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The goal of this study was to investigate a study for the efficient generation of pillar-like nanostructure (nanopillar) on a material surface over a large area. In this research, a vertical cross-scanning (VCS) strategy using two linearly-polarized lasers with different laser conditions was proposed for the generation of nanopillars on a mirror-polished surface on a large scale. It found that the laser fluence and scanning speed of the second laser scanning should be controlled within a specific range to generate the nanopillars. Additionally, the distance between scan lines, which is defined as hatch distance, h, of the second scan, is also a non-negligible factor to induce nanopillars to cover the entire surface. This work demonstrated that the VCS method is a feasible strategy for the fabrication of nanopillars on the entire mirror-polished surface of Ti6Al4V alloy by linearly-polarized picosecond laser conveniently and efficiently.

Owing to their excellent properties such as lightweight and high strength, fiber-reinforced composite materials are widely used as ideal materials for key structural components in extreme service environments (e.g., aviation, aerospace, and nuclear power). However, due to their non-uniformity and anisotropy, they have become typical difficult-to-machine materials, which are prone to cause damage during machining, thereby reducing the service life of the entire component. This review primarily summarizes the damage (including delamination, thermal damage, tool wear, and other processing damages) and suppression strategies of scholars in the process of traditional and non-traditional machining of fiber-reinforced composite materials in the past few years. Besides reviewing the research progress, the limitations of the current research were presented. From this review, it can be seen that traditional and non-traditional (e.g., laser machining) machining methods are difficult to avoid damage. Abrasive waterjet (AWJ) machining is a green and environmentally friendly machining technology. Some key issues are expected to be furtherly solved during the AWJ drilling of fiber-reinforced composite materials with low damage.

Inferring the sentiment polarity or emotion category of subjective text is the fundamental task of sentiment analysis. Recently, emotion detection in conversations that considering context utterances has emerged as a very important and challenging task in this line of research. Most existing studies do not distinguish different speakers in a dialog and fail to characterize inter-speaker dependencies for emotion detection. In this paper, we propose a S peaker I nfluence aware N eural N etwork model (dubbed as SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs (Gated Recurrent Units) and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where an attention mechanism is used to extract the most relevant features for emotion classification. We construct a large-scale multi-turn Chinese dialog dataset WBEmoDialog, where each utterance is manually annotated with an emotion label. Extensive experiments are conducted on public available DailyDialog dataset as well as our constructed WBEmoDialog dataset, and the results show that our model can achieve better or comparable performance with the strong baseline methods.

Background Increasing evidence had suggested that cell division cycle-associated (CDCA) family proteins play prominent roles in multiple types of cancer. However, the expression pattern and prognostic value of CDCAs in gastric cancer were still poorly understood. Methods In this study, bioinformatics was used for the first time to comprehensively discuss the expression changes of the CDCA protein family in gastric cancer. We studied the transcription and survival data of CDCAs in patients with gastric cancer in Oncomine, GEPIA, DAVID, cBioportal, and other databases. Results We identified that the CDCA 1/2/3/4/5/6/7/8 were overexpressed gastric cancer than in normal tissues. There was no significant difference in CDCAs expression among different gastric cancer stages. High expression of CDCA4/6 in patients with gastric cancer was closely related to low overall survival (OS), first progression survival (FPS), and post-progression survival (PPS). In contrast, high CDCA1/2/3/5/7/8 expression predicted a better prognosis. The genetic mutation rate of CDCA2 and CDCA4 was 4%, ranking first. The main biological process of CDCAs protein family enrichment was cell division, the main cell component involved was centromeres of chromosomes, and the main molecular function involved was protein binding. Conclusions The study suggested that CDCA1/2/3/5/7/8 were expected to be new prognostic markers for gastric cancer, and CDCA4/6 might be potential targets for the treatment of gastric cancer.

Ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) of a heart rate over 180 beats min−1, commonly known as shockable rhythms (ShR), are life-threatening ventricular arrhythmias. This study was to assess the feasibility of feeding two-dimensional (2D) time-frequency maps of electrocardiogram (ECG) segment into deep convolutional neural network (CNN) to automatically detect ShR with emphases on optimizing the CNN model and shortening the analysis segment. A total of 115 single-lead surface ECG records collected from four publicly accessible ECG databases were divided into non-overlapping 3-second segments as well as 5-, 8-, and 10-second segments for comparisons. For each one-dimension (1D) ECG segment, its 2D time-frequency maps was obtained by employing a continuous wavelet transform (CWT). Moreover, the performance of automated algorithms with eight different CNN models was evaluated for ShR detection against annotations labeled in the databases. A total of 5,795 3-second maps of ShR and 48,648 maps of non-shockable rhythms (NSR) were analyzed successfully, and a promising performance of the automated algorithm with a twelve-layer CNN model was achieved on this segment length in terms of accuracy (98.82%), sensitivity (95.05%), and specificity (99.43%). Compared with current approaches imposing priority either only on highly real-time or high performance, the presented solution suggested that the deep learning based algorithm together with ECG time-frequency maps may offer the possibility of providing an intelligent and efficient detection of ShR and NSR in short segments especially for public access defibrillation.

Propeller aircraft are widely used in general aviation. The rotating propeller has a strong effect on the aerodynamic performance of the wing. This paper uses an actuator disc to model the effect of the propeller. A wing optimization method is developed with the actuator disc method. Several wing optimizations with different slipstream settings are studied. The twist angle and airfoils of the wing are used as the design variables. The results show that the propeller slipstream and slipstream directions have a strong influence on the optimization process. Powered-on optimization with a slipstream can obtain better drag reduction results than unpowered optimization. The drag decomposition results show that most of the drag reduction comes from the form drag reduction. The symmetric “inboard-up” slipstream configuration is found to have the highest lift-to-drag ratios, which are 18.87 for the twist angle optimization and 19.15 for the airfoil optimization.

Community search is a query-oriented variant of community detection problem, and the goal is to retrieve a single community from a given set of nodes. Most of the existing community search methods adopt handcrafted features, so there are some limitations in applications. Our idea is motivated by the recent advances of node embedding. Node embedding uses deep learning method to obtain feature representation of nodes directly from graph structure automatically and offers a new method to measure the distance between two nodes. In this paper, we propose a two-stage community search algorithm with a minimum spanning tree strategy based on node embedding. At the first stage, we propose a node embedding model NEBRW and map nodes to the points in a low-dimensional vector space. At the second stage, we propose a new definition of community from the distance viewpoint, transform the problem of community search to a variant of minimum spanning tree problem, and uncover the target community with an improved Prim algorithm. We test our algorithm on both synthetic and real-world network datasets. The experimental results show that our algorithm is more effective for community search than baselines.

To what extent did independent directors help firms’ recovery during the COVID-19 pandemic? In this paper, we answer this question by investigating whether independent directors contribute to Chinese listed firms’ operation income growth during the first and second quarters of the year 2020. By employing a triple difference-in-differences (DDD) estimation strategy, we show that firms located in more pandemic-affected regions experienced a more pronounced operating recovery if they receive more independent directors’ opinions and have fewer female and busy directors. The possible reason is that those female and busy directors were likely to be distracted during the pandemic outbreak. We also provide evidence that firms paying higher remunerations to independent directors tend to recover quicker. Moreover, independent directors’ age and education level positively contribute to firms’ recovery. Our work is among the first to study independent directors’ role in shaping firms’ operation performance under the COVID-19.

How does the COVID-19 affect SMEs’ financing in emerging markets? In this paper, we investigate the impact of the COVID-induced shock on Chinese SMEs’ line of credit (LOC) using deal-level data. As Hubei province was mostly affected, we employ a difference-in-differences approach with the propensity score matching (PSM-DID) and compare Hubei SMEs’ credit responses before and after the outbreak relative to those of non-Hubei SMEs. Our results suggest that Hubei SMEs’ credit demand reduced significantly compared to that of non-Hubei SMEs, and the adverse effects were more pronounced for the non-state-owned enterprises (non-SOEs) and the SMEs without prior bank relationships. Moreover, we show a negative impact on non-Hubei SMEs having supply chain relationships with Hubei province. Such effects rippled through the supply chain and exerted an intensified strike on the SMEs with Hubei customers. Finally, we find the state-owned banks eased the LOC to Hubei SMEs during the pandemic outbreak. Plain English Summary Chinese SMEs’ credit demand deteriorated after the COVID-19 outbreak, though supported by the state-owned banks. How does the COVID-19 affect SMEs’ financing in emerging markets? In this paper, we investigate the impact of the COVID-induced shock on Chinese SMEs’ line of credit (LOC) using deal-level data. As Hubei province was mostly affected, our results suggest that Hubei SMEs’ credit demand reduced significantly compared to that of non-Hubei SMEs, and the adverse effects were more pronounced for the non-state-owned enterprises (non-SOEs) and the SMEs without prior bank relationships. Moreover, we show a negative impact on non-Hubei SMEs having supply chain relationships with Hubei province. Such effects rippled through the supply chain and exerted an intensified strike on the SMEs with Hubei customers. Finally, we find the state-owned banks eased the LOC to Hubei SMEs during the pandemic outbreak. According to our study, government COVID-supportive policies should target the SME subgroups such as non-SOEs, firms that heavily rely on supply chain, and those without stable bank relationships.

Molybdenum disulfide (MoS2) as a two-dimensional semiconductor material has been actively explored for field-effect-transistors (FETs). The current prevailing method for MoS2 FET fabrication involves multiple complex steps, including electron beam (e-beam) lithography, annealing, etc., which are time-consuming and require polymer resists. As a consequence, the MoS2 exposed to chemicals during the patterning process may be unfavorably affected by residues and the performance of the final FET could be impaired while the annealing limits materials for FETs. Therefore, there is an urgent need to free the fabrication of FETs from e-beam lithography and annealing. In this study, we introduce an e-beam lithography-free method to fabricate MoS2 FETs by employing maze-like source/drain electrodes. In addition, an ohmic contact in multilayer MoS2 FETs using chromium (Cr) as source/drain electrodes is achieved without annealing. The underlying mechanism for contact performance is studied, and the tightness of the contact and the type of metal are found to be responsible because they determine the contact resistance. Furthermore, the long-term device degradation is explored, in which the oxidation of metal dominates. The facile fabrication process and mechanism explanation in this work might provide a new platform for future electronic devices.

This paper studies the impact of the newly introduced science and technology innovation board (STIB) on stock valuations in China. This Nasdaq-style board features a market-based IPO system that contrasts with the current approved-based arrangement. Event study approach shows that A-share firms pertaining to STIB related industries increased significantly after the reform announcement. The effect is stronger for Non-SOEs and firms with higher R&D capacity. Public shareholders of the firms filing STIB IPO applications experienced salient growth in their abnormal returns while their industry competitors suffered price drops. Financial analysts also broadened their company coverages in STIB related industries and revised their market valuation forecasts positively in line with the market investors.

With the aid of the confined conversion of layered silicate RUB-15, homogeneously dispersed Au and SnO2 nanoparticles (NPs) were generated in the confined layer space of RUB-15. The Au-SnO2/SiO2 composite was obtained with the structure that ultrafine Au and SnO2 NPs were supported on SiO2 lamellas. Benefited by the Sn(II)-assisted in situ reduction strategy, Au NPs were highly uniformed and evenly distributed in/on the RUB-15. This Au-SnO2/SiO2 composite was employed as a catalyst to the reduction of 4-nitrophenol showing excellent catalytic activity. The catalytic rate constant at room temperature was calculated to be 6.64 min-1, which was dramatically higher than that of Au/SiO2 composite produced by reduction with hydrazine hydrate on the same support of layered silicate RUB-15. The interaction between Au and SnO2 NPs increased the electron density around Au NPs, which was demonstrated to be an essential factor to the excellent catalytic activity of the Au-SnO2/SiO2 composite. The simple and universal synthesis method afforded precise control over the size/spatial arrangement of Au and SnO2 NPs on SiO2 lamellas. The high activity of the Au-SnO2/SiO2 composite demonstrated that the strategy used in this study has good potential application prospect. Furthermore, this work provided new perspective on the catalysis mechanism to the metal/semiconductor synergistic catalyst system.

Super ionic channel or shuttle correlated with interfacial engineering of nanocomposite structure and semiconductor-based material electrolytes has shown great potential in electrochemical applications, such as photochemical water splitting or low temperature advanced fuel cell. By adjusting apriority composition between n and p components, the up-to-date semiconductor-ionic membrane fuel cells (SIMFCs) exhibit the improved performance, especially for high ionic conductivity and power outputs at lower temperature. Facing the commercialization of novel advanced ceramic cells, a combined literature survey and phenomenological analysis is proposed to interpret the difference between the current-voltage curve and stability performance of low temperature solid oxide fuel cell (LTSOFC). Meanwhile, it is experimentally determined that the time constant, which is closely related to the interfacial structure and heterostructure, has played a great role in the cell properties. Herein, steady state instead of transient characteristic is preferred, dedicating to provide much more reliable data. Among several prototype cells on semiconductor-based electrolyte, only the candidate that can resist the high current operation shows suppressing inside electronic leakage and survives in short-term test. The results illustrate that though semiconductor-based nanocomposite or heterostructure is an effective methodology in developing low temperature ceramic fuel cells, its durability and the risk of short-circuit should be taken with much care.

In this work, an analogue of electromagnetically induced transparency (EIT) is excited by a periodic unit consisting of a silicon rectangular bar resonator and a silicon ring resonator in terahertz (THz). The analogue of the EIT effect can be well excited by coupling of the "bright mode" and the "dark mode" supported by the bar and the ring, respectively. Using the semimetallic properties of graphene, active control of the EIT-like effect can be realized by integrating a monolayer graphene into THz metamaterials. By adjusting the Fermi energy of graphene, the resonating electron distribution changes in the dielectric structures, resulting in the varying of the EIT-like effect. The transmission can be modulated from 0.9 to 0.3 with the Fermi energy of graphene placed under the ring resonator mold varying from 0 to 0.6 eV, while a modulation range of 0.9-0.3 corresponds to Fermi energy from 0 to 0.3 eV when graphene is placed under the rectangular bar resonator. Our results may provide potential applications in slow light devices and an ultrafast optical signal.

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency, especially for non-mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning in the full 2π range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling binary switching of metasurfaces between arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for reconfigurable meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 μm wavelength. The reconfigurable metalens features a record large switching contrast ratio of 29.5 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents a key experimental demonstration of a non-mechanical tunable metalens with diffraction-limited performance., Here, the authors report an active all-dielectric metasurface platform based on phase change materials, combining phase tuning in the full 2π range, and demonstrate aberration-free and multi-depth imaging with a non-mechanical tunable metalens.

Compared with single-modal content, multimodal data can express users’ feelings and sentiments more vividly and interestingly. Therefore, multimodal sentiment analysis has become a popular research topic. However, most existing methods either learn modal sentiment feature independently, without considering their correlations, or they simply integrate multimodal features. In addition, most publicly available multimodal datasets are labeled by sentiment polarities, while the emotions expressed by users are specific. Based on this observation, in this paper, we build a large-scale image-text emotion dataset (i.e., labeled by different emotions), called TumEmo, with more than 190,000 instances from Tumblr.1 We further propose a novel multimodal emotion analysis model based on the Multi-view Attentional Network (MVAN), which utilizes a memory network that is continually updated to obtain the deep semantic features of image-text. The model includes three stages: feature mapping, interactive learning, and feature fusion. In the feature mapping stage, we leverage image features from an object viewpoint and a scene viewpoint to capture effective information for multimodal emotion analysis. Then, an interactive learning mechanism is adopted that uses the memory network; this mechanism extracts single-modal emotion features and interactively models the cross-view dependencies between the image and text. In the feature fusion stage, multiple features are deeply fused using a multilayer perceptron and a stacking-pooling module. The experimental results on the MVSA-Single, MVSA-Multiple, and TumEmo datasets show that the proposed MVAN outperforms strong baseline models by large margins.

Waveguide structures effectively controlling and guiding terahertz (THz) waves can achieve interesting resonance effects when combined with resonators. At present, achieving high quality factor (Q-factor) resonance in THz resonator-coupled waveguide structure is still a critical consideration to expand its practical application. Here, a high Q-factor Fano resonance based on a metal-dielectric-metal (MDM) waveguide consisting of a stub resonator and a metal wall with an aperture in the center is investigated theoretically and numerically in the THz region. The results show that the sharp and asymmetric Fano resonance peak is induced by the destructive interference between the stub resonator and metal wall which act as a Fabry-Pérot cavity. Q-factor is obviously improved about 60 times (3.72 to ~225) by introducing the metal wall into the stub-coupled MDM waveguide. Moreover, Fano resonance can be effectively tuned by varying different structure parameters. Owing to the high sensitivity of Fano resonance peak to dielectric surroundings, a large-range refractive index (RI) sensor based on the proposed structure with a high sensitivity of 96480 nm/RIU is obtained. The figure of merit (FOM) of 195 is greatly improved compared to other THz Fano-based RI sensors. These results provide possibilities for subwavelength MDM waveguide structure to apply for THz bio/chemical sensing, bandpass filters, and on-chip highly integrated plasmonic device.

International audience; Robust multimodal fusion is one of the challenging research problems in semantic scene understanding. In real-world applications, the fusion system can overcome the drawbacks of individual sensors by taking different feature representations and statistical properties of multiple modalities (e.g., RGB-depth cameras, multispectral cameras). In this paper, we propose a novel central multimodal fusion framework for semantic image segmentation of road scenes, aiming to effectively learn joint feature representations and optimally combine deep neural networks with statistical priors. More specifically, the proposed fusion framework can automatically generate a central branch by sequentially mapping multimodal features into a common space, including both low-level and high-level features. Besides, in order to reduce the model uncertainty, we employ statistical fusion to compute the final prediction, which leads to significant performance improvement. We conduct extensive experiments on various outdoor scene datasets. Both qualitative and quantitative experiments demonstrate that our central fusion framework achieves competitive performance against existing multimodal fusion methods.

Coastal reservoirs are potentially CH4 emission hotspots owing to their biogeochemical role as the sinks of anthropogenic carbon and nutrients. Yet, the fine-scale spatial variations in CH4 concentrations and fluxes in coastal reservoirs remain poorly understood, hampering an accurate determination of reservoir CH4 budgets. In this study, we examined the spatial variability of diffusive CH4 fluxes and their drivers at a subtropical coastal reservoir in southeast China using high spatial resolution measurements of dissolved CH4 concentrations and physicochemical properties of the surface water. Overall, this reservoir acted as a consistent source of atmospheric CH4, with a mean diffusive flux of 16.1 μmol m-2 h-1. The diffusive CH4 flux at the reservoir demonstrated considerable spatial variations, with the coefficients of variation ranging between 199 and 426% over the three seasons. The shallow water zone (comprising 23% of the reservoir area) had a disproportionately high contribution (56%) to the whole-reservoir diffusive CH4 emissions. Moreover, the mean CH4 flux in the sewage-affected sectors was significantly higher than that in the nonsewage-affected sectors. The results of bootstrap analysis further showed that increasing the sample size from 10 to 100 significantly reduced the relative standard deviation of mean diffusive CH4 flux from 73.7 to 3.4%. Our findings highlighted the role of sewage in governing the spatial variations in reservoir CH4 emissions and the importance of high spatial resolution data to improve the reliability of flux estimates for assessing the contribution of reservoirs to the regional and global CH4 budgets.

Human gut microbiome is a promising target for managing type 2 diabetes (T2D). Measures altering gut microbiota like oral intake of probiotics or berberine (BBR), a bacteriostatic agent, merit metabolic homoeostasis. We hence conducted a randomized, double-blind, placebo-controlled trial with newly diagnosed T2D patients from 20 centres in China. Four-hundred-nine eligible participants were enroled, randomly assigned (1:1:1:1) and completed a 12-week treatment of either BBR-alone, probiotics+BBR, probiotics-alone, or placebo, after a one-week run-in of gentamycin pretreatment. The changes in glycated haemoglobin, as the primary outcome, in the probiotics+BBR (least-squares mean [95% CI], −1.04[−1.19, −0.89]%) and BBR-alone group (−0.99[−1.16, −0.83]%) were significantly greater than that in the placebo and probiotics-alone groups (−0.59[−0.75, −0.44]%, −0.53[−0.68, −0.37]%, P, The gut microbiome affects systemic metabolism and is a therapeutic target for type 2 diabetes. Here the authors demonstrate in a randomized controlled trial that effects of berberine, a plant alkaloid known to lower blood glucose, may be explained by the inhibition of Ruminococcus bromii mediated biotransformation of the bile acid deoxycholic acid.

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.

Recast layer (RL) is a major defect in laser drilling of micro holes. In this study, some methods to reduce RL in micro-hole drilling were carried out by using a pulsed Nd:YAG laser (λ = 1.06 µm) o...

The CsI detectors of the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope (HXMT/CsI) can be used for gamma-ray all sky monitoring and searching for the electromagnetic counterpart of gravitational wave source. The instrumental responses are mainly obtained by Monte Carlo simulation with the Geant4 tool and the mass model of both the satellite and all the payloads, which is updated and tested with the Crab pulse emission in various incident directions. Both the Energy-Channel relationship and the energy resolution are calibrated in two working modes (Normal-Gain mode & Low-Gain Mode) with the different detection energy ranges. The simulative spectral analyses show that HXMT/CsI can constrain the spectral parameters much better in the high energy band than that in the low energy band. The joint spectral analyses are performed to ten bright GRBs observed simultaneously with HXMT/CsI and other instruments (Fermi/GBM, Swift/BAT, Konus-Wind), and the results show that the GRB flux given by HXMT/CsI is systematically higher by 7.0 ± 8.8 % than those given by the other instruments. The HXMT/CsI-Fermi/GBM joint fittings also show that the high energy spectral parameter can be constrained much better as the HXMT/CsI data are used in the joint fittings.

Purpose The paper aims to investigate current library instruction programs to help business students make better use of library resources and improve their information. However, students’ information acquisition ability, library usage and their perception toward library user education are inevitably changing along with the rapidly evolving information landscape as well as the socio-cultural environment driven by information technologies. Design/methodology/approach For this study, 90 business students from three different majors at the Faculty of Business and Economics, the University of Hong Kong were invited to participate in the online survey for comparison. Findings The findings of this study suggest that the subjects, in general, recognized the importance of library user instruction. However, when the subjects encountered difficulties in information searching, they preferred to use search engines (such as Google) and seek help from their classmates. Practical implications The results of this study show that there were distinctive differences in library usage and views toward library instruction among students of three different majors. This study sheds light on the feasible actions of academic libraries to enhance library user instruction services and improve the IL skills among business students. Originality/value Although there is much research about the library instruction of academic libraries, scant research focuses on library usage and views toward library instruction of business students, especially on the comparison among different business majors.

Background Body-image dissatisfaction among children and adolescent has become increasingly serious, and may be the result of staged differences in the puberty-development process. The aim of this study was to compare differences in body-image dissatisfaction among children and adolescents at different pubertal stages. Methods A total of 574 students aged 8-15 years were recruited from two nine-year schools via stratified cluster sampling, their secondary sex characteristics and external genital development examined, and body-image cognition surveyed using the teenage body-image annoyance questionnaire. This questionnaire covers body-shape, gender, sexual organ, and appearance dissatisfaction. Lower scores indicate a more negative body image. Results Total, gender, and appearance-dissatisfaction scores in girls were significantly lower than in boys (P

Three high energy particle monitors (HPMs) employed onboard the Hard X-ray Modulation Telescope Insight-HXMT) can detect the charged particles from South Atlantic Anomaly (SAA) and hence provide the alert trigger for switch-on/off of the main detectors. Here a typical design of HPM with high stability and reliability is adopted by taking a plastic scintillator coupled with a small photomultiplier tube (PMT). The window threshold of HPM is designed as 1 MeV and 20 MeV for the incident electron and proton, respectively. Before the launch of Insight-HXMT, we performed in details the ground calibration of HPM. The measured energy response and its dependence on temperature are taken as essential input of Geant4 simulation for estimating the HPM count rate given with an incident particle energy spectrum. This serves as a guidance for choosing a reasonable working range of the PMT high voltage once the real SAA count rate is measured by HPM in orbit. So far the three HPMs have been working in orbit for more than two years. Apart from providing reliable alert trigger, the HPMs data are used as well to map the SAA region., Comment: 7 pages,15 figures

Perovskite solar cells (PSCs) toward practical application relies on high efficiency, long lifetime, low toxicity, and device up-scaling. To realize large-area PSCs, a green solution-bathing strategy is delivered to prepare high-performance PSCs. By utilizing 2-pentanol as a green solvent and formamidinium chloride (FACl) as a solute in the green solution-bathing process, perovskite films with enlarged grain sizes, improved crystallinity, and alleviated defect state density were obtained, resulting in the enhancement in the power conversion efficiency of PSCs. Coupled with 2-pentanol and FACl, both a champion efficiency of 21.03% for small cells (0.103 cm2) and an efficiency of over 18% for large size (1.00 cm2) were obtained based on the GSB process, which can outperform its counterpart made via the commonly used antisolvent-dropping method. In addition, a large perovskite film (5 cm × 5 cm) with obvious mirror effect was successfully prepared. Our innovative approach paves the way to promote device up-scaling of PSCs via an environmentally friendly technique.

In this work, we propose using periodic Au nanoparticles (NPs) in indium selenide-based optoelectronic devices to tune the optical absorption of indium selenide. Electromagnetic simulations show that optical absorption of indium selenide can be manipulated by tuning plasmonic resonance. The effect on the plasmonic resonance of the size, period of NPs, the thickness of silicon oxide, and the insulator spacer is systematically analyzed. A high absorption enhancement over the visible spectrum is achieved through systematic optimization of nanostructures.

Background: Alzheimer’s disease (AD) is a common neurodegenerative disease occurring in the elderly population. The effective and accurate classification of AD symptoms by using functional magnetic resonance imaging (fMRI) has a great significance for the clinical diagnosis and prediction of AD patients. Methods: Therefore, this paper proposes a new method for identifying AD patients from healthy subjects by using functional connectivities (FCs) between the activity voxels in the brain based on fMRI data analysis. Firstly, independent component analysis is used to detect the activity voxels in the fMRI signals of AD patients and healthy subjects; Secondly, the FCs between the common activity voxels of the two groups are calculated, and then the FCs with significant differences are further identified by statistical analysis between them; Finally, the classification of AD patients from healthy subjects is realized by using FCs with significant differences as the feature samples in support vector machine. Results: The results show that the proposed identification method can obtain higher classification accuracy, and the FCs between activity voxels within prefrontal lobe as well as those between prefrontal and parietal lobes play an important role in the prediction of AD patients. Furthermore, we also find that more brain regions and much more voxels in some regions are activity in AD group compared with health control group. Conclusion: It has a great potential value for the AD pathogenesis mechanism study., $\boldsymbol { X } _ { i } ( i

We report integration of chalcogenide phase change materials (GSST and Sb2Se3) with doped silicon-on-insulator heater arrays fabricated in a 90-nm silicon foundry. Reversible electrothermal switching of transmissive metasurfaces and integrated photonic devices are demonstrated.

Multi-frequency multi-bit programmable amplitude modulation (AM) of spoof surface plasmon polaritons (SPPs) is realized at millimeter wave frequencies with interdigital split-ring resonators (SRRs) and In-Ga-Zn-O (IGZO) Schottky diodes. Periodic SRRs on a metal line guide both SRR mode and spoof SPP mode, the former of which rejects the spoof SPP propagation at the SRR resonant frequencies. To actively modulate the amplitude of spoof SPPs, IGZO Schottky diodes are fabricated in the SRR gaps, which continuously re-configure SRRs to metallic loops by applying bias. Interdigital gaps are designed in SRRs to increase the capacitance, thus red shifting the resonant frequencies, which significantly broadens the operation bandwidth of multi-frequency AM. Thus, cascading different kinds of interdigital SRRs with Schottky diodes enables multi-frequency multi-bit AM programmable. As a demonstration, a dual-frequency device was fabricated and characterized, which achieved significant multi-bit AM from −12.5 to −6.2 dB at 34.7 GHz and from −26 to −8.5 dB at 50 GHz independently and showed programmable capability.

Active metamaterials (MTMs) are artificially engineered structures with tunable and exceptional properties that are absent in natural materials. Recently, InGaZnO (IGZO), a widely used semiconductor for large-area and flexible display backplane drivers, has gained interest for active control of MTMs due to its large-area uniformity, ease of thin film deposition, and low cost. In this paper, IGZO Schottky barrier diodes (SBDs) are proposed to reconfigure electric-field-coupled inductor-capacitor (ELC) MTMs and actively control terahertz (THz) waves for the first time. The SBDs are designed to bridge the capacitors of the ELC resonators so that the average conductivity within the capacitor gap can be modulated by bias voltage while keeping the capacitance value almost unchanged. To precisely simulate this mechanism, two U-shaped resistive sheet models beside the gap are built for IGZO SBD in 3-D simulation for maintaining the same capacitance and resonant frequency. Furthermore, a device with 14400 MTM cells is fabricated and characterized using frequency-domain spectroscopy. The measured transmission shows a continuous modulation from -14.2 to -9.4 dB at 0.39 THz, which corresponds to a modulation depth of 14.3%. This work paves a new way for active THz MTMs using industrial compatible thin-film technology.

we propose electrically reconfigurable metamaterials (MTMs) at terahertz (THz) frequencies using InGaZnO (IGZO) Schottky barrier diodes, achieving a continuous amplitude modulation of 13%. The IGZO film was deposited at the capacitive gap of electric-field-coupled inductor-capacitor (ELC) MTMs. By sweeping the depletion region with DC bias, the averaged conductivity of IGZO film is changed, and thus the reconfiguration is accomplished. Upon varying the film conductivity, the simulated transmission can be modulated from 14.5 to -9.9 dB at 0.39 THz, corresponding to a modulation depth of 13%. Such active metamaterials address many attractive advantages, including not only low cost and simple fabrication process but also reasonable modulation depth and large area for several inch-scale.

Optical phase change materials (PCMs) are a unique class of materials which exhibit extraordinarily large optical property change (e.g. refractive index change > 1) when undergoing a solid-state phase transition, and they have witnessed increasing adoption in active integrated photonics and metasurface devices in recent years. Here we report integration of chalcogenide phase change materials in the Lincoln Laboratory 8-inch Si foundry process and the demonstration of electrothermally switched phase-change photonic devices building on a wafer-scale silicon-on-insulator heater platform.

Due to the harsh conditions of the ocean observation environment and the influence of many uncertain factors, data anomalies often appear in the ocean observation system. Therefore, how to detect and diagnosis data anomalies and take corresponding measures has become an urgent problem to be solved. In this paper, a novel method based on deep learning is presented for detecting and diagnosing the data anomalies of ocean observation system, which combines convolution neural network (CNN) and bidirectional long short-term memory recurrent neural network (BiLSTM) with attention mechanism (CBLA for short). By constructing a prediction model based on the CBLA, the short-term trend of ocean observation data is predicted, and the error threshold is set to realize the detection of data anomalies. In addition, through the discussion of different types of data anomalies, the causes of data anomalies are analyzed, and the diagnosis of data anomalies is actively explored. The experimental results show the accuracy of the prediction model and the effectiveness of the overall data anomalies detection and diagnosis algorithm.

Background Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. Methods 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. Findings Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). Interpretation This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. Funding The complete list of funders can be found in the Acknowledgement section.

Immunoglobulin light chain (AL) amyloidosis is an incurable plasma cell disorder characterized by deposition of fibrils of misfolded immunoglobulin free light chains (FLC) in target organs, leading to failure. Cardiac involvement is common in AL amyloidosis and represents the single most adverse prognostic feature. A high index of clinical suspicion with rapid tissue diagnosis and commencement of combinatorial, highly effective cytoreductive therapy is crucial to arrest the process of amyloid deposition and preserve organ function. The clinical use of molecularly targeted drugs, such as proteasome inhibitors and immunomodulatory agents, monoclonal antibodies such as daratumumab, and risk-adjusted autologous stem cell transplant in eligible patients, has radically changed the natural history of AL amyloidosis. Here, we review the state-of-the-art treatment landscape in AL amyloidosis with an eye toward future therapeutic venues to impact the outcome of this devastating illness., Central Illustration, Highlights • Cardiac involvement in AL amyloidosis is common and represents the single most adverse prognostic factor. • Chemo-immunotherapies and autologous stem cell transplant lead to prolonged remission and survival in low-stage patients. • Early diagnosis is critical in AL amyloidosis to avoid irreversible organ damage. • Rapid and deep FLC reduction is necessary to ensure long-term survival and functional recovery of affected organs.

This study evaluated the pharmacokinetics, pharmacodynamics, and safety of a single dulaglutide dose in Chinese healthy subjects and of multiple dulaglutide doses in Chinese patients with type 2 diabetes mellitus (T2DM). This two-part, double-blind, placebo-controlled study included 16 healthy subjects randomized to receive a single dose of placebo and two of three dulaglutide doses (0.5 mg, 0.75 mg, or 1.5 mg) in three treatment periods, and 42 patients with T2DM randomized to receive placebo or one of the three dulaglutide doses once weekly for 4 weeks. Pharmacokinetics and safety parameters were assessed in all participants, and pharmacodynamics effects were investigated in patients with T2DM. Following a single-dose administration of 0.5 mg, 0.75 mg, or 1.5 mg dulaglutide in healthy subjects, geometric mean maximum concentrations (Cmax) were 29.4, 44.2, and 81.5 ng/mL, respectively. Following weekly administration in patients with T2DM for 4 weeks, Cmax were 26.3, 41.4, and 70.2 ng/mL, respectively, with accumulation ratios of 1.33–1.39. Geometric mean for half-life of 4–5 days and median time to Cmax (tmax) of approximately 48 h were observed in both study populations. Dose-proportional increases in drug exposure were observed after both single and multiple dosing. Significant reductions in baseline-corrected fasting glucose and hemoglobin A1c (HbA1c) were observed in patients with T2DM who received dulaglutide 0.75 mg and 1.5 mg. Dulaglutide was well tolerated, with the majority of adverse events being gastrointestinal disorders of mild severity. Pharmacokinetics, pharmacodynamics, and safety profiles of dulaglutide demonstrated in the present study support a once-weekly dosing regimen in Chinese patients with T2DM. NCT01667900 (ClinicalTrials.gov).

The quest for efficient programmable photonic circuits requires active optical components with low insertion loss, small form factor, and low electrical power consumption [1] . The most common low-loss phase shifter – the key building block – employs electrically driven heaters to exploit thermo-optic effects. However, these devices require a constant power supply, suffer from thermal crosstalk, and their sizes are typically >100 µm. Here, we demonstrate a novel alternative that meets all the aforementioned requirements. We use silicon-on-insulator (SOI) waveguides with patterned n ++ - n - n ++ doped-silicon microheaters to electrothermally switch, with a single pulse, a cell of Sb 2 Se 3 between its two optically distinct, nonvolatile states: amorphous and crystalline. Sb 2 Se 3 is a phase change material (PCM) that offers a ∆n ≈ 0.77 upon switching between states with vanishingly small losses at 1550nm [2] . This unique set of properties allows for phase-only modulation [3] , which sets a precedent in a growing PCM photonics field familiar with PCMs for amplitude modulation at the telecom band [4] .

With the development of social networks and intelligent terminals, it is becoming more convenient to share and acquire images. The massive growth of the number of social images makes people have higher demands for automatic image processing, especially in the aesthetic and emotional perspective. Both aesthetics assessment and emotion recognition require a higher ability for the computer to simulate high-level visual perception understanding, which belongs to the field of image processing and pattern recognition. However, existing methods often ignore the prior knowledge of images and intrinsic relationships between aesthetic and emotional perspectives. Recently, machine learning and deep learning have become powerful methods for researchers to solve mathematical problems in computing, such as image processing and pattern recognition. Both images and abstract concepts can be converted into numerical matrices and then establish the mapping relations using mathematics on computers. In this work, we propose an end-to-end multi-output deep learning model based on multimodal Graph Convolutional Network (GCN) and co-attention for aesthetic and emotion conjoint analysis. In our model, a stacked multimodal GCN network is proposed to encode the features under the guidance of the correlation matrix, and a co-attention module is designed to help the aesthetics and emotion feature representation learn from each other interactively. Experimental results indicate that our proposed model achieves competitive performance on the IAE dataset. Progressive results on the AVA and ArtPhoto datasets also prove the generalization ability of our model.

The use of extrapolation of efficacy in pediatric drug development programs is possible when disease progression and treatment response are similar in adult and pediatric populations. Historically, the exposure-response (E-R) similarity was assessed by visual inspection of 2 E-R curves to support pediatric extrapolation. The aim of this study was to develop a quantitative framework to describe the E-R relationship and the difference in E-R between pediatric and adult patients based on accumulated experience in pediatric drug development programs. Using clinical data for 8 drugs with either a linear or nonlinear E-R relationship, we adapted the methodology used in noninferiority testing to assess the E-R similarity between adult and pediatric patients at the targeted drug exposure. We implemented bootstrap-based and Bayesian-based methodologies to estimate the probability of concluding noninferiority of the E-R relationship. This approach provides objective criteria that can be applied to an assessment of E-R noninferiority in 2 populations to support extrapolation of efficacy in drug development programs from adults to pediatric populations.

High energy X-ray telescope (HE) is one of the three instruments of Insight-HXMT (Hard X-ray Modulation Telescope) payload. The HE detector (HED) array is composed of 18 actively NaI(Tl)/CsI(Na) phoswich scintillators with a total geometric area of ∼ 5100 cm 2 and cover the energy range 20∼250 keV. In this paper we describe the on-ground detector-level calibration campaigns and present the principal instrument properties of HEDs.

Surface plasmon polaritons (SPPs) provide subwavelength electric field confinement ranging from microwave to the visible. Several approaches have been explored to manipulate SPPs with a typical modulation capability of only a few percent. Here, active control of SPPs using monolithically fabricated Schottky diodes has been first designed and realized, achieving a continuous and significant modulation of transmission, reflection, and absorption. The SPPs propagating on a metal line are attenuated by using split ring resonators (SRRs) with an In-Ga-Zn-O Schottky diode bridging each SRR split gap. The resistance of the diodes can be tuned over a range of a few orders of magnitude with bias voltage, which continuously transforms each SRR to a metallic quasi-loop with subdued resonance. A remarkable modulation of 40% and 19% was demonstrated for the transmission and absorption, respectively, which to the best of our knowledge has not been achieved before.

Background The lipid accumulation product (LAP) is a powerful marker for predicting metabolic syndrome (MS) in adults. The present study aimed to propose a novel indicator, the children's lipid accumulation product (CLAP), and to assess its association with MS among Chinese children and adolescents. Methods A total of 683 Chinese children aged 8-15 years were recruited using a stratified cluster sampling method in this cross-sectional study. The presence of MS was defined according to the NCEP-ATP III criteria. The effects of BMI, WHtR and the CLAP for predicting MS were compared using logistic regression models and receiver operating characteristic (ROC) curves. Results The prevalence of MS was 5.1% (6.6% and 3.5% among boys and girls, respectively). Overall obesity (based on BMI), abdominal obesity (based on WHtR) and CLAP≥P75 were significantly associated with an increased risk of MS (ORs (95% CIs) were 143.79 (18.78-1101.22), 86.83 (27.19-277.27), 150.75 (20.11-1130.19), respectively). The area under the ROC curve (AUC) for the CLAP was higher than that for BMI and WHtR for predicting MS, with AUC (95% CI) values of 0.944 (0.913-0.975), 0.895 (0.864-0.927), and 0.928 (0.903-0.953), respectively. Conclusion The children's lipid accumulation product (CLAP) was an effective indicator associated with MS in Chinese children and adolescents and was better than BMI and WHtR for predicting MS.

This theoretical modeling and simulation paper presents designs and projected performance of non-volatile broadband on-chip 1 × 2 and 2 × 2 electro-optical switches operating in the telecommunication C-band and based on the silicon-on-insulator technological platform. These optical switches consist of an asymmetric two-waveguide directional coupler and a symmetric three-waveguide directional coupler, in which the optical phase change material Ge[subscript 2]Sb[subscript 2]Se[subscript 4]Te[subscript 1] (GSST) is the top cladding layer for one of the silicon strip waveguides. Reversible crossbar switching is attained by the amorphous (Am) to crystalline (Cr) and Cr-to-Am phase transitions in the GSST induced by heating the GSST in contact with an indium tin oxide (ITO) microstrip through Joule heating. We examined device performance in terms of mid-band insertion loss (IL), crosstalk (CT), and 0.3-dB IL bandwidth (BW). The 2 × 2 results were IL = -0.018 dB, CT < 31.3 dB, and BW = 58 nm for the coupling length Lc of 15.4 μm, and IL = 0.046 dB, CT < 38.1 dB, and BW = 70 nm for the coupling length Lc of 17.4 μm. Simulations of the 1 × 2 devices at 16.7-μm Lc revealed that IL = 0.083 dB and CT < 12.8 dB along with an expanded BW of 95 nm. Thermal simulations showed that a 5-V pulse train applied to 10[superscript 19]-cm[superscript -3] doped ITO would produce crystallization; however, the process of amorphization required a 24-V pulse of 2.9-μs duration to raise the GSST temperature above the melting temperature of 900 K., DARPA (Grant D18AP00070)

Emotion cause analysis has elicited wide interest in both academia and industry, and aims to identify the reasons behind certain emotions expressed in text. Most of the current studies on emotion cause analysis do not consider two types of information: i) the context of the emotional word, which can provide rich emotional details, and ii) the interaction between the candidate clause and the emotional clause (containing the emotional word). The above information is able to provide important clues in emotion cause analysis. In this paper, we propose a multi-attention-based neural network model to address this issue. First, our model encodes the clause via bidirectional long short-term memory, which can incorporate the contextual information of the word. Second, a multi-attention mechanism is designed to capture the mutual influences between the emotion clause and each candidate clause, and then generate the representations for the above two clauses separately. With this design, our model creates better-distributed representations of the emotion expressions and clauses. Finally, these representations are fed into a convolutional neural network to model the emotion cause clause. The experimental results show that our proposed approach outperforms the state-of-the-art baseline methods by a significant margin.

Mariculture shrimp ponds are important CH4 sources to the atmosphere. However, the spatiotemporal variations of CH4 concentration and flux at fine spatial scales in mariculture ponds are poorly known, particularly in China, worlds largest aquaculture producer. In this study, the plot‐scale spatiotemporal variations of water CH4 concentration and flux, both within and among ponds, were researched in shrimp ponds in Shanyutan wetland, Min River Estuary, Southeast China. The average water CH4 concentration and diffusion flux across the water‐air interface in the shrimp ponds over the shrimp aquaculture period varied from 2.29 ± 0.29 to 50.48 ± 20.91 μM and from 0.09 ± 0.01 to 2.32 ± 0.95 mmol·m−2·hr−1, respectively. The CH4 emissions from the estuarine ponds varied greatly between seasons, with peaks in August and September, which was similar to the trend of water temperature and dissolved oxygen concentrations. There was no remarkable difference in CH4 concentration and flux between shrimp ponds but significantly spatiotemporal differences in CH4 concentration and flux within the ponds. Significantly higher emissions occurred in the feeding zone, accounting for approximately 60% of total CH4 emission flux, while much lower CH4 emissions appeared in aeration zone, contributing 14% to total flux. This study suggests the importance of considering spatiotemporal variation in the whole‐pond estimates of CH4 concentration and flux. In light of such high spatial variation within ponds, improving aeration and feed utilization efficiency would help to mitigate CH4 emissions from mariculture ponds.

Objective By neutralizing IL-9 in a nude mouse model, the study aimed to investigate the role of Th9/IL-9 on the growth of gastric cancer in mice. Materials and methods Male BALB/c nude mice were randomly divided into three groups: a normal control group (Control), an SGC-7901 xenografted nude mice model group (Model), and a rIL-9 treatment group (Treat). The weight of the tumors was recorded to calculate the tumor inhibition rate. Flow cytometry was used to detect the cell frequency of Th9, Th17, and Treg in peripheral blood. The IL-4, IL-9, IL-10, IL-25, VEGF, and TGF-β levels in serum were determined by ELISA. The cellular migration and invasion were investigated by transwell assay. Immunohistochemical and Western blot were used to detect the expression of IL-9, CD34, PU.1, p53, and p21 proteins in gastric cancer tissue. The mRNA expression levels of IL-9, IL-21, and PU.1 in gastric cancer tissue were determined by qRT-PCR. Result rIL-9 can significantly inhibit the growth of gastric cancer. The frequency of Th9, Th17, and Treg in peripheral blood was decreased upon treatment. The levels of IL-4, IL-9, IL-10, IL-25, VEGF, and TGF-β in serum were significantly reduced in the Treat group compared with the Model group (P