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With the rapid development of computer technology and the needs of modern higher education reform, computer network multimedia technology has begun to be applied more and more to the application of higher algebra teaching reform in universities. This article mainly analyzes the advantages of using computer network multimedia technology in the reform of advanced algebra teaching, as well as the problems that should be paid attention to when applying it to network multimedia in the teaching process, and puts forward the corresponding problems in how to use its network multimedia to carry out advanced algebraic mathematics reform Suggestions. This article mainly analyzes the advantages of using computer network multimedia technology in the reform of advanced algebra teaching, as well as the problems that should be paid attention to when applying it to network multimedia in the teaching process. The research of this article is dedicated to the deeper understanding of mathematics teachers in colleges and universities. Network multimedia technology the theoretical basis and substantive understanding integrated in the teaching of advanced algebra. Understand the various factors that hinder the effective integration of network multimedia technology and course teaching, and truly realize the real and effective integration of multimedia technology into the reform of higher algebra teaching, and comprehensively improve the quality of teaching. By mainly expounding the time that network multimedia technology is used in the process of advanced algebra teaching, different cases of multimedia teaching are given for analysis from the perspectives of new courses, review courses, research-based learning, and elective courses. Experimental research results show that the application of computer network multimedia technology in the reform of higher algebra teaching is to meet the needs of the development of the times, but the better application of this technology should be combined with the development of the school itself. Teachers continue to understand the teaching skills of network multimedia and give full play to its role.

Growth towards Industry 4.0 has a significant impact on the textile manufacturing industry. In this emerging technology the business and engineering processes are interconnected. The product traceability system is playing a vital role in each sector. Unfortunately, the traditional systems in textile manufacturing industries faced a lot of challenges due to in-house process and supply chain complexity. These systems do not provide a means for reliable and rapid response to backtrack data throughout the textile process of the product. Blockchain and Internet of Things (IoT) based processes have the capacity to overcome these challenges while deploying over the traditional product traceability system. The blockchain and IoT based system provides many benefits such as communication between product process flow to improve the performance and reduce risk, improve quality, continuous involvement of worker, product fault traceability, increase supply chain visibility, and customer’s reliability and trust-ability. In this paper, we proposed Blockchain and IoT based product traceability system. This system facilitates all stakeholders like Raw Material Suppliers, Yarn Manufacturers, Customers, and Consumers to track, monitor, and quality of the product and efficient tracking of the supply chain. This proposed solution will help textile manufacturers to improve the efficiency and quality of each product. The customer’s reliability and trust-ability will boost the manufacturer due to automated data insertion through the IoT technology, decentralized, immutable, auditable, and fault tolerance blockchain technology and traceability features.

As a main composition of the human-associated microbiome, viruses are directly associated with our health and disease. The receptor-binding is critical for the virus infection. So identifying potential virus-receptor interactions will help systematically understand the mechanisms of virus-receptor interactions and effectively treat infectious diseases caused by viruses. Several computational models have been developed to identify virus-receptor interactions based on assumption that similar viruses show similar interaction patterns with receptors and vice versa, but the performance need to be improved. Furthermore, the virus network and the receptor network are also noisy. Therefore, we present a new prediction model (NERLS) to identify potential virus-receptor interactions based on Network Enhancement, virus sequence information and receptor sequence information by Regularized Least Squares. Firstly, the virus network is constructed based on the virus sequence similarity and Gaussian interaction profile (GIP) kernel similarity of viruses by a mean method. They are calculated based on the viral RefSeq genomes downloaded from NCBI and known virus-receptor interactions, respectively. Similarly, we also use the same mean method to construct the receptor network based on the amino acid sequence similarity and known virus-receptor interactions. Then Network Enhancement is applied to denoise the virus network and the receptor network. Finally, we employ the regularized least squares algorithm to identify potential virus-receptor interactions. The 10-fold cross validation (10CV) experimental results indicate that an average Area Under Curve (AUC) values of NERLS is 0.8930, which is superior to other computing models of 0.8675 (IILLS), 0.7959 (BRWH), 0.7577 (LapRLS), and 0.7128 (CMF). Furthermore, the Leave One Out Cross Validation (LOOCV) experimental results also show that NERLS can achieve the AUC values of 0.9210, which is better than other models (IILLS: 0.9061, BRWH: 0.8105, LapRLS: 0.7713, CMF: 0.7491). In addition, a case study also confirms the effectiveness of NERLS in predicting potential virus-receptor interactions.

MicroRNAs (miRNAs) are a class of non-coding RNAs of approximately 22 nucleotides. Cumulative evidence from biological experiments has confirmed that miRNAs play a key role in many complex human diseases. Therefore, the accurate identification of potential associations between miRNAs and diseases is beneficial to understanding the mechanisms of diseases, developing drugs and treating complex diseases. We propose a new method to predict miRNA-disease associations based on a negative sample selection strategy and multi-layer perceptron (called NMLPMDA). For obtaining more similarity information, NMLPMDA integrates the miRNA functional similarity and the Gaussian interaction profile (GIP) kernel similarity of miRNAs as the final miRNA similarity, and integrates the disease semantic similarity and the GIP kernel similarity of diseases as the final disease similarity. In particular, we propose a negative sample selection strategy based on common gene information to select more reliable negative samples from unknown miRNA-disease associations. The 5-fold cross validation is used to evaluate the performance of NMLPMDA and other competing methods. On four datasets (HMDD2.0-Yan, HMDD2.0-Lan, HMDD2.0-You, HMDD3.0), the AUC values of NMLPMDA are 0.9278, 0.9206, 0.9301 and 0.9350, respectively. In addition, we also illustrate the prediction ability of NMLPMDA in Lymphoma. As a result, 28 of the top 30 miRNAs associated with the disease have been validated experimentally in dbDEMC and previous studies, respectively. These experimental results indicate that NMLPMDA is a reliable model for predicting associations between miRNAs and diseases.

A drug-drug interaction(DDI) was defined as the pharmacological effect(s) of a drug influenced by another drug. The positive DDIs can improve the therapeutic effect of patients. However, the negative DDIs can lead serious results, such as drug withdrawal from market and even patient death. Currently, multiple pharmaceutical drugs have widely been used to treat complex diseases, such as cancer. The traditional biomedical experiments are very time-consuming and very costly to validate new DDIs. Therefore, it is appealing to develop computational methods to discover potential DDIs. In this study, we propose a new computational method (called IDNDDI) to predict novel DDIs. Based on the binary vector of drug chemical, biological and phenotype data, IDNDDI computes the integrated drug feature similarity by the cosine similarity method. In addition, the node-based drug network diffusion method is used to calculate the relational initial scores for new drugs. To systematically evaluate the prediction performance of IDNDDI and compare it with other prediction methods, we conduct the 5-fold cross validation and de novo drug validation. In terms of the AUC (area under the ROC curve)value, IDNDDI achieves the better prediction performance in the 5-fold cross validation, specifically, the AUC value is 0.9691, which is larger than the state-of-the-art L1E (L1 Classifier ensemble method) results of 0.9570. In addition, IDNDDI also obtains the best prediction result in the de novo drug validation and the AUC reaches 0.9292. The prediction ability in application of our method is also illustrated by case studies. IDNDDI is an effective DDI prediction method which can help to reduce adverse drug reactions and improve the efficiency of drug development progress.

With the development of the internet of vehicles, the safety of the wireless network communication in the internet of vehicles and the privacy protection of users are in need of an immediate solution. In order to address the interception and tampering by an attacker of the contents among the vehicle mobile terminals, and their consequence of the privacy leakage of the location, and the user’s activity tracks and also the consequence of the user’s receiving the wrong information due to the tampering of the message, this essay comes up with a solution of tamper-proof ciphertext of the revised ciphertext policy attribute-based encryption, which to some degree has reduced the risk of the leakage of the user’s information. This solution realize the encryption protection of the contents of the vehicle mobile terminals by destroying the structure extension of the ciphertext, achieves the goal of non-extensibility security.

Understanding disease-disease associations can not only help us gain deeper insights into complex diseases, but also lead to improvements in disease diagnosis, drug repositioning and new drug development. Due to the growing body of high-throughput biological data, a number of methods have been proposed for the computation of similarity among diseases during past decades. Recently, the disease module theory has been presented, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of protein-protein interaction network. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and protein-protein interaction network data based on disease module theory. By considering the interactions between disease modules and each module’s modularity, ModuleSim outperforms other four popular methods for predicting disease-disease similarity.

To protect users’ privacy and provide efficient and secure key management in Vehicular ad hoc networks (VANETs), this paper proposes a novel Efficient Dynamic Key Management scheme based on Dynamic Secret Sharing, namely EDKM-DSS. The proposed scheme replaces the complex group signature technology with the elliptic curve ElGamal threshold mechanism and the dynamic secret sharing technology, and realizes the key management of the group communication. Experiments results show that the proposed novel scheme can effectively improve the overall performance.