Coping the arsenic toxicity in rice plant with magnesium addendum for alluvial soil of indo-gangetic Bengal, India

Arsenic (As3+) is a toxic metalloid found in the earth’s crust, its elevated concentration is a concern for human health because rice is the staple grain in eastern part of India and the waterlogged rice field environment provides opportunity for more As3+ uptake. Magnesium (Mg2+) is an important plant nutrient. Present work is a search for reducing As3+ toxicity in plants through Mg2+ application. The findings are quite impressive, the root to shoot biomass ratio showed more than 1.5 times increase compared to the control. Total protein content increased 2 folds. Carbohydrate and chlorophyll content increased two to three times compared to control. On the other hand, Malondialdehyde content showed a decline with the application of increased Mg2+ dose. The in-silico study shows a better interaction with As3+ in presence of Mg2+ but interestingly without stress symptoms. These findings from the research indicate that Mg2+ application can be effective in reducing As3+ induced stress in plants

Effect of frying on physicochemical properties of sesame and soybean oil blend

Most common cooking oil, such as soybean oil, can not be used for high-temperature applications, as they are highly susceptible to oxidation. Sesame seed oil rich in natural antioxidants provides high oxidative stability. Therefore, blending sesame oil with soybean oil offer improved oxidative stability. This study aims to determine the effect of frying on the physicochemical properties of sesame and soyabean oil blend. Soybean oil (SO) was blended with sesame seed oil (SSO) in the ratio of A-40:60, B-60:40 and C-50:50 so as to enhance its market acceptability. The changes occurring in soybean and sesame seed oil blend during repeated frying cycles were monitored. The parameters assessed were: Refractive index, specific gravity, viscosity, saponification value, free fatty acid (FFA) , peroxide value, and acid value. Fresh and fried oil blends were also characterised by Fourier Transform Infrared Spectroscopy (FTIR). No significant changes were observed for refractive index and specific gravity values in oil blends. Viscosity of blend B blend was the least, making it desirable for cooking purposes. However, FFA, acid value and peroxide value increased after each frying cycle. The increment of FFA and AV was found low for blend A (10% and 10%,) than blend B (27%,13%) and blend C (13%,13%). The peroxide value of all samples was within the acceptable range. The results of the present study definitely indicated that blending sesame oil with soybean oil could produce an oil blend which is economically feasible and provide desirable physicochemical properties for cooking purposes

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference

The culture of small press publishing in the Pacific Northwest

This report focuses on small press publishing within the context of the Pacific Northwest and has been written in two sections. The first section traces the history of small press publishing in the North American continent, explores its current state of operations—especially within the Pacific Northwest community—lays down the features that set it apart from big press publishing, and highlights the various risks these small press publishers take to continue enriching literary diversity. The second section is a case study of Ronsdale Press as an example of a Pacific Northwest small press publisher. It traces the history of Ronsdale Press, then explores its current work flow and its identifying features, thus establishing it as an essential member of the Pacific Northwest small press publishing community

Acoustic Beamforming : Design and Development of Steered Response Power With Phase Transformation (SRP-PHAT).

Acoustic Sound Source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a soluation for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. This applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and roubst algorithm to localilze acoustic sound sources. However, the algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. This thesis focuses on describe the implementation of the SRP-PHAT algorithm as a function of source type, reverberation levels and ambient noise. The main objective of this thesis is to present different approaches of the SRP-PHAT to verify the algorithm in terms of acoustic enviroment, microphone array configuration, acoustic source position and levels of reverberation and noise

Acoustic Beamforming : Design and Development of Steered Response Power With Phase Transformation (SRP-PHAT).

Acoustic Sound Source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a soluation for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. This applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and roubst algorithm to localilze acoustic sound sources. However, the algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. This thesis focuses on describe the implementation of the SRP-PHAT algorithm as a function of source type, reverberation levels and ambient noise. The main objective of this thesis is to present different approaches of the SRP-PHAT to verify the algorithm in terms of acoustic enviroment, microphone array configuration, acoustic source position and levels of reverberation and noise

On machine learning assisted data-driven bridging of FSDT and HOZT for high-fidelity uncertainty quantification of laminated composite and sandwich plates

First-order shear deformation theory (FSDT) is less accurate compared to higher-order theories like higher-order zigzag theory (HOZT).In case of large-scale simulation-based analyses like uncertainty quantification and optimization using FSDT, such errors propagate and accumulate over multiple realizations, leading to significantly erroneous results. Consideration of higher-order theories results in significantly increased computational expenses, even though these theories are more accurate. The aspect of computational efficiency becomes more critical when thousands of realizations are necessary for the analyses. Here we propose to exploit Gaussian process-based machine learning for creating a computational bridging between FSDT and HOZT, wherein the accuracy of HOZT can be achieved while having the low computational expenses of FSDT. The machine learning augmented FSDT algorithm is referred to here as modified FSDT (mFSDT), based on which extensive deterministic results and Monte Carlo simulation-assisted probabilistic results are presented for the free vibration analysis of shear deformation sensitive structures like laminated composite and sandwich plates considering various configurations. The proposed algorithm of bridging different laminate theories is generic in nature and it can be utilized further in a range of other static and dynamic analyses concerning composite plates and shells for accurate, yet efficient results

Probabilistic characterisation for dynamics and stability of laminated soft core sandwich plates

This paper presents a generic multivariate adaptive regression splines-based approach for dynamics and stability analysis of sandwich plates with random system parameters. The propagation of uncertainty in such structures has significant computational challenges due to inherent structural complexity and high dimensional space of input parameters. The theoretical formulation is developed based on a refined C0 stochastic finite element model and higher-order zigzag theory in conjunction with multivariate adaptive regression splines. A cubical function is considered for the in-plane parameters as a combination of a linear zigzag function with different slopes at each layer over the entire thickness while a quadratic function is assumed for the out-of-plane parameters of the core and constant in the face sheets. Both individual and combined stochastic effect of skew angle, layer-wise thickness, and material properties (both core and laminate) of sandwich plates are considered in this study. The present approach introduces the multivariate adaptive regression splines-based surrogates for sandwich plates to achieve computational efficiency compared to direct Monte Carlo simulation. Statistical analyses are carried out to illustrate the results of the first three stochastic natural frequencies and buckling load