Analysis Of Interdependence Research Articles

Bidirectional associations between parenting stress and coparenting: A longitudinal actor-partner interdependence analysis.

Although parents' parenting stress has been found to be related to coparenting, existing research has mostly focused only on one parent or examined the unidirectional effects between the two factors. The present study investigated the actor and partner effects as well as the bidirectional association between parents' parenting stress and coparenting in Chinese society. The study participants were 522 Chinese father-mother dyads with elementary school-aged children who followed for two measurement occasions (half a year apart). The cross-lagged actor-partner interdependence model (APIM) was used to examine the actor effects and partner effects in the bidirectional associations between both parents' parenting stress and coparenting. The findings revealed that both fathers' and mothers' parenting stress significantly negatively predicted their spouses' coparenting (partner effects), and mothers' coparenting significantly negatively predicted their spouses' parenting stress (partner effects). However, the actor effects between parents' parenting stress and coparenting were not significant. Furthermore, there were significant bidirectional associations between fathers' parenting stress and mothers' coparenting. Findings from the present study reveal the dynamic interaction between parenting stress and coparenting. The results point to the need for providing preventive interventions to parents with high levels of parenting stress and establishing a collaborative interaction framework between parents.

An insight into the combustion analysis of low carbon alcohol infused ternary fuel blends operated by varying the compression ratios

To address the growing scarcity and rising costs of fossil fuels, researchers are exploring alternative energy sources that can mimic the conventional fuels. This study focused on ternary fuel blends made from waste fried oil (WFO) biodiesel, methanol, and diesel. To enhance their stability, 10, 20, and 30 ml of n-butanol per litre of ternary blends are added. The blends were tested in a variable compression ratio (VCR) engine under peak load conditions. The experiments are carried out by varying the compression ratio (CR) of the engine from 16:1 to 18:1. The results showed that increasing the CR improved combustion for all fuel blends. Among the ternary blends, B30M20D50 outperformed pure diesel in terms of combustion characteristics. When compared to diesel, B30M20D50 yielded 5.08 %, 5.31 %, and 4.59 % higher Pmax at CR 16:1, 17:1, and 18:1, respectively. Under the same conditions, NHRR outperformed diesel by 1.46 %, 3.12 %, and 2.44 %, respectively. While ternary blends with up to 20 % methanol exhibited stable combustion, higher methanol concentrations led to erratic rise in the coefficient of variation. The interdependency analysis revealed a strong correlation between the combustion parameters for the B20M30D50 blend across different compression ratios.

Bagehot and the stabilising function of central banks

The analysis of Lombard Street focuses on two dimensions that have received little attention: (1) Bagehot’s analysis of the systemic financial interdependencies between the functional poles of creditors and debtors in financial markets; and (2) the significance of trust and expectations in the functioning of these promise-to-pay markets. These dimensions lead us to Bagehot’s well-known principles of central bank responsibility in the historical legislative-political context of the Bank Charter Act of 1844. Despite its publication 150 years ago, this book deals with the then new and still relevant finance-based interdependencies where functional roles need to be redefined and restructured.

System-of-Systems Resilience Analysis and Design Using Bayesian and Dynamic Bayesian Networks

A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal with the impact of complex internal and external environments. In this paper, we propose a resilience analysis method of an SoS based on a hierarchy structure. Firstly, we establish a hierarchy structure, which is ranked from high to low as capability level, activity level and system level. Then, Bayesian Networks (BNs) and Dynamic Bayesian Networks (DBNs) are used to analyze the resilience of the SoS. A resilience-based system importance metric is introduced, which is used in the budget allocation optimization problem during the development domain of an SoS. This paper proposes a mathematical programming model aimed at optimizing SoS resilience by optimally using budget to the subsystem. The application of the proposed approach is demonstrated using a case study: a Next Generation Air Transportation setting. The study results provide evidence that the proposed inter-dependency analysis based on Bayesian theory and the SoS resilience design approach can assist SoS system engineers in increasing expected SoS resilience during the development domain.

Modeling Interdependent Infrastructure System Vulnerability with Imprecise Information Using Two Fuzzy Inference Systems

Infrastructure systems play important roles in economic development and the social quality of life. Interdependencies exist between infrastructure systems: a functional disruption in one system can affect dependent systems, thereby escalating the impacts. It is vital to properly model interdependencies to understand the full impacts of disruptive events on infrastructure systems. Quantitative data on infrastructure interdependency is often difficult to obtain or unavailable for a variety of reasons. To overcome quantitative data scarcity issues, qualitative subject expert knowledge has been used in interdependency analysis, primarily in the form of linguistic responses. Linguistic data is susceptible to uncertainties arising from variations in intended meanings, which may yield inaccurate results. This paper proposes a framework to address this problem using two fuzzy inference systems to model event-specific, network-wide infrastructure failures. The first fuzzy inference system models the damage induced by interdependencies using verbal descriptions. The second fuzzy inference system accounts for synergistic, compounding effects of multiple incidences of indirect damage caused by interdependencies. A case study is conducted to demonstrate the applicability of the proposed methodology using electric and gas distribution networks in the United Kingdom. Sensitivity analyses are performed to show the flexibility of the fuzzy inference systems. The results show that the proposed method can model the interdependency and vulnerability of infrastructure systems using fuzzy inference systems to handle imprecise input. The proposed framework may assist practitioners in better understanding the interdependency and vulnerability of infrastructure systems, and in making more informed decisions to reduce losses resulting from disruptive events.

Exploring the supply chain ambidexterity: a multilevel micro-foundational perspective

PurposeThis study aims to introduce a multilevel micro-foundational perspective on supply chain (SC) ambidexterity, grounded in organizational learning and adaptation research. It investigates the interplay of contextual factors, strategic orientation and a bundle of supply chain management practices to foster ambidextrous performance.Design/methodology/approachLeveraging a blend of perceptual and objective data and measures, this study explores the intricacies of macro and micro factors at multiple levels, offering empirical support for the research framework. The interrelationships among these factors are scrutinized through three analytical approaches: selection, interaction and system forms of interdependence analysis.FindingsFirst, the authors offer empirical support for their conceptual model, illustrating that ambidexterity behavior and outcomes in the SC emanate from intricate interactions between macro and micro factors across various levels. Second, the authors present robust empirical evidence endorsing a system/gestalt form of interdependence analysis in capturing SC ambidexterity and performance. This analytical approach effectively captures the complementarity and contradictory interdependence among the opposing poles of efficiency and responsiveness.Originality/valueThe organizational and SC activity configuration faces numerous paradoxical tensions, such as profitability versus sustainability. This study offers valuable insights into establishing an ambidextrous system capable of navigating and addressing these paradoxical situations.

Addressing Circularity Strategies by Reconfiguring Smart Products during Their Lifecycle

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach.

Health Security and Cybersecurity: Analysis of Interdependencies

The development of medical digital platforms with data on patients, medical institutions and medicines, the growing use of IoT devices in medicine, the development of telemedicine accelerated by the COVID-19 pandemic, the growth of big data-based treatment technologies – all this makes it necessary to ensure reliable cyberdefence in the field of public health, maintain the confidentiality of financial information of clinics and health insurance companies, protect databases with patient records from hacking, and ensure secure communication. The article uses economic and mathematical modelling to study the relationship between two well-known international indexes and their components: The Global Health Security Index (GHSI) and the Global Cybersecurity Index (GCSI) for 190 countries in 2021. The input base included 6 cybersecurity subindexes and 7 health security subindexes for 2021. Based on the iterative divisive k-means method, all countries were grouped into 3 clusters. The first cluster includes 55 countries (medium levels of the studied indexes), the second – 49 (high levels), the third – 86 (low levels). The feasibility of dividing into clusters and choosing their optimal number is substantiated by means of variance analysis. Via the methods of Sigma-restricted parameterisation, Univariate Tests of Significance, Pareto Chart of t-Values and correlation analysis, all factors, without exception, proved to be relevant. Due to the OLS method, multiple linear regressions describing the relationships between various components of these indexes and their integral values are generated. The statistical significance of the factors included in the model is confirmed, and the model itself is tested for adequacy and accuracy. On the basis of correlation analysis, existence and magnitude of statistical relationship between the components of these indexes are revealed. The strongest correlations are observed between the integral values of these indexes, as well as between pairs of subindexes: 1) Legal Measures (GHSI component) and Prevention (GCSI component); 2) Technical Measures (GHSI component) and Detection and Reporting (GCSI component). To identify the causal relationship between the groups of factors, a canonical analysis was carried out, which showed the GHSI parameters are causal and the GCSI parameters are resultant. The linear regression model showed a significant positive relationship between the GHSI and GCSI indexes.

Movement for Black Lives at Work? Racial Justice Spillover, Labor Organization, and New Worker Militancy

ABSTRACT Emerging from a confluence of events – including Black Lives Matter Movement (BLM) – racial consciousness has intensified in recent years. In the wake of a long declining labor movement, there has also been a surprising upsurge in labor militancy. Are these two currents systematically related? Situating our analysis in the sociological literature on social movement spillover, we ask if there is evidence of racial justice spillover into and stimulating workplace militancy. Employing all known strikes and worker protests during 2021 and 2022, we find: (a) racial justice demands did, indeed, appear as part of workplace militancy during this period; (b) racial justice demands were voiced in both worker protests and strikes launched most heavily by unions but also by alt-labor/SMOs and by unorganized workers; (c) weekly time-series regression models indicate that BLM demonstrations stimulated the frequency of workplace protests and strikes with racial justice demands which, in turn, stimulated wider worker militancy over other issues; and (d) interdependency analysis of worker actions reveals that unionized worker protests served as a key catalyst driving other forms of worker militancy with racial justice demands. We discuss the implications of our findings for social movement spillover theory and for moving racial justice and labor movements forward.

Tracking the size of the estimation window in time-series data

PurposeThis paper introduces a novel method, Variance Rule-based Window Size Tracking (VR-WT), for deriving a sequence of estimation window sizes. This approach not only identifies structural change points but also ascertains the optimal size of the estimation window. VR-WT is designed to achieve accurate model estimation and is versatile enough to be applied across a range of models in various disciplines.Design/methodology/approachThis paper proposes a new method named Variance Rule-based Window size Tracking (VR-WT), which derives a sequence of estimation window sizes. The concept of VR-WT is inspired by the Potential Scale Reduction Factor (PSRF), a tool used to evaluate the convergence and stationarity of MCMC.FindingsMonte Carlo simulation study demonstrates that VR-WT accurately detects structural change points and select appropriate window sizes. The VR-WT is essential in applications where accurate estimation of model parameters and inference about their value, sign, and significance are critical. The VR-WT has also helped us understand shifts in parameter-based inference, ensuring stability across periods and highlighting how the timing and impact of market shocks vary across fields and datasets.Originality/valueThe first distinction of the VR-WT lies in its purpose and methodological differences. The VR-WT focuses on precise parameter estimation. By dynamically tracking window sizes, VR-WT selects flexible window sizes and enables the visualization of structural changes. The second distinction of VR-WT lies in its broad applicability and versatility. We conducted empirical applications across three fields of study: CAPM; interdependence analysis between global stock markets; and the study of time-dependent energy prices.

Effect of Spirituality on Psychological Resilience and Hope in Patient-Family Caregiver Dyads Experiencing Gynecological Cancer: An Actor-Partner Interdependence Analysis.

Women experiencing gynecological cancer (GCPs) and family caregivers (FCGs) use spirituality to cope with cancer. However, the dual relationship between the impact of spirituality on psychological resilience and hope among both GCPs and FCGs has not yet been explored. To examine the interdependent relationship between spirituality, psychological resilience, and hope of women with gynecological cancer and their FCGs at the dyadic level. In this cross-sectional study, 107 dyads were recruited from the gynecological oncology departments of 2 university hospitals in Ankara, Turkey. The GCPs and FCGs completed a Patient/Family Caregiver Information Form, Spiritual Well-being Scale, Connor-Davidson Resilience Scale, and Dispositional Hope Scale. Data were analyzed using descriptive statistics and Pearson's correlations. This study used the Actor-Partner Interdependence Model. Spirituality of both GCPs and FCGs had a significant positive effect on their psychological resilience ( B = 0.918, P < .001; B = 0.435, P < .001, respectively) and hope ( B = 0.350, P < .001; B = 0.246, P < .001, respectively). However, the spirituality of GCPs and FCGs did not have a partner effect on psychological resilience ( B = -0.150, P < .052; B = -0.150, P = .052, respectively) and hope ( B = -0.012, P = .810; B = 0.073, P = .157, respectively). Spirituality of GCPs and their FCGs positively affected their psychological resilience and hope, but the effect on each other was limited. Health professionals, especially nurses, should focus on tailoring spiritual care interventions to the unique needs of each individual within the GCP and FCG dyads. Personalized approaches that recognize and address the specific spiritual concerns of patients and caregivers may be more effective in promoting psychological resilience and hope.

ASAPA: a bioinformatic pipeline based on Iso-Seq that identifies the links among alternative splicing, alternative transcription initiation and alternative polyadenylation.

Although the events associated with alternative splicing (AS), alternative polyadenylation (APA) and alternative transcription initiation (ATI) can be identified by many approaches based on isoform sequencing (Iso-Seq), these analyses are generally independent of each other and the links between these events are still rarely mentioned. However, an interdependency analysis can be achieved because the transcriptional start site, splice sites and polyA site could be simultaneously included in a long, full-length read from Iso-Seq. We create ASAPA pipeline that enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data. We tested this pipeline using Arabidopsis data and found some interesting results: some adjacent introns tend to be simultaneously spliced or retained; coupling between AS and ATI or APA is limited to the initial or terminal intron; and ATI and APA are potentially linked in some special cases. Our pipeline enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data, which is conducive to a better understanding of transcription landscape generation.

Assessment of Parameters in Integrated Photonic Systems Through the Analysis of Interdependencies with the MICMAC Technique

Assessment of Parameters in Integrated Photonic Systems Through the Analysis of Interdependencies with the MICMAC Technique

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

AbstractCyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

Influence-Based Analysis of Disruptions in an Energy Distribution Network Following a Main Channel Outage

Entailing a comprehensive examination of energy distribution systems, this study delves into the repercussions of failures in energy distribution channels, with a specific focus on the impact on substitution channels using the Independent Influence Model (ICM). By transcending conventional fault localization approaches, our research uncovers how these failures influence alternatives, expanding our understanding of outage dynamics in energy networks. The results obtained through the application of the ICM offer valuable insights, emphasizing the crucial importance of understanding how failures in energy distribution channels impact the overall resilience of networks. This approach enables a detailed analysis of interdependencies among substitution channels, providing essential guidance for enhancing the resilience of energy networks in the face of uncertainties and disruptions.

The Innovative Nature of Selected Polish Companies in the Energy Sector Compared to the Use of Renewable Energy Sources from a Financial and an Investor’s Perspective

Analysis of the energy sector from the micro perspective that relates to individual companies is much rarer than a macroeconomic analysis that concerns the power industry as a whole and its impact on the functioning of the economy. However, energy companies directly implement the government’s energy policies and innovation strategies. Thus, this article attempts to answer the question concerning the relationships in three large energy companies operating in Poland (1) between the use of renewable resources for production and the innovative nature of a company, (2) between the use of renewable energy sources and the standing on the stock exchange and profitability. This study used multiple case studies, financial analysis indicators, a time series analysis, and an interdependence analysis. This study covers 2011–2022 and allows consideration of long-term changes in domestic energy policy. Our findings suggest that there is a relationship between a company’s investment activity and the use of renewable energy sources. Unfortunately, the scope of the use of RESs in these companies is small (from ca. 1% to 15%, which demonstrates the low progress of green transformation) and has negative correlations with the investors’ assessment and profitability. In relation to innovation, the ratio of intangible assets to total assets was the highest for Tauron SA, increasing from 1.96% to 5.16%. Its material commitment to innovation is distinguishable from the other two companies. This is also the company with the highest share of RESs in energy production. The second place belongs to Enea SA with its ratio of intangible assets to total assets that increased from 0.72% to 1.69%. The ratio was lowest for PGE SA, increasing from 0.37% to 1.47%. The results and standing of the analyzed energy companies are strongly affected by energy policy amendments, including the improved status of coal and the re-oriented use of RESs (prioritizing solar energy over wind). As a result, these companies, despite the twelve-year period of the implementation of green transformation in the European Union, have achieved little on the path to sustainable energy. Therefore, achieving the goal of a zero-emission economy seems unlikely, since the renewable energy mix is still very slight and not diversified. Changes in energy policy are also not conducive to sectoral and economic innovation.

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

AbstractBattery cell production is a key contributor to achieving a net‐zero future. A comprehensive understanding of the various process steps and their interdependencies is essential for speeding up the commercialization of newly developed materials and optimizing production processes. While several approaches have been employed to analyze and understand the complexity of the process chain and its interdependencies – ranging from expert‐ and simulation‐based to data‐driven – the latter holds significant potential for real‐time application. This is particularly relevant for inline process control and optimization. To streamline the development and implementation of data‐driven models, a holistic framework that encompasses all necessary steps – from identification of relevant parameters and generation of data to development of models – is imperative. This article aims to address this objective by presenting a comprehensive and systematic methodology, demonstrated for efficient cross‐process analysis in electrode manufacturing. Through the combined utilization of design of experiments methods, data‐driven models, and explainable machine learning methods, the interdependencies between production parameters and the physical, mechanical, and electrochemical characteristics of the electrodes were uncovered. These actionable insights are essential for enabling informed decision‐making, facilitating the selection of appropriate process parameters, and ultimately optimizing the production process.

Research on new energy grid-connected load monitoring method based on the network analysis method

Abstract The steady-state characteristic parameters of the loads are used to identify new energy grid-connected loads using an event-based network analysis method in this paper. The analysis of interdependence among elements is done by studying the element layer and special structure of the network analysis method. Using the principle of limit relative ranking vector calculation, the supermatrix and weighting matrix of the ANP element layer are constructed, and the basic steps of ANP model weights are determined. The ANP-based load monitoring algorithm is evaluated by combining the load identification accuracy with the F-value. The results show that event detection algorithms are generally more than 70% accurate. 17 out of 18 times of identifying load events of computers can be correctly identified with an accuracy rate of 89.37%, 9 out of 10 times of identifying fluorescent lamps with an accuracy rate of 89.98%, and 14 out of 15 times of identifying microwave ovens with an accuracy rate of 92.75%. The new energy grid-connected load can also be detected by combining the harmonic content rate. The harmonic content rates when the desktop computer was turned on were 93.04%, 86.67%, 60.16%, 61.76%, and 23.46%, respectively. This study helps to improve the accuracy of new energy grid-connected load monitoring.

Care Management in Scientific Production in Integrative Oncology in Latin America: An Integrative Review

Introduction: Including traditional, complementary and integrative medicines in national health systems is a point of discussion worldwide. This article focuses on the theme within the scope of oncology in Latin America. Objective: To analyze the health care management dimensions included in scientific production in integrative oncology in Latin America. Materials and method: This is an integrative literature review carried out in the LILACS, Mosaico, IBECS, PubMed and Embase databases, whose guiding question was prepared according to The PRISMA 2020 Statement recommendations. The search occurred by the association of free terms related to the descriptors “Integrative Oncology,” “Complementary Therapies,” “Health Care,” “Care Management,” and “Countries That Make Up Latin America”. Data were interpreted from the conceptual perspective of health care management dimensions. Results: The study selection process identified 206 studies. The final sample was made up of eight articles available online in full, published between 2017 and 2022 in Portuguese, Spanish or English. Articles were developed in Brazil (n = 4), Chile, Colombia, Peru, and Uruguay (n = 1, respectively). There was a predominance of the individual health care management dimension as the focus of analyzed studies. The “professional,” “family,” “societal,” and “organizational” dimensions were presented in the discussion of results. Conclusions: Despite the insufficiency of studies, analysis of interdependence between dimensions indicates the complexity of the management process for integrating traditional, complementary and integrative medicine in oncological care in Latin America, which suggests an epistemology in the construction process.

The nonlinear dependence of income inequality and carbon emissions: Potentials for a sustainable future

Understanding the interconnected nature of rising carbon emissions and income inequality is crucial to achieve social and ecological sustainability. We contribute to the literature with a systematic analysis of the conditional interdependence by means of a distributional copula model. The model estimates the nonlinear dependence between a country’s GINI coefficient and CO2 emissions across and within country income groups. This enables us to uncover complex interdependencies that standard linear regression techniques might hide. Using an unbalanced panel data set of 109 countries from 1960 to 2019, composed of different data sets, we show that dependence is related to the prevailing consumption level, energy sources, the structure of the economy and the political system with heterogeneous effects across country income groups. To estimate the potentials for a sustainable future, we define thresholds of potential social and environmental sustainability. We find that richer countries are furthest away from this, but have the highest potential for realizing the defined sustainable space. This further highlights the importance of the service sector and combined policies targeting social and environmental sustainability.