nils-herrmann/clean_scirepeval huggingface dataset:
Regression-based learning for management motivation and earnings management methods

The impact of management motivation on earnings management methods is studied based on 9581 samples from listed firms for the period between 2006 and 2011. This paper examines the influence of management motivation on investor sentiment and intends to study how management motivation influences investor sentiment by real earnings management activities and earnings management methods. The empirical results indicate that management use real earnings management to manipulate the earnings in order to turn a profit, avoid loss, refinance, and change executives. The listed companies with higher executive compensations intend to use real earnings management. However, making big profits by using real earnings management activities is not a universal phenomenon because capital market in our country is not perfect enough.
The use of sucrose gap experiments with molluscan cardiac rhythmicity for undergraduate education in biomedical engineering

The ability to make measurements on and interpret data from living systems is important for undergraduate education in biomedical engineering. Traditional engineering laboratory courses are usually conducted In a time frame shorter than what is necessary for preparing living tissues and conducting biological experiments. This study reports the experience with an undergraduate engineering design course that combines neurophysiology and neuroengineering. Specifically, one of the experiments is to obtain simultaneously recordings of the electrical activity and the contractility of a muscle specimen. The entire heart of the Aplysia californica (oceanic mollusk) is used in a sucrose gap preparation. Neurotransmitters such as 5-hydroxytryptamine (5-HT) and acetylcholine (ACh) are used to modulate the cardiac rhythmicity. The sucrose gap preparation prove to be an effective means to provide hand-on experience in neuroromuscular physiology for undergraduate biomedical engineering students.
Design of semi-active air suspension system based on backstepping sliding mode control

Based on the dynamic model of semi — active air suspension with two degree of freedom quarter-car, a controller is designed using backstepping technology and sliding mode control. In the MATLAB / Simulink environment, the effect of backstepping sliding mode controller on car body acceleration, suspension dynamic travel and tire dynamic load is studied. The backstepping controller of the semi-active air suspension system is as the reference model to ensure the effectiveness of the backstepping sliding mode controller. The results show that, compared with backstepping controller, the proposed backstepping sliding mode controller in this paper is more effective in improving vehicle performance.
A symmetry-based forward vehicle detection and collision warning system on Android smartphone

This study proposes a symmetry-based forward vehicle detection and collision warning system (FCW) on smartphone. The proposed system identifies forward vehicle by shadow with vehicular symmetry. Through Bayes classifier tracking approach, it can reduce the error detection of image processing. Shadow detection with symmetry-based approach could improve the robustness of identifying forward vehicle. The detection system is migrated the FCW algorithm to Android, and employs mobile camera embedded Android smartphone to capture image of road environment. The FCW kernel processes captured images to obtain vehicle-marking of forward vehicle.
Impulsive breakdown in water: Optimisation of energy delivery for high acoustic output

The high voltage impulsive breakdown process in water is complex, with the nature of the impulsive breakdown depending upon the magnitude, polarity and rise time of the HV impulses, the water conductivity, and the electrode topology. In the case of μs and sub-μs high voltage impulses of sufficient magnitude, the breakdown develops through the formation of plasma streamers in the water. When the first streamer crosses the entire inter-electrode gap, the energy released in the breakdown channel transforms this channel into a gas/vapor cavity, which pulsates and radiates acoustic impulse(s). Optimisation of the hydrodynamic (period of cavity oscillation) and acoustic (peak magnitude of the acoustic impulse(s)) parameters is required for practical applications of these underwater spark discharges. The present paper analyses the functional behavior of the period of cavity oscillation and the peak magnitude of the acoustic impulse for spark discharges generated by self-triggered underwater discharges (free discharges), spark discharges triggered by air bubbles injected into the inter-electrode gap, and wire-guided discharges. The advantages and limitations of these methods of generation of underwater acoustic impulses by spark discharges are discussed.
The forecasting method for the furnace bottom temperature and carbon content of submerged arc furnace based on improved BP neural network

In this paper, the forecasting method for the furnace bottom temperature and carbon content in smelting process is proposed to aim at the shortcomings of great energy and low productivity from control strategy, through the analysis of submerged arc furnace smelting process. The method provides a theoretical basis for dynamical control of submerged arc furnace smelting process. The forecasting model is established base on improved BP neural network, and 10 major factors affect the furnace bottom temperature and carbon content are selected to be as input variables. Variable step-size learning algorithm is used to achieve the purpose of global optimization and fast convergence. Data information from the regular and consecutive smelting process of the 801 furnace in 8th branch of Sinosteel Jilin Ferroalloy SCNB are selected to be as training samples and test samples, MATLAB simulation software is used to verify the accuracy and usefulness of the model.
Estimation of Signal Arrival Time Based on UWB Pulse

This paper presented an improved frequency-domain MUSIC algorithm for time of arrival (TOA) estimation based on ultra wideband (UWB) pulse. Making use of the property of UWB signal, the estimated accuracy of signal arrival time is improved. For a two-multipath case, the simulation results show that the improved frequency-domain MUSIC algorithm achieves smaller variance of estimation errors than the frequency-domain MUSIC algorithm, and its accuracy is close to the Cramer-Rao lower bound (CRLB)
Route Research on Critical Factors of Information Construction in Textile Machinery Manufacture Business-Taking Businesses of Shaanxi Province for Example

Information, which is typical of the application of information technology, has great power of expansion, penetration and the characteristics of integration and innovation. In order to research the critical factors of information construction in textile machinery manufacture business, this article constructs the concept model of critical factors, proposes the related assumptions and chooses the businesses of Shaanxi province as samples. According to the data selected, the assumption is proved by statistical analysis methods and the route figure is modified.
Optimal determination of wavelet threshold and decomposition level via heuristic learning for noise reduction

For the research field of adaptive de-noisy, the technique which determinates an adequate threshold in wavelet domain of signal is novel and feasible. The most of threshold determination are developed from universal method proposed by Donoho. Unfortunately, these methods are just performed in some wavelet level and involve several incorrectly estimated factors; therefore, they can't result the best performance and can't work well in general cases. By the reason, this paper replaces the universal threshold determination by an intelligent determination, genetic algorithm (GA). Because original signals and noise are mutually independent, an objective function of GA is created to evaluate the second order correlation and high order correlation. Moreover, GA searching is applied in progressively wavelet levels to explore the most suitable wavelet decomposition and the optimal threshold. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with variant noise degrees are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.
Monte Carlo studies of hot electron transport in insulating films with nanocavities

The problem of hot electron transport and energy loss at high electric fields in insulators is of considerable interest in considering dielectric breakdown and hot carrier related degradation. A Monte Carlo (MC) simulation provides the basis for a study of hot electron transport in thin polyethylene (PE) films at high electric fields based on energy loss to phonons computed using parameter-free, first principles computational quantum mechanics. The electron trajectories, probability densities, and spatial evolution of the electron energy distribution are presented. The electrons with energy greater than the bandgap (8.8eV) trigger impact ionization, which can lead to avalanche breakdown, while electrons with energy larger than 3-4eV can cause degradation of the dielectric. The effect of nanometer scale cavities is also investigated.
Defects in the Bridgman grown lithium tetraborate of three-inch diameter

Defects in lithium tetraborate Li/sub 2/B/sub 4/O/sub 7/ (LBO) and their influence on performance of devices are concerned by crystal material researchers and device engineers with production of 3-inch diameter LBO crystal by the Bridgman (BR) technique and application and design of surface acoustic wave (SAW) devices fabricated on LBO substrate. The defects in the BR-grown LBO of 3-inch diameter are observed by chemical etching and microscopy. The main defects in the crystal are sub-grain boundaries and dislocations. Growth striation, scattering particle and twin occur in the crystal occasionally. The configuration of the defects, their origins and preventive methods are also described.<<ETX>>
Orthogonal sparse eigenvectors: A procrustes problem

The problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications, especially those with high dimensional data set. While classical eigenvectors can be obtained as the solution of a maximization problem, existing approaches formulated this problem by adding a penalty term into the objective function that encourages a sparse solution. Nevertheless, the resulting methods achieve sparsity at a sacrifice of the orthogonality property. In this paper, we develop a new method to estimate dominant sparse eigenvectors without trading off their orthogonality. The problem is highly non-convex and too hard to handle. We apply the minorization-maximization (MM) framework where we iteratively maximize a tight lower bound (surrogate function) of the objective function over the Stiefel manifold. The inner maximization problem turns out to be the rectangular Procrustes problem, which has a closed-form solution. Numerical experiments show that the propose method matches or outperforms existing algorithms in terms of recovery probability and explained variance.
Experiential learning in an undergraduate biomeasurement course: Embedded instrumentation

Biomedical engineering requires a broad base of engineering knowledge and skills, which may include electrical, mechanical, and chemical engineering. Due to the variety of subject areas, it is often difficult to cover all desired materials within reasonable credit hours for an undergraduate biomedical engineering curriculum. A course in biomeasurement has been developed to provide the students with electronic and programming skills for biomedical instrumentation in an efficient way. Through hands-on learning experiences on building an embedded measurement device, the course combines subject matters from the areas of circuits, microprocessors, programming, and basic signal processing.
Analysis and Application for Virtual Learning Community

With the rapid development of informational technology, virtual community has played an important role in the progress of informational education. But on what’s virtual learning community, there are many viewpoints. Someone thinks it’s a learning environment; someone regards it as a learning tool and so on. Based on the research existed, this article restudies the virtual learning community from the visual angle of sociollgy, technology, educational psychology, social culture and gives an example lastly so that we can deeply understand it.
Detecting SYN flood attacks via statistical monitoring charts: A comparative study

Accurate detection of cyber-attacks plays a central role in safeguarding computer networks and information systems. This paper addresses the problem of detecting SYN flood attacks, which are the most popular Denial of Service (DoS) attacks. Here, we compare the detection capacity of three commonly monitoring charts namely, a Shewhart chart, a Cumulative Sum (CUSUM) control chart and exponentially weighted moving average (EWMA) chart, in detecting SYN flood attacks. The comparison study is conducted using the publicly available benchmark datasets: the 1999 DARPA Intrusion Detection Evaluation Datasets.
Modified MUSIC Algorithm for DOA Estimation With Nyström Approximation

Multiple signal classification (MUSIC) can be seen as one of the most popular solutions to the direction-of-arrival estimation problem in array signal processing. Traditionally, this method utilizes the whole array observations to construct the noise subspace. In this letter, based on the randomly chosen sensors outputs, we exploit the Nyström method to approximate the noise subspace, and develop a low-complexity modified MUSIC version for far-field sources localization. Computer simulations confirm that the novel algorithm using only one-fourth sensors outputs provides almost the same estimation performance as its traditional version.
A New Triangulation Algorithm Based on the Determination of the Polygon's Diagonals

This paper presents a triangulation algorithm for the general plane polygon, this algorithm does not consider the polygon's concave or convex and its vertices are clockwise or counter clockwise. It first makes the elimination marks for the diagonals outside of the polygon, then determines the diagonals which intersect with the polygon and makes the elimination marks. In order to avoid the long and narrow triangles we use CDT algorithm to eliminate the inapposite diagonals. Lastly, we draw the remaining diagonals. So we finish the process of triangulation. Keywords-component; polygon; diagonal; triangulations; algorithm
The preliminary study on multi-swarm sharing particle swarm optimization: Applied to UAV path planning problem

This paper presents a preliminary study on multi-swarm sharing scenario for particle swarm optimization, MSSPSO, to deal with uncertain-dimension factor space optimization problems. The proposed MSSPSO can provide more wide capability for unknown solution space exploration. In this paper, the MSSPSO is applied to UAV path planning problem. Based on characteristic number of different paths, it has to use different number of control point to produce varied flight paths. In order to explore suitable solution within suitable characteristic number interval, MSSPSO is employed to explore better solution and the variable-length crossover concept is used to share information among different dimension swarms. The simulation is show that MSSPSO has the ability to explore suitable solution and determine suitable characteristic for flight path. On the other hand, swarm crossover helps swarm to avoid falling local optimal position; swarm manager is applied to enhance computing efficiency and prune the helpless swarms.
Emotional and Satisfactory Research of Grouping Consumers in Service Recovery Process

The industrial service failure is sometimes inevitable and the effective way to re-win customers is through a successful service recovery. Compared with the individual service failure, the group service failure is more complex, thus the loss brought to the service enterprises is more serious. Meanwhile, the implementation of a reasonable remediation strategy and the restoration of community satisfaction will become more difficult. In this article, the service recovery model based on the grouping service failure was established. The proposed model conducts four kinds of experimental designs according to the attribution of responsibility in a service failure. By using the scenario simulation method, the 434 valid questionnaires were analyzed by using the SPSS method and the influence of symbolic compensation and leader persuasion to the recovery satisfactory degree is investigated. The experiment verifies that there is a great influence of symbolic compensation and persuasion to the emotion and satisfaction degree of grouping consumers. It is also proved that the positive emotion after recovery is in positive correlation with the group satisfaction and behavior; the negative emotion after recovery is in negative correlation with the group satisfaction and behavior.
Dynamic target classification in wireless sensor networks

Feature extraction and classification are two intertwined components in pattern recognition. Our hypothesis is that for each type of target, there exists an optimal set of features in conjunction with a specific classifier, which can yield the best performance in terms of classification accuracy using least amount of computation, measured by the number of features used. In this paper, our study is in the context of an application in wireless sensor networks (WSNs). Due to the extremely limited resources on each sensor platform, the decision making is prune to fault, making sensor fusion a necessity. We present a concept of dynamic target classification in WSNs. The main idea is to dynamically select the optimal combination of features and classifiers based on the ldquoprobabilityrdquo that the target to be classified might belong to a certain category. We use two data sets to validate our hypothesis and derive the optimal combination sets by minimizing a cost function. We apply the proposed algorithm to a scenario of collaborative target classification among a group of sensors in WSNs. Experimental results show that our approach can significantly reduce the computational time while at the same time, achieve better classification accuracy, compared with traditional classification approaches, making it a viable solution in practice.
Learning gene regulatory networks based on Dempster-Shafer evidence theory

In the post-genomic era, discovering relationships between genes and further constructing gene regulatory networks (GRNs) is an important problem in system biology. In case the GRNs model for a cell is known, we can simulate the gene expression to predict future states of the cell and discover new drugs based on the relationships in the GRNs. In this paper, our aim is to construct a better gene regulatory network. We present a new informative prior over network structure. In the prior, we combine transcription factor (TF) binding data and gene expression data based on Dempster-Shafer (D-S) evidence theory. In addition, a smooth probabilistic model is used in the TF binding data while the Pearson correlation model is used in the gene expression data. We learn the GRNs through dynamic Bayesian network (DBN) inference algorithms. In order to verify the effectiveness of the proposed method, we use the method on the yeast cell cycle gene expression data and also compare the results with those already reported in the literatures. Results obtained from experimental data demonstrate that combing multiple types of data based on D-S evidence theory in modeling GRNs is more accurate than others.
Parameter estimation for biochemical pathways by resolving inconsistent experimental data

In this study, a parameter estimation method has been developed for models of biomedical pathways. The inconsistency among experimental data that may come from different sources is reduced. The resulting parameters define a well-behaved mathematical model in the steady state. Based on experimental data collected separately from individual sub steps of a pathway, this approach achieves overall optimization in two steps: flux optimization and individual parameter optimization. The method is useful for identifying an initial state in modeling the dynamics of a biochemical pathway. The method has been effective as applied to a model of glycolysis.
Out-of-focus blur estimation for blind image deconvolution: Using particle swarm optimization

This study addresses the blind image deconvolution which uses only blurred image and less point spread function (PSF) information to restore the original image. To identify the blind image it is a very important step for restoring the image. Therefore, the first step is to look for PSF model. In this paper, particle swarm optimization (PSO) is utilized to seek the unknown PSF. The objective function is based on the wavelet transform. It can identify the parameters of PSF exactly. Finally, the feasibility and validity of proposed algorithm are demonstrated by several simulations.
Effect of comprehensive harnessing on water and soil conservation in SanChuanhe river basin

The paper take the SanChuanhe river basin as an example, which is located in the coarser sediment-producing area in the middle reaches of Yellow River, use “Hydrological Method” calculated the flood and sediment reduction by water and soil conservation measures in recent ten years. According to the research: during 1997 to 2006, comprehensive harnessing on water and soil conservation reduced 34.77 million m3 of flood, the rate of flood reduction is 62.7%, reduced 1.482 million tons of sediment, the rate of sediment reduction is 83.6%. Compared with the rate in the past, the flood and sediment reduction rate was raise year after year. During 1997 to 2006, the rate of flood reduction increased nearly two times than in the 1970s and 1980s; the rate of sediment reduction is also significantly higher than past years. Water and soil conservation measures have a good effect in SanChuanhe river basin.
Optimal UAV flight path planning using skeletonization and Particle Swarm Optimizer

The purpose of this paper is to search the best flight route efficiently for unmanned aerial vehicle (UAV) in the 3-dimention complicated topography. The proposed method for the best flight route is mainly utilizing evolutionary algorithm, and give the proper initial population of evolutionary algorithm through skeletonization, efficient pre-processing procedure. In order to provide a smooth flight route for UAV, this paper adopts B-spline Curve method. Several control points of B-spline Curve method must be determined to generate flight route. The best control points can be calculated by Particle Swarm Optimizer (PSO). In this paper, the initial population of PSO is provided by skeletonization. The skeletonization of pre-processing procedure mainly includes two parts: one is Skeletonization and the other is candidate path searching. The purpose of pre-processing procedure is to reduce computation time, to prevent search the best solutions aimless, and execute evolutionary process efficiently. This paper uses Matlab as the experiment environment. The results of the experiments present the proposed method can provide the best flight route for UAV efficiently.