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Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

In the process of tension wire construction of transmission lines, the key state quantities such as tension, inclination and spatial position of the traction walking plate directly affect the construction safety. At present, the construction personnel use visual observation to obtain the state quantity of traction walking plate in the process of tension wire construction. This monitoring method is not only inefficient, and there are personal subjective factors of judgment errors. For this reason, this paper develops a kind of intelligent traction walking plate for tension wire construction, based on the principle of resistance strain type and gyroscope to obtain the tension and inclination of the traction walking plate, respectively, using two front cameras and two rear cameras to obtain the spatial position of the traction walking plate, and finally completing the real‐time transmission of the monitoring data through the Lora technology. The developed intelligent traction walking plate was applied in Shandong Heze Yongfeng 220 kV transmission line project on 11 October 2020, which improved the construction efficiency by 15.71% while ensuring the construction safety. [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]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

In an increasingly digital and interconnected world, the integration of advanced technologies such as robotics, artificial intelligence (AI), and service automation has become pivotal for shaping the future of various industries, including tourism. The paper investigates complex relationship between three independent variables: (robotic adoption, AI adoption, and service automation adoption) and three dependent variables: (social sustainability, economic sustainability, and environmental sustainability). Employing a quantitative research approach, the study gathered data from 608 randomly selected tourism supply chain stakeholders using the Krejcie and Morgan table to determine the sample size. Data collection was facilitated through Google Forms questionnaires, and the analysis relied on structural equation modeling. The statistical findings highlight positive direct significant relationships among these variables, as evidenced by t‐statistic values surpassing the threshold of 1.96. These values ranged from a minimum of 2.156 to a maximum of 10.083. These results suggest that by strategically integrating these technologies, tourism businesses and policymakers in Zimbabwe can enhance tourist experience, the industry's long‐term viability and its positive impact on society, the economy, and the environment. This study's outcomes provide a compelling foundation for informed decision‐making and the development of targeted strategies aimed at advancing sustainability objectives within the Zimbabwean tourism landscape. [ABSTRACT FROM AUTHOR]

The so‐called age of AI, industry 4.0, the fourth industrial revolution, etc. all attempt to conjure into existence a new technological paradigm. Should we believe the hype? This paper draws on neo‐Schumpeterian and régulation theory to widen the scope of this debate and examine techno‐economic and institutional discontinuities. In exploring these discontinuities, this paper argues, first, that growth regimes are not necessarily tenable as indicators of new paradigms, and, second, that there are (infra)structural discontinuities between the ICT/post‐Fordist era and those of the AI/platform era. Platformisation entails a distinct institutional logic, regime of accumulation (RA) and mode of social régulation (MSR). The clusters of technological and institutional changes behind this shift have not yet been sufficiently addressed by economic geography and associated literatures. In reconceptualising the shift in terms of (infra)structural discontinuity, the paper synthesises neo‐Schumpeterian and régulation theory to identify both technological and institutional changes in the régulation of capitalist accumulation. [ABSTRACT FROM AUTHOR]

5G network slicing is promising in prioritizing time‐critical protection communication in wireless networks of smart grids. However, network slicing offered by telecommunication providers encompasses all smart grid applications, lacking granularity. Smart grid automation standards provide recommendations on prioritization of protection communication to improve reliability but only for wired connections. Therefore, this paper investigates hierarchical token bucket (HTB) traffic shaping and uplink (UL) bitrate adaptation of a live video stream for prioritizing protection communication within a 5G slice. An experimental setup combines controller‐hardware‐in‐the‐loop (CHIL) with a quality of service (QoS) measurement system for validation. The system under test consists of commercial 5G networks, intelligent electronic devices (IEDs), and merging units for validation with three smart grid applications: fault location, line differential, and intertrip protection. HTB traffic shaping improves protected faults by 47.57% in congested and 1.16% in non‐congested scenarios. UL bitrate of the video stream adaptation by 2 Mbps increases protected faults by 3.69%. HTB traffic shaping even improves prioritization with a wired connection without introducing additional delays. HTB traffic shaping must be deployed in routers to maintain good QoS for critical applications. Collaboration between utilities and telecommunication providers is essential to effectively deploy 5G network slicing on smart grids. [ABSTRACT FROM AUTHOR]

Summary: One of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%. [ABSTRACT FROM AUTHOR]

When building classification models of complex systems with many classes, the traditional chemometric approaches such as discriminant analysis or soft independent modeling of class analogy often fail. Some people resort to advanced deep neural network, but this is only an option if there is access to very many samples. Another alternative often used is to build hierarchical models where subclasses are sort of peeled off one or a few at a time. Such approaches often outperform classical classification as well as deep neural network on small multi‐class problems. The downside though is that it is very cumbersome to build such hierarchies of models. It requires substantial work of a skilled person. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems. In this paper, we develop a fully automated approach for building hierarchical models and test the performance on a number of classification problems. [ABSTRACT FROM AUTHOR]

As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) and in particular dimensionality reduction methods in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non‐linear dimensionality reduction, spotlighting t‐distributed stochastic neighbor embedding (t‐SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work. [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]

The human factors and ergonomics (HFE) professional has access to a diverse and flexible toolkit of HFE methods. Clearly, HFE methods are thriving and have much to offer the researcher and practitioner communities, some of which are presented in this special issue. In this paper, Stanton et al. describe how OESD were constructed in workshops with HFE engineers, computer scientists, and automotive engineers to develop the driver-automation handover protocol for an automated vehicle simulator. [Extracted from the article]

The role played by technological change in employment trends has long been debated and investigated, but the evidence has proven to be inconclusive. This paper aims to shed light on this topic by critically reviewing a broad and heterogeneous body of literature on the employment implications of technical progress. To this purpose, it briefly discusses the main theories and models that underpin the empirical analysis and reviews the literature following two main criteria, namely, the proxy for technological change and the level of analysis. It also accounts for the effect of technical progress on both overall employment and on distinct occupational, educational and demographic groups. Particular attention is devoted to the results of some very recent studies that attempt to unfold the impact of complex automation technologies, especially robots, and to provide a preliminary account of the evolution, distribution, challenges and potential of Artificial Intelligence. [ABSTRACT FROM AUTHOR]

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data. [ABSTRACT FROM AUTHOR]

Modern product development often generates systems of high complexity that are prone to emergent behavior. The industry has a need to establish better practices to detect inherent emergent behavior when engineering such systems. Philosophers and researchers have debated emergence throughout history, tracing to the time of the Greek philosopher Aristotle (384–322 B.C.) and current literature has both philosophical and practical examples of emergence in modern systems. In this review paper, we investigate the phenomenon of emergent behavior in engineered systems. Our aim is to describe emergence in engineered systems and propose methods to detect it, based on literature. Emergence is in general explained as dynamic behavior seen at macro level that cannot be traced back to the micro level. Emergence can be known or unknown in combination with positive or negative. We find that best practices to engineer complicated systems should contain a sensible suite of traditional approaches and methods, while best practices to engineer complex systems need extensions to this considering a new paradigm using incentives to guide system behavior rather than testing it up‐front. [ABSTRACT FROM AUTHOR]

Recent scholarship on technological change highlights its negative impacts on employment and wages. However, a decade of nationally representative surveys show that Americans hold favorable views toward technology despite concerns over labor displacement. How do people cope with employment threats from a trend they consider desirable? Using a survey experiment, this paper argues that people opt to buffer domestic workers from technological threats with substitute policies against outgroups that they believe could improve wages and employment prospects. Specifically, direct cues about technological displacement make Republicans more likely to demand tighter restrictions on immigration and Democrats more likely to support higher tariffs. In other words, citizens respond to automation anxiety by blaming and penalizing groups that they consider unwelcome or objectionable, depending on their partisanship. Respondents remained reluctant to express support for technological restrictions. Thus, automation anxiety may intensify resistance to globalization, but not necessarily technology. [ABSTRACT FROM AUTHOR]

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

Background: Digital‐first assessments leverage the affordances of technology in all elements of the assessment process: from design and development to score reporting and evaluation to create test taker‐centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and technologies of a test security framework (part of a larger ecosystem; Burstein et al., 2021) that can be used to create systems that protect the integrity of test scores. Methods: We use the Duolingo English Test to exemplify the processes and technologies that are presented. This includes methods for actively detecting and deterring malicious behaviour (e.g., a custom desktop app). It also includes methods for passively detecting and deterring malicious behaviour (e.g., a large item bank created through automatic generation methods). We describe the certification process that each test administration undergoes, which includes both automated and human review. Additionally, we describe our quality assurance dashboard which leverages psychometric data mining techniques to monitor test quality and inform decisions about item pool maintenance. Results and Conclusions: As assessment developers transition to online delivery and to a design approach that places the test taker at the centre, it becomes increasingly important to take advantage of the tools and methodological advances in different fields (e.g., engineering, machine learning, psychometrics). These tools and methods are essential to maintaining the security of assessments so that the score reliability is sustained and the interpretations and uses of test scores remain valid. Lay Description: What is known about this topic?: As more and more testing programmes transition to test taker‐centric administrations, effective measures to prevent cheating and protect content are critical to ensure the validity and integrity of scores.Two of the most common forms of cheating in online testing are (a) having someone other than the person who has registered take the test, and (b) stealing content and providing it to others to assist them in achieving a higher score. What does this paper add?: In designing a test taker‐centric digital‐first assessment, a test security framework must inform decisions from end‐to‐end (i.e., registration, onboarding, communications regarding test taker behaviours, test preparation and practice, test administration, and post‐administrative activities including scoring).Security is safeguarded through active and passive design methods; active methods include having test takers attest that they will follow the rules governing testing and informing them that they will be videoed during testing; passive methods include a computer adaptive design that limits item exposure and test overlap rates, development of a large item pool using automated item generation, and applying artificial intelligence to review test administration videos to flag unauthorized behaviours for human review. Implications: With more educational and assessment programmes transitioning to online digital models, the paper presents a comprehensive review of security issues and identifies an integrated approach for preventing cheating and other unauthorized behaviours. [ABSTRACT FROM AUTHOR]

The primary methods of assessing the reliability of distribution networks comprise analytic and simulation methods. However, both approaches require the identification and computation of network topology, which precludes their expression in explicit, continuous functions, consequently impeding the incorporation of reliability constraints into planning and operational optimization models. To tackle this restriction, the present work puts forth a novel linear‐programming‐based reliability assessment method that is mathematically formulated, considering distribution automation (DA) and distributed generations (DGs), consisting of both conventional and renewable energy sources. In this paper, the clustering method and the scenario‐based method are used to model DGs. Next, a mixed integer linear programming (MILP) model, considering the DA and DGs with the System Average Interruption Duration Index (SAIDI) as the optimization objective, is proposed. Finally, the feasibility and effectiveness of the proposed method are verified in a 37‐node distribution network system. [ABSTRACT FROM AUTHOR]

Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation. [ABSTRACT FROM AUTHOR]

This paper contributes to debates on human–technologies relations and labour geographies. It thinks through how the adoption of automation is mediated by the conditioning effects of atmospheres in space. Taking the occassion of the COVID‐19 pandemic, the paper presents a case of how atmospheres are capable of determining the trajectories of automation, and providing the guiding backdrop for technological (un)development. Drawing on 40 semi‐structured interviews conducted with airport labour in Singapore in 2020 at the height of the COVID‐19 pandemic, the paper offers an analysis of how airport workers variously and viscerally capitulate to, abandon, and/or desire to collaborate with automation, in ways that are both unstable and atmospherically implicated. To the extent that these affective responses have the potential to change the course of technological and labour futures in airport infrastructures, atmospheres — especially those deliberately advocated by the state and airport management — are also a political force to be reckoned with. The paper concludes with a discussion on how a focus on atmospheres can push geographic research on automation in productive and interesting directions. It views automation not just as a collection of abstract artefacts, but projects constantly subject to the conditioning effects of invisible atmospheres. [ABSTRACT FROM AUTHOR]

In the United Kingdom and further afield, policy discourse has focused on the efficiencies technology will afford the care sector by increasing workforce capacity at a time when there are recruitment and retention issues. Previous research has explored the impact of telecare and other technologies on roles within the care sector, but issues related to job quality and the consequences of newer digital technologies that are increasingly being deployed in care settings are under researched. Through an exploration of the literature on robotics and empirical studies of telecare and mainstream 'smart' digital technology use in UK adult social care, this paper examines how these technologies are generating new forms of work and their implications for job quality, arguing the tendency to prioritise technology results in the creation 'machine babysitters' and 'fauxtomatons'. [ABSTRACT FROM AUTHOR]

This study investigated available literature on leveraging the Internet of Things (IoT) to optimize the food fermentation process. Utilizing a subjective examination strategy through bibliometric investigation, this paper assessed 44 articles between 2013 and 2022. The bibliometric study focused on publications per author, publications per country, and a research center based on co‐occurrence keywords, and research patterns. While available publications in this domain show IoT can be used to optimize the fermentation process, uptake has been slow and has been in an infant stage in the past 8 years. The scholarly articles, using the co‐occurrence of keywords, showed five (5) clusters, which are (i) implementing IoT in forecasting the fermentation process to achieve effective quality control, (ii) remotely monitoring and automating the production process to achieve effective process control, (iii) monitoring temperature in real‐time during the fermentation process, (iv) digitally storing parameters during the fermentation process using WLAN, and (v) applying middleware in the processing properties of agricultural produce. These cluster areas revealed how IoT can be used to optimize the food fermentation process. Given the growth and interest in IoT technologies in the food fermentation process, this study explored available literature to highlight available IoT technologies in developing strategies for the food fermentation process and predictive analysis of fermented food products. Practical applications: Using IoT technologies, manufacturers may optimize their regulatory procedures and product quality by monitoring food fermentation process parameters such as temperature, carbon dioxide, humidity, viscosity, and so on in real‐time. Food manufacturers may also obtain and use these real‐time data for food safety. Some cutting‐edge smart sensors and cloud‐based predictive analytics systems can equally predict stages of the fermentation process and possibly detect challenges along the process. [ABSTRACT FROM AUTHOR]

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of "right" and "wrong" examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community. [ABSTRACT FROM AUTHOR]

In pneumatic automation, downstream throttled pneumatic drives are widely used to perform motion tasks because of their low cost, robustness, and high power density. However, this drive technology is generally regarded as having a large energy saving potential. Hence, multiple researchers have proposed control strategies aiming to improve the efficiency of systems using pneumatic drives. However, most of those developments increase the system complexity and commissioning efforts, which hinders a broad application. This paper presents alternatively a simple to implement energy‐saving measure, which relies on a novel energy‐saving speed controller. Furthermore, a design method for the controller is proposed and subsequently analyzed using a lumped parameter model. [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]

The Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings. [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]

Background: Perceptual measures such as speech intelligibility are known to be biased, variant and subjective, to which an automatic approach has been seen as a more reliable alternative. On the other hand, automatic approaches tend to lack explainability, an aspect that can prevent the widespread usage of these technologies clinically. Aims: In the present work, we aim to study the relationship between four perceptual parameters and speech intelligibility by automatically modelling the behaviour of six perceptual judges, in the context of head and neck cancer. From this evaluation we want to assess the different levels of relevance of each parameter as well as the different judge profiles that arise, both perceptually and automatically. Methods and Procedures: Based on a passage reading task from the Carcinologic Speech Severity Index (C2SI) corpus, six expert listeners assessed the voice quality, resonance, prosody and phonemic distortions, as well as the speech intelligibility of patients treated for oral or oropharyngeal cancer. A statistical analysis and an ensemble of automatic systems, one per judge, were devised, where speech intelligibility is predicted as a function of the four aforementioned perceptual parameters of voice quality, resonance, prosody and phonemic distortions. Outcomes and Results: The results suggest that we can automatically predict speech intelligibility as a function of the four aforementioned perceptual parameters, achieving a high correlation of 0.775 (Spearman's ρ). Furthermore, different judge profiles were found perceptually that were successfully modelled automatically. Conclusions and Implications: The four investigated perceptual parameters influence the global rating of speech intelligibility, showing that different judge profiles emerge. The proposed automatic approach displayed a more uniform profile across all judges, displaying a more reliable, unbiased and objective prediction. The system also adds an extra layer of interpretability, since speech intelligibility is regressed as a direct function of the individual prediction of the four perceptual parameters, an improvement over more black box approaches. WHAT THIS PAPER ADDS: What is already known on this subject: Speech intelligibility is a clinical measure typically used in the post‐treatment assessment of speech affecting disorders, such as head and neck cancer. Their perceptual assessment is currently the main method of evaluation; however, it is known to be quite subjective since intelligibility can be seen as a combination of other perceptual parameters (voice quality, resonance, etc.). Given this, automatic approaches have been seen as a more viable alternative to the traditionally used perceptual assessments. What this study adds to existing knowledge: The present work introduces a study based on the relationship between four perceptual parameters (voice quality, resonance, prosody and phonemic distortions) and speech intelligibility, by automatically modelling the behaviour of six perceptual judges. The results suggest that different judge profiles arise, both in the perceptual case as well as in the automatic models. These different profiles found showcase the different schools of thought that perceptual judges have, in comparison to the automatic judges, that display more uniform levels of relevance across all the four perceptual parameters. This aspect shows that an automatic approach promotes unbiased, reliable and more objective predictions. What are the clinical implications of this work?: The automatic prediction of speech intelligibility, using a combination of four perceptual parameters, show that these approaches can achieve high correlations with the reference scores while maintaining a certain degree of explainability. The more uniform judge profiles found on the automatic case also display less biased results towards the four perceptual parameters. This aspect facilitates the clinical implementation of this class of systems, as opposed to the more subjective and harder to reproduce perceptual assessments. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

As full AI‐based automation remains out of reach in most real‐world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human‐AI systems. Our conceptual model is centered around three high‐level aspects ‐ agency, interaction, and adaptation ‐ and is developed through a multi‐step process. Firstly, an initial design space is proposed by surveying the literature and consolidating existing definitions and conceptual frameworks. Secondly, this model is iteratively refined and validated by conducting semi‐structured interviews with nine researchers in this field. Lastly, to illustrate the applicability of our design space, we utilize it to provide a structured description of selected human‐AI systems. [ABSTRACT FROM AUTHOR]

Software projects grow larger every year, which, in turn, makes the testing process harder. One of the most useful methods for testing large projects is unit‐test generation. However, some tests can repeatedly cover the same parts of the code, making it difficult to maintain a growing test codebase. In software testing, test suite minimization plays a crucial role in reducing the cost of testing and improving the efficiency of the testing process. In this paper, we provide an extensible minimization engine that detects redundant tests using one of the supported minimization algorithms without changing the coverage metrics. We also performed a comprehensive analysis of existing approaches and techniques, developed an engine structure, and implemented multiple algorithms of different kinds. Finally, we evaluated our tool on various open‐source projects to demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives: We explored the viability of using product and process features to develop machine learning classifiers that identify low‐ and high‐performing essays in a multi‐text writing task. Methods: We examined learning processes and essay submissions of 163 graduate students working on an authentic multi‐text writing assignment. We utilised learners' trace data to obtain process features and state‐of‐the‐art natural language processing methods to obtain product features for our classifiers. Results and Conclusions: Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re‐reading and planning) features as important predictors of writing quality. Major Takeaways: The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi‐text writing task to help them do well in the task. Lay Description: What is already known about this topic?: Both product and process features should be used to inform formative feedback on writing.Providing product‐ and process‐oriented feedback to learners is challenging.Automatic writing evaluation systems have mainly relied upon product features.Automated analysis of learners' trace data and their essay drafts is a promising venue. What this paper adds?: An accurate machine learning classifier that identifies low‐ and high‐scoring essays.The classifier utilized both product and process features.We obtained process features from learners' trace data in digital learning environment.We computed product features using state‐of‐the‐art text analytical methods. Implications for practice and/or policy: The classifier can be used as a part of a future automated writing evaluation system.We revealed learning processes and essay characteristics that influence performance.Based on important predictors of performance, formative feedback can be given to learners. [ABSTRACT FROM AUTHOR]

Background: Formative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task and requires the application of natural language processing methodology. In this article, we implement and evaluate multiple machine learning models for coding energy knowledge in free‐text responses of German K‐12 students to items in formative science assessments which were conducted during synchronous online learning sessions. Dataset: The dataset we collected for this purpose consists of German constructed responses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were coded for seven core ideas concerning the manifestation and transformation of energy. The data was collected from students in seventh, eighth and ninth grade. Methodology: We train various transformer‐ and feature‐based models and compare their ability to recognize the respective ideas in students' writing. Moreover, as domain knowledge and its development can be formally modeled through knowledge networks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of the descriptive accuracy of our models, we inspect what features played a role for which prediction outcome and if the models pick up on undesired shortcuts. In addition to this, we analyze how much the models match human coders in what evidence within responses they consider important for their coding decisions. Results: A model based on a modified GBERT‐large can achieve the overall most promising results, although descriptive accuracy varies much more than predictive accuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Way benchmark with an English RoBERTa‐large model, where it achieves state‐of‐the‐art results in two out of three evaluation categories. Lay Description: What is already known about this topic?: Formative assessments are needed to test and monitor the development of learners' knowledge throughout a unit to provide them with appropriate automated feedback.Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge.Assessing constructed responses in an automated fashion is a widely researched topic, but the problem is far from solved and most of the work focuses on predicting holistic scores or grades.To allow for a more fine‐grained and analytic assessment of learners' knowledge, systems which go beyond predicting simple grades are required.To guarantee that models are stable and make their predictions for the correct reasons, methods for explaining the models are required. What this papers adds?: A core topic in physics education is the concept of energy.We implement and evaluate multiple systems based on natural language processing technology for assessing learners' conceptual knowledge about energy physics using transformer language models as well as feature‐based approaches.The systems assess students' knowledge about various forms of energy, indicators for the same and the transformation of energy from form into another.As our systems are based on machine learning methodology, we introduce a novel German short answer dataset for training them to detect the respective knowledge elements within students' free‐text responses.We evaluate the performance of these systems using this dataset as well as the well‐established SciEntsBand‐3‐Way dataset and manage to achieve, to our best knowledge, new state‐of‐the‐art results for the latter.Moreover, we apply methodology for explaining model predictions to assess whether predictions are carried out for the correct reasons. Implications for practice and/or policy: It is indeed possible to assess constructed responses for the demonstrated knowledge about energy physics in an analytic fashion using natural language processing.Transformer language models can outperform more specialized feature‐based approaches for this task in terms of predictive and descriptive accuracy.Co‐occurrences of different concepts within the same responses can lead models to learn undesired shortcuts which make them unstable. [ABSTRACT FROM AUTHOR]

At the High Energy Photon Source (HEPS; Beijing, People's Republic of China), where up to 90 beamlines can be provided in the future, minimization of workload for individual beamlines and maximization of knowledge about one beamline that can be applied to other beamlines is essential to minimize the total complexity in beamline control. Presented in this paper are our efforts to achieve these goals by composing relatively simple utilities and mechanisms to automate tasks, while always remembering to keep our automation solutions simple and clear. After an introduction to our choice of basic software in EPICS-based beamline control, the issues encountered in introducing package management to EPICS modules, as well as solutions to them, are presented; the design and implementation of the packaging system is concisely discussed. After a presentation of our efforts to reduce the need for self-built multi-device EPICS 'input/output controller' (IOC) applications by providing reusable modular IOC executables, implementation of easily maintainable multi-IOC setups through the separation and minimization of each user's IOC configurations is given. The ongoing project of comprehensive beamline services at HEPS to further simplify configuration management on multiple scales, ranging from individual beamline devices to all beamlines at HEPS, is introduced. [ABSTRACT FROM AUTHOR]

The explosive growth of information has rapidly ushered people into the era of big data. Due to the large volume, high variety, and rapid velocity characteristics of big data, most traditional data mining methods developed for a centralized data analysis process cannot be applied directly. AI leverages machine intelligence to provide insights, automation, and new methods to interact with data, thereby promoting data literacy throughout the organization. Based on the theory of Big Data Cycle, this paper discusses the relationship between big data and AI and how they interact and influence each other. It adopts the integrative review research method to screen latest literature and summarizes the role of AI in different phases of big data cycle. We also provide an insight into the applications of big data and AI in three different areas, that is, social network, health care, and finance. [ABSTRACT FROM AUTHOR]

Background: Automated Writing Evaluation (AWE) systems to aid writing learning and instruction in primary and secondary education are growing increasingly popular. However, their effectiveness is hardly known. We conducted a systematic review focusing on the effects of these systems providing writing feedback to students in school settings. Objectives: Our goal was to identify and characterize AWE systems tested in the last 20 years for Grades 1–12 and examine their impact on text quality and other writing‐related outcomes. Methods: The review followed PRISMA guidelines. We identified eight studies reporting the effects on writing of six AWE systems on 1659 students 11–17 years of age. Results and conclusions: Our review supported the usefulness of AWE systems for writing learning and instruction. Except for one, all studies showed a positive effect of automated feedback in at least one writing‐related measure. The integration of AWE systems into more extensive instructional programs, the amount of writing practice provided to students, the type of the control groups, and the role of teachers are factors influencing their impact on students' writing outcomes. Relevance: Our review generally supported the value of AWE systems in the teaching/learning process of writing. A closer look into the conditions in which AWE systems are put to practice suggested that they are particularly effective when embedded into comprehensive instructional programs providing ample writing opportunities. Findings from this review expand knowledge on AWE systems as valuable tools to enhance writing in school settings. Lay Description: What is already known about this topic: The use of automated writing evaluation (AWE) systems is increasingly popular in education.Little is known about their effectiveness in school settings. What this paper adds: This paper reviews studies reporting the effectiveness of AWE systems to enhance 11‐ to 17‐year‐old students' writing.The main characteristics of the AWE systems tested in the last 20 years for school settings, and their impact on writing, are identified and discussed. Implications for practice and/or policy: Embedding AWE systems into more extensive instructional programs benefits students' writing more than using the systems in isolation.Providing students with ample opportunity to practice writing seems related to better text quality.The combination of teacher and system feedback seems to positively impact students' writing outcomes.Teachers' and students' perceived impact of use may be a critical factor. [ABSTRACT FROM AUTHOR]

Although the importance of technological change for increasing prosperity is undisputed and economists typically deem it unlikely that labour‐saving technology causes long‐term employment or income losses, people's anxiety about automation and its distributive consequences can be an important shaper of economic and social policies. This paper considers the political economy of automation, proposing that individuals in occupations more at risk of job loss due to automation have stronger preferences for government redistribution. I analyse individual‐level cross‐national data from the European Social Survey and other sources, covering up to 32 countries and more than 170,000 individuals. I find a robust positive association between occupational automation risk and preferences for redistribution. As long as the conditional (mean) independence assumption is satisfied, my estimates suggest that a one standard deviation increase in automatability increases preferences for redistribution with roughly 0.05 standard deviations, which is comparable to the difference in preferences for redistribution between women and men. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell 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.)

A new advancement in test automation is the use of natural language processing (NLP) to generate test cases (or test scripts) from natural language text. NLP is innovative in this context and promises of reducing test cases creation time and simplifying understanding for "non‐developer" software testers as well. Recently, many vendors have launched on the market many proposals of NLP‐based tools and testing frameworks but their superiority has never been empirically validated. This paper investigates the adoption of NLP‐based test automation in the web context with a series of case studies conducted to compare the costs of the NLP testing approach—measured in terms of test cases development and test cases evolution—with respect to more consolidated approaches, that is, programmable (or script‐based) testing and capture&replay testing. The results of our study show that NLP‐based test automation appears to be competitive for small‐ to medium‐sized test suites such as those considered in our empirical study. It minimizes the total cumulative cost (development and evolution) and does not require software testers with programming skills. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

In this paper, a reliability evaluation method based on the hidden Markov model (HMM) considers the influence of degradation trajectory fluctuation. To address the problem in which the performance degradation trajectory modelling of complex systems is limited by sampling time points, this method constructs an approximate degradation trajectory model based on effective discrete data points through a radial basis function to realize the degradation data expansion of small samples. Then, the average degradation rate is defined, and the fluctuation degree of the degradation trajectory is quantitatively evaluated to obtain the evaluation function of the fluctuation degree of the degradation trajectory. In addition, to determine the relationship between the fluctuation degree of the degradation trajectory and the operating state of the system, the method combines HMM with the evaluation function of the fluctuation degree of the degradation trajectory and obtains the transition probability matrix of different operating states of the system affected by the fluctuation degree of the degradation trajectory. On the basis of the probability matrix of the failure state, the reliability change curve of the system is derived. Finally, the effectiveness of the proposed method is verified via an example of a computer numerical control machine tool. [ABSTRACT FROM AUTHOR]

Creating a high‐fidelity railroad station model to match the physical details of hundreds of tracks and switches is never a trivial task. The manual modeling approach often costs engineers significant efforts and constrains the generality and extensivity of many advanced methods. Taking advantage that many stations are drawn proportionally into two‐dimensional drawing exchange format (DXF) files via state‐of‐the‐art computer‐aided design (CAD) techniques, this paper proposed an efficient solution to convert DXF files into meaningful station models. The proposed solution consists of two phases (1) converting graphic basic primitives without explicit engineering interpretations into recognizable railroad symbols and (2) modeling undirected railroad station graphs with necessary configurations such as endpoints and routes. The proposed solution is developed into a graphic user interface application with minimal user interactions and subsequently tested at several real‐world passenger stations in Asia for its validity, productivity, and applicability. [ABSTRACT FROM AUTHOR]

Publishing in journals is crucial in the creation of knowledge within the public administration field and the career advancements of individuals. Each year, more articles enter journal publishing systems, and it is becoming more difficult and time consuming for editors to secure reviewers. A range of technological applications have been developed that apply automation techniques to various aspects of the peer review process and these are explored in this paper. We find some potentially promising applications in terms of assessing aspects of quality and in identifying potential reviewers. The replacement of humans in review processes is less clear cut and there are dangers in exacerbating inequities within the field. The value of peer review is particularly important for early career researchers and building a community of scholarship in the public administration field. It is crucial we recognise this and do not lose positives of peer review processes to address some of the issues currently experienced. Points for practitioners: Academic journals help create knowledge within the field of public administration and play a role in career advancement of individual academics. Yet, there are a number of well‐established challenges with peer review processes.Recent advancements in technologies such as Artificial Intelligence offer the potential to automate some processes associated with peer review.Several automation processes are already available and in use around some areas of peer review processes and these are explored in this paper.But it is unlikely that automation will replace humans in peer review processes, and it is important that the public administration community revisits the importance of these processes and considers how these are valued and rewarded. [ABSTRACT FROM AUTHOR]

This paper studies the implications of business cycle frictions for the diffusion of permanent changes in automation. Incorporating task‐based production in different versions of the New Keynesian model reveals considerable short‐run implications. Price‐distorting nominal rigidities amplify the labor displacement and attenuate the productivity and welfare gains of automation during the transition to the new equilibrium. They exacerbate the falls in the labor income share, the job finding probability, the value of long‐term contracts, and labor market tightness. The inflation response follows a J‐curve. Frictions in capital supply and wage rigidities amplify the labor displacement and attenuate the productivity gains too. [ABSTRACT FROM AUTHOR]

Copyright of Geomechanik und Tunnelbau is the property of Wiley-Blackwell 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.)

Background: Auditory–perceptual assessment of voice is a subjective procedure. Artificial intelligence with deep learning (DL) may improve the consistency and accessibility of this task. It is unclear how a DL model performs on different acoustic features. Aims: To develop a generalizable DL framework for identifying dysphonia using a multidimensional acoustic feature. Methods & Procedures: Recordings of sustained phonations of /a/ and /i/ were retrospectively collected from a clinical database. Subjects contained 238 dysphonic and 223 vocally healthy speakers of Chinese Mandarin. All audio clips were split into multiple 1.5‐s segments and normalized to the same loudness level. Mel frequency cepstral coefficients and mel‐spectrogram were extracted from these standardized segments. Each set of features was used in a convolutional neural network (CNN) to perform a binary classification task. The best feature was obtained through a five‐fold cross‐validation on a random selection of 80% data. The resultant DL framework was tested on the remaining 20% data and a public German voice database. The performance of the DL framework was compared with those of two baseline machine‐learning models. Outcomes & Results: The mel‐spectrogram yielded the best model performance, with a mean area under the receiver operating characteristic curve of 0.972 and an accuracy of 92% in classifying audio segments. The resultant DL framework significantly outperformed both baseline models in detecting dysphonic subjects on both test sets. The best outcomes were achieved when classifications were made based on all segments of both vowels, with 95% accuracy, 92% recall, 98% precision and 98% specificity on the Chinese test set, and 92%, 95%, 90% and 89%, respectively, on the German set. Conclusions & Implications: This study demonstrates the feasibility of DL for automatic detection of dysphonia. The mel‐spectrogram is a preferred acoustic feature for the task. This framework may be used for vocal health screening and facilitate automatic perceptual evaluation of voice in the era of big data. WHAT THIS PAPER ADDS: What is already known on this subject: Auditory–perceptual assessment is the current gold standard in clinical evaluation of voice quality, but its value may be limited by the rater's reliability and accessibility. DL is a new method of artificial intelligence that can overcome these disadvantages and promote automatic voice assessment. This study explored the feasibility of a DL approach for automatic detection of dysphonia, along with a quantitative comparison of two common sets of acoustic features. What this study adds to existing knowledge: A CNN model is excellent at decoding multidimensional acoustic features, outperforming the baseline parameter‐based models in identifying dysphonic voices. The first 13 mel‐frequency cepstral coefficients (MFCCs) are sufficient for this task. The mel‐spectrogram results in greater performance, indicating the acoustic features are presented in a more favourable way than the MFCCs to the CNN model. What are the potential or actual clinical implications of this work?: DL is a feasible method for the detection of dysphonia. The current DL framework may be used for remote vocal health screening or documenting voice recovery after treatment. In future, DL models may potentially be used to perform auditory–perceptual tasks in an automatic, efficient, reliable and low‐cost manner. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) technologies become more ubiquitous for streamlining and optimizing work, they are entering fields representing organizational logics at odds with the efficiency logic of automation. One such field is journalism, an industry defined by a logic enacted through professional norms, practices, and values. This paper examines the experience of technologists developing and employing natural language generation (NLG) in news organizations, looking at how they situate themselves and their technology in relation to newswork. Drawing on institutional logics, a theoretical framework from organizational theory, we show how technologists shape their logic for building these emerging technologies based on a theory of rationalizing news organizations, a frame of optimizing newswork, and a narrative of news organizations misinterpreting the technology. Our interviews reveal technologists mitigating tensions with journalistic logic and newswork by labeling stories generated by their systems as nonjournalistic content, seeing their technology as a solution for improving journalism, enabling newswork to move away from routine tasks. We also find that as technologists interact with news organizations, they assimilate elements from journalistic logic beneficial for benchmarking their technology for more lucrative industries. [ABSTRACT FROM AUTHOR]