Operational Domain Research Articles

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.

Reinhold, Thomas. 2024. Towards a Peaceful Development of Cyberspace: De-Escalation of State-Led Cyber Conflicts and Arms Control of Cyber Weapons. XXVIII, 361 pp. https://doi.org/10.1007/978-3-658-43951-4

The book analyzes cyberspace issues and its global infrastructures, which are essential for the global political system, civiliarchy, political economy and effective administration of political processes. In the modern context, cyberspace is becoming an intelligence and military operational domain for various actors. According to the author, this is evident in the establishment of military cyber departments and the integration of cyberspace into the security and defense strategies of small and large states. Current military technologies as well as established instruments of transparency, de-escalation and arms control measures do not work for cyberspace due to its specific technical characteristics. In this context, it is important for international organizations to achieve de-escalation of state-led conflicts in cyberspace, but in reality it is simply impossible to develop arms control over cyber weapons. In this context, an effective system of classification of cyber weapons, an approach to mutual reduction of stocks of vulnerability and non-interference in cyber conflict are also important.

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.

Strategic Planning of Work Training Center Information System Using TOGAF ADM and ITIL

This study aims to develop an implementation model of TO-GAF and ITIL frameworks in enterprise architecture (EA) for the Technical Implementation Unit (UPTD) of the Tegal Re-gency Vocational Training Center (BLK) and determine the improvement of services provided by BLK after the develop-ment of enterprise architecture. This type of research is a case study research where data collection is done by direct observation or observation, interviews and surveys using questionnaires. The population in this study were employees and students of BLK Tegal Regency, totaling 100 people. While the sample was 15 people. TOGAF ADM is used to un-derstand the strategic planning process systematically, from problem identification to the development of measurable solutions. Analysis and design of enterprise architecture is prelimenary phase, requirement management, architecture vision, business architecture, information systems architecture and opportunities and solutions. While the ITIL approach in addition to providing practical guidance in managing BLK IT services, ITIL is also used to test the level of maturity of the system against the blueprint produced so that a tested sys-tem design is obtained. The results of the maturity level of the service operation domain system are at level 4 with a value of 3.94 and have not yet reached level 5 which means that some activities have not been fully carried out to the maximum. To achieve the expected maturity process, management must always supervise every decision making in accordance with existing procedures.

Automated scenario generation from Operational Design Domain model for testing AI-based systems in aviation

AbstractApplications based on artificial intelligence (AI) promise benefits, ranging from improved performance to increased capabilities in many industries. In the aviation domain, one example is the new Airborne Collision Avoidance System (ACAS X). The current investigation aims at combining ACAS X and AI to maintain its performance while decreasing the memory footprint. However, the anticipation of AI being increasingly used confronts regulators with challenges in terms of safety assurance and certification. Consequently, the European Union Aviation Safety Agency (EASA) published a concept paper for machine learning applications in aviation. Both, the Concept of Operation (ConOps) in combination with an Operational Design Domain (ODD), are listed as objectives to be met for the safety analysis. From a developer’s perspective, this raises questions on how to effectively derive the ODD from ConOps and test the given system based on the ODD description. Based on an exemplary use case of a Near Mid-Air Collision avoidance between two aircraft through the advisories of ACAS X, a highly automated framework for generating and testing synthetic data is proposed. Using this framework, 1800 Near Mid-Air Collision scenario files are created and automatically executed in the simulation environment FlightGear. Scenario-based testing is used for the logging of ACAS X advisory data and evaluating it against predefined requirements. By this approach, an efficient way of verifying system requirements and conducting automated testing based on the ODD definition is demonstrated. Throughout this process, Model-Based Systems Engineering (MBSE) is used to reduce and manage complexity. The framework in this paper enables a systematic and highly automated approach for scenario generation based on the ODD.

Evaluasi Tingkat Kematangan Sistem Informasi Rumah Sakit Menggunakan Information Technology Infrastructure Library Versi 3

Hospital Information System (HIS) is an important component in supporting effective and efficient health services. Evaluation of the maturity of information systems is needed to ensure that the implementation of SIRS is in accordance with the standards and needs of the organization. This study aims to evaluate the maturity level of information systems at Amanah Hospital using the Information Technology Infrastructure Library (ITIL) version 3 framework, specifically in the Service Operation domain. The research method uses a quantitative approach with data collection through questionnaires distributed to respondents involved in managing information systems. The evaluation was carried out on five subdomains, namely Event Management, Incident Management, Request Fulfillment, Problem Management, and Access Management. The results showed that the maturity level of the Amanah Hospital information system was at level 3.81 (Managed), with the highest value in the Incident Management and Access Management subdomains (4.00), while the lowest value in Event Management and Request Fulfillment (3.66). Based on these results, several improvements are recommended including the development of an integrated monitoring system, strengthening operational procedures, improving HR competencies, and implementing more structured periodic evaluations to achieve an optimal level of maturity.

Higher order $\mathcal{S}^{p}$-differentiability: The unitary case

Consider the set of unitary operators on a complex separable Hilbert space \mathcal{H} , denoted as \mathcal{U}(\mathcal{H}) . Consider 1<p<\infty . We establish that a function f defined on the unit circle \mathbb{T} is n times continuously Fréchet \mathcal{S}^{p} -differentiable at every point in \mathcal{U}(\mathcal{H}) if and only if f\in C^{n}(\mathbb{T}) . Take a function U \colon\R\rightarrow\mathcal{U}(\mathcal{H}) such that the function t\in\R\mapsto U(t)-U(0) takes values in \mathcal{S}^{p} and is n times continuously \mathcal{S}^{p} -differentiable on \R . Consequently, for f\in C^{n}(\mathbb{T}) , we prove that f is n times continuously Gâteaux \mathcal{S}^{p} -differentiable at U(t) . We provide explicit expressions for both types of derivatives of f in terms of multiple operator integrals. In the domain of unitary operators, these results closely follow the n -th order successes for self-adjoint operators achieved by the second author, Le Merdy, Skripka, and Sukochev. Furthermore, as for application, we derive a formula and \mathcal{S}^{p} -estimates for operator Taylor remainders for a broader class of functions. Our results extend those of Peller, Potapov, Skripka, Sukochev, and Tomskova.

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach

The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector.

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

Advancing Light Aircraft Health Monitoring with Flight Phase Clustering

This paper addresses the clustering of flight phases of a light aircraft for health monitoring using vibration data. The aim is to improve diagnostic and prognostic functions. Grouping condition monitoring data under similar operating conditions is significant for predictive maintenance. Clustering also supports advanced analytics for fault detection and estimation of remaining life. The proposed framework uses self-organizing maps for flight phase clustering. The findings show that the algorithm can recognize and classify flight phases in various operational domains. Additionally, visualization of cluster maps uncovers complex patterns and non-linear relationships in sensor data under different flight conditions. As a followup, analyzing the vibration properties within these estimated clusters (regimes) provides insights from condition monitoring data behavior during flight phases. The results confirm the effectiveness of the method, but also confirm that determining light aircraft regimes requires more focus due to their unique flight patterns that are absent in commercial airliners. In this context, this research has dealt with these unique patterns and provided the foundation for a new model for clustering with an attempt to contribute valuable insights into improving the reliability and efficiency of light aircrafts.

Maturity Level Framework ITIL V3 dalam Mengukur Kepuasan Mahasiswa Terhadap Web Student Universitas Amikom Purwokerto

In this era of globalization, technology increasingly influences education. Students of Amikom University Purwokerto use Web Student to access academic information such as schedules and grades. Although educational technology is developing, challenges in meeting student needs still exist. Evaluation is needed to assess the effectiveness of Web Student, especially in user satisfaction. This study uses ITIL Version 3 in the service operation domain, with a Likert scale-based questionnaire to 60 respondents to measure the maturity level. The results of the maturity level include: event management 2.90, incident management 2.45, problem management 2.13, request fulfillment 2.24, and access management 3.18. Overall, the average index of all questionnaire results variables obtained was 2.58, which is on a scale of 3 out of 5 maturity levels, namely defined.

From operational design domain to test cases: A methodology to include harsh weather

Background To gain widespread use, assisted and automated driving (AAD) systems will have to cope with harsh weather conditions, such as rain, fog, and snow. This affects the development and testing of perception and decision-making systems. Since the weather cannot be controlled in field tests, the availability and use of virtual simulation and test facilities that can accurately reproduce harsh weather becomes vital. Test cases subjecting the system under test to harsh conditions, covering all expected weather phenomena in both typical and challenging scenarios, must be defined to evaluate all aspects of the system. Methods State-of-the-art in scenario-based and hash weather testing for AAD systems was analysed; based on the analysis, a team with diverse expertise in AAD development and testing defined a methodology for defining a set of harsh weather test cases. Results This paper proposes, and exemplifies the use of, a methodology to develop a representative set of test cases based on the defined operational design domain and use cases for an AAD system under development, considering the possibility of reproducing tests in different test environments with a focus on harsh weather. Conclusions We believe that our proposed methodology can accelerate the overall testing process and contribute to the difficult safety assurance challenges for automated vehicles.

Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System

Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.

Research on the complex dynamical behavior of H-bridge inverter with RLC load

Complex dynamical behaviors such as bifurcation and chaos exist in H-bridge inverter with RLC load, and these nonlinear behaviors will greatly increase the harmonic content of the output current and reduce the stability and reliability of the system. In this paper, a PI controller is added to widen the stable operation domain of the system. The stroboscopic mapping theory is used to model the system, the nonlinear dynamic behavior of the inverter is investigated by the bifurcation diagram, the folding diagram and the phase trajectory diagram are used for comparative verification, and the TDFC method is introduced to inhibit the chaotic behavior of the inverter, which further improves the stable range of the system operation. The fast-change stability theorem is used to analyze the stability of the system theoretically and verify the correctness of the numerical simulation. Therefore, the conclusions of the study provide a reliable theoretical basis for the design of the inverter system, which has important theoretical significance and practical value.

An Optimal Road Network Extraction Methodology for an Autonomous Driving-Based Demand-Responsive Transit Service Considering Operational Design Domains

In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts.

Holistic Information Security Management and Compliance Framework

The growing complexity of cybersecurity threats demands a robust framework that integrates various security domains, addressing the issue of disjointed security practices that fail to comply with evolving regulations. This paper introduces a novel information security management and compliance framework that integrates operational, technical, human, and physical security domains. The aim of this framework is to enable organizations to identify the requisite information security controls and legislative compliance needs effectively. Unlike traditional approaches, this framework systematically aligns with both current and emerging security legislation, including GDPR, NIS2 Directive, and the Artificial Intelligence Act, offering a unified approach to comprehensive security management. The experimental methodology involves evaluating the framework against five distinct risk scenarios to test its effectiveness and adaptability. Each scenario assesses the framework’s capability to manage and ensure compliance with specific security controls and regulations. The results demonstrate that the proposed framework not only meets compliance requirements across multiple security domains but also provides a scalable solution for adapting to new threats and regulations efficiently. These findings represent a significant step forward in holistic security management, indicating that organizations can enhance their security posture and legislative compliance simultaneously through this integrated framework.

The Numerically Leveled Performance Assessment Framework (NLPAF): A Literature-Based Evaluation of Acceptability

ABSTRACT The purpose of this study is to present a literature-based, initial “evaluation of acceptability” (American Educational Research Association et al., 2014, p. 7) of the Numerically Leveled Performance Assessment Framework (NLPAF). Its results enable confidence in continuing its use in the authors’ professional development work and in making it available to professional developers and researchers. The authors developed the NLPAF to guide elementary mathematics teachers enrolled in a professional development program in constructing performance assessments they then administer up to three times annually to their students. These assessments measure growth across time in mathematical proficiency relating to the domain of whole number operations most prominent in the curriculum standards for the grade level taught. Numerically leveled refers to the process teachers learn and apply to estimate the size and numerical complexity of computations to be used in designing performance tasks for their students’ unique levels.

Low-Thrust Fuel-Optimal Guidance with Automatic Control Sequence Detection and Separation

This paper develops a novel fuel-optimal guidance scheme using differential algebraic techniques that is capable of handling changes in the optimal thrust sequence, in addition to a large domain of uncertainty. Taylor polynomials of the solution flow exhibit large approximation errors when the control sequence changes within the domain of operation. This property is leveraged through automatic domain splitting, a technique that tracks the approximation error and splits the domain into smaller subdomains when high errors are observed. After multiple splits, the domain is autonomously separated along the boundaries between different optimal control sequences. Crucially, each resulting subdomain encompasses only a single control sequence, ensuring that any guidance updates obtained from the final polynomial map provide the corresponding optimal control sequence. To ensure high accuracy of the guidance updates, this work also develops an efficient and iterative-less differential-algebra-based refinement algorithm, which is applied to the outputs of the polynomial map. The overall guidance scheme provides highly accurate guidance updates in response to large trajectory deviations, including new optimal control sequences, as demonstrated in an Earth–Psyche transfer.

On the Cauchy problem for logarithmic fractional Schrödinger equation

We consider the fractional Schrödinger equation with a logarithmic nonlinearity, when the power of the Laplacian is between zero and one. We prove global existence results in three different functional spaces: the Sobolev space corresponding to the quadratic form domain of the fractional Laplacian, the energy space, and a space contained in the operator domain of the fractional Laplacian. For this last case, a finite momentum assumption is made, and the key step consists in estimating the Lie commutator between the fractional Laplacian and the multiplication by a monomial.

Air vehicle system health and asset management: modeling, simulation, and decision support

AbstractEffective Asset Management (AM) is essential to achieve operational excellence in the context of large complex Cyber-Physical Systems (CPSs). It involves coordinated activities across all life-cycle stages, including information and service exchange within and between adjacent air operations domains, for effective air operations and collaboration. For enterprises and military air operations, AM presuppose information and service exchange between the adjacent domains of air operations. Important aspects of aviation AM are that CPSs include Integrated Vehicle Health Management (IVHM), CPS models, and Digital Twin (DT), as central concepts used to predict and optimize asset performance. This article provides an overview of the aviation AM phenomenology, environment, and challenges, and how they may impact envisioned realisations of effective AM solutions for support to enterprise air vehicle operations. The article serves as an extension of the paper Cyber-physical Asset Management of Air Vehicle Systems presented at the International Congress and Workshop of Industrial AI and Maintenance (Candell et al., in Proceedings of the IAI2023–7th international congress and workshop on industrial AI and eMaintenance, Luleå, Sweden, 2023). A comprehensive approach is presented, comprising interdependent dimensions of the problem domain, and how they may be integrated into a framework and a platform concept addressing aviation AM needs.