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In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

This study aims to examine how chief executive officer's (CEO) personality traits influence the corporate sustainability performance (CSP) of firms. The paper also examines the moderating effect of board power on this relationship.

Using a linguistic tool (IBM's Watson Personality Insight Service), the authors measured the personality traits of 229 CEOs from 176 firms from 2009 to 2018. Firm-level CSP are obtained from the Sustainalytics database. The hypotheses are tested using multiple regression analysis. The robustness of the results of the study is confirmed by addressing endogeneity concerns and by validating the measurement of CEO personality traits using Personality Recognizer, an alternative linguistic tool.

The results show that CEO personality traits of extraversion and neuroticism are significant predictors of CSP. The paper also identifies board power as a contingent factor that influences the suggested relationships.

Using upper echelon theory and cybernetic big five theory, this paper identifies CEO personality traits as important antecedents of corporate sustainability performance and adds to the micro-foundations of corporate sustainability literature. To the authors’ understanding, this is the first study that examines the influence of CEO personality on CSP using a comprehensive trait framework. The paper also demonstrates the usefulness of text-analytic tools to measure CEO personality traits, thereby contributing to the progress of upper echelon theory.

This study aims to explore brand meaning from a consumer perspective, identifying tangible attributes and intangible associations and their arrangement in brand meaning frameworks. Previous literature has focused on brand meaning flowing from intangible associations, and new insights are offered into the tangible attributes’ contribution to brand meaning.

A phenomenological approach was adopted, and meanings were gathered from lived experiences with consumers of local food brands. Quasi-ethnographic methods were used, including accompanied shopping trips to food fairs and local farm shops, kitchen visits and in-depth interviews in and around the county of Dorset in the south-west of England.

The findings demonstrate that tangible attributes have sensorial and functional brand meanings and are mentally processed. Both hierarchical and flatter patterned approaches are present when connecting attributes and associations. The hierarchical approach reflects both short and long laddering approaches; the flatter alternative offers an interwoven, patterned presentation.

This is a small in-depth study of local food brands, and the findings cannot be generalised across other brand categories.

Local food brand practitioners can promote relevant sensorial (e.g. taste) and functional (e.g. animal welfare) attributes. These can be woven into appropriate intangible associations, creating producer stories to be communicated through their websites and social media campaigns.

A revised brand meaning theoretical framework updates previous approaches and develops brand meaning theory. The study demonstrates that tangible attributes have meaning and hierarchical connections across tangible attributes, and intangible associations should not always be assumed. An additional patterned approach is present that weaves attributes and associations in a holistic, non-hierarchical way.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.

This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text.

The authors evaluate the performance of their deidentification system on two data sets of student-generated text. Each data set was human-annotated for PII. The authors evaluate using two approaches: per-token PII classification accuracy and a simulated reidentification attack design. In the reidentification attack, two reviewers attempted to recover student identities from the data after PII was obfuscated by the authors’ system. In both cases, results are reported in terms of recall and precision.

The authors’ deidentification system recalled 84% of student name tokens in their first data set (96% of full names). On the second data set, it achieved a recall of 74% for student name tokens (91% of full names) and 75% for all direct identifiers. After the second data set was obfuscated by the authors’ system, two reviewers attempted to recover the identities of students from the obfuscated data. They performed below chance, indicating that the obfuscated data presents a low identity disclosure risk.

The two data sets used in this study are not representative of all forms of student-generated text, so further work is needed to evaluate performance on more data.

This paper presents an open-source and automatic deidentification system appropriate for student-generated text with technical explanations and evaluations of performance.

Previous study on text deidentification has shown success in the medical domain. This paper develops on these approaches and applies them to text in the educational domain.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.