Research on Design and Monitoring of a Development Index of an Industrial Internet Platform Based on a Fixed-Base Index Method

Accelerating the innovation and development of the Industrial Internet Platform is inevitably necessary for the integration of new-generation information technology and the manufacturing industry. It is also the key point for promoting the construction of manufacturing power and network power. In this paper, based on the comprehensive analysis of the relevant problems of China’s Industrial Internet Platform development monitoring, the development index of the Industrial Internet Platform is designed. Taking a typical domestic Industrial Internet Platform as an example, the development index of the Industrial Internet Platform in 2018, 2019, and 2020 are comprehensively calculated in this paper. The results show that China’s Industrial Internet Platform is rapidly growing in many aspects, such as industrial equipment cloud and industrial APP. There is a large space for improving the industrial knowledge accumulation reuse and the application promotion of small/medium-sized enterprises. The results in this paper can provide scientific suggestions and practical references for the government, enterprises, scientific research institutions, which is of great significance in promoting the healthiness and sustainability of the Industrial Internet Platform.

Least Square Analysis of Variance for the Assessment of Natural Consequences for Birth Weight, Weaning Weight, Yearling Weight, Pre-weaning Daily Weight Acquire, and Greasy Fleece Weight of Kajli Sheep in Pakistan

The current study was completed to analyze the effect of natural components on various improvement affecting traits of Kajli sheep in Pakistan. For this reason, we apply two huge contemporary datasets in sheep to explore factors that influence the traits. Therefore, the generation information record of 13715 Kajli sheep lambing accumulated from 1994 to 2010 at Livestock Experimental Stations Khushab and Khizarabad, Punjab. Information records were genuinely analyzed through utilizing PC modified Mixed Model Harvey’s Least Squares and Maximum Likelihood. The two farms information data was analyzed by utilizing an animal model program. The factual model was incorporated to evaluate the Birth Weight (BW), 120 days at Weaning Weight (WW), Pre-Weaning Average Daily Gain (PRADG), Yearling Weight (YW) and Greasy Fleece Weight (GFW). Year of Birth (YOB), Birth Season (BS), Birth Types (BT) and sex was the fix effect in the model. Results indicated that, the overall general values for birth weight, weaning weight, yearling weight, pre-weaning weight and fleece weight were noted. Year of birth, type of birth, sex, and herd was influenced altogether significantly while, birth weight and greasy fleece weight, the period of birth showed no essential difference. In weaning weight and pre-weaning increment normally, year of birth, sort of birth and herd showed a critical contact except for sex. Male sheep were heavier than female sheep and single conceived sheep were also basically heavier than twins were during offspring birth. Results emulate that the Kajli sheep breed can be improved on through selection and further developed management. The cascade type of influence of the current investigation has levelheaded ramification not just for sheep farming by and by just as for intensified associate of boundaries which definitely convince deviation of weight, weight has become itself essential forecaster of in a matter of seconds wellness results. These outcomes displayed there are complex associations among hereditary qualities and ecological elements of parental, placental and fetal beginning. These are profoundly affected traits by maternal sustenance, genes, be concerned, the executive, environment, occasional diversity of seasons.

Development of Semantic Analysis Software Tool for Solving Problems of Text Formalization

Problem of safety ensuring of children and underage adolescents in Web–space is becoming increasingly acute. Younger generation information security is the state of children and adolescents protection, in which there is no risk associated with harm to their health and (or) physical, mental, spiritual, moral development from destructive information. This article describes the development of new software product that allows analyzing text from files, Web sites, pages, applications, etc. Analog programs were reviewed; modern text recognition algorithms were described. Screenshots and flowchart of software parts designed functioning for semantic text analysis in Web space are presented. Any Web page is file with extension, and after analyzing, it is possible to evaluate the information and classify it by topics, as well as to determine whether there is destructive content. In the presented study, the software tool was developed for semantic analysis and formalization of text. Exact trigger points from 26 to 30 times evaluate the algorithm functioning. With accuracy of up to 80%, it can be argued that algorithm is suitable for determining the orientation of article and content of ideas expressed in it, taking into account duality of model.

Controlling the thermal conductivity of multilayer graphene by strain

Straintronics is a new concept to enhance electronic device performances by strain for next-generation information sensors and energy-saving technologies. The lattice deformation in graphene can modulate the thermal conductivity because phonons are the main heat carriers. However, the device fabrication process affects graphene’s heat transport properties due to its high stretchability. This study experimentally investigates the change in the thermal conductivity when biaxial tensile strain is applied to graphene. To eliminate non-strain factors, two mechanisms are considered: pressure-induced and electrostatic attraction–induced strain. Raman spectroscopy and atomic force microscopy precisely estimate the strain. The thermal conductivity of graphene decreases by approximately 70% with a strain of only 0.1%. Such thermal conductivity controllability paves the way for applying graphene as high-efficiency thermal switches and diodes in future thermal management devices.

Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Construction and demolition waste (DW) generation information has been recognized as a tool for providing useful information for waste management. Recently, numerous researchers have actively utilized artificial intelligence technology to establish accurate waste generation information. This study investigated the development of machine learning predictive models that can achieve predictive performance on small datasets composed of categorical variables. To this end, the random forest (RF) and gradient boosting machine (GBM) algorithms were adopted. To develop the models, 690 building datasets were established using data preprocessing and standardization. Hyperparameter tuning was performed to develop the RF and GBM models. The model performances were evaluated using the leave-one-out cross-validation technique. The study demonstrated that, for small datasets comprising mainly categorical variables, the bagging technique (RF) predictions were more stable and accurate than those of the boosting technique (GBM). However, GBM models demonstrated excellent predictive performance in some DW predictive models. Furthermore, the RF and GBM predictive models demonstrated significantly differing performance across different types of DW. Certain RF and GBM models demonstrated relatively low predictive performance. However, the remaining predictive models all demonstrated excellent predictive performance at R2 values > 0.6, and R values > 0.8. Such differences are mainly because of the characteristics of features applied to model development; we expect the application of additional features to improve the performance of the predictive models. The 11 DW predictive models developed in this study will be useful for establishing detailed DW management strategies.

On Modernization of Information Services for the National Economy Production Cluster

The purpose of the present article is to draw the specialists' attention to the fact that in order to create a self-sufficient and competitive national economy, it is necessary to start developing a new generation information service, primarily for its production cluster. The submission discusses how this can be done.

The outlook for China’s innovation-driven development beyond COVID-19

In the post-COVID-19 era, high-quality development will be a key characteristic of China’s economic development. Despite the disruption caused by the pandemic, China’s economy has remained on track for stable and long-term development. In the bid to drive China’s economic transition from factor-driven to innovation-driven development, it is imperative to improve total factor productivity, and that effort should focus on five key factors: urbanization and labour migration; the spillover effect of foreign technologies; human capital enhancement; scientific and technological progress; and marketization process. It is important to align with the main trends and directions of the scientific and technological revolution and industrial transformation. It will be increasingly necessary to focus on developing a dominant technological system represented by next-generation information technology, biotechnology, new energy sources and new materials and to leverage key accelerators of progress, including institutional reform, market optimization and indigenous innovation in order to advance high-quality economic development.