Complex Interdependencies Research Articles

Fiscal Policy Instruments for Inclusive Labor Markets: A Review

ABSTRACTThis paper critically reviews fiscal policy instruments—including active labor market policies, social protection measures, care policies, and tax reforms—focusing on their differentiated impacts on underrepresented groups and their potential to foster labor market inclusivity. Our review reveals that empirical studies often overemphasize the capacity of individual policies to mitigate inequalities, neglecting the complex interdependencies among various mechanisms and policies. Instead, we argue for a systematic approach to ensure equitable access to good jobs and address disparities across labor market groups. Although sector‐specific training and reskilling are essential, their effects often take time to materialize, highlighting the necessity of complementary labor demand measures, such as public works schemes, and employment subsidies, to address immediate challenges and promote inclusive growth. Care policies remain central to improving female labor market participation by alleviating their unpaid care burdens. The review also identifies critical research gaps, including the need for longitudinal studies on long‐term policy impacts, an exploration of regional disparities in labor market inequalities, and sector‐specific effects of fiscal measures.

Impact of microgeography on communication dynamics in a healthcare environment

BackgroundFor growing healthcare organisations, anchored resources—assets that are not easily movable—may complicate expansion and distort workflow patterns. We examine work patterns at a radiation oncology department of a major Canadian...

Deep Multi-View Clustering Optimized by Long Short-Term Memory Network

Long short-term memory (LSTM) networks have shown great promise in sequential data analysis, especially in time-series and natural language processing. However, their potential for multi-view clustering has been largely underexplored. In this paper, we introduce a novel approach called deep multi-view clustering optimized by long short-term memory network (DMVC-LSTM), which leverages the sequential modeling capability of LSTM to effectively integrate multi-view data. By capturing complex interdependencies and nonlinear relationships between views, DMVC-LSTM improves clustering accuracy and robustness. The method includes three feature fusion techniques—concatenation, averaging, and attention-based fusion—with concatenation as the primary method. Notably, DMVC-LSTM is well suited for datasets that exhibit symmetry, as it can effectively handle symmetrical relationships between views while preserving the underlying structures. Extensive experiments demonstrate that DMVC-LSTM outperforms existing multi-view clustering algorithms, particularly in high-dimensional and complex datasets, achieving superior performance in datasets like 20 Newsgroups and Wikipedia Articles. This paper presents the first application of LSTM in multi-view clustering, marking a significant step forward in both clustering performance and the application of LSTM in multi-view data analysis.

Optimization of Rendering Parameters of Cesium 3DTiles Model Based on Differential Evolution Algorithm

Cesium is an open-source 3D virtual earth engine based on WebGL and one of the most widely used 3D GIS engines. The 3DTiles rendering parameters in Cesium improve rendering efficiency but involve numerous data types and complex interdependencies, making simultaneous optimization challenging. In this paper, we proposed a multi-strategy probabilistic discrete differential evolution algorithm (MSPDDE) for finding the optimal values of the rendering parameters of Cesium 3DTiles model, which increases the search space and improves the convergence speed by introducing multiple mutation strategies. These strategies effectively reduce the probability of falling into local optimality due to too many parameters and deal with discrete variables in the rendering parameters by applying a probabilistic discretization strategy to the discrete variables so that MSPDDE is able to find all the rendering parameter optima of Cesium 3DTiles. Under three different sizes of 3DTiles model cases, the rendering model time using the optimal parameter configurations found by the algorithm is reduced by 28.84%, 27.89%, and 13.32%, respectively, compared with the default parameter configurations of Cesium, which shortens the time of rendering 3DTiles models by Cesium.

Access to Emerging Technologies and Techniques in the Nigerian Solid Minerals Sector: A Cost-Benefit Analysis

As the global population increases, the scale and impacts of mining have increased exponentially, and there are emerging trends when considering the sector’s future. Therefore, countries are transcending to modern forms of technology to achieve scale economies, decarbonisation, mine health and safety and environmental sustainability through leveraging on Automation, Big Data Analytics, Digital Innovations, and Artificial Intelligence. These innovations have human, social and economic impacts, which can negatively and positively affect the principle of shared value. These complex interdependencies are reshaping the industry on a global scale, and for Nigeria to leverage her mining sector and expand to regional and global value chains, there is a need to scale up access to modern forms of technologies and techniques through linkage optimization, research and development, national and sectoral policies, government intervention, sustainable practices which will ultimately lead to resource-based industrialization. While abundant literature exists on the emergence of technologies in the solid minerals sector, this study, using a desktop and library-based analytical review, finds a dearth of research on the cost-benefit analysis of emerging technologies in the Nigerian mining landscape. This will ensure that technological disruptions are a win-win for communities and stakeholders to drive growth and global competitiveness for the sector. Therefore, Nigeria’s mining laws and policies must be reformed to adapt to these trends and interdependencies

Advancing Middle East Construction Sustainability: A Framework for Addressing Logistics Challenges Through Solutions and Critical Success Factors

The Middle Eastern construction industry significantly contributes to regional economic development but faces persistent sustainability challenges due to logistical inefficiencies. This study aims to address the gap in the existing literature, which often treats logistics challenges, solutions, and critical success factors (CSFs) in isolation without offering a unified framework to integrate these elements. By leveraging structural equation modelling (SEM), this research provides a comprehensive approach to understanding and addressing the interconnections between logistics inefficiencies, proposed solutions, and sustainability outcomes. Using SEM, this study identifies and analyses 23 logistics challenges, 18 solutions, and 8 CSFs, revealing their relationships and collective impact on sustainability in the construction sector. SEM’s robust analytical capabilities enable simultaneous testing of complex interdependencies, offering insights that traditional methodologies cannot achieve. The findings emphasise the critical role of lean methodologies, advanced technologies, and collaboration strategies in overcoming inefficiencies, reducing environmental impact, and optimising resource use. This research fills a crucial gap by developing a cohesive framework that integrates logistics challenges, solutions, and CSFs to advance sustainable practices. The outcomes provide actionable guidance for stakeholders to improve sustainability performance while enhancing competitiveness in Middle Eastern construction logistics

Understanding collaboration in short food supply chains: a focus on collaborative relationships, interaction mechanisms and relational benefits

AbstractThe article provides an original conceptual framework to advance the knowledge of short food supply chains (SFSCs) collaboration by bridging supply chain management approaches with empirical evidence from the agri-food sector. Through an in-depth analysis of 14 initiatives across Europe, it explores the multi-faceted nature of SFSCs collaboration, revealing a complex interplay between collaborative relationships, interaction mechanisms, and relational benefits. By identifying seven key interaction mechanisms—information sharing, decision synchronisation, goal congruence, incentive alignment, resource sharing, joint knowledge creation, and collaborative communication—the article shows how these mechanisms generate both tangible and intangible relational benefits, including fair pricing, reduced transaction costs, enhanced market knowledge, technological skills, and stronger community engagement. The findings demonstrate that SFSCs collaborations operate as dynamic ecosystems characterised by complex interdependencies among diverse actors. This diversity extends beyond traditional business-to-business (B2B) models to include business-to-consumer (B2C), consumer-to-business (C2B), and hybrid business-to-business-to-consumer (B2B2C) relationships. These varied collaborative structures facilitate mutual learning, innovation, and value co-creation, crucial for the resilience and adaptability of local food systems. The presence of robust interaction mechanisms is essential for fostering effective partnerships and generating relational benefits, even if not all mechanisms are consistently present in every type of collaborative relationships.

Pakistan’s Regional Integration via Shared Economic Vision: Prospects and Challenges

Regional cooperation is essential for economic development in a world characterized by complex interdependence. The phenomenon of regional integration is very common these days, and the world witnesses various regional organizations, treaties, and agreements to ensure the optimal utilization of regional collective resources. This indicated that the future is in collective growth. Pakistan also focused on its regional integration lately. It has undergone a shift in its foreign policy from a security-centric state to economic initiatives commonly known as its Shared Economic Vision. This major shift in policy was observed after the capstone economic project of Pakistan CPEC in 2015, which was later confirmed by its National Security Policy (NSP) in 2022. Regional integrations can help countries to overcome the challenges of globalization and to reap the benefits of economic growth and development. Countries can promote regional cooperation by establishing regional trade agreements and regional infrastructure projects and cooperating on security and other issues. In this regard, this paper focuses on the importance of Pakistan's regional integration and the different projects/integrations it is undergoing to enhance its regional integration.

Hybrid MCDM-FMEA Model for Process Optimization: A Case Study in Furniture Manufacturing

The furniture-manufacturing industry is pressured to improve quality, productivity, and profitability, particularly within increasingly volatile market conditions. This study is focused on the development of methods for optimizing production processes in a furniture-manufacturing company through the application of an integrated risk management framework. By integrating Failure Mode and Effects Analysis (FMEA) with advanced multi-criteria decision-making (MCDM) techniques, specifically fuzzy AHP, fuzzy TOPSIS, and fuzzy WINGS, a hybrid model is developed to identify, prioritize, and address critical failure points while accounting for complex interdependencies. Significant failure modes, such as order inaccuracies and delivery delays, are revealed as key findings and are found to notably affect productivity and customer satisfaction. The proposed model’s ability to capture cascading effects and a nuanced prioritization enables a more precise risk assessment, thereby supporting resilience and process efficiency in the furniture-manufacturing sector. This approach is shown to not only optimize production but also provide a foundation for applying such hybrid models in other industries to manage sector-specific interdependencies effectively.

Bridging the gap between vulnerable populations and public health services through outreach initiatives

Introduction: Public health services encounter significant challenges in reaching vulnerable populations, characterized by complex social, economic, and structural barriers that impede healthcare access. The health of marginalized groups is significantly affected by gaps in equality, poverty, and access to resources and social factors. Efficient analysis of such complex relations must entail analyses of various interdisciplinary strategies that can intertwine and traverse the contrasting physiologic specialties of the healthcare field to control diverse barriers towards service provision to culturally and economically deprived populations. Materials and Methods: Comprehensive systematic review approaches were used, including several research approaches to assess existing published literature and empirical research studies. Stringent literature search criteria were employed, the sources included peer-reviewed journals, gray literature, and institution reports of interventions in health care for vulnerable populations. Appropriate bibliometric methods and qualitative synthesis approaches were applied to conduct comprehensive analyses of heterogeneous research fields. Specific methodological techniques used in the study are systematic literature review meta-analysis and comparative evaluation of clinical case studies to make an exclusive and accurate evaluation of the evidence presented. Results: Significant findings emerged regarding outreach initiative effectiveness in enhancing healthcare accessibility. The outcomes established showed that focused equitable services enhanced its use by the vulnerable people, particularly, in preventive health care, and health-related literacy and trust. Specific interventions focusing on certain at-risk groups in contexts that were characterized by interdisciplinary showed promising signs of systematic change. The statistical analysis revealed the effectiveness of the targeted outreach programs and the key role of innovation in improving the health equity indicators. Discussion: Critical interpretations of research findings emphasized complex interactions between structural vulnerabilities and healthcare access mechanisms. Detailed analysis unveiled complex issues that cannot be solved by separate departments in parallel but need an integration of solutions. The context of the intervention was also examined in detail suggesting complex interdependencies between aspects of social environment, intervention-related activities, and health outcomes. Conceptual frameworks were scrutinized to analyze the patterns that impact the healthcare of the high-risk groups. Conclusion: Strategic outreach initiatives represent pivotal mechanisms for addressing healthcare disparities. Multifaceted approaches addressing social multidimensional aspects and economic and cultural factors show great promise for the redesign of public health services. Despite these limitations, collaborative community-centered solutions define vital strategies for realizing health equity. Further research and subsequent practice of such strategies are vital for the continual improvement to close gaps in healthcare.

Multi-objective optimisation for urban road network maintenance based on spatial design network analysis: trade-offs in performance, environmental impacts and costs

ABSTRACT Pavement maintenance decision-making needs to account for multiple conflicting strategic objectives and the complex interdependencies among heterogeneous road sections within road networks. Using spatial design network analysis which allows to capture the complex interdependencies among interconnected road sections, a multi-objective optimisation model is developed for urban road network maintenance. This model integrates multi-dimensional objectives: maximising road network condition, minimising environmental impacts, and minimising agency costs. The model, which uses the non-dominated sorting genetic algorithm II to approximate the Pareto front, is implemented in a real-world urban road network in Shanghai, China. The results show that the trade-offs among the three objectives are more intricate than those found in prior research using traditional decision-making models, which often overlooked the complex interdependencies among heterogeneous road sections. Sensitivity analyses indicate prominently non-linear relationships among objectives: marginal changes in road network condition and environmental impacts fluctuate with different agency costs. As an exploratory effort to develop a network-level multi-objective optimisation model for urban road maintenance through modelling complex interconnections among heterogeneous road sections, this study contributes to a deepened understanding of the complex non-linear associations among performance-environment-cost objectives in pavement maintenance decision-making and offers a network-based approach to perform trade-offs in these conflicting objectives.

Identifying and assessing the impact of 4M1E factors on construction project delays in Afghanistan using structural equation modeling

AbstractConstruction projects in Afghanistan are often challenged by significant delays, posing severe challenges to their successful completion. This study aims to comprehensively assess the impact of various delay factors using the 4M1E framework Man, Machine, Material, Method, and Environment within the Afghan construction sector. By employing a validated Structural Equation Modeling (SEM) approach, the research investigates both direct and indirect effects of these factors on project delays. The analysis reveals that environmental and material factors are particularly influential, with environmental changes often exacerbating delays across other dimensions. The findings underscore the complex interdependencies among the 4M1E factors, highlighting the need for targeted strategies to manage the delay effectively. Practical recommendations are provided to enhance project management practices, with implications that extend beyond Afghanistan to other developing countries facing similar challenges. This study contributes to the academic discourse on construction delays, offering actionable insights for practitioners, policymakers, and stakeholders in the construction industry.

Path Generative Model Based on Conditional β-Variational Auto Encoder for Four-Bar Mechanism Design

Abstract This article introduces a novel methodology based on conditional β-variational autoencoder (cβ-VAE) architecture to generate diverse types of planar four-bar mechanisms for a given coupler curve. Central to our contribution is the novel integration of cross- and self-attention layers within the VAE framework, facilitating an encoding and decoding process that captures the complex interdependencies of mechanism parameters and associated coupler curves. We propose a unified representation scheme for four-bar mechanisms with both revolute and prismatic joints, utilizing a consistent set of joints to describe each mechanism type. To support and validate our methodology, we have compiled an extensive dataset featuring both open and closed coupler curves of the aforementioned mechanism types. Furthermore, the article introduces three metrics aimed at quantifying the efficacy of our model, alongside an innovative algorithm designed to enhance the predictive outcomes by identifying and computing cognate mechanisms.

Men in the making of nations: understanding the nexus between nation, belonging, and complicit masculinity

ABSTRACT The ideal of masculine Hinduism which is currently being employed within the Indian national rhetoric to sustain and build a modern Hindu Rashtra is a direct consequence of India’s tryst with British imperialism. Since it is men who, as real actors of nationalist movement defend their homeland and the honour of women, the current Indian right wing politics ensures that men continue to uphold the ideals of Hindu hegemonic masculinity defined by martial prowess, muscular strength, moral fortitude and a readiness to battle groups to strengthen the nation. However, while the hegemonic notions of Hindu masculinity are achieved by a small minority who become the public face of gender and sexual politics, the majority are those who reap benefits from such gendered arrangements by being complicit in the hegemonic project. This paper makes an attempt to study the complex interdependencies between hegemonic and complicit masculinities in nation building processes in India. The aim is to uncover how complicit masculinities are created to sustain Hindutva nationalism within the current Indian climate and whether there are alternative possibilities and codes of behaviour in which privileges of masculinity and power are confronted and exposed.

Assessing organizational cohesion by the maximum caliber method

PurposeThe aim of this paper is to explore the conditions under which organizational networks achieve high levels of cohesion. It focuses on understanding the roles of interaction cycles, feedback loops and asymmetries in fostering cohesive networks. The study examines how the application of line integrals in closed paths can reveal insights into the cycles and feedback mechanisms influencing cohesion within these networks.Design/methodology/approachThis research uses the maximum caliber method to quantify organizational cohesion through the analysis of interactivity matrices. This approach integrates temporal dynamics and accounts for the complex interdependencies among actors within the network. The study is supported by a case study analysis, which demonstrates the practical application of these theoretical concepts in a real-world organizational context.FindingsThe application of the maximum caliber method offers a comprehensive understanding of organizational cohesion by effectively capturing the dynamic interactions within the network. The study reveals that cycles and feedback mechanisms within the network significantly affect cohesion, with interaction asymmetries playing a crucial role. The method also enables the identification of actors who contribute positively or negatively to cohesion, providing actionable insights for improving organizational performance.Originality/valueThis paper presents a novel methodological approach to analyzing organizational cohesion, enhancing both the theoretical and practical understanding of network dynamics. The findings provide valuable insights for organizational leaders and researchers, emphasizing the importance of managing interaction patterns to strengthen overall network cohesion and effectiveness. This approach offers a new perspective on measuring and influencing organizational cohesion, with significant implications for strategic management and organizational development.

What factors influence research impact? An empirical study on the interplay of research, publications, researchers, institutions, and national conditions

ABSTRACT Purpose This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al. (2024). Design/methodology/approach Using data from a global survey of 630 scientists across diverse disciplines, genders, regions, and experience levels, Structural Equation Modelling (SEM) was employed to assess the influence of 29 factors related to researcher characteristics, research attributes, publication strategies, institutional support, and national roles. Findings The study validated the Quintuple Helix Model, uncovering complex interdependencies. Institutional support significantly affects research impact by covering leadership, resources, recognition, and funding. Researcher attributes, including academic experience and domain knowledge, also play a crucial role. National socioeconomic conditions indirectly influence research impact by supporting institutions, underscoring the importance of conducive national frameworks. Research limitations While the study offers valuable insights, it has limitations. Although statistically sufficient, the response rate was below 10%, suggesting that the findings may not fully represent the entire global research community. The reliance on self-reported data may also introduce bias, as perceptions of impact can be subjective. Practical implications The findings have a significant impact on researchers aiming to enhance their work’s societal, economic, and cultural significance, institutions seeking supportive environments, and policymakers interested in creating favourable national conditions for impactful research. The study advocates for a strategic alignment among national policies, institutional practices, and individual researcher efforts to maximise research impact and effectively address global challenges. Originality/value By empirically validating the Research Impact Quintuple Helix Model, this study offers a holistic framework for understanding the synergy of factors that drive impactful research.

Machine Learning-Based Outlier Detection for Business Intelligence: A Scalable Time Series Analysis Framework

The exponential growth in digital business operations has resulted in an unprecedented volume of time series data generated from diverse business metrics, creating an urgent need for sophisticated anomaly detection systems. This paper presents a comprehensive framework for detecting outliers in business time series data using advanced machine learning techniques, addressing the challenges of scale, accuracy, and real-time processing. We propose a novel hybrid approach that seamlessly integrates statistical methods with deep learning architectures to identify both point anomalies and pattern deviations in multivariate business metrics. The framework incorporates adaptive thresholding mechanisms and contextual awareness, leveraging business domain knowledge to reduce false positives while maintaining high detection accuracy across varying business cycles and seasonal patterns. Our approach addresses the challenges of scalability and real-time processing through a sophisticated distributed computing architecture, making it suitable for enterprise-scale deployments. The framework demonstrates superior performance in handling concept drift, seasonal variations, and complex interdependencies between metrics, while maintaining computational efficiency and interpretability. Keywords—anomaly detection, business intelligence, machine learning, time series analysis, deep learning, statistical methods, distributed computing, real-time processing, adaptive thresholding, feature engineering

A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction

Artificial intelligence has revolutionized various industries, including chemical process optimization. Artificial intelligence (AI) can be applied to various ethylene glycol (EG) production aspects to improve efficiency, quality, and overall process optimization. Process optimization of dimethyl oxalate (DMO) hydrogenation to ethylene glycol (EG) is carried out to minimize/maximize conversion, energy, productivity, bare module cost (CBM), and side product in the presence of pressure and temperature variables. A non-dominated sorting-based multi-objective neural network algorithm (MONNA) is applied to tackle problem optimization for EG production. The results show that the highest productivity, minimum energy cost, side product, and highest conversion are 172 Million RM/year, 0.00600 Million RM/year, 0.0320 kmol/hr, and 99.6%, respectively. The intermediate points in the Pareto Front PF for various three-objective situations provide lower energy costs than the bi-objective function. While bi-objective optimization might seem more straightforward, the intermediate points on the Pareto Front in three-objective optimization can provide lower energy costs due to more effective balancing of trade-offs, richer exploration of the solution space, capturing complex interdependencies, and offering more robust and flexible solutions. Decision makers can use the resulting pareto to decide on the most acceptable alternative according to their preferences. The decision variable plots show that the pressure highly affected the optimal solution with the opposite action. This work’s analysis is predicted to provide insight into optimization literature for energy savings and sustainability and promote commercial growth for the industry. Identifying optimal trade-offs between various objectives, such as energy cost and productivity, can help reduce overall production costs, making the production process more competitive.

The Major Types of Power in the International Arena: Structural Power, Power of Interdependence, “Hard” and “Soft” Power

One of the most ambivalent and complex concepts in world politics is power, which is intangible. There are numerous approaches and interpretations of power. This paper presents the author’s hierarchy of types of power, which includes structural power, complex interdependence, “hard” and “soft power”. For each, the main methodological challenges and prospects for operationalization (including quantitative assessment) of these indicators are presented. It is demonstrated that the distinction between different types of power is conditional. The relationship between the use of one or another power is determined by the national strategic culture. The example of global competition between the United States and China is used to illustrate the distinctive features of the strategies employed by each country to project their respective power.

Exploring the effect of transformational leadership and knowledge management in enhancing innovative performance: a mediating role of innovation capability

PurposeIn the highly competitive sector of state-owned enterprises in Indonesia, particularly within the electricity group, innovation is crucial for sustainability and growth. This study investigates the impact of transformational leadership and knowledge management on innovation performance, emphasizing the mediating role of innovation capability.Design/methodology/approachEmploying a quantitative approach, this research focuses on the upper management of 11 companies, gathering data from 196 out of 280 surveyed individuals through structured questionnaires. Structural equation modeling (SEM) was utilized for analysis.FindingsOur analysis reveals a dual role of transformational leadership; while it significantly enhances innovation capability, it unexpectedly detracts from innovation performance. Conversely, knowledge management boosts both innovation capability and performance. Significantly, innovation capability serves as a crucial mediator, amplifying the effects of both transformational leadership and knowledge management on innovation performance. These results highlight complex interdependencies within organizational leadership and knowledge frameworks.Practical implicationsThese findings can guide organizations in refining leadership and knowledge management strategies to bolster innovation. By enhancing transformational leadership skills, implementing robust knowledge management systems and fostering innovation capabilities, companies can better navigate the complexities of the modern market. The adaptation of these strategies should consider specific organizational cultures and market dynamics to optimize performance.Originality/valueThis research extends the resource-based view (RBV) by detailing the interplay between key organizational variables, offering fresh insights into their collective impact on innovation within the context of state-owned enterprises in Indonesia.