nils-herrmann/clean_scirepeval huggingface dataset:
The vacuum flashover characteristics of dielectrics under ns pulse

The solid dielectrics such as epoxy, high density polyethylene(HDPE), polytetrafluoroethylene (PTFE), nylon and polymethylmethacrylate(PMMA) are often used as vacuum insulator in pulsed power device, in which they were applied under pulse voltage with risetime from several to several tens nanoseconds. Flashover characteristics must be paying more attention because they are the major breakdown models in their applications. A pulse source with output voltage of 50 ns risetime, 320ns pulse width and peak voltage up to 200 kV has been developed in this research. The flashover characteristics including flashover voltage and flashover current of the materials mentioned above under plane electrode were tested under 5E-3Pa vacuum. In order to get the relationship between surface charge deposition and the flashover characteristics, the surface potential distribution before flashover and after once, seventh and thirty-fifth flashovers are presented in this paper.
Lunar Rover Positioning Based on Time of Arrival Measurements of UWB Signals

The application of ultra-wideband (UWB) to lunar rover positioning and navigation shows great foreground for many characteristics of UWB, such as broad transmission wideband, high transmission rate, low power consumption, exceptional multi-path immunity and high localization precision. In the paper, a lunar rover positioning method based on UWB is proposed using exact time-of-arrival (TOA) measurements on the lunar surface. There are two steps in lunar rover localization: the first is to estimate the propagation delays of UWB signals that are sent from a lunar rover. A MUSIC timing estimator is used to estimate the direct-path delay in a DS-UWB system. The second step is a lunar rover positioning algorithm based on obtaining TDOA, AOA and then computing the position. The simulation illustrates that the proposed methods are effective in the lunar rover positioning and navigation.
A multivariate time series approach to forecasting daily attendances at hospital emergency department

Efficient management of patient demands in emergency departments (EDs) has recently received increasing attention by most healthcare administrations. Forecasting ED demands greatly helps ED's managers to make suitable decisions by optimally allocating the available limited resources to efficiently handle patient attendances. Furthermore, it permits pre-emptive action(s) to mitigate and/or prevent overcrowding situations and to enhance the quality of care. In this work, we present a statistical approach based on a vector autoregressive moving average (VARMA) model for a short term forecasting of daily attendances at an ED. The VARMA model has been validated using an experimental data from the paediatric emergency department (PED) at Lille regional hospital centre, France. The results obtained indicate the effectiveness of the proposed approach in forecasting patient demands.
Parallel implementation of ant colony optimization on MPP

An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the ability of search and avoid early convergence. Experimental results show that our algorithm PACO has high convergence speed, high speedup and efficiency.
The Implementation of Fuzzy RBF Neural Network on Indoor Location

The paper presents a fuzzy neural network with radial basis function (RBF) to apply on WLAN indoor location, effectively reducing the cost of indoor location, enhancing real-time indoor location, greatly improving indoor location accuracy. Through the equivalent theorem, the fuzzy inference system and the RBF neural network are combined and form a fuzzy RBF neural network system, which takes advantage of neural network to obtain suitable fuzzy rules and membership function expressions, and then outputs target coordinates by defuzzication. Finally, simulation experiments verify the feasibility and effectiveness of the fuzzy RBF neural network based WLAN indoor location method, with the location accuracy of 2.73m.
A Novel SVC Method Based on K-means

In this paper, a novel support vector clustering algorithm based on k-means (SVC-KM) is presented not only improve the SVC running speed, but also overcome the weaknesses of k-means algorithm. Firstly, SVC algorithm was employed to identify some samples as outliers and some others as intra-cluster points, so that it removed noise and extracted samples near to the cluster core. Then the proposed method used Minimum Spanning Tree Pruning (MSTP) strategy on those intra-cluster points to initialize the number of clusters and clustering centroids which would be the parameters for k-means algorithm. Finally, it run k-means algorithm on subset without outliers to obtain the clustering result and assigned outliers into the nearest cluster. Applying the proposed algorithm to several test datasets, the experiments results compared with initial SVC algorithm and classical k-means strongly validate the efficiency and feasibility of SVC-KM.
Experiential learning in neurophysiology for undergraduate biomedical engineering students

The undergraduate engineering curriculum generally consists of a significant amount of theory and mathematics that are deemed necessary to solve problems in the real world. For biomedical engineering undergraduates this often results in limited hands-on experiences with live tissue samples and biological experimental techniques. In the Biomedical Engineering Program at the University of Rhode Island, this issue is addressed to some extent by implementing an experiential electrophysiology laboratory. The two-semester project course exposes the students to laboratory skills in dissection, instrumentation and physiological measurements. The focus of the projects is: 1) recording of action potentials in cerebral ganglia of the pond snail (Lymnaea stagnalis), 2) measuring of contractile forces and action potentials in odontophore protractor muscles of the American channeled whelk (Busycon canaliculalum) by use of a sucrose gap apparatus. This laboratory has proven to be an effective way to provide undergraduate biomedical engineering students with invaluable experiences in neurophysiology.
The role of AcSDKP on inhibiting myofibroblast differentiation in rats with silicosis

Myofibroblast differentiation, characterized by α-smooth muscle actin (α-SMA) expression, is a key process in organ fibrosis. Our recent studies have shown that AcSDKP has a anti-fibrotic effect in the lung of rats with silicosis. Here we explore the underlying effect of AcSDKP on myofibroblast differentiation during the anti-fibrotic process. SiO2 powders were instilled in trachea of rat to make the silicotic model, and caused 1) increased expressions of TGF-β, 2) escalated serum response factor (SRF) expression, 3) myofibroblast marked by o-SMA being detected in siliconic nodules, and 4) collagen deposition in lung. AcSDKP (both anti fibronic and prevention treatment) can inhibit these changes induced by silica significantly. AcSDKP could inhibit collagen synthesis throuth blocking myofibroblast differentiation in the lung of rats with silicosis, which is a new mechanism of its anti-silicotic effect.
Automated 3D reconstruction of tree-like structures from two orthogonal views

The common use of orthogonal X-ray images for the diagnosis of vascular abnormalities provides a natural motivation for three dimensional reconstruction from two perpendicular views. Reconstruction from two projections is a problem with a non-unique solution. Ambiguity in the 3D reconstruction has previously been resolved by manually matching feature points between the two views. In this study, a priori knowledge of the object's tree-like structure was incorporated into a rule-base in order to help automate the reconstruction process. The procedure for reconstruction of a tree-like structure from two orthogonal images contains three stages: extraction of the skeletal knowledge in each image, determination of the correspondence of tree segments between views by application of a heuristic rule-base, and algorithmic 3D reconstruction from the corresponding segment pairs.<<ETX>>
Double exponential model of ultrasonic signals

The notion of representing discrete-time ultrasonic signals using double exponential model and the method of computing modelpsilas parameters using Prony method are introduced. The rise time tr and full width half maximum tfwhm of double exponential model is discussed and contrasted with the damped exponential model. It is shown that flexibility of double exponent model is superior to damped exponential model. Fitting the measured data and simulation of TOA (Time-Of-Arrived) proved that the double exponent model has better performance than the damped exponential model.
Effect of Postural Changes on Baroreflex Sensitivity: A study on the Eurobavar data set

The effect of changing posture from supine to standing on the heart rate variability (HRV) was analyzed on the EUROBAVAR data set using power spectral analysis. Power spectral analysis has been commonly used to provide indices of autonomic cardiovascular modulation. Although the interpretation of power spectra is in dispute, it is generally agreed that the high-frequency (HF) power components represent the cardiac vagal modulation and the low-frequency (LF) power components are related to sympathetic activity. And then their ratio (LF/HF) is considered as an index of sympathovagal balance. In the supine lying position, relatively fast vagal activity plays a dominant role, whereas the upright standing position results in vagal inhibition and sympathetic predominance. Postural changing from supine to standing position showed the significant decrease in the indexes of vagal influence on HRV; HF power in both absolute and normalized unit decreased (320 to 121 ms2times103/Hz with P<0.005 and 49 to 37 n.u. with P<0.005, respectively) and LF/HF increased (1.4 to 2.65, P<0.005;. The baroreflex sensitivity (BRS) values were estimated by time sequence method, alpha-coefficients and transfer gains in LF and HF bands. There was a clear difference between BRS values in the supine and the standing positions with the averaged supine-to-standing BRS ratios of 2.35plusmn0.4. BRS estimates using HF components indicated higher values than LF analysis. With the evident decrease in normalized HF power and increase in LF/HF, the effect of changing positions to standing from supine lying can be concluded as a clear reduction in vagal influence on the heart rate variability
Hysteresis Compensation Control Algorithm Comparison for the Magnetostrictive Actuators

In the applications of the magnetostrictive actuators (MA), hysteresis of the MA is particularly significant and causes undesired effect in the control system. In order to reduce hysteresis effect and obtain precision position in actual application, a neural network supervisory control was proposed and three different neural networks were used. Hysteresis would be compensated and the precision control of the MA would be obtained. These neural networks were respectively radial basis function neural network (RBFNN), dynamic recurrent neural network (DRNN) and cerebellar model articulation controller (CMAC). Through comparing these neural networks in tracking performance, tracking speed and parameter number, the control performance is better when using DRNN and CMAC, especially CMAC with 4 parameters.
Research of the Psychology and Behavior of Grouping Consumers Facing under Failure Conditions

The service failure is sometimes inevitable because of the high interaction characteristics of service products. Based on the experimental method of scene simulation, this research examines the negative emotion, the recovery expectation, the perception control and the complaining tendency of grouping consumers when facing with service failure, by using the SPSS software. Several conclusions are drawn by the research: (1) After the service failure, different types of grouping consumers react differently to exactly the same incidence due to the differences lying in the size of the groups and the familiarity among the members. (2) The size of the group has a significant influence to the perception control and the negative emotion of grouping consumers; the discontent emotion of consumers affects obviously their recovery expectation; the negative emotion and perception control have a co-effect to the complaining tendency.
Simulation Of Autoregulatory And Baroreflex Effects On Cardiovascular Dynamics

The open-loop gains of systemic autoregulation and carotid baroreflex were incorporated into an analog model that characterizes the cardiovascular dynamics. Computer simulations showed that t he combined model accurately represents the mean aortic pressure vs. cardiac output relationships. The pressure-controlled baroregulation counteracts and slightly dominates the flow-controlled autoregulation. When cardiac output declines, ejection fraction is decreased by baroregulation and increased by autoregulation. The method provides a framework for studying the time-varying properties of hemodynamics and regulatory mechanisms.
Virtual uplink beamforming and power control for space-time coding scheme in DS-CDMA systems with two array antennas

Virtual uplink beamforming and power control technique (V-UBPCT) is an effective means for improving downlink performance in DS-CDMA systems. We present a modified V-UBPCT algorithm for block space-time coding scheme with two array antennas, in which the downlink beamforming weight and transmitted power vectors are generated iteratively when the total transmitted power is minimized while received signal-to-interference-and-noise (SINR) requirements are maintained. Simulations compare the total transmitted power and SINR between conventional orthogonal space-time coding scheme and our method.
Neuron emulation, instrumentation, and communication for a neuroscience instrument

The purpose of the project is to improve on the design and build a neuron emulator and test if it will be sufficient enough for testing a neuroscience instrument currently in development, called the Universal Clamp, as well as develop a graphical user interface that can monitor, control, and save data from the device using Ethernet communication. By improving the neuron emulator and developing the graphical user interface, an initial bench test of the Universal Clamp will be possible before the use of live neurons from animal models.
A Pseudo-Differential Measuring Approach for Implementing Microcontroller-Based Capacitive Touch Sensing in Low-Power Quality Situation

Noise or unstable power supply can influence the sensing results of classic charge-transfer-based capacitive touch sensing methods. If such sensing methods are implemented in devices that share power supply with other heavily changing loads, the sensing methods are inclined to fail because of brownout. This paper proposes an inverse charge-transfer sensing method that can be applied in low-power quality situation. The proposed method includes a reference channel. The sensing capacitors in sensing channels and reference channel can be precharged to the same terminal voltage. Therefore, the inconsistencies in the sensing results induced by low-power quality can be compensated using a reference channel. The experimental result indicates that the proposed method exhibits excellent touch signal-to-noise ratio improvement: from 21.96 to 29.22 dB in the switching noise case and from 18.31 to 31.34 dB in the brownout case, and able to distinguish touched and untouched state under the brownout case.
The reconstruction of gene regulatory network based On Multi-Agent System by fusing multiple data sources

Gene regulatory network (GRN) is a very important biological system during the cell cycle. In this case, the gene regulatory network reconstruction is an important and meaningful work. Based on the network, the future state of a cell can be predicted by the gene expression process. In this paper, we present a new method to reconstruct the network. During this method, we use Multi-Agent System (MAS) to fuse the gene expression data and TF binding data and generate an initial network. Based on the initial network, a final network is learned using Dynamic Bayesian Network (DBN) learning method. In order to verify the performance of our method, we experiment the method using the data of 25 genes and compare the result with the algorithms already raised in the previous papers. The comparing result show that the method based on MAS and DBN has a better performance than others.
Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme

A computer algorithm was developed for automated identification of 2-D vascular networks in X-ray angiograms. This was accomplished by using an adaptive tracking algorithm in a three-stage recursive procedure. First, given a starting position and direction, a segment in the vascular network was identified. Second, by filling it with the surrounding background pixel values, the detected segment was deleted from the angiogram. The detection-deletion scheme was employed to prevent the problem of tracking-path reentry in those areas where vessels overlap. Third, all branch points were detected by use of matched filtering along both edges of the vessel. The detected branch points were used as the starting points in the next recursion. The recursive procedure terminated when no new branch point was found. The algorithm showed a good performance when it was applied to angiograms of coronary and radial arteries. To provide a quantitative evaluation, vascular networks identified by the algorithm were compared to those identified by a human. The algorithm made some false-negative errors, but very few false-positive errors.
Distributed big-data optimization via block communications

We study distributed multi-agent large-scale optimization problems, wherein the cost function is composed of a smooth possibly nonconvex sum-utility plus a DC (Difference-of-Convex) regularizer. We consider the scenario where the dimension of the optimization variables is so large that optimizing and/or transmitting the entire set of variables could cause unaffordable computation and communication overhead. To address this issue, we propose the first distributed algorithm whereby agents optimize and communicate only a portion of their local variables. The scheme hinges on successive convex approximation (SCA) to handle the nonconvexity of the objective function, coupled with a novel block-signal tracking scheme, aiming at locally estimating the average of the agents' gradients. Asymptotic convergence to stationary solutions of the nonconvex problem is established. Numerical results on a sparse regression problem show the effectiveness of the proposed algorithm and the impact of the block size on its practical convergence speed and communication cost.
A distance weighted-based approach for self-organized aggregation in robot swarms

In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.
A robust tracking approach for vision-based forward vehicle detection

Based on the lane marking tracking, this study presents a robust tracking approach for vision-based forward vehicle detection. Following vanishing point of lane which is according to lane detection point and linear regression analysis, five scan lines created as region of interest (ROI) for tracking is adopted to reduce the computational cost in detection process. The proposed algorithm implemented on TI DM648 DSP platform, and get very well results for various road environments.
A feature extraction method based on combined wavelets filter in speech recognition

This paper used wavelet theory in noise-robust feature extraction of speech recognition and introduced two kinds of feature extraction methods based on Gauss wavelet filter and combined wavelets filter. The Gauss wavelet filter and combined wavelets filter with critical frequency bands are obtained by studying human auditory characteristic. Wavelet has flexible characteristic in choosing frequency, the key is making certain the scale parameter. This paper studied the choosing method of scale parameter in designing the two kinds of wavelet filter. The methods used new feature and original feature were simulated. The RBF neural net is used in training and recognition course. The results showed that new feature had higher recognition rate and better robustness than traditional feature.
Notice of RetractionResearch of Innovative Reform Model of Forest Industry Enterprise in Heilongjiang Province

The rich resources of forest provide an advantage of resources for forest industrial enterprise in Heilongjiang Province. However because of special of forest area in Heilongjiang province, the forest industry enterprises are mostly state-owned enterprises and have some problems of inseparable system of government and enterprise, non-flexible system and insufficient funds. Also the scale and collectivized firm can not be achieved. In this paper, we draw on experience of test of forest right system reform carried out in Yichun City of Heilongjiang Province to explore the reform model of the forest industry enterprises in Heilongjiang Province, and suggest related measures to reform model innovation of forest industry enterprise such as policy support from the government, to broaden the financing channels, scientific management and industrial development and so on, so as to further deepen the reform of forest industry enterprises and speed up forest industry enterprise development, to promote the sustainable development of forest and economy in Heilongjiang province comprehensively.
Distributed nonconvex optimization for sparse representation

We consider a non-convex constrained Lagrangian formulation of a fundamental bi-criteria optimization problem for variable selection in statistical learning; the two criteria are a smooth (possibly) non-convex loss function, measuring the fitness of the model to data, and the latter function is a difference-of-convex (DC) regularization, employed to promote some extra structure on the solution, like sparsity. This general class of nonconvex problems arises in many big-data applications, from statistical machine learning to physical sciences and engineering. We develop the first unified distributed algorithmic framework for these problems and establish its asymptotic convergence to d-stationary solutions. Two key features of the method are: i) it can be implemented on arbitrary networks (digraphs) with (possibly) time-varying connectivity; and ii) it does not require the restrictive assumption that the (sub)gradient of the objective function is bounded, which enlarges significantly the class of statistical learning problems that can be solved with convergence guarantees.