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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]

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]

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]

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 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]

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]

One of the key strategies for radiochemical research facilities is the automation of synthesis processes. Unnecessary manual operations increase the radiation exposure of personnel, while simultaneously threatening the reliability of syntheses. We have previously reported an affordable open‐source system comprising 3D‐printed continuous flow reactors, a custom syringe pump, and a pressure regulator that can be used to perform radiofluorinations. In this paper, we address additional essential processes that are needed for radiotracer development and synthesis, with the aim of making laboratory work safer and research more efficient. We have designed and evaluated a fully automated system for rapidly and effectively processing and drying aqueous [18F]fluoride that can be directly connected to the cyclotron. This process relies on triflyl fluoride gas generation and allows nucleophilic [18F]fluoride to be prepared safely in a hotcell within 10 min and an activity recovery of 91.7 ± 1.6% (n = 5). Owing to the need for convenient radiofluorinated prosthetic ligands, we have adapted our continuous flow system to produce [18F]fluoroethyl tosylate (FEOTs) and [18F]fluoroethyl triflate (FEOTf), prosthetic groups that are widely used for late‐stage fluoroethylation of PET tracers. The processes as well as the radiolabeling of different groups are compared and comprehensively discussed. Having a method providing [18F]fluoroethyl tosylate (FEOTs) as well as [18F]fluoroethyl triflate (FEOTf) quickly and highly efficiently is beneficial for radiochemical research. [ABSTRACT FROM AUTHOR]

Summary: Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‐growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‐society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

This paper presents two waveguide quadrature hybrids in the WR‐3 band, which are designed to be fabricated by computer numerical control milling process with relaxed branch width requirements. An E‐plane four‐branch directional coupler gives an amplitude imbalance less than 0.8 dB and a phase difference between 88° and 94° from 240 to 324 GHz. Another design is an H‐plane bandpass coupler composed of cavity resonators, showing a bandwidth of 2% near 300 GHz. [ABSTRACT FROM AUTHOR]

The labour market effects of automation have gained significant attention from scholars and policymakers. Concerns about negative effects are important in emerging countries, where a rapid acceleration of robot adoption and an increasing involvement in global value chains have been observed in recent years, with the subsequent increase in exposure to foreign competition. This paper estimates the effect of local and foreign robots on labour market outcomes and labour shares using a panel dataset composed of 16 sectors and ten emerging countries from 2008 to 2014. The endogeneity of robots' adoption is addressed with an instrumental variables approach and using a shift-share index of exposure to foreign robots. The main results for all sectors show that foreign robot adoption has negatively affected employment in emerging countries. When exploring sectoral heterogeneity, we find that the foreign robots' negative effect on employment has occurred in many sectors, being more prominent in those with higher exposure to foreign robots. Moreover, we found small and negative inter-sectoral spillover effects of local robots on employment and wages. Finally, the results also show that the labour share is affected in some sectors of emerging countries by both the use of local and foreign robots. [ABSTRACT FROM AUTHOR]

This paper studies how automation impacts the structure of decision‐making in organizations. We develop a theoretical model of a firm, where a principal makes a decision about how much to prioritize the new product development division when the division is led by a manager who holds private information specific to this division and has misaligned preferences with the principal. The principal chooses whether to decentralize this decision by delegating it to the manager, resulting in more informed but unbiased decision. In this setting, we investigate how automation which reduces operational variability may alter this choice of organizational structure. The findings from our analysis show that firms deploy automation resources differently depending on their organizational structure: centralized firms choose to automate divisions that face more uncertainty, while decentralized firms do the opposite. Moreover, increasing access to automation results in higher centralization of decision‐making in firms. In the extensions, we show that the strategic use of automation reduces the informativeness of intrafirm communication, and also, that automation can be a strategic substitute to monetary contracts. [ABSTRACT FROM AUTHOR]

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading‐edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5‐m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system. [ABSTRACT FROM AUTHOR]

The current report is the result of the recent EFSA‐EBTC initiative to advance the use of automation tools in systematic reviews. The working group members identified the need for data exchange standards to facilitate transferring data between SR automation tools without loss or changes in data as a first priority. Closely related objectives identified were the concurrent development of data guidance and data content standards, and a strategy for governance of the resulting data standards, including their maintenance and regular updates. These conversations led up to the organization of workshops to initiate the development of data standards, as described in the current report. The EFSA‐EBTC collaboration organized a four half‐day workshop for an extensive group of stakeholders actively involved in the development or use of SR automation tools. The main objectives were 1) to generate a common understanding of the needs and hurdles for data standards in SR automation, 2) to collate viewpoints from all relevant stakeholders on how such standards may be developed, and 3) to kick‐off the development process. Three preparatory workshops preceding the final workshop were organized to clarify the scope and objectives of the main workshop. This external scientific report contains the summary of the preparatory and main workshops, as well as the proposed strategy for the process of how these data standards could be developed. The recommended next steps for development focus on further refining the aims, analysis of the landscape, interoperability with existing standards, setting up the infrastructure (e.g., establishing a governance structure and working groups containing all essential stakeholders), and the process of actual data standard development, launching and monitoring adoption of the standards. [ABSTRACT FROM AUTHOR]

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI‐based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher‐quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety. [ABSTRACT FROM AUTHOR]

Objective: Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). Methods: A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non‐SCQM patients were collected and scored for distal interphalangeal OA (DIP‐OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. Results: The sensitivity and specificity of this model for detecting DIP‐OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP‐OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP‐OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. Conclusion: AutoML platforms are an opportunity to develop clinical end‐to‐end ML algorithms. Here, automated radiographic DIP‐OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging. [ABSTRACT FROM AUTHOR]

Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT‐experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT‐experts in order for a well‐developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory. [ABSTRACT FROM AUTHOR]

Background: With the development and progress of society, not only substations, but also various industries are moving towards automation, intelligence, and unmanned direction. There is an increasing need for machinery to replace human activities. However, how to improve the ability of robotic arms to cope with various working environments and emergencies, and develop real‐time, high‐speed, and accurate trajectory planning algorithms has become a hot topic of discussion. Purpose: In order to develop a reasonable trajectory planning algorithm for robotic arms, this article studies the trajectory planning algorithm of robotic arms based on visual servoing, in order to improve the trajectory operation ability of robotic arms. Method: The feasibility of the algorithm was tested through experiments. Results: It was found that the angle, angular velocity, and angular acceleration curves of both the second and fifth sixth joints of the robotic arm were very smooth, and there were no problems such as faults. Conclusion: This confirms the effectiveness of the algorithm's operation, which can assist the robotic arm in safe, accurate, and efficient operations on the basis of visual servoing. [ABSTRACT FROM AUTHOR]

Industry 4.0 integrates the physical, digital, and biological realms by applying digital automation in systems, processes, and manufacturing facilities. Industry 4.0 is actively shaping the development of intelligent food processing industries in cultivated meat (CM) sector. This integration plays a crucial role in accelerating progress within the global CM sector, facilitating the achievement of its objectives related to food sustainability, security, human health, environmental concerns, and hygiene. Incorporating Industry 4.0 into CM manufacturing systems empowers upstream and downstream production processes to become more intelligent and capable of self‐optimisation. However, enabling rapid adoption of Industry 4.0 by emerging startups and small to medium‐sized enterprises in the CM industry necessitates a thorough understanding of prerequisites and evaluation of technological and biological limitations. Challenges include the substantial initial costs associated with establishing Industry 4.0 infrastructure, robust cybersecurity measures to ensure effective risk management, and acquiring skilled professionals proficient in both operational and maintenance roles. Integrating Industry 4.0 with the evolving CM sector presents an exciting opportunity to foster business‐to‐business investments across various domains, including local markets, export opportunities, and the broader global consumer ecosystem. [ABSTRACT FROM AUTHOR]

In this study, we explored the use of thermal imaging technology combined with computer vision techniques for assessing surgical incision healing. We processed 1189 thermal images, annotated by experts to define incision boundaries and healing statuses. Using these images, we developed a machine learning model based on YOLOV8, which automates the recognition of incision areas, lesion segmentation and healing classification. The dataset was divided into training, testing and validation sets in a 7:2:1 ratio. Our results show high accuracy rates in incision location recognition, lesion segmentation and healing classification, indicating the model's effectiveness as a precise and automated diagnostic tool for surgical incision healing assessment. Conclusively, our thermal image‐based machine learning model demonstrates excellent performance in wound assessment, paving the way for its clinical application in intelligent and standardized wound management. [ABSTRACT FROM AUTHOR]