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2022 ◽  
Vol 13 (1) ◽  
pp. 092-101
Author(s):  
Jay N Patel ◽  
Fenil A Parmar ◽  
Vivek N Upasani

Advancement in green chemistry has increased the use of microbial hydrolyases in various industries and chemical processes because of high catalytic efficiency, specificity, cost-effectiveness and eco-friendly nature. Bioconversion of tannins such as tannic acid is achieved by tannin acyl hydrolase, also known as tannase. It converts tannic acid into glucose and gallic acid by catalyzing the hydrolysis of ester and depside linkages in tannic acid. Tyrosinase is monophenol and O-diphenol oxidase a copper containing enzyme catalyzes the oxidation of tyrosine and generates different types of pigment such as melanin. Xylanases hydrolyze xylan into its constituent sugar with the help of several debranching enzymes. Microbial strains isolated from various sources were screened for these hydrolyases: Bhavnagar marine salterns (Bacillus megaterium BVUC_01 and Bacillus licheniformis BVUCh_02); Okhamadhi marine salterns Aspergillus versicolor; Spoiled/infected pomegranate (Xenoacremonium falcatum, two strains PGF1 and PGF4, Bacillus velezensisPGF2 and Candida freyschussiiPGF3. The other laboratory maintained bacterial cultures namely, Bacillus subtilis, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella typhi were also used in this study. Asp. versicolor and Xen. falcatum (PGF1) produced all the three enzymes (tannase, tyrosinase and xylanase). B. licheniformis, B. megaterium, B. subtilis, B. velezensis produced tyrosinase and xylanase. Xen. falcatum (PGF4) and PGF2 produced tannase and xylanase. PGF3 produced tannase and tyrosinase. While, Bacillus megaterium and Salmonella typhi showed only tyrosinase activity. Candida freyschussii showed tannase activity. Staphylococcus aureus did not produce any of these enzymes.


2022 ◽  
Vol 16 (4) ◽  
pp. 1-55
Author(s):  
Manish Gupta ◽  
Puneet Agrawal

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs) networks, and Transformer [ 121 ] based models like Bidirectional Encoder Representations from Transformers (BERT) [ 24 ], Generative Pre-training Transformer (GPT-2) [ 95 ], Multi-task Deep Neural Network (MT-DNN) [ 74 ], Extra-Long Network (XLNet) [ 135 ], Text-to-text transfer transformer (T5) [ 96 ], T-NLG [ 99 ], and GShard [ 64 ]. But these models are humongous in size. On the other hand, real-world applications demand small model size, low response times, and low computational power wattage. In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation (KD), Parameter Sharing, Tensor Decomposition, and Sub-quadratic Transformer-based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this survey organizes the plethora of work done by the “deep learning for NLP” community in the past few years and presents it as a coherent story.


Author(s):  
Prince Nathan S

Abstract: Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS


2022 ◽  
Vol 89 ◽  
pp. 104930
Author(s):  
Marek Aljewicz ◽  
Beata Nalepa ◽  
Sławomir Ciesielski

Author(s):  
Ms. Vaishnavi Nandurkar

Abstract: To study on different type of irrigation system suitable for south region of Maharashtra. We are attempting to find an irrigation system which would require less water and will be economical with higher yield of the crops for which it is installed. Irrigation is the artificial application of water to the soil through various systems of tubes, pumps, and sprays. Irrigation is usually used in areas where rainfall is irregular or dry times or drought is expected. There are many types of irrigation systems, in which water is supplied to the entire field uniformly Study of various types of irrigation method's such as surface irrigation, subsurface irrigation, drip irrigation and smart irrigation. We discussed about the different types of irrigation systems, there are several types of irrigation systems such as surface irrigation, sub-surface irrigation, drip irrigation, IOT, smart irrigation, sensor based irrigation in combination of traditional and modern type of irrigation. From above study we came to know the difference between automated irrigation system and manual irrigation system. We will know that automated irrigation system gives higher yield of crops using less amount of water as compared to manual irrigation system in accordance to automated and manual. Our study is to compare our system with other irrigation systems in terms of economy and optimum water usage to provide maximum results. Keywords: Surface irrigation, Drip irrigation, Manual Irrigation system, automated irrigation system


2022 ◽  
Author(s):  
Amy Cha

This report presents national estimates of different types of health insurance coverage and lack of coverage (uninsured). Estimates are presented by selected sociodemographic characteristics, including age, sex, race and Hispanic origin, family income, education level, employment status, and marital status.


2022 ◽  
Vol 62 ◽  
pp. 101056
Author(s):  
Molgora Sara ◽  
Corbetta Daniela ◽  
Di Tella Sonia ◽  
Raynaud Savina ◽  
Maria Caterina Silveri

Author(s):  
Jose Alfredo Palacio-Fernádez ◽  
Edwin García Quintero

<span>This article determines the internal parameters of a battery analyzed from its circuit equivalent, reviewing important information that can help to identify the battery’s state of charge (SOC) and its state of health (SOH). Although models that allow the dynamics of different types of batteries to be identified have been developed, few have defined the lead-acid battery model from the analysis of a filtered signal by applying a Kalman filter, particularly taking into account the measurement of noise not just at signal output but also at its input (this is a novelty raised from the experimental). This study proposes a model for lead-acid batteries using tools such as MATLAB<sup>®</sup> and Simulink<sup>®</sup>. First, a method of filtering the input and output signal is presented, and then a method for identifying parameters from 29 charge states is used for a lead-acid battery. Different SOCs are related to different values of open circuit voltage (OCV). Ultimately, improvements in model estimation are shown using a filter that considers system and sensor noise since the modeled and filtered signal is closer to the original signal than the unfiltered modeled signal.</span>


Author(s):  
Ayad Assad Ibrahim ◽  
Ikhlas Mahmoud Farhan ◽  
Mohammed Ehasn Safi

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.


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