tabulated values. It is determined that wall drag is stronger when the magnetic field is inclined at an angle of than at . Additionally, it is observed that passive control of nanoparticles results in better rates of heat and mass transmissions, against varied values of the fluid parameter with . The validation of the envisioned model is also a part of this exploration.

Multigrid simulations of non-Newtonian fluid flow and heat transfer in a ventilated square cavity with mixed convection and baffles

The impact of baffles on a convective heat transfer of a non-Newtonian fluid is experimentally studied within a square cavity. The non-Newtonian fluid is pumped into the cavity through the inlet and subsequently departs from the cavity via the outlet. Given the inherent non-linearity of the model, a numerical technique has been selected as the method for obtaining the outcomes. Primarily, the governing equations within the two-dimensional domain have been discretized using the finite element method. For approximating velocity and pressure, we have employed the reliable P2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb{P}}_{2}$$\\end{document}–P1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb{P}}_{1}$$\\end{document} finite element pair, while for temperature, we have opted for the quadratic basis. To enhance convergence speed and accuracy, we employ the powerful multigrid approach. This study investigates how key parameters like Richardson number (Ri), Reynolds number (Re), and baffle gap bg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{b}}}_{{\ ext{g}}}$$\\end{document} influence heat transfer within a cavity comprising a non-Newtonian fluid. The baffle gap (bg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${b}_{g}$$\\end{document}) has been systematically altered within the range of 0.2–0.6, and for this research, three distinct power law indices have been selected namely: 0.5, 1.0, and 1.5. The primary outcomes of the investigation are illustrated through velocity profiles, streamlines, and isotherm visualizations. Furthermore, the study includes the computation of the Nuavg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${Nu}_{avg}$$\\end{document}(average Nusselt number) across a range of parameter values. As the Richardson number (Ri) increases, Nuavg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${Nu}_{avg}$$\\end{document} also rises, indicating that an increase in Ri results in augmented average heat transfer. Making the space between the baffles wider makes heat flow more intense. This, in turn, heats up more fluid within the cavity.

Uncertainty in the dynamic properties of tall buildings and propagation to the wind‐induced response

SummaryThe response of tall buildings to wind actions is commonly assessed through the quasi‐static approach considering mean, background, and resonant components of the action. The latter accounts for the amplification due to resonance and depends on the dynamic properties of the buildings, that is, modal mass, frequency, and damping ratio. Selecting appropriate values of the modal parameters of tall buildings is not immediate and is usually done using predictive models. These contain uncertainty, which eventually propagates to the dynamic response. The main aim of the paper is the assessment of uncertainty in the dynamic response of flexible buildings to wind action arising from a not perfect knowledge of their dynamic properties. The paper explicitly refers to the dynamic models given by Eurocode 1, but the approach is general, and the analyses can be repeated selecting any other model. It is found that the bias in the dynamic factor is always less than one, with values on average between 0.86 and 0.98. This indicates that the approach of Eurocode 1 is conservative. The only exception is that of the acrosswind response of steel buildings with an high aspect ratio, in which case the bias can be as large as 1.16. As to randomness, the coefficient of variation of the alongwind dynamic factor is very seldom found to exceed 10%, with average values around 5%. Such values are much lower than those of the coefficient of variation of damping, which is in the order of 50% or more. This indicates that uncertainty attenuates when it propagates to the response. On the other hand, the coefficient of variation of the acrosswind and torsional dynamic factors reaches values of 20% or more, indicating that such attenuation is much lower in that case.

Estimating the slip rate in the North Tabriz Fault using focal mechanism data and GPS velocity field

Abstract This study delves into slip distribution on the North Tabriz Fault (NTF), a critical aspect of seismic hazard analysis due to its proximity to the Tabriz metropolis. The study operates within a uniform elastic half-space, maintaining constant values for fault geometry and regional rheological parameters throughout the research. To calculate strain boundary conditions, permanent and periodic global positioning system (GPS) data from the northwest region were utilized. The fault was constrained perpendicularly while allowing tangential movement, facilitating the determination of its annual slip rate using the boundary element method, with the Okada analytical model serving as the fundamental solution. The findings underscore the intricate relationship between the fault’s slip rate and boundary conditions, revealing a predominant right-lateral strike-slip motion. The study offers two slip rate assessments, obtained through earthquake focal mechanisms and GPS velocity data, yielding values of 5 and 5.5 mm / year {\rm{mm}}/{\rm{year}} , respectively. Importantly, the alignment of these calculated slip rates with paleo-seismological data underscores the credibility of the results generated via the boundary element method, distinguishing it as a reliable approach when compared to other numerical and analytical techniques. This research provides valuable insights into the behavior and slip dynamics of the NTF, which is pivotal for assessing seismic risks.

A Critical Review on Room-Temperature Sodium-Sulfur Batteries: From Research Advances to Practical Perspectives.

Room-temperature sodium-sulfur (RT-Na/S) batteries are promising alternatives for next-generation energy storage systems with high energy density and high power density. However, some notorious issues are hampering the practical application of RT-Na/S batteries. Besides, the working mechanism of RT-Na/S batteries under practical conditions such as high sulfur loading, lean electrolyte, and low capacity ratio between the negative and positive electrode (N/P ratio), is of essential importance for practical applications, yet the significance of these parameters has long been disregarded. Herein, it is comprehensively reviewed recent advances on Na metal anode, S cathode, electrolyte, and separator engineering for RT-Na/S batteries. The discrepancies between laboratory research and practical conditions are elaborately discussed, endeavors toward practical applications are highlighted, and suggestions for the practical values of the crucial parameters are rationally proposed. Furthermore, an empirical equation to estimate the actual energy density of RT-Na/S pouch cells under practical conditions is rationally proposed for the first time, making it possible to evaluate the gravimetric energy density of the cells under practical conditions. This review aims to reemphasize the vital importance of the crucial parameters for RT-Na/S batteries to bridge the gaps between laboratory research and practical applications.