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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

Decision Trees (DTs) are predictive models in supervised learning, known not only for their unquestionable utility in a wide range of applications but also for their interpretability and robustness. Research on the subject is still going strong after almost 60 years since its original inception, and in the last decade, several researchers have tackled key matters in the field. Although many great surveys have been published in the past, there is a gap since none covers the last decade of the field as a whole. This paper proposes a review of the main recent advances in DT research, focusing on three major goals of a predictive learner: issues regarding the fitting of training data, generalization, and interpretability. Moreover, by organizing several topics that have been previously analyzed in isolation, this survey attempts to provide an overview of the field, its key concerns, and future trends, serving as a good entry point for both researchers and newcomers to the machine learning community.

Starting a new position often brings significant stress. Amidst the adjustment to a new job, the increasing prevalence of gamification has revealed mixed effects on work-related factors, notably presenting an unclear impact on employee stress levels. Therefore, this article aims to explore the connection between gamification and occupational stress among new employees. The study involved 575 employees from various fields living in the United Kingdom or the United States who have been working in their new jobs for no longer than one year. The study utilized the Perceived Occupational Stress (POS) scale by Marcatto and colleagues (2021) and a questionnaire based on the GAMEFULQUEST model (Högberg, Hamari, and Wästlund, 2019) to evaluate the overall gameful experience in the work environment. Participants were also given descriptions of eight gamification elements and were asked to assess how frequently they encountered and engaged with these elements in their new roles. The obtained results showed that new employees’ limited interaction with gamification, marked by a low number of gamification elements, rare encounters, and low engagement, contributes to a prediction of higher stress experience. This trend was also observed with perceived challenges and competition in the workplace environment. Finally, gameful experiences related to guidance, social connectedness, accomplishments, and playfulness predicted lower stress scores.

Time series forecasting is a critical task that provides key information for decision-making across various fields, such as economic planning, supply chain management, and medical diagnosis. After the use of traditional statistical methodologies and machine learning in the past, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures, ranging from fundamental deep learning models to emerging architectures and hybrid approaches. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and present the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions help lower entry barriers for newcomers by providing a systematic understanding of the diverse research areas in time series forecasting (TSF), while offering seasoned researchers broader perspectives and new opportunities through in-depth exploration of TSF challenges.

This article focuses on how newcomers form social relations when settling in the UK, and the role of these relations in regards to their sense of belonging as well as access to resources that support integration. By bringing together the concept of social integration with scholarship on embedding and sociabilities of emplacement, the article demonstrates how a combination of serendipitous encounters, ‘crucial acquaintances’ and more enduring friendships with other migrants, co-ethnics and members of the majority population support migrants’ settlement. Drawing on two qualitative studies on migrant settlement, it shows the importance of social relations with other migrants during settlement, and subsequently critically reflects on how the notion of ‘bridging social capital’ has been used in policy discourse. By doing so, the article contends that the notion of ‘integration’ needs to reflect the social ‘unit’ into which migrants are supposed to integrate.

This research examines the potential downsides of divestiture socialization. We theorize that supervisor behaviors and attitudes—that is, support for authenticity and creativity expectations—moderate the different stages of a model in which newcomers' authentic self-expression mediates the negative effect of divestiture socialization on newcomer task performance, creativity, social integration, and job satisfaction. Specifically, supervisor support for authenticity allows newcomers to express their authentic self when faced with divestiture processes, and perceived supervisor creativity expectations enable them to deploy their authentic self-expressions to enhance their creativity. A time-lagged, multisource study of 142 new-comer—supervisor dyads provides support for these predictions, offering notable implications for theory and practice.

Increased international tourism in large European cities has been a growing social and political issue over the last few years. As the number of urban tourists has rapidly grown, studies have often focused on its socio-spatial consequences, commonly referred to as touristification, and have linked this to gentrification. This connection makes sense within the framework of planetary gentrification theories because the social injustices it generates in cities have a global pattern. However, gentrification is a complex process that must be analytically differentiated from tourism strategies and their effects. Whereas gentrification means a lower income population replaced by one of a higher status, touristification consists of an increase in tourist activity that generally implies the loss of residents. Strategies to appropriate and marketise culture to sustain tourismled economies can also shape more attractive places for foreign wealthy newcomers, whose arrival has been theorised as transnational gentrification. Discussions on the relationship between gentrification, transnational gentrification and touristification are essential, especially regarding how they work in transforming an urban area’s social fabric, for which Seville, Spain’s fourth largest city with an economy specialised in cultural tourism, provides a starting point. The focus is set on the processes’ timelines and similar patterns, which are tested on three consecutive scales of analysis: the city, the historic district and the Alameda neighbourhood. Through the examination of these transformations, the article concludes that transnational gentrification and touristification are new urban strategies and practices to revalorise real estate and appropriate urban surplus in unique urban areas. 在过去的几年里，欧洲大城市国际旅游业的增长已经成为一个日益突出的社会和政治问题。随着城市游客数量的快速增长，研究往往集中于其社会空间后果（这通常被称为旅游者化，touristification），并将其与绅士化联系起来。这种联系在全球绅士化理论的框架内是有意义的，因为它在城市中产生的社会不公正是一种全球规律。然而，绅士化是一个复杂的过程，必须从分析上区别于旅游战略及其影响。绅士化意味着收入较低的人口被地位较高的人口所取代，而旅游业则包括旅游活动的增加，这通常意味着居民的流失。为维持旅游业主导的经济而采取适当的文化营销策略，也可以为外国富裕的新移民创造更有吸引力的地方，他们的到来被理论上称为跨国绅士化。关于绅士化、跨国绅士化和旅游者化之间关系的讨论至关重要，特别是关于它们如何改变城市地区的社会结构，在这方面，经济以文化旅游业为重点的西班牙第四大城市塞维利亚提供了一个研究的起点。我们的研究重点放在过程的时间表和类似规律上，这些规律在三个连续的分析尺度上进行测试：城市、历史地区和阿拉梅达（Alameda）街区。通过对这些转变的考察，本文得出结论，跨国绅士化和旅游者化是在独特的城市地区稳定房地产和适当的城市剩余的新的城市战略和做法。

Collectives attempting to self-organize without relying on managerial control can leverage open, digital networks to foster information exchange and agility. But, as collectives grow, the open boundaries that enable the mobilization of participants and rapid exchange of ideas can give rise to new organizing challenges that make collective action untenable. We examine this tension by exploring how networked activists self-organize through open, digital networks to achieve shared aims without belonging to a common organization that supports their cause. With a seven-year, inductive field and archival study, we capture how activists from the Anonymous collective organized 70 protest actions while struggling to integrate newcomers and coordinate increasingly complex activities. Rather than succumb to chaos or managerial control, Anonymous learned to self-organize, gradually abandoning normative forms of control in favor of forms of architectural control. By creating a participation architecture—a sociotechnical framework that empowered technical experts and unobtrusively channeled newcomers to designated forums—networked activists enhanced their collective ability to coordinate complex, interdependent actions at scale. Our grounded theoretical model reveals how the challenges of self-organizing emerge with rapid growth and how these can be overcome by configuring architectural control.

PurposeThe purpose of this paper is to examine the effect of mentoring on newcomer well-being, as mediated by newcomer socialization and moderated by proactive personality.Design/methodology/approachData were collected at four time points in a sample of 227 newcomers. Regression analysis and bootstrapping method were used to test the hypotheses.FindingsMentoring had a positive and indirect effect on newcomer well-being through socialization. The moderated mediation analysis also revealed that proactive personality augmented the direct effect of mentoring on socialization and its indirect effect on well-being.Research limitations/implicationsOur data were collected in China, thereby limiting the generalization of the research findings. Future research can test our model in different cultural contexts.Practical implicationsOrganizations should consider establishing a mentoring program to foster newcomer socialization and achieve well-being. Within the mentoring context, cultivating newcomers to become more proactive can predict higher socialization levels, resulting in higher well-being.Originality/valuePrevious research largely focused on the development of the well-being of tenured employees. Drawing on socialization resources theory, this study focuses on the newcomer well-being and proposes the influential mechanism and boundary condition of the relationship between mentoring and newcomer well-being. It sheds light on exploring the well-being development for newcomers.

Industrial visual anomaly detection is critical for ensuring system reliability, safety, and efficiency. This paper presents a comprehensive survey of state-of-the-art anomaly detection techniques, analyzing methodologies, implementations, and recent advancements. Our survey aims to accelerate researchers’ understanding of emerging trends while providing a structured foundation for newcomers. We systematically review 196 recent papers covering five learning strategies, including fully supervised, semi-supervised, self-supervised, weakly supervised, and unsupervised approaches. This paper provides a detailed introduction to twelve industrial anomaly detection methods, revealing their theoretical foundations, technical principles, and practical applications. Additionally, we provide a detailed overview to 2D and 3D datasets for industrial visual anomaly detection. In addition, we critically analyze and summarize the experimental results, identify key performance indicators, and discuss the latest trends in the field of industrial anomaly detection. Beyond analysis, we contribute actionable insights for selecting optimal models for real-world deployment. Finally, we highlight open challenges and outline future research directions to drive innovation in this evolving field. The detailed resources are available at https://github.com/IHPCRits/IAD-Survey .