Complex Dynamics Research Articles

Unveiling the Ethical Dilemmas of Digital Piracy: A Comprehensive Exploration of Motivations, Attitudes, and Behaviors

Driven by several elements, including the unwillingness to pay for material, the spread of subscription services, and the profitability of illegal activity, digital piracy has grown to be a common problem in the contemporary world. Along with financial damages for businesses and content creators, this phenomenon has legal repercussions for individuals involved in piracy and cybersecurity issues. The effective fight against digital piracy depends on an awareness of the drivers and hazards connected with this illegal activity. With an eye towards a thorough knowledge of this intricate problem, in this study, the focus centers on the reasons, attitudes, and actions behind digital piracy. Through examining how people defend their behavior in particular situations, in this study, the aim is to clarify the ethical issues and decision-making procedures related to piracy—even among otherwise law-abiding people. Combining a variety of the relevant literature and ideas, including situation ethics theory, in this study, we seek to provide insights for legislators, business players, and academics eager to meet the difficulties presented by digital piracy. This study adds to the continuous discussion on lessening the detrimental effects of digital piracy on content creators, businesses, and cybersecurity by looking at the underlying reasons for piracy and supporting legal access to material. Along with a dearth of research on the origins and moral questions of digital piracy, there is a lot of contradictory material. By examining the factors behind this unlawful activity, clarifying the complex dynamics involved, and offering understanding of the causes of digital piracy, this study seeks to close a significant void in the knowledge. The findings of this study will enable individuals to develop strategies for handling this prevalent issue in the digital era. Developing successful interventions that can reduce this illegal conduct and safeguard the interests of content producers, businesses, and cybersecurity overall depends on an awareness of the reasons behind digital piracy. This study advances the knowledge of the difficulties presented by this global phenomenon by investigating the ethical issues and decision-making procedures related to digital piracy.

Investigating Charge-Induced Transformations of Metal Nanoparticles in a Radically-Inert Liquid: A Liquid-Cell TEM Study.

We present a novel in situ liquid-cell transmission electron microscopy (TEM) approach to study the behavior of metal nanoparticles under high-energy electron irradiation. By utilizing a radically-inert liquid environment, we aim to minimize radiolysis effects and explore the influence of charge-induced transformations. We observed complex dynamics in nanoparticle behavior, including morphological changes and transitions between amorphous and crystalline states. These transformations are attributed to the delicate interplay between charge accumulation on the nanoparticles and enhanced radiolysis, suggesting a significant role for charge-assisted processes in nanoparticle evolution. Our findings provide valuable insights into the fundamental mechanisms driving nanoparticle behavior at the nanoscale and demonstrate the potential of liquid-cell TEM for studying complex physicochemical processes in controlled environments.

FPGA Implementation for a Class of Generalized Hamiltonian Conservative Chaotic Systems Based on Integrated 4D Euler Equations with Multistability

In this paper, two newly proposed generalized Hamiltonian Conservative Chaotic Systems (CCSs) based on integrated 4D Euler equations are first discussed. Second, another generalized Hamiltonian CCS is also proposed. Furthermore, a class of generalized Hamiltonian CCSs based on integrated 4D Euler equations is given and studied. Subsequently, numerical analysis for the class of generalized Hamiltonian CCSs is further investigated to show their advantages over most of the existing CCSs, where the corresponding Lyapunov exponents’ diagrams, bifurcation diagrams and phase portraits are all given to show their multistability and complex dynamics. Finally, the class of generalized Hamiltonian CCSs is implemented by using FPGA technology, and the results observed in FPGA implementation are consistent with those observed in the numerical analysis. All these results not only show multistability from a physical point of view, but also provide new physical models for chaos applications.

Heart rate variability and insomnia in depressed patients with breast cancer

ObjectivesDepression is associated with unhealthy autonomic regulation. However, whether patients with breast cancer (BC) with different degrees of depression can be identified from linear and non-linear dynamics in the autonomic...

Characterizing surface pressure and wind fields of typhoons approaching Hong Kong

In this paper, 17 severe typhoons that have affected Hong Kong are simulated using an advanced numerical atmospheric simulation system - Weather Research and Forecasting model (WRF). The simulated surface pressure and wind fields of these typhoons are validated against a wide range of field observations. Then azimuth-dependent models for the radius of maximum winds and the Holland parameter are established statistically at the surface level. It is observed that the shape parameter of the Holland pressure model is smaller at the surface than that at the gradient level. And the Holland wind field model cannot well reproduce the simulated radial wind profiles due to the complexities of nonuniform surface conditions and typhoon dynamics. It is found that the modified Rankine model provides satisfactory estimates of typhoon wind speeds in Hong Kong. Additionally, wind field asymmetries of typhoons approaching Hong Kong are highly correlated with the typhoon track velocity, vertical wind shear and the angle between them. The proposed statistical models and identified characteristics of wind field asymmetries of typhoons will provide useful information for rapidly assessing typhoon wind hazards.

A Novel Fourth-Order Finite Difference Scheme for European Option Pricing in the Time-Fractional Black–Scholes Model

This paper addresses the valuation of European options, which involves the complex and unpredictable dynamics of fractal market fluctuations. These are modeled using the α-order time-fractional Black–Scholes equation, where the Caputo fractional derivative is applied with the parameter α ranging from 0 to 1. We introduce a novel, high-order numerical scheme specifically crafted to efficiently tackle the time-fractional Black–Scholes equation. The spatial discretization is handled by a tailored finite point scheme that leverages exponential basis functions, complemented by an L1-discretization technique for temporal progression. We have conducted a thorough investigation into the stability and convergence of our approach, confirming its unconditional stability and fourth-order spatial accuracy, along with (2−α)-order temporal accuracy. To substantiate our theoretical results and showcase the precision of our method, we present numerical examples that include solutions with known exact values. We then apply our methodology to price three types of European options within the framework of the time-fractional Black–Scholes model: (i) a European double barrier knock-out call option; (ii) a standard European call option; and (iii) a European put option. These case studies not only enhance our comprehension of the fractional derivative’s order on option pricing but also stimulate discussion on how different model parameters affect option values within the fractional framework.

Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation.

The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples. This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset, closely mimicking the experimentally measured reaching movements of stroke survivors. This approach not only captures the complex temporal dynamics and common movement patterns after stroke, but also significantly enhances the training dataset. By training deep learning models on both synthetic and experimental data, we enhanced task classification accuracy: models incorporating synthetic data attained an overall accuracy of 80.0%, significantly higher than the 66.1% seen in models trained solely with real data. These improvements allow for more precise task classification, offering clinicians the potential to monitor patient progress more accurately and tailor rehabilitation interventions more effectively.

A Deep Learning Approach for Predicting Aerial Suppressant Drops in Wildland Firefighting Using Automatic Dependent Surveillance–Broadcast Data

This study utilizes Automatic Dependent Surveillance–Broadcast (ADS-B) data sourced by the OpenSky Network to curate a dataset aimed at enhancing the precision of aerial suppressant drop predictions in wildland firefighting. By amalgamating ADS-B data with Automated Telemetry Unit (ATU) drop information, this research constructs a reliable base for analyzing the spatial aspects of aerial firefighting operations. Using sequential machine learning models, specifically Long Short-Term Memory (LSTM) networks and 1D Convolutional Neural Networks (1DCNN), the study interprets complex flight dynamics to predict drop locations. The dataset, covering 2017 to 2023, is labeled and segmented to reflect accurate suppressant release events, facilitating the distinction between drop and non-drop activities in fixed-wing aircraft. The LSTM model demonstrated strong predictive performance with an F1 score of 0.922, effectively identifying suppressant drop events with high accuracy. This model’s reliable predictions can significantly improve situational awareness in real-time aerial firefighting operations, enabling more informed decision-making and better coordination of resources during wildfire events.

Transformations of transnational care in times of the pandemic: spotlights and future prospects

ABSTRACT The COVID-19 pandemic has exacerbated existing inequalities in care between mobile and immobile populations. It has highlighted the precarious situation of many migranticized individuals and their families worldwide, but it has also brought their situation into the public eye, providing opportunities for advances in social science understanding and policy reform. This Special Issue explores the interplay between transnational care arrangements, cross-border movement and mobility, and the production of social inequality in the post-COVID-19 world. The collected articles provide a comprehensive picture from different countries and fields, revealing three interrelated processes that reflect the complex dynamics during the pandemic: (1) the interruption of transnational care arrangements; (2) the reorganization of these arrangements; and (3) coping strategies to adapt to (post)pandemic immobilizations, restrictions, and modified patterns of care. In sum, this issue aims to contribute to the advancement of social science understanding and to benefit vulnerable populations.

Relationships between abiotic factors, foliage chemistry and herbivory in a tropical montane ecosystem.

Herbivore-plant interactions are fundamental processes shaping ecosystems, yet their study is challenged by their complex connections within broader ecosystem processes, requiring a nuanced understanding of ecosystem dynamics. This study investigated the relationship between nutrient availability and insect herbivory in the Australian Wet Tropics. Our objectives were threefold. Firstly, to understand what factors influence nutrient availability for plants and herbivores across the landscape; secondly, to investigate how trees of different species respond to nutrient availability; and thirdly, to unravel how the relationships between resources and plant chemistry affect herbivory. We established a network of 25 study sites covering important abiotic gradients, including temperature, precipitation, and geology. Employing a hierarchical modelling approach, we assessed the influence of climate and geology on resource availability for plants, primarily in the form of soil nutrients. Then, we explored the influence of the above factors on the interaction between herbivory and foliage chemistry across three widespread rainforest tree species, comparing how these relationships emerged across genera. Our findings suggest an overarching influence of climate and geology over soil chemistry, foliar nitrogen, and insect herbivory, both directly and indirectly. However, individual constituents of soil fertility showed equivocal influences on spatial patterns of foliage chemistry once site geological origin was accounted for, suggesting a questionable relationship between individualsoil nutrients and foliar composition. We have demonstrated that herbivore-plant interactions are complex dynamics regulated by an intricate web of relationships spanning different biogeochemical processes. While our results provide some support to the notion that herbivory is affected by resource availability, different species growing under the same conditions can show differing responses to the same resources, highlighting the importance of identifying specific limiting factors rather than simpler proxies of resource availability.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.

Discovering the effectiveness of climate finance for Somalia’s climate initiatives: a dual-modeling approach with multiple regression and support vector machine

IntroductionThis research investigates into the complex dynamics of climate finance in Somalia, a vulnerable region facing the dire consequences of climate change. The study aims to assess how financial inputs for climate-related projects align with the actual needs and identify critical factors that influence funding effectiveness.MethodsA dual-methodological approach was employed, integrating both multiple regression analysis and Support Vector Machine (SVM) techniques. This mixed-method analysis facilitates a robust examination of climate finance data to dissect the relationships and impacts of various determinants on funding effectiveness.ResultsThe results indicate that adaptation finance, robust governance, and the scale of financial interventions significantly enhance the effectiveness of climate finance flows. However, mitigation finance and aspects related to gender equality displayed less significant impacts. Notably, the study identifies a pervasive underfinancing of climate projects in Somalia, illustrating a significant gap between the needed and actual funds disbursed.DiscussionThe findings underscore the need for enhanced governance frameworks and targeted large-scale financial interventions to optimize the allocation and impact of climate finance in vulnerable regions like Somalia. By quantifying the influence of adaptation finance and governance, this study contributes new insights to the literature on climate finance effectiveness and suggests practical strategies for policymakers and practitioners to improve climate resilience initiatives.

Beyond the school: principals’ insights on expanded roles in times of crisis. The case of COVID-19 in Turkey

ABSTRACT This study examined the perspectives of school principals on the workload of the additional administrative responsibilities they were assigned during COVID-19 in Turkey. The purpose of this study was to gain insight into the nature of these additional responsibilities and to assess their impact on the principals who took them on. The research group consisted of 62 school administrators (principals and vice principals), from public elementary schools. The research data were collected through semi-structured interviews. The collected data were analyzed using content analysis. The results showed that principals were assigned tasks without regard to their specific roles, with no differentiation based on their positions. The study also revealed a variety of emotional experiences among the participants, ranging from fear and anxiety to feelings of satisfaction and commitment. Overall, this study provided valuable insights into the complex dynamics of principals’ experiences during COVID-19 and highlighted the need for transparent communication, equitable task allocation, and adequate support mechanisms to maintain their well-being and commitment.

Promoting Sustainable Transportation: Factors Influencing Battery Electric Vehicle Adoption Across Age Groups in Thailand

Battery Electric Vehicles (BEVs) are a crucial innovation for achieving sustainable transportation and reducing greenhouse gas emissions, which are major contributors to global warming and climate change. While previous studies have explored attitudes towards BEV technology acceptance, few have examined the interplay of external factors such as government measures and adoption barriers in promoting sustainable mobility. This study addresses this gap by investigating the roles of government policies, usage obstacles and innovation diffusion in stimulating BEV purchase intentions, while applying the Innovative Diffusion Theory (IDT). Data from 3632 respondents in Thailand were analyzed using structural equation modeling (SEM) to examine causal relationships between factors. The results indicate that government policies supporting BEV users enhance innovation diffusion in society, leading to increased adoption intentions. Furthermore, effective policies help mitigate barriers to BEV usage, further encouraging adoption. The study also reveals that causal relationships of BEV usage intentions vary across age groups, highlighting the need for targeted approaches in promoting sustainable transportation. These findings contribute to the development of evidence-based policy recommendations to accelerate BEV adoption, supporting Thailand’s Carbon Neutrality goals and broader sustainable development objectives. By elucidating the complex dynamics of BEV adoption, this research provides valuable insights for policymakers and stakeholders working towards a more sustainable and environmentally friendly transportation sector.

Navigating the Cultural Paradox: The Dual Role of Globalisation in Propagating Cultural Hybridisation and Nationalism

Abstract: This paper engages in a comprehensive exploration of the cultural paradox that is manifested by globalization's dual role in disseminating cultural hybridization and nationalism.Globalization not only promotes the fusion of different cultural elements, resulting in cultural hybridization phenomena. For example, the global success of South Korean pop music K-Pop, which combines Western and Korean musical elements, embodies a cultural product that is a harmonious blend of global and local cultures generated by globalization. At the same time, globalization also triggers cultural nationalism. Represented by the ideology of "America First", in the face of threats brought by globalization, it emphasizes protecting national culture and economic interests. Nationalism is a counter-reaction to globalization. The paradox of globalization lies in its role as a catalyst for both cultural hybridization and nationalism. Its complex dynamics affect the development of culture and people's perception of identity. In the future, as cultural exchanges continue to be intensified by technology and globalization, elements of hybridization and nationalism will continue to shape our world. They will influence individual and collective assertions of identity and manifest in various fields from arts and media to politics and economics. In this balancing act lies the potential to create a world that honors and appreciates the multiplicity of cultures while preserving diversity, comprising the riveting mosaic of global culture.

Tackling nomophobia: the influence of support systems and organizational practices

ABSTRACT This study investigates the growing issue of nomophobia, characterized by the anxiety of being without a mobile phone, in the workplace. Utilizing the broaden-and-build theory, this research examines the impact of supportive leadership, co-worker support, Human Resource Management (HRM) practices, and affective commitment on reducing nomophobia among employees. Data were collected from 393 employees across various educational organizations. Structural Equation Modeling (SEM) was employed to test the proposed hypotheses. The findings reveal that supportive leadership significantly reduces nomophobia by enhancing HRM practices and affective commitment. However, contrary to expectations, co-worker support was found to increase nomophobia, suggesting that informal communication and social interactions via mobile devices might exacerbate smartphone dependency. HRM practices emerged as a critical factor in mitigating nomophobia, more so than affective commitment. This study contributes to the literature by highlighting the complex dynamics of workplace relationships and offering practical insights for organizations aiming to reduce nomophobia and improve employee well-being.

Dynamics analysis of green supply chain under the conditions of demand uncertainty and blockchain technology

This research investigates the implications of incorporating blockchain technology into the process of making decisions for green supply chains, particularly under conditions of demand uncertainty. A model was formulated to encompass both environmentally friendly products enabled by blockchain technology and those without such enabling technology. The study further explores the optimal method of introducing green input in a duopoly market using game theory. It also examines how consumer uncertainty about green products and acceptance of the technical parameters of blockchain influence this strategy. The findings suggest that increased consumer uncertainty can, in some instances, motivate manufacturers to enhance the eco-friendliness of their products and improve supply chain performance. However, the universal adoption of blockchain does not necessarily ensure better results; on the contrary, it may compromise product sustainability while enhancing supply chain profitability. Moreover, research has indicated that products enabled by blockchain typically have lower prices, thereby offering potential benefits to consumers when acceptance of sustainable energy solutions is high or uncertain. Additionally, this paper analyzes the impact of changes in green supply chain decision-making on system reliability. This involves exploring the relationship between decision parameters and consumer reluctance towards sustainable products and the adoption of blockchain technology. To ensure stable market competition in dynamic complex systems, research shows that feedback control technology can effectively regulate unpredictable behavior.

Unveiling the complexity of civil service effectiveness index: An asymmetric and ANN modeling

Unraveling the factors influencing civil service effectiveness becomes imperative in an era marked by escalating demands for efficient governance. This study attempts to meet this necessity by delving into the complex dynamics among core executive functions, mission support facilities, service delivery functions, and attributes, aiming to elucidate their collective impact on civil service effectiveness. Utilizing a unique methodological blend of Fuzzy-set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Networks (ANN), it delves into the relationships among core executive functions, mission support facilities, service delivery functions, and attributes within the International Civil Service Effectiveness (InCiSE) Index framework. The research uses a configurational model that optimizes CSE and assesses the relative importance of various components. The study reveals significant correlations among the variables. It indicates that all CSE indicators influence but are not equally important in triggering the effectiveness of civil service administration. Key configurations, such as integrating strategic governance with mission support functions and high-level strategy with operational execution, are critical for enhancing civil service effectiveness. It underscores the importance of prioritizing core executive functions and attributes to improve civil service administration. Theoretically, the study enriches contingency theory and contributes to the civil service and administration literature by integrating a configurational approach with machine learning insights. Practically, it provides actionable insights for governance improvement, promoting the application of innovative methodologies in public service to enhance organizational environment and civil service capacities. Original in its approach, this study fills a gap in the literature by applying a hybrid fsQCA-ANN model to explore the configurational and construct ranks influencing civil service effectiveness, offering an inclusive analysis that triangulates qualitative and quantitative data. The findings indicate that civil service effectiveness is highly complex because of its process, involvement of diverse backgrounds of civil servants, critical understanding about the key pillars of good governance and service delivery mechanism. Thus, the findings advance academic understanding and provide practical strategies for policymakers and practitioners to foster better governance through targeted interventions that enhance transparency, accountability, and responsiveness in civil service organizations.

Exploring the roles of Lem2 and Bqt4 in lipid metabolism for nuclear envelope maintenance: a novel perspective

Abstract The nuclear envelope (NE) is a double-membrane structure critical for genome maintenance and cellular function, composed of the inner and outer nuclear membranes. In fission yeast, the inner nuclear membrane (INM) proteins Lem2 and Bqt4 are essential for maintaining NE integrity. The study published by Hiraoka group explores the interactions between Lem2 and Bqt4 with lipid synthesis enzymes, addressing their roles in NE maintenance. The authors identified Lem2- and Bqt4-binding proteins using immunoprecipitation and mass spectrometry, revealing that Lem2 interacts with lipid synthesis enzymes, whilst Bqt4 binds to an enzyme that involves in glucosylceramide synthesis. These findings suggest that Lem2 and Bqt4 independently contribute to NE structure and its integrity through distinct lipid metabolic pathways, highlighting their complementary roles in nuclear membrane homeostasis. This study represents a significant step forward in the field of NE biology to unravel the complexities of nuclear membrane dynamics.

Complex cooptation: How regime complexity affects the stability of cooptation bargains—the case of China and the World Bank

ABSTRACT How do variations in regime complexity affect the overall stability of cooptation bargains amidst shifting great power rivalries? Increased complexity creates opportunities for institutional bargaining during cooptation deal negotiations, enabling cooptees to explore outside-options, engage in regime-shifting and countervailing institutional creation. While great power dynamics influence actors’ decisions regarding cooptation deals, regime complexity shapes their ability to employ cross-institutional strategies, affecting deal stability. Focusing on China's cooptation deal with the World Bank since 1980, we find that during the Cold War, the deal remained stable due to China's limited outside-options and significant power asymmetry with the US. Post-Cold War, increased complexity offered China with only limited outside-options, while it couldn't effectively engage in regime-shifting. The establishment of the AIIB, however, threatens the stability of the cooptation deal made in World Bank by providing China with genuine orum-shopping and regime-shifting opportunities. This analysis highlights how regime complexity and power dynamics interact to affect cooptation bargain stability amid evolving global rivalries.