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The purpose of this paper is to investigate the strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup.

Formulation of the multi-objective design problem-oriented toward execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploits variable fidelity modeling, physics- and approximation-based representation of the structure and model correction techniques. The considered approach is suitable for handling various problems pertinent to the design of microwave and antenna structures. Numerical case studies are provided demonstrating the feasibility of the segmentation-based framework for the design of real-world structures in setups with two and three objectives.

Formulation of appropriate design problem enables identification of the search space region containing Pareto front, which can be further divided into a set of compartments characterized by small combined volume. Approximation model of each segment can be constructed using a small number of training samples and then optimized, at a negligible computational cost, using population-based metaheuristics. Introduction of segmentation mechanism to multi-objective design framework is important to facilitate low-cost optimization of many-parameter structures represented by numerically expensive computational models. Further reduction of the design cost can be achieved by enforcing equal-volumes of the search space segments.

The study summarizes recent advances in low-cost multi-objective design of microwave and antenna structures. The investigated techniques exceed capabilities of conventional design approaches involving direct evaluation of physics-based models for determination of trade-offs between the design objectives, particularly in terms of reliability and reduction of the computational cost. Studies on the scalability of segmentation mechanism indicate that computational benefits of the approach decrease with the number of search space segments.

The proposed design framework proved useful for the rapid multi-objective design of microwave and antenna structures characterized by complex and multi-parameter topologies, which is extremely challenging when using conventional methods driven by population-based metaheuristics algorithms. To the authors knowledge, this is the first work that summarizes segmentation-based approaches to multi-objective optimization of microwave and antenna components.

This paper presents a comparative study of different stator-segmentation topologies of a permanent magnet synchronous machine (PMSM) used in traction drives and their effect on iron losses. Using stator segmentation allows one to achieve more significant copper fill factors, resulting in increased power densities and efficiencies. The segmentation of the stators creates additional air gaps and changes the soft magnetic material’s material properties due to the cut edge effect. The aim of this paper is to present an in-depth analysis of the influence of stator segmentation on iron losses. The main goal was to compare various segmentation methods under equal excitation conditions in terms of their influence on iron loss.

A transient finite element method analysis combined with an extended iron-loss model was used to evaluate discussed effects on the stator’s iron losses. The workflow to obtain a homogenized airgap length accounting for cut edge effects was established.

The paper concludes that the segmentation in most cases slightly decreases the iron losses in the stator because of the overall reduced magnetic flux density B due to the additional air gaps in the magnetic circuit. An increase of the individual components, as well as total power loss, was observed in the Pole Chain segmentation design. In general, segmentation did not change the total iron losses significantly. However, different segmentation methods resulted in the different distortion of the magnetic field and, consequently, in different iron loss compositions. The analysed segmentation methods exhibited different iron loss behaviour with respect to the operation points of the machine. The final finding is that analysed stator segmentations had a negligible influence on the total iron loss. Therefore, applying segmentation is an adequate measure to improve PMSMs as it enables, e.g. increase of the winding fill factor or simplifying the assembly processes, etc.

The influence of stator segmentation on iron losses was analysed. An in-depth evaluation was performed to determine how the discussed changes influence the individual iron loss components. A workflow was developed to achieve a computationally cheap homogenized model.

Additive manufacturing technology significantly simplifies the production of complex three-dimensional (3 D) parts directly from the computer-aided design (CAD) model. Although additive manufacturing (AM) processes have unexampled flexibility, they still have restrictions inhibiting engineers to easily generate some specific geometric shapes, easily. Some of these problems pertain to the consumption of materials as supports, the inferior surface finish of some surfaces with certain angles, etc. One of the approaches to overcome these problems is designing by segmentation.

The proposed methodology consists of two steps: (1) segmentation of the 3 D model and (2) exploring the best orientation for each segment. In the first step, engineers consider the possible number of segments and the connection method of segments. In this paper, a series of segments, called a segmentation pattern (SP), is obtained by the recognition of features and separating them automatically (or manually when needed) with one or more appropriate planes. In the second step, the best fabrication orientation should be chosen. The criteria for choosing the best SP and OPs are minimizing the support volume, building time (directly affected by segments’ height in layer-wise AM processes) and surface roughness. Both steps are performed automatically (or manually when needed) by the algorithm created based on principles of particle swarm optimization (PSO) algorithm using Visual C#.

Experimental tests show that the segmentation design improves AM processes from the aspects of building time, material consumption and the surface quality. Segmentation design empowers users of AM technologies to reduce consumption of material by decreasing the support structures, to decrease the time of building by lowering the segments height and to decrease the surface roughness.

This paper presents an original approach in efficiency improvement of AM technologies, thus bringing the AM one step closer to maturity.

Previous literature tends to combine postponement and modularity or view them as parallel factors to achieve mass customization (MC) while ignoring the sequence of a firm to design and implement operations and supply chain strategy. Based on a customer-oriented strategy and theories of organizational information processing theory, three-dimensional (3D) concurrent engineering and resource dependency, the purpose of this paper is to propose a sequential model reflecting the sequence of practices as well as an overview picture for a firm to achieve MC.

The model links three company antecedents – postponement orientation, operational alignment and information sharing, to three company supply chain practices – product and process modularity and supplier segmentation. These practices, in turn, lead to the company’s MC capabilities. The proposed model is tested with a data set collected from automotive suppliers in China and in the USA. Structural equation modeling is used to analyze the data and test the model.

The results suggest that, for suppliers to achieve MC, postponement orientation and operational alignment are vital antecedents. The results also reveal the important responsibility and role of information sharing practices in coordinating suppliers’ modularity practices.

This research provides three findings that are of value to both academicians and practitioners of supply chain management. First, this study originally proposed and empirically tested that a postponement orientation is an antecedent of product and process modularity and supplier segmentation to achieve MC in the automotive sector, contrary to the traditional view of parallel relationships for both. Second, it developed and verified measures of operational alignment and supplier segmentation for future research use. Third, the vital role of information sharing to coordinate internal and external supply chain practices to achieve MC is empirically supported.

The purpose of this paper is to present a systematic methodology for segmenting customers based on the preferred product features, its cost and worth, so as to facilitate the designer to develop a product that will simultaneously minimize product cost and maximize customer satisfaction.

Post hoc – non-overlapping – non-hierarchical approach has been used for segmentation based on preferred product features by the customers. Allocation of product features to a particular segment is carried out by considering feature cost and customer worth for that feature. Automobile car has been selected as an example to demonstrate the methodology, where features data were collected from dealers and customer worth data were obtained by random generation method.

Methodology facilitates creation of n number of homogeneous segments from a heterogeneous customer group based on the cost and worth of product features. Total product cost decreases though product variety increased due to segmentation.

The proposed approach will help designers in segmenting (grouping) heterogeneous customers based on the preferred product features so that a most compatible (matching) product configuration for each segment, especially during product consolidation stage (beginning of the maturity phase of product lifecycle) can be developed to achieve maximum customer satisfaction.

This exploratory study attempts to cast some light on the state of market segmentation and the issues faced by practitioners amongst a sample of Russian subsidiaries of Western multinational enterprises (MNEs) operating in fast‐moving consumer goods markets.

Qualitative research in the form of depth interviews with seven respondents was undertaken at five MNEs (covering 11 brands across 13 product categories) and one Russian‐owned company which had recently recruited its senior management from Coca‐Cola in Moscow. MNEs were chosen because it was believed they would have the resources to acquire such expertise and knowledge as was necessary to develop a segmentation structure.

The companies researched had indicated that, in most cases, they had not been fully satisfied with their segmentations and so were engaged in continual attempts to improve their segmentations. For the majority, need‐state segmentation was currently seen as a considerable step forward but other options were being investigated. The study shows that factors influencing the search for the better segmentation need to include the nature of the product but also the stage of development of the product category, itself resulting, in most cases, from the pattern of market development in transition economies.

The research uses only a limited number of respondents but there was a large degree of commonality in respect of the key broad issues.

The study has implications for the factors to be taken into account when designing segmentation approaches, including the nature and stage of development of the product category and the purpose of the segmentation.

As far as is known, this is the first study which addresses segmentation issues amongst Russian subsidiaries of Western FMCG multi nationals.

The purpose of this paper is to compare and validate the results of two clustering methods for the segmentation of the market for prestige cosmetics in Korea, and to draw conclusions about their general practical usability.

Segmentation schemes based on scaled data collected by questionnaire from 480 female shoppers, selected by the mall intercept procedure, were analysed for validity, model fit, definability of profiles, and usability of results.

Segmentation by traditional K‐means clustering was not judged useful, whereas segments generated by the innovative alternative of mixture regression modelling had clear marketing strategy potential.

Given the single‐country and single‐market context of the study, its outcomes and implications must be generalised cautiously.

Mixture regression can make a significant contribution to the implementation of segmentation strategies based on deliverable consumer benefits, by helping academics and practitioners to better understand, explain and predict patterns of consumer behaviour.

A segmentation model with proven validity offers a sound basis for such marketing strategies as, for example, positioning.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

Workforce diversity is becoming a crucial matter in the area of internal communication. Realizing that there are multiple brackets within the body of a workforce (i.e. internal audience), the purpose of this paper is to develop an intermediate approach to manage diversity by segmenting the internal audience.

Developing a segmentation approach for managing diversity, the authors recommended the use of a few mathematical methodologies, including the expectation-maximization algorithm, partial least squares structural equation model (PLS-SEM) methodology, and Chow test, on a surveyed data set collected from 1,236 nurses of the US healthcare system. A PLS-SEM model, including employees’ mission awareness, management’s mission fulfillment, employees’ mission fulfillment, and turnover intention, was examined with respect to two internal segments.

Using a simple set of demographic variables, the authors demonstrated a practical approach to segmenting an internal audience and showed that causal relationships in a nomological network of variables regarding mission integration are significantly different between internal segments. Based on the segmentation approach, the authors proved that managers, in an effort to gain maximum diversity, can mix and match both the centrifugal force of diversity and the centripetal force of diversity to value individuals and for mission integration in their practices, respectively.

The authors highlighted a practical matter of internal communication by connecting the concepts of diversity and internal audience segmentation. However, the generalizability of the results must be assessed in other settings.

While managing diversity involves valuing employees as individuals, the segmentation concept can function as a practical and useful intermediate tool for managing diversity. Practitioners can utilize varied sets of segmented variables according to their contexts.

The authors emphasized valuing employees as individuals and developed a managerial way to make personal differences an asset to the productivity of an organization and society.

Introducing a segmentation approach to internal communication and adopting a set of useful statistical techniques, the authors attempted to develop a unique managing model of diversity. The authors suggested a dynamic and substantial segmentation of an internal audience with a smaller set of appropriate variables in each context.

Proposes that the concept of this study regarding market segmentation is at least as applicable to international marketing as to domestic marketing. States the three‐fold purpose is: highlight the nature and importance of international segmentation; to suggest an operational approach; and to review briefly some of the possible bases for segmenting international markets. Concludes that research should be direct not only towards identifying the relevant country and customer characteristics but also towards evaluating cost and benefits anticipated from segmenting on a given base.