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Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosomes type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one localscale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis.

Prognostic is an essential part of condition-based maintenance, which can be employed to enhance the reliability and availability and reduce the maintenance cost of mechanical systems. This paper develops an improved remaining useful life (RUL) prediction method for bearings based on a nonlinear Wiener process model. First, the service life of bearings is divided into two stages in terms of the working condition. Then a new prognostic model is constructed to reflect the relationship between time and bearing health status. Besides, a variety of factors that cause uncertainties toward the degradation path are considered and appropriately managed to obtain reliable RUL prediction results. The particle filtering is utilized to estimate the degradation state, qualify the uncertainties, and predict the RUL. The experimental studies show that the proposed method has a better performance in RUL prediction and uncertainty management than the exponential model and the linear model.

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In this study a complex of naringenin with hydroxypropyl-β-cyclodextrin (HP-β-CD) was prepared to improve the hydrophilicity of naringenin. The physicochemical properties of the complex were analyzed by ultraviolet-visible spectrometry (UV), infrared spectrometry (IR), X-ray diffractometry (XRD), differential scanning calorimetry (DSC). The result showed that naringenin had been molecularly dispersed in the HP-β-CD matrix, not forming a new compound and HPLC analysis showed that the solubility of naringenin in water was enhanced from 4.38 μg/mL to 1,272.31 μg/mL.

To address the problems of high computational complexity, inflexible frame length adjustment, and sub-optimal system efficiency of the RFID tag anti-collision algorithms in the Internet-of-Things systems, a low-complexity, and universal fast RFID tag anti-collision algorithm is proposed in this paper. A faster and less-complex tag number estimation method depends less on computing and storage resources, making it easier to integrate into the Internet of Things. Using the concepts of sub-frame and system efficiency priority, after each sub-frame is identified, the number of tags is estimated quickly and the frame length is adjusted dynamically to ensure the algorithm's efficiency. Moreover, the proposed algorithm is fully compatible with the EPC Class-1 Generation-2 standard, which ensures its universality and compatibility with the existing systems. The simulation results show that the proposed algorithm can achieve a system efficiency of 0.3554, a time efficiency of 0.7851, and an identification speed of 433 n/s. Compared with the standard Q algorithm, the performance is improved by 9.691%, 5.002%, and 8.250%, respectively. It is, hence, demonstrated that the proposed algorithm meets the requirements for the rapid identification of the RFID tags in the Internet-of-Things applications. INDEX TERMS Anti-collision algorithm, Internet of Things, low cost, RFID tag, system efficiency priority. I. INTRODUCTION Radio-frequency identification (RFID) technology has been widely used in industrial, agricultural, and commercial production systems such as warehouse management, logistics tracking and supply chains [1], [2], biological asset inventory, and ticketing systems. The integration of RFID and sensing technology makes it possible for RFID sensor tags to function as intelligent sensing nodes in the Internet of Things and has led to additional research results and commercial products [3]. Passive Ultra High Frequency (UHF) RFID technology is based on the EPC Global Class-1 Generation-2 (EPC C1G2) standard. UHF RFIDs have the advantages of fast identification, no need for a power supply, and a long communication distance. They have been widely studied and applied in Internet of Things systems [3], [4]. The associate editor coordinating the review of this manuscript and approving it for publication was Vyasa Sai.

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