Operational Domain Research Articles

OntoAgency: An agency-based ontology for tracing control, ownership and decision-making in smart buildings

This article proposes an agency-based design ontology (OntoAgency) to systematically trace the relationships between stakeholders who own, control, and decide upon the benefits and experiences provided by smart building services. The ontology associates ‘smartness’ to different building operation domains and assigns their functionalities to agents involved in the flow of data and decision making. It enables control paths to be traced back to specific people, machines, and companies behind smart operations – including those that appear to be ‘external’ to a smart building. Based on Actor-Network Theory (ANT), the ontology connects knowledge from the social sciences to technical knowledge from building design, construction, operation and controls. The need in developing this ontology originates from the limits of conventional design and engineering ontologies, particularly with respect to their unproblematic take on the interplay between different social and technical systems and their neglect to model the varied abilities of stakeholders to exert control over, and benefit from, the technical systems and operations. The proposed ontology addresses these gaps by offering a more realistic and versatile model of inter-agents relationships. It can also highlight points of consented and unconsented data leakage and associated security issues. This knowledge becomes critical for designing and managing increasingly more complex smart building systems.

Optimizing industrial transport with a connected automated vehicle demonstrator for assembly systems and end-of-line production

In recent years, the automotive industry has witnessed significant progress in the development of automated driving technologies. The integration of advanced sensors and systems in vehicles has led to the emergence of various functionalities, such as driving assistance and autonomous driving. Applying these technologies on the assembly line can enhance the efficiency, safety, and speed of transportation, especially at end-of-line production. This work presents a connected automated vehicle (CAV) demonstrator for generating autonomous driving systems and services for the automotive industry. Our prototype electric vehicle is equipped with state-of-the-art sensors and systems for perception, localization, navigation, and control. We tested various algorithms and tools for transforming the vehicle into a self-driving platform, and the prototype was simulated and tested in an industrial environment as proof of concept for integration into assembly systems and end-of-line transport. Our results show the successful integration of self-driving vehicle platforms in the automotive industry, particularly in factory halls. We demonstrate the localization, navigation, and communication capabilities of our prototype in a demo area. This work anticipates a significant increase in efficiency and operating cost reduction in vehicle manufacturing, despite challenges such as current low traveling speeds and high equipment costs. Ongoing research aims to enhance safety for higher vehicle speeds, making it a more viable business case for manufacturers, considering the increasing standardization of automated driving equipment in cars. The main contribution of this paper lies in introducing the general concept architecture of the integration of automated driving functionalities in end-of-line assembly and production systems. Showing a case study of the effective development and implementation of such functionalities with a CAV demonstrator in a more standardized industrial operational design domain.

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

AbstractThe efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.

Examining IT Service Management at Company XYZ through the Lens of ITIL V3 Service Operation Domain

Examining IT Service Management at Company XYZ through the Lens of ITIL V3 Service Operation Domain

Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience

The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.

A comprehensive approach to safety for highly automated off-road machinery under Regulation 2023/1230

As of January 2027, Machine Safety Regulation 2023/1230 replaces Directive 2006/42/EC, and a proposal for the regulation of Artificial Intelligence (AI) systems is soon to be ratified by the European Council. These legislative changes address hazards from increased automation and AI integration. Original Equipment Manufacturers (OEMs) face compliance challenges due to a lack of standardized guidelines, especially for off-road machinery with level 4 ‘high automation’ capabilities.At level 4 ‘high automation’, the machine operator assumes a supervisory role, intervening only in situations beyond the designed operational domain. Relying solely on supervisor intervention at this level of automation is unreasonable. Within this context, legislative safety requirements to maintain safety advocate for robust risk management systems, including both a human-in-the-loop safety option and adequate safety-related decision-making by the level 4 ‘high automation’ off-road machine, i.e., the safe reasoning software becomes paramount.In this research, conducted through a systematic mapping study, we discuss emerging safety trends and integration for the safe reasoning software a level 4 off-road machine requires. Our analyses shows the necessity of safety constraint-based design procedures. These procedures involve constraining the AI system by embedding fault management, reasoning checks, and diagnostic monitoring together with safety-related decision-making, thereby enhancing functional reliability and reducing risks. We consider these trends and patterns through the lens of changing machine safety legislative requirements, and the proposal of the regulation of AI products. Illustrating their implications alongside a case study of a level 4 tree harvesting machine, for a tangible and to industry transferable perspective on our results.

Cyber Resilience, Dependability and Security

There is a continuing skills shortage associated with digital security and DevSecOps (World Economic Forum, 2023), but this paper argues that is due to non-recognition that it is time for cyber security and/or digital security to be defined, and a further separation of specialisms in computing to be made apparent. This has become increasingly important when considering Artificial Intelligence. The problem is not new. This paper presents a refinement of the principles suggested by Milner (2007) of using a model to describe behaviour and organise software, grappling with seemingly intractable and complex problems which cross boundaries between different systems: engineering, technological, social, economic, legal, and political, each with a distinct perspective and goal. It emphasises Hoare’s (1996) assertion that system failures are largely due to failed analysis impacting development of resilient systems. It argues that there are dichotomies between resilience – a system security/safety perspective, dependability – a user/consumer perspective, and security – a technology perspective. Many proposed systems to date have conflated these perspectives in the secure by design paradigm which requires a depth of knowledge and expertise. Unicorns are rare. This paper suggests how to overcome the skills shortage utilising the skill sets that are available in a manner that maximises the contribution to digital security. Recognising that not everyone and everything needs to communicate with the world reduces complexity and can increase trust. Concentration on the operational purpose of a system, resulting in an Operational Design Domain (ODD) reduces complexity further. Additional reduction in complexity is achieved by placing resilience in an engineering and programming development context, grounded in acceptable behaviours, while accepting dependability as a user expectation of system behaviour, and cyber security as a separate specialism addressing access to systems and infrastructure. Much of this paper is a reversion to defensive programming through the ODD. There is a need for any solution to the skills shortage be scalable and economic, and this paper suggests how that can be achieved using existing skill sets targeted at their specialisms.

Neural Network-Driven Reliability Analysis in Safety Evaluation of LiDAR-Based Automated Vehicles: Considering Highway Vertical Alignments and Adverse Weather Conditions

Complex road environments threaten the safe operation of automated vehicles. Among these, adverse weather conditions and road geometries have particularly significant impacts. This study investigates LiDAR-based automated vehicles (LAVs) driving safety on vertical curved roads in adverse weather. A key methodology involves constructing a failure function that incorporates both the available sight distance (ASD) and the required stopping sight distance (RSD). This function is analyzed using a combined approach of neural networks and Monte Carlo simulations to quantitatively evaluate and generalize the reliability of LAVs under various conditions. The results reveal that variations in weather conditions and vertical curve radii significantly impact the ASD of LAVs, while the influence of speed is relatively minor. Notably, dense fog and rainfall can substantially reduce LAVs’ ASD on vertical curves. Furthermore, the vehicle automation level and speed have a significant impact on driving safety, emphasizing the need for road and operational domain design tailored to LAVs under adverse weather conditions and vertical curve radii.

Optimizing [formula omitted] capture in pressure swing adsorption units: A deep neural network approach with optimality evaluation and operating maps for decision-making

This study presents a methodology for surrogate optimization of cyclic adsorption processes, focusing on enhancing pressure swing adsorption units for carbon dioxide (CO2) capture. We developed and implemented a multiple-input, single-output (MISO) framework comprising two deep neural network (DNN) models, predicting key process performance indicators. These models were then integrated into an optimization framework, leveraging particle swarm optimization (PSO) and statistical analysis to generate a comprehensive Pareto front representation. This approach delineated feasible operational regions (FORs) and highlighted the spectrum of optimal decision-making scenarios. A key aspect of our methodology was the evaluation of optimization effectiveness. This was accomplished by testing decision variables derived from the Pareto front against a phenomenological model, affirming the surrogate model’s reliability. Subsequently, the study delved into analyzing the feasible operational domains of these decision variables. A detailed correlation map was constructed to elucidate the interplay between these variables, thereby uncovering the most impactful factors influencing process behavior. The study offers a practical, insightful operational map that aids operators in pinpointing the optimal process location and prioritizing specific operational goals.

Effect of Crisis Leadership on Performance of SMEs During Covid-19 Pandemic in Nairobi County, Kenya

The relationship between crisis leadership and organizational success amidst the COVID-19 pandemic, particularly in the context of Small and Medium-sized Enterprises (SMEs) in Nairobi County, Kenya, remains understudied. This study aimed to address this gap by investigating the impact of crisis leadership on SME performance during the pandemic. Employing a descriptive and correlational research design, data were collected from 237 respondents representing various SME industries in Kenya through a stratified random sampling method and a questionnaire. Analysis of the data using SPSS version 23 revealed significant findings. Firstly, crisis management was found to be positively correlated with safeguarding the reputation of organizations, which in turn can influence long-term sales and profits positively. Secondly, there was a significant linear relationship between crisis leadership and SME performance, with crisis leadership explaining 56.2 percent of the variance in performance. Additionally, the intercept between crisis leadership and performance showed improvement, underscoring the importance of effective crisis leadership in enhancing organizational outcomes. In conclusion, the study highlights the importance of crisis leadership in ensuring business continuity and mitigating the impact of crises, such as the COVID-19 pandemic. It emphasizes the need for leaders to possess the requisite skills, knowledge, and abilities to navigate turbulent times successfully. Furthermore, the study underscores the role of SMEs in developing strong leadership capabilities to enhance performance and mitigate potential crisis-related disruptions. It recommends that executive and management teams within SMEs in Nairobi County focus on strengthening leadership competencies and developing organizational guidelines for effective crisis management across various operational domains. Implementing these recommendations enables SMEs to enhance their crisis preparedness and responsiveness, thus fortifying their continuity and bolstering overall performance amidst adversities. This study enriches the crisis management and organizational resilience literature, especially within the SME domain, offering pertinent insights for practitioners, policymakers, and researchers.

Filling the Gaps: Using Synthetic Low-Altitude Aerial Images to Increase Operational Design Domain Coverage.

A key necessity for the safe and autonomous flight of Unmanned Aircraft Systems (UAS) is their reliable perception of the environment, for example, to assess the safety of a landing site. For visual perception, Machine Learning (ML) provides state-of-the-art results in terms of performance, but the path to aviation certification has yet to be determined as current regulation and standard documents are not applicable to ML-based components due to their data-defined properties. However, the European Union Aviation Safety Agency (EASA) published the first usable guidance documents that take ML-specific challenges, such as data management and learning assurance, into account. In this paper, an important concept in this context is addressed, namely the Operational Design Domain (ODD) that defines the limitations under which a given ML-based system is designed to operate and function correctly. We investigated whether synthetic data can be used to complement a real-world training dataset which does not cover the whole ODD of an ML-based system component for visual object detection. The use-case in focus is the detection of humans on the ground to assess the safety of landing sites. Synthetic data are generated using the methods proposed in the EASA documents, namely augmentations, stitching and simulation environments. These data are used to augment a real-world dataset to increase ODD coverage during the training of Faster R-CNN object detection models. Our results give insights into the generation techniques and usefulness of synthetic data in the context of increasing ODD coverage. They indicate that the different types of synthetic images vary in their suitability but that augmentations seem to be particularly promising when there is not enough real-world data to cover the whole ODD. By doing so, our results contribute towards the adoption of ML technology in aviation and the reduction of data requirements for ML perception systems.

Representative Functions, Variational Convergence and Almost Convexity

We develop a new epi-convergence based on the use of bounded convergent nets on the product topology of the strong topology on the primal space and weak star topology on the dual space of a general real Banach space. We study the propagation of the associated variational convergences through conjugation of convex functions defined on this product space. These results are then applied to the problem of construction of a bigger-conjugate representative function for the recession operator associated with a maximal monotone operator on this real Banach space. This is then used to study the relationship between the recession operator of a maximal monotone operator and the normal–cone operator associated with the closed, convex hull of the domain of that monotone operator. This allows us to show that the strong closure of the domain of any maximal monotone operator is convex in a general real Banach space.

Manajemen Layanan Teknologi Informasi pada Domain Service Operation di Perguruan Tinggi yang ada di Indonesia

The use of information technology which is increasingly developing in increasing effectiveness, especially at tertiary institutions in Indonesia is very useful. In academics, the use of this technology helps tertiary institutions in dealing with a problem that is in it. But there are several factors to a problem in the use and management of information technology. This can be overcome by implementing an Information Technology Infrastructure Library Version 3 framework. Management is carried out in the service operation domain due to the high constrained focus on that domain. There are differences in each tertiary institution analyzed using this framework. Significant differences in each college with each research method used to create a descriptive. Assessment and recommendations are different techniques for each college in a result of the journal. There is a discovery in determining the answer at each stage of higher education research. The results obtained at each tertiary institution are different for each indicator or process in the service operation.

Implementasi Service Strategy ITIL V3 Pada Sektor Perbankan di Indonesia

Companies that develops in technology are that operates in the banking sector. The banking companies used to compare the service strategy are Bank Bapas 69, Bank XYZ, and Bank Mandiri Taspen. The research was carried out by using data collection methods based on literature studies and analyzing them based on ITIL guidelines regarding service strategy. Bank Bapas has used the ITIL V3 framework with a service strategy domain, while XYZ Bank has conducted an evaluation using the ITIL V3 framework with service transition and service operation domain, and Bank Mandiri has used stages in service strategy, namely IT service portfolio, in its development. The results at Bank Bapas reveal that the services used are good and only need periodic maintenance, while at Bank XYZ it shows that IT has been implemented properly and refers to clear and actual procedures, while at Bank Mandiri the output obtained would result in a catalog IT service using service charter for the logistics. The results of the comparison between three journals prove that only a few differences are found, because in the use of service strategy, the majority output is the same in solving problems in the company.

A Critical Analysis of Risk Auditing: An Auditor's Approach

This research aims to critically analyze risk auditing, focusing on the auditor's approach, to uncover complexities and challenges inherent in contemporary auditing practices. The study synthesizes findings from a literature review, discussing implications for auditors, organizations, and future research. The multifaceted role of auditors in risk auditing involves identifying, assessing, and managing risks across financial and operational domains, ensuring organizational objectives and assets' safeguarding. Auditors must possess diverse skills, expertise, and judgment to navigate subjective risk assessment, particularly in evaluating emerging risks and complex financial instruments. Moreover, auditors must adopt a proactive approach to risk management, anticipating potential risks and providing timely recommendations. However, challenges such as the lack of standardized risk assessment practices, the rapid pace of business environment changes, and integrating emerging technologies into risk auditing persist. These challenges underscore the need for ongoing innovation and improvement in risk auditing practices. Empirical findings suggest a positive association between risk disclosures' quality and firm performance, emphasizing governance mechanisms' importance in risk management. By addressing challenges and collaborating with stakeholders, auditors can enhance risk management practices' effectiveness, safeguarding organizational objectives and assets in dynamic business environments.

Controlling a bio-inspired miniature blimp using a depth sensing neural-network camera

Miniature blimps are lighter-than-air vehicles which have become an increasingly common unmanned aerial system research platform due to their extended endurance and collision tolerant design. The UNSW-C bio-inspired miniature blimp consists of a 0.5 m spherical mylar envelope filled with helium. Four fins placed along the equator provide control over the three translatory axes and yaw rotations. A gondola attached to the bottom of the blimp contains all the electronics and flight controller. Here, we focus on using the UNSW-C blimp as a platform to achieve autonomous flight in GPS-denied environments. The majority of unmanned flying systems rely on GPS or multi-camera motion capture systems for position and orientation estimation. However, such systems are expensive, difficult to set up and not compact enough to be deployed in real environments. Instead, we seek to achieve basic flight autonomy for the blimp using a low-priced and portable solution. We make use of a low-cost embedded neural network stereoscopic camera (OAK-D-PoE) for detecting and positioning the blimp while an onboard inertia measurement unit was used for orientation estimation. Flight tests and analysis of trajectories revealed that 3D position hold as well as basic waypoint navigation could be achieved with variance (<0.1 m). This performance was comparable to that when a conventional multi-camera positioning system (VICON) was used for localizing the blimp. Our results highlight the potentially favorable tradeoffs offered by such low-cost positioning systems in extending the operational domain of unmanned flight systems when direct line of sight is available.

Peningkatan Efektivitas Operasional Melalui Implementasi ITIL V3 Pada Sistem Informasi“New Modern” Dengan Fokus Service Operation (Studi Kasus: CV Cahaya Makmur Abadi)

Cv Cahaya Makmur Abadi is a modern cotton distributor company located in Banyumas. Currently, Cv Cahaya Makmur Abadi is facing several operational issues in using the New Modern system. To enhance operational effectiveness, this research implements the ITIL V3 framework, particularly in the service operation domain, which includes Event Management, Incident Management, Problem Management, Request Fulfillment, and Access Management. It is expected that through the implementation of ITIL V3, the issues faced by Cv Cahaya Makmur Abadi can be minimized, and the company's operations can run more efficiently and effectively. In addressing the challenges encountered during the operation of the New Modern System at Cv Cahaya Makmur Abadi, the author recommends the use of the ITIL V3 approach in the Service Operation section. ITIL V3 is a framework that can help improve the operational effectiveness of information systems. Although an analysis has been conducted, there are still some areas within the Service Operation domain that need improvement.

Requirements and software engineering for automotive perception systems: an interview study

Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.

A Study on the Impact of Louis Vuitton's Business Strategy

This paper delves into a comprehensive analysis aimed at understanding the multifaceted impact of Louis Vuitton's business strategy across its operational domains. The research is concentrated on four pivotal areas: Firstly, it scrutinizes the brand's sales performance and its evolving audience, both within physical stores and the digital realm. This includes an exploration of foot traffic patterns, an assessment of marketing methodologies, and an evaluation of the customer experience, both online and offline. Secondly, the study meticulously examines Louis Vuitton's promotional endeavors, encompassing aspects such as advertising campaigns, celebrity endorsements, and various marketing initiatives, providing insights into their contributions to brand visibility and market appeal. Thirdly, the paper delves into Louis Vuitton's adaptability and innovation in refining its product line and mix, addressing the brand's ability to respond to shifting market demands and consumer preferences. By conducting a thorough analysis of these dimensions, this study aspires to offer an in-depth understanding of the far-reaching implications of Louis Vuitton's business strategy. In doing so, it contributes to the discourse surrounding strategic management, marketing, and the dynamics of the luxury fashion industry.

Digital Game Applications on Mathematics Achievement of Students at Risk of Mathematics Learning Difficulty

In this study, the effect of four operations-based digital game applications on the basic mathematics skills of students at risk of math learning difficulties was examined. In this context, the study was designed as a mixed research method. Quantitative and qualitative methods were used in two separate interactive phases following an exploratory design scheme. The study was conducted with seven 5th grade students from two primary schools in two provinces in the Mediterranean region. A pre-test was administered to the students and a 10-session intervention was conducted. Finally, a post-test was administered after the intervention. Participants' responses to the pre-test and post-test were analysed. Shapiro-Wilk test and Wilcoxon signed-rank test were used to analyse quantitative data, and descriptive analysis technique was used to analyse qualitative data. At the end of the study, semi-structured interviews were conducted with the parents of the students. The results of the study showed that the intervention positively affected the academic achievement of the students and the interviews with the parents supported this result. At the end of the study, it can be said that mathematics education with students at risk of mathematics learning difficulties gave positive feedback in the learning domain of numbers and operations, and it may be useful to carry out applications in other learning domains.