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Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely underexplored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments. To bridge this gap, this paper introduces the Dynamic Dataset Generator (DDG). DDG features multiple dynamic Gaussian components integrated with a range of heterogeneous, local, and global changes. These changes vary in spatial and temporal severity, patterns, and domain of influence, providing a comprehensive tool for simulating a wide range of dynamic scenarios.

Objective: This study discusses on 2 aspects. First this article is aimed to give an outlook on V-Commerce as a new marketplace and its business potential. Second this article examines the Impact of V-Commerce and the factors influencing the Consumer Buying Behaviour.   Method: Data from 128 respondents in Salem who have experience with V-Commerce were collected as part of the research using convenience sampling. The GARRETT Ranking method was used to do the analysis of the data.   Result: The study resulted that “Security” and “Virtual Experience” are the major factors which influences the consumers in online shopping.   Conclusion: In today's Digitally driven world, businesses are transitioning from brick business to click businesses. Virtual Reality (VR) has attracted a lot of attention and is now quickly emerging as a disruptive marketing tool in various sectors. V-Commerce has long been regarded as a very viable option for both individuals and businesses looking to expand their customer base.

This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG). These instances cover a broad spectrum of problem features, including varying degrees of modality, ruggedness, symmetry, conditioning, variable interaction structures, basin linearity, and deceptiveness. Purposefully designed, this test suite offers varying difficulty levels and problem characteristics, facilitating rigorous evaluation and comparative analysis of optimization algorithms. By presenting these problems, we aim to provide researchers with a structured platform to assess the strengths and weaknesses of their algorithms against challenges with known, controlled characteristics. For reproducibility, the MATLAB source code for this test suite is publicly available.

As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives, such as the systematic evaluation of algorithms under controllable conditions. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization. Unlike existing approaches that rely on multiple baseline functions and transformations, GNBG utilizes a single, parametric, and configurable baseline function. This design allows for control over various problem characteristics. Researchers using GNBG can generate instances that cover a broad array of morphological features, from unimodal to highly multimodal functions, various local optima patterns, and symmetric to highly asymmetric structures. The generated problems can also vary in separability, variable interaction structures, dimensionality, conditioning, and basin shapes. These customizable features enable the systematic evaluation and comparison of optimization algorithms, allowing researchers to probe their strengths and weaknesses under diverse and controllable conditions.

Purpose: The purpose of this paper is to shed light on the elements that affect college instructors' worries about hierarchies. The measurable device utilized in this exploration is chi square and GARRETT positioning. The investigation came about that numerous educators feel that they will have peaceful work life and they will be able to manage work life if they are given proper awareness and training on handling Emotional Intelligence. Theoretical Framework: Emotional intelligence in teachers has been shown to influence their comfort level, self-efficacy, job happiness, and interpersonal interactions with pupils. Therefore, emotional intelligence has a direct impact on how pupils are taught and learn (Jennings & Greenberg, 2009). Design/methodology/approach: Data was Collected from 500 Respondents for this study using disproportionate stratified random sampling technique Findings: Analysis points that college teachers say EI “helps them to have a stress free work life and have proper balance if they are given awareness and training on Emotional Intelligence”. Future Implications: In order to ascertain the direct and indirect effects of stakeholder demands, awareness on the work life imbalance and training must be given in regular intervals, we first propose that future research make use of our framework and conduct research on teachers to train special children’s Originality Value: The Research Focuses on how work life imbalance and Emotional Intelligence are the key factors among the college teachers. This research will be reference for how Emotional Intelligence can make teachers to balance their work life

To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based approach called variable functioning Fx is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subset of variables are defined with functions using information prior to optimization; thus, instead of modifying the variables in the search process, the function variables are optimized. By using problem structure analysis technique and engineering expert knowledge, the $Fx$ method is used to enhance the steel frame design optimization process as a complex real-world problem. The proposed approach is coupled with particle swarm optimization and differential evolution algorithms and used for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that $Fx$ can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.

This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set of 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization. Although its primary purpose is to provide a coherent basis for running competitions, its generality allows the interested reader to use this document as a guide to design customized problem instances to investigate issues beyond the scope of the presented benchmark suite. To this end, we explain the modular structure of the GMPB and how its constituents can be assembled to form problem instances with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning., Comment: arXiv admin note: text overlap with arXiv:2106.06174

This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes with a broad spectrum of characteristics, offering everything from unimodal to highly multimodal landscapes and ranging from symmetric to highly asymmetric configurations. The landscapes also vary in texture, from smooth to highly irregular surfaces, encompassing diverse degrees of variable interaction and conditioning. This document delves into the intricacies of GMPB, detailing the myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics. GMPB's MATLAB implementation is available on the EDOLAB Platform., Comment: This is the support document for CEC 2024 competition on Dynamic Optimization Problems

While research on EFL teachers’ beliefs and the realization of these beliefs in their classroom practices has recently gained momentum in the field of applied linguistics, the study of teachers’ beliefs as they relate to listening has received insufficient attention in the literature. This study was conducted to investigate Iranian EFL teachers’ beliefs about listening and their beliefs-driven instructional practices. To this end, a listening beliefs questionnaire was administered to a total of 85 teachers (BA= 49, MA= 36), followed by classroom observation of 12 teachers (6 teachers per group) who were given an audio to teach. The results revealed that there was no significant difference between BA and MA teachers regarding their listening beliefs and beliefs-driven practices. The results of the Phi coefficient of correlation indicated that there was no significant relationship between teachers’ beliefs about listening instruction and their listening instructional practices. Furthermore, the results of the interview showed that time, besides other impediments, was the major obstacle for teachers to actualize their listening beliefs. The implications of the study for teacher education are discussed.

The present study reports on the thematic analysis of Iranian EFL teachers' perceptions of dynamic assessment in relation to their academic degree and length of service. To this end, 42 Iranian EFL teachers participated in the study. Of these teachers, 22 held BA and 20 held MA degrees in ELT-related subjects, with varying lengths of service. Semi-structured interviews were used to inquire into the teachers' perceptions of dynamic assessment. The four major themes which emerged from the content analysis of the audiotaped interviews revealed significant variations in participants' patterns of perceptions and concerns towards dynamic assessment. The major themes comprised of teachers' understanding of dynamic assessment as a classroom practice, viewing their own agency in application of dynamic assessment, the place of learners in this practice, and their awareness about contextual constraints affecting application of dynamic assessment. Suggestions for further research as well as limitations of the study are also discussed.

Despite the importance of teacher efficacy, there has been little research on the effects of interventions intended to increase it. Thus, the present study considered the potential of Professional Development (PD) in enhancing teachers’ beliefs about their teaching ability. The study was quantitative in nature and utilized the reliable survey instrument known as “Teacher Sense of Efficacy Scale”. Two groups of English as a Foreign language EFL teachers (an experimental group and a convenience sample of control teachers) were surveyed in the study in a Pre-test Post-test (and delayed Post-test) Control Group Design. After administering a Pre-test on self-efficacy which indicated no significant difference between the two groups, the treatment teachers received three 16-session courses during which they were provided with opportunities for PD using five PD models including In-service Training, Fellow Observation/Assessment, Development/Improvement Process, Mentoring, and Study Groups. The two groups were then compared on the post- and delayed post-tests which showed that the treatment teachers obtained significantly higher efficacy scores than the control group of teachers.