Zhao Jin

The complex chaotic dynamics and multistability of financial system are some important problems in micro- and macroeconomic fields. In this paper, we study the influence of two-delay feedback on the nonlinear dynamics behavior of financial system, considering the linear stability of equilibrium point under the condition of single delay and two delays. The system undergoes Hopf bifurcation near the equilibrium point. The stability and bifurcation directions of Hopf bifurcation are studied by using the normal form method and central manifold theory. The theoretical results are verified by numerical simulation. Furthermore, one feature of the proposed financial chaotic system is that its multistability depends extremely on the memristor initial condition and the system parameters. It is shown that the nonlinear dynamics of financial chaotic system can be significantly changed by changing the values of time delays.

PurposeThe purpose of this study was to introduce an orthogonal experimental design to improve the efficiency of building and optimizing models for freezing of gait prediction.MethodsA random forest model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG. An OED was introduced to optimize the feature extraction parameters.ResultsThe main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time were 27%, 68%, and 2.99 s, respectively.ConclusionThe OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.

There are great individual differences in learning abilities, but their neural bases, especially among normal populations, are not well understood. Using functional magnetic resonance imaging and a training paradigm, the present study investigated individual differences in cerebral asymmetry in fusiform regions when processing a new writing system and their correlation to subsequent visual character learning. Twelve Chinese adults underwent a 2-week training to learn 120 Korean characters and they were scanned before and after the training. Results showed that left-hemispheric dominance during the pretraining task was predictive of better posttraining performance. These results have significant implications for our understanding of the neural basis of language learning, especially in terms of individual differences. © 2006 Massachusetts Institute of Technology.

Existing cognitive and neural imaging studies have suggested a frontoparietal network of multiple, cooperative components for verbal working memory. We used functional MRI to investigate whether this neural network is also involved in the processing of second language by nonfluent bilinguals. Twelve native Chinese speakers who had limited English proficiency were scanned while performing working memory tasks in Chinese and English. They were asked to make judgment continuously whether the word presented on the screen was semantically related to another word presented two words earlier. On a different task, they were asked to make judgment whether the target word rhymed with the other word. A naming and judgment task in each language was adopted to control for the visual process, initial lexical process, and motor responses. Behavioral data showed that subjects performed better at tasks in their native language than in English. Imaging results showed that all working memory tasks in both L1 and L2 elicited a very similar pattern of left-hemisphere-dominated activation in the dorsolateral prefrontal cortex, pars opercularis region, pars triangularis region, precentral cortex, and parietal lobule. Consistent with the behavioral data, the volume of activation in the left opercularis region, left parietal lobule, and right precentral region was greater for L2 than for L1. These results suggest that working memory in L1 and L2 is mediated by a unitary neural system, which is capable of recruiting surrounding cortical resources to meet the increased computational demand caused by low L2 proficiency. © 2004 Elsevier Inc. All rights reserved.

Consumer buying behavior is an important aspect in every marketing strategy to produce maximum output from the market. This study aims to determine how advertisement affects consumer buying behavior and brand loyalty by considering a mediator between brand awareness and the moderating role of perceived quality. For this purpose, this study targets the rising cosmetics industry. This study used the purposive sampling technique to collect data from 300 respondents with the help of an online survey method via Google doc. The partial least squares structural equation modeling PLS-SEM was applied to verify the hypotheses relationships. The findings have confirmed that advertisements substantially predicted brand awareness, brand loyalty, and consumer buying behavior. Furthermore, brand awareness partially mediated the association of advertisement with brand loyalty and consumer buying behavior. Also, perceived quality is significantly moderated on the association of brand awareness with brand loyalty and consumer buying behavior. Based on such findings, this study has contributed to the literature and provided new insights into the practical implications alongside the future roadmap of the survey.

The uses of statistical distributions for modeling real phenomena of nature have received considerable attention in the literature. The recent studies have pointed out the potential of statistical distributions in modeling data in applied sciences, particularly in financial sciences. Among them, the two-parameter Lomax distribution is one of the prominent models that can be used quite effectively for modeling data in management sciences, banking, finance, and actuarial sciences, among others. In the present article, we introduce a new three-parameter extension of the Lomax distribution via using a class of claim distributions. The new model may be called the Lomax-Claim distribution. The parameters of the Lomax-Claim model are estimated using the maximum likelihood estimation method. The behaviors of the maximum likelihood estimators are examined by conducting a brief Monte Carlo study. The potentiality and applicability of the Lomax claim model are illustrated by analyzing a dataset taken from financial sciences representing the vehicle insurance loss data. For this dataset, the proposed model is compared with the Lomax, power Lomax, transmuted Lomax, and exponentiated Lomax distributions. To show the best fit of the competing distributions, we consider certain analytical tools such as the Anderson–Darling test statistic, Cramer–Von Mises test statistic, and Kolmogorov–Smirnov test statistic. Based on these analytical measures, we observed that the new model outperforms the competitive models. Furthermore, a bivariate extension of the proposed model called the Farlie–Gumble–Morgenstern bivariate Lomax-Claim distribution is also introduced, and different shapes for the density function are plotted. An application of the bivariate model to GDP and export of goods and services is provided.

The use of social media is becoming a necessary daily activity in today’s society. Excessive and compulsive use of social media may lead to social media addiction. The main aim of this study was to investigate whether demographic factors, impulsivity, self-esteem, emotions, and attentional bias were risk factors associated with SMA. The study was conducted in a non-clinical sample of college students, ranging in age from 16 to 23 years, including 277 females and 243 males. All participants completed a survey measuring impulsivity, self-esteem, anxiety, depression, social anxiety, loneliness, and attentional bias. The final hierarchical regression model indicated significant risk factors for SMA with an accuracy of 38%. The identified set of associated risk factors included female gender, impulsivity, self-esteem, anxiety, social anxiety, and negative attentional biases. Finally, a discussion of the results is presented, followed by corresponding recommendations for future studies.

Statistical distributions play a prominent role for modeling data in applied fields, particularly in actuarial, financial sciences, and risk management fields. Among the statistical distributions, the heavy-tailed distributions have proven the best choice to use for modeling heavy-tailed financial data. The actuaries are often in search of such types of distributions to provide the best description of the actuarial and financial data. This study presents a new power transformation to introduce a new family of heavy-tailed distributions useful for modeling heavy-tailed financial data. A submodel, namely, heavy-tailed beta-power transformed Weibull model is considered to demonstrate the adequacy of the proposed method. Some actuarial measures such as value at risk, tail value at risk, tail variance, and tail variance premium are calculated. A brief simulation study based on these measures is provided. Finally, an application to the insurance loss dataset is analyzed, which revealed that the proposed distribution is a superior model among the competitors and could potentially be very adequate in describing and modeling actuarial and financial data.

This work focuses on a multiagent system with time-varying interval uncertainty in the system matrix, where multiple agents interact through an undirected topology graph and only the bounding matrices on the uncertainty in the system matrix are known. A reduced-order interval observer, which is named the reduced-order neighborhood interval observer, is designed to estimate the relative state of each agent and those of its neighbors. It is shown that the reduced-order IO can guarantee the consensus of the uncertain multiagent system. Finally, simulation examples are proposed to verify the theoretical findings.