Vacuum gas carburizing fate of hydrocarbons

This work focuses on gaseous reactive flows in ideal and non-ideal reactors. The objective of this research is the development of models for the numerical simulation of homogeneous reactive flows under vacuum carburizing conditions of steel with propane and acetylene. These models can be used for further investigations of heterogeneous reactions during vacuum carburizing of steel to predict the carbon flux on the complex shaped steel parts to understand and, eventually, optimize the behavior of the whole reactor. arburizing is the case-hardening process in which carbon is added to the surface of low-carbon steels at temperatures generally between 850 and 1050 °C. In the conventional gas carburizing at atmospheric pressure, the carbon potential is controlled by adjusting the flow rate of the carburizing gas. Carbon potential of the furnace atmosphere can be related to partial pressure of CO2 or O2 or vapour pressure of water by equilibrium relationships and a sensor can be used to measure it. This method of carbon-potential control cannot be used for vacuum gas carburizing due to the absence of thermodynamic equilibrium which is one of the main difficulties of the vacuum carburizing process. The formation of soot during carburization isalso undesirable and the process parameters should be selected such that the formation of soot is minimized. The amount of carbon available for carburizing the steel depends on the partial pressure of the carburizing gas, carbon content in the carburizing gas and the pyrolysis reactions of the carburizing gas. The pyrolysis reactions of the carburizing gas are also affected by the contacting pattern or how the gas flows through and contacts with the steel parts being carburized. This work focuses on gaseous reactive flows in ideal and non-ideal reactors. The objective of this research is the development of models for the numerical simulation of homogeneous reactive flows under vacuum carburizing conditions of steel with ropane and acetylene. These models can be used for further investigations of heterogeneous reactions during vacuum carburizing of steel to predict the carbon flux on the complex shaped steel parts to understand and, eventually, optimize the behavior of the whole reactor. Two different approaches have been used to model the pyrolysis of propane and acetylene under vacuum carburizing conditions of steel. One approach is based on formal or global kinetic mechanisms together with the computational fluid dynamics (CFD) tool. The other approach is based on detailed chemistry with simplified or ideal flow models. Two global mechanisms developed at the Engler-Bunte-Institut for pyrolysis of propane and acetylene respectively were used in this work. One detailed mechanism developed at the Institute of Chemical Technology by the research group of Professor Deutschmann was used for modeling the pyrolysis of both the propane and acetylene. Experimental data from investigations on vacuum carburizing conducted at the Engler-Bunte-Institut were used to validate the modeling results

Vacuum gas carburizing - fate of hydrocarbons

This work focuses on gaseous reactive flows in ideal and non-ideal reactors. The objective of this research is the development of models for the numerical simulation of homogeneous reactive flows under vacuum carburizing conditions of steel with propane and acetylene. These models can be used for further investigations of heterogeneous reactions during vacuum carburizing of steel to predict the carbon flux on the complex shaped steel parts to understand and, eventually, optimize the behavior of the whole reactor. arburizing is the case-hardening process in which carbon is added to the surface of low-carbon steels at temperatures generally between 850 and 1050 °C. In the conventional gas carburizing at atmospheric pressure, the carbon potential is controlled by adjusting the flow rate of the carburizing gas. Carbon potential of the furnace atmosphere can be related to partial pressure of CO2 or O2 or vapour pressure of water by equilibrium relationships and a sensor can be used to measure it. This method of carbon-potential control cannot be used for vacuum gas carburizing due to the absence of thermodynamic equilibrium which is one of the main difficulties of the vacuum carburizing process. The formation of soot during carburization isalso undesirable and the process parameters should be selected such that the formation of soot is minimized. The amount of carbon available for carburizing the steel depends on the partial pressure of the carburizing gas, carbon content in the carburizing gas and the pyrolysis reactions of the carburizing gas. The pyrolysis reactions of the carburizing gas are also affected by the contacting pattern or how the gas flows through and contacts with the steel parts being carburized. This work focuses on gaseous reactive flows in ideal and non-ideal reactors. The objective of this research is the development of models for the numerical simulation of homogeneous reactive flows under vacuum carburizing conditions of steel with ropane and acetylene. These models can be used for further investigations of heterogeneous reactions during vacuum carburizing of steel to predict the carbon flux on the complex shaped steel parts to understand and, eventually, optimize the behavior of the whole reactor. Two different approaches have been used to model the pyrolysis of propane and acetylene under vacuum carburizing conditions of steel. One approach is based on formal or global kinetic mechanisms together with the computational fluid dynamics (CFD) tool. The other approach is based on detailed chemistry with simplified or ideal flow models. Two global mechanisms developed at the Engler-Bunte-Institut for pyrolysis of propane and acetylene respectively were used in this work. One detailed mechanism developed at the Institute of Chemical Technology by the research group of Professor Deutschmann was used for modeling the pyrolysis of both the propane and acetylene. Experimental data from investigations on vacuum carburizing conducted at the Engler-Bunte-Institut were used to validate the modeling results

Climatic Changes and Their Effect on Wildlife of District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Climatic changes and their impact are increasingly evident in Pakistan, especially in the mountainous regions. Mountain ecosystems are considered to be sensitive indicators of global warming; even slight variations in temperature can lead to significant shifts in local climate, which can, in turn, drastically affect the natural environment, subsequently altering people’s lifestyle and wildlife habitats. The targeted area for the present research was Lower Dir District, Pakistan. The study gathered the required information from primary and secondary sources. Secondary data on temperature and precipitation were obtained from various sources, i.e., local CBO, including WWF Pakistan. Based on information gathered on climate change and wildlife, a detailed questionnaire was designed. Results showed that no regular pattern of the increase was found in temperature from 2010 to 2018; the same was noticed in the rainfall decrease pattern. Results also showed that the leading causes behind climatic changes are an increase in greenhouse gases due to pollution by industries, vehicles, crushing plants, deforestation, and some natural phenomena such as floods. The study showed that more than 80% of the respondents agreed that climatic effects have a significant impact on wildlife, i.e., the existence of wildlife falls in danger due to climatic changes as it may lead to habitat change, making it difficult for the survival and adaptation of the wildlife. Hence, in consequence, it leads to migration, low growth rate, an increase in morbidity and mortality rate, and finally leading to the extinction of the species or population. It is concluded from the study that people are severely noticing the climatic change and its leading causes are greenhouse gases and deforestation. To control climatic changes and wildlife extinction, we need an appropriate policy for forest conservation, wildlife conservation, prevent hunting, industrial pollution control, vehicle pollution control, increase in plantation, awareness of policy for the control of climatic changes, etc

Scholarly Research Output on COVID-2019: The Published Literature Analysis on the ISI Web of Science Databases

The research portrays evaluation of published literature on the topic of COVID-2019 globally. The ISI Web of sciences database was used to access the published literature till December 01, 2020. The types of publications included in the research were, editorials, letters, reviews, articles, case reports, abstracts, and books. The indicators based on the factors; publication period, the most contributing authors, most publishing institutes, countries’ contributions, and research journals titles. A total of 82371 documents were retrieved from the database. The USA has produced 16229 documents that are the almost 20% of the total publications. The contribution on research from China is at second position with the numbers of 6994 (8.491%). Italy in research productivity remained third with the number of 5925 (7.193 %). England, India, Canada, Spain Germany, Australia, and France remained in the top ten productive countries in the publication of Covid-2019 respectively. The research publications percentage of these seven countries remained 2.721- 7.005 percent

ck-NN: A Clustered k-Nearest Neighbours Approach for Large-Scale Classification

k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow

An effective deep learning approach for the classification of Bacteriosis in peach leave

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models

Novel Stimuli-Responsive Pectin-PVP-Functionalized Clay Based Smart Hydrogels for Drug Delivery and Controlled Release Application

Stimuli-responsive drug delivery systems are urgently required for injectable site-specific delivery and release of drugs in a controlled manner. For this purpose, we developed novel pH-sensitive, biodegradable, and antimicrobial hydrogels from bio-macromolecule pectin, polyvinylpyrrolidone (PVP), 3-aminopropyl (diethoxy)methyl silane (3-APDEMS), and sepiolite clay via blending and solution casting technique. The purified sepiolite (40 um) was functionalized with 3-APDEMS crosslinker (ex-situ modification) followed by hydrogels fabrication. FTIR and SEM confirmed crosslinked structural integrity and rod-like morphology of hydrogels respectively. The swelling properties of hydrogels could be controlled by varying the concentration of modified clay in pectin/PVP blends. Moreover, the decrease in pH increased the swelling of hydrogels indicating the pH-responsiveness of hydrogels. All hydrogels were degraded after 21 days in phosphate buffer saline pH 7.4 (human blood pH). In-vitro cytotoxicity against 3T3 mouse fibroblast cell line analysis confirmed cytocompatibility of all hydrogels. Ceftriaxone sodium (CTX-S) was selected as a model drug. The release profile of the hydrogel showed 91.82% release in PBS for 2 h in a consistent and controlled manner. The chemical structure of the drug remained intact during and after release confirmed through UV-Visible spectroscopy. Overall, these hydrogels could be used as potential scaffolds for future biomedical applications

Establishment of the Invasive Cactus Moth, \u3ci\u3eCactoblastis cactorum\u3c/i\u3e (Berg) (Lepidoptera: Pyralidae) in Pakistan: A Potential Threat to Cultivated, Ornamental and Wild \u3ci\u3eOpuntia\u3c/i\u3e spp. (Cactaceae)

Subsequent to the significant accomplishment of biological control of Opuntia weeds in Australia, the larvae of the cactus moth, Cactoblastis cactorum (native to parts of South America), were released in many countries for the biological control of native Opuntia species (Simmonds and Bennett, 1966). Inauspiciously, larvae were also released in the Caribbean, where the moth spread naturally and by the human support all over the region (García-Turudi et al., 1971). Its enhanced dissemination rate and the biological potential for invasiveness, suggests that the cactus moth is likely to become an invasive pest of Opuntia in the Southeast United States, Mexico, and southwestern America. Its damage is restricted mainly to the plants of genus Opuntia (plants with the characteristic of flat prickly pear pads of the former genus Platyopuntia, now considered to be the part of the genus Opuntia). In this region, plants of this genus provide valuable resources for humans, livestock, and wildlife such as food, medicine, and emergency fodder, while in the arid and semi-arid regions, the plants play key roles in ecosystem processes and soil conservation. At present, the cactus moth has developed into a severe threat to the high diversity of prickly pear cacti, all over the world for both the native and cultivated species of Opuntia (IAEA, 2002)