E. Board, Halina Filipowicz, Christopher Garbowski
et al.
The successful design, development, and operation of human-rated and -operated systems requires the combined effort of engineering, science, and human health disciplines. Each of these disciplines produces uniquely trained experts who approach their fields differently from fundamental work to applied practices. Human Systems Integration (HSI) is an important and vital step in the development of human-rated spacecraft and high-performance aircraft. The three disciplines of engineering, life sciences, and health/ medicine are critical disciplines that must engage with one another to ensure the health and safety of the operator. They must also include anthropometric involvement of male and female operators who are integrated into these systems or interact with them. This chapter presents some of the failures, compromises, and lessons learned in the complex field of HSI. These lessons illustrate only a few examples of how HSI is required in the design of complex systems and how its success ensures overall crew and mission safety and success.
Dean Brand, Domenica Dibenedetto, Francesco Petruccione
We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behavior. Applications of this model to a sound localization benchmark show that it outperforms a phenomenological quantum LIF model as well as a classical LIF. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway toward biologically inspired quantum spiking neural networks and paradigms in quantum machine learning.
A comprehensive study of the current state and development prospects of the heat and power industry in Russia has been conducted. The structure of fuel and energy resources consumption, as well as the role of heat supply in ensuring the sustainable functioning of the country’s economy have been analyzed. Special attention has been paid to the intersectoral nature of thermal energy and its impact on industry, housing and communal services, and the social sphere. The high energy intensity of the industry has been emphasized, and the need to optimize the processes of generating, distributing, and consuming thermal energy has been pointed out. Various types of heat supply systems – centralized and decentralized – have been studied in terms of their efficiency, environmental friendliness, and economic feasibility. The current dynamics of these systems development in the context of regional specifics and climatic conditions has been described. The key issues of the industry have been analyzed, including uneven distribution of generating capacities, high level of physical wear of equipment, significant losses of thermal energy in distribution networks, as well as insufficient transparency and accuracy of consumption accounting. An important place in the study is devoted to the issues of tariff formation, and both technical and economic factors have been considered, including excess losses, excess costs, the investment component of tariffs, and the need to modernize infrastructure. A forecast of the Russian thermal power industry development has been proposed, and the main barriers hindering its modernization have been identified. Possible ways to increase the energy and economic efficiency of the industry in the face of rising fuel prices and stricter environmental requirements have been listed.
Haytham Younus, Sohag Kabir, Felician Campean
et al.
This article presents a state-of-the-art review of recent advances aimed at transforming traditional Failure Mode and Effects Analysis (FMEA) into a more intelligent, data-driven, and semantically enriched process. As engineered systems grow in complexity, conventional FMEA methods, largely manual, document-centric, and expert-dependent, have become increasingly inadequate for addressing the demands of modern systems engineering. We examine how techniques from Artificial Intelligence (AI), including machine learning and natural language processing, can transform FMEA into a more dynamic, data-driven, intelligent, and model-integrated process by automating failure prediction, prioritisation, and knowledge extraction from operational data. In parallel, we explore the role of ontologies in formalising system knowledge, supporting semantic reasoning, improving traceability, and enabling cross-domain interoperability. The review also synthesises emerging hybrid approaches, such as ontology-informed learning and large language model integration, which further enhance explainability and automation. These developments are discussed within the broader context of Model-Based Systems Engineering (MBSE) and function modelling, showing how AI and ontologies can support more adaptive and resilient FMEA workflows. We critically analyse a range of tools, case studies, and integration strategies, while identifying key challenges related to data quality, explainability, standardisation, and interdisciplinary adoption. By leveraging AI, systems engineering, and knowledge representation using ontologies, this review offers a structured roadmap for embedding FMEA within intelligent, knowledge-rich engineering environments.
Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser
et al.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.
Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineering approach that transforms a passive monitoring system into an interactive Type 2 DT with real-time control capabilities for pressurized systems operating at seven bar. We contribute details of multi-layered safety protocols, hardware-software integration strategies across Arduino controllers and Unity visualization, and real-time synchronization solutions. We document specific engineering challenges and solutions spanning interdisciplinary integration, demonstrating how our use of the constellation reporting framework facilitates cross-domain collaboration. Key findings include the critical importance of safety-first design, simulation-driven development, and progressive implementation strategies. Our work thus provides actionable guidance for practitioners developing DTs requiring bidirectional control in safety-critical applications.
Vasileios Sideris, Zacharias P. Oikonomou, Sam Gerené
et al.
ABSTRACT The increasing complexity of modern naval vessels due to technological advancements poses challenges for early‐stage ship design (ESSD). Developing well‐defined system architectures and adopting systems engineering approaches are essential to address these challenges. Model‐based systems engineering (MBSE) has emerged as a solution to the issues inherent in traditional document‐centric methods and is considered the future of systems engineering. This paper aims to address the barriers to MBSE adoption by exploring its value in the early design stage of naval vessels. The paper focuses on system architecture development, covering operational, functional, logical, and physical perspectives, and evaluates two MBSE tools: Capella and CDP4‐COMET. The analysis demonstrates that both tools effectively validate anticipated benefits, concluding that MBSE can enhance and accelerate ESSD, with Capella performing better in the early design stages and CDP4–COMET excelling in the later stages. This paper, thus, differentiates itself from traditional performance and detailed design modeling, such as those addressing motion, control, or thermal dynamics.
Mączka Mariusz, Korzeniewska Ewa, Lebioda Marcin
et al.
This study presents a numerical model designed to simulate the transport properties of textronic structures produced by physical deposition from the gas phase. For the numerical implementation of the model, the method of fundamental solutions was used, which was implemented using the author’s iterative algorithm. Such an approach is rarely used due to the difficulty of obtaining accurate solutions with optimal computational cost. The developed algorithm ensures good convergence of solutions (δk ≈ 5%) with acceptable computational time (t ≈ 180 s for 17 × 103 iterations). The software tool has options for calculating the electric field and density of current distributions for the given system power conditions. The ability to define a conductive path in three geometric dimensions makes it possible to study the effect of changes in the surface geometry on the resistance of the textronic structure. The accuracy of the model was verified by measuring the resistance of selected samples of simulated materials. The measurements confirmed the conclusions of the simulations about the clear dependence of the resistivity of the structure on the roughness of the conducting surface. The range of the largest changes in resistivity as a function of the surface roughness of the conducting path was also determined.
In this paper, we start with a comprehensive evaluation of the effect of adding differential privacy (DP) to federated learning (FL) approaches, focusing on methodologies employing global (stochastic) gradient descent (SGD/GD), and local SGD/GD techniques. These global and local techniques are commonly referred to as FedSGD/FedGD and FedAvg, respectively. Our analysis reveals that, as far as only one local iteration is performed by each client before transmitting to the parameter server (PS) for FedGD, both FedGD and FedAvg achieve the same accuracy/loss for the same privacy guarantees, despite requiring different perturbation noise power. Furthermore, we propose a novel DP mechanism, which is shown to ensure privacy without compromising performance. In particular, we propose the sharing of a random seed (or a specified sequence of random seeds) among collaborative clients, where each client uses this seed to introduces perturbations to its updates prior to transmission to the PS. Importantly, due to the random seed sharing, clients possess the capability to negate the noise effects and recover their original global model. This mechanism preserves privacy both at a “curious” PS or at external eavesdroppers without compromising the performance of the final model at each client, thus mitigating the risk of inversion attacks aimed at retrieving (partially or fully) the clients’ data. Furthermore, the importance and effect of clipping in the practical implementation of DP mechanisms, in order to upper bound the perturbation noise, is discussed. Moreover, owing to the ability to cancel noise at individual clients, our proposed approach enables the introduction of arbitrarily high perturbation levels, and hence, clipping can be totally avoided, resulting in the same performance of noise-free standard FL approaches.
Telecommunication, Transportation and communications
Digital transformation has brought unprecedented transformation and opportunities in manufacturing enterprises. Focusing on 65 listed companies in the electric vehicle sector as the research objects and drawing on the “Technology–Organization–Environment” (TOE) framework, this study selects three dimensions—technology, organization, and environment—and six antecedent conditions. Using fsQCA configurational analysis, this research explores diverse paths to improving corporate performance, identifying five pathways. Among these, digital transformation and operational efficiency consistently serve as pivotal bridging conditions across multiple configurations. Furthermore, when enterprises demonstrate strong capabilities in both the technological and organizational dimensions, other conditions tend to act as substitutes, interacting synergistically with these core strengths to enhance overall firm performance. This study organically combines the TOE framework and fsQCA, deepening the application of the TOE theory in the field of electric vehicle manufacturing enterprises. Additionally, based on the configurational paths derived from the research, it provides differentiated countermeasure suggestions for electric vehicle manufacturing enterprises, offering practical guidance for enhancing their performance in the context of digital transformation.