Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectual pre-processing with quantization error elimination in pose detector with the aid of image-guided progressive graph convolution network (IGP-GCN) for multi-person pose estimation

Multi-person pose estimation (MPE) remains a significant and intricate issue in computer vision. This is considered the human skeleton joint identification issue and resolved by the joint heat map regression network lately. Learning robust and discriminative feature maps is essential for attaining precise pose estimation. Even though the present methodologies established vital progression via feature map’s interlayer fusion and intralevel fusion, some studies show consideration for the combination of these two methodologies. This study focuses upon three phases of pre-processing stages like occlusion elimination, suppression strategy, and heat map methodology to lessen noise within the database. Subsequent to pre-processing errors will be eliminated by employing the quantization phase by embracing the pose detector. Lastly, Image-Guided Progressive Graph Convolution Network (IGP-GCN) has been built for MPE. This IGP-GCN consistently learns rich fundamental spatial information by merging features inside the layers. In order to enhance high-level semantic information and reuse low-level spatial information for correct keypoint representation, this also provides hierarchical connections across feature maps of the same resolution for interlayer fusion. Furthermore, a missing connection between the output high level information and low-level information was noticed. For resolving the issue, the effectual shuffled attention mechanism has been proffered. This shuffle intends to support the cross-channel data interchange between pyramid feature maps, whereas attention creates a trade-off between the high level and low-level representations of output features. This proffered methodology can be called Occlusion Removed_Image Guided Progressive Graph Convolution Network (OccRem_IGP-GCN), and, thus, this can be correlated with the other advanced methodologies. The experimental outcomes exhibit that the OccRem_IGP-GCN methodology attains 98% of accuracy, 93% of sensitivity, 92% of specificity, 88% of f1-score, 42% of relative absolute error, and 30% of mean absolute error.
High‐performance geometric nonlinear analysis with the unsymmetric 4‐node, 8‐DOF plane element US‐ATFQ4

A recent unsymmetric 4‐node, 8‐DOF plane element US‐ATFQ4, which exhibits excellent precision and distortion‐resistance for linear elastic problems, is extended to geometric nonlinear analysis. Since the original linear element US‐ATFQ4 contains the analytical solutions for plane pure bending, how to modify such formulae into incremental forms for nonlinear applications and design an appropriate updated algorithm become the key of the whole job. First, the analytical trial functions should be updated at each iterative step in the framework of updated Lagrangian formulation that takes the configuration at the beginning of an incremental step as the reference configuration during that step. Second, an appropriate stress update algorithm in which the Cauchy stresses are updated by the Hughes‐Winget method is adopted to estimate current stress fields. Numerical examples show that the new nonlinear element US‐ATFQ4 also possesses amazing performance for geometric nonlinear analysis, no matter whether regular or distorted meshes are used. It again demonstrates the advantages of the unsymmetric finite element method with analytical trial functions.
Diagnosis of Alzheimer's disease based on logistic regression algorithm

Language impairment may be an important early manifestation of dementia. In this study, we propose a multi-feature fusion to distinguish MCI and AD patients. In this paper, a set of acoustic, demographic and linguistic features were selected, and after data processing, logistic regression algorithm was used to diagnose MCI and AD patients. In order to get better results, comparative experiments are also effectively conducted, including different combination methods and different group distinctions with the three most popular algorithms. Studies have shown that this method is more advantageous and the classification results are more accurate.
Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition

Matrix sketching schemes and the Nystr\"om method have both been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. Matrix sketching methods produce accurate matrix approximations, but they are only computationally efficient on skinny matrices where one of the matrix dimensions is relatively small. In particular, they are not efficient on large square matrices. The Nystr\"om method, on the other hand, is highly efficient on symmetric (and thus square) matrices, but can only achieve low matrix approximation accuracy. In this paper we propose a novel combination of the sketching method and the Nystr\"om method to improve their efficiency/effectiveness, leading to a novel approximation which we call the Sketch-Nystr\"om method. The Sketch-Nystr\"om method is computationally nearly as efficient as the Nystr\"om method on symmetric matrices with approximation accuracy comparable to that of the sketching method. We show theoretically that the Sketch-Nystr\"om method can potentially solve eigenvalue problems and kernel learning problems in linear time with respect to the matrix size to achieve $1+\epsilon$ relative-error, whereas the sketch methods and the Nystr\"om method cost at least quadratic time to attain comparable error bound. Our technique can be straightforwardly applied to make the CUR matrix decomposition more efficiently computed without much affecting the accuracy. Empirical experiments demonstrate the effectiveness of the proposed methods.
Shared Aperture 4G LTE and 5G mm-Wave Antenna in Mobile Phones With Enhanced mm-Wave Radiation in the Display Direction

Radiation in the display direction of the 5G millimeter-wave (mm-wave) antenna in mobile phones is challenging due to the obstruction from the display. In this article, a shared aperture 4G LTE and 5G mm-wave antenna in mobile phones with enhanced mm-wave radiation in the display direction is studied. The 5G mm-wave array, consisting of four slot elements, is constructed in the clearance of a 4G LTE antenna. For the shared aperture design, the interdigital coupling (IDC) structure, which has a slight effect on the 4G LTE antenna, is used as the ground of the slot element. To enhance the mm-wave radiation in the display direction, two groups of floating patches are applied. For the 4G LTE antenna, the measured −6 dB impedance bandwidths are 280 MHz (700–980 MHz) and 1220 MHz (1700–2920 MHz). The measured efficiency is higher than 40%. For the 5G mm-wave array, the measured overlapped −10 dB impedance bandwidth is 2.7 GHz (25.1–27.8 GHz). The measured mutual coupling among each element is lower than −16 dB. Within the 25.5–27.5 GHz, in the display direction, the measured peak gain is 10.4 dBi, and the 3 dB beam steering range reaches 90° (45°– 135°) with a gain higher than 7.5 dBi.
Heat release effects on decaying homogeneous compressible turbulence

Heat Release Effects on Decaying Homogeneous Compressible Turbulence. (May 2008) Kurn Chul Lee, B.S., Seoul National University, Korea; M.S., Seoul National University, Korea Chair of Advisory Committee: Dr. Sharath S. Girimaji High Mach-number compressible flows with heat release are inherently more complicated than incompressible flows due to, among other reasons, the activation of the thermal energy mode. Such flow fields can experience significant fluctuations in density, temperature, viscosity, conductivity and specific heat, which affect velocity and pressure fluctuations. Furthermore, the flow field cannot be assumed to be dilatation-free in high Mach numbers and even in low Mach-number flows involving combustion, or in boundary layers on heated walls. The main issue in these high-speed and highly-compressible flows is the effect of thermal gradients and fluctuations on turbulence. The thermal field has various routes through which it affects flow structures of compressible turbulence. First, it has direct influence through pressure, which affects turbulence via pressure-strain correlation. The indirect effects of thermal fields on compressible turbulence are through the changes in flow properties. The high temperature gradients alter the transport coefficient and compressibility of the flow. The objective of this work is to answer the following questions: How do temperature fluctuations change the compressible flow structure and energetics? How does compressibility in the flow affect the non-linear pressure redistribution process? What is the main effect of spatial transport-coefficient variation? We perform direct numerical simulations (DNS) to answer the above questions. The investigations are
Mycotoxins in milk: Occurrence and evaluation of certain detoxification attempts

Abstract Milk contaminated with mycotoxins is a significant issue affecting human health, especially in infants. The current study aimed to investigate the presence of mycotoxins in milk collected from women farmers' vendors (WFV), and to evaluate certain herbal plant fibers as green mycotoxin binders. Moreover, explore the binding efficiency ratios of mycotoxins using shaking or soaking process incorporated with herbal extracts. Furthermore, compare the taste evaluations of tested milk are enriched with herbal extracts. Results indicated that the fumonisins were not detected in the collected cow milk samples but realized a 25% occurrence ratio in buffalo's milk samples. A high occurrence ratio of aflatoxin M1 (aflaM1) was observed in buffalo and cow milk samples. The soaking process of plant fibers in contaminated milk overnight significantly degrades and adsorbs mycotoxins particles. The shacking process incorporated with plant fibers exhibited more effectiveness in mycotoxins degradation than soaking or shacking processes alone. The speed of shacking process played an important role in the mycotoxin's binding process. All the tested plant fibers effectively reduced all mycotoxin presence in contaminated milk, especially green tea, during the soaking or shacking process. Moreover, the shacking process incorporated with plant fibers promoted and supported the mycotoxins degradation process.
Stable inversion-based multitrace deabsorption method for spatial continuity preservation and weak signal compensation

ABSTRACTNonstationary seismic data can be expressed using a linear matrix-vector multiplication system derived from wave theory when anelastic effects of the earth can be quantified by the intrinsic quality factor (or Q) and Q is frequency independent in the seismic bandwidth. On the basis of the linear modeling system and singular value decomposition, we have assessed the stability using weights associated with the left singular vectors, data, and singular values, and we assessed the compensation/resolution limitation of inversion-based deabsorption using the right singular vectors. In addition, a stable inversion-based multitrace deabsorption method was developed by minimizing the l1-norm of coefficients in the frequency-wavenumber (f-k) domain of reflectivity subject to the time-domain nonstationary data misfit. The optimum deabsorption result can be obtained by sequentially solving a series of lasso subproblems until the stopping condition is reached. As the number of solving lasso subproblems increas.
Speech Emotion Recognition System using SVD algorithm with HMM Model

From many years there has been gaining interest in the field of SER using Matlab. SER states the emotional state by analyzing the input speech. SER has a simple pattern and also including feature extraction, feature matching, classification and database. Here, from the input speech by using algorithm features are extracted by using some models the feature matching takes place. By this process we use to analyze the characteristics of the input speech signal. Hence, the system recognize the state of emotion. The system states some the of emotions: Angry, Boredom, Anxiety, Disgust,Happiness, Neutral, Sadness. The main purpose of this paper is to give survey on two of the algorithms using HMM model with different speech emotion databases. There are several audio features for extracting are available. And also various classifiers are available. The most popular models are Hidden Markov model(HMM), Vector Quantization(VQ), Gaussian Mixture model(GMM), Deep Neural Networks (DNN), Neural Networks(NN) and Artificial Neural Networks(ANN). There are several speech emotion databases included Berlin database. Hence, we reviewed some of the models will be discussed in this paper.
Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study

Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.
Synergistic Antimicrobial Activity of Magnetite and Vancomycin-Loaded Mesoporous Silica Embedded in Alginate Films

The aim of the present study was to obtain a hydrogel-based film as a carrier for the sustained and controlled release of vancomycin, an antibiotic commonly used in various types of infections. Considering the high-water solubility of vancomycin (>50 mg/mL) and the aqueous medium underlying the exudates, a prolonged release of vancomycin from an MCM-41 carrier was sought. The present work focused on the synthesis of malic acid coated magnetite (Fe3O4/malic) by co-precipitation, synthesis of MCM-41 by a sol-gel method and loading of MCM-41 with vancomycin, and their use in alginate films for wound dressing. The nanoparticles obtained were physically mixed and embedded in the alginate gel. Prior to incorporation, the nanoparticles were characterized by XRD, FT-IR and FT-Raman spectroscopy, TGA-DSC and DLS. The films were prepared by a simple casting method and were further cross-linked and examined for possible heterogeneities by means of FT-IR microscopy and SEM. The degree of swelling and the water vapor transmission rate were determined, considering their potential use as wound dressings. The obtained films show morpho-structural homogeneity, sustained release over 48 h and a strong synergistic enhancement of the antimicrobial activity as a consequence of the hybrid nature of these films. The antimicrobial efficacy was tested against S. aureus, two strains of E. faecalis (including vancomycin-resistant Enterococcus, VRE) and C. albicans. The incorporation of magnetite was also considered as an external triggering component in case the films were used as a magneto-responsive smart dressing to stimulate vancomycin diffusion.
Information Extraction from Indonesian Crime News with Named Entity Recognition

Information Extraction on crime domain is the process of extracting information related to crime event. A prior study of crime information extraction on Indonesian text has been carried out by utilizing features from Part-of-Speech tagging and Dependency Parsing. However, there are some misclassifications, especially in location and date/time extraction. The misclassification is mainly due to the system was not able to identify several named entities. In this study, we propose a system capable of extracting criminal information on Indonesian online news by utilizing named entity recognition, with the focus to extract crime location and time. We use Support Vector Machine (SVM) to classify crime type. We evaluate the proposed system performance by comparing with the gold label. The test results show that crime type classification has an overall performance of 92%, the Crime Location Extraction has F1 score of 90.8%, and for Crime Date Extraction the F1 score is 94,1%. Based on analysis, improvement should be conducted especially on Crime Location extraction. Identification of various date time format is also important to be explored further.
A Bayesian Optimized Variational Mode Decomposition-Based Denoising Method for Measurement While Drilling Signal of Down-the-Hole Drilling

Measurement while drilling (MWD) emerges as a reliable technique for assessing rock mass properties. However, the measured MWD signals are often contaminated with noise, leading to distorted signals. To address this issue, this article proposes a denoising method that utilizes variational mode decomposition (VMD) and wavelet soft thresholding (WST). The proposed method employs Bayesian optimization to adaptively determine the optimal VMD parameters. The decomposed modes are then further denoised using WST with appropriate thresholding and optimal wavelet decomposition levels. To validate the effectiveness of the proposed method, four synthetic signals with varying input signal-to-noise ratios (SNRs) and two sets of measured MWD signals were evaluated. The results demonstrate the surpassing denoising efficiency of the proposed technique in terms of higher output SNR, $R^{2}$ , and lower root mean squared error (RMSE) values. Moreover, the denoised MWD signals show promising results, with reduced amplitude and volatility while preserving the characteristics of the original signals.
Boundary Element Analysis Of Rib-stiffenedElastic Plates

The present paper proposes an integral equation formulation for elastostatic deformation problem of eccentrically stiffened plates. Though some boundary element analysis of stiffened plates have been proposed so far [1, 2, 3, 4, 5], many of them consider only concentrically stiffened plates. In case of such eccentrically stiffened plates, inand out-of-plane deformation of the stiffened plate are coupled. Previous studies on concentrically stiffened plates by the boundary element method were based on the Timoshenko theory of elastically supported plates [6], where the equivalent shear force acts as distributed force on stiffener (rib), and the bending moment as distributed twisting moment. This theory requires that the centroidal axis lies in the middle plane of the plate, thus it is difficult to extend to general stiffened plates considering this eccentricity. In this study the boundary integral formulation for general stiffened plates are presented.
Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

Abstract. In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.
FlumeRide: Interactive Space Where Artists and Fans Meet-and-Greet Using Video Calls

Recently, artists and fans meet more often online owing to the COVID-19 pandemic. Therefore, fans need to engage closely with artists and enjoy rich content. FlumeRide is a mobile application that helps artists and fans meet and greet through video calls. Beyond simply connecting the two, it provides detailed features similar to that fans experience in face-to-face meetings. We first investigated the overall contexts and challenges faced by fans during online meet-and-greets and derived the design elements. Then, we designed and implemented a system that supports various communications and contents in situations before, during, and after the conversation between fans and artists. FlumeRide incorporates various features to support communications in highly immersive and efficient ways, such as chat, video calls, streaming, and recordings. Our user study results demonstrate that FlumeRide enables fans to have private video calls with artists and experience rich interactions.