A New Class of Renewable Thermoplastics with Extraordinary Performance from Nanostructured Lignin‐Elastomers

A new class of thermoplastic elastomers has been created by introducing nanoscale‐dispersed lignin (a biomass‐derived phenolic oligomer) into nitrile rubber. Temperature‐induced controlled miscibility between the lignin and the rubber during high shear melt‐phase synthesis allows tuning the material's morphology and performance. The sustainable product has unprecedented yield stress (15–45 MPa), strain hardens at large deformation, and has outstanding recyclability. The multiphase polymers developed from an equal‐mass mixture of a melt‐stable lignin fraction and nitrile rubber with optimal acrylonitrile content, using the method described here, show 5–100 nm lignin lamellae with a high‐modulus rubbery interphase. Molded or printed elastomeric products prepared from the lignin‐nitrile material offer an additional revenue stream to pulping mills and biorefineries.     

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in \(2\times 1\) space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Analytical Modeling of Quantum Mechanical Tunneling in Germanium Nano-MOSFETS

A simple analytical model has been developed to study quantum mechanical effects (QME)in a germanium substrate MOSFET (metal oxide semiconductor field effect transistor),which includes gate oxide tunneling considering the energy quantization effects in the substrate.Some alternate high dielectric constant materials to reduce the tunneling have also been studied.By comparing with the numerically reported results,the results match well with the existing reported work.     

Magneto-thermo-visco-elastic waves in an initially stressed conducting layer

The aim of this paper is to investigate magneto-thermo-viscoelastic surface waves in electrically and thermally conducting layers involving time rates of strain and stress of ordern, the media being under an initial stress in the nature of hydrostatic tension or compression. The theory of magneto-thermo-visco-elastic surface waves in the conducting medium involving strain rate and stress rate ofnth order is derived under initial stress. This theory is then employed to obtain wave velocity equations in specific cases. Results obtained in the above cases reduce to well-known classical results when additional fields are absent.     

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

     

3D study of temperature drop behavior of subsonic rarefied gas flow in microchannel

3D Numerical study of temperature variation for subsonic rarefied gas flow in a microchannel is carried out using an in-house MPI-based parallelized DSMC code. The temperature drop in the microchannel decreases with an increase in the aspect ratio whereas it increases with an increase in the pressure ratio, the cross-aspect ratio (CAR), and the Knudsen number. 3D and 2D simulations results are compared and effect of the CAR and Knudsen number are brought out. Finally, a correlation that predicts the temperature drop is formulated along with a list of conditions that ensures a near isothermal flow.     

Morphology,Mineralogy and Mixing of Individual Atmospheric Particles Over Kanpur (IGP): Relevance of Homogeneous Equivalent Sphere Approximation in Radiative Models

Estimation of the direct radiative forcing (DRF) by atmospheric particles is uncertain to a large extent owing to uncertainties in their morphology (shape and size), mixing states, and chemical composition. A region-specific database of the aforementioned physico-chemical properties (at individual particle level) is necessary to improve numerically-estimated optical and radiative properties. Till date, there is no detailed observation of the above mentioned properties over Kanpur in the Indo-Gangetic Plain (IGP). To fill this gap, an experiment was carried out at Kanpur (IITK; 26.52°N, 80.23°E, 142 m msl), India from April to July, 2011. Particle types broadly classified as (a) Cu-rich particles mixed with carbon and sulphur (b) dust and clays mixed with carbonaceous species (c) Fe-rich particles mixed with carbon and sulfur and (d) calcite (CaCO₃) particles aged with nitrate, were observed. The frequency distributions of aspect ratio (AR; indicator of extent of particle non-sphericity) of total 708 particles from April to June reveal that particles with aspect ratio range >1.2 to ≤1.4 were abundant throughout the experiment except during June when it was found to shift to high AR range, >1.4 to ≤1.6 (followed with another peak of AR i.e. >2 to ≤2.4) due to dust storm conditions enhancing the occurrence of more non-spherical particles over the sampling site. The spherical particles (and close to spherical shape; AR range, 1.0 to ≤1.2) were found to be <20% throughout the experiment with a minimum (11.5%) during June. Consideration of Homogeneous Equivalent Sphere Approximation (HESA) in the optical/radiative model over the study region is found to be irrelevant during the campaign.     

Understanding the relationship between intensity and gratifications of Facebook use among adolescents and young adults

Facebook is a well-known computer-mediated communication platform considered popular among adolescents and young adults. New media scholars have coined the term “intensity of Facebook use” (IFU) for the concept that measures the emotional and affective attitude towards Facebook use among young people. IFU is an important service use concept that has been positively linked with the different psychosocial outcomes of student well-being. However, only a limited amount of the prior literature has investigated the relationship between IFU and different Facebook uses and gratifications (U&G). The existing literature suggests inconsistent findings with a sole emphasis on young adults. To address these gaps, the present study has investigated the differential role of different Facebook U&G among adolescents and young adults in predicting IFU. A total of three cross-sectional data sets (N = 373, 107, 105) represented adolescents and university-attending young-adult Facebook users. The study results suggest that process U&G do and content U&G do not play any significant role in predicting IFU. Adolescents and young adults differ in their sought Facebook U&G. In addition to this, cultural differences were observed in the sought Facebook U&G and their differential role in predicting IFU.     

海尔兹山纪念堂

正海尔兹山纪念堂是一个独特的纪念场所。为了纪念耶路撒冷所有阵亡的士兵,设计中包含了他们名字。这是和平年代期冀希望的象征。尽管临近耶路撒冷繁华路段,纪念堂设计为一个平静的圣经式场地,整个空间孤隐而静谧。建筑主体空间通过挖掘山体而成,形成个人或集体纪念活动的私密空间。纪念堂上方的山体,以耶路撒冷石塑造出新的曲线。纪念堂形成一个波形漏斗状结构,缓缓通向天际。这种不规则的涡旋状结构为纪念堂营造出不断变化的自然光。