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This study aims to evaluate a human rights-informed dementia prevention program promoting better health and social care among older adults. In this study, the authors examined whether a dual-task training would improve cognition in healthy older adults.

Individuals attending the systematic health education program for older adults based in Japan were recruited for study inclusion, and divided into a dual-task training group (TG) and a control group (CG). The TG underwent 90 min of a weekly dual-task training for 12 weeks. Severity of dementia was measured using the Mini-Mental State Examination (MMSE) test. Brain function was assessed using a go/no-go task paradigm, during which cerebral blood flow was additionally measured using functional near-infrared spectroscopy to quantify oxyhemoglobin (oxy-Hb).

MMSE total score, number of errors in the go/no-go tasks and oxy-Hb values showed significant improvements in the TG.

Owing to the small number of participants allocated to the CG, the results must be interpreted with caution. Replication and further validation based on large-scale, randomized-controlled trials is warranted.

This study highlights potential benefits of incorporating an early prevention training for dementia into a human rights-friendly health education program.

This study suggests a potential means to reduce costs of social security and health care by introducing a human rights-informed dementia prevention program.

The results suggest that dual-task training may improve cognitive function in healthy older adults, thereby contributing to better health and improvement of social health care, based on a human rights-informed health education program for the prevention of dementia.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.

The aim of this paper is to generate a streamlined, transparent and effective instrument to fairly measure the contribution made by each student to a group project within a higher education context. The primary aim is to moderate the grades of underperforming students at the end of the project. There is a secondary benefit in alerting underperforming students to raise their contribution mid-task or face a potentially reduced grade at the final stage.

The development of this multi-dimensional instrument is guided by findings from previous research. The quest is to minimise the instructor's administrative work load in applying a moderation-only instrument that is open-source and available at no cost. Based on the literature, the survey instrument seeks to apply a peer-based, equitable and transparent evaluation of each member's contribution to a group task. The survey is applied at mid-task and again at end-task in order to afford underperformers the opportunity to address contribution deficits during the final phase of the project.

The instrument, called TANDEM©, offers a transparent, streamlined, equitable, confidential and practical measure of each student's contribution to a graded group task. Students whose end-task contribution falls below the group average rating receive a proportional reduction in their personal grade. Additionally, the end-task moderation instrument captures a single-item holistic measure of relative contribution that may, in the future, serve as a surrogate for the multi-dimensional measures currently in place.

TANDEM© was developed with group sizes of four or five members in mind. There is no evidence to support its application to three-person groups. Moreover, the application was applied only amongst under-graduate students. It is yet to be applied across post-graduate groups and within online learning environments. Future research into diverse cultural settings would serve to advance understanding of how moderation is perceived across borders.

Several existing group grade moderation methods propose complex algorithms that are “black box” solutions from a student's perspective. In establishing a fair, streamlined, confidential and transparent process for peer-rated moderation, TANDEM© deploys a concise instrument with a relatively small administrative load. TANDEM © may be applied to all groups or can selectively be applied to groups that report moderate, strong or extreme levels of conflict.

Students will appreciate the opportunity to rate peer contributions to group projects. This will dissipate the negative social sentiment that may arise when fellow students benefit from the work of others. Those students seeking conflict resolution within the group will value the transparent and equitable moderation of grades as well as the positive social implications that follow.

This research forms part of an ongoing quest to present a moderation instrument that fairly identifies student contribution to a group project. Whilst the solution proposed is one of many existing alternatives, its focus is on a practical moderation-only instrument that can immediately be applied to a course or major. The benefits lie in the ease of application and minimal administrative workload. This constitutes an original contribution to the individual (course or major) coordinator who seeks to apply a moderation-only instrument without having to commit to an extensive, broad-based group optimisation programme.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been conducted on the design of co-innovation platforms, there is still a need for a more comprehensive understanding of the co-innovation phenomenon. To address this gap, this research aims to identify the critical success factors of co-innovation platforms and provide an extensive analysis of the variables that determine their effectiveness.

This study presents a systematic literature review of co-innovation platforms based on an analysis of 89 articles published in 50 scholarly journals in the disciplines of information systems, marketing and business, covering the years from 2006 to 2022.

The review synthesizes the current state of scientific knowledge and groups prior studies thematically as critical success factors of co-innovation platforms. As a result, eight success factors have been identified in terms of quantity and quality of contributions. These factors include product involvement, perceived fairness, sense of community, interactive environment, employee involvement, participant diversity, assessment structure and task design.

The study consolidates existing research about the critical success of co-innovation platforms. It also provides a research framework that incorporates a diverse set of variables that can be used to assess co-innovation performance in future studies.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

This empirical study explores the profound impact of management functions on the productivity of yard cargo handling equipment within container terminals.

By closely examining crucial management aspects such as planning, organizing, leading, and controlling, a comprehensive managerial behavior framework was developed through focus group studies (FGS) and focal interviews. These qualitative methods were complemented by the distribution of questionnaires to practitioners in Vietnam. To validate the concept of management functions and analyze their influence on effective management practices for equipment efficiency, a structural equation model (SEM) technique was employed using partial least-squares estimation (PLS).

The findings of this study demonstrate that planning (PL), organizing (OR), and controlling (CT) significantly contribute to the productivity of yard cargo handling equipment, while leading (LD) does not exhibit a direct positive impact.

Theoretically, this study contributes by providing clarity to the definition, purpose, and value of management functions in the field of cargo handling equipment management. Furthermore, these research findings offer valuable insights to terminal operators and managers, enabling them to optimize their management strategies and enhance productivity levels, ultimately resulting in improved operational outcomes.

Integrated marketing communications (IMC) is possibly “the richest and most accessible service-learning experience” in the marketing curriculum (Petkus, 2000, p. 68). Yet, despite this recognition, scholars and practitioners continue to lament the pronounced theory-practice gap between how IMC is taught and the practice of it in industry (Schultz and Patti, 2009; Kerr and Kelly, 2017). This research embeds IMC practice within a classroom setting and subsequently explores student marketers' perceptions of their skill development through experiential client-based learning.

An in-depth qualitative study demonstrates the value of integrating experiential learning within an IMC course, captured through students' reflective practice.

Evidence suggests that experiential, client-based projects are suitable for fostering key practice-based skills in the classroom through students “experiencing” IMC at work. However, this is not always easy. In fact, building key skills such as leadership, motivation, communications, organisation and teamwork presents various challenges for students, whilst students appear unaware of other pertinent skills (e.g. persuasion, critical thinking) gained through exposure to “real-world” IMC tasks.

Instructors adopting experiential learning in the marketing classroom have an opportunity to actively design tasks to embed key workplace skills to bridge the theory-practice gap. Client-based projects offer fertile ground for students to experience marketing in action whilst ultimately bolstering their confidence in their workplace skills.

This research contributes to the marketing education literature and acknowledges the importance of embedding key workplace skills into the contemporary marketing curriculum. An overview of challenges and solutions for instructors seeking to adopt experiential learning via client-based projects in the IMC classroom is presented within this research.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.