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Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

My Turn is software used to manage several Covid-19 mass vaccination campaigns in California. The objective of this article is to describe the use of My Turn at two points of dispensing in California and comment on process improvements to reduce manual tasks of six identified processes of vaccination–registration, scheduling, administration, documentation, follow-up, and digital vaccine record–and paper. We reviewed publicly available documents of My Turn and patients vaccinated at George R. Moscone Convention Center in San Francisco and Oakland Coliseum Community Vaccination Clinic. For publicly available documents of My Turn, we examined videos of My Turn on YouTube, and documentation from EZIZ, the website for the California Vaccines for Children Program. For patients, we examined publicly available vaccination record cards on Instagram and Google. At the George R. Moscone Convention Center, 329,608 vaccines doses were given. At the Oakland Coliseum Community Vaccination Clinic, more than 500,000 vaccine doses were administered. The use of My Turn can be used to reduce manual tasks and paper for mass vaccinating patients against Covid-19. [ABSTRACT FROM AUTHOR]

It is well-known that digital learning materials influence the classroom curriculum and didactics. At the same time, few studies examine the role of the data visualizations offered by digital learning materials. Data visualizations signpost the emergence of students as data subjects who can be observed and compared on a computer screen. They thus shape teachers' socio-technical ways of seeing student learning and are likely to influence student assessments. Drawing inspiration from Science and Technology Studies (STS), this paper advances an analytical framework for eliciting the politics of data visualizations, focusing on automation, agencies, and aesthetics. The framework is exemplified through a case study of a popular Danish digital mathematics platform. In a concluding discussion, the paper considers the importance of addressing unsettled and ethical questions about the role of automated digital systems in teachers' knowledge practices. [ABSTRACT FROM AUTHOR]

This paper describes authors' experience with designing and teaching a new course on "Fundamentals of Automation Engineering", based on a hybrid Project-Based Learning approach. The proposed challenge for students was to develop a low-cost Material Handling Machine, equipped with basic control functions. The project was executed in three stages, with different activities/tasks performed during each stage, which finally led to successful completion of the challenge. Detailed course file containing syllabus, learning objectives, assessment rubric, project goals, and deliverables was provided to students at first stage. Handouts and assignments were also provided weekly to students, which required extensive use of library resources (self-learning). Feedback survey was conducted fortnightly and suggestions were considered for course improvement. In this paper, authors discuss course organization, learning and assessment activities, and perception of students. [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]

This scientific paper provides a comprehensive overview of the current state of research concerning the application of additive manufacturing (AM) technology, especially the fused filament fabrication (FFF) and fused granular fabrication (FGF) methods, where robotic manipulator systems are applied in fabrication processes. To achieve this, a Systematic Literature Review (SLR) was conducted to identify, select, and evaluate the most relevant research. A total of 63 papers sourced from the most relevant scientific database covering the period from 2019 to December 2023 were thoroughly analyzed and synthesized. The examination, analysis, and assessment of these papers provide insights about the integration of the AM processes with robot manipulator technology, presenting the types of polymers, blends, and polymeric matrix composites that are processed using this advanced technology. This paper highlights and discusses current gaps in this area and proposes future research directions, emphasizing the potential enhancements in flexibility, quality, consistency, productivity, safety, and cost-effectiveness in AM facilitated by robot-assisted systems. [ABSTRACT FROM AUTHOR]

Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in artificial intelligence (AI) technology. In today's digital era, the ability to decipher emotions from facial expressions has evolved into a fundamental mode of human interaction and communication. As a result, FER has penetrated diverse domains, including but not limited to medical diagnosis, customer feedback analysis, the automation of automobile driver systems, and the evaluation of student comprehension. Furthermore, it has matured into a captivating and dynamic research field, capturing the attention and curiosity of contemporary scholars and scientists. The primary objective of this paper is to provide an exhaustive review of FER systems. Its significance goes beyond offering a comprehensive resource; it also serves as a valuable guide for emerging researchers in the FER domain. Through a meticulous examination of existing FER systems and methodologies, this review equips them with essential insights and guidance for their future research pursuits. Moreover, this comprehensive review contributes to the expansion of their knowledge base, facilitating a profound understanding of this rapidly evolving field. In a world increasingly dependent on technology for communication and interaction, the study of FER holds a pivotal role in human‐computer interaction (HCI). It not only provides valuable insights but also unlocks a multitude of possibilities for future innovations and applications. As we continue to integrate AI and facial emotion recognition into our daily lives, the importance of comprehending and enhancing FER systems becomes increasingly evident. This paper serves as a stepping stone for researchers, nurturing their involvement in this exciting and ever‐evolving field. [ABSTRACT FROM AUTHOR]

One key role in industrial control systems (ICSs) is known as Programmable Logic Controller (PLC). However, with the development of the Internet of Things (IoT), PLCs have become exposed to an increasing number of attacks, which may cause malfunctions of the whole ICS. Thus, it is necessary to identify potential attacks on PLCs and propose effective solutions to mitigate them. Unfortunately, to date, there have not been significant efforts made to provide a detailed overview of existing works on PLC security. With such a concern in mind, in this paper, we focus on summarising PLC security from different components running at different layers of a PLC architecture. We first review the framework of PLCs; then, we discuss several models when considering PLC security. After that, we provide an overview of existing attacks on PLCs and general solutions to those issues from different perspectives. Lastly, we conclude this paper with an overview of future research areas in PLC security. [ABSTRACT FROM AUTHOR]

Requirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering. [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]

AbstractThis article offers a historical inquiry into behaviorism and its impact on standard of judgement concerning education policies. Drawing from Aldous Huxley’s reservation towards behaviorism as a scientific movement that naturalizes the role of control in human affairs, the paper maps the impact of behaviorism on economics of education. By tracing the influence of behaviorism in both rational (human capital theory) and quasi-rational (behavioral insight) economics, we draw attention to the activity of knowledge-making that describes and prescribes agency. The paper demonstrates how policy instruments and outcomes are intimately linked to assumptions about personhood, and in the case of behavioral insight, they contribute to the scope of decision-making entrusted to government and business. Utilizing a genealogical approach, the paper invites ethical reflections on the long-term implications of behavioral economics in light of automation. [ABSTRACT FROM AUTHOR]

The digital economy refers to the economic activities that emerge from connecting individuals, businesses, devices, data, and operations through digital technology. It includes online transactions across multiple sectors and technologies, such as the Internet, mobile technology, big data, and information and communications technology. The digital economy differs from a traditional economy because it relies on digital technology, online transactions, and its transformative effect on traditional industries Digital innovations such as the Internet of Things (IoT), Artificial Intelligence (AI), Virtual Reality, Blockchain, and autonomous vehicles all play a part in creating a digital economy. For this study, various papers on "Digital Economy" and "Industry 4.0" are identified from Scopus, Google Scholar and other research platforms and further studied briefly. This review paper has been developed after studying the digital economy and its needs in the Industry 4.0 Environment. The defining trends, key enablers, features and challenges associated with the digital economy towards Industry 4.0 are discussed briefly. Finally, the paper identifies and discusses the significant requirements of Industry 4.0 fulfilled through the digital economy. Today, customers are becoming aware of goods and services online and are turning to the industry for long-term solutions by deploying digital technologies. The digital economy is built on hyper connectivity, the growing interconnectedness of individuals, organisations, and machines due to the Internet, mobile technologies, and the IoT. Industry 4.0 technologies are automation, data exchange, cloud computing, robotics, big data, AI, the IoT, and other technological trends are all part of the industrial sector's digital transformation, which aims to achieve industrial objectives and intelligent manufacturing practices that engage with customers, emerging technologies, and innovation. The economy is becoming more digital, changing how products and services are provided and consumed. A new road map is being provided by Industry 4.0 services to help many industries adapt their conventional methods and support the new revolution. In the digital economy, scaled-up, integrated ecosystems that leverage software platforms to generate value, build resilience, and stimulate innovation via networked goods, assets, people, and processes rapidly replace old, linear value chains with partner participation. [ABSTRACT FROM AUTHOR]

Paperwork has always been a central part of bureaucratic work. Over the last few years, bureaucratic procedures have become increasingly standardised and digitalised. Based on interviews and ethnographic fieldwork within welfare offices in Switzerland and Belgium, we reflect on the way evidence is constructed within social policy and cases built for or against noncitizen welfare recipients in order to show how paper truths are established and challenged. The focus on digital practices within public policy implementation highlights how it contributes to enhanced control mechanisms on the implementation level and how migration law continues to guide welfare governance for noncitizens. This allows targeting of the most marginalised groups, whose rights to access state support are institutionally impeded. Through database information flows, official forms, paper reports and face-to-face meetings, we further show how a hybrid form of bureaucratic work emerges, where direct contact with the client is still key, yet highly influenced by standardisation processes. [ABSTRACT FROM AUTHOR]

AbstractRecently, the concept of mobile labour has garnered increasing attention among mobilities scholars. Yet, the preponderance of research has emphasised workers’ movements that are fairly large-scale and routes-based. This paper proposes another kind of mobility that is of equal significance—that of micro-mobilities by labour, or more accurately by their bodies. Using original research conducted through semi-structured interviews with 40 customer service agents working in an international airport in Asia, the paper examines three kinds of aesthetic labour that these workers perform alongside passengers. Enacted through various bodily motions intended to speed up aeromobile processes and augment productivity, I argue that these performances produce a (tenuous) aesthetics of assuring presence, orderly movement, and passing time. As more and more work tasks are redistributed across the airport between staff and passengers, ‘new’ automation presents an opportunity to reflect on the mobile practices being invented as self-service technologies infiltrate customer service and other work where human relations and decision-making skills are required. More broadly, it also uncovers the gendered politics of bodily comport, gaits, gestures and other micro-movements in labour (re)production in a wider age of technological change. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners. [ABSTRACT FROM AUTHOR]

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead‐times of producing high‐quality peer‐reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health‐related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health‐ and computer‐science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single‐screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health‐related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Computer numerical control printed disposable paper electrodes for electrochemical sensing. • Proof-of-concept demonstrated by detecting Pseudomonas aeruginosa virulence factor pyocyanin. • Satisfactory analytical performance and validated with gold standard. Disposable paper electrodes (PPE) have significant advantages in biosensor research considering the cost-effectiveness, simplicity, and easy batch manufacturing opportunity. An increase in communicable and non-communicable diseases demands for custom-made sensor platforms. Herein, we report an alternative method for the fabrication of PPE through simple and rapid computer numerical controlled (CNC) printing technique. The fabricated electrodes were electrochemically characterised, and by detecting antibiotic resistant pathogen, Pseudomonas aeruginosa virulence factor pyocyanin, the sensing application is demonstrated. Finally, the sensor is validated with a gold-standard culture-based assay. [ABSTRACT FROM AUTHOR]

This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen interviews of eight engineers, a model is created to understand how the company of study changed a manufacturing activity while noting obstacles identified in the process. This model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in the literature to understand critical elements found within change management. The case study aligns with both a design process and a traditional change management process defined in the literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the change management process. [ABSTRACT FROM AUTHOR]

The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English. The proposed system is developed using the RASA chatbot framework and NLP techniques to comprehend user utterances. RNN and SVM algorithms are employed to ensure smooth conversation flow and interaction with the users. To enhance scheme suggestion accuracy, a knowledge graph is created, containing relevant data on government schemes. The intent classification model achieves an accuracy of 97%, indicating its ability to accurately understand user intentions. The integration of a knowledge graph improves the accuracy of scheme identification and suggestion to users. The system automates the process of filling out government scheme forms based on user inputs. Dhvani voicebot system presents a practical solution to address the challenges faced by monolingual individuals in accessing government schemes in India. The high accuracy of intent classification and the use of a knowledge graph contribute to the system's effectiveness. The study suggests that this system can be extended to other languages. An automated tool called "Dhvani" will solve the problem of aged, illiterate and physically challenged persons filling forms in post offices and banks. Most of the schemes, pension funds, cash withdrawal, cash deposit is through these organizations. So. the tool makes the process easier for the above mention persons without the help of others. An intent recognition and interactive tool developed in Kannada Language which is widely spoken in Karnataka, India. The digital resources available in Kannada Language is very sparce. Use of technology like interactive tool, Knowledge graph, RNN and SVM are used in the development of the tool. Government scheme recommendation interactively makes the users to choose the scheme faster in an interactive way. The form is filled automatically and can be edited to rectify mistakes. [ABSTRACT FROM AUTHOR]

This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research. [ABSTRACT FROM AUTHOR]

Background: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large‐scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short‐term memory (LSTM) was used to detect grade‐text consistency. Furthermore, the automated grading was designed with the BERT‐RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large‐scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour‐intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback. Lay Description: What is already known about this topic: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning.Issues such as disagreement between peer assessors, rough assessment, and plagiarism in large‐scale online learning can decrease peer assessment reliabilityIncorporating extensive data into a training model may result in grading uncertainties. What this paper adds: Detecting the peer assessment reliability before grading is essential in the context of large‐scale online learning.This study aimed to propose and validate an automated grading model with reliability detection for the large‐scale online peer assessment, which will help improve the effectiveness of automated grading, combining the advantages of computer technology and human expertise. Implications for practice and/or policy: The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results.This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. [ABSTRACT FROM AUTHOR]

Background: The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice. Methods: In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies. Results: From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs. Conclusions: Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice. [ABSTRACT FROM AUTHOR]

Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error‐prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule‐based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has transformed the way human-computer interaction (HCI) teams are able to collaborate and coordinate in various domains, including aviation. AI's transformative capabilities can enhance teamwork, efficiency, and safety, particularly in risk management. AI's ability to process vast amounts of data and provide real-time insights enables informed decision-making and automation of repetitive tasks in aviation. By combining the strengths of AI and humans, outlined in our modified version of the 'HABA-MABA' framework, a dynamic teamwork relationship emerges, provided roles are successfully allocated. AI systems are able to act as intelligent assistants, offering timely recommendations, fostering effective communication, and facilitating coordination among crew members. Its adaptability and capacity for learning improve collaboration abilities, tailoring strategies to meet the team's specific needs. This paper explores the theories, considerations, and implications of human-AI teams in aviation, highlighting potential benefits, training recommendations, and future research directions. While human-AI teams offer numerous benefits, addressing the risks, limitations, and ethical considerations is crucial to ensuring safe and efficient operations. Future research must prioritize transparency, explainability, adaptability, and real-world testing to unlock the full potential of human-AI teams and foster successful integration across diverse domains. [ABSTRACT FROM AUTHOR]

Automation in agriculture is a growing area of research with fundamental societal importance as farmers are expected to produce more and better crop with fewer resources. A key enabling factor is robotic vision techniques allowing us to sense and then interact with the environment. A limiting factor for these robotic vision systems is their cross-domain performance, that is, their ability to operate in a large range of environments. In this paper, we propose the use of auxiliary tasks to enhance cross-domain performance without the need for extra data. We perform experiments using four datasets (two in a glasshouse and two in arable farmland) for four cross-domain evaluations. These experiments demonstrate the effectiveness of our auxiliary tasks to improve network generalisability. In glasshouse experiments, our approach improves the panoptic quality of things from 10.4 to 18.5 and in arable farmland from 16.0 to 27.5; where a score of 100 is the best. To further evaluate the generalisability of our approach, we perform an ablation study using the large Crop and Weed dataset (CAW) where we improve cross-domain performance (panoptic quality of things) from 12.8 to 30.6 for the CAW dataset to our novel WeedAI dataset, and 21.2 to 36.0 from CAW to the other arable farmland dataset. Although our proposed approaches considerably improve cross-domain performance we still do not generally outperform in-domain trained systems. This highlights the potential room for improvement in this area and the importance of cross-domain research for robotic vision systems. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to use hybrid manufacturing (HM) to overcome several drawbacks of material extrusion three-dimensional (3D) printers, such as low dimension ranging from 0.2 to 0.5 µm, resulting in a noticeable staircase effect and elevated surface roughness. Design/methodology/approach: Subtractive manufacturing (SM) through computer numerical control milling is renowned for its precision and superior surface finish. This study integrates additive manufacturing (AM) and SM into a single material extrusion 3D printer platform, creating a HM system. Two sets of specimens, one exclusively printed and the other subjected to both printing and milling, were assessed for dimension accuracy and surface roughness. Findings: The outcomes were promising, with postmilling accuracy reaching 99.94%. Significant reductions in surface roughness were observed at 90° (93.4% decrease from 15.598 to 1.030 µm), 45° (89% decrease from 26.727 to 2.946 µm) and the face plane (71% decrease from 12.176 to 3.535 µm). Practical implications: The 3D printer was custom-built based on material extrusion and modified with an additional milling tool on the same gantry. An economic evaluation based on cost-manufacturing demonstrated that constructing this dual-function 3D printer costs less than US$560 in materials, offering valuable insights for researchers looking to replicate a similar machine. Originality/value: The modified general 3D printer platform offered an easy way to postprocessing without removing the workpiece from the bed. This mechanism can reduce the downtime of changing the machine. The proven increased dimension accuracy and reduced surface roughness value increase the value of 3D-printed specimens. [ABSTRACT FROM AUTHOR]

Numerical approaches to computations typically reconstruct the numerators of Feynman diagrams in four dimensions. In doing so, certain rational terms arising from the (D − 4)-dimensional part of the numerator multiplying ultraviolet (UV) poles in dimensional regularisation are not captured and need to be obtained by other means. At one-loop these rational terms of UV origin can be computed from a set of process-independent Feynman rules. Recently, it was shown that this approach can be extended to two loops. In this paper, we show that to all loop orders it is possible to compute rational terms of UV origin through process-independent vertices that are polynomial in masses and momenta. [ABSTRACT FROM AUTHOR]