Complexity最新文献

Consumers in developing countries, particularly Africans, are strongly criticized for their unfavorable feeling about locally produced apparel products. With this concern, this paper explores the determinants of Ethiopian consumers’ purchasing behavior towards locally produced apparel products by using the extended theory of planned behavior (TPB) model. A self-administered questionnaire which consists of open and closed-ended questions was developed based on the literature. Cross-sectional data were collected from 478 Ethiopian consumers by following the convenience sampling technique and were analyzed by structural equation modeling with AMOS V.26.0. The ethnocentric tendency of Ethiopian consumers was found at a moderate level. The consumers had a very high purchasing intention towards locally produced apparel products but had a low actual purchasing behavior. The result also showed that Ethiopian consumers use price, garment fitness, comfort, and color as the most important attributes, while the country of origin and notions were found to be the moderate important attributes. Moreover, consumers’ attitude towards locally produced apparel products was found as the most influential factor, while perceived behavioral control and subjective norms were the least influential factors in affecting consumers’ purchasing intention. The study’s findings provide insight into domestic apparel consumption as well as the factors affecting consumer purchasing intentions and behavior in the Ethiopian context. The study incorporates product attributes and ethnocentrism into the TPB model to investigate their impact on consumer purchasing intentions which are rarely investigated together.

This investigation centers on megastable systems, distinguished by their countable infinite attractors, with a particular emphasis on the Quadratic Megastable Oscillator (QMO). Unlike traditional megastable oscillators reliant on external excitation, our proposed QMO operates autonomously, contributing to its distinctiveness. Through a comprehensive exploration of the QMO, we elucidate various dynamical behaviors, enriching the understanding of its complex system dynamics. In contrast to conventional megastable oscillators, the QMO yields nested types of multiple attractors for diverse initial conditions, elegantly depicted in phase portraits. To gauge the sustainability of chaotic oscillation, we employ influential parameter bifurcation plots, providing a nuanced insight into the system’s dynamical evolution. The complexity of the proposed system is further underscored by its intricate basins of attraction, accommodating an infinite number of coexisting attractors. Exploring trajectory dynamics, we observe that certain initial conditions lead trajectories to distant destinations, evading the influence of local attractors. This behavior underscores the uniqueness of the QMO and highlights its potential applications in scenarios requiring nonlocalized attractor behaviors. Taking a practical turn, the QMO is applied to biometric fingerprint image encryption, demonstrating its efficacy in real-world applications. Rigorous statistical analyses and vulnerability assessments confirm the success of the QMO in providing secure encryption within chaotic system-based frameworks. This research contributes not only to the theoretical understanding of megastable systems but also establishes the QMO as a valuable tool in encryption applications, emphasizing its robustness and versatility in complex dynamical scenarios.

This study, employing convenience sampling of art specialists, investigates the emotional resonance of young consumers with Chinese bamboo patterns, underscoring their significance in the realm of contemporary design. Through the lens of Kansei engineering, we delve into the intricate world of bamboo weaving patterns, scrutinizing their perceptual evaluations and user preferences among 104 art university students aged 18–23. Our analysis not only identifies pivotal factors for innovative redesign but also underscores the pivotal role of these traditional patterns in enhancing the emotional and aesthetic appeal of products for the younger generation. The findings illuminate a notable appreciation among this demographic for the ecofriendly and culturally rich bamboo patterns, positioning them as essential elements in modern lifestyle products. The study advances the discourse on integrating cultural heritage into product innovation, advocating for designs that resonate with the aesthetic sensibilities and environmental values of today’s youth. This aligns with the critical insights into the various categories of patterns and their diverse applications in contemporary design, addressing the cognitive gap between designers and consumers and highlighting the importance of cultural motifs in product development.

This study addresses an adaptive neural funnel fault-tolerant control problem for a class of strict-feedback nonlinear systems with actuator faults and input dead zone. To guarantee the boundedness of the tracking error, a modified transformation for funnel error is devised and incorporated into the control design process. To manage unknown nonlinear functions, radial basis function neural networks (RBFNN) are employed in designing an adaptive neural funnel fault-tolerant controller through the backstepping technique. The proposed controller guarantees the output tracking error stays within a predefined funnel, and all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, simulations of a rigid robot manipulator system and an inverted pendulum system are conducted to validate the practicality and effectiveness of the proposed control method.

In this paper, a mathematical model is formulated, suitable to explain the evolution of income distribution over a population in the presence of production. The model is conceived from the perspective of complexity. Indeed, the income distribution emerges as the result of a myriad of economic exchanges taking place between individuals. In fact, the aim of the paper is to provide a framework and mathematical tools for the construction and the investigation of models having an exploratory character. The framework is expressed in the form of a system of nonlinear ordinary differential equations, as many as the income classes are, involving transition probabilities. Numerical solutions of these systems are constructed under different assumptions on the law of production and in the presence of different fiscal systems, which provides an example of the versatility of the method.

Tourist resorts play a crucial role in providing people with diverse and high-quality leisure and vacation options. The development of tourist resorts is integral to the high-quality growth of the national tourism industry. This study utilizes a combination of the nearest neighbour index, kernel density analysis, and geographic concentration index to examine the spatial and temporal distribution of 758 national and provincial tourist resorts in China. The analysis reveals several key findings: (1) the years 1992 and 2015 were pivotal for the development of China’s tourist resorts, with significant government attention and steady growth in the number of national tourism resorts from 2015 to 2023. (2) The distribution of China’s tourist resorts shows an overall uneven distribution at a national level but a more balanced distribution locally. The southeast region exhibits a high and even distribution density, while the northwest region has a lower density and quantity of resorts. (3) Some specific areas have emerged such as the junction of Anhui, Hubei, and Jiangxi provinces as the first core gathering areas and the junction of Shanxi, Jiangsu, and Zhejiang provinces as the secondary core areas. These core areas have gradually weakened and spread to other regions, leading to changes in the distribution patterns of tourist resorts. (4) The distribution of China’s tourist resorts is influenced by various factors, including economic development, resource endowment, source market, and government support. Government support is a key factor in accelerating the development of tourism resorts. These findings shed light on the complex dynamics shaping the distribution of tourist resorts in China.

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time.

A better understanding of financial contagion and systemically important financial institutions (SIFIs) is essential for the prevention and control of systemic financial risk. Considering the ripple effect of financial contagion, we integrate the relevant spatiotemporal information that affects financial contagion and propose to use the ripple-spreading network to simulate the dynamic process of risk contagion in China’s financial system. In addition, we introduce the smooth-transition vector autoregression (STVAR) model to identify “high” and “low” systemic risk regimes and set the relevant parameters of the ripple-spreading network on this basis. The results show that risk ripples spread much faster in high than in low systemic risk regimes. However, systemic shocks can also trigger large-scale risk contagion in the financial system even in a low systemic risk regime as the risk ripple continues. In addition, whether the financial system is in a high or low systemic risk regime, the risk ripples from a contagion source (i.e., a real estate company) spread first to the real estate sector and the banking sector. The network centrality results of the heterogeneous ripple-spreading network indicate that most securities and banks and some real estate companies have the highest systemic importance, followed by the insurance, and finally the diversified financial institutions. Our study provides a new perspective on the regulatory practice of systemic financial risk and reminds regulators to focus not only on large institutions but also on institutions with strong ripple capacity.

Central actors or opinion leaders are in the right structural position to spread relevant information or convince others about adopting an innovation or behaviour change. Who is a central actor or opinion leader might be conceptualised in various ways. Widely accepted centrality measures do not take into account that those in central positions in the social network may change over time. A longitudinal comparison of the set and importance of opinion leaders is problematic with these measures and therefore needs a novel approach. In this study, we investigate ways to compare the stability of the set of central actors over time. Using longitudinal survey data from primary schools (where the members of the social networks do not change much over time) on advice-seeking and friendship networks, we find a relatively poor stability of who is in the central positions anyhow we define centrality. We propose the application of combined indices in order to achieve more efficient targeting results. Our results suggest that because opinion leaders may change over time, researchers should be careful about relying on simple centrality indices from cross-sectional data to gain and interpret information (for example, in the design of prevention programs, network-based interventions or infection control) and must rely on more diverse structural information instead.

This study aims to investigate the research and development (R&D) competition within the supply chain, focusing on two aspects: R&D competition at the manufacturing level and competition in pricing strategies. This paper establishes a dynamic game model of R&D competition, comprising two manufacturers and two retailers, with both manufacturers exhibiting bounded rationality. The key findings are as follows: (1) an increase in the adjustment speed positively affects the chaotic nature of the R&D competition system, leading to a state of disorder. This chaotic state has adverse implications for manufacturing profitability. (2) The spillover effect exhibits a positive relationship with the level of chaos in the R&D competition system. A greater spillover effect contributes to a more turbulent environment, which subsequently impacts the profitability of manufacturers. (3) R&D cost parameters exert a positive influence on the stability of the R&D competition system. When the system reaches a state of equilibrium, an escalation in the R&D cost parameters poses a threat to manufacturer profitability. (4) Retailer costs play a detrimental role in the stability of the R&D competition system. As retailer costs increase, there is a decline in R&D levels, thereby diminishing manufacturer profitability. (5) To mitigate the chaotic state, we propose the implementation of the time-delayed feedback control (TDFC) method, which reflects a more stable state in the R&D competition system.