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We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}, and the CQU-BPDD dataset is able to be accessed on \url{https://dearcaat.github.io/CQU-BPDD/}., Published on IEEE Trans on ITS

In recent years, the rapid development of UAV technology has greatly improved the efficiency of the detection of concrete bridge cracks. With the increase in the number of bridge inspection UAVs, the number of tasks handled by cloud services has increased linearly, resulting in increased computational pressure on cloud services. In order to reduce the computational load of cloud servers, we proposed a crack segmentation network based on UAV-enabled edge computing. However, due to the limitation of computational capability of edge computing and the strength inhomogeneity and background complexity of cracks, crack detection is still a challenging task. Thus, we proposed an effective concrete crack segmentation network based on UAV-enabled edge computing, the network used feature map fusion to fuse different levels of feature map information into lower level features for crack detection. The atrous spatial pyramid pooling network was used to increase the low-resolution feature map receptive field information for cracks and to enhance the detection accuracy for cracks of different scales. In addition, loss functions for crack datasets were proposed to solve the problem of imbalance due to positive and negative samples in the concrete crack images. Experiments demonstrated that the proposed methods are better than the state-of-the-art edge detection and semantic segmentation methods in terms of accuracy and generality.

The Internet of Things (IoT) has become more prevalent in recent years and is an integral part of everyday life. Thus, the need for ultralow-power sensors and processing units has dramatically increased. The continuous scaling of CMOS transistors has resulted in more severe subthreshold leakage and high power density issues. In recent years, microelectromechanical (MEM) relays have attracted research interests. They are viewed as promising beyond-CMOS candidates due to their nonleaking property and abrupt switch on/off characteristics. This article presents the design and characterization of a few core relay-based digital blocks, including an adder and a D-latch. While the mechanical movement of relays makes them inherently slower than transistors, we show that the scaled 32-bit relay adder offers 60 times less energy per operation than its CMOS counterpart in 40-nm technology. This makes the proposed relay circuits particularly appealing to applications with stringent demand on energy efficiency and moderate performance requirements, such as remote sensors, wearable accessories, and implantable biomedical devices.

As an extremely important part of traffic arteries, bridge structure plays an essential role in national economic construction, social development and smart city. Thus the monitoring of the bridge structure health are increasingly concerned by the bridge industry scholars and engineering people at home and aboard. In this paper, we propose a deep learning framework to evaluate the safety of the bridge structural state. More specifically, the proposed system generates a learnable transformation which attempts to map most of the data network representations into a hypersphere characterized of minimum volume. During inference, mappings of normal examples fall within the learned hypersphere, whereas mappings of anomalies fall outside the hypersphere. The whole system is end-to-end trainable and outperforms other advanced methods in real-world dataset.

Electrocatalytic water splitting is considered as a promising route to use renewable energy for hydrogen production; however, its industrial application is limited by the anodic reaction, oxygen ev...

With the rapid development of society, many industries have integrated intelligent advanced technology, and electrical automation control is one of them. Electrical automation control system is a control system for the efficiency, status, quality and other aspects of electrical operation. Through the system, electrical operation can be well controlled. After the use of intelligence, the system has the ability of sub identification and decision-making. This paper first briefly analyzes the application theory of artificial intelligence, and then expounds the advantages of artificial intelligence controller. This paper studies the application of artificial intelligence technology in electrical automation control system from two aspects of the application of artificial intelligence in DC drive and AC drive, and analyzes the advantages of artificial intelligence control. Taking the application of AI Artificial Intelligence regulator in the control of an electrical appliance as an example, this paper expounds the circuit principle, implementation scheme and application effect of the system.

Structural health monitoring (SHM) systems have been extensively used to ensure the operational safety of long-span bridges. Large-scale bridge structural response and loading data observed from various sensors show obvious big data characteristics. However, serious “data island” problems, which exist in the conventional SHM solutions, inevitably limit the effects of sensory data analysis and information sharing. A unified bridge SHM semantic representation model is much in demand. By taking the advantages of Semantic Web technologies, this article presents a novel model, called the bridge structure and health monitoring ontology, to achieve fine-grained modeling of bridge structures, SHM systems, sensors, and sensory data from multiple perspectives. A bridge SHM big data platform is used to demonstrate the usefulness. Several representative data accessing and rule-based reasoning scenarios are employed as to illustrate the advantages of the proposed manner.

Structural damage detection has become an interdisciplinary area of interest for various engineering fields, while the available damage detection methods are being in the process of adapting machine learning concepts. Most machine learning based methods heavily depend on extracted ``hand-crafted" features that are manually selected in advance by domain experts and then, fixed. Recently, deep learning has demonstrated remarkable performance on traditional challenging tasks, such as image classification, object detection, etc., due to the powerful feature learning capabilities. This breakthrough has inspired researchers to explore deep learning techniques for structural damage detection problems. However, existing methods have considered either spatial relation (e.g., using convolutional neural network (CNN)) or temporal relation (e.g., using long short term memory network (LSTM)) only. In this work, we propose a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection. Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly. Extensive experiments on IASC-ASCE structural health monitoring benchmark and scale model of three-span continuous rigid frame bridge structure datasets have shown that our proposed HCG outperforms other existing methods for structural damage detection significantly., The work has been accepted by Information Sciences!

Purpose We investigate factors that may contribute individually to bladder recurrence and find out the potential candidate to receive postoperative single dose intravesical chemotherapy. Materials and methods A total of 217 patients who were diagnosed with upper tract urothelial carcinoma (UTUC) underwent radical nephroureterectomy (RNU) between 2012 and 2016 in a single hospital. The possible risk factors that may contribute to development of bladder recurrence and overall survival were analysed. In order to find out the relationship between 1st bladder recurrence timing and outcome, we divided the 54 of 56 patients (2 patients with prophylactic intravesical chemotherapy excluded) with bladder recurrence after RNU into 2 groups, using the median time of 1st bladder recurrence and confirmed with the "minimum P-value" approach. The primary endpoint was the development of relapsing high-risk non-muscle invasive bladder cancer (NMIBC). The predictive factors of early recurrence and prognostic factors of survival were also analysed. Results Among 217 patients with UTUC under RNU, intravesical recurrence occurred in 56 (25.8%) patients after a median follow-up of 35.2 (1.18-83.34) months. On multivariable analysis, the preoperative ureter manipulation (p=0.009) was a significant predictor for the development of bladder tumours. As for overall survival, renal rein invasion (p=0.017), neutrophil to lymphocyte ratio (p=0.021), and main tumour size (p=0.015) were significant predictors. For 54 patients who developed bladder recurrence, the optimal cut-off point of early recurrence was determined to be 10 months after surgery (p=0.042). Preoperative ureter manipulation (p=0.005) and tumour located both pelvicalyceally and ureterically (P=0.042) were identified as independent factors associated with early recurrence. An end-stage renal disease history and surgical margin positive patient has more late bladder recurrence. Conclusion Bladder recurrence was common in UTUC after RNU. Early bladder recurrence was associated with more relapsing high-risk NMIBC and preoperative ureter manipulation was identified as an independent factor associated with early recurrence.

A high-quality X-ray source was proposed by modifying the target material structure characteristics driven by ultrahigh laser energy. The experiments were performed on the Ti:sapphire femtosecond laser beam device (4.3–6 J, 30 fs), one of the three XG-III lasers in Laser Fusion Research Center of China Academy of Engineering Physics. The femtosecond laser beam drove the nanowire copper material with an average length of 18–50 μm and a diameter of about 260 nm. A single-photon counting charge-coupled device was employed to measure the copper Kα X-ray emission of the nanowire and foil targets. A clear maximum photon yield of the nanowire target was calculated to be 3.6 × 108 photons sr–1 s–1, the conversion efficiency was up to 0.0087%, and the average yield was 2.5 times that of the copper foil targets. In addition, by using a pinhole imaging method of φ10 μm, the minimum full width at half maximum spot size of the X-ray source was calculated in the range of 85–240 μm, which was similar to that of the copper foil material with a long radius of 170 μm and a short radius of 63 μm. The experimental data illustrate that the nanowire has the potential to enhance the energy absorption of femtosecond laser for X-ray conversion and backlight imaging.

Background Hypersplenism is a serious complication of portal hypertension (PH) and can affect the prognosis of liver disease. Liver stem cells (LSCs) are involved in liver regeneration and hepatocarcinogenesis after liver cirrhosis. Aim To explore the effects and mechanism of the spleen on the proliferation and differentiation of LSCs in PH due to liver cirrhosis. Methods Fetal liver stem cells (FLSCs) were treated with splenic serum from liver cirrhosis patients with hypersplenism and control serum from healthy volunteers, and the proliferation, self-renewal, and IGF-II/ERK signaling pathway of FLSCs were then evaluated. Results We found that splenic serum from PH patients promoted FLSC proliferation, colony formation, and Ki-67 expression in vitro. Splenic serum from PH also enhanced FLSC spheroid formation in vitro. Mechanistically, we determined that insulin-like growth factor (IGF)-II concentration was elevated in splenic serum from PH patients and could promote FLSC proliferation and self-renewal. Furthermore, both IGF-II and splenic serum from PH patients enhanced ERK signaling activation through IGF-I receptor (IGF-I R) in FLSCs. Consistently, blocking IGF-I R or ERK signaling could attenuate the effects of splenic serum from PH patients on FLSCs. Conclusions The spleen in PH patients promotes FLSC proliferation and self-renewal through the IGF-II/ERK signaling pathway.

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high‐dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non‐Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C‐SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

The scaled wind energy bundled with thermal power is an essential way for the consumption of renewable energy in wind farms far away from load center. However, practices have shown that sub synchronous torsional interaction may occur in wind–thermal power bundled transmission systems, which threatens the stability of bundled systems. In this paper, the impedance modeling method based on graphical state space is applied to obtain a detailed impedance model of thermal power unit, which contains the characteristics of shaft torsion. Based on time domain simulation, the responses of each torsional mode of thermal power unit to wind power oscillation are analyzed. The results show that when the frequency of wind power oscillation is close to the torsional mode frequency, the shaft torsional oscillation may be excited, but the risks of different torsional modes are different, which can be explained via the characteristics of shaft torsion in the impedance of thermal power units.

In this paper, development of coupled codes using two-group neutron diffusion kinetics code and computational fluid dynamics (CFD) solver Fluent has been introduced. Way of coupling, time step control algorithm and spatial mesh overlays have been summarized in detail which are basic components and challenges of the coupling methodologies. The implement and verification of coupled code have been modeled on integral pressurized water reactor (IPWR) IP200 with hexagonal fuel assembly in the core and once-through steam generators. The steam line break core transient was analyzed in coupled code simulation of a core boundary conditions derived from system code simulation results. The results presented transient three-dimensional distribution of the key operation parameters such as reactor power and coolant temperature, also demonstrated the inherent safety features of IP200. The current work will bring about the ability to explore multi-scale and multi-dimensional safety transient evaluations and give more precise neutronics/thermal-hydraulics mapping.

Background Patients with locally advanced sigmoid colon cancer (LASCC) have limited treatment options and a dismal prognosis with poor quality of life. This retrospective study aimed to further evaluate the feasibility and efficacy of neoadjuvant chemoradiotherapy (NACRT) followed by surgery as treatment for select patients with unresectable LASCC. Methods We studied patients with unresectable LASCC who received NACRT between November 2010 and April 2019. The NACRT regimen consisted of intensity modulated radiotherapy (IMRT) of 50 Gy to the gross tumor and positive lymphoma node and 45 Gy to the clinical target volume. Capecitabine‑based chemotherapy was administered every 2 (mFOLFOX6) or 3 weeks (CAPEOX). Surgery was scheduled 6–8 weeks after radiotherapy. Results Seventy‑two patients were enrolled in this study. Patients had a regular follow-up (median, 41.1 months; range, 8.3–116.5 months). Seventy‑one patients completed NACRT, and sixty-five completed surgery. Resection with microscopically negative margins (R0 resection) was achieved in 64 patients (88.9%). Pathologic complete response was observed in 15 patients (23.1%), and multivisceral resection was necessary in 38 patients (58.3%). The cumulative probability of 3-year overall survival (OS) and progression-free survival (PFS) were 75.8 and 70.7%, respectively. Conclusions For patients with unresectable LASCC, neoadjuvant chemoradiotherapy is feasible, surgery can be performed safely and may result in increased survival and organ preservation rates.

The adaptive immunity that protects patients from coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is not well characterized. In particular, the asymptomatic patients have been found to induce weak and transient SARS-CoV-2 antibody responses, but the underlying mechanisms remain unknown; meanwhile, the protective immunity that guide the recovery of these asymptomatic patients is elusive. Here, we characterized SARS-CoV-2-specific B-cell and T-cell responses in 10 asymptomatic patients and 64 patients with other disease severity (mild, n = 10, moderate, n = 32, severe, n = 12) and found that asymptomatic or mild symptomatic patients failed to mount virus-specific germinal center (GC) B cell responses that result in robust and prolonged humoral immunity, assessed by GC response indicators including follicular helper T (TFH) cell and memory B cell responses as well as serum CXCL13 levels. Alternatively, these patients mounted potent virus-specific TH1 and CD8+ T cell responses. In sharp contrast, patients of moderate or severe disease induced vigorous virus-specific GC B cell responses and associated TFH responses; however, the virus-specific TH1 and CD8+ T cells were minimally induced in these patients. These results, therefore, uncovered the protective immunity in asymptomatic patients and also revealed the strikingly dichotomous and incomplete humoral and cellular immune responses in COVID-19 patients with different disease severity, providing important insights into rational design of effective COVID-19 vaccines.

Permafrost is important for the climatic, hydrological, and ecological processes on the Qinghai-Xizang Plateau (QXP). The changing permafrost and its impact have been attracting great attention worldwide never before, and more observational and modeling approaches are need to promote an understanding of permafrost thermal state and climatic conditions on the QXP. However, there were almost no synthesis dataset on the permafrost thermal state and climate background on the QXP, but were sporadically reported in different literatures due to the difficulties to access to and work in this region, where the weather is severe, and environmental conditions are harsh and the topographic and morphological features are complex. In this study, a comprehensive dataset under quality controlled consisting of long-term meteorological, ground temperature, soil moisture and soil temperature data were compiled from an integrated, distributed and multiscale observation network in the permafrost regions of the Cryosphere Research Station on the QXP. Meteorological data were observation by automatic meteorological stations from a comprehensive observation network. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature was observed from boreholes. These datasets were helpfur for the scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will greatly promote the verification, development and improvement of the hydrological model, land surface process model and climate model on the QXP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, https://doi.org/10.11888/Geocry.tpdc.271107).

Strong coupling between light and matter is the foundation of promising quantum photonic devices such as deterministic single photon sources, single atom lasers, and photonic quantum gates, which consist of an atom and a photonic cavity. Unlike atom-based systems, a strong coupling unit based on an emitter-plasmonic nanocavity system has the potential to bring these devices to the microchip scale at ambient conditions. However, efficiently and precisely positioning a single or a few emitters into a plasmonic nanocavity is challenging. In addition, placing a strong coupling unit on a designated substrate location is a demanding task. Here, fluorophore-modified DNA strands are utilized to drive the formation of particle-on-film plasmonic nanocavities and simultaneously integrate the fluorophores into the high field region of the nanocavities. High cavity yield and fluorophore coupling yield are demonstrated. This method is then combined with e-beam lithography to position the strong coupling units on designated locations of a substrate. Furthermore, polariton energy under the detuning of fluorophore embedded nanocavities can fit into a model consisting of three sets of two-level systems, implying vibronic modes may be involved in the strong coupling. Our system makes strong coupling units more practical on the microchip scale and at ambient conditions and provides a stable platform for investigating fluorophore-plasmonic nanocavity interaction.

Intravenous (i.v.) glucocorticoid is recommended for active moderate-to-severe thyroid-associated ophthalmopathy (TAO). However, the details of the treatment schedule are still debatable. The present prospective randomized trial was performed to compare clinical outcomes and serum cytokines between the two regimens. A cohort of 90 patients with active moderate-to-severe TAO was randomized to receive i.v. methyl prednisolone on a weekly protocol or daily scheme. The response rate was evaluated at the 12-week follow-up visit. Serum interleukin (IL)-2, IL-6 and IL-17 levels were measured in 160 patients with TAO, 60 patients with isolated Graves' disease (GD) and 60 normal control (NC) at baseline, as well as patients with active moderate-to-severe TAO at the 12(th) week after treatment. The daily scheme had a higher response rate than the weekly protocol without a significant difference (77.8 vs. 63.6%, P>0.05). No major adverse events were recorded under either regimen. Overall, minor events were more common on the daily scheme (11.36 vs. 4.35%, P

BEOL NEM Relay-Based Inductorless DC-DC Converters Ren Li, Dias Azhigulov, Ahmed Allehyani, and Hossein Fariborzi Introduction •The voltage level requirement varies across the integrated circuit chip. Therefore, on-chip voltageconversions are crucial for anychip to be operational. •This work proposes a novel solution for voltage regulation using Back-End-Of-Line (BEOL)NEM relays. •The proposed Buck and Boost converters offer low voltage ripple, high conversion efficiency and low area overhead. Relay and its Operation •The relay is actuated electrostatically by the voltage difference between G and B. •Two inherent threshold voltages:VPI (pull-in voltage) and VPO(pull-out voltage). Relay Buck Converter •The buck converter uses Switched-Capacitor Buck Converter (SCBC) configuration. Relay Boost Converter •The boost converter utilizes Switched-Capacitor Voltage Doubler (SCVD) concept. Results and comparisons •Custom Verilog-A relay model is used for Relays •Validated against a commercialFEM tool, MEMS Conclusion The BEOL NEM relay-based converters have the following benefits: •Capable of handling higher voltages; •Utilize switched-capacitorconfiguration to avoid bulky on-chip inductors; •Zero sub-threshold leakage andsharp turn on/off curves; •Low output voltage ripple and highconversion efficiency. •Perfect for ultra low power applications, such as IoT and biological, implantable medical devices. References 1 R. Li, D. Azhigulov, A. Allehyani and H. Fariborzi, BEOL NEM Relay-Based Inductorless DC-DC Converters in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020. 2 D. Ma and R. Bondade, Reconfigurable switched-capacitor power converters: principles and designs for self-powered microsystems, 1 ed. New York: Springer-Verlag New York, 2013, p. 178. 3 R. Li, R. Alhadrami, and H. Fariborzi, BEOL NEM Relay Based Sequential Logic Circuits, in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019.

Hyperthyroid heart disease (HHD) is one of the most severe complications of overt hyperthyroidism and increases the risk of mortality in affected patients. Early identification of patients at a higher risk of developing HHD can improve clinical outcomes through active surveillance and management. Connective tissue growth factor (CTGF), a secreted extracellular protein, plays a significant role in cardiac remodeling and dysfunction. We aimed to investigate the association between plasma CTGF level and the risk of HHD in this study. A total of 142 overt hyperthyroid patients without HHD and 99 patients with HHD were included. The plasma CTGF levels were measured using ELISA kits. Routine clinical medical data and echocardiography parameters were recorded for analysis. The plasma CTGF level was significantly higher in patients with HHD than in those without HHD (P=0.002). The plasma CTGF level was positively correlated with free triiodothyronin, tryrotropin receptor antibody, troponin I and lactate dehydrogenase levels and the left atrium diameters, right atrium diameters, and right ventricular end-diastolic diameters (all P

A low-profile conformal endfire antenna is proposed in this paper. The proposed antenna is a traveling wave antenna, which can be integrated on a curved surface. By changing exciting ports, a forward or backward radiation can be realized. The endfire antenna can form array and realize beam scanning. A prototype of the proposed endfire antenna array is fabricated and tested. The measured results are consistent with the full-wave simulation results. Foam is filled in the proposed antenna array to increase the bearing capacity. The height of the antenna is only 0.07 λ 0 , where λ 0 is the wave length of the highest frequency.

SummaryThe adaptive immunity that protects patients from coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is not well characterized. In particular, the asymptomatic patients have been found to induce weak and transient SARS-CoV-2 antibody responses, but the underlying mechanisms remain unknown; meanwhile, the protective immunity that guide the recovery of these asymptomatic patients is also not well studied. Here, we characterized SARS-CoV-2-specific B-cell and T-cell responses in 10 asymptomatic patients and 49 patients with other disease severity (mild, n = 10, moderate, n = 32, severe, n = 7) and found that asymptomatic or mild symptomatic patients failed to mount virus-specific germinal center (GC) B cell responses that result in robust and long-term humoral immunity, assessed by GC response indicators including follicular helper T (TFH) cell and memory B cell responses as well as serum CXCL13 levels. Alternatively, these patients mounted potent virus-specific TH1 and CD8+ T cell responses. In sharp contrast, patients of moderate or severe disease induced vigorous virus-specific GC B cell responses and associated TFH responses; however, the virus-specific TH1 and CD8+ T cells were minimally induced in these patients. These results therefore uncovered the protective immunity in COVID-19 patients and revealed the strikingly dichotomous and incomplete adaptive immunity in COVID-19 patients with different disease severity, providing important insights into rational design of COVID-19 vaccines.

COVID-19 patients exhibit differential disease severity after SARS-CoV-2 infection. It is currently unknown as to the correlation between the magnitude of neutralizing antibody (NAb) responses and the disease severity in COVID-19 patients. In a cohort of 59 recovered patients with disease severity including severe, moderate, mild, and asymptomatic, we observed the positive correlation between serum neutralizing capacity and disease severity, in particular, the highest NAb capacity in sera from the patients with severe disease, while a lack of ability of asymptomatic patients to mount competent NAbs. Furthermore, the compositions of NAb subtypes were also different between recovered patients with severe symptoms and with mild-to-moderate symptoms. These results reveal the tremendous heterogeneity of SARS-CoV-2-specific NAb responses and their correlations to disease severity, highlighting the needs of future vaccination in COVID-19 patients recovered from asymptomatic or mild illness.

Intrauterine adhesion (IUA) is one of the most common reproductive system diseases in women worldwide. The role of lncRNAs in multiple diseases has been confirmed, but the role and mechanism of lncRNA taurine upregulated gene 1 (TUG1) in the progression of IUA need to be elucidated further. Here, we found that lncRNA TUG1 was upregulated in the endometrial tissues of IUA and TGF-β1-treated human embryonic stem cells (hESCs). Moreover, lncRNA TUG1-silenced alleviated TGF-β1-induced the proliferation and migration abilities of hESCs and enhanced inflammatory cytokines secretion in vitro. In vivo experiments showed that inhibition of lncRNA TUG1 promoted endometrium regeneration in IUA rats through downregulating inflammatory response and epithelial-to-mesenchymal transition (EMT) process. Mechanistically, lncRNA TUG1 suppression attenuated EMT process and inflammation through competitively binding miR-590-5p to downregulate Fasl expression. Collectively, our findings provide vital theoretical evidence for explaining the mechanisms of the lncRNA TUG1/miR-590-5p/Fasl axis in the progression of IUA, and may provide a new biomarker for the treatment of IUA patients.

Entity Linking (EL) is a task for mapping mentions in text to corresponding entities in knowledge base (KB). This task usually includes candidate generation (CG) and entity disambiguation (ED) stages. Recent EL systems based on neural network models have achieved good performance, but they still face two challenges: (i) Previous studies evaluate their models without considering the differences between candidate entities. In fact, the quality (gold recall in particular) of candidate sets has an effect on the EL results. So, how to promote the quality of candidates needs more attention. (ii) In order to utilize the topical coherence among the referred entities, many graph and sequence models are proposed for collective ED. However, graph-based models treat all candidate entities equally which may introduce much noise information. On the contrary, sequence models can only observe previous referred entities, ignoring the relevance between the current mention and its subsequent entities. To address the first problem, we propose a multi-strategy based CG method to generate high recall candidate sets. For the second problem, we design a Sequential Graph Attention Network (SeqGAT) which combines the advantages of graph and sequence methods. In our model, mentions are dealt with in a sequence manner. Given the current mention, SeqGAT dynamically encodes both its previous referred entities and subsequent ones, and assign different importance to these entities. In this way, it not only makes full use of the topical consistency, but also reduce noise interference. We conduct experiments on different types of datasets and compare our method with previous EL system on the open evaluation platform. The comparison results show that our model achieves significant improvements over the state-of-the-art methods.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel corona virus disease (COVID-19). To date, no prophylactic vaccines or approved therapeutic agents are available for preventing and treating this highly transmittable disease. Here we report two monoclonal antibodies (mAbs) cloned from memory B cells of patients recently recovered from COVID-19, and both mAbs specifically bind to the spike (S) protein of SARS-CoV-2, block the binding of receptor binding domain (RBD) of SARS-CoV-2 to human angiotensin converting enzyme 2 (hACE2), and effectively neutralize S protein-pseudotyped virus infection. These human mAbs hold the promise for the prevention and treatment of the ongoing pandemic of COVID-19.

We aimed to evaluate the feasibility of interventional treatment of atrial septal defect (ASD) in low weight infants under 2-year-old. Seven hundred and ninety-three secundum ASD patients were divided into 2 groups: 665 were above 2-year-old and 128 were under 2-year-old. The basic conditions before the operation, postoperative complications within 24 hours, and adverse outcomes during a three-year follow-up were compared between the 2 groups using multivariate analysis. There were significant differences in age, weight, and the diameter of the ASD between the 2 groups (p0.001). The immediate success rate of the procedure was 96.7%. There were no significant differences in the success rate of the procedure, the incidence of residual shunt, arrhythmia, procedure-related arrhythmia, and occluder shedding between 2 groups (p0.05). Similarly, we found no association between age ≤2-year-old and any adverse outcomes postprocedure within 24 hours, including procedure failure (OR = 0.35; 95%CI: 0.04 to 2.93), residual shunt (OR = 1.07; 95%CI: 0.54 to 2.14), arrhythmia (OR = 0.68; 95%CI: 0.32 to 1.43), or procedure-related arrhythmia (OR = 0.34; 95%CI: 0.04 to 2.87). In the follow-up data, we found no association between age ≤2-year-old and arrhythmia (HR = 0.95; 95%CI: 0.50 to 1.80) and procedure-related arrhythmia (HR = 0.96;95%CI:0.25 to 3.64). Kaplan-Meier survival curves indicated no significant difference in the occurrence of arrhythmia between the 2 groups (log-rank test: p = 0.776). In conclusion, percutaneous ASD closure in young and low weight infants has a high success and low complication rate, along with reliable effects.

Since December 2019, the infection of the new coronavirus (COVID-19) caused an outbreak of new coronavirus pneumonia in Wuhan, China, and caused great public concern. Both COVID-19 and SARS-CoV belong to the coronavirus family and both invade target cells through ACE2. An in-depth understanding of ACE2 and a series of physiological and physiological changes caused by the virus invading the human body may help to discover and explain the corresponding clinical phenomena and then deal with them timely. In addition, ACE2 is a potential therapeutic target. This article will summarize the role of ACE2 in multiple organ damage caused by COVID-19 and SARS-CoV, targeted blocking drugs against ACE2, and drugs that inhibit inflammation in order to provide the basis for subsequent related research, diagnosis and treatment, and drug development.

We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics-1.0 are carefully collected, and fake videos are generated by a newly proposed end-to-end face swapping framework. The quality of generated videos outperforms those in existing datasets, validated by user studies. The benchmark features a hidden test set, which contains manipulated videos achieving high deceptive scores in human evaluations. We further contribute a comprehensive study that evaluates five representative detection baselines and make a thorough analysis of different settings., CVPR 2020. Project page: https://liming-jiang.com/projects/DrF1/DrF1.html

This paper considers the problem of 3D patient body modeling. Such a 3D model provides valuable information for improving patient care, streamlining clinical workflow, automated parameter optimization for medical devices etc. With the popularity of 3D optical sensors and the rise of deep learning, this problem has seen much recent development. However, existing art is mostly constrained by requiring specific types of sensors as well as limited data and labels, making them inflexible to be ubiquitously used across various clinical applications. To address these issues, we present a novel robust dynamic fusion technique that facilitates flexible multi-modal inference, resulting in accurate 3D body modeling even when the input sensor modality is only a subset of the training modalities. This leads to a more scalable and generic framework that does not require repeated application-specific data collection and model retraining, hence achieving an important flexibility towards developing cost-effective clinically-deployable machine learning models. We evaluate our method on several patient positioning datasets and demonstrate its efficacy compared to competing methods, even showing robustness in challenging patient-under-the-cover clinical scenarios.

In western China, large-scale wind power is generally bundled with thermal power, and transmitted to eastern China by high-voltage direct current (HVDC) systems. This constitutes the wind–thermal bundled system transmitted by HVDC (WTBH). In this study, a typical studied system of WTBH is presented, and its models for eigenvalue analysis are introduced. To improve the efficiency of eigenvalue analysis, an improved block modelling method is proposed for building the state-space model of WTBH. By eigenvalue analysis and electromagnetic transient (EMT) simulation, the impact of doubly-fed induction generator (DFIG)-based wind farm integration on sub-synchronous torsional interaction (SSTI) between thermal generators (TGs) and HVDC was investigated. The sensitivity of the operating parameters of DFIG-based wind farms was analysed, including wind speed, wind farm scale, and distance between the wind farm and HVDC rectifier station. The obtained results show that the integration of DFIG-based wind farms can mitigate the SSTI between the TG and HVDC. The system becomes more stable as the wind speed and wind farm scale increase or the distance between the wind farm and HVDC rectifier station decreases. The conclusions of this study are validated through EMT simulations in PSCAD/EMTDC, and provide theoretical reference for practical WTBH projects.

The present study describes the design of a simple teaching module for each student to fabricate a dye-sensitized solar cell (DSSC) that could power a small fan motor. The significance of this laboratory exercise is to stimulate students’ motivation by visualizing the light being converted into electricity, which is then switched over to kinetic energy, and to inspire them to understand the mechanistic working principles of solar cells and gain the concepts of sustainable green energy. The developed DSSC teaching module could easily power a small fan motor under the light of a halogen lamp, a conventional light bulb, or a compact fluorescent lamp. In addition, the fabrication process allowed replacing the expensive massive pressing machine by using electric irons that are readily available in daily life and made this module cost-effective and easy to assemble by school students. The field studies reveal that both junior and senior high school students were able to complete the module in 2.5 h class sessio...

As an important data source in the field of bridge management, bridge inspection reports contain large-scale fine-grained data, including information on bridge members and structural defects. However, due to insufficient research on automatic information extraction in this field, valuable bridge inspection information has not been fully utilized. Particularly, for Chinese bridge inspection entities, which involve domain-specific vocabularies and have obvious nesting characteristics, most of the existing named entity recognition (NER) solutions are not suitable. To address this problem, this paper proposes a novel lexicon augmented machine reading comprehension-based NER neural model for identifying flat and nested entities from Chinese bridge inspection text. The proposed model uses the bridge inspection text and predefined question queries as input to enhance the ability of contextual feature representation and to integrate prior knowledge. Based on the character-level features encoded by the pre-trained BERT model, bigram embeddings and weighted lexicon features are further combined into a context representation. Then, the bidirectional long short-term memory neural network is used to extract sequence features before predicting the spans of named entities. The proposed model is verified by the Chinese bridge inspection named entity corpus. The experimental results show that the proposed model outperforms other mainstream NER models on the bridge inspection corpus. The proposed model not only provides a basis for automatic bridge inspection information extraction but also supports the downstream tasks such as knowledge graph construction and question answering systems.

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false‐negative rate. This article proposes a new imbalanced SVM (termed ImSVM)‐based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over‐sampling techniques and several existing imbalanced SVM‐based techniques.

The safety and efficacy of intraoperative chemotherapy in colorectal cancer have not yet been extensively investigated. This randomized control trial was designed to compare the safety and efficacy of intraoperative chemotherapy in combination with surgical resection to those of traditional surgical resection alone. From January 2011 to January 2016, 696 colorectal cancer patients were enrolled in this study: 341 patients were randomly assigned to the intraoperative chemotherapy, which consist of portal vein chemotherapy, intraluminal chemotherapy and intraperitoneal chemotherapy, plus surgery group, whereas 344 patients were randomized to the control group to undergo surgery alone. Eleven patients withdrew consent. Intraoperative chemotherapy did not increase the rate of surgical complications, and no severe chemotherapy-associated side effects were observed. Four patients in each of the intraoperative chemotherapy and the control groups experienced anastomotic leakage and underwent a second operation (1.2 vs. 1.2%, P = 0.99). There were no deaths within 90 days after surgery in the chemotherapy group, whereas one patient died in the control group. Intraoperative chemotherapy did not decrease the rate of patients who received postoperative chemotherapy between the intraoperative group and control group (29.3 vs. 30.2%, P = 0.795). Intraoperative chemotherapy can be safely performed during colorectal surgery; however, follow-up is necessary for a better assessment of its efficacy. ClinicalTrial.gov Register Number: NCT01465451.

Micro/nano-electromechanical resonator based logic computation has attracted significant attention in recent years due to its dynamic mode of operation, ultra-low power consumption, and potential for reprogrammable and reversible computing. Here we demonstrate a 4-bit parity checker circuit by utilizing recently developed logic gates based on MEMS resonators. Toward this, resonance frequencies of shallow arch shaped micro-resonators are electrothermally tuned by the logic inputs to constitute the required logic gates for the proposed parity checker circuit. This study demonstrates that by utilizing MEMS resonator based logic elements, complex digital circuits can be realized.

A time and covariance threshold triggered optimal maneuver planning method is proposed for orbital rendezvous using angles-only navigation (AON). In the context of Yamanaka-Ankersen orbital relative motion equations, the square root unscented Kalman filter (SRUKF) AON algorithm is developed to compute the relative state estimations from a low-volume/mass, power saving, and low-cost optical/infrared camera’s observations. Multi-impulsive Hill guidance law is employed in closed-loop linear covariance analysis model, based on which the quantitative relative position robustness and relative velocity robustness index are defined. By balancing fuel consumption, relative position robustness, and relative velocity robustness, we developed a time and covariance threshold triggered two-level optimal maneuver planning method, showing how these results correlate to past methods and missions and how they could potentially influence future ones. Numerical simulation proved that it is feasible to control the spacecraft with a two-line element- (TLE-) level uncertain, 34.6% of range, initial relative state to a 100 m v-bar relative station keeping point, at where the trajectory dispersion reduces to 3.5% of range, under a 30% data gap per revolution on account of the eclipse. Comparing with the traditional time triggered maneuver planning method, the final relative position accuracy is improved by one order and the relative trajectory robustness and collision probability are obviously improved and reduced, respectively.

Patent quality has a decisive position in national intellectual property development strategy. Evaluating patent quality objectively and scientifically, is important to promote the transfer of patent technology and achievements, and enhance the country's independent innovation capability. According to our review of the literature on the evaluation standards of patent quality, we conclude that the current evaluation for the patent quality mainly takes "three characteristics of genetically patent" as the unified standard. However, because the evaluation standards ignore patent technology to some extent, which is the essential characteristics of a patent, the standard is difficult to accurately measure regarding the level of patent quality. In this study, based on patent technical perspective, combined with the classification methods in technology and a contemporary system of science and technology, we divide patent into three types,: basic-type patent, technological patent and applied-type patent. Next, we further analyze the different evaluation standards of patent quality under these three levels based on the characteristics of three types of patents. Next the scientific nature of the classification evaluation index of patent quality from the perspective of empirical research is investigated, which can provides a certain basis and reference to construct a scientific and reasonable evaluation index system and improve our country's evaluation standard of patent quality.

In this work, we present a reprogrammable micro-resonator logic gate designed with an industrial standard process. The device operation is based on altering the resonance frequency of the beam using DC digital inputs. The proposed design methodology reduces design complexity by 4 to 10 times compared to traditional CMOS design techniques and has great potential for improving energy efficiency. The proposed device has been simulated by MEMS+ software provided by Coventor using XMB10_M30 process design kit (PDK) and then exported into Cadence for further simulations and layout creation following XMB10 process provided by X-FAB.

Functional echo state network (FESN) is a new kind of recurrent neural network which has been successfully used for time series classification. In order to make FESN more suitable for multi-variable time series data classification task, we present a novel FESN model by modifying the output layer of original FESN with softmax regression, and the L-BFGS algorithm is employed to train such proposed model. Moreover, the genetic algorithm is used to determine the hyper-parameter of the improved FESN. The experimental results show that the proposed approach can achieve better accuracy than classical classifiers such as support vector machine, Long Short-Term Memory neural network and original FESN, in the context of multi-variable series data classification.

Industry 4.0, started in Germany in 2011, was fervently promoted by various advanced countries and followed by the introduction of &ldquo, smart manufacturing&rdquo, As an innovative strategy of the manufacturing industry &ldquo, has attracted considerable attention in the field of academics and practice. However, the issues of huge investment and differentiation of individual enterprises have created a big gap between policy promotion and production at the site of actual industrial manufacturing and resulted in limited progress in smart manufacturing. In view of the situation, the authors have previously developed in relation to the practice of individual companies in the industry a practical structure of &ldquo, Lean Smart Manufacturing: a conceptual framework and solution based co-creative platform&rdquo, combining Lean Production with smart manufacturing to achieve customer value. In addition to filling the gap between government policy and industry in Industry 4.0, the practical structure suggests the possibility of balancing differentiation and open innovation. Taiwan&rsquo, s bicycle industry is one of Taiwan's most internationally competitive industries. GIANT, the leading manufacturer of bicycles in Taiwan, has been actively promoting TPS since 2000. With the development of Industry 4.0, GIANT has been working on the application of Industry 4.0 related technologies in order to further enhance customer value. In the application of the practical structure of &ldquo, Lean Smart Manufacturing&rdquo, there are few cases of corporate practice. This study focuses on the A-Team which is constructed by the bicycle industry in Taiwan and GIANT for empirical research, to see how GIANT implement the &ldquo, and construct the co-creative platform, to balance differentiation and open innovation, then strengthen its competitiveness. The research results show that although this case is still in the development stage, it has the basic form of &ldquo, This case, as we advocate the use of differentiated advantages of &ldquo, pragmatically from the establishment of the smart factory platform on the physical system, gradually extended to the strategic co-creative platform of the cyber system.

Through a simple self-assembly method, the as-synthesized NaY/MnO2 nanocomposites were uniformly distributed and immobilized on textile fibers, which make the nanocomposite suit for indoor formaldehyde (HCHO) removal. Compared with NaY-based textiles and MnO2-based textiles, the NaY/MnO2-based textiles using polyester (PET) as substrate exhibited superior HCHO removal activity and better stability at room temperature. Furthermore, for low-level indoor environment, HCHO removal efficiency of NaY/MnO2-based textiles was up to 90 % even after 30 cycles tests under the conditions of original HCHO concentration of 1 ppm. Therefore, the NaY/MnO2-based textiles highlighted new avenues to the practical use for low-level indoor HCHO removal.

High-performance Si thin-film transistors (TFTs) on the recessed-channel Si strips with the under-layered nitride film have been fabricated using excimer laser crystallization (ELC). A nitride film was added as a light absorption layer to suppress solidification along the edge of the Si strip. Thus, only one primary grain boundary perpendicular to the Si strip formed in the middle of the recessed region during ELC. The single-crystal-like Si TFTs fabricated on one-half of the recessed region are capable of excellent field-effect mobility of 640 cm2/V-s, with only minor deviation.