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Accurate object detection requires correct classification and high-quality localization. Currently, most of the single shot detectors (SSDs) conduct simultaneous classification and regression using a fully convolutional network. Despite high efficiency, this structure has some inappropriate designs for accurate object detection. The first one is the mismatch of bounding box classification, where the classification results of the default bounding boxes are improperly treated as the results of the regressed bounding boxes during the inference. The second one is that only one-time regression is not good enough for high-quality object localization. To solve the problem of classification mismatch, we propose a novel reg-offset-cls (ROC) module including three hierarchical steps: the regression of the default bounding box, the prediction of new feature sampling locations, and the classification of the regressed bounding box with more accurate features. For high-quality localization, we stack two ROC modules together. The input of the second ROC module is the output of the first ROC module. In addition, we inject a feature enhanced (FE) module between two stacked ROC modules to extract more contextual information. The experiments on three different datasets (i.e., MS COCO, PASCAL VOC, and UAVDT) are performed to demonstrate the effectiveness and superiority of our method. Without any bells or whistles, our proposed method outperforms state-of-the-art one-stage methods at a real-time speed. The source code is available at https://github.com/JialeCao001/HSD.

OBJECTIVE To clarify whether systemic lymph node dissection (LND) influences the safety of surgery and the survival of patients with locally advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemoradiotherapy (nCRT). SUMMARY BACKGROUND DATA Prognostic impact of systemic lymphadenectomy during surgery after nCRT for ESCC is still uncertain and requires clarification. METHODS This is a secondary analysis of NEOCRTEC5010 trial which compared nCRT followed by surgery versus surgery alone for locally advanced ESCC. Relationship between number of LND and perioperative, recurrence and survival outcomes were analyzed in the nCRT group. RESULTS Three-year overall survival was significantly better in the nCRT group than the S group (75.2% vs 61.5%; P=0.011). In the nCRT group, greater number of LND was associated with significantly better overall survival (HR, 0.358; P < 0.001) and disease-free survival (HR, 0.415; P=0.001), but without any negative impact on postoperative complications. Less LND (< 20 vs ≥ 20) was significantly associated with increased local recurrence (18.8% vs 5.2%, P=0.004) and total recurrence rates (41.2% vs 25.8%, P=0.027). Compared to patients with persistent nodal disease, significantly better survival was seen in patients with complete response and with LND ≥ 20, but not in those with LND < 20. CONCLUSIONS Systemic lymph node dissection does not increase surgical risks after nCRT in ESCC patients. And it is associated with better survival and local disease control. Therefore, systemic lymphadenectomy should still be considered as an integrated part of surgery after nCRT for ESCC.

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates., Accepted by IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI). The official version is at https://ieeexplore.ieee.org/document/9609659

BACKGROUND Developing a nomogram to predict improvement in moderate ischemic mitral regurgitation (IMR) after coronary artery bypass grafting (CABG) is in need. METHODS Between 2010 and 2018, data were retrospectively collected from 112 patients with prior myocardial infarction and moderate IMR undergoing CABG. Patients were divided into two groups based on IMR degree 1 year after CABG as follows: Improved Group with no or mild IMR (n=54) and Failure Group with moderate or severe IMR (n=58). To determine the predictors of postoperative IMR improvement, preoperative clinical and echocardiographic data were compared, and a nomogram was formulated based on all independent predictors. Discriminative ability, calibration, and clinical usefulness of the prediction model were assessed. RESULTS Independent predictors of IMR improvement after CABG constructing the nomogram included duration between infarction and operation, poster-inferior to left ventricular volume ratio, maximum difference of the time to reach minimum systolic volume of 16 segments, P3 leaflet tethering angle, and annular Non-planar angle. The nomogram exhibited well-fitted calibration curves and excellent discriminative ability. The area under receiver operating characteristic curve was 0.974. Patients with a score > 236 demonstrated a high probability of IMR improvement (sensitivity, 90.7%; specificity, 93.1%). Patients in the Improved Group demonstrated greater actuarial survival rates than those in the Failure Group. CONCLUSIONS The nomogram combining 5 preoperative clinical and echocardiographic predictors provides an accurate preoperative estimation of moderate IMR improvement after surgery, with excellent discriminative ability. Based on this nomogram, patients with a higher score predict higher probabilities of IMR improvement.

In this article, a Ka-band satellite communication (SATCOM) transceiver is first presented using a standard CMOS technology. The proposed Ka-band SATCOM transceiver consists of a high-linearity transmitter (TX) and dual-channel receiver (RX); both TX and RX are based on direct-conversion architecture. By implementing the dual-channel RX, multiple multiplexing modes, including polarization multiplexing and frequency multiplexing, can be enabled depending on the application. The RX variable gain is distributed in both RF blocks and baseband blocks to achieve a wide input dynamic range. The LNA employs a dual-coupling transformer for a low-noise figure (NF) and wideband input matching; moreover, it enhances the amplifier unilateralization characteristic to mitigate the loading effect from switched attenuators. An adjacent channel interference (ACI) cancellation scheme is proposed to further enhance the RX linearity in the frequency multiplexing mode. In the TX, single-turn high-quality-factor transformer is employed to realize matching network and four-way power combining. A prototype of the SATCOM transceiver is fabricated in a standard 65-nm CMOS process. Under 1.05-V supply voltage, the TX achieves a ${P{_{SAT}}}$ of 20.5 dBm and an average output power of 12 dBm with 2% error vector magnitude (EVM) and a 37.6-dB ACPR. The dual-channel RX achieves an NF of 5.4 dB, an IIP3 of -30 dBm on the high-gain mode, the ACI cancellation is measured of 7.9 dB with a 100-MHz signal bandwidth.

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. To solve this problem, we propose an edge computing based privacy-preserving truth discovery mechanism, PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers reliabilities. A truth-dependent budget recycle mechanism is designed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude based on worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy w-event (,)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.

Fetal congenital heart disease (CHD) is the most common type of fatal congenital malformation. Fetal four-chamber (FC) view is a significant and easily accessible ultrasound (US) image among fetal echocardiography images. Automatic detection of four fetal heart chambers considerably contributes to the early diagnosis of fetal CHD. Furthermore, robust and discriminative features are essential for detecting crucial visualizing medical images, especially fetal FC views. However, it is an incredibly challenging task due to several key factors, such as numerous speckles in US images, the fetal four chambers with small size and unfixed positions, and category confusion caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying the fetal four chambers' precise detection. Therefore, we propose an intelligent feature learning detection system (FLDS) for FC views to detect the four chambers. A multistage residual hybrid attention module (MRHAM) presented in this paper is incorporated in the FLDS for learning powerful and robust features, helping FLDS accurately locate the four chambers in the fetal FC views. Extensive experiments demonstrate that our proposed FLDS outperforms the current state-of-the-art, including the precision of 0.919, the recall of 0.971, the F1 score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43. In addition, our proposed FLDS is also validated on other visualizing nature images such as the PASCAL VOC dataset, achieving a higher mAP of 0.878 while input size is 608 * 608.

Purpose: Although medical care for transgender and gender nonbinary (TGNB) individuals is rapidly expanding, numerous gaps in the organization of quality care for TGNB individuals remain. In 2018, ...

Background: The aim of the study is to evaluate clinical outcomes of patients with ovarian metastases from colorectal cancer (OM-CRC) treated with complete resection combined with chemotherapy and ...

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle's uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of uncertain task delay and incurred payment. To maximize the requester's utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a truthful budgeted utility maximization auction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s. The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. We identify that deep forest has high time costs and memory requirements---this has inhibited its use on large-scale datasets. In this paper, we propose a simple and effective approach with three main strategies for efficient learning of deep forest. First, it substantially reduces the number of instances that needs to be processed through redirecting instances having high predictive confidence straight to the final level for prediction, by-passing all the intermediate levels. Second, many non-informative features are screened out, and only the informative ones are used for learning at each level. Third, an unsupervised feature transformation procedure is proposed to replace the supervised multi-grained scanning procedure. Our theoretical analysis supports the proposed approach in varying the model complexity from low to high as the number of levels increases in deep forest. Experiments show that our approach achieves highly competitive predictive performance with reduced time cost and memory requirement by one to two orders of magnitude.

Few-shot human-object interaction (FS-HOI) recognition aims at inferring new interactions between human actions and surrounding objects merely with a few available instances. It is beneficial to alleviate the long-tail and combinatorial explosion problems in human-object interaction (HOI). Nevertheless, the existing FS-HOI methods only focus on modeling the relationships between labeled samples and unlabeled samples in the Euclidean domain, which neglects the rich relational structures of the visual information among labeled samples and between human actions and objects. Accordingly, we tackle the few-shot HOI task in the non-Euclidean domain and present a graph-based model, namely, task-oriented high-order context graph network (THCG-Net). It contains a task attention module (TA-Module) and a high-order context graph module (HG-Module). In TA-Module, an attention mechanism is designed by utilizing task information to build a task-oriented space, in which the discriminative information for the current task (episode) is captured by embedding the visual features into the task-oriented space. The HG-Module is proposed to construct a task-level graph and takes the context information as high-order knowledge, which provides discriminative guidance for propagating visual information. It captures the discriminability among different categories while highlights the commonality of related categories adaptively, which effectively transfers knowledge to related categories. Extensive experimental results on two benchmark datasets, HICO-FS and TUHOI-FS, are provided. It demonstrates that our THCG-Net significantly outperforms the state-of-the-art approaches, which proves its impressive effectiveness in recognizing various human actions and surrounding objects in few-shot scenarios.

Deep neural networks (DNNs) have been widely used in many Cyber-Physical Systems (CPS). However, it is still challenging work to deploy DNNs in real-time systems. In particular, the execution time of DNN inference must be predictable, s.t. it could be known whether the run-time inference can complete within a required timing constraint. Moreover, the timing constraints may change dynamically with the run-time environment in many embedded applications, such as autonomous cars. A possible way to meet such dynamic real-time requirements is to execute different sub-networks of a DNN at run-time. However, improper construction of sub-networks may not only introduce unpredictable inference time, s.t. the real-timing constraints could be violated unexpectedly, but also has poor compatibility with the well-optimized machine learning framework (e.g., TensorFlow). In this paper, we study the predictability when executing different sub-networks of a DNN. In particular, we present a feature-wise run-time adaptation framework for DNN inference, which is implemented and validated on NVIDIA Jetson TX2 and Nano with TensorFlow. The experimental results show that our method can achieve predictable inference time in comparison with the state-of-the-art methods.

The head-mounted displays (HMDs) provide immersive experiences in virtual reality (VR). However, the face interactions are limited due to the serious occlusion of the user’s face. Existed approaches try to recover the user’s facial expression by adding additional sensors to HMD. In this paper, we develop a novel framework to reconstruct the userb’s 3D face in VR only using an RGB camera. Given a reference face, a realistic full-textured avatar is created by fitting a 3D Morphable Model (3DMM). A self-supervised UV map Generative Adversarial Network (GAN) is proposed to make the facial texture look more realistic. Next, we propose a novel landmark detection method to locate the landmark positions under HMD occlusion since facial landmarks are commonly used for driving the avatar. To this end, we synthesize a face dataset with HMD. Our method is easy to build and popularize with low cost. The experiments on the synthetic and real HMD data demonstrate that the proposed method can detect landmark accurately and restore facial expressions faithfully despite the large occlusion of HMD.

A considerable number of surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve expensive optimization problems (EOPs) with continuous objective functions. However, in the real-world applications, we may face EOPs with discontinuous objective functions, which are also called EOPs with discontinuous responses (EOPDRs). Indeed, EOPDRs pose a great challenge to current SAEAs. In this paper, a surrogate-assisted differential evolution (DE) algorithm with region division is proposed, named ReDSADE. ReDSADE includes three main strategies: the region division strategy, the Kriging-based search, and the radial basis function (RBF)-based local search. In the region division strategy, we define a new distance measure, called the objective-decision distance. Based on this distance, the evaluated solutions are partitioned into several clusters, and several support vector machine (SVM) classifiers are trained to classify them. These SVM classifiers divide the decision space into several subregions, with the aim of making the objective function continuous in them. In the Kriging-based search, a Kriging model is established in each subregion and combined with DE to search for the optimal solution. In the RBF-based local search, DE is coupled with RBF to search around the best solution found so far, thus accelerating the convergence. By combining these three strategies, ReDSADE is able to solve EOPDRs with limited function evaluations. Three set of test problems and a real-world application are utilized to verify the effectiveness of ReDSADE. The results demonstrate that ReDSADE exhibits good convergence accuracy and convergence speed.

Three-dimensional (3D) printing, also known as additive manufacturing, has the advantages of low cost, easy structure operation, rapid prototyping, and easy customization. In the past few years, materials with different structures, compositions, and properties have been widely studied as prospects in the field of 3D printing. This paper reviews the synthesis methods and morphologies of one-, two- and three-dimensional micro/nano materials and their composites, as well as their applications in electrochemistry, such as supercapacitors, batteries, and electrocatalysis. The latest progress and breakthroughs in the synthesis and application of different structural materials in 3D-printing materials, as well as the challenges and prospects of electrochemical applications, are discussed.

Background: The purpose of our study was to review a series of patients with scaphoid fractures to determine whether there was an association between lunate morphology and progression to delayed union or nonunion when treated operatively or nonoperatively. Secondary aims included evaluation of the relationship between lunate morphology and scaphoid fracture location. Methods: A retrospective review of all patients with a diagnosis of scaphoid fracture was performed at our institution between 2014 and 2017. Medical records and radiographs were evaluated to determine lunate morphology, scaphoid fracture location, treatment, and time to union. Differences between groups were determined using χ2 analysis with significance set at P

In conventional medium voltage Solid-State Circuit Breakers (SSCBs), series-connected semiconductor switches are used with Metal Oxide Varistors (MOVs) in parallel with each of them to extend the voltage ratings of the breakers without incurring the dynamic voltage unbalance. Besides, for isolating the faults at faster speeds, the clamped surge voltages of the MOVs are always much higher than the system voltages. For those reasons, the voltage ratings of all the switches in the SSCBs have to be overdesigned according to the surge voltage values, which causes high material costs along with nonideal conduction losses in the solid-state circuit breakers employed for medium voltage DC distribution systems. To solve this dilemma, a Surgeless Diode-Clamped Multilevel Solid-State Circuit Breaker has been proposed in this paper. It offers an ultrafast isolation speed with no significant surge voltages or dynamic unbalanced voltage across the semiconductor switches. With these attributes, the proposed SSCB can potentially use the semiconductor switches rated at lower voltages compared to conventional SSCBs and have higher efficiency along with faster isolation speed. The simulation and experimental results are presented to validate the technical feasibility and practical values of the proposed solid-state circuit breaker.

Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.

Background: To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regard...

The Line Commutated Converter (LCC)-based High Voltage Direct Current (HVDC) transmission system is an effective scheme for long-distance transmission of wind power. Commutation failure is a common failure of LCC-HVDC transmission system. During the commutation failure, the overvoltage of the sending Alternating Current (AC) grid will occur to disconnect the wind farm from the utility grid and limit the transmission capacity of the system. It is necessary to suppress the overvoltage of the sending AC grid under commutation failure to increase the transmission capacity of the LCC-HVDC system. In this paper, a mathematical model of the transient overvoltage peak value of the sending AC grid under commutation failure is established. The mathematical model can quantitatively analyze the influence of the reactive power of the wind farm and the transmission capacity of the LCC-HVDC system on the overvoltage peak value of the sending AC grid. Based on the mathematical model, a power coordinated control strategy for wind farm is proposed to improve the reactive power support capability under grid overvoltage. The proposed control strategy can effectively suppress the overvoltage of the sending AC grid and increase the transmission capacity of the LCC-HVDC system. The accuracy of the mathematical model is verified by simulation, and the superiority of the power coordinated control strategy is verified by the control hardware-in-the-loop (CHIL) experiment.

The performance of stereo vision tasks degrades when haze exists in the input stereo image pair. Independently applying single image dehazing algorithm on left and right images is not optimal. To overcome the problem, we propose an effective framework, called SRDNet, for simultaneously dehazing stereo images. The main idea of SRDNet is to make full use of the stereo information from cross views improving dehazing performance. It does not explicitly employ the disparity estimation and the correlation matrix. SRDNet comprises two parts: a weight-sharing coarse dehazing network (WSCDN) and a guided separated refinement network (GSRN). The WSCDN is utilized to predict a coarse dehazed image pair. Then the GSRN is introduced to predict the residues for different views by extracting the fused information of cross views and separating the features of different views with a guided channel and spatial refinement module. The residues are added to the coarse dehazed pair so as to make refinement and remove the remained haze. Experimental results demonstrate that our proposed SRDNet surpasses previous image dehazing methods by a significant margin both quantitatively and qualitatively. Moreover, our SRDNet could be a preprocessing step of the stereo-based 3D object detection and boosts the 3D detection accuracy in hazy scenes.

Modular multilevel converters (MMCs) have been a promising topology in medium-voltage motor drive applications over the past decades. The main challenge that hinders the widespread use of the MMC in medium-voltage motor drives is the large voltage fluctuation under low-speed conditions. In this paper, we propose a model predictive control (MPC) for the MMC operating in a wide frequency range. Unlike conventional MPC methods, a novel cost function has been designed to realize the output current control and the voltage balancing control for both low- and high-frequency operations. Compared with the high-frequency circulating current injection method, the proposed method can easily achieve a trade-off between the modulation range and the amplitude of injected common-mode voltage under different operating conditions. In addition, due to the exclusion of the switching process between the low- and high-frequency operation, the control structure of this method is more straightforward. At last, the experimental results verify the effectiveness and superiority of the proposed method.

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods.

© Copyright 2021 The Author(s). At present, Solid-State Fermentation (SSF) is mainly controlled by artificial experience, and the product quality and yield are not stable. Therefore, predicting the quality and yield of SSF is of great significance for improving the utility of SSF. In this works, we propose a Deep Learning Utility Prediction (DLUP) scheme for the SSF in the Industrial Internet of Things (IIoT), including parameter collection and utility prediction of the SSF process. Furthermore, we propose a novel Edge-rewritable Petri net to model the parameter collection and utility prediction of the SSF process and further verify their soundness. More impor- tantly, DLUP combines the generating ability of Least Squares Generative Adversarial Networks (LSGAN) with the predicting ability of Fully Connected Neural Network (FCNN) to realize the utility prediction (usually use the alcohol concentration) of SSF. Experiments show that the proposed method predicts the alcohol concentration more accurately than the other joint prediction methods. In addition, the method in our paper provides evidences for setting the ratio of raw materials and proper temperature through numerical analysis. Tai Shan Industry Leading Talent Project (Grant Number: tscy20180416); Major Science and Technology Innovation Project of Shandong Province (Grant Number: 2019TSLH0214).

This paper proposes and implements an integrated wireless motor system using the laminated magnetic coupler (LMC) and commutative-resonant control (CRC). Prominently, a unique wireless power transfer (WPT) system is developed to directly drive the switched reluctance motor (SRM) over a long distance with all controls centralizing in the transmitter. Differing from the multifrequency wireless motor system, the proposed system firstly employs the single resonant frequency to wirelessly drive a multiphase SRM through the single power transfer channel. Particularly, two orthogonal bipolar coils and one unipolar coil are skillfully integrated and naturally decoupled to form an LMC which acts as the transmitter, intermediate and receiver coils, respectively. By utilizing the CRC to sequentially energize coils in the transmitter, the SRM phase windings can be properly excited by relevant coils in the receiver, which takes the distinct advantages of balanced phase currents, flexible transfer distance, high integration and free maintenance. A prototype is constructed with the system efficiency of 89.14 % and motor speed of 1500 rpm under the distance of 200 mm. Both theoretical and experimental results are provided to validate the feasibility of the proposed system.

The discovery and large-scale exploration of unconventional oil/gas resources since 1980s have been considered as the most important advancement in the history of petroleum geology. It has not only changed the balance of supply and demand in the global energy market, but also improved our understanding of the formation mechanisms and distribution characteristics of oil/gas reservoirs. However, misconceptions widely exist in studies of different types of petroleum accumulations because of the lack of a unified genetic classification. Unconventional reservoir has been used in the literature as a general name for various oil/gas reservoirs that are formed under complex dynamic mechanisms as long as buoyancy is not the major controlling factor. On the other hand, different terms have been given to the reservoirs even with the same formation mechanism. This paper selected six representative basins in China as the major subject of the study, analyzed the drilling results of 80,762 reservoir layers from 12,237 exploration wells in these basins. By investigating the progress of unconventional oil/gas exploration in North America in the past 30 years and addressing the distribution characteristics of discovered 52,926 oil/gas reservoirs in 1,186 basins around the world, this study revealed the correlations and differences of formation and distribution characteristics between conventional and unconventional oil and gas reservoirs. It was found that there are five correlations between conventional and unconventional reservoirs, including sharing the same oil and gas source, occurring in strata with the same geological age, coexisting in the same sedimentary basins, being confined in the same petroleum systems, and being enriched in the same reservoir layers. It was also found that there are five differences between conventional and unconventional reservoirs, including differences in hydrocarbon compositions, spatial relations to source rocks, reservoir lithology and quality, distribution in geological settings, and reservoir formation mechanism. Then, the unified genetic classification scheme is put forward to clarify their differences and correlations. All kinds of conventional and unconventional oil and gas resource are classified into 3 categories and 6 subcategories according to their dynamic mechanisms of formation, reclassified into 15 types based on main controlling factors, and further divided into 49 styles considering their underground occurrences. The application results show that all different oil/gas reservoirs can find their corresponding positions in the classification scheme, and all the oil/gas reservoirs with the same genetic mechanism, major controlling factor and occurrence can find their particular position in this classification scheme.

SARS-CoV-2 is a newly identified member of the coronavirus family that has caused the Coronavirus disease 2019 (COVID-19) pandemic. This rapidly evolving and unrelenting SARS-CoV-2 has disrupted the lives and livelihoods of millions worldwide. As of 23 August 2021, a total of 211,373,303 COVID-19 cases have been confirmed globally with a death toll of 4,424,341. A strong understanding of the infection pathway of SARS-CoV-2, and how our immune system responds to the virus is highly pertinent for guiding the development and improvement of effective treatments. In this review, we discuss the current understanding of neutralising antibodies (NAbs) and their implications in clinical practice. The aspects include the pathophysiology of the immune response, particularly humoral adaptive immunity and the roles of NAbs from B cells in infection clearance. We summarise the onset and persistence of IgA, IgM and IgG antibodies, and we explore their roles in neutralising SARS-CoV-2, their persistence in convalescent individuals, and in reinfection. Furthermore, we also review the applications of neutralising antibodies in the clinical setting—from predictors of disease severity to serological testing to vaccinations, and finally in therapeutics such as convalescent plasma infusion.

Objective To investigate the risk factors for lateral lymph node metastasis (LLNM) in papillary thyroid carcinoma (PTC). Methods A retrospective analysis of 209 patients with PTC who underwent primary surgery at the Beijing Friendship Hospital affiliated with Capital Medical University from November 2014 to November 2018 was performed. The patients were divided into the LLNM group and the non-LLNM group. The clinical and pathological characteristics of the patients were analysed. The risk factors for LLNM were analysed by univariate and multivariate analyses. Results The incidence of LLNM was 13.4% in PTC patients. Univariate analysis showed that the maximum diameter of the primary tumour >2 cm (P 2 cm, ETE, and CLNM were independent risk factors for LLNM (OR values were 3.880, 5.202, and 4.474, respectively). There were 6 patients with skip lateral cervical lymph node metastasis, accounting for 21% of all LLNM patients. Conclusion This study revealed several independent risk factors for predicting LLNM in PTC patients, such as the maximum diameter of the primary tumour >2 cm, ETE and CLNM. Lateral neck dissection may be recommended in PTC patients with those risk factors. Paying attention to the occurrence of skip lateral cervical lymph node metastasis during the clinical diagnosis and treatment processes is necessary.