Infectious mononucleosis in children and differences in biomarker levels and other features between disease caused by Epstein-Barr virus and other pathogens: a single-center retrospective study in China.

Background: Infectious mononucleosis (IM) is a common viral infection that typically presents with fever, pharyngitis and cervical lymphadenopathy. Our aim was to identify the different pathogens causing IM in children admitted to our hospital and to analyze the differences in features of infection with different organisms. Methods: We retrospectively analyzed the data of children aged 0-17 years admitted to Wuhan Children's Hospital during 2013-2022 with IM. We compared symptoms, physical findings, blood counts, and serum biomarkers between patients with IM due to Epstein-Barr virus (EBV) and IM due to other pathogens. Results: Among 1480 enrolled children, 1253 (84.66%) had EBV infection, 806 (54.46%) had M. pneumoniae infection, 796 (53.78%) had cytomegalovirus infection, 159 (10.74%) had parvovirus infection, 38 (2.57%) had influenza virus infection, and 25 (1.69%) had adenovirus infection. Receiver operating characteristic curves were used to determine the area under the curve for alanine transaminase (ALT), aspartate transaminase (AST), Alkaline phosphatase (ALP), total bilirubin (TBil), indirect bilirubin (IBil) levels to assess liver damage, and for creatine kinase (CK), CK-MB, and lactate dehydrogenase (LDH) levels to assess myocardial damage. The optimal cutoff values of these biomarkers were then determined. In multivariate analysis, elevated ALT, AST, ALP, TBil, and IBil were independently associated with liver damage, and age <3 years, CK, CK-MB, and LDH with myocardial damage. Conclusion: Evaluation of biomarkers and pathogen detection may help physicians to take preventive actions to avoid serious complications in children with infectious mononucleosis.

Impact of psychological contract breach on firm's innovative performance: A moderated mediation model.

Organizations are seeking ways to be more competitive in the market. Globalization also paves the way for additional challenges for firms to compete in today's knowledge-based economy and competitive corporate settings. The psychological contract breach (PCB) of employees could be a possible reason to slow down the firm's innovative performance. Based on the social exchange theory, the present study assumes that a PCB negatively affects a firm's innovative performance. The present study also assessed the mediating role of knowledge hiding (KH) and moral disengagement (MD) in the relationship between PCB and a firm's innovative performance. This study also attempts to check the moderating role of perceived supervisor support (PSS) in the relationship between PCB and KH and between PCB and MD. For empirical investigation, the present study collected the data from 303 employees of various textile organizations in china through a structured questionnaire method using a convenient sampling technique. The present study applied partial least square structural equation modeling for empirical analyses using Smart PLS software. The present study revealed that a PCB does not directly influence a firm's innovative performance. However, the results confirmed that KH negatively mediates the relationship between PCB and a firm's innovative performance. On the other hand, results also confirmed that MD negatively mediates the relationship between PCB and a firm's innovative performance. The finding also acknowledged that the PSS does not moderate the relationship between PCB and KH. Additionally, the findings confirmed that PSS positively moderates the relationship between PCB and moral disengagement. The present study offers important practical, theoretical, and managerial implications.

Object detection based on an adaptive attention mechanism.

Object detection is an important component of computer vision. Most of the recent successful object detection methods are based on convolutional neural networks (CNNs). To improve the performance of these networks, researchers have designed many different architectures. They found that the CNN performance benefits from carefully increasing the depth and width of their structures with respect to the spatial dimension. Some researchers have exploited the cardinality dimension. Others have found that skip and dense connections were also of benefit to performance. Recently, attention mechanisms on the channel dimension have gained popularity with researchers. Global average pooling is used in SENet to generate the input feature vector of the channel-wise attention unit. In this work, we argue that channel-wise attention can benefit from both global average pooling and global max pooling. We designed three novel attention units, namely, an adaptive channel-wise attention unit, an adaptive spatial-wise attention unit and an adaptive domain attention unit, to improve the performance of a CNN. Instead of concatenating the output of the two attention vectors generated by the two channel-wise attention sub-units, we weight the two attention vectors based on the output data of the two channel-wise attention sub-units. We integrated the proposed mechanism with the YOLOv3 and MobileNetv2 framework and tested the proposed network on the KITTI and Pascal VOC datasets. The experimental results show that YOLOv3 with the proposed attention mechanism outperforms the original YOLOv3 by mAP values of 2.9 and 1.2% on the KITTI and Pascal VOC datasets, respectively. MobileNetv2 with the proposed attention mechanism outperforms the original MobileNetv2 by a mAP value of 1.7% on the Pascal VOC dataset.

Resonator fiber-optic gyro with intensity error suppression using a bias-sampling compensation technique.

Resonator fiber-optic gyros (RFOGs) are one of the most promising candidates for next-generation inertial rotation sensors. The frequency servo loop is used to lock the laser frequency to the resonance frequency of fiber ring resonator via adjusting the laser diode current, which increases accompanying intensity variation and induces RFOG drift. RFOG output compensation using a bias-sampling technique is proposed to suppress intensity error. The linear relationship between the RFOG output and bias is verified by theory and experimental results, and the bias-sampling compensation technique by monitoring the bias signal is analyzed in detail. With the compensation technique, intensity error is significantly suppressed, and the RFOG bias stability is effectively improved from 127.2 to 7.4 deg/h, which demonstrates tactical-grade performance.

Frequency tracking out-of-lock control in a resonator fiber-optic gyro.

A resonator fiber-optic gyro (RFOG) is being pursued because of its theoretical potential to meet navigation-grade performance with small size, high precision, and lower cost. The stability of the RFOG operation is based on the synchronization of laser frequency to the fiber ring resonator (FRR) resonance frequency. Frequency tracking out-of-lock will lead to peak pulse and zero-bias change at the output of the RFOG, which seriously degrades the performance. First, the influence mechanism of frequency tracking out-of-lock is analyzed. The change of current and temperature in frequency tracking and the symmetry change caused by backscatter and polarization are the main reasons for the peak pulse and zero-bias error. Second, a scheme of out-of-lock control of the RFOG based on temperature closed-loop operation using digital signal processing is proposed. The improved scheme, signal processing, and implementation method are investigated in detail. Finally, a RFOG prototype is assembled and tested, and 10 min tracking of the laser frequency to the FRR's single-resonance frequency is realized by temperature closed-loop operation. The static performance of the RFOG over 1 h shows that the RFOG output errors caused by frequency tracking out-of-lock are successfully eliminated. The output peak pulse is reduced from 3000 to 200 deg/h, the zero bias is eliminated from 50 to 600 deg/h to 0, and the bias stability of the RFOG is improved from 15.2 to 1.85 deg/h, which indicates a remarkable advance in the performance of the RFOG to satisfy civil navigation application requirements.

Frequency tuning ratio testing of a laser via a hollow photonic crystal fiber resonator.

In order to satisfy the requirements of laser frequency tuning ratio (FTR) measurement, experimental equipment based on a hollow photonic crystal fiber resonator (HPCFR) is proposed in this paper. First, the principle scheme of the equipment consisting of HPCFR is designed, and the resonance curves of the HPCFR are theoretically analyzed, calculated, and simulated; second, the transmissive HPCFR sample is fabricated and the resonance curve is obtained; eventually, the experimental results from the established laser FTR experimental setup demonstrate that the FTRs of a narrow-linewidth fiber laser and semiconductor laser are 17.6 MHz/V and 30.9 MHz/mA, respectively, which are basically in accordance with the factory parameters of the lasers. This work shows that the FTR experimental equipment via HPCFR has the advantages of high precision and good long-term stability.

FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse.

Most of the recent successful object detection methods have been based on convolutional neural networks (CNNs). From previous studies, we learned that many feature reuse methods improve the network performance, but they increase the number of parameters. DenseNet uses thin layers that have fewer channels to alleviate the increase in parameters. This motivated us to find other methods for solving the increase in model size problems introduced by feature reuse methods. In this work, we employ different feature reuse methods on fire units and mobile units. We solved the problem and constructed two novel neural networks, fire-FRD-CNN and mobile-FRD-CNN. We conducted experiments with the proposed neural networks on KITTI and PASCAL VOC datasets.