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The purpose of this study is to evaluate and minimize the losses of alternating current (AC) in the winding of electrical machines. AC winding losses are frequently disregarded at low frequencies, but they become a significant concern at high frequencies. This is the situation where applications require a high speed. The most significant applications in this category are electrical propulsion and drive systems.

An analytical model is used to predict the AC losses in the winding of electrical machines. The process involves dividing the slot into separate layers and then calculating the AC loss factor for each layer. The model aims to calculate AC losses for two different winding arrangements involving circular conductors. This application focuses on the stator winding of a permanent magnet synchronous motor that is specifically designed for electric vehicles. The model is integrated into an optimization process that makes use of the genetic algorithm method to minimize AC losses resulting from the arrangement of conductors within the slot.

This study and its findings demonstrate that the arrangement of the conductors within the slot has a comparable effect on the AC losses in the winding as the machine's geometric and physical properties. The effectiveness of electrical machines depends heavily on optimizing the arrangement of conductors in the slot to minimize AC winding losses.

The proposed strategy seeks to minimize AC winding losses in high-speed electric machines by providing a cost-effective and precise solution to improve energy efficiency.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

This study aims to solve the problem of high output voltage fluctuation and low efficiency caused by the misalignment of the magnetic coupling structure in the wireless charging system for electric vehicles. To address these issues, this paper proposes a dual LCC-S wireless power transfer (WPT) system based on the double-D double-layer quadrature (DDDQ) coil, which can realize the anti-misalignment constant voltage output of the system.

First, this paper establishes the equivalent circuit of a WPT system based on dual LCC-S compensation topology and analyzes its constant-voltage output characteristics and the relationship between system transmission efficiency and coupling coefficient. 1. Quadruple D (Ahmad et al., 2019) and double-D quadrature pad (DDQP) (Chen et al., 2019) coils have good anti-misalignment in the transverse and longitudinal directions, but the magnetic induction intensity in the center of the coils is weak, making it difficult for the receiving coil to effectively couple to the magnetic field energy. 2. Based on the double-D quadrature (DDQ) structure coil that can eliminate the mutual inductance between coupling coils and cross-coupling, Gong et al. (2022a) proposed a parameter optimized LCC-LC series-parallel hybrid topology circuit, which ensures that the output current fluctuation is controlled within 5% only when the system is misaligned within the 50% range along the X direction, achieving constant current output with anti-misalignment. The magnetic coupling structure’s finite element simulation model is established to analyze the change in magnetic induction intensity and the system’s anti-misalignment characteristics when the coil offsets along the x and y axes. Finally, an experimental prototype is developed to verify the constant voltage output performance and anti-misalignment performance of the system, and the proposed anti-misalignment system is compared with the systems in existing literature, highlighting the advantages of this design.

The experimental results show that the system can achieve a constant voltage output of 48V under a time-varying load, and the output voltage fluctuates within ±5% of the set value within the range of ±60 mm lateral misalignment and ±72 mm longitudinal misalignment.

Based on the dual LCC-S WPT system, the mutual inductance between the same side coils is reduced by adding decoupling coils, and the anti-misalignment characteristics and output power of the system are improved in a certain range. It is aimed at improving the stability of the system output and transmission efficiency.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

This paper sets out to solve a common and crucial fundamental theoretical problem of gray incidence cluster analysis: to [X]={X|ρ(X,Y)≥1−ε0} constitute an approximate classification, it must first be proven that [X]={X|ρ(X,Y)=1} constitutes a rigorous classification.

This paper does not study the concrete expressions of various incidence degrees but rather the perfect correlation essence of such incidence degrees, that is, sufficient and necessary conditions.

For any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree, it is proven that the perfect correlation relation is an equivalence relation. The set composed of all sequences Y that are equivalent to sequences X is studied, that is, the equivalence class of X. The structure and mutual relations of these equivalence classes are discussed, and the topological homeomorphism concept of incidence degree is introduced. The conclusion is obtained that the equivalence classes of the two incidence degrees must be the same when the topological homeomorphism is obtained.

In this paper, only the perfect correlation relation of any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree are studied as equivalent relations.

Not only are the research results of several incidence degrees involved in this paper original but also many other effective incidence degrees have not done this basic research, so this paper opens up a research direction with theoretical significance.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

This article aims to identify and describe the research outcomes of studies that have employed the theoretical framework of lesson study (LS) in initial science teacher preparation programs. The focus is on the impact of LS on preservice teachers’ (PST) pedagogical and content knowledge, beliefs, routines and norms for professional learning and instructional practices.

A systematic approach was employed to compile pertinent literature by initially searching scholarly databases using specific keywords and phrases related to prospective science teacher preparation. Seventeen studies, encompassing both qualitative research and mixed-methods research, met the inclusion criteria and significantly contributed to the study’s findings. The authors independently conducted a coding process, applying a predefined code scheme based on Lewis et al.'s (2019) theoretical framework. The outcomes of the coding process were compared, and reliability tests were conducted to ensure the consistency of the coding.

In preservice science teacher (PSST) education, LS proves transformative, enriching pedagogical and content knowledge, shaping beliefs, fostering collaboration and influencing instructional practices. Its collaborative, reflective and iterative nature significantly contributes to the professional growth of preservice science teachers, preparing them for effective, student-centered teaching practices. Further investigation is warranted in the realm of LS, particularly concerning preservice science teachers and their beliefs.

This literature review on science PSTs is one of the pioneering efforts to employ the professional development framework crafted by Lewis et al. (2019).

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

This paper focuses on prospective teachers’ (PTs) participation in a lesson study (LS) that prompted them to research their own practice. We seek to describe the dimensions of PTs’ knowledge of student learning developed during the process and the LS features fostering it.

The participants were two PTs, a teacher educator, a cooperating teacher and a researcher. The LS was integrated into a Portuguese initial elementary teacher education program. Following a qualitative approach, we used participant observation.

The PTs developed their knowledge of students’ learning of the concept of area in four dimensions: theories; students’ interests and expectations; ways students interact with the content and students’ strengths and weaknesses in learning the concept. To support this development, the LS design considered follow-up sessions and emphasised collaborative work.

This study focuses on PTs researching their practice and disseminating the results, which has been overlooked in previous research of LS with PTs. The results highlight the potential of LS to motivate PTs to research their practice and emphasise the importance of involving them in disseminating LS results.