Operational Domain Research Articles

The future of education in sleep science and medicine in Europe: Report of an ESRS workshop.

The European Sleep Research Society (ESRS) is the leading voice for sleep medicine and research in Europe and includes in its mission the objective to promote and develop sleep education. As part of this objective, the Society established a Multidisciplinary Task Force on sleep education, which proposed a workshop on this topic to define the role of ESRS in the future of sleep education in Europe. This includes defining the domains of operation by the ESRS, its most important links and networks, and to recognise areas for development. A concrete goal was to make an action plan with suggestions for eventual Task Forces. Recommendations from the workshop include active collaboration with other Societies and University degree courses in sleep with the objective to position the ESRS as the central focus for sleep education in Europe. Predefined learning objectives and Entrustable Professional Activities were identified as key aspects of future direction. The ESRS Textbook in Sleep Medicine provides a valuable educational resource, and the next edition should expand on the traditional format to include digital material and an integrative platform. The workshop also identified the need to develop a more compact publication to inform non-sleep specialists, university students, and interested members of the public with basic information on sleep and sleep disorders.

Operational Design Domain of Automated Vehicles at Freeway Exit Terminals

Operational Design Domain of Automated Vehicles at Freeway Exit Terminals

A New Method to Find the Balanced Solution to the Transportation Problem with Fuzzy Sets of Type-2 for Supply and Demand

The Transportation Problem is a fundamental problem widely studied in the Operational Research domain. The main objective of this paper is to contribute to a deeper study of the topics of fuzzy transportation problems of linear programming when the supply and demand are represented as a fuzzy set of type-2. Here, we are using a newly proposed method to solve a transportation problem with fuzzy sets type-2 of supply and demand to find a balanced solution. The membership function of a fuzzy set of type-2, which is the set of its feasible solutions, is constructed. The properties of this set are investigated, and the problems of choosing balanced solutions are considered. The newly proposed method of the fuzzy transportation problem is discussed with the help of an illustrative numerical example.

Digital twin-based shop-floor reconfiguration design for uncertainty management

Reconfiguration has emerged as a paradigm that adjusts shop-floor operations based on production requirements in such a way as to obtain desired performance and adapt to changes quickly. Existing research puts more emphasis on optimising the layout and configuration of shop floors based on simplified mathematical models and algorithms. However, uncertainty and complexity of shop-floor operations could diminish the effectiveness of the reconfiguration solution. This paper presents a digital twin-based design method for shop-floor reconfiguration to solve the problems. Digital twin models with dynamic fidelity are used for reconfiguration design to provide effective information of shop-floor complexity. To analyse the impact of uncertainty on shop-floor performance and then guide the shop-floor reconfiguration design, methods of shop-floor performance fluctuation identification, uncertain event extraction, reconfiguration operation domain division are proposed. The proposed method is verified with the case study of a chemical fibre silk cake packing shop floor.

Transfer Learning for Thickener Control

Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, making it a strong candidate for advanced surrogate modeling techniques. However, traditional data-driven approaches often require extensive datasets, which are frequently unavailable, especially in new plants or unexplored operational domains. Developing data-driven models without enough data representative of the dynamics of the system could result in incorrect predictions and consequently, unstable response of the controller. This paper proposes the application of a methodology that leverages transfer learning to address these data limitations to enhance surrogate modeling and model predictive control (MPC) of thickeners. The performance of three approaches—a base model, a transfer learning model, and a physics-informed neural network (PINN)—are compared to demonstrate the effectiveness of transfer learning in improving control strategies under limited data conditions.

Three-dimensional spatial differentiation based on a single metalens.

Spatial differentiation is the key element for edge detection and holds unquestionable significance in the current information era. All-optical computation based on metasurfaces has emerged as a powerful platform for spatial differentiation due to its advantage of high integration and parallel processing. However, while most current works focus on one- or two-dimensional (2D) spatial differentiation, three-dimensional (3D) all-optical computation for compact spatial differentiator remains elusive. In this work, by leveraging handedness multiplexing through the combination of Pancharatnam-Berry (PB) and propagation phases, we propose and demonstrate a high-integrated meta-differentiator system by a single multifocal metalens. This can simultaneously achieve 3D imaging and edge detection, i.e., work for objects at varying distances with the functionality being switchable by merely changing the handedness of the incident light, which significantly extends the capacity of spatial differentiation from a single 2D plane to a comprehensive 3D space. This expansion not only broadens the operational domain but also accelerates the speed of information processing. We anticipate this work will provide exciting opportunities to advance the application of metasurfaces in all-optical computation, information processing, and advanced imaging.

Verification of Vulnerable Road User Pre-Crash Scenarios in Vision-Based Safety Applications

<div>Verifying training datasets in vision-based vehicle safety applications is crucial to understanding the potential limitations of detection capabilities that may result in a higher safety risk. Vision-based pedestrian safety applications with crash avoidance technologies rely on prompt detection to avoid a crash. This research aims to develop a verification process for vulnerable road user safety applications with vision-based detection functionalities. It consists of reviewing the application’s safety requirements, identifying the target objects of detection in the operational design domain and pre-crash scenarios, and evaluating the safety risks qualitatively by examining the training dataset based on the results of pre-crash scenarios classification. As a demonstration, the process is implemented using open-source pedestrian tracking software, and the pre-crash scenarios are classified based on the trajectories of pedestrians in an example training dataset used in a pedestrian automatic emergency braking system. The dataset provides abundant vulnerable road users under various operating conditions. Results show that the dataset does not cover snowy weather, fog, smoke, smog, and dust/dirt of particulate matter conditions. Crossing, cut-in, and front pre-crash scenarios are less well-represented in the dataset than pedestrians on the sidewalk. Very few pedestrians were found in the front-near zone, which suggests the dataset lacks sufficient imminent pre-crash scenarios that pose the greatest safety risk. The performance of the pedestrian tracking software is evaluated and future research for improving the proposed mechanism is discussed.</div>

UNPACKING EXPERTISE IN THE PRIVACY TECH INDUSTRY

Companies that collect personal data have spent billions of dollars complying with a patchwork of global data privacy laws since 2018. In response, a nascent privacy tech industry has emerged, consisting of tech startups, consultants, investors, platforms, and domain experts that collectively help companies build compliant data governance programs. This study recognizes the key role of translating the law into software products by asking: how is expertise defined and encoded in the privacy tech industry? I draw on fieldwork from a broader ethnographic study of the privacy tech industry to identify three findings. First, the privacy tech industry constitutes a networked arena of relations structured by partitioning professional expertise across technical, legal, and operational domains. Second, technical expertise in the privacy tech industry is often tenuous and contingent, which could be strengthened by applying scrutiny and deliberation to evaluate the content of expertise rather than its performance. Third, boundaries of expertise are increasingly encoded in compliance software, perpetuating performative rather than scrutinized expertise. At scale, these products promote managerial processes that manifest as checkbox compliance, codifying splintered accountability and undermining the spirit of data privacy law. I argue that technology policy would benefit from scrutinizing the contents of expertise, inviting agonistic deliberation, and including lay expertise to counter the technocratic structural relations of a surveillance economy.

Use of Artificial Intelligence in Operational Efficiency and Business Management Strategic

Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.

A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.

Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.

Enhancing Impact for Key Leadership Domains

Significant operational and functional action domains require health, aged and social care leaders and managers’ expertise and responsibilities. Contemporary strategic; development and operational activities involving patient/client centred care; strategic vision articulation and alignment; financial performance; workforce management; effective communications; informatics challenges; enabling strong and positive organisational cultures as well as managing conflict and resistance to change are required and need to deeply focused on the context of clinical and social health care.....

System Proposal for Supervision of Critical Adverse Processes in Patients with Implanted Ventricular Assist Devices

Ventricular assist devices (VADs) are designed to provide sufficient blood flow to patients with severe heart failure. Once implanted, the patient becomes dependent on the VAD, making it essential to prevent situations that could harm the patient while receiving circulatory support. VADs are classified as critical systems (CS), and adverse events (AEs) can lead to serious consequences, including hospitalization or even death. At present, patient care is provided through in-person consultations, with incidents reported via medical device reports (MDRs) to the Food and Drug Administration (FDA). However, there is no real-time monitoring of AEs or oversight of these events. In response to this gap, a system for supervising critical adverse processes in patients with implanted VADs (SCVAD) is proposed, based on horizontally and vertically integrated architecture. This system aims to address the complexity of AEs by considering multiple domains of operation: the device, the patient, and the medical team, as well as the interactions between these entities. In this context, the formalism of Petri nets (PN) is used to develop models that represent adverse processes based on the actions recommended by the medical team. These models allow for the mapping of events with the potential to cause harm to the patient. Therefore, the medical team will be able to monitor adverse processes, as the models in interpreted PN can be isomorphically transcribed into computable algorithms that can be processed on compatible devices, enabling the tracking of complications caused by adverse processes.

Meta-Entrepreneurship

Metaverse entrepreneurship has emerged as an innovative topic alongside the development of generative AI, agentic AI and metaverse. This study conceptualizes meta-entrepreneurship as a novel form of entrepreneurial activity that enables value creation within virtual and physical realms and proposes an analytical theoretical framework based on a systematic literature review, observations, and focus group study. Our framework is structured around three layers (infrastructure, content, and experience) and two domains (metaverse-based operational domain and AI-based production domain), aims to conceptualize “what is meta-entrepreneurship” and identify new possibilities. The research highlights the multifaceted impact of meta-entrepreneurship on individuals, corporations, industries, societies, and countries. Further, we delineate three pathways to achieve meta-entrepreneurship, analyze nine critical challenges, and propose potential future research directions to contribute to the theoretical foundation of this emerging field.

Emerging technologies in military space operations: current applications and future research for educational and training purposes

ABSTRACT The rapidly evolving domain of military space operations has become increasingly integral to national security and defense strategies. Emerging technologies, including artificial intelligence (AI), simulators and extended reality (XR) tools such as virtual reality (VR) and augmented reality (AR), are revolutionizing how military forces train and operate. These innovations not only enhance the effectiveness of military missions but also necessitate a revaluation of the education and training required for space personnel. This paper explores the current applications of these technologies in military space operations, identifies potential challenges associated with their integration and proposes future research to guide the development of educational and training programs. By examining the historical evolution of military space activities, the roles and activities of space personnel and the integration of advanced technologies, this paper aims to provide an understanding of how emerging technologies are shaping military space operations and the current and future implications for education and training.

Off-Highway Collision Avoidance System Leveraging Cellular Vehicle-to-Everything and Software-Defined Vehicle Architecture

<div>The off-highway industry witnesses a vast growth in integrating new technologies such as advance driver assistance systems (ADAS/ADS) and connectivity to the vehicles. This is primarily due to the need for providing a safe operational domain for the operators and other people. Having a full perception of the vehicle’s surrounding can be challenging due to the unstructured nature of the field of operation. This research proposes a novel collective perception system that utilizes a C-V2X Roadside Unit (RSU)-based object detection system as well as an onboard perception system. The vehicle uses the input from both systems to maneuver the operational field safely. This article also explored implementing a software-defined vehicle (SDV) architecture on an off-highway vehicle aiming to consolidate the ADAS system hardware and enable over-the-air (OTA) software update capability. Test results showed that FEV’s collective perception system was able to provide the necessary nearby and non-line-of-sight detections to the vehicle’s controls algorithms and the vehicle was able to navigate the environment safely.</div>

A step towards digital transformation within a utility company through process automation for a sustainable energy transition

AbstractSustainable energy management systems are part of the global energy transition by digitally transforming utility companies throughout the past decade. Industry 4.0 (4IR) has been adopted to improve competitiveness and align its processes with trends that require changes in the producer‐consumer relationship. This study examines how AES Colombia uses Industry 4.0 to improve efficiency and promote a sustainable energy transition. It focuses on automating key processes within the company's hydropower operations, representing 6% of Colombia's energy market. The research shows that robotic‐process‐automation technologies reduce pricing simulation time from 27 to 5 h and tax declaration manual labour from 1560 to 584 h. These advancements enhance operational efficiency and significantly improve decision‐making capabilities, thereby boosting the reliability of information utilized across various operational domains, including financial, market, and regulatory processes. The findings underscore the critical role of flexibility, cultural change, and continuous training in facilitating successful digital transformation initiatives. This work contributes to understanding how intelligent systems can optimize energy companies’ processes while promoting a human‐centred approach to Industry 4.0 technologies. The results underscore the importance of aligning technological advancements with sustainability goals to drive significant improvements in the energy sector, paving the way for a more resilient and adaptable energy landscape.

Analysis of the Ferry Service Network in Hong Kong

Ferry transportation enhances urban mobility and connectivity across the Hong Kong region. This study analyses the ferry network in Hong Kong using social network analysis. It has been found that the network consists of 31 piers linked by 28 maritime routes. Specific routes, particularly those departing from Hong Kong’s Central Ferry Pier, serve as the network’s backbone with its high service frequency. The existence of subnetworks within the broader ferry network indicates specialised clusters of maritime connectivity that cater to distinct geographic and operational domains. This study extends beyond network description to illuminate the implications of ferry network configurations for urban planning, sustainable transportation, and regional tourism development. That offers insights into strategies for optimising ferry services and infrastructure investment.

Analysis of potential risks during the control of an unmanned underwater vehicle (UUV)

This article explores the analysis of potential risks associated with controlling unmanned underwater vehicles (UUVs), focusing on operational, environmental, and technological challenges. UUVs play a critical role in marine research, defense, and offshore industries but are subject to unique risks due to limited visibility, communication latency, and unpredictable underwater conditions. While UUVs have revolutionized underwater exploration and navigation, their reliance on advanced technologies introduces unique challenges. The study identifies the primary risk domains of UUV operations, including operational risks, human-machine interaction vulnerabilities, and environmental unpredictability. Using comprehensive scenario analysis and data from practical implementations, the article evaluates key risks such as navigation errors, system malfunctions, and communication disruptions. It further examines how artificial intelligence (AI) can be leveraged to mitigate these risks through real-time trajectory optimization, adaptive learning, and enhanced situational awareness. This research incorporates scenario-based analyses for UUVs to evaluate the most critical risk factors affecting mission safety. Furthermore, it explores how modern technologies can mitigate these risks by enhancing trajectory optimization and monitoring of UUVs during underwater navigation. By providing a holistic understanding of UUV risks, the findings underscore the need for robust control systems and operator training protocols. The research contributes actionable insights for improving UUV safety, efficiency, and reliability, laying the groundwork for further advancements in maritime robotics

Doing GOOD: defining a green operational outcomes domain for nitrogen use in New York corn silage production

CONTEXTEffective nitrogen (N) management is an essential aspect of productivity and sustainability of corn silage (Zea mays L.) production in New York (NY) dairy systems. Implementing end-of-season evaluations for N use can help farms identify opportunities to refine N management over time, and support field experimentation through the NY adaptive N management process. OBJECTIVESThe objectives of this study were to (1) define operational or feasible outcome values for end-of-season field N use efficiency (NUE) indicators in NY corn silage production, (2) characterize NUE indicators across a dataset of field observations, and (3) delineate complementary graphical tools and numerical indicators to identify opportunities for N management refinement over time. METHODSData were collected for 994 corn silage field*year observations (10,048ha) across eight NY dairies. For each observation, N balances (N supply - N removal) and N removal/N supply were calculated based on land-grant university (LGU) guidelines and farm management data. A green operational outcomes domain (GOOD) was defined, and a series of complementary graphical tools and numerical indicators were created to identify opportunities for N management refinement over time. RESULTS AND CONCLUSIONSFeasible outcome values for NUE indicators in the GOOD framework were established at a 50% minimum N removal/N supply and a 159kgN ha-1 maximum tolerable available N balance. Feasible values were in partial agreement with previous literature, did not constrain P-based manure applications, and identified fields with large N balances and low efficiencies in the context of adaptive N management. Considerable farm-to-farm differences existed, with 66% of field*year observations meeting the GOOD feasible values and 34% displaying opportunities for refinement. The strategies with largest potential for enhancing N management included reducing N inputs and evaluating non-N yield barriers (e.g. drainage, pests) for fields with low yields and high balances, increasing manure N utilization efficiency (with spring injection or incorporation), and crediting N contributions from sod. SIGNIFICANCEThe current work proposes a practical end-of-season evaluation framework for N management in corn silage that is easy to scale across NY dairies to facilitate implementation of the NY adaptive N management process. The framework also allows farmers to assess current practices and identify opportunities for management refinement over time. Enhancing NUE in corn silage should result in financial benefits for farms and reduced N losses to prevent water quality impairment and/or direct or indirect nitrous oxide emissions that contribute to global warming.

The work organization in teleoperated driving – a human reliability and socio-technical systems perspective

Abstract Very high levels of automation in road traffic are accompanied by the need for teleoperation. If the driverless autonomous vehicle is not able to solve a complex driving situation, then the problem should be solved by a teleoperated intervention by getting a human into the loop. Such a system is highly complex, interacting in a socio-technical system. Therefore, in addition to the empirical research of user-centered development of teleoperated driving, an analytical and systemic approach is crucial in the first place to understand the interrelationships between the subsystems and to optimize a teleoperated driving system. This work provides a base for the systemic consideration by taking two different perspectives focusing on the work organization and vehicle operation dealing with how many vehicles can be monitored and serviced by one operator. First, a brief analytical perspective of human reliability is taken, emphasizing the appropriate definition of the operational design domain and knowledge of its variability. Second, a comprehensive systemic, socio-technical perspective is applied using the functional resonance analysis method (FRAM) for a reflexive and computational purpose highlighting effects on operator workload and traffic performance. Finally, the implications of the research results are discussed and integrated into socio-technical interactions in everyday teleoperated driving between blunt- and sharp-end factors from a safety and reliability perspective to demonstrate the complex leverage of the work organization. Overall, the insights contribute to an enhanced understanding of the complex socio-technical interactions in teleoperated driving.