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

Rapid diagnostic tests (RDTs) for point-of-care (POC) testing of infectious diseases are popular because they are easy to use. However, RDTs have limitations such as low sensitivity and qualitative responses that rely on subjective visual interpretation. Additionally, RDTs are made using paper-bound reagents, which leads to batch-to-batch variability, limited storage stability and detection of only the analytes they were designed for. This work presents the development of a versatile technology, based on short magneto-assays and inexpensive paper-based microfluidic electro-analytical devices (PMEDs). PMEDs were produced locally using low-cost equipment, they were stable at room temperature, easy to use, and provided quantitative and objective results. The devices served to detect alternatively a variety of magneto-assays, granting quantitation of streptavidin-HRP, biotinylated HRP and Pasmodium falciparum lactate dehydrogenase (Pf-LDH) in less than 25 min, using either commercial or customized screen-printed electrodes and measurement equipment. Furthermore, Pf-LDH detection in diluted lysed whole blood displayed a linear response between 3 and 25 ng mL−1, detection and quantification limits ranging between 1 and 3 ng mL−1 and 6–12 ng mL−1, respectively, and provided results that correlated with those of the reference ELISA. In short, this technology is versatile, simple, and highly cost-effective, making it perfect for POC testing. [ABSTRACT FROM AUTHOR]

This paper introduces a kind of intelligent lubricating oil quality detector integrating computer technology. On the basis of the traditional lubricating oil quality detection, through the introduction of advanced computer technology, the equipment realizes the automation of the detection process, the intelligence of data processing and the convenience of remote monitoring and management. This paper not only expounds the system structure and working principle of the detector, but also discusses the application of computer technology in the detection process. [ABSTRACT FROM AUTHOR]

• The process of automation could hinder political trust in government. • The impact results from unemployment, higher down-side risk and lower group participation. • The performance-based legitimacy structure in a developmental state may hinder its political legitimacy when promoting development. • The government should focus more on citizens' trust when pursuing technological development. • Social security is crucial for governance faced with fast technological advancement. The side effects of automation on the economy have been discussed frequently, but little is known regarding its political consequences. This paper examines the causal effect that automation induces on political costs for the local government. By combining the national individual-level panel data of political trust with the prefecture-level robot exposure rate in China, we find that the development of automation would reduce individuals' political trust in the Chinese local government. Furthermore, we explore the channels through which the automation process could affect political trust, namely the risk of unemployment, intensified pessimism about local government, higher downside risk, and declining organization participation. This paper provides empirical evidence for the impact of automation and the source of political legitimacy, emphasizing the crucial role of governments in coping with technological progress and making good use of endogenous creative destruction. [Display omitted] [ABSTRACT FROM AUTHOR]

Safety is paramount to any process plant facilities and its design process. To ensure that safety is considered in design stages, process safety analysis such as hazard and operability studies are often carried out to address safety concerns throughout the stages of plant design to operation. However, conventional HAZOP studies are labor-intensive and expensive. This paper aims to provide a semi-automated HAZOP study using a computer-aided tool called Stateflow. It describes the rule-based approach and algorithm used to develop the automated HAZOP process. The paper also highlights the limitations and challenges faced and finishes with the potential areas for further works on Stateflow to enhance the HAZOP automation capabilities. [ABSTRACT FROM AUTHOR]

Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines. [ABSTRACT FROM AUTHOR]

In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints. [ABSTRACT FROM AUTHOR]

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. • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety. • Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance. • AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated. [ABSTRACT FROM AUTHOR]

This paper explores the parallel collaboration of multimodal physiological signals, combining eye tracker output signals, motor imagery, and error-related potentials to control a computer mouse. Specifically, a parallel working mechanism is implemented in the decision layer, where the eye tracker manages cursor movements, and motor imagery manages click functions. Meanwhile, the eye tracker output signals are integrated with electroencephalography data to detect the idle state for asynchronous control. Additionally, error-related potentials evoked by visual feedback, are detected to reduce the cost of error corrections. To efficiently collect data and provide continuous evaluations, we performed offline training and online testing in the designed paradigm. To further validate the practicability, we conducted online experiments on the real-world computer, focusing on a scenario of opening and closing files. The experiments involved seventeen subjects. The results showed that the stability of the eye tracker was optimized from 67.6% to 95.2% by the designed filter, providing the support for parallel control. The accuracy of motor imagery conducted simultaneously with fixations reached 93.41 ± 2.91%, proving the feasibility of parallel control. Furthermore, the real-world experiments took 45.86 ± 14.94 s to complete three movements and clicks, and showed a significant improvement compared to the baseline experiment without automatic error correction, validating the practicability of the system and the efficacy of error-related potentials detection. Moreover, this system freed users from the stimulus paradigm, enabling a more natural interaction. To sum up, the parallel collaboration of multimodal physiological signals is novel and feasible, the designed mouse is practical and promising. [ABSTRACT FROM AUTHOR]

Workforce education and development are key cornerstones in advancing and maturing the Cell & Gene Therapy sector. A skilled worker shortage can significantly impact and delay progress as well as the quality of output for any developer, thereby negatively impacting a patient's access to life-saving treatments. Several roundtable discussions were held at the International Society for Cell & Gene Therapy (ISCT) 2023 Annual Meeting to dive deeper into the current state of workforce development and solutions to address this bottleneck. One roundtable discussion was co-hosted by the Alliance for Regenerative Medicine (ARM) and ISCT, which focused on the gap analysis provided for the United States Cell & Gene Therapy (CGT) sector, highlighting the lack of skilled workers in manufacturing and quality control. In this manuscript, the roundtable participants continue this conversation, review the roles and staffing requirements in both academic and industry as well as small and large company settings. The adoption of increased manufacturing automation is one promising solution to propel the sector forward. However, automation alone won't replace on-site staff, but will lower the bar to entry for a larger pool of people and require different training. This paper also addresses the workforce development and training paradigm shift as advanced manufacturing techniques are implemented, which will differ considerably based on the type of manufacturing efforts, thus emphasizing the need for a well-thought-out strategy to up-skill and re-skill the technical workforce to adapt to these advancements. Organizations such as ISCT and ARM have a role to play in propelling the field forward, providing awareness and education to stakeholders at all levels, as well as acting as a convener and participating as a key stakeholder in discussions and partnerships between academia and industry towards solutions for training the best personnel for CGT manufacturing. This scope includes novel digital tools and technologies to simplify training to increase access to new talent pools interested in careers in a rapidly advancing sector. [ABSTRACT FROM AUTHOR]

Short-term automation solutions for industrial companies have potential to compensate for volatile demand, employee turnover or shortages. Current solutions are either not available at short notice or economically unviable, as integration in existing control systems and machine tools would be necessary. The paper proposes a cognitive robotic system that circumvents this problem by emulating the behaviour of operators through automated, cognitive skills. The approach uses a perception system, handling and skill modules and a skill-based control to automate in an industrial brownfield use case without additional interfaces to the existing control systems to reduce integration effort to a minimum. [ABSTRACT FROM AUTHOR]

Meat, as protein-rich food, provides essential nutrition to humans. Meat consumption is expected to increase by 15% over the next decade, requiring the meat industry to boost its capacity. However, the meat cutting industry's harsh working environment and long-term heavy workload result in a labor shortage. Low-margin and low capacity require innovative processing methods. Hence, some novel automation methods are urgently demanded to meet these challenges. Accompanied by the development of Industry 4.0, robotization and intelligent systems are progressively applied in the meat cutting industry, which can provide a flexible, adaptive, and sustainable processing approach. The paper selected 50 papers using the systematic literature review method. This paper provides an overview of the current research and commercial applications of cutting robotization from livestock, poultry, and fish in the meat industry. Additionally, intelligent sensing technologies, advanced cutting techniques, and a novel manufacturing concept called the meat factory cell, are emphasized to promote efficiency, scalability, and modularization. Finally, we discuss the potential applications of digital technologies in the meat cutting industry and the focal points of future research aiming to promote cutting robotization. The meat cutting process can be highly robotized by integrating dexterous cutting robots, advanced sensing techniques, and digital systems, driving the transformation from manual labor to robotic, efficient, and intelligent manufacturing. Consequently, robotization and intelligent digital systems can provide a brand-new manufacturing method to the meat cutting industry and advance the Meat factory 4.0. [Display omitted] • Advanced perception techniques assist robots to perform adaptive cutting. • Intelligent robotic systems in the meat cutting industry are reviewed. • Commercial applications regarding meat cutting robotization are presented. • Advanced cutting techniques are highlighted to promote cutting performance. • Potential applications of advanced digital systems are provided for sustainability. [ABSTRACT FROM AUTHOR]

In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarized comparison of two tools. [ABSTRACT FROM AUTHOR]

This paper presents research performed on set-based multi-criteria optimization for the preliminary design of slab frame bridges. As a result of immense CO2 emissions emerging from concrete production, there is a need for optimization methods decreasing the volume of concrete without affecting the function of the structure. Previous research suggests a general correlation between CO2 footprint- and cost reduction, due to minimized material use. However, the aspect of buildability may conflict with lessened material, as optimized designs might simultaneously be less buildable. This research aimed to develop an optimization method with respect to the investment cost and environmental impact while also considering buildability cost aspects. The optimization algorithm shows the possibilities of reducing the environmental impact by up to 13.7% for a slightly increased cost of 2.3%. Thus, by implementing optimization procedures in the early stages of the planning process the holistic cost effects related to green solutions can be presented, favoring the choice of sustainable designs amongst clients during tendering procedures. [ABSTRACT FROM AUTHOR]

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs. [ABSTRACT FROM AUTHOR]

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) technology can organically combine data modeling and virtual reality, and compare them with actual construction objects to realize the smart collaboration of entity and the model in the construction processes, greatly reducing early stage mistakes and improve construction efficiency. This paper proposed an automation system framework of smart construction based on BIM technology and collaboration platform, and discussed the element configuration of smart construction based on BIM technology and collaboration platform, and finally elaborated the working mechanism of automated system based on BIM technology and collaboration platform. The study results show that the automation system based on the BIM technology and collaboration platform supports the rapid construction of large-scale three-dimensional scenes and the precise integration of multi-source data, and can bring together construction information and asset information from different sources to form an open, safe, and accessible digital environment. The application of smart construction technology in construction cost management has brought huge economic benefits, construction efficiency has been increased by 65%, construction period has been shortened by 30%, labor intensity has been reduced by 27%, and labor productivity has been increased by 39%, which has brought considerable indirect benefits to construction projects. [ABSTRACT FROM AUTHOR]

The growing complexity of the logistics chain, the need to take on an ever longer and distributed logistic chain, which in some cases even reaches the final stages of the production of the same goods, highlights the problem of managing the post-production phases of products and goods, but also of the personnel who work on these activities, charged to the companies that deal with logistics. We present in this paper the Integrated Logistic Platform (ILP 4.0), a software architectural model, whose purpose is to lead warehouse logistics to new levels of efficiency, not reachable as the sum of the operational functions within a single logistic company, but reachable only through a unique and cross-functional system, which surpasses the usual fragmented vision and consequent approaches, in favor a strategic coordination of all activities. In this paper it is described the general architecture of the realized platform, with its research contributions and innovations, in particular related to some typical problems of warehouse environments, solved with the help of machine learning and computer vision approaches, but also with the integration of augmented reality (AR) and virtual reality (VR) devices. The aim of the integration of these technologies in the project is the definition of a "smart" warehouse model environment, capable of mitigate problems of warehouse logistics such as: automation of the inventory process (through an UAV), control of warehouse movements, and management of logical / physical security of the premises. Based on the experiences made on the project, and focusing the attention on the safety and security aspects in logistics, that the platform covers, we discuss further a possible federated learning approach in order to mitigate the lack of data in order to solve some security and safety problems in the logistic area. [ABSTRACT FROM AUTHOR]

Selecting the best microscope parameters for optimal image quality currently relies on microscopists; there exist no procedures or guidelines for tuning parameters to ensure the desired image quality is achieved. More importantly, for quantitative analysis purposes, adequate image quality for segmentation should be prioritized. This paper is the second of two parts, describing a regression model, mixed input, multiple output with Keras TensorFlow, trained to predict the beam energy and probe current, two important parameters for image quality. Specifically, parameters are predicted to optimize the image quality for segmentation, using a generated training set, as described in Part 1 of this paper. Model performance is then tested on models trained with multiple different training sets, and with different proportions of simulated and acquired data. First, to examine the impact of the training set on the prediction accuracy and then, to evaluate the importance of including real data during training. The model successfully predicted the beam energy and probe current to set on the microscope to improve image quality for segmentation. Models trained with both simulated and acquired data performed the best, as evaluated by their efficacy at improving the image quality for feature segmentation. [ABSTRACT FROM AUTHOR]

The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT-3.5, GPT-4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT-4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87–100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT-3.5, exhibited slightly lower accuracy across all tasks and data sources (average accuracy across tasks: 81–97%), whereas LLaMA-2-70B showed the worst performance (37–73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data. • Large Language Models (LLMs) are tested for extracting biodiversity data from text. • Tasks include identification of relevant sources, geocoding, and data structuring. • LLMs, especially GPT-4, show over 90% accuracy on multiple tasks. • LLMs promise effective data assimilation from unstructured text in ecology. [ABSTRACT FROM AUTHOR]

The automation system of the future will consist of an increasing amount of complex resources, such as collaborative robots and/or autonomously roaming robots for material handling. To control these devices in an environment shared with human operators require state of the art computer perception and motion planning algorithms to be used as part of the automation system. This new type of intelligent automation system, where intelligent machines and learning algorithms are replacing more traditional automation solutions, requires new methods and workflows to keep up with the increase in complexity. This paper presents an interactive and iterative framework for solving some of these new challenges. The framework supports model-based control system preparation performed simultaneously to preparation of 3D geometries, positioning of robots, and tool design. The workflow enables an interactive preparation process, where new resources and constraints can be added to a live (real or simulated) automation system and control system failures can be analyzed in familiar tools for virtual preparation. Additionally, the paper describes how the integrated preparation process was applied to reconfiguring an industrial use case that includes a collaborative robot working side by side with a human operator, smart tools, and a vision system for localizing both work objects and tools. [ABSTRACT FROM AUTHOR]

Automated Fibre Placement (AFP) is receiving increasing attention from academia and industry. This is due to its widespread application in major sectors such as the automotive, marine, aerospace, renewables, and rail industry. AFP defects pose a significant barrier to efficient part throughput due to their lengthy processing time in inspection and rework. While there is a plethora of research on the causes and effects of various defect types, the subsequent reworking of these defects has not been thoroughly investigated. Although some papers have examined manual rework, their scope is limited. Insights from manual hand layup and similar methods can be applied to this context. This article presents the first comprehensive review of the academic literature in the field of defect rework, alongside an analysis of rework process experiments conducted by the authors to provide insight into current industry best practices. Following this, the article focuses on analysing the rework process in AFP and proposes guidelines and best practices. It emphasises the need for further research in this area, along with the findings of other researchers, to demystify the rework activity and inspire new investigations. Such research can offer valuable insights into enhancing reworking techniques and exploring automation possibilities. Automation can assist in manual reworking or even eliminate the need for it entirely, thereby reducing manufacturing time. The suggested directions for future work include developing automated rework processes to achieve near-full automation of AFP. [ABSTRACT FROM AUTHOR]

In the modern business world, where hybrid business processes are widespread, effective resource and operations management defines the success of companies. One of the most critical areas is logistics, which is a critical component for many organizations, regardless of size or industry. The management of logistics processes is becoming more complex every year, as the demands on the industry are becoming more complicated and today consists not only of efficiency issues but also of environmental friendliness, and globalization. In this situation, RPA technology is one of the main solutions, enabling companies to achieve new levels of efficiency, accuracy, and speed in managing warehouse processes. In this article, we will consider modern approaches to managing hybrid business processes using the example of warehouses and explore how RPA technology has become the main optimization tool. The paper observes specific examples of the use of RPA in the context of warehouse management, identifies the benefits and risks associated with automation, and defines the development prospects and future of this innovative technology in logistics. [ABSTRACT FROM AUTHOR]

Future human exploration missions require new concepts of operations since the international roadmap foresees stations with uncrewed periods (e.g. Lunar Gateway) and deep space missions with communication delays making real-time monitoring and control impossible (e.g. any vehicle on its way to or in orbit of Mars). As part of the commitment to achieve human presence in lunar orbit and prepare for missions to Mars, the goal for future operations is to become independent from ground and enable crews to live and work autonomously. However, as a first step towards on-board crew autonomy, the ground segment itself needs to be automated and its functions eventually transferred on-board. For this purpose, this paper introduces a novel tool, based on Columbus operations, conducted at the Columbus Control Center (COL-CC), allowing the interaction between scheduled activities and the commanding infrastructure, by automatically generating a command sequence, transferring the command sequence to the operators' command interface for inspection and approval, and finally sending the commands to the receiver for execution. This tool is implemented as a ground prototype, in order to show how ground operations can be automated and how such a tool can ultimately lead to increased crew autonomy. • Crewed deep-space missions cannot use real-time commanding, due to signal delays. • Real-time commanding is shifted from the ground controller to an on-board system. • An interface allows interaction between crew, mission timeline and command system. • The system is capable of automatically generating commands, based on constraints. • An activity database stores all necessary information for activity execution. [ABSTRACT FROM AUTHOR]

In this paper, two systematic literature searches are used to pursue questions about digitization in management reporting on the one hand and role-specific questions on the other. We assume that greater digitization in terms of efficiency and effectiveness will change the role of management accountants in general and in reporting in particular, as this should lead to more automation and possibly more time for consulting activities from management accountants/controllers. After a strictly documented filtering process, a total of 51 papers remained for the analysis of the research questions posed. As a result, in addition to research gaps in management reporting, the most important information technology (IT) tools in management accounting and management reporting can be identified. Concerning corporate performance and management accounting performance, the research also reveals a connection with digitization/IT trends in controlling, controller roles, and role conflicts. The systematic literature evaluations serve as a basis for further research to verify the possible causal relationships found. [ABSTRACT FROM AUTHOR]

LoRa (Long-Range) has become the Deoxyribo Nucleic Acid (DNA) of the Internet of things (IoT) for equipping smart solutions. Home automation is responsible for providing a safe and stylish home. This paper proposes a capable architecture of home automation for both short-range and long-range utilizing multiple communication technologies, namely LoRaWAN, server-based LoRa gateway, and Bluetooth connectivity. This integrated system effectively controls distinct types of home appliances and keeps smart management among all the electronics components. A regular user can easily manage these unified systems by using an Android application. This paper also presents experimental data analysis. The results and discussion section provide a set of experiments like estimated transmission delay calculation for LoRa, Wi-Fi, and Bluetooth, a coverage area calculation for LoRa with RSSI and SNR values, and a System Usability Scale (SUS). The scheme has achieved a SUS score of 93%. However, the proposed architecture can be called an outright package for smart home and will be very workable, abuzz, and handy. [ABSTRACT FROM AUTHOR]

In additive manufacturing with metallic base material, Laser Powder Bed Fusion (L-PBF) is an industrially established technology for tool-free production of complex and individualized components and products by applying material layer by layer. In addition to in-processing, the L-PBF process chain consists of several other process steps in pre- and post-processing. A holistic view of the additive manufacturing process chain shows that many process steps currently involve non-value-adding (NVA) operations and that there is generally great potential for reducing throughput times and costs. Due to the great importance of efficiency in industrial manufacturing process chains, this paper proposes a concept for analyzing the L-PBF process chain and deriving measures to eliminate NVA operations and increase the efficiency of value-adding (VA) operations. First, this paper presents a methodology for identifying VA and NVA operations that encompasses the entire chain. After the process mapping, the process steps are divided into sub-tasks to subsequently analyze them with regard to the level of automation. This analysis is the basis for selecting the appropriate automation strategy and optimizing the individual process steps by applying automation-aligned lean production methods to increase the proportion of VA operations. The potential for a significant increase in the productivity of the additive manufacturing process chain is demonstrated through systematic analysis and requirements-based optimization of the level of automation. The approach is structured in an adaptive manner so that, beyond the possibilities of lean production and automation, other optimization alternatives from further disciplines, such as digitization and networking, can be integrated. [ABSTRACT FROM AUTHOR]

Reconfigurable Pilot Lines (RPLs) are one practical concept for implementing the test-before-invest strategy to improve skills and capabilities of European small and medium enterprises (SMEs). SMEs typically have limited resources for systematically exploring the potential benefits of available technologies. By utilizing the RPLs offered by research institutes and universities, companies can get both commercially uncommitted and realistic proof-of-concept studies for their applications, and valuable information about the skills and personnel competences needed for successful implementation of the new technologies. Test-before-invest services offered by RPLs can also foster the process of adopting new digital technologies in manufacturing industry. RPLs are versatile. RPL can act as a test bed for manufacturing company's investment study, industry-scale product development laboratory for technology provider, as well as a platform for scientific research and education. Due to versatile roles and functions, defining pilot line requirements specifications call for systematic contribution from both industry and academia. This paper introduces a collaborative approach for defining requirements specifications for RPLs from the future technology skills development perspective. As a basis for this research, we combine results of three joined industry-academia workshops and one academic workshop with total 87 participants utilizing Affinity Wall method. This data was supplemented with an online survey for 74 companies from manufacturing industry. The analysis indicates that defining requirements specifications and development roadmaps for RPLs is a complex process, requiring multidisciplinary collaboration. Based on the analysis, a conceptual model supporting the requirements specification work is proposed in this paper. The model was applied to an existing testbed environment for further evaluation. [ABSTRACT FROM AUTHOR]

With the increasing performance of computer hardware and the resulting computing capabilities, machine learning more and more transforms into a common tool for automating a wide range of applications. While today's society is aware of the potential and possibilities this technology provides, most small companies often lack the manpower, problem solving imagination or basic knowledge to implement and use machine learning for those purposes, which is why many process steps are still carried out manually. Consequences are increasing expenses and manufacturing time caused by process delays. The use case described in this paper regards the manufacturability check (feasibility of the coating process) of sheet metal objects; a task that still has to be performed by an expert. The system proposed in this paper aims to fully automate this process by applying convolutional neural networks (CNN) on transformed computer-aided design (CAD) objects in order to determine their coatability. [ABSTRACT FROM AUTHOR]

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively. [ABSTRACT FROM AUTHOR]

Simultaneous Multithreading (SMT) allows for a processor to concurrently execute multiple independent threads while sharing certain data path components to optimize resource waste. Speculative execution allows for these processors to take advantage of Instruction-Level Parallelism but the penalty for a miss speculation includes the wasting of resources amongst these shared resources where clock cycles are wasted at a time. In this paper we show that an average of 13 % of instructions are flushed as a result of incorrect predictions. These flushed out instructions could have potentially taken up shared resources which other non-speculative threads could have used. This paper proposes a technique that can dynamically adjust how many speculative instructions a thread can rename and decode aiming to diminish the waste of the shared resources. Our simulation results show, with the proposed technique, that the average flushed out instruction rate is reduced by 23 % and average throughput is improved by 13 %. [ABSTRACT FROM AUTHOR]

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education. • SBTC/RBTC and firms' innovation behavior theories are incorprated in a unified framework. • R&D inputs promote the demands for high- and low-skilled labor, and rising TFP reduces the demand for low-skilled labor. • Industrial concentration leads to a cyclical pattern of the demand for low-skilled labor rather than for high-skilled labor. • There exists a paradoxical relationship between higher education and skill demand. [ABSTRACT FROM AUTHOR]

• A general mechanism reliability analysis framework. • Probability space non-equal weight profiling is used to reduce computational effort. • The direct probability integral method is used to ensure accuracy with a small sample size. • For the time-varying mechanism, the extreme value method is used to calculate the reliability. Reliability of mechanism systems is very important. However, actual engineering machinery systems are affected by various random factors, such as limited manufacturing accuracy, unavoidable assembly errors, essential motion clearance, etc., which cause the actual motion of the mechanism system to deviate significantly from its ideal design, resulting in a significant deficiency in the dynamic reliability of the mechanism system. Therefore, in this paper, we start from the general dynamic equation of the mechanism system to study the high-performance computational method of dynamic response reliability variation due to parameter uncertainty. Firstly, the multi-body dynamics equations of the mechanism system with parameter uncertainties are established. Secondly, the variable weight sampling point strategy is used to substitute the sampling point into the multi-body dynamics equation to obtain the corresponding dynamic time-domain response. Then, the direct probability integral method (DPIM) is used to calculate the extreme value reliability of the dynamic time-domain response. Finally, a typical computer numerical control (CNC) machine tool is taken as an example to verify the effectiveness of the dynamic reliability method proposed in this paper for general mechanism systems. The main advantage of this method is that it eliminates the need for repeated derivations of dynamic models for different mechanism systems and can handle reliability problems with complex performance functions, thus offering greater generality. In addition, it solves the problem of the time-variant reliability of mechanical systems. In particular, the use of low-discrepancy sequences as the initial point set further improves the computational efficiency of the dynamic reliability analysis of the mechanism system. [Display omitted] [ABSTRACT FROM AUTHOR]

• A recognized microwave waveform generation is proposed using time-delayed feedback reservoir computing. • This work demonstrates that an SOA-based time-delayed feedback reservoir computing is constructed and theoretically analyzed. • A variety of optimization algorithms are used to demonstrate and compared the automated capability of waveform synthesis. • The paper demonstrates the six waveforms with low RMSE generated by the proposed microwave waveform generation. This paper proposes and demonstrates a model for generating microwave waveforms using a delay feedback photonic reservoir computing system with SOA. Our scheme involves building a photonic reservoir computer with SOA, which provides rich nonlinearity functionalities and memory capability for the reservoir. The automatic optimization of output waveform is implemented through the incorporation of gradient descent regression and a reservoir output layer. In theory, the system is capable of generating arbitrary microwave waveforms towards the target automatically. In our work, the capacity of the model is tested through theoretical analysis and simulation. We also compared the optimization effects of several regression algorithms applied to the reservoir computing, and generated six typical microwave waveforms, including triangle, square, sawtooth, negative, oblique triangle 1, and oblique triangle 2. [ABSTRACT FROM AUTHOR]

Automation has the potential to transform entire agricultural value chains and the nature of agricultural business. Recent studies have emphasised barriers to adoption, as well as issues related to labour market and cultural outcomes of automation. However, thus far, very little attention has been afforded to the regional variations in the potential for automation adoption or threats to agricultural employment. Specifically, research to date does not take into account the local availability of similar occupations including those in different sectors to which displaced workers may transition. Threats to employment and lower numbers of similar jobs locally are particularly salient in rural contexts, given the thin and specialized local labour markets. The aims of this paper are to show the regional distribution of risk to automation for the agricultural sector specifically, and to link these patterns to indicators for occupation specific labour market thickness in Ireland. Using detailed occupational skills data, we construct indices for local labour market thickness conditioned on occupational skills and knowledge requirements. We show that there is substantial regional heterogeneity in the potential threat of automation to the employment prospects of workers currently active in the agricultural sector. This regional heterogeneity highlights the importance of the regional context for designing effective labour market policy in the face of job automation. • The K-means cluster analysis in this paper reveals four distinct types of agricultural employment separated on knowledge- and skills-bases. • Agricultural employment at high risk of automation can transfer to low risk jobs in other sectors. • The availability of low risk alternative jobs is heterogeneous across regions. • We reveal regional clusters of automation risk and low numbers alternative jobs for farmers and forestry workers in the Borders region in Ireland. [ABSTRACT FROM AUTHOR]

This paper offers an alternative approach for engaging sociologically, ontologically, and politically with digital farming platforms. A challenge faced by any approach looking to upend intellectual conventions, especially ontological ones, lies in the question of representation, namely, how do we talk about digitization in novel theoretical ways using language rooted in more than two millennia of western human-centered thought? One way to deal with this challenge, the one adopted in this paper, involves decentering familiar terms and repurposing them. Thus, rather than organizing the argument around familiar terms like small and big data, I argue instead that we organize data-assemblages according to what they do , which makes this a political ontological project. To do this, I offer the relational predicate of weak data and strong data, suggesting that they are good to think with in this regard. The argument ultimately lands at a place for analyzing these platforms that offers the potential for critique and perhaps even a degree of optimism, as I suggest a framework for normatively evaluating these practices. The paper draws upon various empirical studies of digital agriculture conducted by the author, which include interviews with farmers, farm laborers, hacktivists, investors, and engineers from numerous countries and locales. • Digital farming platforms ought to be judged by what they do. • The outcomes and impacts of Agriculture 4.0 are unevenly distributed. • More social science research is needed on the subject. [ABSTRACT FROM AUTHOR]

Automation systems are soaring due to the innovation of artificial intelligence (AI) technologies. In this paper, we mainly concentrate on the security and efficiency of data transmission in AI-based automation systems, especially, for data sharing in a group manner in distributed networks. To this end, an authenticated group key agreement protocol is proposed for secure data transmission in AI-based automation systems. In order to release the computational overhead of distributed nodes, a semi-trusted authority (STA) is introduced to enable precomputation operations. Moreover, to overcome the predominantly distributed denial of service (DDoS) attack, a dynamic batch verification mechanism is devised. The presented dynamic batch verification mechanism guarantees that the proposed protocol can be run properly among legitimate nodes regardless of the existence of nodes who suffered from the DDoS attack. Finally, the session key security of the proposed protocol is proved and the performance is evaluated. [ABSTRACT FROM AUTHOR]

• In-depth discussion on ML application for ground prediction in TBM tunneling. • Analyzes significant research in ground prediction for tunnel automation. • Critically examines the limitations of existing research. • Argues that current models are not yet fully ready for tunnel automation. • Identifies opportunities for further research in tunneling and automation. Automation of construction machines has grown rapidly in recent years as a response to the need to increase productivity, increase construction safety, decrease costs, and overcome the lack of availability of qualified labor. However, tunnel automation still lags. Automation of mechanized tunneling is essential to the future of tunneling construction, as the current practice still relies heavily on the experience and the human judgment of the machine operator to steer the TBM, which could lead to undesirable events. The primary motivation for this review paper stems from the statement: the success of tunneling automation relies on precise ground prediction. Even small inaccuracies can have significant implications, and the current reliance on human experience and judgment presents limitations and risks. With the abundance of machine data now available from TBMs, and the advancements in data analytics and machine learning (ML), many models have been proposed. These models have the potential to revolutionize tunneling by providing better decision-making support, including geology forecasts and anomaly detection. However, despite the numerous research studies and advantages of these models, they are not widely implemented in real-world scenarios. Thus, this review paper aims to address this issue by focusing on ground prediction models for TBM tunnels, providing a comprehensive overview of the current state-of-the-art, illuminating the existing practices, and highlighting the limitations that hinder these models from being the catalysts of tunneling automation. By emphasizing these challenges, the paper seeks to not just critique but also guide, providing recommendations for future research that promise to bridge the gaps and potentially usher in an era of fully automated TBMs. [ABSTRACT FROM AUTHOR]

The increasing usage of social media networks has raised concerns about the growing frequency of cyberbullying incidents. The definition of cyberbullying lacks universal consensus, yet according to several authors, cyberbullying is characterized by aggressive, repetitive, and intentional communication among peers. However, existing cyberbullying detection datasets often focus solely on classifying texts as aggressive or non-aggressive, neglecting the other cyberbullying aspects, thus hindering research progress. This paper proposes a framework for designing a new dataset incorporating all four aspects of cyberbullying to address this gap. The text messages are sourced from a real dataset, while the users' data is generated synthetically. The resulting dataset contains messages exchanged randomly among different pairs of users, thus inculcating repetition. Additionally, the degree of peerness, defined and calculated to measure the likelihood of two users being peers, is used. The intent of harm is quantified as a numeric value using the ratios of aggression and repetition. As a result, the proposed dataset encompasses all four aspects of cyberbullying by providing repeated aggressive messages among users along with quantitative values of the degree of peerness and intent to harm. The proposed dataset is adaptable, with adjustable threshold values for peerness, repetition, and intent to harm, offering flexibility for various applications. The paper concludes by presenting the results of some baseline machine-learning methods on the proposed dataset. • Generation of a semi-synthetic dataset for automatic cyberbullying detection. • The dataset includes all aspects of cyberbullying: aggression, repetition, intent to harm, and occurrence among peers. • Presentation of baseline results on the proposed dataset. [ABSTRACT FROM AUTHOR]

In this constantly evolving landscape of urbanization, the relationship between technology and automation, in regards to sustainability, holds immense significance. The intricate strands of human intelligence are seamlessly interwoven with the fabric of technological progress, giving rise to exquisite patterns of synergy and collaborative innovation. Automation is just another step in this process which started with the industrial revolution and now has paved way towards urbanization. Smart homes or home automation is a subset of Internet of Things (IoT) based automation that has added into the comfort, ease, and quality of our living standards and is now being integrated to form the concept of Smart Cities. In the past decade, various techniques and processes of smart home automation have been proposed and implemented. To extend and translate the existing methods into new one, the understanding of the former is imperative to the research procedure. This review stands as a comprehensive exploration, diving into the pivotal role of intelligent systems and expert knowledge in driving the transformation of smart homes into sustainable smart cities. By meticulously analyzing and aggregating an array of contemporary techniques used in smart homes, this paper offers profound contributions to the intersection of urban evolution and technological innovation. The review's holistic approach not only facilitates a deep understanding of smart homes' contributions but also charts a course for innovative strategies in city planning, infrastructure, and technological integration. In bridging the gap between technology and sustainable urban development, this exploration underscores the transformative power of leveraging smart home techniques to lay the foundation for harmonious and forward-thinking smart cities. The technologies cover a wide range of methodologies and intelligent systems used for communication, security and management in an urban infrastructure. The paper focuses on analysis of the technology to provide an outlook into achieving the goal of sustainable smart cities and deal with challenges like scalability and big data computation. Our comprehensive analysis yields a holistic set of technology comparisons and illuminates the promising future prospects within this domain. The information is highly insightful in creating a bigger picture for adopting state of the art technologies like Federated Learning (FL), Digital Twin and Embedded Edge computing in better planning and infrastructure management in smart cities. These findings offer reliable and potent methods to chart not only the course of research but also to enhance these technologies for the betterment of mankind's convenience and advancement. • Technical advancement needed for transformation of home automation to a next level. • Smart cities that build from the foundation of homes automations have been discussed. • Comparison with the existing papers to unlock new research directions in this area. [ABSTRACT FROM AUTHOR]

The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

The paper evaluates the sensitivity of sonic tomography imaging of historic masonry structures to variations in stress level and damage. Six stone masonry walls with different geometries representative of historical typologies were constructed by a professional mason. A laboratory campaign was carried out, subjecting the walls to cyclic uniaxial compression tests. During the test, an automated sonic tomography system was used to inspect the wall under loading cycles of increasing amplitude, which led to obtain tomographic images during loading and compare them under different stress level and damage condition. The use of robotic systems was proved essential to carry out the sonic inspections simultaneously to the compression tests. Results show that sonic wave propagation is not only sensitive to damage level, but also to the stress state. Thus, sonic tomography has the potential to be used to measure damage and stress level of masonry components over time, for example during renovation works on an existing construction. • A novel automated sonic tomography system has been used to inspect masonry walls during laboratory tests. • Sonic tomography of stone masonry structures is sensitive to variations in the stress level and damage propagation. • Automated sonic tomographic systems may be used to monitor historic buildings during construction works. • The paper explores the correlation between the sonic wave velocity through stone masonry and material properties. [ABSTRACT FROM AUTHOR]

The red meat processing industry has a harsh work environment where tasks performed in abattoirs are physically and mentally demanding. In addition, the high financial costs associated with employing skilled labour, the shortage of such workers, and the rise in worldwide meat consumption, there has been a growing push towards integrating automation as a potential solution for the industry. This paper describes the complexities of implementing robotics technology in red meat processing. The complexity when processing deformable natural meat mediums is significantly sensitive to the variations of workpieces caused by mechanical properties, physical shape and the position of tissues. These differences hinder conventional robotic systems from succeeding. Experimental and commercial robotic systems in red meat processing are shown to perform cutting tasks in the deboning room, whose systems capabilities are limited by executing cuts requiring little to no adaptability during the process. The review shows that X-ray, optical probes, and ultrasonic are the most effective sensing technologies in determining the cutting trajectories prior to the task. Some experimental systems utilised tactile sensing to follow more complex cutting paths but have not yet produced a commercially viable product. The evaluation of these sensing technologies' applicability to guide a robotic system in real-time is critical to tackling more complex cuts. A combination of preoperative scanning and real-time perception for adaptive control is recommended to automate tasks in red meat cutting. Also, it is recommended that to fully automate the meat cutting process, a gradual approach should be taken by shifting abattoirs by first utilising assistive technologies such as cobots, exoskeletons augmented reality, and virtual reality. • Explores automation integration in red meat processing, its motivation, and challenges for implementation. • Recommends combining preoperative scanning and real-time perception for adaptive control in meat cutting. • Reviewed the experimental and commercially available automatic systems for carcass cutting and deboning. • Evaluates the developed sensing technologies' applicability to guide a real-time automatic system. • Assistive robotics technologies are a recommended approach towards fully automating the meat industry. [ABSTRACT FROM AUTHOR]

The increase in computer numerical control machine efficiency highly contributes to environmental emission reduction and energy-savings. Path and trajectory optimizations are used to improve machine efficiency in a coverage motion such as pocket milling, polishing, inspection, gluing, and additive manufacturing. Several studies have proposed coverage motion optimization in improving machine efficiency for time and energy consumption. Ensuring the smoothness and satisfaction of the machine constraints in coverage motion is necessary. This paper proposes a multi-objective path and trajectory optimization to obtain a trade-off between time and energy consumption for coverage motion. Jerk limited acceleration profiles describe the trajectory where velocity profiles generated for each linear segment attain desirable velocities. The energy model of an industrial two-axis feed drive system is used in finding solutions to the optimization problem. The non-dominated sorting genetic algorithm II generates a Pareto front for trade-off time and energy consumption solutions. Simulation results of the proposed method are validated through experiments using the industrial two-axis feed drive system. Experimental results show the effectiveness of the proposed approach where time reduction and energy savings are 10.05% and 2.10%, respectively. In addition, the optimized path has a lower maximum error of 76.6% compared to the constantly commanded velocity optimized path. • A method of simultaneous trajectory generation and path optimization is proposed. • Jerk limited acceleration profiles describe the trajectory. • The NSGA II is used to find the trade-off between time and energy consumption. • The best trade-off solution achieves time reduction and energy savings of 10.05% and 2.10%. • Optimized path motion with variable velocities reduces maximum error by 76.6%. [ABSTRACT FROM AUTHOR]

Industry 4.0 has driven the need for Information Technology (IT) & Operational Technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as Programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure. [ABSTRACT FROM AUTHOR]

Integration of management information systems is crucial to optimizing internal and external processes in enterprises' environments. One of the technologies sup-porting this integration is OPC UA. An embedded automation device OPC UA server can transmit either dead-band tested analog attributes or all changes of ana-log attributes to OPC UA clients. Typically OPC UA server to OPC UA client data transmission is based on subscriptions. Subscription interval, sampling interval, and dead-band are usually controlled by OPC UA clients [1]. It is, however, desirable that OPC UA data traffic is controlled by automation de-vice control programs and the embedded OPC UA servers and not by OPC UA clients as specified in the norm. This is because the OPC UA server is the limiting component in the client sever communication scheme. Floating-point attributes in an automation device have small changes all the time, but in real-world plants, analog attributes do not necessarily have to be frequently communicated to OPC UA clients on small changes. In addition to an attribute dead-band test, changes in some attributes can safely be blocked a minimum time since the last attribute update before a new value is transmitted to the UA clients. Analog attributes can also be cyclically transmitted to OPC UA clients with a low frequency to ensure that they receive the exact attribute value after a set maximum time. This paper demonstrates an implementation of this communication scheme by using a device function block to control traffic between an OPC UA server and OPC UA clients for analog attributes. [ABSTRACT FROM AUTHOR]

Over the past several decades, the global economy has undergone a significant transformation, primarily driven by advancements in automation and robotics. This paper aims to examine recent progress in manufacturing automation and shed light on potential challenges that contribute to the observed disparities in automation adoption. Despite the limitations imposed by a lack of comprehensive statistical data, this analysis has yielded several significant insights. These include insufficient investment in automation, variations in enterprise size structure, and ongoing disparities in labor costs and productivity. Of these factors, the latter appears to be the primary obstacle to rapid industrial automation, as it encourages the reshoring of manufacturing operations to home countries and offshoring to medium- and low-income economies. [ABSTRACT FROM AUTHOR]

Advances in automotive engineering have brought about a range of new vehicle technologies, such as automation, connectivity and electrification, which are expected to have multiple effects on different aspects of people's lives and have the potential of radically transforming the transportation status quo. This paper explores the effects that such new technologies can have on land use by employing a Land Use and Transportation Interaction (LUTI) modelling approach. A new methodology is developed to simulate the effects of automation, connectivity and electrification on accessibility and to conduct a sensitivity analysis. The analysis is carried out on a regional scale to determine the effects of the new technologies on multiple cities that form a region. The case study for this application is the region of the West Midlands (UK). The results suggest that new vehicle technologies can affect accessibility, population and employment in the cities of the region, the region itself, as well as the peripheral zones beyond the boundary of the region in a 15-year modelling period. Most notably, the two main urban cores of the region are found to not lose their financial power, despite increases in employment opportunities and population density in more rural outskirts. The increased accessibility also results in the relocation of lower-income households to places with lower housing rent. The sensitivity analysis shows that these findings are mostly impacted by changes in road capacity (which is expected to be generally higher as a result of these new technologies), as well as different penetration levels in the fleet. • Regional accessibility increases with the existence of new vehicle technologies. • Activity is affected from vehicle automation, connectivity and electrification. • The centres of the largest cities will not lose their financial power. • Activity increases in the peripheral zones of the cities, indicating urban sprawl. [ABSTRACT FROM AUTHOR]

This paper presents a new AI-based smart factory construction model for SMEs based on the Korean manufacturing industry and proposes an effective construction process that implements the informatization, automation, and intelligentization characteristics of smart factories. The Korean AI-based smart factory construction process starts with the accumulation of digital information by attaching sensors to the manufacturing processs, and then creates a customized AI model for the company through cloud accumulation of sensor data and big data analysis through CNN techniques. The Korean SME Smart Factory is an AI-based smart factory through AHP analysis and smart factory level diagnosis system, which is a construction process, which means a consumer-oriented intelligent factory that combines next-generation digital technology and manufacturing technology beyond the existing level of factory automation (FA). Various products can be produced on one production line, and modularization is expected to change from mass orders to individual flexible production systems. The transition to smart factories is expected to dramatically improve the productivity of manufacturing, as well as realize energy savings and a more human-centered work environment. By monitoring and controlling manufacturing sites in a virtual space, it is expected to facilitate factory management and lead to improved quality and cost competitiveness. Keywords: AI Smart Factory, Level Diagnosis System, Korean SMEs, AHP Analysis [ABSTRACT FROM AUTHOR]