Below-Ground Growth of Alpine Plants, Not Above-Ground Growth, Is Linked to the Extent of Its Carbon Storage

Understanding carbon allocation in plants is essential for explaining their growth strategies during environmental adaptation. However, the role of mobile carbon in plant growth and its response to habitat conditions is still disputed. In degraded meadow (alpine sandy grassland) and non-degraded meadow (typical alpine meadow and swamp meadow) on the Qinghai–Tibetan Plateau, we measured the monthly averages of above-ground biomass (AGB) and below-ground biomass (BGB) of the investigated species in each meadow and the average concentration of non-structural carbohydrates (NSCs), an indicator of carbon storage. Below-ground organs had higher concentrations and showed more seasonal variation in NSCs than above-ground organs. BGB had a positive correlation with below-ground NSCs levels. However, AGB had no clear relationship with above-ground NSCs levels. Plants in sandy grasslands had higher total NSC, soluble sugars, fructose, and sucrose concentrations and lower starch concentrations in below-ground organs than plants in alpine or swamp meadows. Overall, NSCs storage, particularly soluble sugars, is a major process underlying the pattern of below-ground growth, but not above-ground growth, in the meadow ecosystem of the Qinghai–Tibetan Plateau, and degraded meadow strengthens this process. These results suggest that the extent of carbon storage in non-photosynthetic organs of alpine herbs impacts their growth and habitat adaptation.

Virtual Reality in Education System

In the education process, student faces Problem with understanding due to the Complexity. So that many students Conducted on the use of VR in education and training. In today world were surrounded by a wonderful array of technologies. We have as simulated many of these technologies in to our everyday lives. We know that computer is the heart of this generation. Virtual reality technologies for supporting teaching and Learning have been an academic research topic for decades. Virtual reality sometime called virtual environment. In the five years, major process has been made and the Virtual reality technology is getting closer to being implemented in education. This paper Present an overview of the uses of virtual reality in education. The purpose of this Research is to explore the current use of virtual reality to support teaching and Learning. We present new opportunities with VR and put together the most interesting, recent virtual reality application used in education in relation to several education areas such as general, engineering and health related education. This paper presenting methods for testing and validation.

Evolution of the Early Spliceosomal Complex—From Constitutive to Regulated Splicing

Pre-mRNA splicing is a major process in the regulated expression of genes in eukaryotes, and alternative splicing is used to generate different proteins from the same coding gene. Splicing is a catalytic process that removes introns and ligates exons to create the RNA sequence that codifies the final protein. While this is achieved in an autocatalytic process in ancestral group II introns in prokaryotes, the spliceosome has evolved during eukaryogenesis to assist in this process and to finally provide the opportunity for intron-specific splicing. In the early stage of splicing, the RNA 5′ and 3′ splice sites must be brought within proximity to correctly assemble the active spliceosome and perform the excision and ligation reactions. The assembly of this first complex, termed E-complex, is currently the least understood process. We focused in this review on the formation of the E-complex and compared its composition and function in three different organisms. We highlight the common ancestral mechanisms in S. cerevisiae, S. pombe, and mammals and conclude with a unifying model for intron definition in constitutive and regulated co-transcriptional splicing.

Heuristic-Assisted BERT for Twitter Sentiment Analysis

The identification of opinions and sentiments from tweets is termed as “Twitter Sentiment Analysis (TSA)”. The major process of TSA is to determine the sentiment or polarity of the tweet and then classifying them into a negative or positive tweet. There are several methods introduced for carrying out TSA, however, it remains to be challenging due to slang words, modern accents, grammatical and spelling mistakes, and other issues that could not be solved by existing techniques. This work develops a novel customized BERT-oriented sentiment classification that encompasses two main phases: pre-processing and tokenization, and a “Customized Bidirectional Encoder Representations from Transformers (BERT)”-based classification. At first, the gathered raw tweets are pre-processed under stop-word removal, stemming and blank space removal. After pre-processing, the semantic words are obtained, from which the meaningful words (tokens) are extracted in the tokenization phase. Consequently, these extracted tokens are classified via optimized BERT, where biases and weight are tuned optimally by Particle-Assisted Circle Updating Position (PA-CUP). Moreover, the maximal sequence length of the BERT encoder is updated using standard PA-CUP. Finally, the performance analysis is carried out to substantiate the enhancement of the proposed model.

A Desirability Function for Evaluation of Corrosion Behavior for Nanocoated- Steel Using Electroless Technique

The current work is conducting an experimental investigation into the effect of those technical parameters, called nanomaterial, bath temperature and plating time on the micro-hardness and corrosion rate of electroless plated low carbon steel undergoing electroless deposition operation. It was used to prepare (Ni-P/ Nano TiO2), (Ni-P/ Nano Al2O3) and (Ni -P/ Nano SiO2) alloys in this research. The Taguchi design is used to describe the variations located within the corrosion and mechanical properties. To achieve a comprehensive study, a Taguchi-based design was used to account for all applicable combinations of factors. Experimental models had been advanced that linking the response and method parameters to the results of those experiments. Validation of these models is done using analysis of variance (ANOVA). The desirability function is used to simultaneously optimize all the response. Finally, the optimum combination of method parameters resulting (bath temperature=90 oC, plating time =120 min. and Nanomaterial=(Al2O3)), nanomaterial was observed to be the major process parameter on the responses of the electroless-plated low carbon steel with an impact ratio of (47%) based on the (ANOVA) results.

Using Consultants

There are two fundamental types of consultants. Process consultants offer methods to use to solve a problem but rely on the client organization to decide how to use the methods, including making the decisions on what to actually do. The more well-known type of consultation is referred to as expert consultation. These consultants are contracted to fully complete a project or set of tasks. There should be a very detailed process for assessing and selecting a consultant and developing clear plans regarding roles and change activities. Issues, including confidentiality, how “deep” the consultation will potentially go into issues in the organization, and how much the consultation will include developing internal skills and knowledge of staff, are especially important. Organization development is a major process consultation method and field of practice. Such consultants are experts in change management processes, including conducting employee surveys and facilitating workshops and problem-solving groups.

A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection.

A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection

Comparison of Moving-Bed Catalytic Tar Hydrocracking Processes

In recent years, there has been a trend in the global oil industry to improve the proportion of heavy high-sulfur crude oils in the total volume of extracted and processed resources, reserves of which are estimated at over 800 billion metric tons. Therefore, the main line of oil refining is processing of heavy crudes and residua to allow maximum use of the hydrocarbon potential and yield of high-margin products. Hydrogenation processes of heavy raw materials are most attractive in terms of product quality. This article analyzes tar hydrocracking processes that are either in operation or at the stage of full-scale testing. These include Veba Combi-Cracker (VCC), Uniflex, suspended-bed catalyst hydrocracking (ENI), and vacuum residue hydroconversion (TIPS RAS). These technologies use heterogeneous catalysts and are designed to obtain the largest possible amount of liquid products. This article discusses the features of each technology, highlights their advantages and disadvantages, shows the main approaches to process management, and speculates about the development of these technologies. Tar refining is a major process in heavy oil upgrading, and the development of efficient tar-processing methods will influence refinery configurations and management.