Process automation is a crucial strategy for improving business processes, but little attention has been paid to the effects that automation has once it is operational. This paper addresses this research problem by reviewing the literature on human-automation interaction. Although many of the studies in this field have been conducted in different domains, they provide a foundation for developing propositions about process automation effects. Our analysis focuses on how humans perceive automation technology when working within a process, allowing us to propose an effective engagement model between technology, process participants, process managers, and software developers. This paper offers insights and recommendations that can help organizations optimize their use of process automation. We further derive novel research questions for a discourse within the process automation community.
Although several accounts of scientific understanding exist, the concept of understanding in relation to technology remains underexplored. This paper addresses this gap by proposing a philosophical account of technological understanding: the type of understanding that is required for and reflected by successfully designing and using technological artefacts. We develop this notion by building on the concept of scientific understanding. Drawing on parallels between science and technology, and specifically between scientific theories and technological artefacts, we extend the idea of scientific understanding into the realm of technology. We argue that, just as scientific understanding involves the ability to explain a phenomenon using a theory, technological understanding involves the ability to use a technological artefact to realise a practical aim. Both theories and artefacts are tools, and using them successfully requires the cognitive skill of understanding. Technological understanding is thus conceived as the ability to recognise how a practical aim can be achieved by using a technological artefact. In a context of design, this general notion of technological understanding is specified as the ability to design an artefact that, by producing a phenomenon through its physical structure, achieves the intended aim. By analogy with De Regt's criterion of the intelligibility of theories, we give, as a precondition for technological understanding, a criterion for the intelligibility of a technological artefact. We illustrate our concept of technological understanding through two running examples: magnetic resonance imaging (MRI) and superconducting quantum computers. Our account highlights the epistemic dimension of engaging with technology and, by allowing for context-dependent specifications, provides guidance for testing and improving technological understanding in specific contexts.
This paper studies the macroeconomic effects of news about future technological advancements in the green sector. Utilizing the economic value of green patents granted to publicly listed companies in the U.S., we identify green technology news shocks via a convenient and meaningful rotation of the innovations from a Bayesian Vector Autorregresion Model (BVAR). These shocks are decomposed into two orthogonal components: i) a common technological component shared by both green and non-green innovation, that reproduces response patterns similar to those expected from a technology news shock with long-run impacts on productivity; and ii) an idiosyncratic component to green innovation inducing inflationary pressures and stock price reductions. The responses to the idiosyncratic component suggest the existence of a green transition news mechanism related to expectations of more rigorous carbon policies or stricter environmental standards in the future. The focus on green innovation deepens our understanding about the effect of technology-specific news shocks and provides information of practical importance for macroeconomic and environmental policies.
Julien Kindle, Michael Loetscher, Andrea Alessandretti
et al.
Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash in various parts along the kinematic chain. In this work, we introduce a novel approach that integrates deflection and backlash compensation models with high-accuracy accelerometers, significantly enhancing position accuracy. Our method employs a modular framework based on a factor graph formulation to estimate the state of the kinematic chain, leveraging acceleration measurements to inform the model. Extensive testing on publicly released datasets, reflecting real-world construction disturbances, demonstrates the advantages of our approach. The proposed method reduces the $95\%$ error threshold in the xy-plane by $50\%$ compared to the state-of-the-art Virtual Joint Method, and by $31\%$ when incorporating base tilt compensation.
Advances in technology, growing concern about climate change, and the setting of greenhouse gas emission reduction targets in many countries have contributed to a significant increase in the demand for alternative fuel vehicles globally over the last decade. Electric vehicles, which include all-electric vehicles (BEVs) and plug-in hybrids (PHEVs), are the most promising alternative to conventional hydrocarbon vehicles. It is very likely that in some regions of the world electric vehicles will dominate the market as early as the 2030s. However, compared to internal combustion engine vehicles, the production of electric vehicles requires a wider range of non-ferrous metals, which may become one of the bottlenecks for further electrification of transportation. This paper presents a scenario analysis of the development of the electric vehicle market, and then calculates the key metal requirements for each of the scenarios considered. The results of this analysis reveal that, between now and 2050, the accelerating spread of electric vehicles will have a significant impact on the cobalt market, a moderate impact on the lithium, nickel, and copper markets, and a minor impact on the manganese and aluminum markets. The results of the analysis demonstrate that the increasing use of electric vehicles in the coming decades opens up significant opportunities for countries specializing in the production of non-ferrous metals, including Russia, to increase their supply to global markets.
Appendices
This study introduces an innovative approach to automating Cyber Threat Intelligence (CTI) processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds. This study introduces an innovative approach to automating CTI processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds, security logs, and dark web forums -- a process prone to inefficiencies, especially when rapid information dissemination is critical. By employing the capabilities of GPT-4o and advanced one-shot fine-tuning techniques for large language models, our research delivers a novel CTI automation solution. The outcome of the proposed architecture is a reduction in manual effort while maintaining precision in generating final CTI reports. This research highlights the transformative potential of AI-driven technologies to enhance both the speed and accuracy of CTI and reduce expert demands, offering a vital advantage in today's dynamic threat landscape.
Automated software testing is integral to the software development process, streamlining workflows and ensuring product reliability. Visual testing, particularly for user interface (UI) and user experience (UX) validation, plays a vital role in maintaining software quality. However, conventional techniques such as pixel-wise comparison and region-based visual change detection often fail to capture contextual similarities, subtle variations, and spatial relationships between UI elements. In this paper, we propose a novel graph-based approach for context-aware visual change detection in software test automation. Our method leverages a machine learning model (YOLOv5) to detect UI controls from software screenshots and constructs a graph that models their contextual and spatial relationships. This graph structure is then used to identify correspondences between UI elements across software versions and to detect meaningful changes. The proposed method incorporates a recursive similarity computation that combines structural, visual, and textual cues, offering a robust and holistic model of UI changes. We evaluate our approach on a curated dataset of real-world software screenshots and demonstrate that it reliably detects both simple and complex UI changes. Our method significantly outperforms pixel-wise and region-based baselines, especially in scenarios requiring contextual understanding. We also discuss current limitations related to dataset diversity, baseline complexity, and model generalization, and outline planned future improvements. Overall, our work advances the state of the art in visual change detection and provides a practical solution for enhancing the reliability and maintainability of evolving software interfaces.
Von Schomberg offers a compelling examination of key open science principles and their potential role in fostering responsible research and innovation (RRI). Utilizing Merton's Ethos of Science framework, the paper constructs a series of arguments supporting a central thesis: “the transition towards open science is vital to facilitate RRI.” This transition necessitates significant institutional reforms within the scientific community and adjustments to incentive structures that promote the adoption of open and mutually responsive practices.
The manuscript reframes the discourse surrounding responsibility and responsiveness in light of the evolving landscape of open science, shifting the focus from normative commitments to actionable frameworks in research and open science practices. Overall, the position paper strives to bridge the gap between idealised models of scientific communities based on RRI principles and the reality of actual scientific endeavour (Anderson et al., 2007; Politi, 2021, 2024).
However, it is important to acknowledge certain omissions that could enrich the analysis. Firstly, a more comprehensive examination of the profound crisis facing science amidst the increasing marketisation and commodification of academia and research would provide valuable context beyond discussions of system failures related to productivity and reproducibility. Secondly, a more nuanced and critical approach to conceptualising open science would enrich the discussion, considering its multifaceted nature and potential pitfalls. Thirdly, the validity of the Mertonian framework and its selective analysis of values, particularly its exclusive focus on the norm of communism. Lastly, a deeper exploration of the challenges and promises inherent in the pursuit of responsible Open Science within ongoing institutional processes.
This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO<sub>2</sub> levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO<sub>2</sub> and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.
Engineering machinery, tools, and implements, Technological innovations. Automation
This study examines the relationship between automation and income inequality across different countries, taking into account the varying levels of technological adoption and labor market institutions. The research employs a panel data analysis using data from the World Bank, the International Labour Organization, and other reputable sources. The findings suggest that while automation leads to an increase in productivity, its effect on income inequality depends on the country's labor market institutions and social policies.
Notorious cases of corporate misconduct often revolve around the misapplication of pay to performance. Yet many business schools have too easily given themselves up to these kinds of high-powered incentives in the management of research. This practice is contrary to the very management knowledge taught in business school classrooms and it can wreak havoc with business schools’ mission of knowledge production. The reduction of managing research to a bean-counting performance evaluation, that is, keeping count of discrete units of research outputs as A-class journal hits and citation counts, has arguably tilted the scales in favor of form and against content. This undermines both the quality of knowledge produced and the autonomy that academics need to create knowledge. Much as combat sports, football or soccer, and democratic societies prevent certain traits and actions from conferring an unfair advantage, academics need to reclaim the principle of a level playing field to prevent practices inimical to the academic enterprise.
Context. A universally accepted definition of what a vortex is has not yet been reached. Therefore, we lack an unambiguous and rigorous method for the identification of vortices in fluid flows. Such a method would be necessary to conduct robust statistical studies on vortices in highly dynamical and turbulent systems, such as the solar atmosphere. Aims. We aim to develop an innovative and robust automated methodology for the identification of vortices based on local and global characteristics of the flow. Moreover, the use of a threshold that could potentially prevent the detection of weak vortices in the identification process should be avoided. Methods. We present a new method that combines the rigor of mathematical criteria with the global perspective of morphological techniques. The core of the method consists in the estimation of the center of rotation for every point of the flow that presents some degree of curvature in its neighborhood. For that, we employ the Rortex criterion and combine it with morphological considerations of the velocity field. We then identify coherent vortical structures by clusters of estimated centers of rotation. Results. We demonstrate that the Rortex is a more reliable criterion than are the swirling strength and the vorticity for the extraction of physical information from vortical flows, because it measures the rigid-body rotational part of the flow alone and is not biased by the presence of pure or intrinsic shears. We show that the method performs well on a simplistic test case composed of two Lamb-Oseen vortices. We combine the proposed method with a state of the art clustering algorithm to build an automated vortex identification algorithm. (Abridged)
Marfri Gambal, Aleksandre Asatiani, Julia Kotlarsky
Competition in the Information Technology Outsourcing (ITO) and Business Process Outsourcing (BPO) industry is increasingly moving from being motivated by cost savings towards strategic benefits that service providers can offer to their clients. Innovation is one such benefit that is expected nowadays in outsourcing engagements. The rising importance of innovation has been noticed and acknowledged not only in the Information Systems (IS) literature, but also in other management streams such as innovation and strategy. However, to date, these individual strands of research remain largely isolated from each other. Our theoretical review addresses this gap by consolidating and analyzing research on strategic innovation in the ITO and BPO context. The article set includes 95 papers published between 1998 to 2020 in outlets from the IS and related management fields. We craft a four-phase framework that integrates prior insights about (1) the antecedents of the decision to pursue strategic innovation in outsourcing settings; (2) arrangement options that facilitate strategic innovation in outsourcing relationships; (3) the generation of strategic innovations; and (4) realized strategic innovation outcomes, as assessed in the literature. We find that the research landscape to date is skewed, with many studies focusing on the first two phases. The last two phases remain relatively uncharted. We also discuss how innovation-oriented outsourcing insights compare with established research on cost-oriented outsourcing engagements. Finally, we offer directions for future research.
This study examines the impact of technological leapfrogging on manufacturing value-added in SSA. The study utilizes secondary data spanning 1990 to 2018. The data is analyzed using cross-sectional autoregressive distributed lags (CS-ARDL) and cross-sectional distributed lags (CS-DL) techniques. The study found that technological leapfrogging is a positive driver of manufacturing value-added in SSA. This implies that SSA can copy the foreign technologies and adapt them for domestic uses, rather than going through the evolutionary process of the old technologies that are relatively less efficient. If the governments of SSA could reinforce their absorptive capacity and beef up productivity through proper utilization of the existing technology. The productive activities of the domestic firms will stir new innovations and discoveries that will eventually translate into indigenous technology
The fourth industrial revolution (4IR) is likely to have a substantial impact on the economy. Companies need to build up capabilities to implement new technologies, and automation may make some occupations obsolete. However, where, when, and how the change will happen remain to be determined. Robust empirical indicators of technological progress linked to occupations can help to illuminate this change. With this aim, we provide such an indicator based on patent data. Using natural language processing, we calculate patent exposure scores for more than 900 occupations, which represent the technological progress related to them. To provide a lens on the impact of the 4IR, we differentiate between traditional and 4IR patent exposure. Our method differs from previous approaches in that it both accounts for the diversity of task-level patent exposures within an occupation and reflects work activities more accurately. We find that exposure to 4IR patents differs from traditional patent exposure. Manual tasks, and accordingly occupations such as construction and production, are exposed mainly to traditional (non-4IR) patents but have low exposure to 4IR patents. The analysis suggests that 4IR technologies may have a negative impact on job growth; this impact appears 10 to 20 years after patent filing. Further, we compared the 4IR exposure to other automation and AI exposure scores. Whereas many measures refer to theoretical automation potential, our patent-based indicator reflects actual technology diffusion. Our work not only allows analyses of the impact of 4IR technologies as a whole, but also provides exposure scores for more than 300 technology fields, such as AI and smart office technologies. Finally, the work provides a general mapping of patents to tasks and occupations, which enables future researchers to construct individual exposure measures.
Stefan Zaichenko, Natalia Jukova, Dmitro Yakovlev
et al.
Сучасний етап розвитку енергетики характеризується широким використанням альтернативних та відновлюваних джерел енергії, вітрогенератори сонячні панелі. Такі системи, як правило, мають надскладну структуру і мають високу питому вартість електроенергії. Наявність поновлюваних джерел енергії дозволяє використовувати їх як окремі, але ефективність та надійність повністю залежать від добових ритмів та пори року. Ці особливості істотно обмежують використання альтернативних джерел енергії як надійного автономного джерела енергії. Наявність надійного резервного джерела живлення на сучасному підприємстві - запорука безпечної та якісної роботи.
Claudia Ayim, Ayalew Kassahun, Bedir Tekinerdogan
et al.
According to the latest World Economic Forum report, about 70% of the African population depends on agriculture for their livelihood. This makes agriculture a critical sector within the African continent. Nonetheless, agricultural productivity is low and food insecurity is still a challenge. This has in recent years led to several initiatives in using ICT (Information Communication Technology) to improve agriculture productivity. This study aims to explore ICT innovations in the agriculture sector of Africa. To achieve this, we conducted a SLR (Systematic Literature Review) of the literature published since 2010. Our search yielded 779 papers, of which 23 papers were selected for a detailed analysis following a detailed exclusion and quality assessment criteria. The analysis of the selected papers shows that the main ICT technologies adopted are text and voice-based services targeting mobile phones. The analysis also shows that radios are still widely used in disseminating agriculture information to rural farmers, while computers are mainly used by researchers. Though the mobile-based services aimed at improving access to accurate and timely agriculture information, the literature reviews indicate that the adoption of the services is constrained by poor technological infrastructure, inappropriate ICT policies and low capacity levels of users, especially farmers, to using the technologies. The findings further indicate that literature on an appropriate theoretical framework for guiding ICT innovations is lacking.