Miracle-dz/UniFAR-FTUs huggingface dataset:
Levinson and Schur algorithms for Toeplitz matrices with singular minors

A simple general method for overcoming singularities in the Levinson (and Schur) recursions for Toeplitz systems is presented. It is based on a generalization of the conventional three-term recursion for polynomials orthogonal on the unit circle: the scalar coefficients of the conventional three-term recursion are replaced by polynomial coefficients whose degree is determined by the depth of singularity. The depth of the singularity is related to the number of additional zero elements that occur in the Schur recursion. The authors' method also makes it possible to recursively determine the inertia of a Hermitian Toeplitz matrix. <<ETX>>
Combinatorial Optimization of Non-linear Multicommodity Network Flow Using Pseudo Quasi-Newton Method

This paper presents a new approach to solve the Nonlinear Multicommodity Network Flow Problem (NMNFP). We intend to use a multi-algorithm approach to resolve the Convex Multicommodity Network Flow Problem into Multicommodity Flow Quadratic Subproblems (MFQSPs) using the projected Jacobi approach. The proposed methodology combines the well-known Jacobi Method and innovative Dual Projected Pseudo-Quasi-Newton (DPPQN) approach to improve the computational efficiency of the DPPQN problem. A sparse-block-matrix methodology and finite iteration scale technique have been used in the comparative analysis of Frank-Wolfe with PARTAN and the DPPQN approach. We show the efficiency of the purpose algorithm with a numerical example.
A Machine Learning-Driven Movie Performance Prediction System to Improve Decision-Making Capability of Movie Investors

Moviegoers refer to online audience movie ratings before deciding to watch a movie. They are more inclined to watch a movie with a high average rating. We develop a system to predict average audience movie ratings based on the lead cast and crew at an early stage of movie production. After valuing multiple scenarios, investors can use our study to select the lead cast and crew objectively. Judicious selection of the key cast and crew is extremely important as investors commit to large sums of money as professional fees while signing contracts with them. Our study uses a relatively large sample of 1687 Indian movies spread across 10+ languages released in India between 2010 and 2019 to identify the important predictors influencing average audience movie rating. Identification of important predictors improves the explainability of the prediction model, which increases the investors’ trust in the predicted values. The best model, random forest, reduces the baseline prediction error of the average rating by 10.21%.
Exploring the Relative Importance of Number of Edge Crossings and Size of Crossing Angles: A Quantitative Perspective

Recent research has indicated that human graph reading performance can be affected by the size of crossing angles. The aesthetic of crossing angles is closely related to another aesthetic factor: edge crossings. Although the number of edge crossings has been previously identified as the most important aesthetic, its relative impact on human graph reading, compared to the size of crossing angles, has not been investigated. In this paper, we present an exploratory user study investigating the relative importance between crossing number and crossing angle. This study also aims to further examine the effects of crossing number and crossing angle not only on task performance measured as response time and accuracy, but also on cognitive load and visualization efficiency. The experimental results reinforce the previous findings that the two aesthetics each significantly affect performance of human graph reading. Further, in terms of the relative importance, the study demonstrates that given the current setting of the user study, the number of edge crossings is relatively more important than the size of crossing angles. To be more specific, crossing number and crossing angle together explain about 40% of the variance in response time, mental effort and visualization efficiency, with about 83% of the explained variance being attributed to crossing number. In regard to response accuracy, crossing number and crossing angle together explain about 14% of the variance, with a slightly larger portion of the explained variance being attributed to crossing number.
Leuconostoc citreum Inhibits Adipogenesis and Lipogenesis by Inhibiting p38 MAPK/Erk 44/42 and Stimulating AMPKα Signaling Pathways

Probiotics provide a range of health benefits. Several studies have shown that using probiotics in obesity treatment can reduce bodyweight. However, such treatments are still restricted. Leuconostoc citreum, an epiphytic bacterium, is widely used in a variety of biological applications. However, few studies have investigated the role of Leuconostoc spp. in adipocyte differentiation and its molecular mechanisms. Therefore, the objective of this study was to determine the effects of cell-free metabolites of L. citreum (LSC) on adipogenesis, lipogenesis, and lipolysis in 3T3-L1 adipocytes. The results showed that LSC treatment reduced the accumulation of lipid droplets and expression levels of CCAAT/ enhancer-binding protein-α & β (C/EBP-α & β), peroxisome proliferator-activated receptor-γ (PPAR-γ), serum regulatory binding protein-1c (SREBP-1c), adipocyte fatty acid binding protein (aP2), fatty acid synthase (FAS), acetyl CoA carboxylase (ACC), resistin, pp38MAPK, and pErk 44/42. However, compared to control cells, adiponectin, an insulin sensitizer, was elevated in adipocytes treated with LSC. In addition, LSC treatment increased lipolysis by increasing pAMPK-α and suppressing FAS, ACC, and PPAR-γ expression, similarly to the effects of AICAR, an AMPK agonist. In conclusion, L. citreum is a novel probiotic strain that can be used to treat obesity and its associated metabolic disorders.
Unsteady Hydromagnetic Flow over an Inclined Rotating Disk through Neural Networking Approach

The goal of this research is to examine how a magnetic field affects the unsteady flow of a hybrid nanofluid over a spinning disk that is inclined and stretched while the flow is surrounded by a non-Darcy porous medium. Furthermore, for heat transmission mechanisms, Joule heating and viscous dissipation are considered. The current article is made more realistic by imposing thermal radiation to enhance the heat transmission system under the effects of convection. Moreover, thermal and velocity slip conditions have also been incorporated into the current study. The equations that administer the flow problem along with constraints at the boundaries are converted to dimension-free form by employing a set of appropriate similarity transformations, which are then solved by the numerical technique Runge-Kutta method of order four (RK-4). The new and advanced trend for the convergence of the obtained results is validated through a neural networking approach. The temperature of hybrid nanofluid is augmented by an increase in the porosity parameter, the unsteadiness factor, the Eckert number, the magnetic field, and the Forchheimmer number, while for the values of the radiation factor, the thermal heat is decreasing near the disk and increasing away from the disk. The precision of the obtained results has been ensured by comparing them with established results, with good agreement among these results.
AI-Based CXR First Reading: Current Limitations to Ensure Practical Value

We performed a multicenter external evaluation of the practical and clinical efficacy of a commercial AI algorithm for chest X-ray (CXR) analysis (Lunit INSIGHT CXR). A retrospective evaluation was performed with a multi-reader study. For a prospective evaluation, the AI model was run on CXR studies; the results were compared to the reports of 226 radiologists. In the multi-reader study, the area under the curve (AUC), sensitivity, and specificity of the AI were 0.94 (CI95%: 0.87–1.0), 0.9 (CI95%: 0.79–1.0), and 0.89 (CI95%: 0.79–0.98); the AUC, sensitivity, and specificity of the radiologists were 0.97 (CI95%: 0.94–1.0), 0.9 (CI95%: 0.79–1.0), and 0.95 (CI95%: 0.89–1.0). In most regions of the ROC curve, the AI performed a little worse or at the same level as an average human reader. The McNemar test showed no statistically significant differences between AI and radiologists. In the prospective study with 4752 cases, the AUC, sensitivity, and specificity of the AI were 0.84 (CI95%: 0.82–0.86), 0.77 (CI95%: 0.73–0.80), and 0.81 (CI95%: 0.80–0.82). Lower accuracy values obtained during the prospective validation were mainly associated with false-positive findings considered by experts to be clinically insignificant and the false-negative omission of human-reported “opacity”, “nodule”, and calcification. In a large-scale prospective validation of the commercial AI algorithm in clinical practice, lower sensitivity and specificity values were obtained compared to the prior retrospective evaluation of the data of the same population.
The complete mitogenome of Paratanakia chii (Cypriniformes; Cyprinidae)

Abstract Paratanakia chii is a bitterling fish of the genus Paratanakia, subfamily Acheilognathinae and family Cyprinidae. The mitochondrial DNA sequence of P. chii is reported in this paper. The complete mitochondrial genome of P. chii is 16,575 bp in length, including 13 protein-coding genes (PCGs), 2 rRNA genes, 22 tRNA genes, and 1 displacement loop (D-loop). The genome sequence is consistent with those of most other carp. The majority of PCGs have AT- (Met) start codons and TA- end codons. The A + T contents of the genome, PCGs, transfer RNAs (tRNAs), and ribosomal RNAs (rRNAs) are 56.92%, 58.07%, 56.34%, and 54.21%, respectively. Phylogenetic analysis showed that P. chii is most closely related to Tanankia himantegus. These data will benefit relative ecological and phylogenetic studies.
Synthesis of Enzyme-based Organic-Inorganic Hybrid Nanoflower Particles

Enzyme-incorporated hybrid nanostructures are the immobilization of enzymes and inorganic components that exhibits promising characteristics in various industries. The immobilization of enzymes onto nanomaterial is naturally based to accommodate the enzymatic activity, stability, recyclability as well as their catalytic functions. The designing of these conjugates can improve the overall enzymatic performance by imparting their novel properties onto the system in comparison to conventional free enzymes which experience drawbacks in terms of deactivation or denaturing. A facile and ultrafast method is described in this paper to synthesize a novel enzyme-incorporated lipase/Cu3(PO4)2 hybrid nanoflower (NF). The physical properties of the hybrid NF allow easier retrieval which indicates its higher reusability and recyclability value. The enzyme loading capacity was found to be 95.1% whereas, the catalytic performance of lipase/Cu3(PO4)2 hybrid NF at the optimal conditions resulted in a specific enzyme activity of 1752 U/g corresponding to an increment of 90.5% to that of free lipase. This indicates that the well-designed lipase/Cu3(PO4)2 hybrid NF to be highly efficient in industrial biocatalytic applications. Meanwhile, in future work, we aim to study its operational stability and reusability to enzymatically degrade biopolymers through hydrolysis process.
IV.—On the Integrals of Hua and James.

Synopsis Recently a power series representation of Hypergeometric functions with matrix argument has been established. This representation involves a special type of spherical function from the theory of semi-simple Lie groups, called the zonal polynomials. A general theory of these polynomials is well established; however an explicit representation of them is lacking. This paper considers two integrals which are related to this explicit representation. The final paragraph considers a third integral which gives an application of a result from a previous paper of the author.
Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.
Elliptic and triangular instabilities in rotating cylinders

In this article, the multipolar vortex instability of the flow in a finite cylinder is addressed. The experimental study uses a rotating elastic deformable tube filled with water which is elliptically or triangularly deformed by two or three rollers. The experimental control parameters are the cylinder aspect ratio and the Reynolds number based on the angular frequency. For Reynolds numbers close to threshold, different instability modes are visualized using anisotropic particles, according to the value of the aspect ratio. These modes are compared with those predicted by an asymptotic stability theory in the limit of small deformations and large Reynolds numbers. A very good agreement is obtained which confirms the instability mechanism; for both elliptic and triangular configurations, the instability is due to the resonance of two normal modes (Kelvin modes) of the underlying rotating flow with the deformation field. At least four different elliptic instability modes, including combinations of Kelvin modes with azimuthal wavenumbers m = 0 and m = 2 and Kelvin modes m = 1 and m = 3 are visualized. Two different triangular instability modes which are a combination of Kelvin modes m = −1 and m = 2 and a combination of Kelvin modes m = 0 and m = 3 are also evidenced. The nonlinear dynamics of a particular elliptic instability mode, which corresponds to the combination of two stationary Kelvin modes m = −1 and m = 1, is examined in more detail using particle image velocimetry (PIV). The dynamics of the phase and amplitude of the instability mode is shown to be predicted well by the weakly nonlinear analysis for moderate Reynolds numbers. For larger Reynolds number, a secondary instability is observed. Below a Reynolds number threshold, the amplitude of this instability mode saturates and its frequency is shown to agree with the predictions of Kerswell (1999). Above this threshold, a more complex dynamic develops which is only sustained during a finite time. Eventually, the two-dimensional stationary elliptic flow is reestablished and the destabilization process starts again.
Exploring the quality change mechanism of tea polyphenol-assisted cured grass carp from perspectives of physicochemical and flavor

ABSTRACT In this study, the quality changes of grass carp during tea polyphenol-assisted curing were studied from physicochemical and flavor perspectives. The addition of tea polyphenols has a positive effect on maintaining the water content of fish as the curing time increases (p < 0.05). The tea polyphenol-assisted treatment maintains the brightness of grass carp meat. The addition of tea polyphenols also slowed down the rate of pH growth and enhances the texture of grass carp (p < 0.05). The addition of tea polyphenols reduced His content, which had a positive effect on the taste of the cured grass carp products. When the pickling time reached 60 min, the inosine monophosphate content of tea polyphenol-treated groups was higher than that of the group soaked in salt only. Combined with equivalent umami concentration and taste activity values, it was found that the addition of 0.1% TP could improve freshness of grass carp cured products.
A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Straight Quantum Waveguide with Robin Boundary Conditions

We investigate spectral properties of a quantum particle confined to an infinite straight planar strip by imposing Robin boundary conditions with variable coupling. As- suming that the coupling function tends to a constant at infinity, we localize the essential spectrum and derive a sufficient condition which guarantees the existence of bound states. Further properties of the associated eigenvalues and eigenfunctions are studied numerically by the mode-matching technique.
Traducción, adaptación cultural y validación de la Escala de Apoyo a Políticas Asociadas al Alcohol en universitarios mexicanos

Background: Health protection through prevention and reduction of alcohol consumption is a public health priority. Some scales have been identified to assess public alcohol policies but in English language. Objective: To evaluate the psychometric properties of the translation and cultural adaptation of a scale to assess support for alcohol policies in young Mexican university students. Methodology: Methodological study, with the participation of 367 university students from southern Mexico. Construct validity was estimated by means of an exploratory factor analysis and the internal consistency of the questionnaire was checked to verify its reliability. Results: Internal consistency was 0.96. Construct validity was performed through exploratory factor analysis, identifying a three factors structure: harm reduction, marketing and information, and price and availability. Conclusion: The scale showed adequate reliability and validity, and can be applied in Spanish as an empirical indicator in studies concerning alcohol public policies in young university students.
Probability density evolution method of fatigue strength-life relationship

Taking degradation of fatigue strength with increase in loading cycles into account,the joint probability density evolution equation of fatigue strength and random factors was derived using Euler description.A probability density S-N(p-S-N) surface was obtained with a numerical method,it was used to calculate the p-S-N curve with a given survivability.The analysis based on experimental fatigue data indicated that p-S-N curves with 95% survivability predicted by p-S-N,Monte Carlo simulation and S-N relation ship having parameters with a given fractile agree well;p-S-N provides a complete probability description of S-N relationship,independent of a probability distribution assumption.
Brain Activity Recognition using Deep Electroencephalography Representation

Advances in neurotechnology have enhanced and simplified our ability to research brain activity with low-cost and effective equipment. One such scalable and noninvasive technique is Electroencephalography (EEG), which detects and records electrical brain activity. Brain activity recognition is one of the emerging problems as EEG wearables become more readily available. Our research has modeled EEG signals to classify three states (i) music listening, (ii) movie watching, and (iii) meditating. The datasets incorporating the brain signals induced while performing these activities are NMED-T for music listening, SEED for movie watching, and VIP_Y_HYT for meditating. EEG activity is transformed into deep representation using a convolutional neural network comprising three different types of 2D convolutions: Temporal, Spatial, and Separable, to capture dependencies and extract high-level features from the data. The Depthwise Convolution function is responsible for learning spatial filters within each temporal convolution, and combining these spatial filters across all temporal bands optimally is learned by the Separable Convolutions. EEGNet and EEGNet-SSVEP are specially designed for EEG Signal Processing and Classification, and the DeepConvNet has incorporated more convolution layers. Our finding demonstrates that increasing the number of layers in the Network provided a higher accuracy of 99.94% using DeepConvNet. In contrast, the accuracy of EEGNet and EEGNet-SSVEP resulted in 85.63% and 75.76%, respectively.
Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases—A Review

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.
Elliptically Polarized THz Generation and Manipulation from Layered Two-dimensional Materials

Elliptically polarized terahertz (THz) emitter with tunable polarization state utilized in THz time-domain spectroscopy has intriguing potential for various applications, such as data processing, medical diagnosis, chiral molecular analysis, and so on. With the rapid development of material science, two-dimensional (2D) materials have been seen as the efficient and burgeoning nanoscale THz sources due to the high carrier concentration and mobility, broad spectral response, adjustable band gap, and especially the spin-related physics. Herein, we have observed the elliptically polarized THz generation from the tin sulfide (SnS) excited by linearly polarized incident light. Interestingly, the ellipticity and the orientation angle of the reconstructed THz polarization ellipses are dependent on the pump light polarization angle, realizing the arbitrary control of the polarized THz emission. Our study could lay the foundation for the 2D materials-based compact and integrated THz emitters and modulators.