Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Insider Trading Impact Innovation? Evidence from Insider Trading Restrictions and Enforcement

In accordance with insider trading law, U. S. Securities and Exchange Commission enforcement actions are intended to limit insider benefits at the expense of outsiders. Enforcement can also act as restrictions to the incentives of firm and employee decisions, which relate to firm value. As a result, we explore innovation and corporate outcomes following insider trading restrictions imposed by both regulators and firms. Using a manually assembled database of insider trading indictments against corporate insiders, we observe that innovative activities positively respond to changes in insider trading restrictions. Further, using firm insider trading patterns to observe the role of firm blackout periods and internal corporate governance restrictions offers similar observations, in addition to links with investment, capital access and performance. Finally, we use rule changes affecting enforcement and indictments for identification and inferences. Overall, these results suggest insider trading restrictions and enforcement actions have important consequences for managerial decision-making.
FACTORS INFLUENCING ENTREPRENEURIAL ORIENTATION OF THE STUDENTS: A REVIEW

: University education is an essential pillar for developing the country's business environment. Entrepreneurial education affects entrepreneurial proclivity, innovativeness, and risk-taking capability, which is essential for developing entrepreneurship. The present study reviews the existing literature to determine the factors influencing the Entrepreneurial Orientation of students. The researcher searched the Web of Science database to find papers published on the EO of the students. The search was done using two keywords i.e. ‘Entrepreneurial Orientation’ + ‘Students’. Overall, 246 papers with these keywords were extracted. Out of 246 papers, the top 50 papers, based on their citation, were analyzed to explore the relevant study in the field of EO in the context of students. Entrepreneurship Education in universities is a profound reform of the higher education paradigm. It is necessary for universities to break through the traditional educational model. It is an essential demand of the time that universities cultivate students’ entrepreneurial consciousness, entrepreneurship, and entrepreneurial ability to explore and rapidly development of entrepreneurship in the country.
Ca 2 1 -induced Phosphoethanolamine Transfer to the Outer 3-Deoxy- D - manno -octulosonic Acid Moiety of Escherichia coli Lipopolysaccharide A NOVEL MEMBRANE ENZYME DEPENDENT UPON PHOSPHATIDYLETHANOLAMINE*

Certain strains of Escherichia coli and Salmonella contain lipopolysaccharide (LPS) modified with a phosphoethanolamine (pEtN) group at position 7 of the outer 3-deoxy- D - manno -octulosonic acid (Kdo) residue. Using the heptose-deficient E. coli mutant WBB06 (Brabetz, W., Muller-Loennies, S., Holst, O., and Brade, H. (1997) Eur. J. Biochem. 247, 716–724), we now demonstrate that the critical parameter determining the presence or absence of pEtN is the concentration of CaCl 2 in the medium. As judged by mass spectrometry, half the LPS in WBB06, grown on nutrient broth with 5 m M CaCl 2 , is derivatized with a pEtN group, whereas LPS from WBB06 grown without supplemental CaCl 2 is not. Membranes from E. coli WBB06 or wild-type W3110 grown on 5–50 m M CaCl 2 contain a novel pEtN transferase that uses the precursor Kdo 2 -[4 * - 32 P]lipid IV A as an acceptor. Transferase is not present in membranes of E. coli grown with 5 m M MgCl 2 , BaCl 2 , or ZnCl 2
Point Estimation and Risk Preferences

Abstract The decision-theoretic approach to point estimation involves the choice of an estimate to minimize the expected loss associated with the estimate. The purpose of this paper is to indicate the influence of risk aversion on point estimates for classes of payoff functions including the piecewise linear and quadratic payoff functions. Increased risk aversion results in a point estimate closer to zero for a quadratic payoff function and a lower estimate with a piecewise linear payoff function, for example.
A Method for Leakage Current and Power Reduction of Buffer in 65-nm CMOS Technology Based on the Pileup-Effect

In small-size Complementary Metal Oxide Semiconductor (CMOS) technology, the size of Very Large-Scale Integration (VLSI) below 90nm becomes higher and higher due to the enhancement of the short channel effect of transistors. CMOS Buffer is a very common circuit unit in VLSI. In this paper, a Pileup effect transistor (PET) is proposed to reduce the subthreshold leakage current of the CMOS buffer. The main principle of PET technology is to reduce the voltage difference between gate and source and the voltage difference between drain and source by lowering the voltage of source so as to reduce the subthreshold leakage current. Through simulation-based on Cadence and Taiwan Semiconductor Manufacturing Company (TSMC) N65 library, PET technology can reduce leakage current power consumption by about one-twelfth of the original while maintaining the function of the original CMOS buffer. PET technology has also been found to reduce current consumption in the transition state by about 40%.
An adaptive noise removal method for EEG signals

EEG signals are the response to the electrophysiological activity of brain nerve cells in the cerebral cortex, but the collected EEG signal generally contains noise. In order to effectively remove the noise and retain useful information, after research and analysis, this paper proposes an improved EEG noise-removing algorithm based on the threshold method where wavelet contraction is used. The improved adaptive threshold selection algorithm makes it possible that the threshold changes with the number of layers of decomposed signal, so it can be applied flexibly in practice. The wavelet transform is used to decompose the EEG signal into multiple layers of high- and low-frequency coefficients. Adaptive threshold processing is then applied to the wavelet coefficients in accordance with the various decomposition levels, and the scaled wavelet coefficients are then reconstituted to produce the denoised EEG signal. Using the Root Mean Square Error (RMSE) and Signal Noise Ratio (SNR) as quantitative measures of the denoising effect Through trials, the improved threshold method, hard and soft threshold method, and adaptive threshold method were contrasted. The experimental findings demonstrate that the wavelet shrinkage-based improved threshold approach has a better denoising effect than the other three threshold methods.
Mathematical foundations of equilibrium classical statistical mechanics of charged particles

CONTENTS Introduction Chapter I. Statistical description of classical systems of charged particles ? 1. Mathematical features of the description of Coulomb systems in the thermodynamic limit ? 2. The problem of stability of systems with electrostatic interactions ? 3. Classical equilibrium statistical mechanics as a model of Euclidean quantum field theory ? 4. The limit . The Mayer expansions in the method of Brydges and Federbush Chapter II. Low-temperature and high-temperature expansions ? 5. The Peierls expansion and transition to the screened potential ? 6. Construction of cluster expansions ? 7. Convergence of cluster expansions Chapter III. Theory of Debye screening ? 8. Rigorous justification of the Debye-H? ckel theory ? 9. Debye screening in ion-dipole systems ? 10. Influence of dielectric-type boundary conditions on the character of the screened interaction References
Space Manipulator Assembly Operation Technique based on Deep Residual Reinforcement Learning

In recent years, there are more and more space complex operational tasks such as the maintenance and assembly of on-orbit aircraft. Traditional robot planning and control methods require precise dynamic models, which are difficult to accommodate to on-orbit assembly operations in extreme space environments. Typical space operation tasks, such as plug and pull operation, whose control strategy can be artificially designed. Being artificially designed by combining the output control strategy and deep reinforcement learning algorithm, which can simplify the training difficulty of deep reinforcement learning, making the learning process more efficient and training results better. In this paper, a deep Residual reinforcement learning algorithm combined with a heuristic control strategy is constructed to complete the space mechanical arm assembly operation training in a highly realistic simulation environment. Based on the experimental data, the Residual deep reinforcement-learning algorithm designed in this paper shows the performance of rapid convergence and can complete the on-orbit assembly operation task with a high probability.
GSM-R wireless field strength coverage prediction algorithm based on PSO-RBF neural network algorithm

In the wake of the successive construction of new railway lines, new lines and existing lines are adjacent, approached and surpassed. In the early stage of line construction, if the influence of adjacent lines is not considered in subsequent planning, the original lines need to be adjusted, and the reconstruction of the base station (BS) coverage along the railway increases the hardness of buliding and the invested funds. Therefore, a Global System for Mobile Communications – Railway (GSM-R) wireless field strength coverage prediction model based on particle swarm optimization (PSO) algorithm optimized radial basis function neural network (RBFNN) was proposed. Aiming at the problem of slow network convergence caused by improper selection of network parameters and structure of RBFNN, PSO algorithm is used to optimize the parameters of RBFNN, and combined with the actual measurement data on site, a PSO-RBFNN model is established to simulate and predict the field strength coverage. The results show that the prediction effect of PSO-RBFNN is the best, followed by RBFNN, and the worst of HATA model, which is very beneficial to the future railway GSM-R wireless network coverage and provides a feasible idea.
A novel Ultra-wideband Wide-angle scanning Dual-polarized antenna with multilayer dielectric

In this paper, a dual-polarized planar ultra-wideband modular antenna (PUMA) with wide-scanning angle matching layers is proposed. The multiple dielectric layers and frequency selective surfaces (FSS) are loaded on the dipoles. Each frequency selective surface is etched on the dielectric plate as one wide-angle -matching layer. By applying three wide-angle-matching layers, the wide-scanning angle performance is guaranteed. As for the ultra-wideband performance, a metal cylinder is added to the lower space between adjacent dipoles, and the upper end of the cylinder has a certain distance from the dipole. This forms a “ridge” structure which shift problematic common-mode resonances caused by circulating current out of band. A dual-polarized 256-port array (16×16) is set and simulated. The simulation result indicates that the proposed antenna operating over 4.79GHz -20.15GHz (4.20:1) achieves VSWR<3 while scanning to 60° in E-plane and D-plane, and to 45° in H-plane respectively.
First infrared VLTI fringes on Galactic Center sources

We present the frst VLTI observations of targets in the Galactic Center region. The encouraging results are establishing the scientifb capabilities of infrared interferometry in the outstanding but - for interferometric means - distant and faint dust obscured Central Parsec. We successfully combined the light of two 8m-class telescopes in K and N band with ESO's AMBER, and MIDI beam combiners. The enigmatic dust-forming and spectrally featureless source IRS 3 could be clearly resolved into two components with different physical properties. We estimated lower limits of the 10 μm sizes of the most prominent Northern Arm bow-shocks IRS 1W, 10W, and 8. Investigations on the suitability of IRS 7 as a phase reference for long baseline interferometry at NIR, wavelengths are underway.
A Rigid and Flexible Combined Finger Exoskeleton Design with Variable Curvature

Some hand exoskeletons have been used in the rehabilitation treatment of paralyzed patients caused by stroke and cerebral apoplexy as a result of research into hand exoskeletons. However, how to make the patient more comfortable to use and how to make the exoskeleton more precise are still key issues that need to be studied. Aiming at the motion law and skeletal characteristics of the human hand, this paper proposes a soft exoskeleton machine for hand rehabilitation training that combines rigidity and flexibility. Using a simplified two-layer sliding spring mechanism, the device combines the comfort and fit of a flexible device with the accurate kinematics of a rigid device. The mechanism requires only one degree of freedom to drive the normal flexion and expansion of the three finger joints. By changing the section thickness of the spring sheet to change its section moment of inertia, the bending effect of the mechanism is optimized to better fit the bending characteristics of the finger joints. To simplify the simulation of the mechanism, the method of the pseudo-rigid-body model is used to target the spring leaf’s nonlinear deformation, to achieve intuitively understandable kinematics. Finally, the accuracy of the design is demonstrated through simulation and prototype models.
Skin Disease Classification Using Mobilenet-RseSK Network

The traditional deep learning method has a large amount of calculation, long time training and complex network structure, which is not easy to be applied to embedded or mobile devices. To solve the these problems, we proposed an improved lightweight mobile network named as Mobilenet-RseSK for skin disease classification. Firstly, a new attention mechanism seSK module is proposed, and seSK module is used to replace the position of SE in the original network. This module can better perform feature extraction and improve network performance than the original network attention module. Secondly, using RBN normalization, RBN maintains the advantages of BN, and strengthens the representation of specific features, which strengthens the degree of accuracy of skin disease identification. We compare the Mobilenet-RseSK network with MobilenetV3, Ghost and other advanced networks on the HAM10000 dataset. The proposed network promotes the accuracy of skin disease classification by 1.7% compared with the original network. Compared with advanced mobilenet network, our method achieves 85% accuracy on the test set. This network has certain practical value in skin disease classification, and is an effective lightweight skin disease classification algorithm.
Design of 1T2M integration of storage and calculation based on threshold memristor

Memristor is a new type of electronic component with small size and non-volatile, and it is the most promising new electronic component to replace MOS tube. The computing and storage parts of the computing electronic machines we use today are separate. With the development of technology and new materials, the indicators of CPU and memory have undergone qualitative changes, but the data transmission of CPU and memory still needs to rely on the bus, which greatly reduces the performance of computing and storage. This paper studies the integration of storage and computing. The ultimate purpose of this experiment is to integrate computing and storage into one chip. This paper proposes a 1T2M memory-computing integrated circuit design based on threshold-type memristors. This circuit is a new circuit structure. This circuit consists of an NMOS transistor and two threshold-type memristors. This structure can realize “OR logic operation” and “storage”. The circuit structure is simple, the performance is better, the number of transistors is less, the integration level is higher, and the reading and writing speed is faster. The simulation verification is carried out by LTSpice.
The Principles, Algorithms and State-of-Art Applications of Quantum Computing

Contemporarily, quantum computing has become a popular topic, which is a new approach that is different from classic computing. To be specific, it can deal with information by using mathematical modeling that used to represent quantum mechanics, including superposition, interference, and entanglement. In quantum Computing, information is storage in ‘qubit’, whereas classic computer use bit to store information. This article aims to introduce the principles and some algorithms of quantum computing, and then discuss some up-to-date important development. There are two important algorithms are mentioned in this article, Grover’s algorithms and Shor’s algorithms. According to the analysis, Grover’s algorithms is potentially faster than classic algorithms. However, it requires millions of qubits to make it. Besides, Shor’s algorithms have challenged classic encryption (RSA encryption). Two quantum computer Google Sycamore and Jiuzhang are also mentioned here. The interesting thing is, for some specific question, Jiuzhang performed better than classic computers. Finally, it is found out that the quantum computing is generally slower that classic computer now, due to the restriction of qubit numbers. However, quantum computing still has an advantage in some specific question. These results shed light on guiding further exploration of quantum computer designing.
Decoding of the turbo code created on the basis of the block code using the syndrome grid

Turbo codes came closest to the Shannon limit. They can be built on the basis of both block and convolutional codes. When decoding a block code using a syndrome lattice, it is possible to use well-studied algorithms for decoding convolutional codes with soft input and soft output. This publication demonstrates the application of the maximum a posteriori information decoding algorithm. The process of decoding a code sequence of a turbo code generated by an encoder consisting of two encoders of a block code and an interleaving device, in the presence of three errors in the received code word, is considered in detail.
Superamphiphobic coatings based on graphene oxide with abrasion resistant property by a simple method

Due to the excellent physiochemical properties of graphene oxide (GO), the fabrication of superamphiphobic coatings based on GO can explore the application fields of GO. Herein, superamphiphobic GO coatings are fabricated by simple dip coating GO and organosilane polymer (GO-FAS) dispersion on the cotton fabric, with GO acting as solid lubricant originating from its low friction coefficient. Through compound with PS/PTFE and PVDF-HFP/FAS, the PVDF-HFP/FAS/GO-FAS coating exhibit enhanced anti-abrasion property and can withstand 200 cycles of sandpaper abrasion. This study provides an opportunity for the applications of graphene oxide as superamphiphobic materials.
Research on Heat Dissipation Scheme of Active Magnetic Bearing Based on ANSYS

Compared to mechanical bearings, active magnetic bearings eliminate mechanical friction, resulting in lower energy losses. However, due to the magnetization characteristics and high-frequency working characteristics of the core material of the active magnetic bearing, there will be high wind friction loss, eddy current loss, hysteresis loss and copper loss in the active magnetic bearing. These losses will generate heat and increase the internal temperature of the bearing, which will not only affect the control accuracy of the active magnetic bearing and the stability of the rotor suspension, but may also cause irreversible damage to the components inside the active magnetic bearing. In this paper, different heat dissipation schemes are established to discuss the influence of the active magnetic suspension bearing on the loss and heat generation of the active magnetic bearing.
An effective algorithm for offshore air temperature prediction with LSTM neural network and wavelet decomposition and reconstruction

Offshore air temperature is an important parameter in marine scientific research. The change of offshore air as an indicator of the marine ecological environment is not only related to the growth of offshore organisms but also affects the development of the marine economy. Effective prediction of offshore air temperature is significant. The prediction model of offshore air temperature data is established by using wavelet decomposition and reconstruction algorithm combined with long short-term memory neural network (LSTM). We use the wavelet decomposition to decompose the offshore air temperature data of the ocean station into the overview signal and the detail signal, and the decomposed signal is reconstructed by a single branch to obtain the reconstructed signal. Then, input the reconstructed signals into LSTM model to predict the future offshore temperature. Finally, through the experiments, the proposed model is verified that has more advantages than the prediction effect of the LSTM offshore air temperature prediction model based on seasonal-trend decomposition procedure based on seasonal trend loss (STL) decomposition and the traditional LSTM prediction model. The proposed model has better prediction accuracy for offshore air temperature, and the prediction model can achieve effective prediction of the offshore air temperature.
ADS1299-Based Array Surface Electromyography Signal Acquisition System

A multi-channel sEMG signal acquisition system based on the analog front-end chip ADS1299 is designed. The whole acquisition system consists of a 2×9 high-density electrode array, ADS1299 multi-channel high-precision A/D conversion chip; A MCU named STM32F103C8, an upper computer, and PC. We carried out electrode array design, The introduction of the function of the ADS1299 chip, and the circuit design of the analog signal acquisition part. The test results show that the acquisition system designed in this paper can ideally collect the sEMG signal of 8 channels on the back of the hand, which proves the effectiveness of this design in extracting weak EMG signals. Therefore, it has reference significance for designing larger-scale sEMG signal acquisition circuits.
Influence of substitutional metallic impurities on the performances of p-type crystalline silicon solar cells : The case of gold

The influence of a gold bulk contamination on the performances of boron doped p-type crystalline silicon solar cells is investigated for different base doping levels and different kinds of materials, such as float zone Si, Czochralski Si, and multicrystalline Si. Solar cells are made from intentionally contaminated silicon wafers. By monitoring the evolution of the electrically active substitutional gold concentration by means of bulk lifetime and minority carrier diffusion length measurements, this paper highlights the eventual gettering or hydrogenation effects occurring throughout the whole process but also of the danger of such an impurity in materials containing large densities of extended defects generating recombination centers by means of the impurity-defect interaction.
Power transformers fault diagnosis using graph neural networks based on dissolved gas data

The Dissolved Gas Analysis (DGA) is an important way and is now extensively used to detect incipient faults in oil-immersed transformers. Some conventional methods for DGA, such as the Three-Ratio Method, usually misunderstood the gas data and failed to accurately detect multiple fault combinations. Recently, most researchers investigating DGA have utilized machine learning algorithms to improve the accuracy of fault diagnosis. The dissolved gas data are usually treated as tabular data by using machine learning algorithms to achieve better results compared to conventional methods. However, those machine learning methods ignore the internal connection of the dissolved gas data in the time dimension. Therefore, this paper adopted a new DGA method for transformer fault diagnosis based on Graph Neural Network (GNN) and Multivariate Time Series (MTS) model. The results finally showed that the model outperforms the existing machine learning baseline methods and conventional methods on the dataset.