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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.
Chief Editor, Professor Tramontana, is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.
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An Approach of Spectra Standardization and Qualitative Identification for Biomedical Materials Based on Terahertz Spectroscopy
Terahertz time-domain spectroscopy (THz-TDS) systems are widely used to obtain fingerprint spectra of many different biomedical substances, and thus the identification of different biological materials, medicines, or dangerous chemicals can be realized. However, the spectral data for the same substance obtained from different THz-TDS systems may have distinct differences because of differences in system errors and data processing methods, which leads to misclassification and errors in identification. To realize the exact and fast identification of substances, spectral standardization is the key issue. In this paper, we present detailed disposal methods and execution processes for the spectral standardization and substance identification, including feature extracting, database searching, and fingerprint spectrum matching of unknown substances. Here, we take twelve biomedical compounds including different biological materials, medicines, or dangerous chemicals as examples. These compounds were analyzed by two different THz-TDS systems, one of which is a commercial product and the other is our verification platform. The original spectra from two systems showed obvious differences in their curve shapes and amplitudes. After wavelet transform, cubic spline interpolation, and support vector machine (SVM) classification with an appropriate kernel function, the spectra from two systems can be standardized, and the recognition rate of qualitative identification can be up to 99.17%.
Use of Big Data Tools and Industrial Internet of Things: An Overview
Big data is ever playing an important role in the industry as well as many other organizations. With the passage of time, the volume of data is increasing. This increase will create huge bulk of data which needs proper tools and techniques to handle its management and organization. Different techniques and tools are being used to properly handle the management of data. A detailed report of these techniques and tools is needed which will help researchers to easily identify a tool for their data and take help to easily manage the data, organize the data, and extract meaningful information from it. The proposed study is an endeavour toward summarizing and identifying the tools and techniques for big data used in Industrial Internet of Things. This report will certainly help researchers and practitioners to easily use the tools and techniques for their need in an effective way.
Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach
Due to the enhancements of Internet of Things (IoT) and sensors deployments, the production of big data in Industrial Internet of Things (IIoT) is increased. The accessing and processing of big data become a challenging issue due to the limited storage space, computational time, networking, and IoT devices end. IoT and big data are well thought-out to be the key concepts when describing new information architecture projects. The techniques, tools, and methods that help to provide better solutions for IoT and big data can have an important role to play in the architecture of business. Different approaches are being practiced in the literature for evaluating the role of big data in IIoT. These techniques are not handling the situations when complexity of dependency arises among parameters of the alternatives. The proposed research uses the approach of Analytic Network Process (ANP) for evaluating the role of big data in IIoT. The results show that the proposed research works well for evaluating the role of big data in IIoT.
Chinese Microblog Sentiment Detection Based on CNN-BiGRU and Multihead Attention Mechanism
With the rapid development of the Internet, Weibo has gradually become one of the commonly used social tools in society at present. We can express our opinions on Weibo anytime and anywhere. Weibo is widely used and people can express themselves freely on it; thus, the amount of comments on Weibo has become extremely large. In order to count up the attitudes of users towards a certain event, Weibo managers often need to evaluate the position of a certain microblog in an appropriate way. In traditional position detection tasks, researchers mainly mine text semantic features through constructing feature engineering and sentiment dictionary, but it takes a large amount of manpower in feature selection and design. However, it is an effective method to analyze the sentiment state of microblog comments. Deep learning is developing in an increasingly mature direction, and the utilization of deep learning methods for sentiment detection has become increasingly popular. The application of convolutional neural networks (CNN), bidirectional GRU (BiGRU), and multihead attention mechanism- (multihead attention-) combined method CNN-BiGRU-MAttention (CBMA) to conduct Chinese microblog sentiment detection was proposed in this paper. Firstly, CNN were applied to extract local features of text vectors. Afterward, BiGRU networks were applied to extract the global features of the text to solve the problem that the single CNN cannot obtain global semantic information and the disappearance of the traditional recurrent neural network (RNN) gradient. At last, it was concluded that the CBMA algorithm is more accurate for Chinese microblog sentiment detection through a variety of algorithm experiments.
Urban Traffic Signal Control Based on Multiobjective Joint Optimization
This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.
Research on Multifeature Data Routing Strategy in Deduplication
Deduplication is a popular data reduction technology in storage systems which has significant advantages, such as finding and eliminating duplicate data, reducing data storage capacity required, increasing resource utilization, and saving storage costs. The file features are a key factor that is used to calculate the similarity between files, but the similarity calculated by the single feature has some limitations especially for the similar files. The storage node feature reflects the load condition of the node, which is the key factor to be considered in the data routing. This paper introduces a multifeature data routing strategy (DRMF). The routing strategy is made based on the features of the cluster, including routing communication, file similarity calculation, and the determination of the target node. The mutual information exchange is achieved by routing communication, routing servers, and storage nodes. The storage node calculates the similarity between the files stored, and then the file is routed according to the information provided by the routing server. The routing server determines the target node of the route according to the similar results and the node load features. The system prototype is designed and implemented; also, we develop a system to process the feature of cluster and determine the specific parameters of various features of experiments. In the end, we simulate the multifeature data routing and single-feature data routing, respectively, and compare the deduplication rate and data slope between the two strategies. The experimental results show that the proposed data routing strategy using multiple features can improve the deduplication rate of the cluster and maintain a lower data skew rate compared with the single-feature-based routing strategy MCS; DRMF can improve the deduplication rate of the cluster and maintain a lower data skew rate.