Interconnected Systems Research Articles

The Global Integration Dilemma: Functionalist Efficient Stability Versus Geoeconomic Vulnerability Risks

This article explores the complex trade-offs involved in global economic integration. While economic integration, through mechanisms such as customs unions, promotes efficiency, trade, and cooperation, it also introduces significant vulnerabilities, particularly in the context of geoeconomics and weaponized interdependence. Drawing on the theories of Jacob Viner and David Mitrany, this paper illustrates how functionalist economic stability and efficiency are essential but must be balanced with considerations of resilience and security. Case studies such as the COVID-19 pandemic and Europe’s reliance on Russian gas during the Ukraine war highlight the risks of overdependence in interconnected global systems. Ultimately, this paper argues for a nuanced approach that balances economic benefits with the geopolitical risks of interdependence, advocating for a mixed framework that integrates both efficiency and stability.

Trends in Urbanization and Urban Size Distribution Over the Last 5000 Years

Throughout history, the population of the largest city areas have followed a hyperbolic growth pattern. However, when the viewed on a logarithmic scale, a more detailed structure appears, revealing periods of stability (stasis) punctuated by episodes of rapid expansion. This study explores the implications of this historical trend for the size-frequency distribution and complexity of urban areas relative to the largest city of each era. Several key assumptions underpin the research. First, the size-frequency distribution of urban areas is assumed to follow a power-law (Zipf-Pareto) distribution. Second, the maximum urban area sizes are treated as historical upper limits, shaped by the type and availability of energy sources—such as solar energy for agrarian societies and fossil fuels for industrial systems. By fitting historical city size distributions to a power-law for each century, a pattern of punctuated equilibria emerges. The concept of "complexity" can be interpreted in various ways, but in this study, it refers to the diversity of urban area sizes within an interconnected system. Analyzing this proxy for complexity over time reveals a trend of increasing complexity, with alternating phases of positive and negative growth. Periods of significant complexity increase align with times of systemic reorganization, such as the Dark Ages as described by Sing C. Chew (2006). Additionally, the correlation between power-law steepness and complexity highlights distinct phases of societal development, corresponding to transitions between agrarian societies, Bronze Age empires, and the shift to industrial and post-industrial eras.

Optimizing deployment model of flexible interconnection systems for new energy 6/10 kV medium-voltage grids

Abstract To improve the voltage instability, overloading, and suboptimal economic performance resultant from the integration of flexible interconnections within new energy 6/10 kV medium-voltage distribution grids, we present a novel methodology for the deployment and optimization of flexible interconnection systems. We incorporate generative adversarial networks to assimilate and delineate prototypical deployment scenarios, mitigating voltage disparities. Then, we formulate a two-tiered deployment model for interconnection systems that synchronizes across various time scales, enabling a comprehensive assessment of safety and cost variables intrinsic to the distribution grid framework. Results proved that our refined deployment strategies and models bolster the operational dependability and economic efficacy.

Cybersecurity of Automotive Wired Networking Systems: Evolution, Challenges, and Countermeasures

The evolution of Electrical and Electronic (E/E) architectures in the automotive industry has been a significant factor in the transformation of vehicles from traditional mechanical systems to sophisticated, software-defined machines. With increasing vehicle connectivity and the growing threats from cyberattacks that could compromise safety and violate user privacy, the incorporation of cybersecurity into the automotive development process is becoming imperative. As vehicles evolve into sophisticated interconnected systems, understanding their vulnerabilities becomes essential to improve cybersecurity. This paper also discusses the role of evolving standards and regulations, such as ISO 26262 and ISO/SAE 21434, in ensuring both the safety and cybersecurity of modern vehicles. This paper offers a comprehensive review of the current challenges in automotive cybersecurity, with a focus on the vulnerabilities of the Controller Area Network (CAN) protocol. Additionally, we explore state-of-the-art countermeasures, focusing on Intrusion Detection Systems (IDSs), which are increasingly leveraging artificial intelligence and machine learning techniques to detect anomalies and prevent attacks in real time. Through an analysis of publicly available CAN datasets, we evaluate the effectiveness of IDS frameworks in mitigating these threats.

Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Ethical, Regulatory, and Socioeconomic Dimensions of Theranostic Digital Twins.

Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT. To this aim, the major social, ethical, and regulatory challenges related to TDT implementation and adoption have been analyzed. Methods: The artificial intelligence-dosimetry working group of the Society of Nuclear Medicine and Molecular Imaging is actively proposing, motivating, and developing the field of computational nuclear oncology, a unified set of scientific principles and mathematic models that describe the hierarchy of etiologic mechanisms involved in RPT dose response. The major social, ethical, and regulatory challenges to realize TDTs have been highlighted from the literature and discussed within the working group, and possible solutions have been identified. Results: This technology demands the implementation of advanced computational tools, harmonized and standardized collection of large real-time data, and modeling protocols to enable interoperability across institutions. However, current legislations limit data sharing despite TDTs' benefiting from such data. Although anonymizing data is often sufficient, ethical concerns may prevent sharing without patient consent. Approaches such as seeking ethical approval, adopting federated learning, and following guidelines can address this issue. Accurate and updated data input is crucial for reliable TDT output. Lack of reimbursement for data processing in treatment planning and verification poses an economic barrier. Ownership of TDTs, especially in interconnected systems, requires clear contracts to allocate liability. Complex contracts may hinder TDT implementation. Robust security measures are necessary to protect against data breaches. Cross-border data sharing complicates risk management without a global framework. Conclusion: A mechanism-based TDT framework can guide the community toward personalized dosimetry-driven RPT by facilitating the information exchange necessary to identify robust prognostic or predictive dosimetry and biomarkers. Although the future is bright, we caution that care must be taken to ensure that TDT technology is implemented in a socially responsible manner.

Iterative Learning Control Applied to Interconnected RGB Light Strip

In this paper, an iterative learning control scheme is applied to the interconnected RGB light strip modeled with so-called spatially interconnected systems. The proposed strategy starts with formulating a set of Kirchhoff equalities, which lead to the 2D state-space model. In the next step, the system model is transformed into its 1D equivalent using the so-called lifting approach. Subsequently, the ILC design strategy is proposed. Due to the fact that it eventually takes the form of a differential linear repetitive process, the well-established stability theory along the trial is applied. This enables the application of computationally efficient stability tests, which employ linear matrix inequalities. Such a strategy ensures a numerically tractable design procedure. The application of this strategy ensures the convergence of the output signal to the desired reference. It is important to emphasize that the considered system can be affected by disturbances, and hence, it reflects practical situations that are inevitable in engineering practice. The effectiveness of the proposed approach is illustrated with a comprehensive simulation study.

Novel application of sinh cosh optimizer for robust controller design in hybrid photovoltaic-thermal power systems

Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional–integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm’s balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25–50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller’s effectiveness and reliability in modern power systems with hybrid and renewable energy sources.

Narration, Memory, and the Construction of Self Fatna El Bouih’s Talk of Darkness as a Case Study

This paper investigates the intricate interplay between Memory and Literature as two interconnected symbolic systems that significantly conjure up in their representational capacities. It posits that literature transcends its marginalization in contemporary discourses by asserting its pivotal role in preserving and articulating both personal and collective memories. Literature’s unique ability to transform historical events, cultural identities, and collective experiences into enduring textual forms underpins its capacity as a mnemonic medium. Drawing on Paul Ricoeur's theory of mimesis and Astrid Erll's studies on memory, the paper examines how literature, particularly autobiography, mediates the reconstruction of Self and Memory within the frameworks of time and power structures. The analysis delves into the dual capacity of literature as both a locus of memory and a framework for organizing disparate experiences into coherent narratives, emphasizing its role in identity construction. It critically engages with Dariush Shayegan’s concept of "double fascination," which highlights the dynamic correlation of past and present in collective memory, alongside Ricoeur's tripartite model of mimesis that elucidates the narrative dimensions of memory. The article further critiques the tensions between individual and collective representations of memory, exploring how literature navigates these through emplotment and imaginative engagement with history. By foregrounding the relationship between time, narrative, and memory, the article underscores literature’s enduring relevance in preserving and interrogating cultural histories and identities, resisting homogenized narratives, and fostering a nuanced understanding of collective and individual consciousness. The article concludes by situating literature as a dynamic mediator that reshapes the past to resonate with present realities, thus contributing to both individual and collective consciousness in memory culture.

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks. The proposed IDS leverages machine learning (ML) and deep learning (DL) techniques, employing a stacking ensemble method to enhance detection accuracy by integrating the strengths of multiple classifiers. To ensure real-time performance, the IDS is implemented within a Kappa Architecture framework, enabling continuous processing of IoMT data streams. The system effectively detects and classifies a wide range of cyberattacks, including ARP spoofing, DoS, Smurf, and Port Scan, achieving an outstanding detection accuracy of 0.991 in binary classification and 0.993 in multi-class classification. This research highlights the potential of combining advanced ML and DL methods with ensemble learning to address the unique cybersecurity challenges of IoMT systems, providing a reliable and scalable solution for safeguarding healthcare services.

Transient Allosteric Regulation of Catalysis by Effector Switching in a Pt2L4 Cage.

The complexity of allosteric enzymatic regulation continues to inspire synthetic chemists seeking to emulate interconnected biological systems. In this work, a Pt2L4cage capable of catalyzing the cyclization reaction of an alkynoic tosyl amide is orthogonally coupled to a diacid-catalyzed carbodiimide-hydration cycle. This new Pt-catalyzed cyclization reaction is demonstrated to exhibit electronic regulation by inclusion of different guest effectors. The orthogonal diacid-catalyzed carbodiimide hydration cycle produces transiently diverse guests that influence the rate of the Pt-catalyzed cyclization reaction to different extents. Further complexity can be introduced to the system through displacing the transiently-formed, weakly bound anhydride guest with the stronger binding fumaronitrile, affecting the catalytic rate to a larger extent for the duration of the orthogonal reaction cycle. The modulation of a Pt-catalyzed cyclization reaction can thus be regulated transiently over the course of the reaction-either up- or down-regulating the turnover frequency (TOF)-via coupling with a temporally controllable orthogonal process. This study demonstrates that principles of allosteric enzymatic regulation can also be applied to simple artificial systems.

Transient Allosteric Regulation of Catalysis by Effector Switching in a Pt2L4 Cage

The complexity of allosteric enzymatic regulation continues to inspire synthetic chemists seeking to emulate interconnected biological systems. In this work, a Pt2L4 cage capable of catalyzing the cyclization reaction of an alkynoic tosyl amide is orthogonally coupled to a diacid‐catalyzed carbodiimide‐hydration cycle. This new Pt‐catalyzed cyclization reaction is demonstrated to exhibit electronic regulation by inclusion of different guest effectors. The orthogonal diacid‐catalyzed carbodiimide hydration cycle produces transiently diverse guests that influence the rate of the Pt‐catalyzed cyclization reaction to different extents. Further complexity can be introduced to the system through displacing the transiently‐formed, weakly bound anhydride guest with the stronger binding fumaronitrile, affecting the catalytic rate to a larger extent for the duration of the orthogonal reaction cycle. The modulation of a Pt‐catalyzed cyclization reaction can thus be regulated transiently over the course of the reaction—either up‐ or down‐regulating the turnover frequency (TOF)—via coupling with a temporally controllable orthogonal process. This study demonstrates that principles of allosteric enzymatic regulation can also be applied to simple artificial systems.

Decentralized control of interconnected systems with actuator faults and disturbances via cooperative interaction observer approach

This work investigates decentralised control problems of interconnected systems under actuator faults and disturbances via cooperative interaction observers. Firstly, the influence of faults and matched disturbances is described by an unknown signal generated from any unknown input-to-state stable dynamics. Next, by using current and historical state and estimation information, two virtual auxiliary systems are constructed that involve estimation error information of the unknown signal. And then, a robust decentralised observer with a cooperative interaction structure is developed. Through the cooperative interaction between auxiliary systems and estimation error systems, the estimation error information of the unknown signal is fully used to obtain a more accurate estimation signal. Furthermore, based on cyclic-small-gain and H ∞ techniques, a decentralised control scheme with the estimated signal is proposed, which not only effectively eliminates the influence of faults and matched disturbances, but also improves the robustness of systems. Finally, simulation results validate the presented method.

Implementation of a Bayesian Optimization Framework for Interconnected Systems

Implementation of a Bayesian Optimization Framework for Interconnected Systems

Fuzzy Control of Multivariable Nonlinear Systems Using T–S Fuzzy Model and Principal Component Analysis Technique

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms.

Low-Code BPM meets IoT: A Framework for Real-Time Industrial Automation

Abstract:As we enter the age of Industry 4.0 with smart factories, these interconnected systems, industrial automation has come a long way. To enable real time automation of industrial processes, this paper proposes a novel framework for combining Low-Code Business Process Management (BPM) platforms with the Internet of Things (IoT) technologies. Low Code BPM brings about agility and ease of development as well with IoT offering real time data from sensors and devices closing the gap between generation of data and getting actionable insights. It addresses critical problems including response latency, process inefficiency and scalability limits. The study shows substantial improvements in response time by integrating IoT data streams with Low Code BPM workflows, from 12x faster, to better process efficiency from 70% to 92% and robust scalability to 100 devices with minimal latency. Middleware, edge computing, and intuitive interfaces were used to mitigate challenges such as device integration complexity, data overload and users adaptability. This research serves to support Industry 4.0 goals of predictive maintenance, workflow optimization, and dynamic decisions making. Keywords: Low-code BPM, IoT integration, real-time automation, industry 4.0, smart manufacturing, predictive maintenance, workflow optimization

Event-triggered Decentralized H∞ Control for Nonlinear Interconnected System Based on Adaptive Dynamic Programming: Application to Roller Kiln

Event-triggered Decentralized H∞ Control for Nonlinear Interconnected System Based on Adaptive Dynamic Programming: Application to Roller Kiln

The Impact of Cloud Technology and Integration Solutions on Public Sector Regulatory Systems

The integration of cloud technology and advanced digital solutions has revolutionized public sector regulatory systems, transforming traditional paper-based processes into efficient, automated operations. This transformation encompasses comprehensive frameworks for compliance monitoring, enforcement mechanisms, and public service delivery. The implementation of sophisticated API architectures and real-time monitoring capabilities has enhanced system interconnectivity while ensuring robust security protocols. The adoption of cloud-based platforms has significantly improved citizen engagement through intuitive interfaces and streamlined application processes. Integration challenges, including data sovereignty and legacy system compatibility, have been addressed through structured migration strategies and enhanced security frameworks. Looking ahead, emerging technologies such as artificial intelligence, machine learning, and Internet of Things are shaping the future of regulatory management, enabling predictive compliance monitoring and automated enforcement actions while maintaining transparency and accountability in public sector operations.

Development of 3D-Formed Textile-Based Electrodes with Flexible Interconnect Ribbon.

The integration of electronics into textiles has gained considerable attention in recent years, due to the development and high demand of wearable and flexible electronics. One of the promising fields is healthcare, which often involves the utilization of textile-based electrodes. These electrodes often offer advantages such as conformability, breathability, and comfort. This article presents the development of 3D-formed textile-based electrodes together with a narrow fabric-based interconnect system. This study showcases the methods and materials for the fabrication of the textile-based electrodes and the interconnect system, including a durability assessment, by performing standardized washing (ISO 6330-2012) and user tests. The results demonstrated that the developed 3D-formed textile-based electrodes and stretchable interconnect system are durable and effective for wearable applications, maintaining performance under extensive washing.

An evaluation method for power supply guarantee capability of multi-city interconnected power system based on security region

An evaluation method for power supply guarantee capability of multi-city interconnected power system based on security region

Pediatric behavioral health medical-legal partnerships: a novel approach to child and adolescent psychiatric care.

The state of pediatric mental health in the United States remains an ongoing challenge. Contributing to this challenge is the biopsychosocial nature of mental health - an interconnected system of biological, psychological, social, and legal factors. Consequently, addressing pediatric mental health requires interdisciplinary collaboration. Medical-legal partnerships (MLPs) integrate legal assistance into traditional health care. Though MLPs have gained momentum in general pediatric health care delivery, they are surprisingly underutilized in the pediatric mental health landscape. This current work highlights the Yale Child Study Center Medical-Legal Partnership Project (YCSC-MLPP), which is to our knowledge, the first MLP in a children's behavioral health setting in the country. The YCSC-MLPP emerged as an interdisciplinary collaboration between the Yale Schools of Medicine and Law as well as the Center for Children's Advocacy. Between November 2021 to October 2022, the YCSC-MLPP received 150 referrals regarding patients whose care was complicated by health-harming legal needs. Of these referrals, 70% were non-client consultations, 26% were direct consultation with families, and 4% were full legal representation. The most pertinent topics addressed included education, health, housing, individual rights, and immigration. The creation of the YCSC-MLPP sets an example for what a reimagined, interdisciplinary approach to pediatric mental health can look like.