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The Systematic Review Toolbox aims provide a web-based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a search strategy that could be regularly and automatically run to identify eligible records for the SR Toolbox, thus reducing time on task and burden for those involved. We undertook a mapping exercise to identify the PubMed IDs of papers indexed within the SR Toolbox. We then used the Yale MeSH Analyser and Visualisation of Similarities (VOS) Viewer text-mining software to identify the most commonly used MeSH terms and text words within the eligible records. These MeSH terms and text words were combined using Boolean Operators into a search strategy for Ovid MEDLINE. Prior to the mapping exercise and search strategy development, 81 software tools and 55 'Other' tools were included within the SR Toolbox. Since implementation of the search strategy, 146 tools have been added. There has been an increase in tools added to the toolbox since the search was developed and its corresponding auto-alert in MEDLINE was originally set up. Developing a search strategy based on a mapping exercise is an effective way of identifying new tools to support the systematic review process. Further research could be conducted to help prioritise records for screening to reduce reviewer burden further and to adapt the strategy for disciplines beyond healthcare.

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations--many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human-centered AI in education.

This chapter provides an overview of a novel computer-based learning environment, the Diagnostic Assessment and Achievement of College Success (DAACS), designed to generate and leverage diagnostic assessment data about college students' academic and self-regulated learning (SRL) skills to create effective success pathways for students as they transition to college life. As implemented across multiple large-scale research projects conducted in online, hybrid, and traditional brick and mortar college contexts, DAACS integrates diagnostic assessments with automated and actionable feedback, social supports (i.e., advisors), and various open educational resources (OERs), such as an SRL Lab and writing support. In this paper, we reveal two key challenges when implementing DAACS technology, specifically in relation to student SRL functioning and skills: (1) quality and level of participation among stakeholders, and (2) uptake of diagnostic data and feedback. Through illustrative examples and recommended solutions to these challenges, readers will gain insights about a few important yet thorny issues related to implementing and using SRL-enhanced technological supports in college contexts.

App-based habit building has been shown to be a good tool for forming desired habits; however, it is unclear how much individual features that are present in many apps contribute to the success of habit building. In this paper, the authors consider the influence of social support features by developing an app in which habit progress was shared with peers -- 'buddies' in the app. In the study, 38 participants created habits and monitored their progress regularly with the app over three weeks. The participants were divided into a control group without a 'buddy' and a treatment group cohort in which they were assigned to buddies based on their desired habits. With each habit repetition, the app gave feedback on the number of repetitions and the automaticity of the user's habit. The results obtained show that the reproduction of app-based intentional habit building is effective and that automaticity could be predicted by habit repetition.

Schools produce multiple products and digitization articulates with them in different ways. In this paper we expand the frame for analyzing instructional automation by examining its implications for three scholastic products -- embodied learning, grades and test scores, and the narratives that connect the two. We draw on data from interviews with 47 teachers in four full-time virtual elementary and secondary schools in the US to argue that at present most of the actual work of automation in virtual schools is focused on the production of marks and grades, and that the narrative products of digitization efforts -- routinization discourses, potentials discourse, and 'live' teaching discourse, play key roles in shaping how we understand the connections between those products and student learning.

The term "Smart Classroom" has evolved over time and nowadays reflects the technological advancements incorporated in educational spaces. The rapid advances in technology, and the need to create more efficient and creative classes that support both in-class and remote activities, have led to the integration of Artificial Intelligence and smart technologies in smart classes. In this paper we discuss the concept of Artificial Intelligence in Education and present a literature review related to smart classroom technology, with an emphasis on emerging technologies such as AI-related technologies. As part of this survey key technologies related to smart classes used for effective class management that enhance the convenience of classroom environments, the use of different types of smart teaching aids during the educational process and the use of automated performance assessment technologies are presented. Apart from discussing a variety of technological accomplishments in each of the aforementioned areas, the role of AI is discussed, allowing the readers to comprehend the importance of AI in key technologies related to smart classes. Furthermore, through a SWOT analysis, the Strengths, Weaknesses, Opportunities, and Threats of adopting AI in smart classes are presented, while the future perspectives and challenges in utilizing AI-based techniques in smart classes are discussed. This survey targets educators and AI professionals so that the former get informed about the potential, and limitations of AI in education, while the latter can get inspiration from the challenges and peculiarities of educational AI-based systems.

The digitalisation of processes is a current topic in accounting. New technologies can change activities which in turn may require different skills from accounting graduates. This paper aims to shed light on the changes that digitalisation brings about in various areas of accounting by assessing the types of activities (non-routine and routine) and corresponding competences in the context of progressing stages of digitalisation. In addition, it is analysed how different technologies are used in these activities and where their execution is placed within the supply chain. The systematic literature review shows a lack of expertise in the field of digitalisation that enables graduates and employees to successfully manage respective processes in the workplace. While routine activities are continuously being automated or digitalised, non-routine activities and the corresponding skills have a similarly increasing importance for employees in accounting as the acquisition of general digital competences.

Automated writing evaluation (AWE) systems are developed based on interdisciplinary research and technological advances such as natural language processing, computer sciences, and latent semantic analysis. Despite a steady increase in research publications in this area, the results of AWE investigations are often mixed, and their validity may be questionable. To yield a deeper understanding of the validity of AWE systems, we conducted a systematic review of the empirical AWE research. Using Scopus, we identified 105 published papers on AWE scoring systems and coded them within an argument-based validation framework. The major findings are: (i) AWE scoring research had a rising trend, but was heterogeneous in terms of the language environments, ecological settings, and educational level; (ii) a disproportionate number of studies were carried out on each validity inference, with the evaluation inference receiving the most research attention, and the domain description inference being the neglected one, and (iii) most studies adopted quantitative methods and yielded positive results that backed each inference, while some studies also presented counterevidence. Lack of research on the domain description (i.e., the correspondence between the AWE systems and real-life writing tasks) combined with the heterogeneous contexts indicated that construct representation in the AWE scoring field needs extensive investigation. Implications and directions for future research are also discussed.

This paper presents a study analyzing the perception of artistic products created by or with the support of artificial intelligence (AI). The research builds on previous studies revealing that the output of artificial creativity processes can indeed rival human-made products, satisfy consumer expectations, and derive enjoyment. However, in contrast to earlier works, the study focuses on the awareness of how AI is used during production processes (full automation, human-AI co-creation, and solely human) and product types (levels of involvement and product tangibility). A laboratory experiment was conducted to compare the value and creativity perceptions of 199 participants. Results revealed that, overall, the use of AI enhanced perceptions of process novelty. Additionally, the use of AI led to more positive evaluations of intangible and high involvement products such as songs compared to tangible and high involvement products such as paintings. This shows a decrease in perceived quality as more AI is involved in the production process. Furthermore, the level of product involvement level indicated no causal relationship to the degree of AI used in the production process.

In this paper, we present an agenda for the research directions we recommend in addressing the issues of realizing and evaluating communication in CPS instruments. We outline our ideas on potential ways to improve: (1) generalizability in Human-Human assessment tools and ecological validity in Human-Agent ones; (2) flexible and convenient use of restricted communication options; and (3) an evaluation system of both Human-Human and Human-Agent instruments. Furthermore, in order to demonstrate possible routes for realizing some of our suggestions, we provide examples through an introduction of the features of our own CPS instrument. It is a Human-Human pre-version of a future Human-Agent instrument and a promising diagnostic and research tool in its own right, as well as the first example of transforming the so-called MicroDYN approach so that it is suitable for Human-Human collaboration. We offer new alternatives for communication in addition to pre-defined messages within the test, which are also suitable for automated coding. For example, participants can send or request visual information in addition to verbal messages. As regards evaluation as a hybrid solution, not only are the pre-defined messages proposed as indicators of different CPS skills, but so are a number of behavioural patterns.

The automation of creativity measurement is a promising avenue of development, given that classic creativity assessments face challenges such as resource-intensive expert judgments, subjective creativity ratings, and biases in people's self-reports. In this paper, we present a construct validation study for CollaboUse, a test developed to deliver fully automated creativity assessments for individuals and teams, based on the testing logic of the Alternative Uses Task. The CollaboUse can be taken online, with test-takers prompted to think up many, diverse and original ideas in three minutes. A construct validation study has been hosted on Amazon Mechanical Turk (MTurk), yielding valid data from N = 103 participants. A comprehensive set of metrics was obtained via automated assessment tools (CollaboUse, Immune Defense, C-Tracer), classic expert ratings of creative performance (Alternative Uses Task, and CollaboUse test responses judged by human experts), as well as self-reports (Creative Achievement Questionnaire, BIG5 Personality Inventory). Results indicate that automated tests as available with the CollaboUse deliver similar results to expert-judged creative performance.

Creativity is now accepted as a core 21st-century competency and is increasingly an explicit part of school curricula around the world. Therefore, the ability to assess creativity for both formative and summative purposes is vital. However, the "fitness-for-purpose" of creativity tests has recently come under scrutiny. Current creativity assessments have up to five key weaknesses that create a barrier to their widespread use in educational settings. These are: (a) A lack of domain/subject specificity; (b) Inconsistency, leading to a lack of trust; (c) A lack of authenticity in classroom settings; (d) Slowness (in providing useful results); (e) High cost to administer. The aim of the present study is to explore the feasibility of the automated assessment of mathematical creativity, drawing on tools and techniques from the field of natural language processing, as a means to address these weaknesses. This paper describes the performance of a machine learning algorithm, relative to human judges, demonstrating the practicality of automated creativity assessment for large-scale, school-based assessments.

This paper explores the literature on AWE feedback, particularly its perceived impact on enhancing EFL student writing proficiency. Prior research highlighted the contribution of AWE in fostering learner autonomy and alleviating teacher workloads, with a substantial focus on student engagement with AWE feedback. This review strives to illuminate these facets and offer critical insights on AWE effectiveness, feedback quality, reliability, and usefulness. Guided by the research questions, 16 studies were selected, adopting specific inclusion criteria to assess the effectiveness of AWE in enhancing EFL learner writing performance. Recommendations and implications from the reviewed articles regarding AWE implementation were synthesized and discussed. The review concludes that AWE can improve EFL student writing skills, with varying effectiveness based on student proficiency levels. AWE provides quality feedback and can be a reliable and valuable tool. However, despite its effectiveness, human intervention is essential to maximize its outcomes and mitigate limitations.

Automatic Item Generation (AIG) refers to the process of using cognitive models to generate test items using computer modules. It is a new but rapidly evolving research area where cognitive and psychometric theory are combined into digital framework. However, assessment of the item quality, usability and validity of AIG relative to traditional item development methods lacks clarification. This paper takes a top-down strong theory approach to evaluate AIG in medical education. Two studies were conducted: Study I--participants with different levels of clinical knowledge and item writing experience developed medical test items both manually and through AIG. Both item types were compared in terms of "quality" and "usability" ("efficiency and learnability"); Study II--Automatically generated items were included in a summative exam in the content area of surgery. A psychometric analysis based on Item Response Theory inspected the validity and quality of the AIG-items. Items generated by AIG presented quality, evidences of validity and were adequate for testing student's knowledge. The time spent developing the contents for item generation (cognitive models) and the number of items generated did not vary considering the participants' item writing experience or clinical knowledge. AIG produces numerous high-quality items in a fast, economical and easy to learn process, even for inexperienced and without clinical training item writers. Medical schools may benefit from a substantial improvement in cost-efficiency in developing test items by using AIG. Item writing flaws can be significantly reduced thanks to the application of AIG's models, thus generating test items capable of accurately gauging students' knowledge.

Term extraction from textbooks is the cornerstone of many different intelligent natural language processing systems, especially those that support learners and educators in the education system. This paper proposes a novel unsupervised domain-independent model that automatically extracts relevant and domain-related key terms from a single PDF textbook, without relying on a statistical technique or external knowledge base. It only relies on the basic linguistic techniques of the natural language processing: pattern recognition, sentence tokenization, part-of-speech tagging, and chunking. The model takes a PDF textbook as an input and produces a list of key terms as an output. Furthermore, the model proposes a novel classification of sentences from which the concept of defining sentences is proposed. The defining sentences are the main textual units that the model revolves around to identify the key terms. The architecture of the proposed work consists of 21 processes distributed across three phases. The first phase consists of five processes for extracting text from a PDF textbook and cleaning it for the next phases. The second phase consists of eight processes for identifying the defining sentences and extracting them from all the textbook's sentences. The last phase consists of eight processes for identifying and extracting the key terms from every defining sentence. The proposed work was evaluated by two experiments in which two PDF textbooks from different fields are used. The experimental evaluation showed that the results were promising.

Quality control and assurance plays a fundamental role within higher education contexts. One means by which quality control can be performed is by mapping the course learning outcomes (CLOs) to the program learning outcomes (PLO). This paper describes a system by which this mapping process can be automated and validated. The proposed AI-based system automates the mapping process through the use of natural language processing. The framework underwent testing using two actual datasets from two educational programs, and the findings were promising. A testament to the potential of the suggested framework was the precision of the mapping detected (83.1% and 88.1% for the two programs, respectively) compared to the mapping performed by the domain experts. A web-based tool was created to help teachers and administrators execute automatic mappings (https://dsaluaeu.github.io/mapper.html). The data and software used in this research project can be found at the following URL: https://github.com/nzaki02/CLO-PLO.

Computational analysis methods and machine learning techniques introduce innovative ways to capture classroom interactions and display data on analytics dashboards. Automated classroom analytics employ advanced data analysis, providing educators with comprehensive insights into student participation, engagement, and behavioral trends within classroom settings. Through the provision of context-sensitive feedback, automated classroom analytics systems can be integrated into the evidence-based pedagogical decision-making and reflective practice processes of faculty members in higher education institutions. This paper presents TEACHActive, an automated classroom analytics system, by detailing its design and implementation. It outlines the processes of stakeholder engagement and mapping, elucidates the benefits and obstacles associated with a comprehensive classroom analytics system design, and concludes by discussing significant implications. These implications propose user-centric design approaches for higher education researchers and practitioners to consider.

Purpose: Industry 4.0 is characterised by the exponential pace of technological innovations compelling organisations to transform or be displaced. Industry 4.0 transformation of construction enterprises lacks systematic guidance and notable earlier studies have utilised maturity models to map transformation of enterprises. This paper proposes a conceptual maturity model for construction enterprises for business scenarios leading to Industry 4.0. Design/methodology/approach: The requirements for designing maturity models, including comparison with existing models and scientifically documenting the design process, make Systematic Literature Reviews (SLR) appropriate. Two systematic literature reviews (SLRs) are conducted to shortlist a total of 95 papers, which are subjected to subsequent content analysis. Findings: The first SLR identifies the following process categories as critical levers of industry 4.0 maturity; data management, people and culture, leadership and strategy, collaboration and communication, automation, innovation and change management. The second SLR ascertains that the existing maturity models in construction literature do not adequately correspond to Industry 4.0 business scenarios with limited emphasis on data management, automation, change management and innovation. The findings are assimilated to propose a conceptual Smart Modern Construction Enterprise Maturity Model (SMCeMM). Originality/value: The paper systematises the transformation of construction enterprises in Industry 4.0 and leads to state-of-the-art development of Industry 4.0 and maturity model research in construction. The proposed conceptual model addressed both the demands of the construction industry as well as what is required to navigate Industry 4.0 better. [ABSTRACT FROM AUTHOR]

Rapid diagnostic tests (RDTs) for point-of-care (POC) testing of infectious diseases are popular because they are easy to use. However, RDTs have limitations such as low sensitivity and qualitative responses that rely on subjective visual interpretation. Additionally, RDTs are made using paper-bound reagents, which leads to batch-to-batch variability, limited storage stability and detection of only the analytes they were designed for. This work presents the development of a versatile technology, based on short magneto-assays and inexpensive paper-based microfluidic electro-analytical devices (PMEDs). PMEDs were produced locally using low-cost equipment, they were stable at room temperature, easy to use, and provided quantitative and objective results. The devices served to detect alternatively a variety of magneto-assays, granting quantitation of streptavidin-HRP, biotinylated HRP and Pasmodium falciparum lactate dehydrogenase (Pf-LDH) in less than 25 min, using either commercial or customized screen-printed electrodes and measurement equipment. Furthermore, Pf-LDH detection in diluted lysed whole blood displayed a linear response between 3 and 25 ng mL−1, detection and quantification limits ranging between 1 and 3 ng mL−1 and 6–12 ng mL−1, respectively, and provided results that correlated with those of the reference ELISA. In short, this technology is versatile, simple, and highly cost-effective, making it perfect for POC testing. [ABSTRACT FROM AUTHOR]

In a recent article, Madelaine Ley evaluates the future of work, specifically robotised workplaces, via the lens of care ethics. Like many proponents of care ethics, Ley draws on the approach and its emphasis on relationality to understand ethical action necessary for worker wellbeing. Her paper aims to fill a research gap by shifting away from the traditional contexts in which care ethics is employed, i.e., health and care contexts and instead appropriates the approach to tackle the sociotechnicity of robotics and how caring should be integrated into non-traditional contexts. This paper comments on that of Ley’s, making the case that the author does, in fact, achieve this end while still leaving areas of potential future research open to buttressing the approach she presents. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

In recent years, the use of asynchronous discussion forums in online learning has increased rapidly. In earlier studies, content analysis is the most-used research method in exploring the discussion transcripts. However, conventional content analysis is a time-consuming task and requires experienced coders. There is a need for an automated approach to analyse the online discussion transcripts to help instructors optimise the learners' learning experiences. This article presents a systematic literature review of the automated content analysis approach in online discussion transcripts. Fifty-four relevant peer-reviewed conference and journal papers were found between January 2016 and October 2021, using the PRISMA and snowball methods. Eight measurement dimensions were studied from online learning transcripts: cognitive, social, relevance/importance, summary, pattern, behaviour, topic, and learning resources. Six theoretical frameworks were used in the selected studies, namely the Community of Inquiry, Stump's Post Classification, Arguello's Speech Acts, ICAP, Wise's Speaking Behaviour, and Bloom's Taxonomy. All selected studies are experimental, with 93% using machine learning to analyse discussion transcripts. English is the most-used language dataset, used in 78% of studies. These studies reported promising results in accuracy and precision. However, this research area still has room for improvement, especially in the reliability and generalisability of cross-domain context.

Background: Digital-first assessments leverage the affordances of technology in all elements of the assessment process--from design and development to score reporting and evaluation to create test taker-centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and technologies of a test security framework (part of a larger ecosystem; Burstein et al., 2021) that can be used to create systems that protect the integrity of test scores. Methods: We use the Duolingo English Test to exemplify the processes and technologies that are presented. This includes methods for actively detecting and deterring malicious behaviour (e.g., a custom desktop app). It also includes methods for passively detecting and deterring malicious behaviour (e.g., a large item bank created through automatic generation methods). We describe the certification process that each test administration undergoes, which includes both automated and human review. Additionally, we describe our quality assurance dashboard which leverages psychometric data mining techniques to monitor test quality and inform decisions about item pool maintenance. Results and Conclusions: As assessment developers transition to online delivery and to a design approach that places the test taker at the centre, it becomes increasingly important to take advantage of the tools and methodological advances in different fields (e.g., engineering, machine learning, psychometrics). These tools and methods are essential to maintaining the security of assessments so that the score reliability is sustained and the interpretations and uses of test scores remain valid.

Purpose: To support senior leaders and HR practitioners with building the internal leadership capabilities to oversee automation in a virtual teams environment. Design/methodology/approach: This point of view paper presents the topic of workplace automation in a virtual teams environment through contextual practitioner literature sources. Findings: Six new capabilities are suggested for the modern leader. Originality/value: This paper introduces a scarcely researched area which is of enormous relevance in the post-covid age of remote working and digital transformation agendas, alongside presenting recommendations for HR practitioners and senior leaders to build internal leadership capabilities. [ABSTRACT FROM AUTHOR]

This paper explores the following questions: What is reading all about, as our technical milieu becomes increasingly digital and our reading increasingly automated? What is entailed in closely reading a book, in studying and handling the book as an object? And what is the role of philosophy—and in reading philosophy—as we grapple with new technical modes of reading? Guided by philosopher Gilbert Simondon, this paper compares the language heuristics of large language models (LLM) with human reading practices, revealing parallel and diverging technical tactics, with the aim of increasing our understanding of how and why these algorithms are part of our technical reality. This comparison moves beyond concerns with automation and alienation, using Simondon's notions of technicity and transindividuality to philosophically analyze the nature of collaborative reading in a distraction economy, and the extent to which transformer neural network models achieve an implicit embodied or grounded sense of language-use. [ABSTRACT FROM AUTHOR]

In this interview article, we embark on a fascinating journey through time alongside the winners of the 2023 DRUID Best Paper Award. DRUID, an annual research conference renowned as the hub of cutting-edge research on innovation and the dynamics of structural, institutional, and geographic change, bestows this award on the most innovative and exceptional conference submission. As longstanding allies of DRUID, Industry and Innovation offers an exclusive peek behind the curtains, unveiling the untold stories that underlie award-winning research. In 2023, this coveted DRUID prize was awarded to a paper by Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, and Frank Neffke. Their work stands out through an impressive data collection effort and the exploration of a compelling and urgent research question – how technological change has impacted the gender pay gap. Throughout this interview, the author team takes us down memory lane, retelling the story behind their research project. On this journey through time, we trace the genesis of the authors' innovative ideas and the intricate pathways they navigated in their quest to understand the past as a means of unravelling the future of work and its implications for gender inequality in the labour market. This journey not only takes us back in time but also points to potential avenues for future research and open questions that lie ahead. [ABSTRACT FROM AUTHOR]

What drives investment in automation technologies? This paper documents a positive relationship between labor-friendly institutions and investment in industrial robots in a sample of advanced and developing economies. Institutions explain a substantial share of cross-country variation in automation. The relationship between institutions and robots is stronger in sunk cost-intensive industries, where producers are vulnerable to holdup. The result suggests that one reason for producers to invest in automation is to thwart rent appropriation by labor. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

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In automated essay scoring (AES) systems, similarity techniques are used to compute the score for student answers. Several methods to compute similarity have emerged over the years. However, only a few of them have been widely used in the AES domain. This work shows the findings of a ten-year review on similarity techniques applied in AES systems and discusses the efficiency and limitations of current methods. In the final review, thirty-four (34) articles published between 2010 and 2020 were included. The metrics used to evaluate the performance of the AES systems are also elaborated. The review was conducted using the Kitchenham method, whereby three research questions were formulated and a search strategy was developed. Research papers were chosen based on pre-defined inclusion and quality assessment criteria. This review has identified two types of similarity techniques used in AES systems. In addition, several methods were used to compute the score for student answers in the AES systems. The similarity computation in AES systems is dependent on several factors, hence many studies have combined multiple methods in a single system yielding good results. In addition, the review found that the quadratic weighted kappa (QWK) was most frequently used to evaluate AES systems.

Checking essays written by students is a very time consuming task. Besides spellings and grammar, they also need to be evaluated on their semantic content such as cohesion, coherence, etc. In this study we focus on one such aspect of semantic content which is the topic of the essay. Putting it formally, given a prompt or essay-statement and an essay, this study aims to address the problem of predicting whether the essay is off-topic or not using machine learning techniques. With an increase in online learning and evaluation platforms especially during the COVID-19 pandemic, the off-topic detection system can be very useful to check essays that are mainly submitted online. In this paper, we answer the question: given a prompt and an essay written in Pakistani English, can the process of detecting whether the essay is off-topic or not be reliably and completely autonomized with zero human intervention using currently available tools and techniques? For this purpose, we explore and implement various embedding techniques proposed in recent years to extract similarity or dissimilarity features between question and response, and compare the performance of these techniques using 10 benchmark data sets and 6 classifiers. With different classifiers and different datasets along with different embeddings, we conclude that combining word mover distance, average word embeddings and idf weighted word embeddings together and then using random forest as the classifier is the best combination for off-topic essay detection. The accuracy obtained is 93.5%.

Electric motor development is a challenging task, as higher efficiency requirements and various interdependencies between different engineering domains must be considered. Established design approaches often lack the ability to address these interdependencies because they focus on specific domains and properties. Automated, multidisciplinary design approaches hold untapped potential for optimizing motors in terms of diverse requirements and advancing the development of more efficient and reliable motors. This paper presents a systematic literature review of the current state of research in the multidisciplinary design automation of electric motors. The literature basis comprises 1005 publications that are identified by a systematic internet search. The review of the existing approaches is based on twelve criteria that characterize the design automation task in general, such as knowledge representation or reasoning methods used, as well as criteria specific to electric motor design, such as domains considered and their coupling. The analysis reveals what current approaches are lacking: Consequent analysis and integration of domains, applicability of suggested methods, incorporation of established multidisciplinary design optimization (MDO) architectures, alongside the consideration of passive components in the motor. Aside from the introduction of twelve criteria for systematic charaterization of multidisciplinary design automation of electric motors, this article expands the state of the art by proposing an initial framework to establish process chains tackling the identified gaps in the review. [ABSTRACT FROM AUTHOR]

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible's powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI). [ABSTRACT FROM AUTHOR]

This paper deals with the use of automation in rail transport to provide transfer links in public passenger transport. The methods of railway transport management in the Czech Republic are presented and their suitability for the use of automation in regional passenger transport is assessed. The authors consider the impact of automation only from a technological point of view. In this paper, the authors propose a modification of the train departure using a layout criterion and time stops to provide transfer links using the Automatic Route Setting System. The authors consider the use of the Real Time Rescheduling considering the traffic on the railway line, as well as the traffic connections of the public line transport. The paper proposes the use of automation software to improve the quality of public passenger transport. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore the past and future impacts of automation on family businesses, with a focus on the opportunities for human capital empowerment. Design/methodology/approach: This paper draws upon a contemporary literature search to examine a range of scholarly and practitioner perspectives of the challenges and benefits of automation, exploring the evolvement towards hyperautomation and the empowerment of human capital in family businesses. Findings: Automation, transforming to hyperautomation, general purpose artificial intelligence (AI) and beyond has the possibility of radically improving productivity. Fear of job obsolescence has been present since the birth of modern automation, and whilst some jobs are at risk of redundancy, a net gain towards higher-skilled labour is already evident. Family business leaders must be prepared to react appropriately to the accelerating war for talent by implementing a strategy for human capital empowerment. Originality/value: This unique paper synthesises developments in automation and proposes a future perspective centred upon the empowerment of human capital in family businesses. [ABSTRACT FROM AUTHOR]

Rubber industry is one of the major industries in Malaysia. Rubber stamping machine is a machine that cuts rubber sheets into desired shapes and dimensions, facing challenges due to the elastic properties of rubber sheets. These challenges include long process time and non-identical dimension. This review paper focuses on the design of the rubberstamping machine to address the challenges by reducing process time and producing identical dimensions products. The rubber-stamping machine was fabricated, and analysis was performed to verify its efficiency. The core of the designs is to ensure the user safety, friendliness, and increase productivity and product consistency. Based on the findings from existing studies, the review highlights significant improvements in machine design and operational efficiency. The paper also discusses the impact of these innovations on the competitiveness of rubber stamping operations and provides insights into future directions for research in mechanical design and automation systems. This review can be a crucial resource for developers and manufacturers looking to enhance the efficiency and product quality of rubber-stamping machines, contributing to the advancement of manufacturing practices in the rubber industry. [ABSTRACT FROM AUTHOR]

Virtual commissioning (VC), defined as the simulation and testing of systems in a virtual environment before physical implementation, plays a key role in addressing the challenges of integrating and validating complex systems efficiently and effectively. This paper focuses on the topic of virtual commissioning, summarizing and organizing existing research in the field. The paper provides a comprehensive overview of various design methods and technologies currently in use. A case study of virtual commissioning is also presented within the area of the Cyber-Physical Manufacturing Systems Laboratory of the Széchenyi István University, detailing the solution steps taken. Drawing on both research and practical experience, the paper proposes a novel framework to support virtual commissioning design, referred to as the "Virtual Commissioning House" (VCH). The methodology is evaluated through comparisons with existing virtual commissioning solutions, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

The closed architecture of modern control systems impedes them from further development in the environment of the industrial IoT. The open control system is proposed to tackle this issue. Numerous open control prototypes have been proposed, but they do not reach high openness. According to the definition and criteria of open control systems, this paper suggests that the independence between control tasks and the independence between control tasks and infrastructures are the keys to the open control system under the industrial IoT. Through the control domain's formal description and control task virtualization to deal with the keys, this paper proposes a new method to implement open control systems under the industrial IoT. Specifically, given the hybrid characteristic of the control domain, a hierarchical semantic formal based on an extended finite state machine and a dependency network model with the time property is designed to describe the control domain. Considering the infrastructure's heterogeneity in the industrial IoT, a hybrid virtualization approach based on containers and WebAssembly is designed to virtualize control tasks. The proposed open control system implementation method is illustrated by constructing an open computer numerical control demonstration and compared to current open control prototypes. [ABSTRACT FROM AUTHOR]