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The purpose of this study is to evaluate the extent to which there are communication gaps among auditors and two user groups in their understanding of the messages conveyed by the standard audit report on the financial statements (SAR). Auditors, bankers, and nonprofessional investors reviewed background information on a hypothetical company that had received a SAR. Compared to auditors, users consider the SAR to be more important in assessing fraud risk even though they assess a lower likelihood that auditors have detected fraud. Further, the SAR gives users a significantly higher level of confidence in the company's management, investment soundness, and accomplishment of strategic goals than auditors. We also find several instances where the auditors and bankers differed from the nonprofessional investors in the interpretation of technical terms in the SAR, suggesting a between-user disagreement in interpreting the technical terms. Taken together, the results suggest that there are important differences between auditors and users in their understanding of the broad messages conveyed by the SAR (i.e., roles, responsibilities, and conclusions of an audit), and those differences are not driven by disagreements in interpreting the technical terms in the SAR.

Building a good understanding of the technical terms is an essential achievement for aircraft maintenance learners for their future careers in the field of aircraft maintenance. With the recent evolution in educational technologies, teaching and learning technical terms need to be shaped differently. Most studies on technical terms acquisition have been conducted at college levels via the traditional mode. Hence, this qualitative study explored the implementation of the online learning approach as a medium to learn technical terms. Data were collected from in-depth interviews as well as online observations with fourteen first-year aircraft maintenance learners on how the online platform assists in acquiring technical terms. Additional data was obtained from the instructor teaching them. The results of the interview indicated that the learners improved on their technical terms acquisition in four significant aspects such as blended learning, group learning, the role of the lecturer and the utilisation of an online dictionary. Learners also reported that the implementation of the online learning approach enabled collaboration between the lecturer and the learner to improve their online learning experiences. The results presented a much-needed and currently lacking, view into the actual use and the online learning approach for technical terms acquisition among aircraft maintenance learners in this private university.

This contribution applies Steen’s (e.g., 2008, 2010, 2015, 2017a,b) notion of ‘deliberate metaphor’ to authentic language data from US-American academic lectures. The analysis of excerpts from these data demonstrates several problems with the concept of deliberate metaphor and its proposed ‘identification procedure’ ( , ). As the analysis shows, problems in distinguishing deliberate from non-deliberate metaphors are posed by metaphorical technical terms, the assumption of ‘idealized language users’ inherent in the identification procedure of deliberate metaphors, and the dynamics of discourse. Thus, while in its current state, deliberate metaphor can draw our attention to important uses of striking metaphors, it appears to be inadequate for the analysis of less striking cases of metaphor whose use in particular discourse contexts nevertheless suggests important communicative functions for part of the participants.

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice. This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective.

Purpose This paper aims to undertake a thematic review of academic papers on financial technology (FinTech) to identify three broad categories for the purpose of classifying extant literature. The paper summarizes the research and findings in this emerging field. Thereafter, it identifies the gaps and provides directions for further research. Simultaneously, the paper collates technical terms related to FinTech that appear repeatedly in each category and explains them. Finally, the study highlights the lessons that growing FinTech firms and their regulators can learn from the experiences of their counterparts across the globe. Design/methodology/approach A systematic review of literature consisting of 130 studies (social science research network [SSRN]-29 papers, Scopus-81, other sources-20) on FinTech is carried out in this thematic paper. Findings This thematic paper divides FinTech into three themes, i.e. financial industry, innovation/technology and law/regulation. The paper suggests that a thorough impact of FinTech on various stakeholders can be understood using three dimensions, namely, consumers, market players and regulatory front. It is noted that FinTech is in its nascent phase and is undergoing continuous development and implementation through product and process innovation, disruption and transformation. Research limitations/implications The paper reports that FinTech promises huge potential for further study by various stakeholders in the FinTech industry – from academia to practitioners to regulators. Practical implications The paper summarizes lessons that could be of significance for FinTech users, producers, entrepreneurs, investors, policy designers and regulators. Originality/value The paper is believed to add value to the understanding of FinTech in light of the emerging threats and opportunities for its various stakeholders.

This paper is devoted to the study of vocabulary units that are used in definitions of technical terms with the help of semantic network analysis. Since a semantic network represents a model of a definite system of knowledge it is claimed that it may reveal what predetermines the choice of adjacent technical terms as well as words of general vocabulary in definitions of technical terms denoting academic concepts that belong to different categories. The research demonstrates how some prototypic semantic schemes that consist of semantic relations of definite type between the adjacent technical terms of certain categories are reflected in definitions. The examples are mainly drawn from the actively developing terminologies of nanotechnology and space research as well as physics when necessary.

Exploring the key technology evolution paths in specific technological domains is essential to stimulate the technological innovation of enterprises. There have been many methods to identify the technology evolution path, but many of them still had some limitations. Firstly, many studies consider only a single type of data source without analyzing and comparing multiple data sources, which may lead to incomplete evolution paths. Secondly, the text mining methods ignore the semantic relationships between technical terms, making path tracing inaccurate. In this study, we develop an integrated approach for mapping the technology evolution paths of scientific papers and patents. To better forecast the technology development trends, the gap analysis between scientific papers and patents and the identification of potential topics are also applied. The all-solid-state lithium-ion battery technology is selected for the empirical study and the related technology evolution trends and the technology opportunities are focused on. The empirical case research results show the proposed method’s validity and feasibility. This method can be helpful for understanding and analyzing the specific technology, which provides clues for forecasting technology development trends in enterprises. Furthermore, it contributes to the coordination of research and development efforts, which provides a reference for enterprises to identify technology innovation opportunities.

Background Artificial intelligence (AI) has transformative potential in dementia care. However, for such tools to be effective, they must be designed to meet the diverse needs of patients, carers, and healthcare professionals. The LUMEN project (Large Language Model for Understanding and Monitoring Elderly Neurocognition) is developing an AI‐assisted tool for dementia assessment, which uses a Large Language Model to take structured collateral histories from a patient’s relatives or carers. Co‐production with stakeholders is integral to ensuring LUMEN is not only clinically effective but also user‐friendly and culturally relevant across different user groups. Method A series of co‐production workshops have been conducted with patients, carers, and healthcare professionals. Participants have been recruited from diverse cultural, linguistic, and digital backgrounds, with strategic partnerships formed with community organisations, including those from underserved communities. These workshops, currently in progress, focus on evaluating LUMEN’s interface, language clarity, and cultural appropriateness. Participants engage with the LUMEN prototype, providing feedback on language, interface usability, and overall user experience. Using a ‘Think Aloud’ methodology, participants articulate their immediate thoughts while interacting with the tool, allowing facilitators to capture valuable data on usability and engagement. Feedback is audio recorded, transcribed and systematically analysed using thematic analysis, identifying key themes and patterns that highlight challenges related to language, interface design, and cultural sensitivity. Result Although the workshops are ongoing, preliminary analysis has identified key areas for improvement, including interface complexity and terminology usage. Participants with lower digital literacy may experience difficulties navigating the tool and understanding some of its more technical terms. Thematic analysis of the workshop transcripts will reveal deeper insights into how specific design features impact usability, with a particular focus on the cultural appropriateness of language and accessibility for users with varying levels of technical expertise. Conclusion The LUMEN project highlights the crucial role of co‐producing artificial intelligence tools in healthcare. By integrating feedback from diverse stakeholders and community partners, LUMEN is being refined to ensure it is clinically effective, culturally sensitive, and accessible to all. This approach will not only improve dementia care but also pave the way for more inclusive, user‐friendly AI tools in healthcare.