Interconnected Systems Research Articles

Dynamic Linkages Between U.S and Indian Equity Markets: An Empirical Study

In this study, we investigate the dynamic linkages between US and Indian equity markets by examining the daily Total Return Index values of NIFTY 50 and NASDAQ Composite over a decade ending October 2024. While the indices are not found to be cointegrated, suggesting potential long-term diversification benefits, they exhibit significant short-term correlations. Through various statistical analyses including regression, correlation, and variance tests, we find strong positive associations between the markets, with NASDAQ Composite movements explaining approximately 89% of variations in NIFTY 50 Total Return Index (TRI). The study reveals distinct volatility patterns, with the Indian market showing higher price fluctuations but stronger absolute growth compared to its US counterpart. Despite the strong correlation, both markets maintain unique characteristics influenced by their respective economic fundamentals. The findings demonstrate the evolving nature of market integration between developed and emerging economies, offering valuable insights for international portfolio diversification strategies and risk management in an increasingly interconnected global financial system. Keywords: Market Integration, Volatility Spillover, Portfolio Diversification, Stock Market Returns, Emerging Markets, Cross-market Correlation

Empowering sustainability: bridging science, community, and youth for a brighter future

BackgroundSustainability science has become a crucial aspect of interdisciplinary research aimed at finding practical solutions and driving lasting change. This article examines the challenges facing sustainability science to move beyond the analysis of problems within interconnected systems and critically evaluate the interplay of social, political, and technological factors in the application of knowledge to action.ResultsThe study highlights the influence of innovation implemented by companies on their sustainability performance, the establishment of innovations that positively affect sustainability performance, and analyzes the youth and local community perspectives on scientific innovations for a sustainable future.ConclusionsThe main conclusion is that sustainable development needs to focus on four areas of research: the impact of values ​​on research and decisions; community perspectives on sustainability; fostering socio-technical progress; and promoting learning for sustainable development. By decoding science, positive change can be achieved through the implementation of strategies such as transparency, understandability, and openness to scientific knowledge.

Synchronization of networked mechanical systems without velocity information

AbstractThis paper studies the synchronization problem in a network of interconnected mechanical systems where the velocity information of the interacting agents is unavailable. To achieve this, a distributed law related to the kinetic and potential energies of the considered system is proposed. Specifically, by introducing an auxiliary system and a distributed filter, it is shown that the proposed algorithm enables the synchronization of the considered system by merely using the local position measurements. Moreover, it is also shown that, compared with the existing literature, the derived synchronization conditions are irrelevant to the eigenvalues of Laplacian matrix, thus circumventing the global information of the underlying system. Moreover, on condition that the absolute position information is available, it is also shown that apart from the synchronization of agents, the discrepancy between the position variable of the original system and that of the auxiliary variable tends to zero eventually. Finally, a concrete example and some comparisons are conducted to support the formulated setup and the obtained theoretical results.

Metrics of physiological network topology are novel biomarkers to capture functional disability and health.

Physiological networks are highly complex, integrating connections among multiple organ systems and their dynamic changes underlying human aging. It is unknown whether individual-level network could serve as robust biomarkers for health and aging. We used personalized network analysis to construct single sample network and examine the associations between network properties and functional disability in the Rugao Longevity and Aging Study (RuLAS), the China Health and Retirement Longitudinal Study (CHARLS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and the National Health and Nutrition Examination Survey (NHANES). We observed impairments in interconnected physiological systems among long-lived adults in RuLAS. Single sample network analysis was applied to reflect the co-occurrence of these multi-system impairments at the individual level. The ADL-disabled individuals' networks exhibited notably increased connectivity among various biomarkers. Significant associations were found between network topology and functional disability across RuLAS, CHARLS, CLHLS and NHANES. Additionally, network topology served as novel biomarkers to capture risks of incident ADL disability in CHARLS. Furthermore, these metrics of physiological network topology predicted mortality across four cohorts. Sensitivity analysis demonstrated that prediction performance of network topology remained robust, regardless of the chosen biomarkers and parameters. These findings showed that metrics of network topology were sensitive and robust biomarkers to capture risks of functional disability and mortality, highlighting the role of single sample physiological networks as novel biomarker for health and aging.

POLICY DEVELOPMENT FOR EFFECTIVE SCHOOL MANAGEMENT AMIDST DETERIORATING DISCIPLINE IN SECONDARY SCHOOLS IN KWAZULU-NATAL: CHALLENGES AND STRATEGIES

The current increasing indiscipline in the secondary schools undermines the overall management of schools hence the importance of proper school management for the achievement of the set objectives is critical important(Daka, 2021). Another problem that has come to light is eroding discipline among students in KwaZulu-Natal, South Africa: the issues under the management of misbehaviour disrupt school administration and jeopardize the educational process. In this paper, I discuss the numerous multifaceted causes of the difficulties that current secondary schools encounter in establishing discipline. Through a review of literature, coupled with interviews with educational administrators personally encountered by the researcher, important areas of consideration, among which include the socio-economic conditions, poor school policies, and a bad influence from the community have been found to be among the important factors that contribute to the current disciplinary crisis. The study employs Bronfenbrenner’s Ecological Systems Theory as a framework to analyse the interconnected systems influencing school discipline, including the microsystem (family and school environments), mesosystem (interactions between school and community), exosystem (external policies and socio-economic conditions), and macrosystem (cultural and societal norms). The findings highlight the need for comprehensive and multi-faceted strategies to address these challenges effectively. Some of the key suggestions include providing practical, effective and clear disciplinary measures to be taken, enhancing school community relations, and providing teachers with training and focusing on the socio-economic factors leading to indiscipline. The study also highlights the practical recommendation as ethical leadership and inclusion of the African philosophy, specifically Ubuntu, in developing the better-discipline school climate. This study provides a valuable contribution to the literature on educational management in South and provides practical research that may be useful for policymakers, educators, and school administrators who want to improve school discipline and management. The strategies put forward present an opportunity for schools in KwaZulu-Natal to foster appropriate environment that would facilitate learning and ensure that learners gain improved education outcomes, and instil discipline insisting on accountability among llearners.

Optimization of Marginal Price Forecasting in Mexico through applying Machine Learning Models

The Local Marginal Price (LMP) represents the value of energy at a specific moment and location, and its proper management is crucial for the development of the country's strategic sectors. This study compares the ADR, RPSG, SARIMA, and LSTM-H models for predicting the LMP, achieving an approximate effectiveness of 88%. By implementing it in 28 nodes of the three interconnection systems (SIN, BCA, and BCS) in Mexico, the results of the enhanced LSTM network analysis are presented through sensitivity analysis and an ensemble with Prophet, yielding the following metrics: MAE: 0.0189, MSE: 0.0101, RMSE: 0.1007, and MAPE: 12.18, at node 05PAR-115 in Hidalgo del Parral, Chihuahua. This model can construct tree diagrams (ADR) that identify the critical variables for predicting the LMP of any node, significantly contributing to the accuracy of predictive analysis models.

A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.

HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IoMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

Adaptive heterogeneous graph reasoning for relational understanding in interconnected systems

Adaptive heterogeneous graph reasoning for relational understanding in interconnected systems

Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton–Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.

Strengthening the pain care ecosystem to support equitable, person-centered, high-value musculoskeletal pain care

Abstract Improving health and wellbeing outcomes for people experiencing chronic musculoskeletal pain requires collective efforts across multiple levels of a healthcare ecosystem. System-wide barriers to care equity must however be addressed (eg, lack of co-designed services; overuse of low value care/underuse of high value care; inadequate health workforce; inappropriate funding models; inequitable access to medicines and technologies; inadequate research and innovation). In this narrative review, utilizing a systems’ thinking framework, we synthesize novel insights on chronic musculoskeletal pain research contextualized through the lens of this complex, interconnected system, the “pain care ecosystem.” We examine the application of systems strengthening research to build capacity across this ecosystem to support equitable person-centred care and healthy ageing across the lifespan. This dynamic ecosystem is characterized by three interconnected levels. At its centre is the person experiencing chronic musculoskeletal pain (micro-level). This level is connected with health services and health workforce operating to co-design and deliver person-centred care (meso-level), underpinned further upstream by contemporary health and social care systems (macro-level context). We provide emerging evidence for how we, and others, are working towards building ecosystem resilience to support quality musculoskeletal pain care: at the macro-level (eg, informing musculoskeletal policy and health strategy priorities); at the meso-level (eg, service co-design across care settings; health workforce capacity); and downstream, at the micro-level (eg, person-centred care). We outline the mechanisms and methodologies utilized and explain the outcomes, insights and impact of this research, supported by real world examples extending from Australian to global settings.

Novel Wafer-Level Ta-Ta Direct Thermocompression Bonding for 3D Integration of Superconducting Interconnects for Scalable Quantum Computing System

Novel Wafer-Level Ta-Ta Direct Thermocompression Bonding for 3D Integration of Superconducting Interconnects for Scalable Quantum Computing System

H∞- Static Output Feedback based Load Frequency Control of an Interconnected Power System with Regional Pole Placement

H∞- Static Output Feedback based Load Frequency Control of an Interconnected Power System with Regional Pole Placement

Congestion Management in Interconnected Power System using Water Cycle Algorithm

Nowadays, it is desirable for the power industry to transmit power between different sites of the transmission system in the most cost-effective manner. Congestion management is one of the system operator's most challenging responsibilities in a deregulated context. Congestion would increase electricity costs and transmission loss and have a negative impact on the system's stability and security, so system operators work on it to reduce congestion in deregulated power systems. In this investigation, congestion is handled by considering three objective functions. The first objective is to minimize the generation cost and the second objective is to minimize the transmission loss of the system and third objective is to minimize the total congestion expanse. A water cycle algorithm is employed to mitigate the proposed congestion management and an IEEE 30 bus and 118 bus test system is employed to demonstrate the effectiveness of the suggested approach.

Dynamic load frequency control in Power systems using a hybrid simulated annealing based Quadratic Interpolation Optimizer

Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.

Frequency stabilization of interconnected diverse power systems with integration of renewable energies and energy storage systems

Recent improvements in renewable energy sources (RESs) and their extensive use in the power industry have created significant issues for their operation, security, and management. Due to the ongoing reduction of power system inertia, maintaining operational frequency at its nominal value and minimizing tie-line power variations constitute essential variables for these improvements. A novel improved frequency stabilization approach based on modified fractional order tilt controller is presented for interconnected diverse power systems with integration of sea wave energy, photovoltaic, wind, energy storage system and biodiesel generator. The fractional order tilt integral double derivative (FOTIDD2) is a novel controller that is developed to address the frequency stabilization problems of a hybrid power structure that is integrated with sources of clean energy and devices for storing energy. A recent optimization algorithm inspired by human behavior called mother optimization algorithm (MOA) is applied to adjust the coefficient of the proposed cascaded controller. The FOTIDD2 controller was compared with other control methods in terms of its transient performance, in order to determine its effectiveness. Further, the mother optimization algorithm results are compared with those of sine cosine algorithm (SCA), fox optimization algorithm (FOA), and grey wolf optimization (GWO). FOTIDD2 controller was found to be more effective than PID controller in respect of reducing overshoot (Osh) by 79.31%, 83.78% and 67.99%, improving time settlement by 33.22%, 29.87%, and 22.45% and reducing undershoot (Ush) by 79.13%, 89.34%, and 86.90% for the (∆F1), (∆F2), and (∆Ptie), respectively.

Characteristics of interventions that address racism in the United States and opportunities to integrate equity principles: a scoping review

BackgroundAs a driver of racial and health inequities, racism is deeply ingrained in the interconnected systems that affect health and well-being. Currently, no common frame is employed across researchers, interventionists, and funders to design, implement, and evaluate comprehensive interventions to address racism. Consequently, there is a need to examine the characteristics of interventions implemented in the United States that address racism across social and structural determinants of health and socio-ecological levels. Additionally, we utilized a Health Equity Action Research (HEART) framework to assess how interventions integrate equity principles.MethodsThis scoping review examined the characteristics of multi-level interventions that addressed racism and appraised the interventions using a Health Equity Action Research frame. A comprehensive search strategy was conducted across nine electronic databases between 24 October 2022 through 15 November 2022. Records were included if they were available in English, discussed or evaluated a multi-level intervention or program conducted in the United States, and discussed or evaluated the intervention or program regarding the health and well-being of racialized and ethnically minoritized groups.ResultsA total of 13,391 records were identified, of which 91 met the eligibility criteria and were included in the analysis. Most records reported the racialized group impacted by an intervention, of which the majority were racialized as African American or Black (n = 42) and Hispanic or Latino/a/x (n = 18). Eighty-one (89%) of interventions reported health outcomes and concentrated on the individual level. Most funders reported across the records, and 86 (51%) were a federal agency or department. A further 43 (25%) were private foundations, 12 (7%) were nonprofit organizations, 10 (6%) were private universities, and 4 (2%) were public universities. Regarding alignment with the HEART framework, 14% of interventions reported a mixed-methods approach, 45% reported community engagement, and less than 1% reported researcher self-reflection.ConclusionsMost interventions prioritized people who are racialized as Black and report health outcomes. Since intervention designs, objectives, and methodological approaches vary, no standard frame defines racism and health equity. Applying the HEART framework offers a standard approach for interventionists and researchers to examine power, integrate community voice, and self-reflect to advance health equity.

Credit spread drivers and cross-country connectedness: a study of emerging economies in Asia

PurposeWhile the existing literature lacks a holistic approach to determining credit spreads and is limited to mostly developed countries, this study investigates credit spread determinants and their cross-country connectedness in the context of four emerging economies in Asia by incorporating bonds, market risk, macroeconomic and global factors.Design/methodology/approachThis study utilizes principal component analysis for dimensionality reduction and variable representation. Furthermore, we employ the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity model to capture the cross-country credit spread connectedness between the variables.FindingsThe findings indicate that market volatilities are the most significant drivers of credit spreads, while global factors play a moderating role. Furthermore, the results provide compelling evidence of cross-country credit spread connectedness, with China as the primary transmitter and Malaysia as the primary receiver among the selected emerging economies.Originality/valueThis study addresses the limitations of previous research by extending the analysis beyond the commonly studied developed economies and focusing on emerging economies in Asia. It also employs a comprehensive approach to determine credit spread and explores cross-country credit spread connectedness in developing economies, thereby shedding light on financial risks and vulnerabilities within interconnected global financial systems.

An innovative dynamic observer for nonlinear interconnected systems with uncertainties

In this paper, an innovative dynamic observer is applied to complex nonlinear interconnected systems with matched and mismatched uncertainties. This dynamic observer can estimate system states, which cannot be achieved during the design process for nonlinear interconnected systems with uncertainties. The proposed method has great identification ability with small estimated errors for the states of nonlinear interconnected systems with matched and mismatched uncertainties. It should be pointed out that the considered uncertainties of nonlinear interconnected systems have general forms, which means that the proposed method can be effectively used in more generalised nonlinear interconnected systems. Finally, simulation results for the lateral flight control system are presented to ensure the effectiveness of this strategy.

Miocene to Quaternary evolution of the northern Danube Basin: a review of geodynamics, depositional systems and stratigraphy

This review aims to present an updated lithostratigraphic framework of the northern Danube Basin in Slovakia, consistent with recent shifts in geochronology and depositional system redefinitions based on sedimentology and seismic stratigraphy. Several transgressive–regressive sequences are distinguished: The Lower Badenian (Middle Miocene), represented by the Špačince Fm, mainly occurred in peripheral Blatné and Želiezovce sub-basins. The central part of the Danube Basin formed an elevation during this period, coinciding with the onset of intermediate volcanic activity. The Upper Badenian (Middle Miocene), encompassed within the Pozba Fm, saw the flooding of the central Danube Basin, signifying a change in transport direction. The inception of this cycle is well constrained by recently published multiple 40 Ar/ 39 Ar dating to c. 13.8 Ma. The Sarmatian deposition (Middle Miocene), characterized by the Vráble Fm, also occurred in newly significantly deepened Komjatice and Rišňovce sub-basins, coinciding with a gradual cessation of calc-alkaline volcanism, observed only at the basin's eastern edge. The Pannonian (Upper Miocene) deposition includes lacustrine Ivanka, deltaic Beladice and alluvial Volkovce formations, representing interconnected depositional systems prograding towards Lake Pannon. The subsequent evolution entails a post-rift stage (9.5–6 Ma), primarily marked by the alluvial Volkovce Fm deposition, and basin inversion, with alluvial deposition mostly confined to the basin centre and differentially eroded basin margins (from 6 Ma to the present).