Development of Mathematical Functions to Predict Deflection of Radial and Bias Tractor Tires on Rigid Surface

The objective of this study was to develop mathematical functions to predict deflection for radial and bias tires. In order to develop the models, the data were obtained from the tire manufacturing companies and organized in Excel first and then transferred to Minitab® for stepwise regression analysis. The variables considered in the study were inflation pressure, load and tire width and overall diameter. Tire width (w) and overall diameter (d) was considered in a multiplication form. The tire deflection models in two different form (linear and non-linear) were developed for both, radial and bias tires. The model selection was achieved by three different criteria and % differences between the measured and predicted data. Based on the results of applying model selection criteria, the models for radial and bias tire in non-linear form were found to be adequate for predicting the tire deflection. The results from the stepwise analysis indicated that the load on tire was the predominant variable in the models and made the highest contribution to the prediction functions. The developed models were verified against to published literature data and found a good agreement.

The Characteristics of Intestinal Bacterial Community in Three Omnivorous Fishes and Their Interaction with Microbiota from Habitats

Artificial fishery habitats have been extensively used for fishery resource protection and water habitat restoration, and they could attract a large number of omnivorous fishes to gather together. This study intended to reveal the relationship between bacterial communities in the habitats (water and sediment) and intestines of omnivorous fishes (Oreochromis mossambicus, Toxabramis houdemeri and Hemiculter leucisculus). Therefore, we investigated the bacterial communities of samples collected from intestines, water, and sediments in artificial fishery habitats via 16S rRNA metabarcoding high-throughput sequencing technology. The results showed that there were significant differences in the composition, core indicators, diversity and prediction functions in water, sediments, and intestinal microbial communities of the three omnivorous fish. The microbial diversities were significantly higher in habitats than in intestines. The analysis of similarity (ANOSIM) and nonmetric multidimensional scaling (NMDS) results indicated that the intestine microbial communities (T. houdemeri and H. leucisculus) were more similar to the water microbiota, but the intestine microbial communities (O. mossambicus) were more similar to the sediments. Source tracking analysis also confirmed that the contribution of habitat characteristics to omnivorous fish intestinal microorganisms was different; the sediment had a greater contribution than water to the intestinal microbiota of O. mossambicus, which was consistent with their benthic habit. Moreover, the functional prediction results showed that there were unique core indicators and functions between the bacterial community of habitats and intestines. Altogether, these results can enhance our understanding of the bacterial composition and functions about omnivorous fish intestines and their living with habitats, which have provided new information for the ecological benefits of artificial fishery habitats from the perspective of bacterial ecology and contributed to apply artificial fishery habitats in more rivers.

Co-Expression Network Revealed The Potential Regulatory Mechanism of lncRNAs In Relapse Hepatocellular Carcinoma.

Background: The mechanistic basis for the relapse of hepatocellular carcinoma (HCC) remains poorly understood. Recent research has highlighted the important roles of long non-coding RNAs (lncRNAs) in HCC. However, there are only a few studies on lncRNAs associated with the relapse of HCC.Methods:We analyzed lncRNA and mRNA profiles in the GSE101432 dataset associated with HCC relapse. The differentially expressed lncRNAs and mRNAs were used to construct a lncRNA-mRNA co-expression network. Weighted gene co-expression network analysis followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on the database. Furthermore, correlation and survival analyses were performed using The Cancer Genome Atlas database, and the clinical samples were verified by qRT-PCR.Results:In this study, lncRNAs and mRNAs associated with HCC relapse were identified. Two gene modules were found to be closely linked to HCC relapse. The functional enrichment analysis results of lncRNAs and co-expression mRNAs indicated that they were closely related to the relapse of HCC. In addition, we verified that the overall survival and recurrence-free survival of these genes in HCC have survival prediction functions. In total, we identified and validated two lncRNAs (LINC00941 and LINC00668) and six mRNAs (LOX, MICB, OTX1, BAIAP2L2, KCTD17, NDUFA4L2) associated with HCC relapse.Conclusion: In summary, we identified the key gene modules and central genes associated with relapse of HCC, and constructed lncRNA-mRNA networks related to this cancer type. These results provide a foundation for future basic research on the mechanism of relapse of HCC.

Co-Expression Network Revealed the Potential Regulatory Mechanism of Lncrnas in Relapse Hepatocellular Carcinoma.

Background: The mechanistic basis for the relapse of hepatocellular carcinoma (HCC) remains poorly understood. Recent research has highlighted the important roles of long non-coding RNAs (lncRNAs) in HCC. However, there are only a few studies on lncRNAs associated with the relapse of HCC.Methods:We analyzed lncRNA and mRNA profiles in the GSE101432 dataset associated with HCC relapse. The differentially expressed lncRNAs and mRNAs were used to construct an lncRNA-mRNA co-expression network. Weighted gene co-expression network analysis followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on the database. Furthermore, correlation and survival analyses were performed using The Cancer Genome Atlas database, and the clinical samples were verified by qRT-PCR.Results:In this study, lncRNAs and mRNAs associated with HCC recurrence were identified. Two gene modules were found to be closely linked to HCC relapse. The functional enrichment analysis results of lncRNAs and co-expression mRNAs indicated that they were closely related to the recurrence of HCC. In addition, we verified that the overall survival and recurrence-free survival of these genes in HCC have survival prediction functions. In total, we identified and validated two lncRNAs (LINC00941 and LINC00668) and six mRNAs (LOX, MICB, OTX1, BAIAP2L2, KCTD17, NDUFA4L2) associated with HCC relapse.Conclusion: In summary, we identified the key gene modules and central genes associated with recurrent HCC, and constructed lncRNA-mRNA networks related to this cancer type. These results provide a foundation for future basic research on the mechanism of recurrent liver cancer.

Stochastic optimization and modeling of high-velocity impact tests on high-temperature carbon–carbon composites

In this study, it is intended to optimize a high-velocity impact case of a composite plate. The case selected from literature focused on the failure response of advanced carbon–carbon (C/C) composites under high-velocity impacts. Based on the stochastic optimization method, three unique models are introduced within the present study's scope as dimensionless damage areas of front and back sides and the composite impact energy response. The difference between the equations found in the present study and the base study is the number of variables. Obtained prediction models consist of only the tests' input variables; thus, these models can be considered the essential prediction functions of high-velocity impact response of C/C composites under high temperatures. Multiple nonlinear regression method is used for objective functions of the optimization problem. Since the determination coefficient values have been found quite similar to the ones in the literature, the presented models can be considered successful in predicting the results. By utilizing the novel regression functions presented in this study, the damaged areas are minimized. Without the necessity of experimental research, further predictions can be made by operating the models found in the present study.

The Impact of Global Health Treat COVID-19 on the Implementation of Corporate Innovations in E-commerce Within the Slovak Republic

Research background: The pandemic of the new coronavirus causing COVID-19 poses a global health threat with a direct impact on individual companies and the country’s economy. Estimating the overall effects of COVID-19 is very difficult at the moment, as the situation is evolving every day, and the length of the restrictive measures is not known in advance. As the viruses know no borders, the governments of the affected countries have had to take stringent standards to slow the spread of COVID-19. Among these countries is Slovakia, which has taken many measures that have affected the operation of service companies as well as industry. The disease brought challenges in the field of e-commerce and technology, as isolation and social distancing fundamentally changed the shopping habits of the population. Purpose of the article: The purpose of this research paper is to highlight how nowadays challenges have affected service businesses and led to the more innovative use of technology and e-commerce. The importance of innovation in adverse conditions caused by business constraints due to Covid-19. Methods: The analysis in the form of a questionnaire through a questionnaire survey and the subsequent processing of the created database using SPSS using prediction functions were used. Findings & Value added: The analyses demonstrated the impact of COVID-19 on e-commerce innovation on a random sample of companies that included companies stimulated to change by this environment to secure their sales, but those that tried to operate without radical change further on the market.

Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

Making WAVES in Breedbase: An Integrated Spectral Data Storage and Analysis Pipeline for Plant Breeding Programs

ABSTRACTVisible and near-infrared (vis-NIRS) spectroscopy is a promising tool for increasing phenotyping throughput in plant breeding programs, but existing analysis software packages are not optimized for a breeding context. Additionally, commercial software options are often outside of budget constraints for some breeding and research programs. To that end, we developed an open-source R package, waves, for the streamlined analysis of spectral data with several cross-validation schemes to assess prediction accuracy. Waves is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of vis-NIRS spectra with reference measurements. Furthermore, we have integrated this package into the Breedbase family of open-source databases, expanding the analysis capabilities of this growing digital ecosystem to a number of crop species. Taken together, the standalone and Breedbase versions of waves enhance the accessibility of tools for the analysis of spectral data during the plant breeding process.Core ideaswaves is an open-source R package for spectral data analysis in plant breedingBreeding relevant cross-validation schemes to evaluate predictive accuracy of modelsExtension of Breedbase—an open-source database—to support spectral data storageGraphical user interface developed for implementation of waves in Breedbase

Side-Effects Causing Hidden Conflicts in Software-Defined Networks

The Software-Defined Networking (SDN) architecture facilitates the flexible deployment of network functions by detaching them from network devices to a logically centralized point, the so-called SDN controller, and maintaining a common communication interface between them. While promoting innovation for each side, this architecture also induces a higher chance of conflicts between concurrent control applications compared to existing traditional networks. We have discovered a new type of anomalies that we call hidden conflicts. They appear to occur only due to side-effects of control application’s behaviour and to be independent of and distinct from the class of conflicts between rules present in the network devices. We analyse the SDN interaction primitives susceptible to such disruptions and present experiments supporting our analysis, the result of which indicates the necessity of the knowledge on the control mechanics in detecting hidden conflicts. We present a hidden conflict prediction approach that employs speculative provocation to determine the deployed applications’ behaviour. The observed behaviour can be leveraged to predict undesired network state. Evaluation of our prediction prototype suggests that prediction functions should be integrated into control applications.