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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]

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]

The article presents commentary from Ananth Madhavan about a paper written by Kumar Venkataraman entitled "Automated Versus Floor Trading: An Analysis of Execution Costs on the Paris and New York Exchanges," which appeared in the August, 2001 issue of the "Journal of Finance." In that paper Venkataraman concluded that automated stock exchanges could not yet match the benefits afforded by floor traders on traditional exchanges. The author of the current paper raises questions about some of Venkataraman's interpretations.

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]

Tourism is one of the economic activities that has changed most since large‐scale multidimensional data set, so‐called big data, was provided. The scientific literature on big data and tourism with the hospitality industry has been growing exponentially, which is, for this reason, hard to comprehend and synthesise manually. Our research question is how to familiarise oneself with the current knowledge of the relationship between the big data and the complex changes in the tourism and hospitality industry. An automated content analysis procedure was used to identify key topics and concepts of interest to the researchers. A careful review of the abstracts and full texts was carried out, resulting in a final list of 308 papers published in 154 journals. We used the Leximancer software 5.0 to create an automated content analysis. Seven topics (tourism, data, search, model, industry, online and travel) from papers published between 2011 and 5/2020 were found. We propose future development trends of big data in sustainable tourism. [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]

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]

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]

Summary: The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B‐cell and T‐cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality‐checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea‐sharing among players in this ever‐growing space. [ABSTRACT FROM AUTHOR]

Industrie 4.0, also called Industry 4.0, represents the current trend of manufacturing industry characterized by high level of automation, industrial integration, and industrial information integration. It mainly includes enabling technologies such as the Internet of Things (IoT), cyber‐physical systems (CPS), cloud computing, industrial integration, industrial information integration, and other technologies. Systems science has been emerged and well developed since the second half of the twentieth century. From 1940s until now, systems science has been called systems science, systems engineering, systems theory, cybernetics, systems analysis, systems methodology, systems approach, and systems thinking. Since its emergence, a wealth of research has produced an astonishing array of theoretical results and empirical insights, and a large suite of methods and techniques. Systems science has been continuously, widely, and successfully applied to many subjects in natural science and social sciences. This paper aims at discussing the potential contribution of systems science to Industry 4.0. As we approach the era of Fourth Industrial Revolution, this paper points out that systems science is a necessity to deal with the overwhelming systems complexity in Industry 4.0 and the surrounding industrial ecosystem. [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]

Chronic wounds are associated with significant clinical, economic and quality‐of‐life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5‐week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter‐rater and intra‐rater reliability between clinical and TA‐based measurements were analysed using intra‐class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5‐week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter‐rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799‐0.919, P < 0.001). Separate measurements of intra‐rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967‐0.985 and range 0.977‐0.984 respectively, P < 0.001). Inter‐rater reliability between TA used on the iOS and Android systems was also excellent (0.987‐0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity. [ABSTRACT FROM AUTHOR]

The field of biology has seen tremendous technological progress in recent years, fuelled by the exponential growth in processing power and high‐level computing, and the rise of global information sharing. Low‐cost single‐board computers are predicted to be one of the key technological advancements to further revolutionise this field.So far, an overview of current uptake of these devices and a general guide to help researchers integrate them in their work has been missing. In this paper I focus on the most widely used single‐board computer, the Raspberry Pi, and review its broad applications and uses across the biological domain.Since its release in 2012, the Raspberry Pi has been increasingly taken up by biologists, in the laboratory, the field and in the classroom, and across a wide range of disciplines. A hugely diverse range of applications exists that ranges from simple solutions to dedicated custom‐build devices, including nest‐box monitoring, wildlife camera trapping, high‐throughput behavioural recording, large‐scale plant phenotyping, underwater video surveillance, closed‐loop operant learning experiments and autonomous ecosystem monitoring. Despite the breadth of its implementations, the depth of uptake of the Raspberry Pi by the scientific community is still limited.The broad capabilities of the Raspberry Pi, combined with its low cost, ease of use and large user community make it a great research tool for almost any project. To help accelerate the uptake of the Raspberry Pi by the scientific community, I provide detailed guidelines, recommendations and considerations, and 30+ step‐by‐step guides on a dedicated accompanying website (http://raspberrypi‐guide.github.io). I hope this paper will help generate more awareness about the Raspberry Pi among scientists and thereby both fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro‐ to the macro‐scale. [ABSTRACT FROM AUTHOR]

The article comments on a paper published within the issue. "Technology, Communication and the Performance of Financial Markets: 1840-1975," by Kenneth D. Garbade and William L. Silber, investigates the effects of innovations in information technology on security markets. The author discusses two points of criticism regarding the paper in question. He feels that the authors exaggerate the policy significance of their findings. He also believes they overstate their case regarding the importance of the consolidated ticker tape, noting that no impact on inter-market price differentials could be observed. The author feels that the automation of the trading processes is the next logical step for market integration, which is currently under active consideration by the U.S. Securities and Exchange Commission.

Highly automated vehicles (HAVs) are expected to fundamentally change the on‐road transportation field by increasing traffic flow efficiency, reducing road crashes caused by human errors, and increasing comfort and driving productivity. With the help of recent developments in cognitive management techniques and machine learning analysis, intelligent on‐board computing services are gaining acceptance. The main goal of the present paper is to introduce and develop a novel on‐board cognitive decision‐making functionality that dynamically and automatically enables HAVs to operate each time in the best available level of driving automation (LoDA). The proposed functionality utilizes several attributes associated with the driving environment and the driver's personality characteristics and personal preferences. The cognitive nature of the proposed functionality is based on previous knowledge, turned into experience, by implementing the Naive Bayes classifier supervised machine learning method. The effectiveness of the proposed cognitive functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal LoDA, is illustrated by modelling and analysing three scenarios with regards to drivers with different profile data and to driving scenes with different environment characteristics. Therefore, it can operate as an in‐car intelligent personal assistant for the drivers. [ABSTRACT FROM AUTHOR]

The development of multi‐pixel receivers on the millimetre‐wave and terahertz bands demands on the integration of key waveguide devices. In this paper, two waveguide bandpass filters operating at the W‐band are developed based on the layered integration architecture. First, the traditional fourth‐order waveguide filter is perpendicularly folded to realise dual‐layered arrangement, meanwhile a cross‐coupling between layers is introduced to obtain the standard quasi‐elliptical response. Then, two layered approaches including 5‐layer and 3‐layer are proposed to fabricate filter prototypes integrating with 90° curved waveguides based on the computer numerical control (CNC) technology. Finally, the measurement exhibits that both filters have achieved the 3 dB fractional bandwidth of 14.8% from 85 to 98.5 GHz, low insertion loss of 0.6 dB (5‐layer) and 0.4 dB (3‐layer), the return loss with better than 20 dB, as well as two obvious transmission zeros in the out‐of‐band. Besides, the layering error analysis and performance comparison of both filters are discussed, which verify the feasibility of the layered integration for CNC‐machined W‐band waveguide filters. [ABSTRACT FROM AUTHOR]

Safety management determines and implements safety policy. It is closely intertwined with industrial development. As the Fourth Industrial Revolution, Industry 4.0 presents new challenges and opportunities for safety management. The paper identifies three stages, namely, Safety 1.0, Safety 2.0 and Safety 3.0, for the evolution of safety management by considering safety principles, technologies and modes. A theoretical framework that integrates Safety 3.0 and Industry 4.0 is presented. Furthermore, knowledge graphs are developed with Citespace to explore the status of cross‐topic research on safety management and Industry 4.0. Potential opportunities for integrating Safety 3.0 and Industry 4.0 are discussed. [ABSTRACT FROM AUTHOR]

This paper increases understanding of the concept of smart factories system and its implementation in the automotive industry in terms of addressing necessary changes in human aspect of system, namely, employees' competencies, as well as to new job profiles. A qualitative research approach was used by utilizing a case study of the automotive industry in Slovenia. For the purpose of data collection, we used a semistructured interview on a sample of experts from governmental, educational, and private automotive sectors. In order to analyse gathered data, we used the method of content analysis. Research results indicate that significant changes are essential in the transformation from traditional to a smart factory system, especially regarding organizational mindset, the architecture of system and its processes, and the inclusion of Industry 4.0 technologies at all levels. Also, results indicate changes are essential regarding future job profiles and competencies. Although there are different perceptions about future jobs and competencies needed for Industry 4.0‐induced changes, depending on the sector in which the expert is employed, there is an overall agreement that job profiles related to programming, mechatronics, robotics, data analysis, Internet of Things, design, and maintenance of smart systems, process analysis, and bionics are the new job profiles needed in smart factory systems. In addition to the high emphasis on technical skill and knowledge, soft competencies are considered important, as well as competencies of continues learning, flexibility, creativity, problem solving, critical, and analytical thinking. Our findings possess important implications for conceptualizing job profiles and competencies in smart factories systems and practical implications for managers concerned with human resource management activities. We provide an outlook to the future job profiles that are underresearched, especially in the field of smart manufacturing. As well, our paper provides insight into the importance of different competencies recognized as crucial for complete functioning and optimal performance of smart factories system. This study provides a starting point for further research regarding human resource management issues in smart factories in the automotive sector, especially related to future job profiles and employee competencies, and their role in system performance. [ABSTRACT FROM AUTHOR]

This paper examines the choice of trading venue by dealers in U.S. Treasury securities to determine when services provided by human intermediaries are difficult to replicate in fully automated trading systems. When Treasury securities go “off the run” their trading volume drops by more than 90%. This decline in trading volume allows us to test whether intermediaries' knowledge of the market and its participants can uncover hidden liquidity and facilitate better matching of customer orders in less active markets. Consistent with this hypothesis, the market share of electronic intermediaries falls from 81% to 12% when securities go off the run. [ABSTRACT FROM AUTHOR]

A global trend towards automated trading systems raises the important question of whether execution costs are, in fact, lower than on trading floors. This paper compares the trade execution costs of similar stocks in an automated trading structure (Paris Bourse) and a floor-based trading structure (NYSE). Results indicate that execution costs are higher in Paris than in New York after controlling for differences in adverse selection, relative tick size, and economic attributes across samples. These results suggest that the present form of the automated trading system may not be able to fully replicate the benefits of human intermediation on a trading floor. [ABSTRACT FROM AUTHOR]

Epilepsy is a common non‐communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state‐of‐the‐art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start‐ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA advancements have shown immense potential and paved a path to successful real‐time detection, prediction, and management of epileptic seizures. [ABSTRACT FROM AUTHOR]

Tunnel construction is one of the areas of civil engineering where automation systems can increase both safety and productivity. That is why, to promote a continuous improvement and optimization of construction processes, the implementation of intelligent transport systems and, specifically, autonomous vehicles has been proposed as a catalyst for public works construction activity, including tunnels, reducing personnel, and operation costs, as well as improving safety. Under this scope, this paper presents the development of the automation system for a 25‐ton hauler truck, focusing on the task of removing debris from public works, including tunnels. This automation system has been designed, developed, and tested under real conditions in the facilities of two companies dedicated to civil engineering works, including vehicle guidance with and without gobal positioning system, obstacle detection, and tele‐operation for load/unload tasks. The main novelty of the system is the capacity of easy integration into the production system of the works, with a capacity to switch between manual, tele‐operation, or autonomous driving modes, and with specific sensors and control systems for combining indoor and outdoor navigation. The presented work includes the description of the automation of the vehicle, the general architecture, the environment perception, and the tele‐operation capacity, including indoor navigation and obstacle detection algorithms. The proposed system has been implemented and tested in real conditions, demonstrating the performance of the system and the benefit of the automation for this application. [ABSTRACT FROM AUTHOR]

This paper describes a holistic, yet simple and comprehensible, ecosystem model to deal with multiple and complex challenges in eyecare. It aims at producing the best possible wellbeing and eyesight with the available resources. When targeting to improve the real‐world cost‐effectiveness, what gets done in everyday practice needs be measured routinely, efficiently and unselectively. Collection of all real‐world data of all patients will enable evaluation and comparison of eyecare systems and departments between themselves nationally and internationally. The concept advocates a strategy to optimize real‐life effectiveness, sustainability and outcomes of the service delivery in ophthalmology. The model consists of three components: (1) resource‐governing principles (i.e., to deal with increasing demand and limited resources), (2) real‐world monitoring (i.e., to collect structured real‐world data utilizing automation and visualization of clinical parameters, health‐related quality of life and costs), and (3) digital innovation strategy (i.e., to evaluate and benchmark real‐world outcomes and cost‐effectiveness). The core value and strength of the model lies in the consensus and collaboration of all Finnish university eye clinics to collect and evaluate the uniformly structured real‐world outcomes data. In addition to ophthalmology, the approach is adaptable to any medical discipline to efficiently generate real‐world insights and resilience in health systems. [ABSTRACT FROM AUTHOR]

Crack is a common form of road distress and a key study of an intelligent transportation system. However, automatic pavement crack detection is a very challenging task due to noisy texture background, intensity inhomogeneity, and topology complexity. In this paper, a new pavement crack detection algorithm to address these issues is proposed. First, non‐local block matching strategy and local statistical mean are put together to generate the probability map of cracks, which has advantages on automatic threshold choosing and strong resistance to intensity inhomogeneity. Second, an iterative seed points sampling algorithm is proposed, which makes full use of the area and shape of connected regions where the seeds lie in, thus exploiting high reliable crack seeds for following curves extraction. Finally, a minimum spanning tree (MST) is adopted to connect points into crack curves and employ a crack growth method to find out the cracks, which is specified to deal with complex topology of cracks. For parameters, a robust and optimal parameters selection rule is obtained by data driven method. The algorithm is compared with other state‐of‐the‐art algorithms on two datasets. The experiment result shows that the proposed method has a better detection performance on F1$F1$‐measure score over other methods. [ABSTRACT FROM AUTHOR]

Automation risk prevails less in large cities compared to small cities but little is known about the drivers of this emerging urban phenomenon. A major challenge is that automation risk is quantified by work‐related tasks that allows for measurement through occupation, which is in turn implicitly related to local economic structure and to individual career paths. This paper examines the role of working in cities on changes in automation risk through individual career mobility. Using panel data on Swedish workers, we show that the metropolitan effect of reducing automation risk is mainly induced through inter‐firm job mobility. Separate estimates for different groups show that this effect accrues mostly to native, high‐skilled and male workers. [ABSTRACT FROM AUTHOR]

Pagination problems deal with questions around transforming a source text stream into a formatted document by dividing it up into individual columns and pages, including adding auxiliary elements that have some relationship to the source stream data but may allow a certain amount of variation in placement (such as figures or footnotes). Traditionally, the pagination problem has been approached by separating it into one of micro‐typography (eg, breaking text into paragraphs, also known as h&j) and one of macro‐typography (eg, taking a galley of already formatted paragraphs and breaking them into columns and pages) without much interaction between the two. While early solutions for both problem areas used simple greedy algorithms, Knuth and Plass introduced in 1981 a global‐fit algorithm for line breaking that optimizes the breaks across the whole paragraph. This algorithm was implemented in (see Computers & Typesetting, Volume B: TeX: The Program by Knuth in 1986) and has since kept its crown as the best available solution for this space. However, for macro‐typography there has been no (successful) attempt to provide a globally optimized page layout: All systems to date (including) use greedy algorithms for pagination. Various problems in this area have been researched, and the literature documents some prototype development. However, none of them have been made widely available to the research community or ever made it into a generally usable and publicly available system. This paper is an extended version of the author's work in 2016 originally presented at the 16th ACM Symposium on Document Engineering in Vienna, Austria. It presents a framework for a global‐fit algorithm for page breaking based on the ideas of Knuth/Plass. It is implemented in such a way that it is directly usable without additional executables with any modern installation. It therefore can serve as a test bed for future experiments and extensions in this space. At the same time, a cleaned‐up version of the current prototype has the potential to become a production tool for the huge number of users worldwide. This paper also discusses 2 already implemented extensions that increase the flexibility of the pagination process (a necessary prerequisite for successful global optimization): the ability to automatically consider existing flexibility in paragraph length (by considering paragraph variations with different numbers of lines) and the concept of running the columns on a double spread a line long or short. It concludes with a discussion of the overall approach, its inherent limitations and directions for future research. [ABSTRACT FROM AUTHOR]

Large‐scale infrastructure and industrial projects face schedule delays, cost overruns and performance shortfalls. One cause is the late delivery of their control systems that end up being complex and unsuited. To overcome this problem, an innovative industrial control system is proposed that can be commissioned almost effortlessly and delivered on time without functional defects in most large‐scale industrial and infrastructure construction projects, while offering high capacity (>25,000 filed devices scanning >100,000 signals) and straightforward scalability, upgradability and maintainability. Designed with a central controller and distributed sensing and actuating devices, and equipped with a cluster of servers and an Ethernet network transporting video, voice and data (>300 video and >750 voice streams), it integrates plant automation technologies with data communication techniques, allowing industrial and information technology applications to coexist and cooperate. While being tolerant to faults and fast in its response, it provides high safety integrity level (>SIL 2) and real‐time automatic reaction in repetitive stimuli or occasional events (programmable from 5 to 250 ms). In this paper, with the provided system blueprint and engineering guidelines, this novel system architecture can be built with off‐the‐shelf components seamlessly integrated in the context of Cyber Physical Systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how data about public preferences may be used to inform policy around the use of controversial novel technologies, using public preferences about autonomous vehicles (AVs) as a case study. We first summarize the recent 'Moral Machine' study, which generated preference data from millions of people regarding how they think AVs should respond to emergency situations. We argue that while such preferences cannot be used to directly inform policy, they should not be disregarded. We defend an approach that we call 'Collective Reflective Equilibrium in Practice' (CREP). In CREP, data on public attitudes function as an input into a deliberative process that looks for coherence between attitudes, behaviours and competing ethical principles. We argue that in cases of reasonable moral disagreement, data on public attitudes should play a much greater role in shaping policies than in areas of ethical consensus. We apply CREP to some of the global preferences about AVs uncovered by the Moral Machines study. We intend this discussion both as a substantive contribution to the debate about the programming of ethical AVs, and as an illustration of how CREP works. We argue that CREP provides a principled way of using some public preferences as an input for policy, while justifiably disregarding others. [ABSTRACT FROM AUTHOR]

This paper studies trajectory tracking control problem of quadrotor in the presence of external disturbances. A new control scheme integrating adaptive sliding mode disturbance observer (ASMDO) with backstepping funnel control is designed in both position loop and attitude loop. Adaptive sliding mode disturbance observer is utilized to estimate and compensate the external disturbances of quadrotor system, which relaxed the upper bound requirement in conventional sliding mode disturbance observer (SMDO) whilst adapting the gain of discontinuous term to a level as small as possible for consideration of chattering mitigation. The modified funnel function not only guarantees the dynamic quality in the tracking process but also avoids derivative operation of Euclidian norm of tracking error in the backstepping design. To guarantee stabilization of closed‐loop system, Lyapunov stability criterion is utilized in the overall controller design process. Finally, the effectiveness and superiority of the proposed scheme are illustrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper proposed a saturated adaptive barrier sliding mode control (SABSMC) for disturbed systems with parameter uncertainty, external disturbance, and input saturation. One distinctive feature of the proposed method is that even under the influence of input saturation, it is able to drive the system trajectory into a prespecified neighbourhood of the sliding surface, independent of the upper limit of lumped disturbance. Moreover, attributing to the designed adaptive laws, the switching gain of the proposed method can be adaptively adjusted in a wise manner. Another attractive feature of the proposed method is that it removes any presumption of constant upper limit on the lumped disturbance. Instead, SABSMC utilises a more rational state‐dependent upper limit assumption. The system dynamics of the closed‐loop system is analysed via the Lyapunov method in the presence of parameter uncertainty, external disturbance, and input saturation. The effectiveness of the proposed method is confirmed through numerical simulations. [ABSTRACT FROM AUTHOR]

The Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) algorithms are widely used to detect overlapping communities in complex networks. The main problem with these two algorithms is that they are extremely sensitive to the setting of the hyperparameters. Previous methods set the hyperparameters of LFM or SLPA based on either empirical values or random choices, resulting in a large amount of computation for adjusting those parameters. To solve this problem, in this paper, a machine‐learning‐based approach is proposed to automatically set the hyperparameters of these two algorithms. Experimental results show that compared with the manual method, automatically setting the hyperparameter using machine learning models can lead to higher‐quality divisions. Furthermore, in comparison to the well‐known hyperparameter tuning method using Bayesian Optimization, the proposed predictive model‐based approach can find suitable para meters for LFM and SLPA much faster, while achieving competitive results in terms of division quality. [ABSTRACT FROM AUTHOR]

Despite the increasing number of automated systems that have been introduced in vehicles over the past decade, highly automated vehicles are not yet capable of driving reliably and safely in all traffic scenarios and conditions. Until they are, humans will need to remain in the loop - to take back vehicle control when the driving capabilities of the automated system(s) are limited or systems fail. This automation-to-manual transition may be problematic if the driver is inattentive or distracted by competing activities. If so, this may compromise the driver's ability to take back control in a safe and timely manner. The aims of this study are to: (a) review what is known about driver inattention and distraction during periods of highly automated driving, (b) to outline countermeasures that have or may have potential to prevent and mitigate the effects of inattention and distraction during automated driving and (c) to highlight future research directions that may further inform the understanding and management of distraction and inattention as vehicles become increasingly automated. The paper concludes by contemplating whether a fully self-driving vehicle may itself be distracted or inattentive to activities critical for safe driving. [ABSTRACT FROM AUTHOR]

Abstract: In distributed and open environments, MASs (multiagent systems) generally have no mechanisms for prior coordination and self‐organization has been believed to be the necessary selection to achieve the coordination of agents. This paper first presents a values‐driven model for self‐organization in which the expected emergent properties of a system are specified as the social values while the social values are realized via implicitly inducing members to regulate their individual values and adjust their behaviors to fit the expectations of the system. Based on the values‐driven self‐organization, this paper proposes an automated coordination mechanism for decentralized MASs. In this mechanism, by indirectly changing the difficulties in acquiring resources (which may be delegated to some special agents since MASs generally do not have substantial bodies), MASs can lead agents to regulate their values to be consistent with the social values of MASs so that the coordination of MASs can spontaneously emerge from the local behaviors of agents. Finally, this paper implements a simulation traffic system using the coordination mechanism based on values‐driven self‐organization to validate the emergence of coordination among multiple agents. [ABSTRACT FROM AUTHOR]

A correction in the paper by Lazo et al. [(2021). J. Synchrotron Rad.28, 1649–1661] is made. [ABSTRACT FROM AUTHOR]

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The future of risk assessment cannot neglect to consider the vast literature produced through the application of new approach methodologies (NAMs). This, though, constitutes a challenge for the risk assessor, as the production in this context is huge and heterogeneous both for the methods applied and the standardisation and quality of the results. The integration of results from NAMs is hence only feasible under some degree of automation of the risk assessment workflow, specifically for searching, extracting and integrating such results in "AOP‐like" knowledge networks (AOP – Adverse Outcome Pathway). Artificial intelligence (AI) with its state‐of‐the‐art methods and tools is the most promising source for help among modern technologies supporting automation of manual tasks. Within the broader context of investigating possible applications of AI to achieve this goal, the present paper illustrates an evaluation framework to quantitatively assess these tools and methods and, in turn, support the selection of the most promising among them for a (at least partial) automation of the overall workflow. It also provides a survey of the state‐of‐the‐art tools and methods identified at present which can then be assessed for specific use cases with the help of the introduced evaluation framework. [ABSTRACT FROM AUTHOR]

This paper is concerned with the changing nature of space. More and more of the spaces of everyday life come loaded up with software, lines of code that are installing a new kind of automatically reproduced background and whose nature is only now starting to become clear. This paper is an attempt to map out this background. The paper begins by considering the nature of software. Subsequently, a simple audit is undertaken of where software is chiefly to be found in the spaces of everyday life. The next part of the paper notes the way in which more and more of this software is written to mimic corporeal intelligence, so as to produce a better and more unobtrusive fit with habitation. The paper then sets out three different geographies of software and the way in which they are implicated in the reproduction of everyday life before concluding with a consideration of the degree to which we might consider the rise of software as an epochal event or something much more modest. [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]

The DIAD beamline for Dual Imaging and Diffraction at Diamond Light Source has opted to use an industrial robot to position its Dectris Pilatus 2M CdTe diffraction detector. This setup was chosen to enable flexible positioning of the detector in a quarter-sphere around the sample position whilst reliably holding the large weight of 139 kg of detector, detector mount and cabling in a stable position. Metrology measurements showed that the detector can be positioned with a linear repeatability of <19.7 mm and a rotational repeatability of <16.3 mrad. The detector position stays stable for a 12 h period with <10.1 mm of movement for linear displacement and <3.8 mrad for rotational displacement. X-ray diffraction from calibration samples confirmed that the robot is sufficiently stable to resolve lattice d-spacings within the instrumental broadening given by detector position and beam divergence. [ABSTRACT FROM AUTHOR]

Till this moment, the model of interconnected power systems in the automatic generation control (AGC) loops relies only on the synchronous generating units. In today's world, a high level of penetration of renewable energy sources (RES) is integrated to the power grids. This paper presents a novel power system model, which includes both conventional generating units and RES for studying the AGC problem of such systems. The control strategy in the AGC loops is based on the proportional-integral-derivative (PID) controller, which is optimally designed by the whale optimization algorithm (WOA). It represents a great challenge to this controller to deal with the RES uncertainties. The effectiveness of the WOA-based PID controller is compared with other computation evolutionary algorithms-based PID controller. The system performance is evaluated under different operating conditions. For achieving a realistic study, 1) real wind speed data that extracted from Zafarana location in Egypt are used, 2) solar irradiation and temperature data that extracted from a field test are implemented, and 3) an irregular wave energy condition is applied. The validity of the control strategy is verified using the simulation results, which are carried out using MATLAB environment. [ABSTRACT FROM AUTHOR]

Effectiveness in lab workflows-despite progresses made in automation and lab informatics-is often hindered by insufficient integration of devices and data. The iLAB software framework, a middleware connecting and integrating devices and data, provides a plugin architecture that can be adapted to individual lab environments. Integration of devices is preferably based on standardized data and communication protocols. In addition to device integration, process data and result data from different sources (e.g. readers) are converted to a standard format and administered by a powerful database for further processing. In this paper, the use of iLAB in a bioprocess development application is described. Process parameters and measured values of two high-throughput bioreactor systems (96-well plates and 10 mL reactor vessels) are collected in the iLAB database. Data from screening experiments and offline data are visualized, analyzed, and compared. A filter algorithm allows searching for matching parameters in experiments as well as the comparison of correlated datasets, independently from the used bioreactor system. The database model enables the consolidation of data, the transition from data to information, and a solid base for management decisions on enterprise level. [ABSTRACT FROM AUTHOR]

Ecological data are collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring‐width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, which have been developed for ecological analysis and are publicly available, remains the exception.We developed the Wood Image Analysis and Dataset (WIAD), an open‐source application including a web interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or X‐ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood.WIAD provides user‐friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early‐ and latewood widths and fire scars, and to visualise, process and archive images, metadata, and the derived data.WIAD constitutes a step towards the reproducible automation of tree‐ring analysis while establishing an open‐source foundation to create improved community‐developed repositories which would enable novel ecological studies harnessing the wealth of existing visual data. [ABSTRACT FROM AUTHOR]