Tradeoff analysis between time cost and energy cost for fixed-time synchronization of discontinuous neural networks.

This article focuses on the tradeoff analysis between time and energy costs for fixed-time synchronization (FXTS) of discontinuous neural networks (DNNs) with time-varying delays and mismatched parameters. First, a more comprehensive lemma is systematically established to study fixed-time stability, which is less conservative than those in most current results. Besides, theoretical proof has proven that the upper bounds of the settling time (ST) in this article are more accurate compared to existing results. Second, on the grounds of the new fixed-time stability lemma, fixed-time synchronization problem for discontinuous neural networks with time-varying delays and mismatched parameters is explored, and sufficient conditions for fixed-time synchronization are obtained. Further, the upper bounds of energy cost during the synchronization process are estimated. Third, in order to achieve a balance between time cost and energy cost, the genetic algorithm is utilized to find the satisfactory control parameter. Finally, a numerical example is provided to verify the theoretical analysis's correctness and the control mechanism's feasibility.

A Small Target Localization Method Based on the Magnetic Gradient Tensor.

Currently, many small target localization methods based on a magnetic gradient tensor have problems, such as complex solution processes, poor stability, and multiple solutions. This paper proposes an optimization method based on the Euler deconvolution localization method to solve these problems. In a simulation, the Euler deconvolution method, an improved method of the Euler deconvolution method and our proposed method are analyzed under noise conditions. These three methods are evaluated in the field with complex magnetic interference in an experiment. The simulations show that the accuracy of the proposed method is higher than that of the improved Euler deconvolution method and is slightly lower for noisy conditions. The experimental results show that the proposed method is more precise and accurate than the Euler deconvolution and enhanced methods.

Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR.

Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features.

Characteristics of prescriptions and costs for acute upper respiratory tract infections in Chinese outpatient pediatric patients: a nationwide cross-sectional study.

BACKGROUND: To understand the characteristics of prescriptions and costs in pediatric patients with acute upper respiratory infections (AURI) is important for the regulation of outpatient care and reimbursement policy. This study aims to provide evidence on these issues that was in short supply. METHODS: We conducted a retrospective cross-sectional study based on data from National Engineering Laboratory of Application Technology in Medical Big Data. All outpatient pediatric patients aged 0-14 years with an uncomplicated AURI from 1 January 2015 to 31 December 2017 in 138 hospitals across the country were included. We reported characteristics of patients, the average number of medications prescribed per encounter, the categories of medication used and their percentages, the cost per visit and prescription costs of drugs. For these measurements, discrepancies among diverse groups of age, regions, insurance types, and AURI categories were compared. Kruskal-Wallis nonparametric test and Student-Newman-Keuls test were performed to identify differences among subgroups. A multinomial logistic regression was conducted to examine the independent effects of those factors on the prescribing behavior. RESULTS: A total of 1,002,687 clinical records with 2,682,118 prescriptions were collected and analyzed. The average number of drugs prescribed per encounter was 2.8. The most frequently prescribed medication was Chinese traditional patent medicines (CTPM) (36.5% of overall prescriptions) followed by antibiotics (18.1%). It showed a preference of CPTM over conventional medicines. The median cost per visit was 17.91 USD. The median drug cost per visit was 13.84 USD. The expenditures of antibiotics and CTPM per visit (6.05 USD and 5.87 USD) were among the three highest categories of drugs. The percentage of out-of-pocket patients reached 65.9%. Disparities were showed among subgroups of different ages, regions, and insurance types. CONCLUSIONS: The high volume of CPTM usage is the typical feature in outpatient care of AURI pediatric patients in China. The rational and cost-effective use of CPTM and antibiotics still faces challenges. The reimbursement for child AURI cases needs to be enhanced.

Inaugural editorial: Military Medical Research.

Military medicine is one of the most innovative part of human civilization. Along with the rapid development of medicine and advances in military techniques, military medicine has become the focus and intersection of new knowledge and new technologies. Innovation and development within military medicine are always ongoing, with a long and challenging path ahead. The establishment of "Military Medical Research" is expected to be a bounden responsibility in the frontline of Chinese military medicine.

[Oil atomic spectrometric feature selection by Parzen window based vague sets theory].

Large quantity and ambiguity of oil atomic spectrometric information greatly affects the applicable efficiency and accuracy in fault diagnosis. A novel method for choosing less and effective spectrometric features is presented. Based on gearbox test bed, we simulated the normal wear state and two typical faults to acquire the lubricant samples. The three wear states are regarded as three vague sets, and spectrometric feature values are vague values on vague sets. Based on similarity between vague values, mean vague sensibility (MVS) is defined to describe the sensitive degree of spectrometric feature to wear state. Besides, the membership degrees of vague sets greatly depend on human experience. The probability density distribution of spectrometric data of three wear states was estimated with Parzen window. Combined with Bayesian formula, the range of vague sets membership was calculated. Experimental results verify that the proposed method is of efficient help in choosing high fault-sensitive features from so many spectrometric features.

[Research on oil atomic spectrometric data semi-supervised fuzzy C-means clustering based on Parzen window].

A Parzen window based semi-supervised fuzzy c-means (PSFCM) clustering algorithm was presented. The initial clustering centers of fuzzy c-means (FCM) were determined with training samples. The membership iteration of FCM was redefined after the membership degrees of testing samples relatively to each state were calculated using Parzen window. Two typical faults of gear box were simulated through the gear box bed in order to acquire the lubricant samples. Concentration of Fe, Si and B, which were the representative elements, was selected as the three-dimensional feature vectors to be analyzed with FCM and PSFCM clustering methods. The clustering results were that the correct ratio of FCM was 48.9%, while that of PSFCM was 97.4% because of integrating with supervised information. Experimental results also indicated that it can reduce the dependence of the experience and lots of faults data to introduce PSFCM into oil atomic spectrometric analysis. It was of great help in improving the wear faults diagnosis ratio.

[Research on monitoring mechanical wear state based on oil spectrum multi-dimensional time series model].

A new method using oil atomic spectrometric analysis technology to monitor the mechanical wear state was proposed. Multi-dimensional time series model of oil atomic spectrometric data of running-in period was treated as the standard model. Residues remained after new data were processed by the standard model. The residues variance matrix was selected as the features of the corresponding wear state. Then, high dimensional feature vectors were reduced through the principal component analysis and the first three principal components were extracted to represent the wear state. Euclidean distance was computed for feature vectors to classify the testing samples. Thus, the mechanical wear state was identified correctly. The wear state of a specified track vehicle engine was effectively identified, which verified the validity of the proposed method. Experimental results showed that introducing the multi-dimensional time series model to oil spectrometric analysis can fuse the spectrum data and improve the accuracy of monitoring mechanical wear state.