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This article classifies, describes, and critically compares different compensation schemes, converter topologies, control methods, and coil structures of wireless power transfer systems for electric vehicle battery charging, focusing on inductive power transfer. It outlines a path from the conception of the technology to the modern and cutting edge of the technology. First, the base principles of inductive coupling power transfer are supplied to give an appreciation for the operation and design of the systems. Then, compensation topologies and soft-switching techniques are introduced. Reimagined converter layouts that deviate from the typical power electronics topologies are introduced. Control methods are detailed alongside topologies, and the generalities of control are also included. The paper then addresses other essential aspects of wireless power transfer systems such as coil design, infrastructure, cost, and safety standards to give a broader context for the technology. Discussions and recommendations are also provided. This paper aims to explain the technology, its modern advancements, and its importance. With the need for electrification mounting and the automotive industry being at the forefront of concern, recent advances in wireless power transfer will inevitably play an essential role in the coming years to propel electric vehicles into the common mode of choice.

Objective: The objective of this study was to evaluate how individuals in a specific culture perform the linguistic categorization of the dominant color in an image and to identify potential differences in such categorization.   Theoretical Framework: Language is a fundamental component not only of culture but also of the mechanisms of the human brain, influencing both information processing and perception of the world. In this sense, linguistic categorization can provide insight into the functioning of the human mind, the perceptual experience of certain phenomena, and how language and visual perception interact across different cultures.   Method: An empirical study was conducted in which participants were exposed to images with a dominant color. The linguistic categorization of this color was evaluated based on the participants' gender.   Results and Discussion: The results showed variation in the linguistic categorization of the dominant color by gender. Women exhibited a broader range of categorization for different colors, which was also influenced by the intrinsic qualities of the object as well as by the visual processing derived from human structure   Implications of the Research: The findings could influence how colors are used in products and advertising in a culturally sensitive manner, where color perception is critical based on gender differences.   Originality/Value: This study examines how the intrinsic qualities of objects and human visual structure can alter perception and categorization, offering a new perspective on the relationship between language, culture, and the recognition of static systems.

Aging spirits in wooden casks is a traditional and mandatory process for the production of certain products, such as whisky. The physicochemical and sensory changes that occur during aging are shaped by the characteristics of the barrels and the aging method used. In this paper, we examined the behavior of the same malt spirit when aged using two different Sherry Casks® methods. The first one was static aging, with the distillate remaining still in the cask, and the second one was a dynamic system, characterized by the regular racking of the spirit between casks at different aging stages (Criaderas and Solera). For 36 months, the aging spirits were sampled and analyzed to determine any changes in acidity, volatile, and phenolic compound content that might indicate changes in their chemical profile. The spirits were also subjected to sensory evaluations. The analysis revealed a significant evolution of the distillate in either system, although with different chemical profiles. Multiple Linear Regression Models (MLR and PLS) were successfully used to estimate the age of the distillates at a high level of confidence. Although, after the first racking operation, the distillates in the dynamic system had an average age greater than the theoretical one, these differences tended to fade away as the system gradually stabilized.

The paper discusses a novel method, which addresses robust design optimization of dynamic and static multi-objective processes. For a dynamic process, the optimal setting of the graded signal and input parameters are sought so that it is least sensitive to internal and external noises. In addition to addressing planned and unplanned experiments (cross-sectional and panel data), the method estimates the random and nonrandom variance components variably (i.e., returns a non-constant uncertainty at each combination level or treatment). The stochastic frontier model is utilized to ensure this purpose. For dynamic processes, the method operates in three main steps, (i) data preparation by transforming the outputs to maximization functions, (ii) estimate of the composed variation (random and non-random error components), (iii) and, composition of the process uncertainty array for each output across the signal levels. The robust design optimization solution corresponds to the levels combination of the signal and the input factors, which adds up to the lowest global uncertainty score. The applicability of the approach is then illustrated with a case study that uses one signal factor at two levels and four input factors (x1, x2, x3, and x4) at three levels each. The process responses, Y1, Y2, and Y3 are of types Dynamic Larger the Best (DLB), Dynamic Nominal the Best (DNB), and Dynamic Smaller the Best (DSB), respectively.

Increasing demand for electricity has placed heavy stress on power system security. Therefore, this paper focuses on the problem of how to maximize power system static security in terms of branch loading and voltage level under normal operation and even the most critical single line contingency conditions. This paper proposes a hybrid approach to find out the optimal locations and settings of two classical types of flexible AC transmission system (FACTS) devices, namely thyristor-controlled series compensators (TCSCs) and static var compensators (SVCs) for solving this problem. Our proposed approach requires a two-step strategy. Firstly, the min cut algorithm (MCA) and tangent vector technique (TVT) are applied to determine the proper candidate locations of TCSC and SVC respectively so as to reduce the search scope for a solution to the problem, and then the cuckoo search algorithm (CSA) is employed to solve this problem by simultaneously optimizing the locations and settings for TCSC and SVC installation. The proposed hybrid approach has been verified on the IEEE 6-bus and modified IEEE 14-bus test systems. The results indicate that CSA outperforms particle swarm optimization (PSO), proving its effectiveness and potential, and they also show that our proposed hybrid approach can find the best locations and settings for TCSC and SVC devices as an effective way for enhancing power system static security by removing or alleviating the overloads and voltage violations under normal operation and even the most critical single line contingency conditions. Using this hybrid approach, the search space for solution to the problem becomes limited hence the computational burden will be decreased.

This paper addresses the problem of robust identification of gross errors affecting both measurements and network parameters in power system state estimation. The study is conducted within a steady-state framework and focuses on improving bad data identification in the presence of modeling and measurement uncertainties, explicitly accounting for the limited observability of gross errors. Building on an Extended Weighted Least Squares (EWLS) estimator and a theoretically refined eigenvalue-based clustering of dominant error components, a novel Bayesian identification framework is introduced. The proposed Bayesian approach assigns probabilities to competing gross error models, including scenarios involving multiple simultaneous errors, given the observed clusters of dominant errors. This probabilistic formulation enables a systematic and quantitative decision-making process for identifying the most likely sources of gross errors, extending existing deterministic or heuristic approaches. The methodology is evaluated through numerical simulations on the IEEE-14 bus test system, considering several gross error scenarios and significant parameter uncertainties. The results demonstrate that the proposed Bayesian framework enhances the interpretability and discriminative capability of gross error identification, highlighting its potential for robust bad data identification in power system state estimation.

The goal of this work is to determine the possibilities to synthesize stable latex with a narrow particle size distribution by homogeneous polymerization of methyl methacrylate in an aqueous solution. For the first time, methyl methacrylate is polymerized under static conditions in an aqueous solution of a hydroquinone–potassium persulfate redox system. It is assumed that semiquinone radical anions formed at the intermediate stage of hydroquinone oxidation can participate in the termination of growing radicals and affect the process of formation of latex particles by changing parameters of polymer molecules. The article presents the results of studying the colloidal parameters of the obtained latex, which show that the selected polymerization conditions make it possible to reproducibly synthesize monodisperse stable latexes.

In this study, a neural network-based approach is proposed for the identification of nonlinear static systems. A variant called ABCES (ABC Based on Effective Scout Bee Stage) is introduced for neural network training. Two important changes are carried out with ABCES. The first is an update of “limit” control parameters. In ABC algorithm, “limit” value is fixed. It is adaptively adjusted according to number of iterations in ABCES. In this way, the efficiency of the scout bee stage is increased. Secondly, a new solution-generating mechanism for the scout bee stage is proposed. In ABC algorithm, new solutions are created randomly. It is aimed at developing previous solutions in the scout bee stage of ABCES. The performance of ABCES is analyzed on two different problem groups. First, its performance is evaluated on 13 numerical benchmark test problems. The results are compared with ABC, GA, PSO and DE. Next, the neural network is trained by ABCES to identify nonlinear static systems. 6 nonlinear static test problems are used. The performance of ABCES in neural network training is compared with ABC, PSO and HS. The results show that ABCES is generally effective in the identification of nonlinear static systems based on neural networks.

Static in vitro permeation experiments are commonly used to gain insights into the permeation properties of drug substances but exhibit limitations due to missing physiologic cell stimuli. Thus, fluidic systems integrating stimuli, such as physicochemical fluxes, have been developed. However, as fluidic in vitro studies display higher complexity compared to static systems, analysis of experimental readouts is challenging. Here, the integration of in silico tools holds the potential to evaluate fluidic experiments and to investigate specific simulation scenarios. This study aimed to develop in silico models that describe and predict the permeation and disposition of two model substances in a static and fluidic in vitro system. For this, in vitro permeation studies with a 16HBE cellular barrier under both static and fluidic conditions were performed over 72 h. In silico models were implemented and employed to describe and predict concentration–time profiles of caffeine and diclofenac in various experimental setups. For both substances, in silico modeling identified reduced apparent permeabilities in the fluidic compared to the static cellular setting. The developed in vitro–in silico modeling framework can be expanded further, integrating additional cell tissues in the fluidic system, and can be employed in future studies to model pharmacokinetic and pharmacodynamic drug behavior.

In this study, the aspects of voltage stability related to voltage control and reactive power support in an active distribution grid are analysed, considering both static and dynamic system constrains. The analysis is carried out based on different international and local utility standards for voltage source converters (VSCs) and distribution. The ability of VSCs to provide reactive power support is explored in a structural way to improve voltage stability as well as power quality. The functional requirements of the VSCs are identified for this purpose in a generalised platform with the assumption of extension of the network automation till the distributed resources level. The step-by-step formulation of voltage stability and reactive power support requirements from system level are translated to VSC level for both static and dynamic system operation, aiming for a better system performance and new energy market scenario. The opportunity of tariff benefit would attract the DG operators as well as the customer for participating in system security through reactive power support and voltage profiling. The main contribution of the study lies in providing a comprehensive guideline for voltage control and reactive support of VSC interfaced DGs in active distribution combining the static and dynamic limitations.