Miracle-dz/UniFAR-FTUs huggingface dataset:
EgpuIP: An Embedded GPU Accelerated Library for Image Processing

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm; 2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm; 3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm; 4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to $\mathbf{19}\times,\ \mathbf{88}\times$, and $\mathbf{3}\times$ speedup respectively.
Research on Dynamic Simulation Technology of Urban 3D Landscape Based on Virtual Reality APP Platform

Urban planning is a very important work in urban management, which involves almost all fields such as land, electricity, water conservancy, landscaping, environmental protection and so on. With the rapid development of spatial information technology, urban planning and geographic information technology have become inseparable, and the traditional two-dimensional geographic information technology can no longer meet the development needs of current urban planning. With the rise of “digital city”, urban 3D landscape reconstruction and 3D visualization are becoming the focus of current geographic information system research, which is of great significance to optimizing urban planning and management. With the development of geographic information system and related digital technologies, the traditional two-dimensional urban geographic information system can no longer meet the application requirements, and the three-dimensional digital city has become an important means in the process of urban planning and management. The method adopted in this paper is the virtual reality APP platform. The realization of this system platform can not only provide users with a more vivid, intuitive and detailed experimental environment for teaching or production; Promotion and application of science and technology.
Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semi-empirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sun-synchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R2 = 0.88; root mean square (rms) error = 2.21 °C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed – a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various land-cover types. The standard error of this coefficient estimate was 0.002 mm h−1 K−1 for both time periods. A strong correlation (R2 > 0.87; rms. error <0.17 mm h−1) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.
Dynamic analysis of the fifth order Chua circuit memristor system

According to the basic idea of Chua circuit, more variables are introduced to construct a fifth order memristor system, which is composed of inductor, capacitor, memristor and other components. In order to better study the dynamics characteristics of the fifth-order Chua circuit memristor system, multi-dimensional analysis methods such as bifurcation diagram, Lyapunov exponent, phase diagram and Poincare cross section diagram will be used. Finally, Simulink simulation will be used to verify its effectiveness. This circuit will be paid attention to in many fields such as chaotic dynamics and chaotic secure communication.
Social environment and moment-to-moment variation in self-construal among Latinx emerging adults: An ecological momentary assessment approach

Self-construal involves how one thinks of themselves in relations to others, including how much one thinks of themselves as separate from others (independent) or connected (interdependent). Researchers have typically assumed these self-perceptions are stable. However, given self-construal’s basis in the perceptions of others and the self, it is highly likely that self-construal can vary in different social environments. Thus, the goals of the present study were to examine (1) whether independent and interdependent self-construal fluctuates within-person among Latinx emerging adults from day-to-day and moment-to-moment, and (2) how individuals’ social environment is concurrently associated with fluctuations in self-construal. Using ecological momentary assessment (EMA), Latinx emerging adults (N = 191) reported on their independent and interdependent self-construal, as well as aspects of their social environment (occurrence of social interaction, interaction partner, quality of social interaction, and closeness of relationship) twice a day for 14 days, for up to 4058 observations in total. Using multilevel modeling to partition the variance in self-construal at the moment, day, and person level, we found substantial moment-to-moment variation. Further analyses indicated that aspects of the social environment were consistently associated with interdependent, but not independent, self-construal. The findings highlight the need to re-conceptualize changes in self-construal as a function of the social environment, and the need to measure aspects of culture, including self-construal, using ecologically valid measures that allow for more dynamic and sensitive assessments.
Analysis of Users' Emotional Attitudes to Vulgar Videos Based on Data Mining

With the development of network video, Vulgar Videos with its unique video content and form quickly became popular, but in its early stage, there were some problems such as vulgar content and poor user experience. In order to optimize user experience, this study explores the emotional attitude of users when they participate in the Vulgar Videos based on data mining. By using Python language to write the data collection logic, run the program to crawl the microblog comment data about Vulgar Videos, use TF-IDF algorithm to extract keywords and make word frequency statistics, use SnowNLP library for sentiment analysis, build semantic network and LDA topic model, and carry out topic analysis and extraction. Meanwhile, Jieba database will be used for word co-occurrence analysis to further excavate and analyze users' emotional attitude. Through the above process, this study draws the following conclusions: Vulgar Videos not only have entertainment properties, but also social properties and information properties. From the perspective of emotion analysis, a large number of netizens hold a positive attitude towards Vulgar Videos, while a small number of users are dissatisfied with the video content.
Reporting Valid and Reliable Overall Scores and Domain Scores

In educational assessment, overall scores obtained by simply averaging a number of domain scores are sometimes reported. However, simply averaging the domain scores ignores the fact that different domains have different score points, that scores from those domains are related, and that at different score points the relationship between overall score and domain score may be different. To report reliable and valid overall scores and domain scores, I investigated the performance of four methods using both real and simulation data: (a) the unidimensional IRT model; (b) the higher-order IRT model, which simultaneously estimates the overall ability and domain abilities; (c) the multidimensional IRT (MIRT) model, which estimates domain abilities and uses the maximum information method to obtain the overall ability; and (d) the bifactor general model. My findings suggest that the MIRT model not only provides reliable domain scores, but also produces reliable overall scores. The overall score from the MIRT maximum information method has the smallest standard error of measurement. In addition, unlike the other models, there is no linear relationship assumed between overall score and domain scores. Recommendations for sizes of correlations between domains and the number of items needed for reporting purposes are provided.
USE OF THE GENERALIZED LINEAR MODEL IN FORECASTING THE AIR PASSENGERS' CONVEYANCES FROM EU COUNTRIES

Some regression models to forecast the air passengers' conveyances from EU countries are considered. Two different approaches for the above-mentioned task of forecasting are shown. The first one is the classical method of linear regression and the second one is its generalized approach. The considered regression models contain many explanatory factors and their combinations. The advantage of using the generalized linear model (GLM) in comparison with the classical linear regression model is shown. Most the literature which is devoted to forecasting of transport flows contains only simple forecasting models on the base of the time series methods or linear regression methods with a small number of explanatory variables. Two approaches for the forecasting of air passengers' conveyances from EU countries are considered in this article: the classical method of linear regression and its generalized approach. The difference of linear regression models considered in this article comparing with the models presented in other papers (6) (autoregression integrated moving average models) and (3, 4, 8) (multiple regression models) consists in using the greater number of the explanatory factors and their combinations. Some models on the base of GLM are considered in the article as well. The aim of this article is to illustrate the advantage of using the GLM comparing with the linear regression models. The verification of the models and the evaluation of the unknown parameters are included in the research as well. This article has the following structure. The second section contains the description of the informative base of the mentioned investigation. The used models for analyzing and forecasting of air passengers' conveyances are considered in the third section. The elaboration of linear regression models and generalized linear models are presented in the fourth and fifth sections. In the fifth section the advantage of using GLM in comparison with the classical linear regression is shown. 2. Informative Base In this article the number of carried air passengers was our index of interest and we intend to forecast their volumes. We use the following factors influencing the volumes of air passengers' conveyances: t1 total population of the country (TP), millions of inhabitants; t2 area of the country (AREA), thousands of km 2 ;
Groupes géométriques de rang de Morley fini

Abstract We consider a new subgroup In(G) in any group G of finite Morley rank. This definably characteristic subgroup is the smallest normal subgroup of G from which we can hope to build a geometry over the quotient group G/ In(G). We say that G is a geometric group if In(G) is trivial. This paper is a discussion of a conjecture which states that every geometric group G of finite Morley rank is definably linear over a ring K1 ⊕…⊕ Kn where K1,…,Kn are some interpretable fields. This linearity conjecture seems to generalize the Cherlin–Zil'ber conjecture in a very large class of groups of finite Morley rank. We show that, if this linearity conjecture is true, then there is a Rosenlicht theorem for groups of finite Morley rank, in the sense that the quotient group of any connected group of finite Morley rank by its hypercentre is definably linear.
The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle

Simple Summary The objective of the present study was to develop a tool to detect mildly lame cows by combining already existing data from sensors, automated milking systems (AMSs), routinely recorded animal and farm data and other phenotypes. Ten dairy farms were visited every 30–42 days from January 2020 to May 2021, and locomotion scores (LCSs) and body condition scores (BCSs) were assessed at each visit. For each farm, a lameness incidence risk was calculated. Further, the impact of lameness on the derived sensor parameters was inspected. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables and compared for best results. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78 for predicting an LCS ≥ 2. We conclude that the combination of data from an available sensor device with routinely available AMS data, animal and farm information, and performance records can provide promising results for the detection of mild lameness in dairy cattle. Abstract This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.
Identification Method for Pulse Wave Resulted From Fault Transient of Overhead Transmission Line

This paper presents a simple and effective method to extract the identification quantity of transient pulse waveform produced by the overhead transmission line short-circuit fault. This method employs the second-order time harmonic equation of the power frequency component to convert the fault full-wave data. After the conversion, only the identification quantity remains. This method can amplify high-frequency spectral content by the times of the square of the angular frequency, and hence improve the sensitivity of identifying fault transient. Using the identification quantity, one can clearly observe the arrival time and relative intensity of fault transient. A three-phase half wavelength transmission line of 50Hz/3000km is simulated with PSCAD to illuminate the effectiveness of this method. The effects of fault type, soil resistivity, fault resistance, power frequency fluctuation, sampling rate, transposition length, fault inception angle and white noise are analyzed. Calculated results show that this method can distinguish different fault type and can work effectively at low sampling rate down to 10kHz. This method is helpful for improving the performance of the traveling-wave based fault location method.
Production and diagnosis of energetic particles in FAST

The Fusion Advanced Study Torus (FAST) has been proposed as a possible European satellite facility to study fast-ion physics in deuterium plasmas under conditions relevant to a burning plasma. Energetic minority ions (H or 3He) accelerated by ion cyclotron resonance heating (ICRH), with dimensionless parameters close to those of fusion-born alphas in ITER, can be produced in FAST via 30 MW power ICRH minority heating. This work provides a first assessment of the extent to which the 3He fast-ion population can be diagnosed in FAST with a set of dedicated diagnostics for confined fast particles. Neutron emission spectroscopy (NES), gamma-ray spectroscopy (GRS) and collective Thomson scattering (CTS) diagnostics have been reviewed with a description of the state-of-the-art hardware and a preliminary analysis of the required lines of sight. The results of the analysis, based on numerical simulations of the spatial and energetic particle distribution function of the ICRH-accelerated ions for the standard FAST H-mode scenario, suggest that NES and GRS measurements can provide information on the fast 3He population effective tail temperature, with time resolutions in the range 20–100 ms. The proposed CTS diagnostic can measure the fast-ion parallel and perpendicular temperature with a spatial resolution of 5–10 cm and a time resolution of 10 ms. The paper provides a scientific basis for the prediction of the production and diagnosis of energetic ions in FAST.
Karl Pearson and the Establishment of Mathematical Statistics

At the end of the nineteenth century, the content and practice of statistics underwent a series of transitions that led to its emergence as a highly specialised mathematical discipline. These intellectual and later institutional changes were, in part, brought about by a mathematical‐statistical translation of Charles Darwin's redefinition of the biological species as something that could be viewed in terms of populations. Karl Pearson and W.F. R. Weldon's mathematical reconceptualisation of Darwinian biological variation and “statistical” population of species in the 1890s provided the framework within which a major paradigmatic shift occurred in statistical techniques and theory. Weldon's work on the shore crab in Naples and Plymouth from 1892 to 1895 not only brought them into the forefront of ideas of speciation and provided the impetus to Pearson's earliest statistical innovations, but it also led to Pearson shifting his professional interests from having had an established career as a mathematical physicist to developing one as a biometrician. The innovative statistical work Pearson undertook with Weldon in 1892 and later with Francis Galton in 1894 enabled him to lay the foundations of modern mathematical statistics. While Pearson's diverse publications, his establishment of four laboratories and the creation of new academic departments underscore the plurality of his work, the main focus of his life‐long career was in the establishment and promulgation of his statistical methodology.
Global asymptotic stability of positive equilibrium in a 3-species cooperating model with time delay

The asymptotic behavior of the time-dependent solution for a 3-species cooperating model was investigated with the effects of both diffusion and time delay taken into consideration. We proved the global asymptotic stability of a positive steady-state solution to the model problem by using coupled upper and lower solutions for a more general reaction-diffusion system that gives a common framework for 3-species cooperating model problems. The result of global asymptotic stability implies that the model system coexistence is permanent. Some global asymptotic stability results for 2-species cooperating reaction-diffusion systems are included in the discussion, and some known results are extended.
Analysis of Facial Occlusion Challenge in Thermal Images for Human Affective State Recognition

Several studies have been conducted using both visual and thermal facial images to identify human affective states. Despite the advantages of thermal facial images in recognizing spontaneous human affects, few studies have focused on facial occlusion challenges in thermal images, particularly eyeglasses and facial hair occlusion. As a result, three classification models are proposed in this paper to address the problem of thermal occlusion in facial images, with six basic spontaneous emotions being classified. The first proposed model in this paper is based on six main facial regions, including the forehead, tip of the nose, cheeks, mouth, and chin. The second model deconstructs the six main facial regions into multiple subregions to investigate the efficacy of subregions in recognizing the human affective state. The third proposed model in this paper uses selected facial subregions, free of eyeglasses and facial hair (beard, mustaches). Nine statistical features on apex and onset thermal images are implemented. Furthermore, four feature selection techniques with two classification algorithms are proposed for a further investigation. According to the comparative analysis presented in this paper, the results obtained from the three proposed modalities were promising and comparable to those of other studies.