Complex Interdependencies Research Articles

Media Culture - Between Technology and Communication

Media culture is a major challenge for media and communication studies. Without media culture, there can be no understanding of media reality, just as there can be no orientation in a media society consisting of a complex interdependence of media, culture, technology, and communication. It is a categorical self-identification of the contemporary media person in the conditions of dynamic media transformation and an important condition for presence and interaction in the media environment of the information society. This article examines some theoretical approaches to media culture and argues that the debate about culture should be a discussion about media development. It concludes that there is no culture outside the media or media communication, and the responsibility for media culture now also lies with the audience.

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Systemic risks and solar climate engineering research. Integrating technology ethics into the governance of systemic risks

The paper explores how the framework of systemic risks can help govern the risks imposed through solar climate engineering research. The central argument is that a systemic perspective of risk is a useful tool for analysing and assessing the risks imposed through Stratospheric Aerosol Injection (SAI) research. SAI is a form of climate engineering that could cool the planet by enhancing its albedo through the injection of aerosols into the stratosphere. Researching such a technology creates systemic risks with a strong sociotechnical component. This component consists of the potential societal harm that a developing or new technology might cause to existing norms, values, institutions, and politics. The systemic risk framework is a valuable heuristic for this case, given the complex interdependencies of societal systems, infrastructures, markets, etc. At the same time, the systemic risk framework can be enhanced through the inclusion of a more robust and reflected ethical considerations on technological risks. Consequently, this article seeks to supplement the systemic risk governance framework with insights from technology ethics. Specifically, the paper offers an ethically reflective conception of societal value dynamism and stakeholder engagement and participation, tying it to existing systemic risk governance approaches.

Climate Diplomacy and Geopolitics: Exploring the Role of Climate Policy in International Relations

Purpose: The main objective of this study was to explore the role of climate policy in international relations.
 Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The findings revealed that there exists a contextual and methodological gap relating to climate policy and geopolitics. Preliminary empirical review revealed that climate diplomacy and geopolitics are inextricably linked in the 21st century, with climate policy playing a central role in shaping international relations. The urgency of addressing climate change has forced nations to collaborate and confront shared challenges, while also introducing new dimensions of competition and complexity. Non-state actors, climate justice considerations, and the evolving global energy landscape all contribute to the intricate tapestry of climate diplomacy. The future of international relations will depend, to a large extent, on how effectively nations navigate these challenges and seize the opportunities presented by climate action to build a more sustainable and equitable world.
 Unique Contribution to Theory, Practice and Policy: Realism theory, Complex Interdependence theory and the Regime theory may be used to anchor future studies on climate policy. The study recommended promotion of multilateralism, incorporation of climate into foreign policy, supporting vulnerable nations, engagement of non-state actors and incorporation of climate security.

Diktyo: Network-Aware Scheduling in Container-Based Clouds

Containers have revolutionized application deployment and life-cycle management in current cloud platforms. Applications have evolved from single monoliths to complex graphs of loosely-coupled microservices. However, the efficient allocation of microservice-based applications is challenging due to their complex inter-dependencies. Further, recent applications are becoming even more delay-sensitive, demanding lower latency between dependent microservices. Scheduling policies in popular container orchestration platforms mainly aim to increase the resource efficiency of the infrastructure, insufficient for latency-sensitive applications. Application domains such as the Internet of Things and multi-tier web services would benefit from network-aware policies that consider network latency and bandwidth in the scheduling process. Previous works have studied network-aware scheduling via theoretical formulations or heuristic-based methods evaluated via simulations or small testbeds, making their full applicability in popular platforms difficult. This paper proposes a novel network-aware framework for the popular Kubernetes (K8s) platform named Diktyo that determines the placement of dependent microservices in long-running applications focused on reducing the application’s end-to-end latency and guaranteeing bandwidth reservations. Simulations show that Diktyo can significantly reduce the network latency for various applications across different infrastructure topologies compared to default K8s scheduling plugins. Also, experiments in a K8s cluster with microservice benchmark applications show that Diktyo can increase database throughput by 22% and reduce application response time by 45%.

Energy Diplomacy and Strategic Engagements: Nigeria's role in Shaping the Global Energy Landscape.

In an ever-evolving global energy landscape driven by climate change and technological advancements, Nigeria stands at a pivotal crossroads. Historically reliant on its abundant oil and gas reserves, the nation now confronts the imperative to diversify its energy mix while skillfully navigating the complex realm of international energy diplomacy. These challenges and opportunities demand a strategic response.The urgency to address these issues cannot be overstated, considering Nigeria's pivotal role on the African continent and its significant influence on the world's energy markets. Failing to adapt not only jeopardizes Nigeria's energy security and economic stability but also undermines global endeavours to achieve sustainable energy solutions.This study scrutinizes three critical facets of Nigeria's energy landscape: firstly, how its historically oil-centric sector adapts to the evolving energy era; secondly, the transformative impacts emerging from Nigeria's growing investments in renewable energy; and finally, how Nigeria's participation in international energy diplomacy aligns with global energy security and sustainability objectives.Drawing on Nigeria as a case study, data from diverse documentary sources such, including government policies and scholarly inputs, are analyzed within the framework of Complex Interdependence Theory.The findings underscore Nigeria's dual role—confronting challenges in its oil and gas sector while embracing opportunities in renewable energy. Recommendations include a swift transition to renewables, robust infrastructure, enhanced security measures, and strategic partnerships. These actions align with global sustainability goals and uphold the principles of complex interdependence in the global energy arena.

Dynamic Modeling of Nonlinear Systems in Cyber-Physical Environments

Complex behavior understanding and prediction relies on dynamic modeling of nonlinear systems in cyber-physical contexts. Critical for improving control systems, anticipating maintenance needs, and bolstering resilience, it permits accurate portrayal of complex interactions between cyber and physical components. Guaranteeing efficient and adaptable performance in networked ecosystems is made possible by this insight. Dynamic modeling of nonlinear systems (DM-NS) in cyber-physical settings has a number of challenges, such as the need to accurately depict complex interdependencies, accommodate changes in real-time, and reduce uncertainty. Advanced modeling techniques are required to capture the subtle behaviours and maintain the accuracy of the models while trying to balance the complicated interactions between physical and cyber components. A novel strategy is suggested to tackle these obstacles: Analysing Nonlinear Systems Cyber-Physical Modeling (ANSC-PM). By integrating complex mathematical models with adaptive learning algorithms, ANSC-PM is able to characterize nonlinear system behaviours, taking into account the dynamic interplay between cyber and physical components. To remain relevant as system dynamics change, the suggested method adjusts in real-time. Numerous disciplines find ANSC-PM useful, such as control system optimization, robotic predictive maintenance, and networked environment resilience enhancement. The effectiveness of ANSC-PM is determined by careful simulation analysis, which provides insight into its possible benefits in improving system performance, flexibility, and resilience in complex cyber-physical settings. Developing a thorough and flexible strategy that is adapted to the intricacies of cyber-physical systems, this research makes a substantial contribution to the progress of dynamic modeling approaches.

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencoder methods that could not fully exploit the contextual information as supervision signals for effective anomaly detection. To overcome these challenges, in this paper, we propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD). Our method constructs different contextual subgraphs (views) based on a target node and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection. While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms state-of-the-art methods by a large margin.

Diversifying agroecological systems: Plant-pollinator network organisation and landscape heterogeneity matter

Agroecology is a process-based agriculture that implements agrobiodiversification to stabilize ecosystem processes and crop yield, leading to sustainable food systems. Traditionally, agrobiodiversification focused on increasing within-farm plant richness, which may increase local species richness of higher trophic levels. However, it is increasingly recognized that stabilizing ecosystem processes involving plant-animal interactions –e.g., pollination- requires practices that consider the organisation of interactions and heterogeneity effects at landscape level. In this work, we investigated how plant-pollinator interactions organised in agricultural landscapes harbouring agroecological farms and how landscape heterogeneity modulated interactions organisation. We characterized the organisation of 36 plant-pollinator networks describing interactions in nine agricultural landscapes -i.e., agroecological farms and their surroundings- across four flowering peak periods in the Agri-food region of central Córdoba, Argentina. We evaluated (1) centrality to network organisation of plant and pollinator only registered interacting in agroecological farms, farm surroundings (unique species) or in both locations (shared species); and (2) network organisation across gradients of landscape configurational (mean edge density) and compositional (forest proportion) heterogeneity. We found that plant-pollinator networks at landscape scale showed nested structures. Shared plant and pollinator species were the less represented in such networks but were central to networks organisation, i.e., they were connected to most species and with the highest frequency. Results also showed that network organisation changed with landscape configurational heterogeneity. Higher landscape mean edge density was associated with higher total and unique plant richness in networks and lower richness of shared plants. Network connectance and nestedness decreased with higher landscape mean edge density, i.e., plant and pollinator species interacted with less species, shared less interaction partners with more specialist species and showed lower interaction frequency with generalist partners in more heterogeneous landscapes. Our study showed that in agricultural landscapes harbouring agroecological farms (1) a few species inhabiting and showing interactions in both agroecological farms and their surroundings are key to the organisation of plant-pollinator interactions, and (2) landscape heterogeneity modulates such organisation. As interaction networks dynamic is influenced by network organisation, landscape configurational heterogeneity may modulate the stability of the pollination process and ultimately agroecological production. Thus, the agrobiodiversification of agroecological systems may benefit from considering the complexity of plant-pollinator interdependencies across agricultural landscapes and the effects of landscape heterogeneity on such complexity.

Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry

This research paper presents an all-encompassing methodology for multi-criteria decision-making in the apparel sector, with the particular objective of aiding in the determination of the most appropriate location within the CLMV subregion. The research is conducted in three crucial stages. The process began with the administration of a survey to proprietors of garment businesses in both Thailand and the CLMV countries. This survey resulted in the compilation of an exhaustive list of site-selection criteria and sub-criteria. Based on the findings of subject matter-expert interviews, Cambodia (C), Vietnam (V), and Myanmar (M) were identified as feasible alternatives. Subsequently, the questionnaire criteria and sub-criteria were evaluated utilizing the Fuzzy Analytic Network Process (Fuzzy ANP), which involved the utilization of meticulously designed pair-wise comparison matrices and local priorities. Five specialists from the Thai entrepreneurial community affirmed the effectiveness of Fuzzy ANP and expressed interest in expanding manufacturing operations in the CLMV subregion. The optimal location for Thai apparel manufacturers was subsequently determined using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The results indicated that Vietnam is the most favorable option. In order to improve the dependability of results, an amended mixture-design scenario analysis was implemented. This analysis assessed the sensitivity and dependability of the proposed model in different scenarios, ensuring its applicability in real-world situations. In contrast to traditional models, this study integrates managerial judgments and preferences into the decision-making procedure, thereby accounting for the complex interdependencies among numerous criteria. The suggested methodology functions as a beneficial instrument for decision-makers, both domestic and international, as it integrates effortlessly into the organizational structure of the CLMV region. By harmonizing objectives pertaining to data acquisition, manipulation, retention, and dissemination, this framework not only enables enhanced decision-making processes, but also optimizes system efficiency.

Machine Learning Driven Channel Thickness Optimization in Dual-Layer Oxide Thin-Film Transistors for Advanced Electrical Performance.

Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual-layer oxide semiconductor thin film transistors (OS TFTs). This approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial-and-error. Considering field effect mobility (𝜇) and threshold voltage (Vth ) simultaneously, the dual-layer structure designed by the BO model allows to produce OS TFTs with remarkable electrical performance while significantly saving an amount of experimental trial (only 15 data sets are required). The optimized dual-layer OS TFTs achieve the enhanced field effect mobility of 36.1cm2 V-1 s-1 and show good stability under bias stress with negligible difference in its threshold voltage compared to conventional IGZO TFTs. Moreover, the BO algorithm is successfully customized to the individual preferences by applying the weight factors assigned to both field effect mobility (𝜇) and threshold voltage (Vth ).

Short-term wind power forecasting based on multivariate/multi-step LSTM with temporal feature attention mechanism

Precision enhancement for short-term wind power forecasting can alleviate negative impact of the forecasting results on wind power generation. Due to complexities and nonlinearities among factors and facets in wind power, it is essential to achieve reliable and stable power generation via the long short-term memory (LSTM) forecasting. To this purpose, multi-task temporal feature attention (MTTFA) based LSTM, namely MTTFA-LSTM, is proposed for multivariate/multi-step wind power forecasting with historical power and meteorological data, in which task-sharing and task-specifying layers are designed for task co-features extracting and task specifics discriminating, respectively. More specifically, in the task-sharing layer, multi-dimensional inputs are fed into LSTM to extract long-term trends, while in the task-specifying layer, one-dimensional convolution operations extract temporal features hidden in each and all time steps. Furthermore, an attention mechanism is adopted to adaptively tune weights for temporal features. Finally, the proposed model is leveraged to cope with different short-term wind power forecasting (SWPF) problems based on the national renewable energy laboratory’s (NREL) wind power data. Simulation results show that the proposed MTTFA-LSTM achieves persistent excellent forecasting accuracy, comparing its backbone STL model, TFA-LSTM as well as the benchmark MTL models in the same setting, which indicate that the complex and non-linear interdependencies among multi-dimensional data can be well depicted by the proposed model.

Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes

The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic’s main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations.

Why is safety in intrapartum electronic fetal monitoring so hard? A qualitative study combining human factors/ergonomics and social science analysis

BackgroundProblems in intrapartum electronic fetal monitoring with cardiotocography (CTG) remain a major area of preventable harm. Poor understanding of the range of influences on safety may have hindered improvement. Taking...

Enhancing Project Management through Exploratory Factor Analysis of Safety Performance in Dubai's Construction Industry

The construction industry in Dubai, known for its rapid growth and towering skyscrapers, has faced significant challenges ensuring the safety of its workforce. The purpose of this study was to enhance project management practices through an exploratory factor analysis of safety performance within this sector. This research aimed to identify the underlying factors that contribute to safety outcomes and to provide a data-driven foundation for improving safety measures. Addressing the problem of frequent construction accidents and the high rate of safety incidents, this study hypothesized that a multifaceted approach to safety analysis would reveal complex interdependencies affecting worker safety. The methodology involved a comprehensive review of incident reports and safety records from various construction projects in Dubai over the past five years, coupled with surveys and interviews with industry professionals. The data underwent exploratory factor analysis to ascertain the primary variables impacting safety performance. Findings indicated that key factors affecting safety included worker training, communication efficacy, adherence to safety protocols, and the use of protective equipment. Surprisingly, statistical analysis revealed that projects with high safety investments did not always correlate with lower incident rates, suggesting the importance of qualitative factors over mere financial commitment. The study concludes that enhancing safety performance in Dubai’s construction industry requires a holistic approach that goes beyond financial investment in safety. Recommendations include implementing regular, targeted training programs, improving on-site communication channels, strict enforcement of safety protocols, and fostering a safety-centric organizational culture. In light of these conclusions, the study advocates for policy revisions at the regulatory level and a shift in the industry’s safety paradigm. The recommended strategic changes aim to not only mitigate the risks inherent in the construction industry but also to enhance the overall management of construction projects in the dynamic landscape of Dubai. Keywords. Project Management, Exploratory Factor Analysis, Safety Performance, Construction Industry, Dubai

Location and time embedded feature representation for spatiotemporal traffic prediction

As a fundamental spatiotemporal sequence forecasting problem, traffic prediction is pivotal in transportation management and urban computing. Nonetheless, the intricate and dynamic nature of spatiotemporal correlations presents significant obstacles in acquiring precise forecasts. Existing techniques utilize graph convolutional networks in conjunction with temporal modules, such as recurrent neural networks or transformer-based structures, to effectively extract spatiotemporal features. Unfortunately, current approaches struggle with outliers and fail to capture potential global correlations between different timestamps. In this study, we propose an innovative Spatio-Temporal Graph Convolution Network with Embedded location and time features (STEGCN) for traffic prediction problems, which can generate precise and prompt predictions. STEGCN effectively captures the complex interdependencies among location, time, and traffic volume by leveraging the TransD algorithm to embed their representations. For each timestamp, a graph convolution module is exploited to capture the spatial features, merged with the embeddings of location and time that serve as global external information. Then, we leverage a temporal module composed of 1-D convolutions to capture the spatiotemporal patterns. The traffic volume embedding is employed to constrain predictions within a reasonable range. Extensive experiments and rigorous analysis show that our STEGCN model outperforms state-of-the-art baselines, demonstrating exceptional performance and potential for practical application.

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.

Ukrainos visuomenės regioninė tapatybė: socialinis-humanitarinis diskursas

The purpose of the article is to review the development of regional studies, to understand the strategies of geopolitical dynamics in the Ukrainian space, civilizational and cultural features of regional development, complex interdependencies between na­tional and regional dimensions of the historical process. Attention is paid to the regional problems in Ukraine, related to the specificity of regional identities, different socio-cul­tural values and mental stereotypes. The application of methodological approaches of re­gionalism is seen as the most appropriate tool both for clarifying the conflict dimension in the formation of Ukrainian identity in the past, and for finding a constructive solution to the problem of “conflict of identities” in modern conditions. It has been proven that the scientific space of modern regional history is an acceptable field for the analysis of re­gional societies, the economic and demographic situation in the regions, individual and group, social and territorial identities, and local political and cultural preferences. It is emphasized that during the post-war recovery in Ukraine regional characteristics took on a role of system-forming norms and structural and functional determinants that effec­tively stimulate and strengthen the reconstruction processes in the regions and condition the formation of a nationwide safe spatial environment.

Regional Identity of Ukrainian Society in Socio-Humanitarian Discourse

The purpose of the article is to review the development of regional studies, to understand the strategies of geopolitical dynamics in the Ukrainian space, civilizational and cultural features of regional development, complex interdependencies between na­tional and regional dimensions of the historical process. Attention is paid to the regional problems in Ukraine, related to the specificity of regional identities, different socio-cul­tural values and mental stereotypes. The application of methodological approaches of re­gionalism is seen as the most appropriate tool both for clarifying the conflict dimension in the formation of Ukrainian identity in the past, and for finding a constructive solution to the problem of “conflict of identities” in modern conditions. It has been proven that the scientific space of modern regional history is an acceptable field for the analysis of re­gional societies, the economic and demographic situation in the regions, individual and group, social and territorial identities, and local political and cultural preferences. It is emphasized that during the post-war recovery in Ukraine regional characteristics took on a role of system-forming norms and structural and functional determinants that effec­tively stimulate and strengthen the reconstruction processes in the regions and condition the formation of a nationwide safe spatial environment.

Statistical Approaches to Study Exposome-Health Associations in the Context of Repeated Exposure Data: A Simulation Study.

The exposome concept aims to consider all environmental stressors simultaneously. The dimension of the data and the correlation that may exist between exposures lead to various statistical challenges. Some methodological studies have provided insight regarding the efficiency of specific modeling approaches in the context of exposome data assessed once for each subject. However, few studies have considered the situation in which environmental exposures are assessed repeatedly. Here, we conduct a simulation study to compare the performance of statistical approaches to assess exposome-health associations in the context of multiple exposure variables. Different scenarios were tested, assuming different types and numbers of exposure-outcome causal relationships. An application study using real data collected within the INMA mother-child cohort (Spain) is also presented. In the simulation experiment, assessed methods showed varying performance across scenarios, making it challenging to recommend a one-size-fits-all strategy. Generally, methods such as sparse partial least-squares and the deletion-substitution-addition algorithm tended to outperform the other tested methods (ExWAS, Elastic-Net, DLNM, or sNPLS). Notably, as the number of true predictors increased, the performance of all methods declined. The absence of a clearly superior approach underscores the additional challenges posed by repeated exposome data, such as the presence of more complex correlation structures and interdependencies between variables, and highlights that careful consideration is essential when selecting the appropriate statistical method. In this regard, we provide recommendations based on the expected scenario. Given the heightened risk of reporting false positive or negative associations when applying these techniques to repeated exposome data, we advise interpreting the results with caution, particularly in compromised contexts such as those with a limited sample size.