Miracle-dz/UniFAR-FTUs huggingface dataset:
Stock price prediction in Chinese stock markets based on CNN-GRU-attention model

Stock price prediction is a hot topic and has attracted the sufficient attention of both regulatory authorities and financial institutions. Because the fluctuation of stock prices is the result of many different factors, it is not easy to make stock price prediction. Traditional prediction solutions are mainly using simple linear models based on statistical and econometric models, these solutions are difficult to support nonstationary time series data. With the development of deep learning, some newly models can not only support non-linear data, but also retain useful information for better forecasting the stock prices. This paper aims to construct a CNN-GRU-Attention based model for price prediction in Chinese stock markets. First, the convolutional and pooling layers of CNN are used to extract features of factor correlation information from the input data; then, the output of feature matrix is used as input for the GRU model to forecast correlation; finally, the Attention mechanism is used to focus on the important characteristics of stock prices and optimize model structure. We collect multi-dimensional stock data of the China SSE 50 index from 2011 to 2021 as our dataset and conduct a set of experiments to compare the performance, which measured in terms of their Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R²) score. The proposed model is superior to other models: MAPE decreased by 11.23%, RMSE decreased by 5.71% and R²score improved by 0.41%, which shows that the CNN-GRU-Attention model outperforms state-of-the-art approaches in forecasting stock price.
Pim-2 Kinase Regulates Energy Metabolism in Multiple Myeloma

Simple Summary This study reveals the role of Pim-2 kinase in regulating the metabolism and proliferation of multiple myeloma (MM) cells. We believe that our study makes a significant contribution to the literature because, using patient data as well as cell line-based metabolomic studies, we establish that Pim-2 kinase directly regulates glycolysis, energy production, and survival and that Pim-2 is a potential target for MM treatment. Abstract Pim-2 kinase is overexpressed in multiple myeloma (MM) and is associated with poor prognosis in patients with MM. Changes in quantitative metabolism, glycolysis, and oxidative phosphorylation pathways are reportedly markers of all tumor cells. However, the relationship between Pim-2 and glycolysis in MM cells remains unclear. In the present study, we explored the relationship between Pim-2 and glycolysis. We found that Pim-2 inhibitors inhibited glycolysis and energy production in MM cells. Inhibition of Pim-2 decreased the proliferation of MM tumor cells and increased their susceptibility to apoptosis. Our data suggest that reduced Pim-2 expression inhibits the energy metabolism process in MM, thereby inhibiting tumor progression. Hence, Pim-2 is a potential metabolic target for MM treatment.
Tutela del diritto di proprieta'delle immagini digitali: Implementazione di un algoritmo di Watermark mediante funzioni Wavelet

Protection of copyright of the digital images is a critical element for the multimedia Web applications, e-books, virtual picture gallery. This problem is today receiving growing attention due to the pervasive diffusion of Internet technology. This work shows the watermark as solution to this problem and describes a new wavelet-based algorithm, called WM1.0, which is invisible, private, strong. WM1.0 watermaks a subset of digital images building the ecclesiastic on-line art collection. The Owner of the images and related information is the Italian Episcopal Conference, whereas the Publisher is I.D. S. , an ICT company located in Messina.
Subendocardial Viability Ratio Predictive Value for Cardiovascular Risk in Hypertensive Patients

Background: The subendocardial viability ratio (SEVR), also known as the Buckberg index, is a parameter of arterial stiffness with indirect prognostic value in assessing long-term cardiovascular risk. Materials and Methods: We conducted a prospective cohort study on 70 patients with uncomplicated hypertension admitted to a county medical reference hospital. We analyzed demographics, laboratory data, arterial stiffness parameters and cardiovascular risk scores (SCORE and Framingham risk scores) and aimed to identify paraclinical parameters associated with increased cardiovascular risk. Results: Of the arterial stiffness parameters, SEVR correlates statistically significantly with age, central and peripheral systolic blood pressure, as well as with heart rate. SEVR seems to have prognostic value among hypertensive patients by increasing the risk of major cardiovascular events assessed by SCORE and Framingham risk scores. SEVR correlates statistically significantly with serum fibrinogen (p = 0.02) and hemoglobin (p = 0.046). Between pulse wave velocity and lipid parameters (p = 0.021 for low-density lipoprotein cholesterol and p = 0.030 for triglycerides) a statistically significant relationship was found for the study group. The augmentation index of the aorta also correlated with serum LDL-cholesterol (p = 0.032) and the hemoglobin levels (p = 0.040) of hypertensive patients. Conclusions: Age, abdominal circumference and Framingham score are independent predictors for SEVR in our study group, further highlighting the need for early therapeutic measures to control risk factors in this category of patients.
Tertiary Education and Economic Growth: How Much Does Physical Capital Matter?

: This paper addresses the impact of tertiary education on economic growth through physical capital accumulation. Results indicate the presence of a threshold of physical capital development, particularly in regions such as Northern Africa, Western Asia, and Latin America and the Caribbean, where tertiary education becomes in an important factor to the economic growth. This impact is not found in the absence of a physical capital analysis, which could suggest that the impact of tertiary education on economic growth could dilute when estimating the effect with traditional regressions.
Pattern Recognition Of One-Dimensional Cellular Automata Using Markov Chains

A technique is presented for the identification of rule that generates a given complex pattern of linear one-dimensional cellular automata (LCA). The technique is based on the construction of a Markov transition matrix for the Markov chains that correspond to the evolution of the automaton. Such chain is generated by the evolution of a sequence of symbols representing the value of a string composed by small portion of the sites of the automaton. Excellent results are obtained for the identification of the rules that generate different complex patterns.
Health technology, quality and safety in a learning health system

Health technology quality and safety is an important issue for health informatics (i.e. digital health) professionals. Health technologies have been used to (1) collect data that can be analyzed to improve the quality and safety of healthcare activities and (2) re-engineer and/or automate error-prone processes. Health technologies are also able to introduce new types of errors (i.e. technology-induced errors) and have been implicated in propagating errors across digital health ecosystems. To develop a learning health system, health technologies need to be considered in terms of how they can improve the quality and safety of health activities traditionally carried out by humans (patients and health professionals) and also how the technology’s quality and safety can be improved. This article outlines how this can be done by integrating evidence from health informatics research into practice using a learning health systems approach.
Two-Stage Beamforming With Arbitrary Planar Arrays of Differential Microphone Array Units

Differential Microphone Arrays (DMAs) are of great interest in the literature on small-sized microphone arrays, due to their good directivity properties and nearly frequency-invariant spatial responses. Recently developed beamforming techniques combine multiple DMA units to form flexible two-stage spatial filtering systems, where the output of each DMA is fed into a higher-level filter, called virtual filter, for further processing. In this manuscript, we analyze and discuss some properties of a broad class of two-stage beamformers with arbitrary planar geometry. In this context, the DMA units are all assumed to have the same directivity pattern of arbitrary order and can be characterized by a variable number of omnidirectional sensors organized in an arbitrary geometry. For any given choice of the virtual array filter, we introduce a closed-form optimization procedure to design DMA filters that maximize the White Noise Gain (WNG) or the Directivity Factor (DF) of the resulting two-stage beamformer at any frequency. Based on this frequency-dependent design, we propose a frequency-invariant design of the two-stage beamformer and we compare the performance of the two approaches. Finally, we propose two possible computational schemes for the proposed generic two-stage spatial filtering system and discuss their efficiency in performing filtering, steering, and changing beampattern.
Therapeutic Use of Video Games in the Treatment of Posttraumatic Stress Disorder (PTSD): A Case Study of an Immersed 10-Year-Old Boy

Working therapeutically with video gamers has almost entirely consisted of an overshadowing addiction perspective. This undoubtedly leads any therapist working with video gamers to assume they are addicted, further guiding treatment into a reduction or complete termination of all video games. To date, there are no known uses of video games in a clinical setting as therapeutic tools. This case study illustrates use of Archetypal and Jungian therapies prescribing video games for a 10-year-old male diagnosed with Posttraumatic Stress Disorder (PTSD). Video games were utilized as a main form of inclusion and immersion playability. He experienced a reported improvement in his symptoms across his environments. Parental involvement, therapist’s knowledge of video game worlds, and using video games’ heroic motifs were critical to the client’s understanding of himself. If an addiction lens had been utilized, the client may not have been successful. Through this comprehensive study, the research will showcase that clinicians should aim to think outside of the addiction lens while working with video gamers. As therapists are presumed to be experts in communication, it makes sense to utilize the language of the video game world to communicate and understand where the client may be presently situated. Utilizing this immersive experience opens the door for a more collaborative therapy, increasing communication about video games, playing, and extrapolating players’ experiences into real life contexts.
Heart Rate Variability (HRV): Nonlinear HRV

The cardiovascular system is considered to be a nonlinear system. Therefore, an effort exists to study its signals, such as heart rate and blood pressure, by applying nonlinear dynamics tools in addition to the commonly used linear tools. This chapter provides a brief introduction to the nonlinear field in conjunction with the main results obtained in studying heart rate variability. The main nonlinear methods discussed here are phase space portrait, surrogate data test for nonlinearity, and the two most commonly used parameters: the correlation dimension and the largest Lyapunov exponents. In order to complete the view on nonlinear analysis of the cardiovascular system, nonlinearity in blood pressure is presented briefly. Keywords: heart rate variability; blood pressure; nonlinear dynamics; surrogate data test; correlation dimension; Lyapunov exponents
A note on the steady high-Reynolds-number flow about a circular cylinder

The most detailed theoretical description of the steady flow at high Reynolds number about a circular cylinder is given by Smith (1979). It appears to give a satisfactory description of much of the flow field. Numerical solutions for this flow computed by Fornberg (1985) are in conflict with some aspects of Smith's model. An acknowledged weak point in Smith's argument is the lack of a detailed flow for the interior of the eddies, particularly with respect to their rear closure. This paper discusses this aspect of the flow with particular emphasis on the vorticity distribution and with the aid of a solution of the Navier–Stokes equations which is valid at the rear of the eddies. As a result an interpretation of Fornberg's results is possible. The self-induced velocity of translation of the vorticity distribution is considered and discussed with reference to the flow as Re → ∞.
WIDE-FIELD NEAR-INFRARED IMAGING OF THE L1551 DARK CLOUD

We present wide-field near-infrared images of the densest part of the L1551 dark cloud taken with the narrow-band filters for [Fe ii] λ 1.644 μm and H2 v = 1–0 S(1) lines, together with the broadband H and Ks filters. Numerous [Fe ii] and H2 emission features were detected from the regions around HL/XZ Tau, HH 30, HH 262, L1551 NE, and L1551 IRS 5. Most of the [Fe ii] features are compact or jet-like, suggesting that the emission arises from fast shocks occurring in the ejecta of jets. The H2 features are more diffuse and widely distributed in outflow lobes, with none of the H2 features showing the well-collimated emission associated with jets. This implies that the H2 emission originates from slower shocks where the ejecta interacts with ambient material. The outflow structure in the vicinity of the deeply embedded object L1551 NE is revealed, featuring a well-collimated, spatially continuous [Fe ii] jet penetrating a fan-shaped infrared reflection nebula. The tangential velocities of knots in the L1551 NE jet are estimated to be 140–190 km s−1 from their proper motions, implying an inclination of 45°–60° for the jet axis. A counter-jet from L1551 IRS 5 is detected for the first time in [Fe ii]; this probably corresponds to the northern-most of the two jets on the blueshifted side. The relative brightness of the counter-jet suggests a visual extinction of 20–30 mag. The [Fe ii] emissions from collimated jets are relatively strong toward L1551 NE and L1551 IRS 5 compared to those toward HL Tau and HH 30. This implies that the jets from the former objects, which are more embedded, have a higher shock velocity and/or a larger gas density than the latter, more revealed objects. The results presented here show that the near-infrared [Fe ii] emission is a useful probe of well-collimated jets from deeply embedded sources, in much the same way that optical [S ii] emission is used for relatively revealed objects.
PEMBELAJARAN KOSAKATA BAHASA JEPANG TINGKAT DASAR DENGAN MENGGUNAKAN MEDIA RIDDLE GOI

Penelitian ini bertujuan untuk mengetahui bagaimana cara pembuatan media Riddle Goi. Penelitian ini menggunakan metode ADDIE. Media ini berisi kosakata bahasa Jepang dan bahasa Indonesia yang terdapat pada buku Kiat Sukses Mudah dan Praktis Mencapai N5. Media ini juga memuat contoh latihan untuk melatih kemampuan kosakata yang sudah dipelajari. Pembuatan media Riddle Goi menggunakan Adobe Flash CS6 sebagai software dalam proses pembuatannya. Hasil dari penelitian ini menghasilkan media pembelajaran kosakata bahasa Jepang yang diberi nama Riddle Goi. Aplikasi ini diharapkan dapat membantu pembelajar dalam menguasai kosakata Bahasa Jepang tingkat dasar.
E‐cadherin‐dependent coordinated epithelial rotation on a two‐dimensional discoidal pattern

In vivo, cells collectively migrate in a variety of developmental and pathological contexts. Coordinated epithelial rotation represents a unique type of collective cell migrations, which has been modeled in vitro under spatially confined conditions. Although it is known that the coordinated rotation depends on intercellular interactions, the contribution of E‐cadherin, a major cell–cell adhesion molecule, has not been directly addressed on two‐dimensional (2D) confined substrates. Here, using well‐controlled fibronectin‐coated surfaces, we tracked and compared the migratory behaviors of MDCK cells expressing or lacking E‐cadherin. We observed that wild‐type MDCK II cells exhibited persistent and coordinated rotations on discoidal patterns, while E‐cadherin knockout cells migrated in a less coordinated manner without large‐scale rotation. Our comparison of the collective dynamics between these two cell types revealed a series of changes in migratory behavior caused by the loss of E‐cadherin, including a decreased global migration speed, less regularity in quantified coordination, and increased average density of topological defects. Taken together, these data demonstrate that spontaneous initiation of collective epithelial rotations depends on E‐cadherin under 2D discoidal confinements.
Android Malware Classification Using LSTM Model

From last two decades, smartphone use is essentially widespread around the world, and Android is the most popular open-source operating system, with the largest market share and active user population of any open-source operating system. This has resulted in malicious actors turning their attention toward the Android operating system to exploit user reliance and vulnerabilities that exist inside the system. Hackers can take advantage of consumers' sensitive data to engage in advertising, extortion, and theft. Most of the existing anti-malware software’s cannot be able to detect all the malwares because of the intelligent malwares. In this paper we use the deep learning based Long short-term memory (LSTM) network for android malware classification. The model is effective in classification of intelligent malwares. The proposed model is implemented using google colab. The model is archiving more than the 98% accuracy in classification of android malwares.
Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

Hyperspectral reflectance (350∼2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
Extensions of Bayesian P-splines models for fitting PK curves

In clinical experiments, the evolution of a product concentration in tissue over time is often under study. Different products and tissues may be considered. For instance, one could analyse the evolution of drug concentration in plasma over time. One could also observe the evolution of radioactivity uptakes in different regions of the brain during a PET scan (Positron Emission Tomography). Such evolutions are named, generically, pharmacokinetic curves (PK curves). Some clinical measures of interest are derived from PK curves. For instance, when analysing the evolution of drug concentration in the plasma, PK parameters such as the area under the curve (AUC), the maximal concentration (Cmax) and the time at which it occurs (tmax) are usually calculated. In a PET study, one could measure receptor occupancy (RO) in some regions of the brain. These clinical measures could be badly estimated if the observed PK curves are noisy. The objective of this paper is to provide some extensions of the Bayesian P-splines model for fitting this type of curves. Two extensions are provided. The first one introduces adaptive penalties in the Bayesian P-splines model. Jullion and Lambert (2007) already proposed an adaptive Bayesian P-splines model. Here, two alternative specifications relying on multivariate normal (Nelsen 1999; Sklar 1959) and Archimedean copulas (Genest and MacKay 1986) are proposed. Simulations show that the best performer to fit PK-like curves is the model of Jullion and Lambert (2007). In the second extension, two solutions are proposed to deal with heterogeneous variance. In clinical studies, the variance is often not constant but varies either with the mean response or with time. The first solution relies on the Yeo-Johnson transformation while the second expresses the variance as a parametric function of the mean response. Simulations have been performed on PK-like curves, to compare these two extensions with the Bayesian P-splines model applied on the original and on the log-transformed data, in three different scenarios. In the first one, the conditional variance increases with the conditional mean. In the second one, the variance is homogeneous while in the third one, it increases with the independent variable, x. It turns out that, in the first scenario, the best performer is the model where the variance is a parametric function of the conditional mean followed by the Bayesian P-splines model applied on the logarithm of the data. The simulation performed from scenario 2 shows that, when the variance of the data is constant, the Bayesian P-splines model applied on the original data is the best performer. Finally, in the third scenario, when the variance increases with x, the best choice is the Yeo-Johnson transformation model. These two extensions can be combined to obtain a satisfactory fit of PK curves.
Minimal triangulations of simplotopes

We derive lower bounds for the size of simplicial covers of simplotopes, which are products of simplices. These also serve as lower bounds for triangulations of such polytopes, including triangulations with interior vertices. We establish that a minimal triangulation of a product of two simplices is given by a vertex triangulation, i.e., one without interior vertices. For products of more than two simplices, we produce bounds for products of segments and triangles. Our analysis yields linear programs that arise from considerations of covering exterior faces and exploiting the product structure of these polytopes. Aside from cubes, these are the first known lower bounds for triangulations of simplotopes with three or more factors. We also construct a minimal triangulation for the product of a triangle and a square, and compare it to our lower bound.
Some Practical Experience with the Time Integration of Dissipative Equations

Different methods for the numerical integration of evolution dissipative partial differential equations are tested with the Kuramoto-Sivashinsky equation. Discretizations in space include Galerkin and nonlinear Galerkin methods. For integration in time three different codes are used, including standard stiff ODE methods. Numerical tests show that standard codes for stiff ODE render a gain of computing time of several orders of magnitude with respect problem tailored methods.