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Artificial intelligence (AI) has the potential to positively impact library and archives collections and services--enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. [ABSTRACT FROM AUTHOR]

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]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis, their role in automating drug delivery, and the design of an expert system that leverages these technologies to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]

This paper investigates how investments in automation affect the gender pay gap. The evidence of the effects of automation on the labor market is growing; however, little is known about the implications of automation for the gender pay gap. The data used in this paper are from a matched employer–employee dataset incorporating detailed information on firms, their imports, and employee–level data for Estonian manufacturing and service employers for the period of 2006–2018. Through the use of the imports of automation goods as a proxy for the introduction of automation at the firm level, this paper estimates the effect of automation using simple Mincerian wage equations. The causality of the effect is further validated using propensity score matching (PSM). We find that introducing automation enlarges the gender pay gap, and PSM confirms that this also has a higher causal effect on the wages of male employees than female employees. The results imply that a higher representation of women in higher-paid positions does not guarantee a reduction in the gender pay gap in the presence of automation, and appropriate measures in education and retraining are needed to tackle the effect of automation on gender inequality. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hypothesis Descriptions are a type of manuscript dedicated to the formal description of a hypothesis, as introduced in an accompanying editorial and an examplary Hypothesis Description for the Enemy Release Hypothesis that is used in invasion biology. This questionnaire provides a template for such a Hypothesis Description manuscript. The template's format was designed for simplicity to facilitate adoption, and it can be easily extended to capture additional information, e.g. instructions for falsification or generalization, taxonomic or geographic scope, etymology, or relevant information in other research fields or other languages. The template reflects the recommended structure for a Hypothesis Description manuscript in that each of its sections provides the title for a section in a Hypothesis Description manuscript and indicates whether that section is mandatory or optional. Four sections - Keywords (mandatory), Conflicts of interest (optional), Acknowledgments (optional) and References (mandatory) - are in this template filled in for the template itself but should otherwise be adjusted for the hypothesis at hand. Comments to guide authors who work on a Hypothesis Description manuscript are provided as well. [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]

Nishijin is famous for its traditional technique of cutting Japanese paper to a thickness of 0.2 to 0.3 mm and weaving it with warp and weft threads. This technique is called ' hikibaku '. In the previous paper, we reported on the creation of a new textile with a new tactile feeling by using natural cowhide instead of Japanese paper used in hikibaku. In this paper, we describe the details of the automation of the hikibaku technique that created the new textile, and conduct a durability test of the newly produced textile using artificial leather, which is of stable quality, instead of natural cowhide, and discuss its new applications. [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]

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]

Introduction: The paper introduces a novel optimal feedforward controller for Hydraulic manipulators equipped with a passive grapple, addressing the issue of sway during and after movement. The controller is specifically applied to a forwarder machine used in forestry for log-loading tasks. Methods: The controller is designed for smooth operation, low computational demands, and efficient sway damping. Customizable parameters allow adjustments to suit operator preferences. The implementation was carried out using the Amesim model of a forwarder. Results: Simulation results indicate a significant reduction in sway motions, averaging a decrease of more than 60%. This performance was achieved without the need for additional sway-detection sensors, which simplifies the system design and reduces costs. Discussion: The proposed method demonstrates versatility and broad applicability, offering a new framework for anti-sway controllers in various fields such as construction cranes, forestry vehicles, aerial drones, and other robotic manipulators with passive end-effectors. This adaptability could lead to significant advances in safety and efficiency. [ABSTRACT FROM AUTHOR]

Purpose: Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles. Design/methodology/approach: The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method. Findings: The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models. Originality/value: The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase. [ABSTRACT FROM AUTHOR]

Recent years have seen an increasing recognition of the necessity to historicize and conceptualize ecological degradation in relation to capitalist and racist regimes of exploitation, epitomized by the popular concept of the "racial Capitalocene." While constituting an important effort to move beyond a color-blind environmentalism and class analysis, many of these accounts tend to abstract away from the place- and time-determined specificities of contemporary racial capitalism. This paper reconstructs the ecological thought latently present in the work of revolutionary theorists, activists, and life-long partners Grace Lee Boggs and James Boggs to explore an alternative analysis. It shows that, as part of their analysis of automation of the capitalist production process, the Boggses developed a powerful account of environmental harm alongside social deprivation. Extrapolating their concepts of social and material "waste" and "wastelands," this paper argues that James and Grace Lee Boggs saw racist exploitation and ecological harm as intimately tied to the capitalist processes of valorization and devaluation. It shows that there emerges from their conjunctural analysis a novel theory of ecological racialization as well as some concrete implications for anti-capitalist, antiracist, and ecological struggles. [ABSTRACT FROM AUTHOR]

The advent of blockchain technology has revolutionized various sectors by providing transparency, immutability, and automation. Central to this revolution are smart contracts, which facilitate trustless and automated transactions across diverse domains. However, the proliferation of smart contracts has exposed significant security vulnerabilities, necessitating advanced analysis techniques. Data dependency analysis is a critical program analysis method used to enhance the testing and security of smart contracts. This paper introduces Sligpt, an innovative methodology that integrates a large language model (LLM), specifically GPT-4o, with the static analysis tool Slither, to perform data dependency analyses on Solidity smart contracts. Our approach leverages both the advanced code comprehension capabilities of GPT-4o and the advantages of a traditional analysis tool. We empirically evaluate Sligpt using a curated dataset of Ethereum smart contracts. Sligpt achieves significant improvements in precision, recall, and overall analysis depth compared with Slither and GPT-4o, providing a robust solution for data dependency analysis. This paper also discusses the challenges encountered, such as the computational resource requirements and the inherent variability in LLM outputs, while proposing future research directions to further enhance the methodology. Sligpt represents a significant advancement in the field of static analysis on smart contracts, offering a practical framework for integrating LLMs with static analysis tools. [ABSTRACT FROM AUTHOR]

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system's various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems. [ABSTRACT FROM AUTHOR]

The growing demand for sustainable and affordable construction, paired with labor shortages, requires more efficient tools and methodologies. Industrialized construction (IC) is one solution that has the ability to address construction efficiency by utilizing techniques such as modularization, construction automation, and building information modeling. One particular aspect of IC is Design for Manufacture and Assembly (DfMA) since the design phase determines up to 80% of the operating costs. However, DfMA is yet to be implemented in the construction industry due to some challenges in DfMA that inhibit full implementation. The majority of construction manufacturers have to regenerate the construction drawings for manufacturing which add substantial time to the schedule. Delays are caused by the lack of coordination of these components, as well as assemblies' connections, compliance with building codes, and other related policies. Although many scholarly manufacturing and assembly works have studied and reviewed various aspects, techniques, and applications of DfMA, none have looked at the design process, tools and methods, or challenges in design automation. Using a systematic literature review, this study reviews the most recent innovative design responses for manufacturing and assemblies, and tools and methods. The study then analyzes the identified challenges of DfMA automation and processes in the existing literature on the construction industry, such as the lack of autonomous design and construction manufacturing. The literature analysis suggests future studies are required to improve DfMA not only as a technique but as a design strategy, particularly during the design process and prior to the production phase. Finally, future directions are provided to lead the next generation of research to advance building construction. Significantly, the results of this research provide an up-to-date status on autonomous DfMA and contribute to the body of knowledge of IC by synthesizing the state-of-the-art of building design automation and methods for manufacture and assembly. Practical Applications: Shifting the construction work from a jobsite to a factory environment and utilizing automation are resolutions to a lack of construction efficiency. Factory automation highly relies on DfMA processes. Automating the manufacturing process itself requires advanced knowledge, particularly DfMA; which is a common term referring to both management and technology that enables designers to ease a product's material selection, cost, and factory production. Studies have addressed the state-of-the-art for DfMA, but none have looked at the design process from the early design stage, tools and methods, and the associated challenges. This paper provides a review of design automation techniques in various thematic categories including (1) design scale, from an entire building scale to a small component like a wall, (2) design parameters like structural strengths and sustainability, (3) building material types, (4) tools and methods, and (5) the challenges for implementation. The provided paper is a fundamental resource for DfMA research in construction manufacturing. Manufacturers, developers, and builders require such a resource to learn more about various aspects of the design process and associated challenges to either improve their production lines or establish new state-of-the-art factories. [ABSTRACT FROM AUTHOR]

Mobile bed to move patients in hospitals is a critical aspect in a healthcare system. Typical hospital beds often require significant manpower and impose physical stresses on caregivers. This paper explores potential solutions to improve hospital bed mobility, focusing on the integration of robotic technologies. Existing literatures on the subject are reviewed, including studies on hospital bed design structures, ergonomic considerations, and implementations of robotics. Different types of wheels suitable for robot movements are discussed, covering conventional wheels, omniwheels, mecanum wheels, swerve drives, and powered fifth wheels. Each wheel type is described and its advantages and disadvantages are highlighted. The comparison of these wheel types helps identify the most suitable option for modifying hospital beds while maintaining their maneuverability. Furthermore, the paper draws inspiration from many applications of mobile robots in other industries, such as automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), to explore potential innovations for the healthcare. By leveraging robotic technologies, hospitals can enhance efficiency and alleviate the burden on healthcare workers. [ABSTRACT FROM AUTHOR]

There is broad consensus that audio description (AD) is a modality of intersemiotic translation, but there are different views in relation to how AD can be more precisely conceptualised. While Benecke (Audiodeskription als partielle Translation. Modell und Methode, LIT, Berlin, 2014) characterises AD as 'partial translation', Braun (T 28: 302–313, 2016) hypothesises that what audio describers appear to 'omit' from their descriptions can normally be inferred by the audience, drawing on narrative cues from dialogue, mise-en-scène, kinesis, music or sound effects. The study reported in this paper tested this hypothesis using a corpus of material created during the H2020 MeMAD project. The MeMAD project aimed to improve access to audiovisual (AV) content through a combination of human and computer-based methods of description. One of the MeMAD workstreams addressed human approaches to describing visually salient cues. This included an analysis of the potential impact of omissions in AD, which is the focus of this paper. Using a corpus of approximately 500 audio described film extracts we identified the visual elements that can be considered essential for the construction of the filmic narrative and then performed a qualitative analysis of the corresponding audio descriptions to determine how these elements are verbally represented and whether any omitted elements could be inferred from other cues that are accessible to visually impaired audiences. We then identified the most likely source of these inferences and the conditions upon which retrieval could be predicated, preparing the ground for future reception studies to test our hypotheses with target audiences. In this paper, we discuss the methodology used to determine where omissions occur in the analysed audio descriptions, consider worked examples from the MeMAD500 film corpus, and outline the findings of our study namely that various strategies are relevant to inferring omitted information, including the use of proximal and distal contextual cues, and reliance on the application of common knowledge and iconic scenarios. To conclude, consideration is given to overcoming significant omissions in human-generated AD, such as using extended AD formats, and mitigating similar gaps in machine-generated descriptions, where incorporating dialogue analysis and other supplementary data into the computer model could resolve many omissions. [ABSTRACT FROM AUTHOR]

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Europeana digital library features cultural heritage collections from over 3,000 European institutions described in 37 languages. However, most textual metadata describe the records in a single language, the data providers' language. Improving Europeana's multilingual accessibility presents challenges due to the unique characteristics of cultural heritage metadata, often expressed in short phrases and using in-domain terminology. This work presents the EuropeanaTranslate project's approach and results, aimed at translating Europeana metadata records from 23 EU languages into English. Machine Translation engines were trained on a cleaned selection of bilingual and synthetic data from Europeana, including multilingual vocabularies and relevant cultural heritage repositories. Automatic translations were evaluated through standard metrics and human assessments by linguists and domain cultural heritage experts. The results showed significant improvements when compared to the generic engines used before the in-domain training as well as the eTranslation service for most languages. The EuropeanaTranslate engines have translated over 29 million metadata records on Europeana.eu. Additionally, the MT engines and training datasets are publicly available via the European Language Grid Catalogue and the ELRC-SHARE repository. [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]

This conceptual article theorises the tensioned interplay between digitalization, datafication and automation and subjectness in education by asking what intensifying datafication and automation means for teacher--student relationships and how we understand and approach education. Theoretically, the paper draws on Buber's ideas of the dialogical I--Thou and objectified I--It as the key forms of human relationships. The core argument is that increasing datafication and automation steers the teacher--student relationship towards an objectified I--It relationship instead of the dialogical I--Thou relationship, which Buber (and others such as Biesta, another main influencer of the present paper) saw as the ideal. Literature-informed examples of various forms of educational datafication and automation are provided to support and concretise the arguments. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) systems into administrative procedures can revolutionize the way processes are conducted and fundamentally change established forms of action and organization in administrative law. However, implementing AI in administrative procedures requires a comprehensive evaluation of the capabilities and limitations of different systems, including considerations of transparency and data availability. Data are a crucial factor in the operation of AI systems and the validity of their predictions. It is essential to ensure that the data used to train AI algorithms are extensive, representative, and free of bias. Transparency is also an important aspect establishing trust and reliability in AI systems, particularly regarding the potential for transparent representation in rule-based and machine-learning AI systems. This paper examines the potential and challenges that arise from integrating AI into administrative procedures. In addition, the paper offers a nuanced perspective on current developments in artificial intelligence and provides a conceptual framework for its potential applications in administrative procedures. Beyond this, the paper highlights essential framework conditions that require continuous monitoring to ensure optimal results in practice. [ABSTRACT FROM AUTHOR]

This paper examines the evolution of unemployment in a task-based model that allows for two types of technical change. One is automation, which turns labor tasks into mechanized ones. The second is addition of new labor tasks, which increases specialization, as in the expanding variety literature. The paper shows that in equilibrium the unemployment caused by automation converges to zero over time. [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]

With increasing globalisation, organisations are confronted with intensifying market competition. Consequently, organisations continually implement novel technologies to address the challenges posed by market competition. In business process management (BPM), robotic process automation (RPA) is regarded as a tool or methodology that can automate specific tasks or activities within a business process. RPA is widely employed as a standard solution in various industries to assist enterprises in automating their processes, enhancing their operational efficiency, and reducing operational costs. Nevertheless, it is prudent to consider which processes can be automated by applying RPA and the most appropriate deployment methods for RPA. A literature review reveals that process identification is the optimal method for identifying processes suitable for automation, and three approaches - process mining, process quality-based and interview-based can be considered. There are three principal models for deploying RPA: local, cloud and hybrid. The RPA managers in an organisation are thus able to select the most appropriate deployment option based on the organisation's policies, requirements, and cost considerations. Furthermore, this study identifies the critical success factors (CSFs) and analyses their relationships using a 'why-why' diagram. This study could assist organisations in a more comprehensive understanding of RPA technology, which enables more effective utilisation of RPA for enhanced process management. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation. Design/methodology/approach: This paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature. Findings: BACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security. Originality/value: To the best of the authors' knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature. [ABSTRACT FROM AUTHOR]

Background: Effective strategies for developing scholarly writing skills in postsecondary nursing students are needed. Generative artificial intelligence (GAI) tools, such as ChatGPT, for automated writing evaluation (AWE) hold promise for mitigating challenges associated with scholarly writing instruction in nursing education. This article explores the suitability of ChatGPT for AWE in writing instruction. Method: ChatGPT feedback on 42 nursing student texts from the Michigan Corpus of Upper-Level Student Papers was assessed. Assessment criteria were derived from recent AWE research. Results: ChatGPT demonstrated utility as an AWE tool. Its scoring performance demonstrated stricter grading than human raters, related feedback to macro-level writing features, and supported multiple submissions and learner autonomy. Conclusion: Despite concerns surrounding GAI in academia, educators can accelerate the feedback process without increasing their workload, and students can receive individualized feedback by incorporating AWE provided by ChatGPT into the writing process. [J Nurs Educ. 2023;62(12):721–727.] [ABSTRACT FROM AUTHOR]

This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 5th International Conference on Energy Systems, Drives and Automations (ESDA2022) held in Kolkata, India. The conference covered various interdisciplinary areas such as electrical, mechanical, computer, electronics, and medical electronics. The papers included in this special issue have undergone a rigorous review process and were selected by guest editors. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

Unit testing is a fundamental aspect of software development, ensuring the correctness and robustness of code implementations. Traditionally, unit tests are manually crafted by developers based on their understanding of the code and its requirements. However, this process can be time-consuming, errorprone, and may overlook certain edge cases. In recent years, there has been growing interest in leveraging large language models (LLMs) for automating the generation of unit tests. LLMs, such as GPT (Generative Pre-trained Transformer), CodeT5, StarCoder, LLaMA, have demonstrated remarkable capabilities in natural language understanding and code generation tasks. By using LLMs, researchers aim to develop techniques that automatically generate unit tests from code snippets or specifications, thus optimizing the software testing process. This paper presents a literature review of articles that use LLMs for unit test generation tasks. It also discusses the history of the most commonly used large language models and their parameters, including the first time they have been used for code generation tasks. The result of this study presents the large language models for code and unit test generation tasks and their increasing popularity in code generation domain, indicating a great promise for the future of unit test generation using LLMs. [ABSTRACT FROM AUTHOR]

Addressing health inequity is a central component of the Sustainable Development Goals and a priority of the World Health Organization (WHO). WHO supports countries in strengthening their health information systems in order to better collect, analyze and report health inequality data. Improving information and research about health inequality is crucial to identify and address the inequalities that lead to poorer health outcomes. Building analytical capacities of individuals, particularly in low-resource areas, empowers them to build a stronger evidence-base, leading to more informed policy and programme decision-making. However, health inequality analysis requires a unique set of skills and knowledge. This paper describes three resources developed by WHO to support the analysis of inequality data by non-statistical users using Microsoft Excel, a widely used and accessible software programme. The resources include a practical eLearning course, which trains learners in the preparation and reporting of disaggregated data using Excel, an Excel workbook that takes users step-by-step through the calculation of 21 summary measures of health inequality, and a workbook that automatically calculates these measures with the user's disaggregated dataset. The utility of the resources is demonstrated through an empirical example. [ABSTRACT FROM AUTHOR]

Process knowledge reuse technology can improve the computer numerical control (CNC) programming efficiency and shorten the production cycle for similar parts. However, current process knowledge reuse methods focus on retrieving similar machining cases based on the machining features or topological structures of a part, which failed to deeply consider the close relationship between the 3D part geometry and CNC machining process, can result in incomplete matches between the reused knowledge and CNC process. It is more obvious for the similar complex surface parts that the most existing reuse methods are hardly to integrate 3D part geometry with CNC machining process. To overcome the problem, a reuse method for similar ruled surface blades is proposed through dual mapping mechanism in this paper, which is based on the 3D solid mapping model and spatial transformation relationship. First of all, a dual mapping mechanism is constructed to transfer process knowledge from the parameter domain to physical domain for similar ruled surface blades. Secondly, the acquired process knowledge of the ruled surface blade is characterized to the process knowledge template by the multi-vector function, followed by its inheritability is analyzed for the process knowledge reuse. Finally, two similar ruled surface blades are machined in an experiment, the machined surfaces of both blades are smooth without obvious pits in no violent vibration machining, furthermore, the surface roughness value is 4.314 µm and 4.933 µm of the two similar blades, which can meet the requirement of semi-fishing, this result indicates the correctness and validity of the process knowledge reuse method presented in this paper. [ABSTRACT FROM AUTHOR]

Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities. [ABSTRACT FROM AUTHOR]

With the integration of connected devices, artificial intelligence, and heterogeneous networks in IoT-driven cyber-physical systems, our society is evolving as a smart, automated, and connected community. In such dynamic and distributed environments, various operations are carried out considering different contextual factors to support the automation of connected devices and systems. These devices often perform long-lived operations or tasks (referred to as activities) to fulfill larger goals in the connected environment. These activities are usually mutable (change states) and interdependent. They can influence the execution of other activities in the ecosystem, requiring active and real-time monitoring of the entire connected environment. Traditional access control models are designed to take authorization decisions at the time of access request and do not fit well in dynamic and connected environments, which require continuous active checks on dependent and mutable activities. Recently, a vision for activity-centric access control (ACAC) was proposed to enable security modeling and enforcement from the perspective and abstraction of interdependent activities. The proposed ACAC incorporates four decision parameters: Authorizations (A), oBligations (B), Conditions (C), and activity Dependencies (D) for an object agnostic continuous access control in smart systems. In this paper, we take a step further towards maturing ACAC by focusing on the mutability of activities (the ability of changing states of activities), activity dependencies (D) and developing a family of formal mathematically grounded models, referred to as ACAC D . We propose six practically suitable sub-models for ACAC D to support the state transition of a mutable activity incorporating the dependent activities' state-check and state-update procedures. These formal models consider the real-time mutability of activities as a critical factor in resolving active dependencies among various activities in the ecosystem. Activity dependencies can form a chain where it is possible to have dependencies of dependencies. In ACAC, we also consider the chain of dependencies while handling the mutability of an activity. We highlight the challenges (such as multiple dependency paths, race conditions, circular dependencies, and deadlocks) while dealing with a chain of dependencies, and provide solutions to resolve these challenges. We also present a proof of concept implementation of our proposed ACAC D models with performance analysis for a smart farming use case. This paper addresses the formal models' intended behavior while supporting activities' dependencies. Specifically, it focuses on developing and categorizing mathematically grounded activity dependencies into various ACAC sub-models without formal policy specification and analysis of theoretical complexities, which are intentionally kept out of the scope of this work. [ABSTRACT FROM AUTHOR]