Interconnected Subsystems Research Articles

The benefits of the dairy value chain to inclusive sustainable development in European highlands: a systems thinking approach

This research investigates the benefits of the dairy value chain beyond food provision, and its contribution to inclusive sustainable development (ISDH) in European highlands. Transdisciplinary teams interviewed and analysed 11 cases related to the dairy value chain sourced from three highland regions in Europe: Alps (Austria, France, Italy), Massif Central (France), Pyrenees (Spain). Actors interviewed were farmers, cooperatives, retailers and associations. A systems thinking approach was applied to develop a conceptual map (Causal Loop Diagram, CLD) describing the contribution of the dairy value chain to ISDH, through the interpretation of the viewpoints of interviewed actors. For each case, the factors contributing to ISDH were identified, then clustered within related topics. Topics from all the cases were used to develop a CLD, and subsystems and leverage points identified. Six interconnected subsystems were identified: local resources, environment, dairy production, collaboration, supply chain and socio-economic. Four leverage points were also identified: public subsidies, feed autonomy, certification of product, and added value. In conclusion, the dairy value chain provides the following benefits aligned with the dimensions of ISDH: landscape maintenance and ecosystem services, quality of dairy products and added value, inclusive governance for actors, reduction of environmental impact, and local development. This model delivers a decision-making tool for the actors involved in the dairy value chain, to prioritise strategic interventions.

Enhancing Energy Systems and Rural Communities through a System of Systems Approach: A Comprehensive Review

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure.

Accelerating Transition to a Low-Carbon Economy: A Coupling Analysis of Agricultural Products and Resource Environment

This study examines the low-carbon economy, agricultural products, and the resource environment as three interconnected subsystems, establishing an evaluation framework for their coordinated growth across eight regions of China. The results highlight significant regional imbalances, particularly in North China, Northwest China, and Northeast China. Principal component analysis (PCA) shows that the agricultural product system captures 99.502% of its information, while the resource-environment system accounts for 84.823%, demonstrating robust explanatory power. The national Economic–Agricultural–Resource–Environment (EARE) system progressed from sub-coordinated growth (2010–2014) to coordinated growth (2019–2020), moving from mild imbalance to high-quality growth. Initially, resource growth lagged behind economic development (2010–2015), which then shifted to economic growth lagging behind resource and environmental growth (2015–2020). This study underscores the need for targeted policies to enhance regional sustainability and balanced development.

A new distributed robust H∞ control strategy for a class of uncertain interconnected large-scale time-delay systems subject to actuator saturation and disturbance

In the realm of large-scale systems, the complexity of controller design has long been exacerbated by the proliferation of decision variables and inherent conservatism. This study introduces a novel approach to address these challenges, presenting a new distributed robust controller design methodology tailored for large-scale systems grappling with disturbances, uncertainties, and actuator saturations. The primary objectives include reducing conservatism, minimizing decision variables, and significantly curtailing computation time. To surmount these hurdles, the research leverages descriptive and reciprocally convex methods, formulating the design procedure using linear matrix inequalities. This enables the adjustment of uncertain parameters and robust disturbance rejection, thereby ensuring stability in large-scale systems. Additionally, a feedback control law is proposed to accommodate saturation constraints and ensure the closed-loop system’s stability. Notably, the effectiveness of the proposed control scheme is demonstrated through the evaluation of a full-car active suspension system, which is partitioned into interconnected subsystems to a large-scale system. Comparative analyses underscore the superior performance and technical advancements offered by the proposed methodology over existing approaches.

A performance-driven framework with a system-of-systems approach for augmented asset management of railway system

The railway system is a complex technical system-of-systems (SoS). To address the complexity of the railway system, a holistic approach is needed that facilitates the development of an appropriate asset management regime. A systems-of-systems (SoS) approach considers the complex nature of the railway system, comprising interconnected subsystems like rolling stock and infrastructure. Neglecting these interdependencies risks sub-optimization of the overall system performance. Asset management of the railway system utilising a SoS approach ensures the focus of asset management on overall system requirements. The efficiency and effectiveness of the railway system is based on aspects such as availability, reliability, and safety performance. To enhance these aspects, monitoring, and improvement of key performance indicators (KPIs) emphasizing increased capacity and reduced operational costs is essential. The KPIs offer quantifiable parameters for performance optimization. Augmenting asset management through data-driven technologies can improve the efficiency and effectiveness of asset management. However, challenges persist in the implementation of data-driven solutions due to the railway system’s complexity and lack of a holistic perspective. A systematic performance-driven framework with a system-of-systems approach for augmented asset management of railway system provides handrail for the utilisation of data-driven technologies with railway system requirements at the centre while developing an asset management regime. The proposed framework aims to establish a clear relationship between system KPIs, and the performance of sub-systems and components aiding railway organizations in asset management design and implementation. This paper explains the important components of the proposed framework and demonstrates the application the framework for asset management and maintenance planning of high value components in the fleet of railway rolling stock. Adoption of the proposed framework is expected to enhance asset management through development and implementation of data-driven solutions that are aligned with system KPIs, to support asset management decision making.

Spatial and functional dynamics of regional economic subsystems maintenance intensity by road infrastructure

Subject. This article discusses the role of road infrastructure in the development of the economy and the issue of ensuring the interconnection of economic subsystems of the region. Objectives. The article aims to determine the degree of influence of the road infrastructure of the regions of Russia on economic subsystems. Methods. For the study, we used the virtual clustering method and a regression analysis. Results. The study outlines the spatial and functional configuration of the Russian regions distribution by intensity of road infrastructure service of economic subsystems. The relevant statistical indicators are correlated with the length of public roads of federal, regional or inter-municipal and local significance. Calculations help predict the increase in the intensity of service for almost all subsystems. Relevance. The practical application of the study results will contribute to the strengthening of strategic and operational management systems for the development of road infrastructure.

Reduced-order modeling of modular, position-dependent systems with translating interfaces

Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. To support such a design approach, a modular model framework is needed to reduce computational complexity and, at the same time, enable multiple teams to develop and analyze the subsystems in parallel. In such a modular framework, the subsystem models are typically interconnected by means of a static interconnection structure. However, many complex dynamical systems exhibit position-dependent behavior (e.g., induced by translating interfaces) which cannot be captured by such static interconnection models. In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure. Additionally, this framework allows to apply model reduction on subsystem level, enabling a more effective reduction approach, tailored to the specific requirements of each subsystem. Furthermore, we show the effectiveness of this framework on an industrial wire bonder. Here, we show that including a position-dependent model of the interconnection structure (1) enables to accurately model the dynamics of a system over the operating range of the system and, (2) modular model reduction methods can be used to obtain a computationally efficient interconnected system model with guaranteed accuracy specifications.

Feature Engineering for a MIL-STD-1553B LSTM Autoencoder Anomaly Detector

The MIL-STD-1553B data bus protocol is used in both civilian and military aircraft to enable communications between subsystems. These interconnected subsystems are responsible for core services such as communications, flow of instrument data and aircraft control. With aircraft modernization, threat vectors are introduced through increased inter-connectivity internal and external to the aircraft. The resulting potential for exploitation introduces a requirement for an intrusion detection capability in order to maintain the integrity, availability and reliability of data transmitted using the MIL-STD-1553B protocol, safety of the aircraft and overall, to achieve mission assurance. Research in recent years has investigated signature, statistical and machine learning based solutions to detect attacks on MIL-STD-1553B buses. Of the different techniques, those based on machine learning have shown extremely good results. The aim of this research is to improve the performance of an existing Long Short-Term Memory Auto-Encoder by refining the feature engineering phase of its pipeline. The improvement in the detector’s overall effectiveness was accomplished through feature engineering focused on feature generation and selection. Five different attack datasets were used as the starting point, consisting of four different denial of service attacks and one data integrity attack. From initial feature extraction of 155 features, two feature generation techniques were employed to create over 38,000 features as a starting point. Using five different MIL-STD-1553B datasets and three feature selection techniques, fifteen different Long Short-Term Memory Auto-Encoder models were created, trained and evaluated using common performance metrics and compared to those of the original anomaly detector. This research demonstrated marked performance improvement achieved by the feature engineering refinements made in comparison to those of the original model. Equally important, this research also showed a significant reduction in the number of features required to achieve this performance gain. In the context of miliary air operations, the ability to improve detection capabilities with less data is important to the technical solutions that contribute to the achievement of cyber mission assurance.

ТРАНСФОРМАЦІЯ МЕНЕДЖМЕНТУ БУДІВЕЛЬНИХ ПРОЄКТІВ В УМОВАХ ГЛОБАЛЬНОЇ ЦИФРОВІЗАЦІЯ

Introduction. The article discusses the need for systemic qualitative changes in the management structure and requirements for the construction project management process in the context of digital transformation, characterized by a combination of advanced technologies and the integration of physical and digital systems. The importance of considering the problems and aspects of digital transformation in the context of new generations (a qualitatively new cluster of human capital), digital talent, changing the mindset of project managers, as well as key changes in construction project management systems in the digital environment is highlighted. Problem statement. The construction industry is a fundamental sector of the economy of any country. A construction project is a structural element of the “construction” system, which allows to form the economic effect of the industry's development as a whole. Modern construction project management includes a set of interconnected but heterogeneous subsystems that together should increase the level of economic profitability. The defining subsystems are: 1. Strategic risk management: Given the volatility of raw material markets and the high value of some materials, the introduction of circular approaches in construction management can help reduce the vulnerability of projects to risks associated with fluctuations in material prices. 2. Efficient use of resources: A central goal of construction management is to optimize the use of resources to achieve project goals. The use of circular economy principles will allow to use resources more efficiently and reduce the cost of purchasing new materials.

Role of BP-ANN in simulating greenhouse gas emissions from global aquatic ecosystems via carbon component-environmental factor coupling

Inland waters (IW), estuarine areas (EA), and offshore areas (OA) function as aquatic systems in which the transport of carbon components results in the release of greenhouse gases (GHGs). Interconnected subsystems exhibit a greater greenhouse effect than individual systems. Despite this, there is a lack of research on how carbon loading and its components impact GHG emissions in various aquatic systems. In this study, we analyzed 430 aquatic sites to explore trade-off mechanisms among dissolved organic carbon (DOC), particulate organic carbon, dissolved inorganic carbon (DIC), and GHGs. The results revealed that IW emerged as the most significant GHG source, possessing a comprehensive global warming potential (GWP) of 0.78 ± 0.08 (10−2 Pg CO2-ep ha−1 year−1) for combined carbon dioxide, methane, and nitrous oxide. This surpassed the cumulative potentials of EA and OA (0.35 ± 0.05 (10−2 Pg CO2-ep ha−1 year−1)). Additionally, structural equation modeling indicated that GHG emissions resulted from a combination of carbon component loading and environmental factors. DOC exhibited a positive correlation with GWPs when influenced by biodegradable DOC. Total alkalinity and pH influenced DIC, leading to elevated pCO2 in aquatic systems, thereby enhancing GWPs. Predictive modeling using backpropagation artificial neural networks (BP-ANN) for GWPs, incorporating carbon components and environmental factors, demonstrated a good fit (R2 = 0.6078, RMSEaverage = 0.069, p > 0.05) between observed and predicted values. Enhancing the estimation of aquatic region feedback to GHG changes was achieved by incorporating corresponding water quality parameters. In summary, this study underscores the pivotal role of carbon components and environmental factors in aquatic regions for GHG emissions. The application of BP-ANN to estimate greenhouse effects from aquatic regions is highlighted, providing theoretical and experimental support for future advancements in monitoring and developing policies concerning the influence of water quality on GHG emissions.

IMPLEMENTATION OF A SYSTEMATIC APPROACH TO THE ANALYSIS OF THE STRUCTURE OF ECONOMIC SECURITY OF ENTERPRISES IN THE AGRICULTURAL SECTOR OF UKRAINE

The article is devoted to implementing a systematic approach to developing the economic security structure of enterprises in Ukraine's agrarian sector. It is substantiated that the economic security of enterprises of the agrarian sector of the economy as a system should include the following components: food; environmental safety; biological (resource); financial and credit; social; inventory management and management; social indicators; insurance coverage; state regulation; agricultural cooperation; Monitoring; agricultural information; agriculture and animal husbandry; Machines; employment; Trade; wholesale food markets; scientific support, etc. This creates an idea of the structural and logical scheme of interconnection and mutual influence of the main elements of the system of economic security of agricultural enterprises. It is determined that the elements and subsystems of the system of economic security of enterprises in the agricultural sector are marked by specific principles. In particular, it is determined that the resource principle means that the subsystems interact with resource flows to provide all the necessary resources for the implementation of the main goal – economic security in the agricultural sector; the information principle provides for the exchange of information between subsystems to ensure the stability of the entire system; the organizational principle defines the interconnection of subsystems for the effective achievement of the main goal; technological principle denotes the technological coupling of subsystems in the process of transforming resources to achieve the goals of food and raw material supply; The functional-target principle determines that subsystems with their target functions contribute to the achievement of the overall goal - the economic security of the agricultural sector. A typology of structural components of an agrarian enterprise's management of economic security has been developed. The substantiation of the allocation of functional components and elements of this system is carried out. The typology of structural components of the system of management of economic security of an agricultural enterprise includes such elements as environmental, resource, financial and credit, integration, monitoring, information, production, technical and technological, personnel and sales, as well as a component of scientific and innovative support, inventory management, insurance provision, state regulation.

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing.

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

Reliability Analysis of Electric Vehicle Charging Station Using Risk-Based Inspection Method and Reliability Block Diagram

Due to the increasing market demand for electric vehicles, particularly electric cars, the infrastructure for electric vehicles is also being fulfilled. This research utilized two methods: Risk Based Inspection and Reliability Block Diagram. Through the Risk Based Inspection method, a Risk Priority Number was obtained which identifies critical points in the Electric Vehicle Charging Station (EVCS). The Block Diagram method established relationships between interconnected subsystems. The research findings indicate that the critical point of the EVCS lies within the Body subsystem. This discovery significantly contributes to raising awareness about EVCS, ensuring that stakeholders entering the EVCS business have a clearer understanding and knowledge of the system.

Dynamic Short Circuit Ratio for Stability Assessment of Grid Connected VSC-HVDC Systems Considering the Impact of Time-Varying Phase Shift

As more renewable resources are integrated into the receiving-end AC grid through VSC-HVDC, the grid connected VSC-HVDC system is facing a critical dynamic stability issue involving multiple-frequency oscillatory behaviors, which mainly stems from the low-system-strength and the time-varying phase shift. This paper proposes a general stability analysis method to measure the dynamic stability of grid connected VSC-HVDC system from the point of view of input to state stability (ISS). A nonlinear time-varying discrete-time state space model (NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M) of the grid connected VSC-HVDC system is built considering the impact of the time-varying phase shift. Then, the NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M is decomposed into several interconnected subsystems. The general asymptotic gain matrix (GAGM), whose elements are derived from the proposed dissipative-form sum-type inequalities (DSIs), is constructed to quantify the interactions between multiple subsystems. Finally, a general dynamic stability analysis index, called dynamic short circuit ratio (DSCR), is proposed to effectively explain the critical dynamic instability point of the grid connected VSC-HVDC system, based on the spectral radius property of the GAGM. Moreover, the real-time simulations and multiple comparative studies are implemented in this paper to verify the effectiveness of the proposed NTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M and stability analysis method by using RT-LAB platform.

Compositional Synthesis of Controllers via Co-Büchi Barrier Certificates

The success of barrier certificates to ensure the safety of control systems has led to their adoption in designing controllers to guarantee more general ω-regular automata specifications. Recent developments in co-Büchi barrier certificates propose the direct construction of the product of the system and the automaton, ensuring that the accepting states of the automaton are visited finitely often. Despite the benefits, challenges arise when finding these certificates for large-scale systems. To overcome such challenges, we introduce a compositional controller synthesis via co-Büchi barrier certificates. We decompose the large-scale system into an interconnection of smaller subsystems, construct local certificates for these subsystems, and utilize a dissipativity-type condition to guarantee the existence of a certificate for the large-scale system. We design controllers for the subsystems that, when concatenated, act as a formally correct controller for the large-scale system. Finally, we illustrate our approach with a numerical example.

Port-Hamiltonian modeling and control of a curling HASEL actuator

This paper is concerned with the modeling and control of a curling Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator using the port-Hamiltonian (PH) approach. For that purpose, we use a modular approach and consider the HASEL actuator as an interconnection of elementary subsystems. Each subsystem is modeled by an electrical component consisting of a capacitor in parallel with an inductor connected through the conservation of volume of the moving liquid to a mechanical structure based on inertia, linear, and torsional springs. The parameters are then identified, and the model is validated on the experimental setup. Position control is achieved by using Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) with integral action (IA) for disturbance rejection. Simulation results show the Efficiency of the proposed controller.

Integrating multiscale data for digital twin-enhanced manufacturing systems: A conceptual and case demonstration

This paper explores the integration of data within digital twin-driven multiscale manufacturing systems, examining the core characteristics of data flows in digital twin (DT) environments tailored to manufacturing. In DT frameworks, data are inherently bidirectional, automated, and real-time, which are critical for maintaining the operational integrity of interconnected manufacturing subsystems. Different scales (from machine tool focus to supply chains) and timelines (from operations events to long term planning) make manufacturing system operation complex. This in turn means data integration across these scales and timelines is inherently complex. This study focuses on the dynamics of data flows in DT-driven manufacturing systems that support both forward and backward communication across DT modules and the central DT platform. The overall DT data scheme manages the supervision and cataloging of actions, which includes technical data related to machinery, processes, resources, and rules, alongside performance metrics across various scales and levels over time. A case study of a DT-driven manufacturing unit illustrates the data flow and interdependency in DT data framework. It highlights how data integration across multiple manufacturing stages can improve operational efficiency, fault diagnosis, and resource optimisation. This study lays the groundwork for the implementation of DT in complex, multiscale manufacturing systems by providing an extendible structured framework.

Mathematical modeling of food production technological processes in a computer training complex for operator education

A computer training complex (CTC) structure has been designed for training food production operators, comprising interconnected subsystems generating output parameters. This CTC structure enables the configuration and storage of knowledge on specific technological processes, facilitating the customization of exercises by instructors. Individualized exercises are generated for students to perform in a virtual production environment, utilizing computer programs, tablets, smartphones, or simulated production equipment, including VR/AR technologies. During exercises, student actions are automatically recorded, and a quantitative assessment is provided based on standards, forming a comprehensive evaluation. A mathematical model, represented by a Markov transition graph, captures various states of the virtual production environment during simulated processes at the CTC. This model serves as the basis for constructing specific technological process models in the food industry, tailored to CTC hardware, software, and training methodologies. The methodology presented streamlines the development of training courses for CTC exercises, enhancing personnel knowledge and skills in food production. By utilizing these models, methods, and algorithms, CTCs can be tailored for training operators in diverse food production sectors. The approach outlined simplifies course creation, ensuring personnel development at the desired proficiency level through exercise quality assessment algorithms.

Mother-father and parent-grandmother coparenting conflict and caregiver involvement in adolescent parent families

Family systems theory recognizes the complex interconnected subsystems within families, yet little is known about how multigenerational coparenting affects coparenting and caregiving outcomes within adolescent parent families. The current study estimated auto-regressive cross-lag models to evaluate the interrelation between mother-father and parent-grandparent coparenting subsystems and caregiver involvement using reports of coparenting conflict and caregiver involvement from 280 adolescent parents ( Mage = 17.02, SD = 1.42; 83.6% Latine; 69.6% women) at two time-points, collected nine-months apart. Results showed stability in coparenting conflict and caregiver involvement among mother-father and parent-grandparent dyads and one intergenerational cross-lag effect. Grandmother involvement at time 1 (T1) was negatively linked to parent-grandparent coparenting conflict at time 2 (T2). Gender differences also emerged, such that (1) father’s report of mother involvement at T1 was positively associated with mother-father coparenting conflict at T2; (2) father’s report of mother involvement at T1 was negatively associated with father’s report of grandmother involvement at T2; and (3) father’s report of parent-grandparent coparenting conflict at T1 was negatively associated with father’s report of mother involvement at T2. Findings underscore connections between intergenerational coparenting subsystems and suggest gender differences in sensitivity, malleability, and the influence of the coparenting dyad on coparenting conflict and caregiver involvement. Further research on adolescent coparenting and caregiving dynamics across coparenting subsystems is needed to strengthen interventions aimed at serving young mothers and fathers.

Enhancing Limited Authority Adaptive Control: Peudocontrol Hedging for Addressing Input Saturation

This paper introduces an innovative approach to address the challenges of input saturation and interconnected subsystem control in limited authority adaptive control systems. Limited authority adaptive control faces issues when encountering input saturation, which can severely impact the system's performance and adaptability. Additionally, when dealing with cascaded control systems, ensuring smooth adaptation for both subsystems is essential. To overcome these challenges, we propose a method known as Pseudocontrol Hedging (PCH), which allows adaptation to continue even during input saturation and ensures effective control in interconnected subsystems. The PCH technique modifies the reference model response to accommodate expected system behavior, enabling reliable adaptation regardless of system response.