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In visual art and design, color composition not only affects the aesthetic value of the work but also plays a deeper role in the psychological feelings and emotional reactions of the audience. The purpose of this study is to explore how color composition affects the prediction of the psychological quality of images so as to provide scientific guidance for artistic creation and design. By using the theories and methods of psychology, aesthetics and computer science, this study conducted a series of experiments and analyses to reveal the mechanism of different color configurations in visual perception and their impact on image quality evaluation. The relevant literature on color theory and visual psychology is understood, and the theoretical framework of the research is established. Then, using computer vision technology, a large number of images with different colors were evaluated for visual quality to obtain objective data. Viewers’ subjective evaluations of these images were collected to examine how color composition affects an individual’s visual experience and emotional response. Color saturation, contrast and tone matching have significant effects on the prediction of the visual psychological quality of images. Color configurations with high saturation and moderate contrast can enhance the appeal and viewing value of the image, while harmonious color combinations can help trigger a positive emotional response from the viewer. In addition, the study also found that individual differences, such as gender, age and cultural background, also affect the effect of color composition on the prediction of visual psychological quality.

Thailand is currently grappling with a severe dengue fever outbreak, with a rising threat to public health as the rainy season and El Niño draw near. This year has witnessed a troubling surge in dengue cases, prompting the Ministry of Public Health (MoPH) to issue warnings that the numbers may hit a three-year peak. Dengue outbreaks in Thailand have historically followed a cyclical pattern, excluding COVID-19 years. This research employs data analysis and predictive modeling to forecast the forthcoming dengue case numbers in Thailand, facilitating better public health preparedness. It also incorporates data visualization for enhanced data exploration. Various forecasting models, including Exponential Smoothing, Polynomial Fitting and Random Forest, are deployed to predict dengue cases within the constraints of our data. This study offers valuable insights into the potential trajectory of dengue cases in Thailand, aiding proactive measures to combat the outbreak.

Hair mineral content has become a useful biomarker to measure exposure to elements both safe and unsafe. To promote the use of hair mineral volumes in clinical practice, it would seem crucial to first determine their normal ranges by the statistical analysis of hair minerals sampled from a large healthy population. This paper used Proton Induced X-ray emissions (PIXE) to measure mineral concentrations in the hair of 842 mother-infant pairs. The data were then statistically analyzed to determine their normal ranges and categorized into 4 groups based on distributional characteristics. Intra-individual variations are also identified and discussed with regard to risk analysis.

The objective of this study is to examine the ability of PIXE to distinguish the country of origin of duvets. The stable isotope method that is currently used is not recognized as reliable. Mineral concentrations of down feathers from France, Poland and Taiwan were measured by PIXE. The measurements were statistically analyzed. The results show even a scatter plot of appropriately chosen minerals distinguished between those countries at 95% accuracy.

We investigate and quantify the multifractal detrended cross-correlation of return interval series for Chinese stock markets and a proposed price model, the price model is established by oriented percolation. The return interval describes the waiting time between two successive price volatilities which are above some threshold, the present work is an attempt to quantify the level of multifractal detrended cross-correlation for the return intervals. Further, the concept of MF-DCCA coefficient of return intervals is introduced, and the corresponding empirical research is performed. The empirical results show that the return intervals of SSE and SZSE are weakly positive multifractal power-law cross-correlated, and exhibit the fluctuation patterns of MF-DCCA coefficients. The similar behaviors of return intervals for the price model is also demonstrated.

Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.

A financial time series model is developed and investigated by the oriented percolation system (one of the statistical physics systems). The nonlinear and statistical behaviors of the return interval time series are studied for the proposed model and the real stock market by applying visibility graph (VG) and multifractal detrended fluctuation analysis (MF-DFA). We investigate the fluctuation behaviors of return intervals of the model for different parameter settings, and also comparatively study these fluctuation patterns with those of the real financial data for different threshold values. The empirical research of this work exhibits the multifractal features for the corresponding financial time series. Further, the VGs deviated from both of the simulated data and the real data show the behaviors of small-world, hierarchy, high clustering and power-law tail for the degree distributions.

The geometrical aspects of the saccular aneurysm are an important factor for the rupture of aneurysm. In this paper, the effects of the sac centerline on the aneurysm rupture are fully investigated. Our attention is to disclose the important factors related to aneurysm rupture in different time instants. CFD method is applied for the analysis of WSS, OSI, pressure and velocity inside the saccular aneurysm with different sac centerlines. Our results also show that the coiling technique could sufficiently decrease the risk of rupture since decreasing the coil porosity (increasing the coil permeability) would increase the OSI and pressure and decrease WSS and blood velocity inside the aneurysm sac.

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0MB, 180.0MB, and 360.0MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

Radix Scutellariae has been widely used to hasten the process of heat clearing and dampness drying in traditional Chinese medicine. The resource of wild Radix Scutellariae is scarce; an increasing amount of cultivated Radix Scutellariae has become available in the market. To determine the clinical effects of Radix Scutellariae, we conducted a comparative analysis of the chemical compositions of cultivated and wild Radix Scutellariae. An HPLC fingerprint method was developed to determine simultaneously the amounts of baicalin, baicalein, and wogonin, which have been identified as active compounds in Radix Scutellariae. Chinese pharmacopoeia methodology was also applied to measure the ethanolic extract content of the wild and cultivated samples. Although the cultivated and wild Radix Scutellariae have similar concentrations of baicalein and wogonin, the concentrations of baicalin and ethanolic extracts are higher in the cultivated samples (i.e., 15.14% ± 1.11% and 56.90% ± 2.83%, respectively, compared to 11.17% ± 1.11%, and 44.16% ± 2.02%, respectively, in the wild Radix Scutellariae). Data from fingerprint analysis were statistically analyzed using the decision tree and hierarchical cluster methods. The study was carried out with 58 samples. Thus, the current study provides significant guidelines for distinguishing cultivated and wild Radix Scutellariae.

Nested simulation has been an active area of research in recent years, with an important application in portfolio risk measurement. While majority of the literature has been focusing on the continuous case where portfolio loss is assumed to follow a continuous distribution, monetary losses of a portfolio in practice are usually measured in discrete units, oftentimes due to the practical consideration of meaningful decimal places for a given level of precision in risk measurement. In this paper, we study a nested simulation procedure for estimating conditional Value-at-Risk (CVaR), a popular risk measure, in the case where monetary losses of the portfolio take discrete values. Tailored to the discrete nature of portfolio losses, we propose a rounded estimator and show that when the portfolio loss follows a sub-Gaussian distribution or has a sufficiently high-order moment, the mean squared error (MSE) of the resulting CVaR estimator decays to zero at a rate close to ${\mathrm{\Gamma }}^{-1}$, much faster than the rate of the CVaR estimator in the continuous case which is ${\mathrm{\Gamma }}^{-2/3}$, where $\mathrm{\Gamma }$ denotes the sampling budget required by the nested simulation procedure. Performance of the proposed estimator is demonstrated using numerical examples.

The neglected 35 MeV/c² particle mass quantization hypothesis has recently been reassessed for all known meson states. The rule is found to be statistically relevant, once the states are grouped by quark composition and J^PC, with slightly different mass units for each group. In certain groups the mass unit is spin-dependent. Also the mass units are linearly quantized, with highly structured correlation patterns. The baryon masses are organized along similar lines. These results support an indication that hadrons might be shell-structured.

The technical breakthroughs of multiple detectors developed by Daya Bay and RENO collaborations have gotten great attention. Yet the optimal determination of neutrino mixing parameters from reactor data depends on the statistical method and demands equal attention. We find that a straightforward method using minimal parameters will generally outperform a multi-parameter method by delivering more reliable values with sharper resolution. We review standard confidence levels and statistical penalties for models using extra parameters, and apply those rules to our analysis. We find that the methods used in recent work of the Daya Bay and RENO collaborations have several undesirable properties. The existing work also uses nonstandard measures of significance which we are unable to explain. A central element of the current methods consists of variationally fitting many more parameters than data points. As a result, the experimental resolution of sin²(2θ₁₃) is degraded. The results also become extremely sensitive to certain model parameters that can be adjusted arbitrarily. The number of parameters to include in evaluating significance is an important issue that has generally been overlooked. The measures of significance applied previously would be consistent if and only if all parameters but one were considered to have no physical relevance for the experiment's hypothesis test. Simpler, more transparent methods can improve the determination of the mixing angle θ₁₃ from reactor data, and exploit the advantages from superb hardware technique of the experiments. We anticipate that future experimental analysis will fully exploit those advantages.

There are a substantial number of empirical relations that began with the identification of a pattern in data; were shown to have a terse power-law description; were interpreted using existing theory; reached the level of "law" and given a name; only to be subsequently fade away when it proved impossible to connect the "law" with a larger body of theory and/or data. Various forms of allometry relations (ARs) have followed this path. The ARs in biology are nearly two hundred years old and those in ecology, geophysics, physiology and other areas of investigation are not that much younger. In general if X is a measure of the size of a complex host network and Y is a property of a complex subnetwork embedded within the host network a theoretical AR exists between the two when Y = aX^b. We emphasize that the reductionistic models of AR interpret X and Y as dynamic variables, albeit the ARs themselves are explicitly time independent even though in some cases the parameter values change over time. On the other hand, the phenomenological models of AR are based on the statistical analysis of data and interpret X and Y as averages to yield the empirical AR: 〈Y〉 = a〈X〉^b. Modern explanations of AR begin with the application of fractal geometry and fractal statistics to scaling phenomena. The detailed application of fractal geometry to the explanation of theoretical ARs in living networks is slightly more than a decade old and although well received it has not been universally accepted. An alternate perspective is given by the empirical AR that is derived using linear regression analysis of fluctuating data sets. We emphasize that the theoretical and empirical ARs are not the same and review theories "explaining" AR from both the reductionist and statistical fractal perspectives. The probability calculus is used to systematically incorporate both views into a single modeling strategy. We conclude that the empirical AR is entailed by the scaling behavior of the probability density, which is derived using the probability calculus.

Urban bus transit is a major mode of transportation in modern cities and plays an important role in mitigating the traffic pressure in urban road networks. We used smartcard data collected by three-million bus passengers in Shenzhen, a major southern city of China, to study the statistical properties and dynamics of bus passenger flows. In this study, the recorded passenger flows were cross-grained into each 1 km × 1 km square grids to avoid large flow variations at a single bus station. The temporal and spatial patterns of passenger flows were analyzed and a machine learning-based model for passenger flow prediction was generated.

Entering big data era, individual GPS trajectory data have created great opportunities for human mobility and collective behavior studies. Individual GPS trajectories can be collected by location-based services on mobile phones. However, GPS data often do not record transportation modes (e.g., walking, riding a bus, or driving a car). In this study, we analyzed the statistical characteristics of individual trajectories and present a collaborative isolation forest (Co-IF) model to identify the transportation modes of mobile phone GPS trajectories. Unlike previous models that identify multiple transportation modes simultaneously, the proposed Co-IF model builds a single-class classifier for each transportation mode and then combines their results. Compared to the existing models, the Co-IF model offers competitive performance and shows improved reliability with noisy trajectories.

The purpose behind this research is to utilize the knack of Bayesian solver to determine numerical solution of functional differential equations arising in the quantum calculus models. Functional differential equations having discrete versions are very difficult to solve due to the presence of delay term, here with the implementation of Bayesian solver with means of neural networks, an efficient technique has been developed to overcome the complication in the model. First, the functional differential systems are converted into recurrence relations, then datasets are generated for converted recurrence relations to construct continuous mapping for neural networks. Second, the approximate solutions are determined through employing training and testing steps on generated datasets to learn the neural networks. Furthermore, comprehensive statistical analysis are presented by applying various statistical operators such as, mean squared error (MSE), regression analysis to confirm both accuracy as well as stability of the proposed technique. Moreover, its rapid convergence and reliability is also endorsed by the histogram, training state and correlation plots. Expected level for accuracy of suggested technique is further endorsed with the comparison of attained results with the reference solution. Additionally, accuracy and reliability is also confirmed by absolute error analysis.

In this research paper, the authors wish to examine the effects of couple stress on hybrid nanofluid considering magnetohydrodynamic three-dimensional transient flow between two parallel plates. Stretching of the lower plate causes fluid flow in the channel. The fluid flow model is shown in mathematical form using a set of coupled nonlinear partial differential equations, which are then translated into coupled nonlinear ordinary differential equations using the proper transformation. The authors used the spectral quasi linearization method (SQLM), an effective numerical technique, to solve the updated equations and study the effects of various flow parameters on fluid temperature and velocity. The Nusselt number and skin friction coefficients were also investigated from an engineering standpoint. The generated solutions are verified using the residual analysis. Statistical analysis is performed on the skin-friction coefficients and the Nusselt number using quadratic regression models.

High quality bus service is considered as an efficient way to mitigate traffic congestion in big cities. Global positioning system (GPS) data provide sufficient sources to evaluate the performance of bus vehicles that both passengers and operator concern about. This paper aims to propose a framework to assess the operational performance of bus routes based on the GPS trajectory data collected from Jinan, China. Several important indicators of bus operation including travel time of routes, section running time, dwell time and bus bunching have been studied. The results show that the travel time of routes follow right skewed distributions. Moreover, section running time between two consecutive stations varies in different time period and it is larger in evening peak hours. Additionally, the dwell time has been discussed and the results show that there is no big variation in most stations except some stations, which provides a help to identify the key stations. Furthermore, we propose an approach to detect the bunching points. The results indicate the bunching points are easy to occur in the peak hours and the congested road section.

This article describes how to use statistical data analysis to obtain models directly from data. The focus is put on finding nonlinearities within a generalized additive model. These models are found by means of backfitting or more general algorithms, like the alternating conditional expectation value one. The method is illustrated by numerically generated data. As an application, the example of vortex ripple dynamics, a highly complex fluid-granular system, is treated.