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The function of pseudoknots cannot be ignored in the RNA secondary structure. Existing methods for analyzing RNA secondary structures with pseudoknots exhibit many shortcomings. This paper presents a novel RNA secondary structure visualization method in the case of a joint analysis of RNA primary structures and secondary structures. The way is based on the page number representation of the RNA secondary structure. It innovatively uses five vectors to represent bases, which are sequentially connected to outline the characteristics of the RNA secondary structure. The method covers almost all the constituent elements of the RNA secondary structure and extracts features completely. Experiments are based on the available techniques for large-scale annotation of RNA secondary structures, using a combination method of discrete wavelet transform and fractal dimension. The classification effect is compared with the previous RNA secondary structure representation methods. Experimental results show that the RNA secondary structure visualization method proposed in this paper has good application prospects in RNA secondary structure classification.

Smart home is an emerging form of the Internet of Things (IoT), enabling people to enjoy a convenient and intelligent life. The data generated by smart home devices are transmitted through the public channel, which is not secure enough, so the secret data in smart home are easily intercepted by malicious adversaries. In order to solve this problem, this paper proposes a smart home privacy protection method combining DES encryption and the improved Least Significant Bit (LSB) information hiding algorithm, changing the practice of directly exposing smart home secret information to the Internet, first, using Data Encryption Standard (DES) encryption to encrypt the smart home information and second, the improved LSB information hiding algorithm is used to hide the ciphertext, so that the adversary cannot detect the smart home secret information. The goal of the scheme is to provide a double protection for the secure transmission of the smart home secret information. If an attacker wants to carry out an attack, it has to break through at least two defense lines, which seems impossible to do. Experiment results show that the improved LSB algorithm is more robust than the existing algorithms, and it is very safe. Therefore, the scheme proposed in this paper is very practical for protecting the smart home secret information.

Background Limited data exist on VTE risk and prophylaxis in Chinese inpatients. The Identification of Chinese Hospitalized Patients' Risk Profile for Venous Thromboembolism-2 (DissolVE-2), a nationwide, multicenter, cross-sectional study, was therefore designed to investigate prevalence of VTE risks and evaluate VTE prophylaxis implementation compliant with the latest prophylaxis guidelines (American College of Chest Physicians [CHEST], 9th edition). Methods Adults admitted (≥ 72 h) to 60 urban, tertiary Chinese hospitals due to acute medical conditions or surgery from March to September 2016 were assessed for VTE risk. Risk assessments were made by using the Padua Prediction Scoring or Caprini Risk Assessment model, risk factors, and prophylaxis based on the CHEST guidelines, 9th edition. Results A total of 13,609 patients (6,986 surgical and 6,623 medical) were analyzed. VTE risk in surgical inpatients was categorized as low (13.9%; 95% CI, 13.1-14.7), moderate (32.7%; 95% CI, 31.6-33.8), and high (53.4%; 95% CI, 52.2-54.6); risk in medical patients was categorized as low (63.4%; 95% CI, 62.2-64.6) and high (36.6%; 95% CI, 35.4-37.8). Major risk factors in surgical and medical patients were major open surgery (52.6%) and acute infection (42.2%), respectively. Overall rate of any prophylaxis and appropriate prophylactic method was 14.3% (19.0% vs 9.3%) and 10.3% (11.8% vs 6.0%) in surgical and medical patients. Conclusions A large proportion of hospitalized patients reported VTE risk and low rate of CHEST-recommended prophylaxis. The data highlight the insufficient management of VTE risk and show the great potential for improving physicians' awareness and current practices across China. Trial Registry Chinese Clinical Trial Registry; No.: ChiCTR-OOC-16010187; URL: http://www.chictr.org.cn/showproj.aspx?proj=17077.

In organisms, ribonucleic acid (RNA) plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform (DWT) and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

Recently, the deep learning method that integrates image features has gradually become a hot development trend in hyperspectral image classification. However, these studies did not fully consider the fusion of image features, and did not remove the interference to the classification process caused by the difference in the size of the objects. These factors hinder the further improvement of the classification effect. To eliminate these drawbacks, this paper proposes a more effective fusion scheme (MSF-MIF), which realizes the fusion from the perspective of location characteristics and channel characteristics through 3D convolution and spatial feature concatenation. In view of the size discrepancy of the objects to be classified, this method extracts features from several input patches of different scales and uses the novel calculation method proposed to fuse them, which minimizes the interference caused by size differences. In addition, this research also tried to quote the coordinate attention structure for the first time that combines spatial and spectral attention features to further improve the classification performance. Experimental results on three commonly used data sets prove that this framework has achieved a breakthrough in classification accuracy.

Monitoring is a significant issue for finishing the assembly interfaces of large-scale components before final assembly. Acquisition and supervision of the pivotal data is essential to ensure the security and reliability for machining the large and complicated components with high-value. This process is generally cumbersome and time-consuming because there are various types of data coming from different components and sensors. The problem becomes more serious when considering the whole shop floor. Recently, MTConnect has been proven to be an effective method to realize standardized data collection and monitoring process. However, MTConnect is still under development and cannot cover the whole finishing process such as on-machining measuring (OMM) and fixturing. To address the issue, an MTConnect compliant method with extended data models is proposed in this paper to implement a standardized monitoring system. Firstly, a finishing system for the assembly interfaces is introduced, including the framework, workflow and key procedures and data. Then extended MTConnect data models are proposed to represent the finishing system including on-machine touch-trigger probe and sensor-based intelligent fixturing related information. Based on the extended MTConnect data models, a web-based monitoring system is developed for data collection and monitoring by combining an MTConnect agent and an OPC adapter. The proposed approach is validated by collecting and monitoring the key process data using an airplane vertical tail as an application. The advantages of using MTConnect would be more significant when extended to the entire factory and implemented in cloud manufacturing in the future.

In this paper the authors discuss the applications of LSTM Neural Network in Protein Structure Prediction. The main idea is to construct a LSTM neural network. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. In this article, a position-specific scoring matrices (PSSM) containing evolutionary information is linked to other features to construct a completely new feature set. The CB513 data set is selected to construct LSTM neural networks to predict the secondary structure of the sequence. Experiments have shown that the proposed method effectively improves the prediction accuracy and is better than the previous method. The idea in this paper can also be applied to the analysis of other sequences.

DNA-binding proteins are an essential part of the DNA. It also an integral component during life processes of various organisms, for instance, DNA recombination, replication, and so on. Recognition of such proteins helps medical researchers pinpoint the cause of disease. Traditional techniques of identifying DNA-binding proteins are expensive and time-consuming. Machine learning methods can identify these proteins quickly and efficiently. However, the accuracies of the existing related methods were not high enough. In this paper, we propose a framework to identify DNA-binding proteins. The proposed framework first uses PseKNC (ps), MomoKGap (mo), and MomoDiKGap (md) methods to combine three algorithms to extract features. Further, we apply Adaboost weight ranking to select optimal feature subsets from the above three types of features. Based on the selected features, three algorithms (k-nearest neighbor (knn), Support Vector Machine (SVM), and Random Forest (RF)) are applied to classify it. Finally, three predictors for identifying DNA-binding proteins are established, including [Formula: see text], [Formula: see text], [Formula: see text]. We utilize benchmark and independent datasets to train and evaluate the proposed framework. Three tests are performed, including Jackknife test, 10-fold cross-validation and independent test. Among them, the accuracy of ps+md is the highest. We named the model with the best result as psmdDBPs and applied it to identify DNA-binding proteins.

In particular, the reason when organisation competitors win business, is the gain of organisation sensitive and important data. This happen as results of having a local employee active as an agent inside the organisation or because of a persistent threat attack. The data sharing, data protection, data retention, data risk management, and personnel physical security are the responsibilities of the organisation Chief Security Officer (CSO), who solves the intellectual property theft problems before and when occurred, by combining approached of Information Risk Management (IRM) and Information Security Governance (ISG). In this paper, we propose a new novel security management approach that improve information security return of investment.

Cardiac toxicity represents one of the leading causes of drug failure along different stages of drug development. Multiple very successful pharmaceuticals had to be pulled from the market or labeled with strict usage warnings due to adverse cardiac effects. In order to protect clinical trial participants and patients, the International Conference on Harmonization published guidelines to recommend that all new drugs to be tested preclinically for hERG (Kv11.1) channel sensitivity before submitting for regulatory reviews. However, extensive studies have demonstrated that measurement of hERG activity has limitations due to the multiple molecular targets of drug compound through which it may mitigate or abolish a potential arrhythmia, and therefore, a model measuring multiple ion channel effects is likely to be more predictive. Several phenotypic rapid-throughput methods have been developed to predict the potential cardiac toxic compounds in the early stages of drug development using embryonic stem cells- or human induced pluripotent stem cell-derived cardiomyocytes. These rapid-throughput methods include microelectrode array-based field potential assay, impedance-based or Ca(2+) dynamics-based cardiomyocytes contractility assays. This review aims to discuss advantages and limitations of these phenotypic assays for cardiac toxicity assessment.

To efficiently improve the accuracy of hyperspectral image (HSI) classification, the spatial information is usually fused with spectral information so that the classification performance can be enhanced. In this paper, we propose a new classification method called wavelet transform-based smooth ordering (WTSO). WTSO consists of three main components: wavelet transform for feature extraction, spectral–spatial based similarity measurement, smooth ordering based 1D embedding, and construction of final classifier using interpolation scheme. Specifically, wavelet transform is first imposed to decompose the HSI signal into approximate coefficients (ACs) and details coefficients (DCs). Then, to measure the similar level of pairwise samples, a novel metric is defined on the ACs, where the spatial information serves as the prior knowledge. Next, according to the measurement results, smooth ordering is applied so that the samples are aligned in a 1D space (called 1D embedding). Finally, since the reordering samples are smooth, the labels of test samples can be recovered using the simple 1D interpolation method. In the last step, in order to reduce the bias and improve accuracy, the final classifier is constructed using multiple 1D embeddings. The use of wavelet transform in WTSO can also reduce the high dimensionality of HSI data. By converting the hight-dimensional samples into a 1D ordering sequence, WTSO can reduce the computational cost, and simultaneously perform classification for the test samples. Note that in WTSO, the smooth ordering based 1D embedding and interpolation are executed in an iterative manner. And they will be terminated after finite steps. The proposed method is experimentally demonstrated on two real HSI datasets: IndianPines and University of Pavia, achieving promising results.

Many important reasons influence the organization needs of adoption change as strategic decision. Among this reasons the internal and external forces that inforce organization to find new ways to improve performance on both organizational and individual levels. By applying change, the organization need an effective leadership management. Organization effective change management understood by referring to John Kotter book 'Leading Change'. In his book, Kotter proposed an 8-Stage change model that, defines 8 process to successfully implementation of change. In this paper, Kotter's model extend with information security stage for answering the question "To what extent does leading change management model improve university announcement system performance?".

In industry, chip vision-based detection system cannot detect the dots and shatter within 2 pixels. In the process of chip image detection, image processing algorithm has great influence on the effectiveness and accuracy of detection and recognition. Among them, the image enhancement and edge extraction are the primary characteristics. The classical edge extraction methods mainly include Prewitt operator, Sobel operator, and traditional canny operator. By using these, the processing speed is fast and simple, but to shatter edge extraction is not efficient. In this paper, an enhancement algorithm based on fussy sets algorithm for the chip image processing is presented. We expect that the proposed algorithm can improve the detection accuracy within 2 pixels and improve the processing efficiency.

The development of the Internet of Things will occur within a new ecosystem that will be driven by a number of key players. The public security as one of the key players is going to make real-time communications will be possible not only by humans but also by things at anytime and from anywhere. This research will present the advent of the Internet of Things to create a plethora of innovative applications and services, which will enhance quality of life and reduce inequalities.

The future of mobile wireless communication networks will be experienced several generations as which have been experienced. This kind of development will drive the researches of information technology in industrial area. In this paper, we predict the future generations of mobile wireless communication networks including 4th, 5th, 6th and 7th generations. The main objective of this paper is to propose a technical frame for industry in the future. Thus, this paper is focused on the specification of future generations of wireless mobile communication networks.

The current existing multimedia services have been achieved mainly by fixed internet networks. The 4th Generation (4G) wireless mobile multimedia internet networks integrate with IPv6, LAS-CDMA, OFDM, MC-CDMA, UWB, Networks-LMDS and fixed internet networks to support mobile multimedia services as the same quality of service as fixed internet, which is an evolution not only to move beyond the limitations and problems of 3G, but also to enhance the quality of services, to increase the bandwidth and to reduce the cost of the resource. The 5th wireless mobile multimedia internet networks are real wireless world, which are completed wireless communication without limitation. In this paper, we propose Multi-network data path for 5G real wireless multimedia world.

Monitoring is an important issue for finish machining the assembly interface of the large-scale component, as it plays a crucial role in ensuring security and reliability in the finish machining system. To overcome the interoperable problems caused by different proprietary interfaces and communication protocols, this paper proposes an MTConnect compliant monitoring system for acquisition and supervision of the pivotal data during the finish machining process. The framework of the finish machining system is introduced at first, in which the main components and the workflow are described. What is more, the vital procedure and the involved process data are pointed out. Then the MTConnect standard is used to model the finish machining system, including the architecture and the data items. Based on the presented MTConnect model, a web-based monitoring system is developed for data collection and monitoring while machining the assembly interface of the large-scale component. The validity and the interoperability of the proposed approach are verified by collection and monitoring of the process data during finish machining the assembly interface of a vertical tail.