Miracle-dz/UniFAR-FTUs huggingface dataset:
STATISTICAL POWER AND PROBABILITIES OF TYPE I ERROR IN TERMS OF SUITABLE NUMBER OF SIMULATIONS IN NONPARAMETRIC TESTS

Recently, simulation techniques have been widely used in social and medical sciences as well as natural and applied sciences. One of the most common fields of application of these simulation techniques is determination of statistical powers and probabilities of type I error of both parametric and nonparametric tests. Simulation number plays a vital role in such deterministic studies. In case of number of simulations not being sufficient for a clear analysis, then study findings may be inconsistent and instable. In contrast, if number of simulations exceeds the suitable number then it means waste of time. In this study, the effects of different numbers of simulations on determination process of statistical powers and probabilities of type I error of nonparametric tests were discussed. Furthermore, optimum numbers of simulations for determining statistical powers and probabilities of type I error were suggested for future researchers. In this context, one of the nonparametric tests which is used for testing data Statistical Power And Probabilities Of Type I Error In Terms Of Suitable Number Of Simulations In Nonparametric Tests 1316 obtained from two samples, namely Wald Wolfowitz runs test was applied and results were generalized for all nonparametric tests. In study, four equal and small sample sizes were used, these were as follows: (5, 5), (10, 10), (15, 15) and (20, 20). Simulation runs were performed for twenty different numbers of simulation. Significance levelα was assumed as 0, 05 for each sample size. Study was performed by taking the prerequisites of normality and homogeneity of variance into account. According to results; if researchers perform 80.000 simulation runs for a sample size of 5, 60.000 for a sample size of 10 and 50.000 for a sample size of 15 and 40.000 for a sample size of 20 they will have the optimum numbers of simulations in practice.
Home Current Issue Table of Contents THE USE OF FUNCTIONAL CONSTRAINTS FOR THE NEUROELECTROMAGNETIC INVERSE PROBLEM: ALTERNATIVES AND CAVEATS.

The use of functional neuroimages as a constraint for the solution of the neuroelectromagnetic inverse problem (NIP) constitutes an appealing alternative to deal with the non uniqueness of the solution. Among the functional techniques it is probably the fMRI the most attractive one due to its high spatial resolution. A limitation to this integration is that the relationships between neuronal activity and BOLD responses are poorly defined. This paper starts discussing some alternatives to integrate functional information as constraints for the inverse solution. Concrete examples of situations where functional images substantially diverge from electrophysiological methods are presented to promote the discussion about the most reasonable alternatives to combine these image modalities. The results of an anatomically constrained inverse solution that employs a sound physical model are compared with the EEG triggered fMRI in an epileptic patient. This example serves to show that the spatial resolution attainable with inverse solutions is comparable in some situations with that of functional images. Finally, some concrete strategies to ameliorate the quality and reliability of linear inverse solutions maps in more general situations are briefly described. The main conclusion of this paper is that integration of functional modalities into the solution of the NIP should be cautiously considered until a more tight coupling between BOLD effects and electrophysiological measurements could be established.
A Lipreading Model Based on Fine-Grained Global Synergy of Lip Movement

Lipreading is a type of speech recognition based on visual information. It is instructive to design a lipreading model according to the lip movement law. Algorithms in the field of computer vision cannot fully satisfy the characteristics of lipreading, and direct use does not necessarily improve the performance of lipreading. In this paper, we propose that lipreading has fine-grained global synergy by comparing other computer vision tasks and analyzing lip muscle motion patterns. To address this feature, we propose a tailored model and name it Fine-Grained Global Synergy Lipreading (FGSLip). Our model aims to make features synergistic to improve lipreading performance. We introduce global features to represent the overall characteristics of the lip, and local features to learn coarse-grained and fine-grained correlations between features. Then, diffusion and fusion methods are used to make the local features and global features synergistic. Based on the above, several different feature extraction structures are constructed to demonstrate the fine-grained global synergy of lipreading. To verify the effectiveness of the proposed model, extensive experiments are conducted on the laboratory record dataset ICSLR and the public dataset CMLR, and the experimental results show that the proposed method can effectively improve the accuracy of lipreading.
A Finite Volume Upwind-Biased Centred Scheme for Hyperbolic Systems of Conservation Laws: Application to Shallow Water Equations

We construct a new first-order central-upwind numerical method for solving systems of hyperbolic equations in conservative form. It applies in multidimensional structured and unstructured meshes. The proposed method is an extension of the UFORCE method developed by Stecca, Siviglia and Toro [25], in which the upwind bias for the modification of the staggered mesh is evaluated taking into account the smallest and largest wave of the entire Riemann fan. The proposed first-order method is shown to be identical to the Godunov upwind method in applications to a 2×2 linear hyperbolic system. The method is then extended to non-linear systems and its performance is assessed by solving the two-dimensional inviscid shallow water equations. Extension to second-order accuracy is carried out using an ADER-WENO approach in the finite volume framework on unstructured meshes. Finally, numerical comparison with current competing numerical methods enables us to identify the salient features of the proposed method. AMS subject classifications: 65M08, 76M12
EV Hosting Capacity and Voltage Unbalance: An Australian Case Study

The widespread adoption of residential electric vehicles (EVs) will result in larger voltage drops due to the extra demand. Since most residential EV chargers are single-phase, they might also contribute to voltage unbalance which, in turn, can make voltage drop issues on certain phases worse. This paper investigates the extent to which voltage unbalance affects the EV hosting capacity of distribution networks using a Monte Carlo-based time-series analysis to capture the uncertainties of EV location, charger size, and charging behavior. Using a realistically modeled Australian MV-LV network with 1,300+ customers, results show that with increasing EV penetrations, the voltage unbalance keeps increasing too, even for 100% EV penetration. Moreover, it is demonstrated that large voltage unbalance significantly limits EV hosting capacity, suggesting the need for considering balancing strategies when possible.
Arcing Characteristics of Electromechanical Contactor under Different Currents

The structure of arc extinguishing chamber of electromechanical contactor has an important influence on the occurrence and the development of arc. In this paper, an experimental platform was built to analyse the critical current effect in the breaking process. Based on the magnetohydrodynamic model, the mathematical arc model for small DC contactor is established. Consider the near polar region between fractures, the physical fields in arc extinguishing chamber under different breaking conditions are analyzed. The simulation model is verified by comparing the simulation result with the experiment result. The research conclusion is helpful to optimize the arc extinguishing chamber structure, and to improve the breaking capacity of the contactors.
Smart IOT Based Pothole Detection and Filling System

In most developing nations, a failure to maintain the current road infrastructure frequently results in the growth of potholes, cracks, etc. One of the difficulties in preventing accidents is dealing with excessive traffic. The foundation of our project is the ability to spot the potholes and fix them in two layers to prevent accidents. Additionally, the device keeps track of potholes and reduces labor expenses. The proposal describes an Internet-of-Things platform using basic ultrasonic sensor, a Raspberry Pi 3 Model B, a camera module for the Raspberry Pi 5MP and a mobile phone with Wi-Fi access. Our strategy is to fix the potholes in two layers after the detection phase is complete. Plastic trash will be used in the first layer so that it can be recycled, and the concrete layer of the road material will fill the pothole in the second layer. This filling allowed the plastic waste to be handled more effectively. The governments and road maintenance authorities have access to the information stored in the system. The suggested solution provides local authorities with real-time, objective data regarding the condition of a city's roadways a specific area so they can take further action.
Hyperspectral Image Acquisition Methods and Processing Techniques Based on Traditional and Deep Learning Methodologies-A Study

Hyperspectral image processing is a very rapidly growing technology these days. It extends the scope of its applications across diversified fields such as Remote sensing, precision agriculture, medicine, Computer Vision applications and many more. Especially Hyperspectral Imagery, benefiting from its very own nature of providing rich spectral information, find many apllications in the field of Remote sensing and Earth observation. With the development of hyperspectral imaging sensor technology, the supply of HSI data has increased greatly, creating a need for proportionate rise in the HIS Processing techniques for better interpretation of data. In this context Deep Learning has become a viable option to handle huge datasets like Hyperspectral Imagery. Thus many enthusiastic researchers are showing interest in the domain of Hyperspectral Image Processing. This paper attempts to provide the basic concepts of HSI acquisition and processing at an abstract level with more focus given to Deep Learning based HSI processing tasks. So that the work will come in handy to the beginners in this field to get the preliminary concepts of HSI at a glance. To the best of our knowledge this work is first of its kind to bring to together in this study the the concepts of HSI acquisition and HSI processing.
R-AQM: Reverse ACK Active Queue Management in Multitenant Data Centers

TCP incast has become a practical problem for high-bandwidth, low-latency transmissions, resulting in throughput degradation of up to 90% and delays of hundreds of milliseconds, severely impacting application performance. However, in virtualized multi-tenant data centers, host-based advancements in the TCP stack are hard to deploy from the operators’ perspective. Operators only provide infrastructure in the form of virtual machines, in which only tenants can directly modify the end-host TCP stack. In this paper, we present R-AQM, a switch-powered reverse ACK active queue management (R-AQM) mechanism for enhancing ACK-clocking effects through assisting legacy TCP. Specifically, R-AQM proactively intercepts ACKs and paces the ACK-clocked in-flight data packets, preventing TCP from suffering incast collapse. We implement and evaluate R-AQM in NS-3 simulation and NetFPGA-based hardware switch. Both simulation and testbed results show that R-AQM greatly improves TCP performance under heavy incast workloads by significantly lowering packet loss rate, reducing retransmission timeouts, and supporting 16 times (i.e., 60 to 1000) more senders. Meanwhile, the forward queuing delays are also reduced by 4.6 times.
Investigating Heuristic and Optimization Energy Management Algorithms to Minimize Residential Electricity Costs

Recently, renewable and distributed resources have been emerging to fulfill the ever-increasing energy demand to promote sustainable development globally. An energy management system (EMS) is used to integrate these resources cost-effectively. The intermittent nature of Solar energy, however, might have an impact on the energy security and stability of power systems. To guarantee the effectiveness, dependability, and quality of electricity provided, an optimal control approach is crucial. In this research, a Heuristic and Optimization-based EMS is developed to deliver energy from available resources. The supply system of the microgrid consists of Solar PV, and Energy Storage Systems (ESS) in addition to the main utility grid to serve the residential loads through smart EMS with optimal cost. Inside the Heuristic EMS logical decisions like - when to utilize the main grid or, cut it down are made based on the accessibility of Solar PV and battery state-of-charge (SoC). While, an optimization objective function is formulated within the EMS, aiming at reducing grid intake, utilizing off-peak hours for charging ESS from the grid, maximizing renewable penetration, and potentially exploiting these on-site generators during on-peak hours. Manipulating some practical datasets two plausible cases have been studied to test and validate the performance of the proposed model. Finally, the simulation outcomes of the two methods are compared with each other. As the electricity price significantly changes, the optimization-based EMS was found effective to manage the battery charge-discharge operation and can minimize the residential electricity bill consequently.
A Study on Dynamic Response Character of High-Speed Railway Joint

As the increasing of the speed, the requirements for the regularity of the high-speed railway track are also getting higher and higher. Rail joints are one of the weak links of the track. Due to the presence of rail joints, high-frequency impact and vibration occur when the train passes. In this paper, the vehicle dynamic response data when the train passed by the welded joint, is analyzed to obtain the response data characteristics. A time-frequency analysis method combined with Ensemble Empirical Mode Decomposition (EEMD) and adaptive short time Fourier transform(E-ADSSTFT) is used for data analysis. The response characteristic at different speed of one joint and the dynamic response data characteristics of vehicles at different heights of welded joints are studied. The data are studied from time domain, frequency domain and time-frequency plane respectively. The research shows that with the increase of the speed the energy around the weld joint are increasing. With the increase of the damage degree of the welded joint, the amplitude of the acceleration vibration of the axle box of the high-speed train is getting larger and larger, and the frequency band of the energy distribution is narrower and narrower, which eventually causes the nonlinear vibration of the vehicle-track coupling system. Long-term coupling vibration, behind the high-speed railway rail defect joint, the rail surface is easy to form rail corrugation.
Glaucoma Detection using HOG and Feed-forward Neural Network

The second most common cause of blindness in the world is glaucoma. It is an eye condition that doesn't show any early symptoms until considerable vision loss has already taken place. To reduce the risk of vision loss, glaucoma diagnosis is therefore crucial. This study provides an assessment of detecting retinopathy, glaucoma, and healthy images using a feature extraction method called Histogram of Oriented Gradients (HOG), and feed-forward neural network used as a classifier to classify the refunds images. The Contrast Limited Adaptive Histogram Equalization (CLAHE) method, morphology-based homomorphic filter, wavelet-based homomorphic filter, andmulti-scale top-hat transformation can all be used to enhance retinal images and increase their quality and dynamic range. Retinal fundus images are enhanced to increase contrast and bring out the retinal vessels. From the HOG we extracted the different mean, variance, Skewness, kurtosis, entropy, and energyfeatures set. Further, the extracted features set are given to the feedforward neural networks to classify the images.
DyLESC: A Dynamic LiDAR Extrinsic Self-Calibration Method for Intelligent Driving Vehicles

In this paper, we propose a novel LiDAR extrinsic parameter self-calibration method, DyLESC. With the driving trajectories obtained from GPS/IMU, a set of point cloud maps can be established by accumulating the LiDAR frames. The method introduces a function to evaluate the blurriness of the point cloud maps, which involves both geometrical features and point-number factors. By minimizing the blurriness of the point cloud maps, LiDAR extrinsic parameters are automatically calibrated. The method limits the frame number in a point cloud map to avoid the accumulated bumpy errors of GPS/IMU. For improving the robustness, the moving objects are removed from the LiDAR frames. We test the method both in a simulation environment and in the real world. The experiment result shows that DyLESC can accurately calibrate the LiDAR extrinsic parameters.
Fake News Detection: A Graph Mining Approach

The proliferation of the internet and social media has dramatically expanded the reach of information, but it has also made it easier for false information to be disseminated. Automating the classification of misinformation and disinformation is a difficult task, as experts must consider multiple factors when determining the accuracy of an article. This study employs a graph mining technique to identify fake news stories. The linguistic features examined differentiate fake content from credible information. The proposed method was tested using a dataset from the real world, and our experiments demonstrate that it performs similarly to existing fake news detection algorithms on this dataset.
Approximation Solution of Volterra Integral Equation Using Adomian Decomposition Method

In this paper, Adomian Decomposition method has been used to find the approximation solution for the linear Volterra integral equation of the second kind. In this method the solution of a functional equation is considered as the sum of an infinite series usually converging to the solution, and the Adomian decomposition method is also used to solve linear integral equation. Finally, numerical example are prepared to illustrate these considerations.
Edge Detection Based on Multi-Structure

Edge detection is an important pre-processing step in image analysis. Conventionally, mathematical morphology edge detection methods use single and symmetrical structure elements. But they are difficult to detect complex edge feature, because they are only sensitive to image edge which has the same direction of structure elements. This paper proposed a novel edge detection algorithm based on multi-structure elements morphol- ogy of eight different directions. We got eight different edge de- tection results by using morphological gradient algorithm respec- tively, and final edge result was got by using synthetic weighted method. The experimental results showed that the proposed algo- rithm was more efficient for edge detection than conventional mathematical morphological edge detection algorithms and dif- ferential edge detection operators.
Generalization of Soliton Solutions

Supersymmetric quantum mechanics techniques are used in generating new sets of kink/soliton bearing Hamiltonians. We consider the Lagrangian density in (1+1) dimensions as L = 1 (∂µϕ)(∂ µ ϕ)−V (ϕ) and work out the soliton solution for some potential models. Further, the generalized soliton solutions are obtained using q-deformed hyperbolic functions.
Fast Calculation of Prices and Sensitivities of European Options Under Variance Gamma

We present new, fast and accurate methods to calculate the prices and sensitivities of European vanilla and digital options under the Variance Gamma model, and the Variance Gamma PDF. For ATM and near-ATM options with short times to maturity, they are usually much faster than methods based on the (inverse) Fourier transform. We show that interpolation near ATM can be very inaccurate, especially for digitals, whereas the results calculated with our method agree with the benchmarks obtained with the most efficient iFT algorithm.
Spatial Distributions of the Axis of Coherent Fine Scale Eddies in Turbulence

Spatial distributions of axis of coherent fine scale eddies in turbulence are discussed by using visualization with some physical insights. The axes are determined from DNS data of decaying homogeneous isotropic turbulence up to Re λ =87.9. Visualizations of the axes in the volume unit of integral length scale ( lE), Taylor microscale ( λ) and Kolmogorov microscale (η) are compared by selecting the visualized width of axes as constant value, square root of Qc * and Qc * , respectively, where Qc * is the second invariant of the velocity gradient tensor. It is shown that the spatial distribution of the fine scale eddies can be characterized by λ and square root of Qc * . The visualization in η unit volume emphasizes that the distribution of the strongest fine scale eddy is more intermittent for high Re λ.
A NEW BASIC ONE-DIMENSIONAL ONE-LAYER MODEL OBTAINS EXCELLENT AGREEMENT WITH THE OBSERVED EARTH TEMPERATURE

The Earth radiation and energy budget is calculated by a manifold of rather complex Global Circulation Models. Their outcome mostly cannot identify integral radiation or energy budget relations. Therefore it is reasonable to look at more basic models to identify the main aspects of the model results. The simplest one of all of those is a one-dimensional one-layer model. However, most of these models — two are discussed here — suffer the drawback that they do not include essential contributions and relations between the atmospheric layer and the Earth. The one-dimensional one-layer model presented here integrates sensible and latent heat, the absorption of solar radiation and the direct emission of the long wave radiation to space in addition to the standard correlations. For the atmospheric layer two different long wave fluxes are included, top of atmosphere to space and bulk emission to Earth. The reflections of long wave radiation are taken into account. It is shown that this basic model is in excellent agreement with the observed integrated global energy budget.
SOME SEQUENCE SPACES DERIVED BY RIESZ MEAN IN A REAL 2-NORMED SPACE

In the present paper, we introduce some new sequence spaces derived by Riesz mean and the notions of almost and strongly almost convergence in a real 2normed space. Some topological properties of these spaces are investigated. Further, new concepts of statistical convergence which will be called weighted almost statistical convergence, almost statistical convergence and , n Rp       statistical convergence in a real 2normed space, are defined. Also, some relations between these concepts are investigated.
The Direct Estimation of Near-Bottom Turbulent Fluxes in the Presence of Energetic Wave Motions

Abstract Velocities produced by energetic waves can contaminate direct covariance estimates of near-bottom turbulent shear stress and turbulent heat flux. A new adaptive filtering technique is introduced to minimize the contribution of wave-induced motions to measured covariances. The technique requires the use of two sensors separated in space and assumes that the spatial coherence scale of the waves is much longer than the spatial coherence scale of the turbulence. The proposed technique is applied to an extensive set of data collected in the bottom boundary layer of the New England shelf. Results from the oceanic test demonstrate that the technique succeeds at removing surface-wave contamination from shear stress and heat flux estimates using pairs of sensors separated in the vertical dimension by a distance of approximately 5 times the height of the lower sensor, even during the close passage of hurricanes. However, the technique fails at removing contamination caused by internal motions that occur oc.