Analysis of time delays in scheduled and unscheduled communication used in process automation

This paper introduces a network model for analysing the time delays of scheduled and unscheduled communication services among field devices used in process automation. The proposed model is implemented by configuring multiple control loops of real-time field devices into a network. The consensus of the network is designed using segment checkerTM simulation software. The simulated network of the field devices is re-configured for the proposed network model by mapping virtually. Every device is treated as a node in the network model and the real-time data is accessed. The time delays recorded for both scheduled and unscheduled communication of field-bus topology in simulation environment and the performance is compared with scheduled communication delay. The better bandwidth utilization and assignment of field device is achieved by introducing the unscheduled communication time delays in the network. It helps in the improvement of network capacity by accommodating more devices and reduces the commissioning cost

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.Comment: 19 fig, 2 tabl

Gibbs’ energy of formation of YBa₂Cu₃O_7-x (tetragonal)

The high temperature ceramic oxide superconductor YBa2Cu3O7-x (1–2–3 compound) is generally synthesized in an oxygen-rich environment. Hence any method for determining its thermodynamic stability should operate at a high oxygen partial pressure. A solid-state cell incorporating CaF2 as the electrolyte and functioning under pure oxygen at a pressure of 1·01 × 105 Pa has been employed for the determination of the Gibbs’ energy of formation of the 1–2–3 compound. The configuration of the galvanic cell can be represented by: Pt, O2, YBa2Cu3O7-x , Y2BaCuO5, CuO, BaF2/CaF2/BaF2, BaZrO3, ZrO2, O2, Pt. Using the values of the standard Gibbs’ energy of formation of the compounds BaZrO3 and Y2BaCuO5 from the literature, the Gibbs’ energy of formation of the 1–2–3 compound from the constituent binary oxides has been computed at different temperatures. The value ofx at each temperature is determined by the oxygen partial pressure. At 1023 K for O content of 6·5 the Gibbs’ energy of formation of the 1–2–3 compound is −261·7 kJ mol−1

Electrolysed reduced water decreases reactive oxygen species-induced oxidative damage to skeletal muscle and improves performance in broiler chickens exposed to medium-term chronic heat stress

1. The present study was designed to achieve a reduction of reactive oxygen species (ROS)-induced oxidative damage to skeletal muscle and to improve the performance of broiler chickens exposed to chronic heat stress. 2. Chickens were given a control diet with normal drinking water, or diets supplemented with cashew nut shell liquid (CNSL) or grape seed extract (GSE), or a control diet with electrolysed reduced water (ERW) for 19 d after hatch. Thereafter, chickens were exposed to a temperature of either 34°C continuously for a period of 5 d, or maintained at 24°C, on the same diets. 3. The control broilers exposed to 34°C showed decreased weight gain and feed consumption and slightly increased ROS production and malondialdehyde (MDA) concentrations in skeletal muscle. The chickens exposed to 34°C and supplemented with ERW showed significantly improved growth performance and lower ROS production and MDA contents in tissues than control broilers exposed to 34°C. Following heat exposure, CNSL chickens performed better with respect to weight gain and feed consumption, but still showed elevated ROS production and skeletal muscle oxidative damage. GSE chickens did not exhibit improved performance or reduced skeletal muscle oxidative damage. 4. In conclusion, this study suggests that ERW could partially inhibit ROS-induced oxidative damage to skeletal muscle and improve growth performance in broiler chickens under medium-term chronic heat treatment

The potentiality of bioethanol production from corn (Zea mays L.) as a renewable source

Corn (Zea mays L.)  is one of the versatile crop which is used as food, feed, fodder and in recent past as a source of bio-fuel. The sub-tropical climate is very favorable for corn cultivation. Traditionally, corn was grown in South and Southeast Asia primarily as a subsistence food crop. Worldwide it is being cultivated in over 170 countries representing an area of 185 million ha with a productivity of 5.62 t ha-1 (FAO, 2017). Out of world corn production of 1037 million MT, SAARC countries comprising of Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka represent 3.2 % with a productivity of 3.8 t ha-1. Among SAARC countries, the highest productivity of 6.9 t ha-1 is reported in Bangladesh. Corn can be an important renewable source for bioethanol production. This research was carried out to evaluate Bangladeshi Corn for optimum bioethanol production. A 100 g of corn flour was mixed with 300 ml distilled water was blended and sterilized. The experiment was conducted with a temperature of 30 oC, pH 6.0 and 20 % sugar concentration. For alcoholic fermentation, 200 ml yeast (Saccharomyces cerevisiae CCD) was added to make the total volume 500 ml. Addition of small amount of 1750 unit α-amylase enzyme to the substrate solution was found to enhance the fermentation process quicker. After 6-days of incubation time corn produces 63.57 ml of ethanol with 13.33 % (v/v) purity. The non-filtered solution produces comparatively more ethanol (63.57 ml with 13.33 % purity) than the filtered solution (53.66 ml with 10 % purity). The purity can be increased by re-distillation process.  &nbsp

Numerical Modeling of Salinity and Sediement Transport in Lake Pontchartrain During the Bonnet Carre Spillway Flood Release

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Prediction of plant promoters based on hexamers and random triplet pair analysis

<p>Abstract</p> <p>Background</p> <p>With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.</p> <p>Methods</p> <p>In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.</p> <p>Results</p> <p>Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.</p> <p>Conclusions</p> <p>We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.</p