Selection of Statistical Software for Solving Big Data Problems for Teaching

The need for analysts with expertise in big data software is becoming more apparent in 4 today’s society. Unfortunately, the demand for these analysts far exceeds the number 5 available. A potential way to combat this shortage is to identify the software sought by 6 employers and to align this with the software taught by universities. This paper will 7 examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and 8 it will outline the cost, training, statistical methods/tests/uses, and specific uses within 9 industry for each of these software. It will further explain implications for universities and 10 students (PDF

Does sustainable financial inclusion and energy efficiency ensure green environment? Evidence from B.R.I.C.S. countries

Continuous rise in a global economy with a 3–4% annual growth rate poses a severe risk to environmental sustainability due to high energy demand. Since the Paris climate accord, countries worldwide have implemented numerous strategies to attain the target of carbon neutrality. With the rising environmental challenges, it is important to consider global financial inclusion (F.I.) policies. This study uses panel data for the B.R.I.C.S. countries to investigate the impact of F.I. and energy efficiency in limiting trade adjusted emissions (T.A.E.) taking technological innovation and trade as control variables. This study uses panel data consisting small sample size and large time period; therefore, keeping in mind the potential econometric problems, this study uses AMG method, which can efficiently deal with endogeneity problems and small sample bias. We find a positive impact of F.I. and energy efficiency on CO2 emissions. Moreover, we find that technological innovation, exports and output amplify CO2 emissions

A Study Of a Wide-Angle Scanning Phased Array Based On a High-Impedance Surface Ground Plane

This paper presents a two-dimensional infinite dipole array system with a mushroom-like high-impedance surface (HIS) ground plane with wide-angle scanning capability in the E-plane. The unit cell of the proposed antenna array consists of a dipole antenna and a four-by-four HIS ground. The simulation results show that the proposed antenna array can achieve a wide scanning angle of up to 65$^{\circ}$ in the E-plane with an excellent impedance match and a small $S11$. Floquet mode analysis is utilized to analyze the active impedance and the reflection coefficient. Good agreement is obtained between the theoretical results and the simulations. Using numerical and theoretical analyses, we reveal the mechanism of such excellent wide scanning properties. For the range of small scanning angles, these excellent properties result mainly from the special reflection phase of the HIS ground, which can cause the mutual coupling between the elements of the real array to be compensated by the mutual coupling effect between the real array and the mirror array. For the range of large scanning angles, since the surface wave (SW) mode could be resonantly excited by a high-order Floquet mode $\textrm{TM}_{-1,0}$ from the array and since the SW mode could be converted into a leaky wave (LW) mode by the scattering of the array, the radiation field from the LW mode is nearly in phase with the direct radiating field from the array. Therefore, with help from the special reflection phase of the HIS and the designed LW mode of the HIS ground, the antenna array with an HIS ground can achieve wide-angle scanning performance

How Can We Clinically Apply Ultrasound-Guided BoNT-A Injection Technology for Muscle Spasticity in Stroke Patients?

In this chapter, the primary focus is towards four topics related to the ultrasound (US)-guided injection: (1) the advantages of various guided injection techniques including US-guided injection, (2) a brief review of recently published studies on the US-guided botulinum toxin type A (BoNT-A) injection in stroke patients, (3) standardized operational procedures for the US-guided injection and (4) a description of the skills necessary to properly locate the probe and limb during the US-guided injection operation. Illustrations will be presented in the chapter to assist the readers in gaining a better understanding of the US-guided BoNT-A injection technique

The Study of Graininess for Tibetan Named Entity Recognition

Tibetan named entity recognition (NER), which is a fundamental part in Tibetan natural language processing, is the important subtask of Information extraction. In this paper, we surveyed the methods, effect and problems of Tibetan NER. And we discussed which kind of tokens that should be taken as the graininess for Tibetan NER task. The paper used two kinds of different graininess in a comparative experiment for Tibetan person names, location names and organization names, based on syllables, or based on words. From the result, we know that the person names based on syllable have better result than that based on words. Location names have small difference while species differ. But the organization names are more suitable based on words

Superconducting Properties of Carbonaceous Chemical Doped MgB2

The discovery of superconductivity in magnesium diboride (MgB2: 39 K, in January 2001) (Nagamatsu et al., 2001) has generated enormous interest and excitement in the superconductivity community and the world in general, but especially among researchers into superconductivity in non-oxide and boron related compounds

Exploring Temporal Preservation Networks for Precise Temporal Action Localization

Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal boundaries of action segments precisely. Most works use segment-level classifiers to select video segments pre-determined by action proposal or dense sliding windows. However, in order to achieve more precise action boundaries, a temporal localization system should make dense predictions at a fine granularity. A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization. However, CDC network loses temporal information partially due to the temporal downsampling operation. In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters. TPC network can fully preserve temporal resolution and downsample the spatial resolution simultaneously, enabling frame-level granularity action localization. TPC network can be trained in an end-to-end manner. Experiment results on public datasets show that TPC network achieves significant improvement on per-frame action prediction and competing results on segment-level temporal action localization