Facilitative responses to species-specific calls in cortical FM-FM neurons of the mustached bat

FM-FM neurons in the auditory cortex of the mustached bat are highly specialized for echolocation, responding facilitatively to the combination of frequency modulated (FM) components of biosonar pulse and its echo. Here we propose they are also specialized for processing bat communication calls. FM-FM neurons respond facilitatively to natural call syllable pairs, and exhibit inter-syllable interval tuning to the natural range of intervals. Our results support a role for 'combination-sensitive' neurons in communication, and suggest that cortical neurons can possess multiple distinct specialized modes of response.     

基于微生物和纳米TiO2的双负载 核壳水凝胶的制备和性能

摘要：设计了能同时利用微生物和光催化剂，分步逐级降解有机污染物的多功能双负载核壳水凝胶，核为聚乙烯醇（PVA）和羟乙基纤维素（HEC）包覆酵母菌（YE），壳为海藻酸钠（SA）和钙离子（Ca2+）负载纳米二氧化钛（TiO2 NPs）。采用SEM观察形貌可知，核壳结构清晰可辩，显微镜照片和荧光标记照片证明了核中YE的存在，XRD分析结果证明了壳中TiO2 NPs的负载，同时FTIR、XPS验证了核壳结构的分子间存在着相互作用力。并且在不同负载量下，研究了核壳水凝胶对亚甲基蓝（MB）染料去除的分步降解效果，壳中TiO2 NPs负载量越高，对MB的降解最高，可达96.65%； YE负载量的增加而MB的降解率先增大后减小，最高可达92.15%。在循环降解4次后，对MB染料的降解率仍有68.97%。本研究为分步多次降解多种有机污染物，提供并拓展了研究思路和新方法。     

Bug Prioritization to Facilitate Bug Report Triage

The large number of new bug reports received in bug repositories of software systems makes their management a challenging task.Handling these reports manually is time consuming,and often results in delaying the resolution of important bugs.To address this issue,a recommender may be developed which automatically prioritizes the new bug reports.In this paper,we propose and evaluate a classification based approach to build such a recommender.We use the Na¨ ve Bayes and Support Vector Machine (SVM) classifiers,and present a comparison to evaluate which classifier performs better in terms of accuracy.Since a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a bug.To evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives (NFN) and Nearest False Positives (NFP),which provide insight into the results produced by precision and recall.Our findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than SVM.The highest accuracy is achieved with SVM when categorical and text features are combined for training.     

Scattering of elastic waves by an elastic sphere

The problem of scattering of plane compressional wave by an elastic sphere embedded in an isotropic elastic medium of different material properties is solved. Approximate formulas are derived for the displacement field, stress tensor, stress intensity factors, far-field amplitudes and the scattering cross-section. It is assumed that the wave length is large compared to the radius of the scatterer. Various elastostatic limits are also presented.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Design of a hybrid magnetomotive force electromechanical valve actuator

We propose a novel axis-symmetric modified hybrid permanent magnet (PM)/electromagnet (EM) magnetomotive force actuator for a variable valve timing camless engine. The design provides a large magnetic force with low energy consump-tion, low coil inductance, PM demagnetization isolation, and improved transient response. Simulation and experimental results confirm forces of about 200 N (in the presence of coil current) at the equilibrium position and 500 N (in the absence of coil current) at the armature seat. We compared our proposed design with a double solenoid valve actuator (DSVA). The finite element method (FEM) designs of the DSVA and our proposed valve actuator were validated by experiments performed on manufactured prototypes.     

One-step synthesis of the 3D flower-like heterostructure MoS2/CuS nanohybrid for electrocatalytic hydrogen evolution

In this work, a facile one-step hydrothermal method was developed to fabricate three types different of nanomaterials: the two-dimension (2D) of MoS₂ nanosheets; 3D spherical CuS nanoparticles; and 3D flower-like heterostructure of MoS₂/CuS nanohybrid, respectively. The as-synthesized MoS₂, CuS and MoS₂/CuS were characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (SEM) and X-ray diffraction (XRD) etc. The morphology of the MoS₂/CuS nanohybrid is different from the MoS₂ nanosheets and CuS nanoparticles. The hydrogen evolution reaction (HER) activity of MoS₂ nanosheets, CuS nanoparticles and MoS₂/CuS nanohybrid, were investigated by the Linear Sweep Voltammetry (LSV) and Tafel slope. The HER activity of MoS₂/CuS nanohybrid is better than those of MoS₂ nanosheets and CuS nanoparticles, which can be attributed to the good electron-transport ability of CuS and the strong reduction ability of hydrogen ions by MoS₂. Thus, MoS₂/CuS nanohybrid exhibited excellent activity for HER with a small onset potential of 0.15 V, a low Tafel slope of 63 mV dec^?1, and relatively good stability. However, the MoS₂ nanosheets and CuS nanoparticles respectively shows a bigger onset potential of 0.25 V and 0.35 V, a higher Tafel slope of 165 and 185 mV dec^?1. This 3D flower-like heterostructure of MoS₂/CuS nanohybrid catalyst exhibits great potential for renewable energy applications.     

Effect of Strong Pump Depletion on Two-beam Coupling in Photorefractive BaTiO3 at 442 nm

Abstract

Photorefractive BaTiO₃ crystal illuminated at 442 nm gives a large initial scattering level, which leads to strong pump depletion into beam fanning. Studies on the time development of beam fanning show a temporal shift of the fanned beam maximum towards the c-axis direction. The effect on the signal beam amplification of the strong pump depletion into beam fanning has been investigated, revealing different temporal development of the amplified signal for various cross-angles. Our observations on beam fanning in the presence and in the absence of a signal reveal that there is appreciable coupling between the fanned beam and the signal.     

Energy Efficiency and Superlative TTT for Equitable RLF and Ping Pong in LTE Networks

Mobile Networks and Applications - Data hungry users engage radio resources over long periods of time thus resulting into higher energy consumption by Base Stations (BSs). Mobile operators’...