Bulk and lattice thermal expansion in Ce1−xSrxO2−x (0.0≤x≤0.10)

In this communication, we report on the bulk and lattice thermal expansion studies on a number of compounds, within the homogeneity range of solid solutions, in a series with the general composition Ce_1−xSr_xO_2−x (0.0≤x≤0.10). The XRD pattern of each product was refined to determine the solid solubility of SrO into the lattice of CeO₂, and the homogeneity range. The composition with maximum solid solubility limit of SrO in CeO₂ lattice, under the slow cooled conditions, was delineated as Ce_0.91Sr_0.09O_1.91 (i.e. 9 mol.% of SrO). The bulk thermal expansion measurements from ambient to 1123 K, as investigated by a dilatometer, revealed that the _l (293 to 1123 K) values for the compositions within the homogeneity range increase from 11.58×10⁻⁶ to 12.13×10⁻⁶ K⁻¹ on increasing the Sr²⁺ content from 0 mol.% (i.e. CeO₂) to 9 mol.%, i.e. the upper solubility limit of SrO into the lattice of CeO₂. A similar trend was observed in the lattice thermal expansion coefficients _a (293 to 1473 K) as obtained by a high temperature-XRD.     

ADYTIA: Adaptive and Dynamic TCP Interface Architecture for heterogeneous networks

Due to the widespread popularity and usage of Internet of things (IoT)‐enabled devices, there is an exponential increase in the data traffic generated from these IoT devices. Most of these devices communicate with each other using heterogeneous links having constraints such as latency, throughput, and interference from concurrent transmissions. This results in an extra burden on the underlying communication infrastructure to manage the traffic within these constraints between source and destination. However, most of the existing applications use different Transmission Control Protocol (TCP) variants for traffic management between these devices and are dependent on the stage of the sender, irrespective of the application types and link characteristics. Each operating system (OS) has different TCP variant for all applications, irrespective of path characteristics. Hence, a single TCP variant cannot select the best suitable link, which results in degradation in throughput compared to the existing default. Moreover, it cannot use the full capacity of the available link for different applications and network links, especially in heterogeneous network such as IoT. To cope up with these challenges, in this paper, we propose an Adaptive and Dynamic TCP Interface Architecture (ADYTIA). ADYTIA allows the usage of different TCP variants based on application and link characteristics, irrespective of the physical links of the entire path. It allows the usage of different TCP variants based on their design principle across heterogeneous technologies, platforms, and applications. ADYTIA is implemented on NS‐2 and Linux kernel for real testbed experiments. Its ability to select the best suitable TCP variant results in 20% to 80% improvement in throughput compared with the existing default and single TCP variant on Linux and Windows.     

Multi-step Training of a Generalized Linear Classifier

Neural Processing Letters - We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class linear classifier is found through regression. Then...     

Mechanical and thermal behaviour of biodegradable composites based on polycaprolactone with pine cone particle

Polycaprolactone (PCL) was reinforced with natural fibres as they not only permit a substantial reduction of the material costs, but also play a role as reinforcement in mechanical properties. This work was focused on the estimation of mechanical and thermal behaviour based on PCL and Pine Cone particles (PCP) filler at different weight percentages (0, 5, 10, 15, 30 and 45 wt%). Tests results indicated considerable improvement in mechanical properties, corresponding to a gain in impact strength and % elongation of 6 and 9.2% at 15 wt% particle loading, respectively. Some decrease in thermal stability was observed for composites with increasing filler content where as composite at 15% PCP was not significantly affected. Lower melting and crystallization enthalpies and higher crystallinity values were obtained for bio-composites compared with neat PCL. Some decrease in thermal stability and increase in oxygen and water vapour barrier properties were also observed for composites with increasing filler content.     

Mobility-Aware and Radio Characteristics Enabled Routing for Video Streaming over MANETs

Wireless Personal Communications - Providing an adequate level of quality-of-experience (QoE) for multimedia applications in mobile ad-hoc networks (MANETs) is a challenging task due to its...     

Engineering the Interlayer Spacing by Pre-Intercalation for High Performance Supercapacitor MXene Electrodes in Room Temperature Ionic Liquid

MXenes exhibit excellent capacitance at high scan rates in sulfuric acid aqueous electrolytes, but the narrow potential window of aqueous electrolytes limits the energy density. Organic electrolytes and room-temperature ionic liquids (RTILs) can provide higher potential windows, leading to higher energy density. The large cation size of RTIL hinders its intercalation in-between the layers of MXene limiting the specific capacitance in comparison to aqueous electrolytes. In this work, different chain lengths alkylammonium (AA) cations are intercalated into Ti₃C₂T_x, producing variation of MXene interlayer spacings (d-spacing). AA-cation-intercalated Ti₃C₂T_x (AA-Ti₃C₂), exhibits higher specific capacitances, and cycling stabilities than pristine Ti₃C₂T_x in 1 m 1-ethly-3-methylimidazolium bis-(trifluoromethylsulfonyl)-imide (EMIMTFSI) in acetonitrile and neat EMIMTFSI RTIL electrolytes. Pre-intercalated MXene with an interlayer spacing of ≈2.2 nm, can deliver a large specific capacitance of 257 F g⁻¹ (1428 mF cm⁻² and 492 F cm⁻³) in neat EMIMTFSI electrolyte leading to high energy density. Quasi elastic neutron scattering and electrochemical impedance spectroscopy are used to study the dynamics of confined RTIL in pre-intercalated MXene. Molecular dynamics simulations suggest significant differences in the structures of RTIL ions and AA cations inside the Ti₃C₂T_x interlayer, providing insights into the differences in the observed electrochemical behavior.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis

An Artificial Neural Network (ANN) classifier trained by a hybrid GA-BP method for diagnosis of gear faults is presented here that can be incorporated in an online fault diagnostic system of vital gearboxes. The distinctive features obtained from vibration signals of a running gearbox; that was operated in normal and with faults induced conditions were used to feed the GA-BP hybrid classifier. Time domain vibration signals were divided in 40segments. From each segment features such as magnitude of peaks in time domain and spectrum along with statistical features such as central moments and standard deviations were extracted to feed the classifier. Based on the experimental results it was shown that the GA-BP hybrid classifier can successfully identify gear condition. It was also shown that the network trained by GA-BP hybrid method performs much better than ANN that is trained by standard BP or GA individually. Further, it was also shown that if prior to extraction of features; the vibration signals are pre-processed by Discrete Wavelet Transform (DWT) then efficacy of the GA-BP hybrid is significantly enhanced.     

Effect of curcumin and capsaicin on arachidonic acid metabolism and lysosomal enzyme secretion by rat peritoneal macrophages

The inflammatory mediators secreted by macrophages play an important role in autoimmune diseases. Spice components, such as curcumin from turmeric and capsaicin from red pepper, are shown to exhibit antiinflammatory properties. The influence of these spice components on arachidonic acid metabolism and secretion of lysosomal enzymes by macrophages was investigated. Rat peritoneal macrophages preincubated with 10 μM curcumin or capsaicin for 1 h inhibited the incorporation of arachidonic acid into membrane lipids by 82 and 76%: prostaglandin E₂ by 45 and 48%; leukotriene B₄ by 61 and 46%, and leukotriene C₄ by 34 and 48%, respectively, but did not affect the release of arachidonic acid from macrophages stimulated by phorbol myristate acetate. However, the secretion of 6-keto PG F_1α was enhanced by 40 and 29% from macrophages preincubated with 10 μM curcumin or capsaicin, respectively, as compared to those produced by control cells. Curcumin and capsaicin also inhibited the secretion of collagenase, elastase, and hyaluronidase to the maximum extent of 57, 61, 66%, and 46, 69, 67%, respectively. These results demonstrated that curcumin and capsaicin can control the release of inflammatory mediators such as eicosanoids and hydrolytic enzymes secreted by macrophages and thereby may exhibit antiinflammatory properties.