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Purpose Vulnerable plaque of carotid atherosclerosis is prone to rupture, which can easily lead to acute cardiovascular and cerebrovascular accidents. Accurate identification of the vulnerable plaque is a challenging task, especially on limited datasets. Methods This paper proposes a multi-feature fusion method to identify high-risk plaque, in which three types of features are combined, i.e. global features of carotid ultrasound images, echo features of regions of interests (ROI) and expert knowledge from ultrasound reports. Due to the fusion of three types of features, more critical features for identifying high-risk plaque are included in the feature set. Therefore, better performance can be achieved even on limited datasets. Results From testing all combinations of three types of features, the results showed that the accuracy of using all three types of features is the highest. The experiments also showed that the performance of the proposed method is better than other plaque classification methods and classical Convolutional Neural Networks (CNNs) on the Plaque dataset. Conclusion The proposed method helped to build a more complete feature set so that the machine learning models could identify vulnerable plaque more accurately even on datasets with poor quality and small scale.

Vision-based intelligent systems such as driver assistance systems and transportation systems should take into account weather conditions. The presence of haze in images can be a critical threat to driving scenarios. Haze density measures the visibility and usability of hazy images captured in real-world conditions. The prediction of haze density can be valuable in various vision-based intelligent systems, especially in those systems deployed in outdoor environments. Haze density prediction is a challenging task since the haze and many scene contents have a lot in common in appearance. Existing methods generally utilize different priors and design complex handcrafted features to predict the visibility or haze density of the image. In this article, we propose a novel end-to-end convolutional neural network (CNN) based method to predict haze density, named as HazDesNet. Our HazDesNet takes a hazy image as input and predicts a pixel-level haze density map. The density map is then refined and smoothed, and the average of the refined map is calculated as the global haze density of the image. To verify the performance of HazDesNet, a subjective human study is performed to build a Human Perceptual Haze Density (HPHD) database, which includes 500 real-world hazy images and 100 synthetic hazy images, and the corresponding human-rated perceptual haze density scores. Experimental results show that our method achieves the best haze density prediction performance on our built HPHD database and existing databases. Besides the global quantitative results, our HazDesNet is capable of predicting a continuous, stable, fine, and high-resolution haze density map. We will make the database and code publicly available at https://github.com/JiaheZhang/HazDesNet.

Many stereoscopic image retargeting (SIR) methods have been developed for automatically and intelligently resizing stereoscopic images and we cannot always rely on time-consuming subjective user studies to validate the performance of different SIR methods. It is therefore required to design reliable objective metrics for SIR quality evaluation. This paper extends our previous 2D aspect ratio similarity (ARS) metric to a stereo 3D version termed as StereoARS where the key idea is to investigate into retargeting inconsistency between the original stereo correspondences. Our proposed StereoARS operates via two stages: monocular quality estimation and binocular inconsistency detection. In the first stage, monocular quality estimation is performed by applying a modified ARS measure on the left and right views separately to quantify the quality degradation within each monocular view. In the second stage, binocular inconsistency detection is performed in both pixel-level and grid-level to characterize the influence of binocular rivalry and stereo visual discomfort on SIR quality. In addition, we also measure to what extent the original pixel visibility relation is preserved after SIR as another binocular quality factor. Finally, these monocular and binocular quality estimates are fused to produce an overall SIR quality score. Extensive experiments have demonstrated that StereoARS achieves better alignment with human subjective ratings than the existing metrics by a large margin.

Low-density parity-check (LDPC) codes are widely used as error correction codes in new generation digital TV standards, such as the second generation of terrestrial digital video broadcasting standard (DVB-T2), Advanced Television Systems Committee (ATSC) 3.0, etc. The nonlinear belief propagation (BP) algorithm has excellent decoding performance for LDPC codes, but is often simplified in hardware implementations by linear min-sum (MS) algorithm due to its high complexity. This simplification also leads to over-estimation problems, which can be corrected by adding factors in conventional algorithms (e.g., normalized min-sum (NMS), offset min-sum (OMS), and variable scaling normalized min-sum (VMS) algorithms). However, the correction factors of these modified MS algorithms cannot adapt to different channels and modulations, and the performance needs further improvement. In this paper, the concepts of over-estimation value (OEV) and over-estimation rate (OER) are introduced to describe the over-estimation problem of the MS algorithm. Then, under the guidance of OEV and OER, a polygonal line min-sum (PMS) algorithm with correction factors adapted to different channels and modulations is proposed according to LLR distribution. Following the properties of OEV and OER, PMS algorithm is further simplified into Simplified PMS (SPMS) algorithm. LDPC codes from ATSC 3.0 are adopted in this paper to evaluate SPMS algorithm in comparison with the conventional algorithms. Extensive simulation results show that the SPMS algorithm for ATSC 3.0 LDPC decoder has at most 1.61dB, 0.24dB and 0.36dB gain over NMS, OMS and VMS algorithms respectively when frame error rate (FER) is at 10⁻⁴ level over additive white Gaussian noise (AWGN) channel with QPSK modulation. More importantly, the simulation results show that the SPMS algorithm can achieve much better performance than these modified MS algorithms over AWGN and Rayleigh channel with higher-order modulations or under limited maximum iteration number.

The non-invasive treatment of glioblastoma (GBM) is of great significance and can greatly reduce the complications of craniotomy. Sonodynamic therapy (SDT) is an emerging tumor therapeutic strategy that overcomes some fatal flaws of photodynamic therapy (PDT). Different from PDT, SDT has deep tissue penetration and can be applied in the non-invasive treatment of deep-seated tumors. However, effective sonosensitizers that can be used for SDT of GBM are still very rare. Herein, we have prepared a suitable assembly based on a hypocrellin derivative (CTHB) with good biocompatibility. Excitedly, the hypocrellin-based assembly (CTHB NPs) can effectively produce reactive oxygen species under ultrasound stimulation. The inherent fluorescence and photoacoustic imaging characteristics of the CTHB NPs are conducive to the precise positioning of the tumors. It has been proved both in subcutaneous and in intracranial tumor models that CTHB NPs can be used as an effective sonosensitizer to inhibit tumor growth under ultrasound irradiation. This hypocrellin-based assembly has a good clinical prospect in the non-invasive treatment of GBM.

With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images.

Broadband high-efficiency luminescent materials have become a hot spot in lead-free perovskite research. There are relatively few broadband yellow-green phosphors with both ultraviolet and blue excitation ranges, which make them more suitable for phosphor-converted white LEDs. Through the ion-exchanged strategy, Cs2Hf1-xTexCl6 (CH1-xTxC) vacancy halide double perovskites were successfully prepared at room temperature. Using different excitation ranges of CH1-xTxC, two types of high-quality white LEDs are obtained. By combining density functional theory calculations and experiments, it is proved that this bright broadband yellow-green emission (photoluminescence quantum yield of 83.46%) is not only derived from the ion transitions of Te4+ but also exhibits the inherent characteristics of self-trapped exciton emission. Our results not only broaden the application fields of lead-free halide perovskites but also provide further insights into the luminescence mechanism.

The rapid development of flexible triboelectric nanogenerators (TENGs) has become an alternative to batteries for wearable devices. Stretchable, multifunctional, and low-cost are the primary development directions for these wearable devices based on TENG. Herein, a stretchable triboelectric generator with coplanar integration was designed for energy harvesting and force sensing. The industrial conductive silicone and silicone were used to fabricate the TENG with a thickness of less than 0.9 mm. When the elongation was less than 150%, TENG exhibited excellent linear characteristics in the resistance-tensile strain correspondence, and the coefficient of determination was 0.99. This stretchable TENG with a sufficient contact area of 9 cm2 could generate a short-circuit current of 2 µA when it was in contact with the skin. Lastly, an intelligent tension monitoring wearable device that can effectively measure the tensile force was developed. Such a stretchable, coplanar integration-based, and low-cost wearable device has excellent applicability in wearable electronics.

Photothermal therapy (PTT) has inherent advantages in the treatment of hypoxic tumors due to its optically controlled selectivity on tumor ablation and oxygen-independent nature. The subcellular organelle-targeting capability and photothermal conversion efficiency (PCE) at near-infrared (NIR) wavelength are the key parameters in the assessment of the photothermal agent (PTA). Here, we report that carbon dots (CDs) prepared by the hydrothermal treatment of coronene derivatives show a high PCE of 54.7% at 808 nm, which can be attributed to the narrow band gap and the presence of amounts of continuous energy bands on CDs. Moreover, the vibrations in the layered graphite structures of the CDs also increase the rate of nonradiative transition and thus enhance the PCE. Furthermore, the CDs also possess excellent photostability, biocompatibility, and cell penetration capability and could mainly accumulate in the lysosomes. These experiment results have proved that the CDs are suitable as an efficient NIR light-triggered PTA for efficient PTT against cancer.

It is a very well-known fact, that the high complexity of the High Efficiency Video Coding standard (HEVC) is the main hurdle for its wide deployment and use. To tackle this problem, a number of recent research outcomes exploit heuristic algorithms and machine learning, including deep learning, to reduce the coding complexity. However, in most cases, each encoder module, i.e., encoding process, is first accelerated individually, and then different acceleration algorithms are manually combined. Without a holistic strategy, the acceleration potential of multi-module combination is not exploited and the Rate-Distortion (RD) loss is generally not well controlled. To tackle these shortcomings, this paper exploits the acceleration properties of different modules, i.e., the numerical representation of potential time saving and possible RD loss, from which a heuristic model is explored. Then a Heuristic Model Oriented Framework (HMOF) is proposed which adapts the properties of modules to underlying acceleration algorithms. In the framework, two advanced acceleration algorithms, including Border Considered CNN (BC-CNN)-based Coding Unit (CU) partition and Naive Bayes-based Prediction Unit (PU) partition, are proposed for the CU and PU modules, respectively. Further, by leveraging the heuristic model as the guidance to combine the proposed acceleration algorithms, HMOF is globally optimized, where different time saving budgets are wisely allocated to different modules and a theoretically minimal RD loss is achieved. According to the experimental results, through fusing a suitable deep learning technique and a Bayes-Based prediction, the proposed acceleration framework HMOF enable multiple acceleration choices. Here the proposed joint optimization strategy help to make a choice leading to the best cost-performance. Furthermore, within the proposed framework, intra coding time can be precisely controlled with negligible Bjontegaard delta bit-rate (BDBR) loss. In this context, as a complexity control method, HMOF outperforms the state-of-the-art complexity reduction algorithms under a similar complexity reduction ratio. These results partially demonstrate the superiority of the proposed technique.

In Multimedia Broadcast/Multicast Service(MBMS), Multicast/Broadcast Single Frequency Network (MBSFN) and Single-cell Point-to-multipoint Network (SC-PTM) are two essential ways to organize networks to provide multicast services. MBSFN shows its unique advantage of enhancing signals at the boundary of two cells, however, highly requiring synchronization of symbols. SC-PTM shows the advantage of the flexibility on the network deployment. The comparison and complementarity of these two modes are explored. In this paper, we analyze the reception performance of MBSFN and SC-PTM from multiple perspectives. We first compare the successful transmission probability (STP) of MBSFN with that of SC-PTM. Then, we consider optimal power control problems in MBSFN and SC-PTM. The corresponding power control optimization problems are with fraction and max-min forms and solved by the Dinkelbach algorithm. Furthermore, we propose a joint mode-selection and power-control method in the hybrid mode of MBSFN and SC-PTM, where each cell could select the multicast mode from MBSFN and SC-PTM. This is a mixed-integer nonlinear programming (MINLP) problem and solved by the concave-convex procedure (CCCP) and the Dinkelbach algorithm. To further improve the throughput, the appropriate successful reception proportion is selected under the opportunistic multicast. Finally, the numerical simulations show the performance of our algorithms and compare the throughput of two modes, which verify our analysis.

This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise informatio...

Background: Chronic diabetes accelerates vascular dysfunction often resulting in cardiomyopathy but underlying mechanisms remains unclear. Recent studies have shown that the deregulated unfolded protein response (UPR) dependent on highly conserved IRE1α-spliced X-box- binding protein (XBP1s) and the resulting endoplasmic reticulum stress (ER-Stress plays a crucial role in the occurrence and development of diabetic cardiomyopathy (DCM). In the present study, we determined whether targeting MAPK/ERK pathway using MEK inhibitor U0126 could ameliorate DCM by regulating IRE1α-XBP1s pathway.Method: Three groups of 8-week-old C57/BL6 mice were studied: one group received saline injection as control (n=8) and two groups were made diabetic by streptozotocin (STZ) (n=10 each). 18 weeks after STZ injection and stable hyperglycemia, one group had saline treatment while the second group was treated with U0126 (5mg/kg/day), 8 weeks later, all groups were sacrificed. Cardiac function/histopathological changes were determined by echocardiogram examination, Millar catheter system, hematoxylin-eosin staining and western blot analysis. H9c2 cardiomyocytes were employed for in vitro studies.Results: Echocardiographic, hemodynamic and histological data showed overt myocardial hypertrophy and worsened cardiac function in diabetic mice. Chronic diabetic milieu enhanced SUMOylation and impaired nuclear translocation of XBP1s. Intriguingly, U0126 treatment significantly ameliorated progression of DCM, and this protective effect was achieved through enriching XBP1s’ nuclear accumulation. Mechanistically, U0126 inhibited XBP1s’ phosphorylation on S348 and SUMOylation on K276 promoting XBP1s’ nuclear translocation. Collectively, these results identify that MEK inhibition restores XBP1s-dependent UPR and protects against diabetes-induced cardiac remodeling. Conclusion: The current study identifies previously unknown function of MEK/ERK pathway in regulation of ER-stress in DCM. U0126 could be a therapeutic target for the treatment of DCM.

BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin lymphoma. The development of immunotherapy greatly improves the patient prognosis but there are some exceptions. Thus, screening for better biomarkers for prognostic evaluation could contribute to the treatment of DLBCL patients. AIM To screen the novel mediators involved in the development of DLBCL. METHODS The GSE60 dataset was applied to identify the differentially expressed genes (DEGs) in DLBCL, and the principal components analysis plot was used to determine the quality of the included samples. The protein-protein interactions were analyzed by the STRING tool. The key hub genes were entered into to the GEPIA database to determine their expressions in DLBCL. Furthermore, these hub gene alterations were analyzed in cBioportal. The UALCAN portal was employed to analyze the expression of the hub genes in different stages of DLBCL. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Score was conducted to evaluate the correlation between the gene expression and tumor purity. The gene-gene correlation analysis was conducted in the GEPIA. The stromal score analysis was conducted in TIMER to confirm the correlation between the gene expression and infiltrated stromal cells. The correlation between the indicated genes and infiltration level of cancer-associated fibroblasts (CAFs) was also completed in TIMER with two methods, MCP-Counter and Tumor immune dysfunction and exclusion. The correlation between fibronectin (FN1) protein level and secreted protein acidic and cysteine-rich (SPARC) messenger ribonucleic acid expression was confirmed in the cBioportal. RESULTS The top 20 DEGs in DLBCL were identified, and the principal components analysis plot confirmed the quality of the significant DEGs. The pairwise correlation coefficient analysis among all samples showed that these DEGs have a certain co-expression pattern. The DEGs were subjected to STRING to identify the hub genes, alpha-2-macroglobulin (A2M), cathepsin B (CTSB), FN1, matrix metallopeptidase 9 (MMP9), and SPARC. The five hub genes were confirmed to be overexpressed in DLBCL. The cBioportal portal detected these five hub genes that had gene alteration, including messenger ribonucleic acid high amplification and missense mutation, and the gene alteration percentages of A2M, FN1, CTSB, MMP9, and SPARC were 5%, 8%, 5%, 2.7%, and 5%, respectively. Furthermore, the five hub genes had a potential positive correlation with tumor stage. The correlation analysis between the five genes and tumor purity confirmed that the five genes were overexpressed in DLBCL and had a positive correlation with the development of DLBCL. More interestingly, the five genes had a significant correlation with the stromal infiltration scores. The correlation analysis between the fives genes and CAFs also showed a significant value, among which the top two genes, FN1 and SPARC, had a remarkable co-expression pattern. CONCLUSION The top DEGs were identified, and the five hub genes were overexpressed in DLBCL. Furthermore, the gene alterations were confirmed and the positive correlation with tumor purity revealed the overexpression of the five genes and close association with the development of DLBCL. More interestingly, the five genes were positively correlated with stromal infiltration, especially in CAFs. The top two genes, FN1 and SPARC, showed a co-expression pattern, which indicates their potential as novel therapeutic targets for DLBCL.

With the widespread of application scenarios such as remote office and cloud collaboration, Screen Content Video (SCV) and its processing which show different characteristics from Natural Scene Video (NSV) and its processing, are increasingly attracting researcher’s attention. Among these processing techniques, quality evaluation plays an important role in various media processing systems. Despite extensive research on general Image Quality Assessment (IQA) and Video Quality Assessment (VQA), quality assessment of SCVs remains undeveloped. In particular, SCVs always suffer from compression degradations in all kinds of application scenarios. In this article, we first study subjective SCV quality assessment. Specifically, we first construct a Compressed Screen Content Video Quality (CSCVQ) database with 165 distorted SCVs compressed from 11 most common screen application scenarios using the H.264, HEVC and HEVC-SCC formats. Twenty subjects were recruited to participate in the subjective test on the CSCVQ database. Then we study objective SCV quality assessment and propose a SCV quality measure. We observe that localized protruding information such as curves and dots can be well captured by the local relative standard deviation which then can be used to measure the intra-frame quality. Base on this observation, we develop a MutiScale Relative Standard Deviation Similarity (MS-RSDS) model for SCV quality evaluation. In our model, the relative standard deviation similarity between the reference and distorted SCVs is measured from frame differences between two adjacent frames, which can capture the spatiotemporal distortions accurately. A multiscale strategy is also applied to strengthen the original single-scale model. Extensive experiments are performed to compare the proposed model with the most popular and state-of-the-art quality assessment models on the CSCVQ database. Experimental results show that our proposed MS-RSDS model which has relatively low computation complexity, outperforms other IQA/VQA models.

Colon cancer is the third most common cancer in the world, and it is increasingly threatening people’s health. Early diagnosis is crucial to reducing the threat; however, the chance of missed polyps in today’s colonoscopy examination is still high (about 10%) due to limitations in diagnosis techniques and data analysis methods. The colonoscope is a kind of robot and on its tip there is a camera to acquire images. This paper presents a study aimed to improve the rate of successful diagnosis with a new image data analysis approach based on the faster regional convolutional neural network (faster R-CNN). This new approach has two steps for data analysis: (i) pre-processing of images to characterize polyps, and (ii) incorporating of the result of the pre-processing into the faster R-CNN. Specifically, the pre-processing of colonoscopy was expected to reduce the influence of specular reflections, resulting in an improved image, upon which the faster R-CNN algorithm was aplied. There are several improvements of the faster r-CNN tailoring to the task of colon polyps detection. To confirm the superiority of this new approach, the mean average precision (mAP) was used to compare the results obtained with the new approach and the faster R-CNN algorithm. The experimental result shows that the mAP of the new approach is 91.43%, as opposed to 90.57% with the faster R-CNN, which shows a significant improvement.

In this paper, three dimensional numerical models for shield tunnel lining bolted joints, which are suitable for seismic excitation and static pressure of surrounding rocks, are established respectively. The influence of different seismic directions on the stress and deformation characteristics of bolted joints is studied in detail. Based on the existing tests, the numerical model of double segment bolted joint was verified. Then, the mechanical properties of the lining joint with different bolted forms were explored, and the applicability analysis of connecting bolted under seismic excitation and surrounding rock static pressure was improved. The results showed that: Compared with the deformation of the circumferential joint under the earthquake transverse excitation, the deformation of the circumferential joint under the longitudinal earthquake excitation is relatively more significant, and the deformation of the inclined bolted joint is the largest. Under the static pressure of surrounding rocks, the opening of the inclined bolted joint is the smallest, but the dislocation is the largest. The opening of the bent bolted joint is the largest, while the dislocation is the smallest. The stress value of the inclined bolted is the largest, whereas the stress value of the bending bolted is the lowest.

The development of wireless monitoring is currently restricted by the short lifetime of batteries, requiring frequent replacement. Utilization of abundant mechanical energy from the surrounding environment has attracted increasing attention in real-time monitoring. Herein, a coil-levitated hybrid generator was developed for the efficient harvesting of mechanical energy from mechanical motion. The novel coil-levitated structure adapted to the metal and magnetic environment. The output currents were systematically analyzed at different operation modes based on the unique combination of triboelectrification, electromagnetic induction, and piezoelectric effect. Under the excitation of vibration frequency and amplitude of 8 Hz and 5 mm respectively, the as-constructed triboelectric nanogenerator delivered a peak power density of 11.40 W/m3 at 10 MΩ. Meanwhile, the middle electromagnetic part and bottom piezoelectric generator provided peak power densities of 6.97 and 79.93 W/m2 at 10000 Ω, respectively. More importantly, the battery charging experiment was verified, in which a 30 mA h Li-ion battery can be charged from 2.57 to 3.27 V in about 90 min. In sum, a self-powered temperature and vibration monitoring system was successfully developed based on hybrid generator, promising for realizing wireless monitoring of mechanical equipment without any external power supply.

Non-uniform constellation (NUC) is an advanced technology in digital terrestrial television broadcasting (DTTB) systems to reduce the shapping gap of BICM capacity to Shannon theoretical limit and provide performance gain. Two-dimensional NUC (2D-NUC) is a kind of NUC providing more gain but bringing higher demapping complexity at the receiver, which hinders its application prospects, especially in power limited systems. This paper proposes three novel demapping algorithms with reduced complexity for low to medium code rate 2D-NUCs in Advanced Television Systems Committee 3rd Generation (ATSC 3.0) standard. The proposed algorithms are based on the introduction of virtual points, the strategy of condensed symbols reduction and some reasonable approximations. There is a trade-off between the demapping complexity and performance. These three algorithms have different degrees of reduction in complexity and performance degradation, so they accommodate for different practical requirements. Theoretical analysis and simulation results are also given in this paper to prove the efficiency of the proposed demapping algorithms with reduced complexity.

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome is a newly-described adult-onset inflammatory syndrome characterized by vacuoles in myeloid and erythroid precursor cells and somatic mutations affecting methionine-41 (p.Met41) in UBA1. The VEXAS syndrome often overlaps with myelodysplastic syndromes (MDS) with autoimmune disorders (AD). By screening the UBA1 gene sequences derived from MDS patients with AD from our center, we identified one patient with a p.Met41Leu missense mutation in UBA1, who should have been diagnosed as MDS comorbid with VEXAS syndrome. This patient respond poorly to immune suppressive drugs. Patients with MDS and AD who have characteristic vacuoles in myeloid and erythroid precursor cells should be screened for UBA1 mutation, these patients are likely to have VEXAS syndrome and unlikely to improve with immunosuppressive drugs and should be considered for other alternative therapies.

Light utilization is one of the key factors for the improvement of photocatalytic performance. Herein, we design C-TiO2 hollow nanoshells with strong Mie resonance for enhanced photocatalytic hydrogen evolution in a dye-sensitized system under visible light irradiation (λ ≥ 420 nm). By tuning the inner diameters of hollow nanoshells, the Mie resonance in hollow nanoshells is adjusted for better excitation of dye molecules, which thus greatly enhances the light utilization in visible light region. This work shows the potential of Mie resonance in nanoshells can be an alternative strategy to increase the light utilization for photocatalysis.

Advanced anti-counterfeiting materials have great research significance in the fields of economy, safety and human health. Compared with independent color mixed anti-counterfeiting materials, ultraviolet (UV) excitation wavelength-dependent discoloration, which can realize the change of the emission peak shape and color under different excitation wavelengths, is more attractive. In this work, the Sb-doped Cs2ZrCl6 vacancy-ordered double perovskite was designed and synthesized for anti-counterfeiting materials. Due to the singlet and triplet transitions of Sb3+ ions and self-trapping exciton (STE) emission of the Cs2ZrCl6 host, the material showed emission variability from blue to orange-yellow color. In addition, room temperature synthesis and excellent air and humidity stability further enhanced the potential of Sb-doped Cs2ZrCl6 for application.

Conventional photodetectors are usually “color blind” and require additional dispersive components or complicated structures to recognize colors. In this work, an organic photodetector (OPD) is designed with a layer of molybdenum(VI) oxide (MoO3) stacked on an organic donor/acceptor charge transfer interface. The band structure of the OPD is designed with multiple charge separation channels that can be activated/deactivated under reverse and forward biases. The combination of a high or low photocurrent under reverse and forward biases determines the colors of the received light, and that forms the heart of “non-color blind” ability to recognize light colors in the OPD. The energy structure and charge collection pathways in the OPD are investigated with biased external quantum efficiency (EQE), ultraviolet photoelectron spectroscopy (UPS) and current density–voltage (J–V) studies. The OPD with the ability of color and wavelength determination demonstrates its potential to recognize colors without using additional components and the dual mode operation enables it to work under different conditions via switching its detection ranges with bias voltages. This design principle can be utilized to fabricate multifunctional devices by simply selecting different donor–acceptor materials according to the desired detection ranges.

Built on the explosive advancement of cloud and telecommunication technologies, Gaming-as-a-Service (GaaS) or cloud gaming system is expected to revolutionize the traditional multi-billion video game market in the near future. This wave is analogous to the rise of live-video-streaming-based-Netflix to replace conventional DVD rental business for movies and TVs. In practice, a successful GaaS platform need to operate in a transparent mode without requiring substantial efforts from both content providers and end users, and offer the pristine quality of experience (QoE) at an affordable cost. Our analysis suggests that GaaS provisioning cost can be reduced significantly by enforcing the game video rendering and streaming at a lower resolution (so as to increase the user concurrency in the cloud and reduce the streaming bandwidth over the network). However, streaming video at a lower resolution may deteriorate the QoE. To maintain the client QoE at the level using the default-native resolution for streaming or even enhance it, we introduce the learned resolution scaling (LRS), which leverages the computational capabilities at clients/edges to restore/improve the reconstructed image/video quality via stacked deep neural networks (DNN). We integrate this LRS into a commercialized GaaS platform - AnyGame, to study its efficiency and complexity quantitatively. Extensive real-life experiments have shown that LRS-powered AnyGame offers the state-of-the-art performance, and the lower operational cost, paving the road for a potential success of GaaS over the Internet. Additionally, we dive into proposed LRS via ablation studies to further demonstrate its consistent performance, including the discussions on trade-off between efficiency and complexity, alternative training sets, etc.

Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.

Conventionally, geometric computing problems are treated as algebraic computing problems by representing a geometric object in a global reference coordinate system. This approach has two problems. The first problem is that the intuitive view of the interaction of objects is lost, and the second problem is that algebraic computing is prone to errors for degenerated cases related to the interaction of (e.g., notable case of “divided by zero”). In this paper, we propose a new approach to geometric computing especially to analyze the interaction of two objects (e.g., two triangles). The main idea behind this new approach is a specially-tailored local coordinate system for two interacting objects is defined, which makes the projection of the objects on this local coordinate system represent the true geometry of the objects. This idea significantly departs from the conventional approach which is primarily based on the concept of the global coordinate system. Three examples are provided to illustrate the effectiveness of the proposed approach. Among them, one example is related to the robustness of methods for analyzing the relations of two interacting objects, which is still an open issue in the field of geometric computing and suggests that the proposed approach could have some benefit to robustness in analysis.

Breast cancer is the leading cause of cancer-related death among women despite the significant improvement in diagnosis and treatment. Tumor-associated neutrophils have been shown to suppress antitumor functions of the host. However, how breast cancer tumor microenvironment influences the phenotype and functions of neutrophils to potentiate T cell immunosuppression is unknown. Herein, neutrophils isolated from peripheral blood of healthy donors were treated with supernatants from breast cancer cell lines or recombinant human CCL20. PD-L1 expression on neutrophils was then evaluated by immunofluorescence and flow cytometry. Neutrophils and Jurkat T cells were cocultured to evaluate the effect of tumor-associated neutrophils on T cell functions. Finally, immunohistochemical staining was performed to evaluate the clinical relevance of neutrophils infiltrating breast tumor tissues. Tumor-derived CCL20 activated and upregulated PD-L1 expression on neutrophils. A significant positive correlation was found between CCL20 and CD66b+ neutrophils in tumor tissues. Through in vitro experiment, tumor-associated neutrophils (TANs) effectively suppressed T cell immunity which was reversed upon PD-L1 blockade. Moreover, a high density of TANs was associated with short disease free survival in breast cancer patients. Furthermore, receiver operating curve showed that the density of TANs could accurately predict disease-free survival in breast cancer patients. Our findings suggest that targeting TANs via CCL20 immunosuppressive pathway may be a novel therapeutic strategy for breast cancer treatment.

To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-of-the-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.

The interface material is an indispensable part of perovskite solar cells (PSCs) because of its special functions. In this work, a conjugated polyelectrolyte, PTPADT-SO3Na, was synthesized and intr...

Objective To establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels. Methods Totally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve. Results Differences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P

The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a bag of instances through visual instance generation module, which is composed of a pre-trained convolutional neural network (CNN) and two adaptation layers. Then, a fuzzy c-means routing algorithm is introduced for generating clustered instances as semantic mid-level representation to bridge the instance-to-bag gap. To explore the relationships between clustered instances and bags, we construct an attention based MIL pooling layer for representing bag features. A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. Finally, we conduct extensive experiments on several datasets, and the experimental results verify the feasibility of our proposed approach for visual sentiment analysis.

As an important indicator for the appearance and intrinsic quality of textiles, fabric flatness is the immediate cause affecting the aesthetic appearance and performance of textiles. In this paper, the objective evaluation system of fabric flatness based on 3D scanner and convolutional neural network (CNN) is constructed by using the height data of AATCC flatness template. The 3D scanner is responsible for the collection of the height value data of the sample. The effect of different sub-sample cutting sizes, cutting offsets, and network model depths on the objective evaluation coincidence rate of multiple flatness level was studied. The experimental results show that the coincidence rate of the system reaches 98.9% when the collected sample data are cut into subsamples of 20 pixel × 20 pixel with 12 pixel cutting offsets and the 11-layer network model is selected. Finally, this scheme is used to evaluate the flatness of four real fabrics with different colors and textures. The result shows that all of the samples can achieve a higher coincidence rate, which further verifies the adaptability and stability of the objective evaluation system constructed in this paper for fabric flatness evaluation.

Scale variation of pedestrians in a crowd image presents a significant challenge for vision-based people counting systems. Such variations are mainly caused by perspective-related distortions due to the camera pose relative to the ground plane. Following the density-based counting paradigm, we postulate that generating density values adaptive to object scales plays a critical role in the accuracy of the final counting results. Motivated by this, we distill the underlying information from depth cues to obtain scale-aware representations that can respond to object scales considering the fact that the scale is inversely proportional to the object depth. Specifically, we propose a depth embedding module as add-ons into existing networks. This module exploits essential depth cues to spatially re-calibrate the magnitude of the original features. In this way, the objects, although in the same class, will attain distinct representations according to their scales, which directly benefits the estimation of scale-aware density values. We conduct a comprehensive analysis of the effects of the depth embedding module and validate that exploiting depth cues to perceive object scale variations in convolutional neural networks improves crowd counting performances. Our experiments demonstrate the effectiveness of the proposed approach on four popular benchmark datasets.

Being able to accurately predict the common gaze point of a group of persons is of particular interest to precise marketing and automatic group attention assessment. Group gaze estimation faces challenges including small face/head size and outlier observers. To address these challenges, we proposed a novel framework called Multi-stream Group Gaze Learning and Estimation (MUGGLE). The MUGGLE infrastructure includes two inference streams: 1) a holistic stream which utilizes fused attention map as input to a global deep convolutional structure to explore the global geometric configurations and contexts of interesting persons in the scene; and 2) an aggregative stream which robustly aggregates individual gazes via a recurrent structure (e.g., LSTM) to obtain outlier-tolerant estimation. Both streams are seamlessly integrated via a fusion network. Extensive experiments are performed on a fully annotated group gaze image dataset with 8,000+ images and 100,000+ faces (which is publicly releasable). The results demonstrate the effectiveness of the proposed MUGGLE framework in group gaze estimation.

Regularly assembled structures of nanowires, such as aligned arrays, junctions and interconnected networks, have great potential for the applications in logical circuits, address decoders, photoelectronic devices and transparent electrodes. However, for now it is still lack of effective approaches for constructing nanowire bifurcated junctions and crosslinked networks with ordered orientations and high quality. Herein, we report the controlled growth of Bi2S3 semiconductor nanowire bifurcated junctions and crosslinked networks with well-aligned directions and high crystalline degree by utilizing the proportional lattice match between nanowires and substrates. Taking advantages of the “tip-to-stem splice” assembly of individual nanowires, the precise orientation alignments of Bi2S3 semiconductor nanowire bifurcated junctions and crosslinked networks were successfully realized. The controlled growth mechanism and structural evolution process have been elucidated by detailed atomic structure characterizations and modeling. The highly crystal quality and direct energy bandgap of as-assembled photodetectors based on individual bismuth sulfide nanowires enabled high photoresponsivity and fast switch time under light illumination. The three-terminal devices based on nanowire bifurcated junctions present rapid carrier transport across the junction. The flexible photodetectors based on nanowire crosslinked networks show very minimal decay of photocurrent after long-term bending test. This work may provide new insights for the guided construction and regular assembly of low-dimensional ordered functional nanostructures towards advanced nanotechnologies.

Broadcasting and broadband network is moving towards integration and LTE-based 5G terrestrial broadcast is now researched in Release 16 in Third Generation Partnership Project (3GPP) standardization meetings. However, the work scope of LTE-based 5G terrestrial broadcast focuses on specifying new numerologies and some minor improvement on cell acquisition subframe, which is insufficient. In this paper, limitations in coding and modulation schemes of LTE-based 5G terrestrial broadcast system, e.g., Turbo codes and Quadrature Amplitude Modulation (QAM), are detailedly analyzed. To further enhance the spectrum efficiency of LTE-based 5G terrestrial broadcast system, LDPC (Low Density Parity Check) codes from 5G new radio (NR) standard and newly designed non-uniform constellations (NUCs) are adopted in this paper to replace Turbo codes and QAM respectively. Extensive simulations and complexity analysis show that the proposed LDPC coding and NUC modulation scheme, either standalone or combined, can provide significant performance gain over Additive White Gaussian Noise (AWGN) and Tapped Delayline (TDL) channels, without additional complexity. To summarize, this paper investigates the weakness of the coding and modulation schemes in current systems and provides potential alternatives for the enhanced future broadcast in 3GPP standard.

Screen content image (SCI) prevails because of the explosive growth of screen oriented applications. This leads to extensive studies on SCI quality assessment and modeling for application optimization. In this paper, we propose a full reference multiscale edge attention (MSEA) similarity index to efficiently measure the perceptual quality of a screen image. This model considers the perceptual impacts of fixation attention, edge structure and edge contrast jointly, to accurately capture the masking phenomena (e.g., frequency selectivity, luminance, contrast, etc.) of our human visual system (HVS) when viewing a screen image. Specifically, we decompose the images using Gaussian and Laplacian pyramids which are then used to derive the edge structure, and edge contrast feature maps. Together with the fixation attention map generated by weighted luminance difference between the reference and distorted SCIs, we could eventually offer a MSEA similarity map for final index score. We have evaluated this model using a publicly accessible screen image database. Simulation results have shown that the MSEA similarity index correlates with the collected subjective mean opinion score (MOS) very well. In fact, it is ranked at the first place for both Pearson linear correlation coefficient (PLCC) and Root mean squared error (RMSE), and ranked at the second place for Spearman rank-order correlation coefficient (SROCC) measurements, among existing quality metrics.

3GPP is now carrying forward the development of the point to multipoint transmission mode of 5G on the basis of the cellular infrastructure and standard. Since the dedicated proposal for multimedia broadcast multicast system (MBMS) was approved, the MBMS technologies are evolving with the update of the requirements from 3G to 5G. This paper reviews the latest progress during the evolved MBMS standardization, which focuses on the LTE-based 5G terrestrial broadcast mode. Some agreements in terms of two widely discussed topics during 3GPP meetings, cell acquisition subframe (CAS) enhancement and numerology refinement, are presented. The enhancement of CAS and the update of numerology aim to deal with the service outage issue in the case of covering large area and serving high-mobility users. To verify the improvement brought by the CAS enhancement and the new numerology, simulations from system level and link level are carried out by several organizations and the representative results are presented and analyzed in this paper.

A few prognostic predicting systems existed for primary cutaneous B-cell lymphoma (PCBCL). However, none of them took age into consideration. We sought to declare the prognostic role of age in PCBCL. A total of 4859 patients were identified in the surveillance, epidemiology and end results data, spanning 1975–2016. The association between age and survival was determined using unadjusted and adjusted proportional hazard analysis. There was a uni-modal distribution of age, with most in the seventh decade (22%), followed by the sixth and eighth decades (19%). As a continuous variable, age was demonstrated to have an adverse effect on overall survival (OS, HR, 1.077, 95% CI 1.073–1.082, p = 0.000) and cancer-specific survival (CSS, HR, 1.099, 95% CI 1.092–1.106, p = 0.000) after adjusted proportional hazard analysis. Patients aged ≤ 60 years had significantly higher survival rates than those aged > 60 (5-year OS was 93% vs 64%, 10-year OS was 90% vs 45%, p = 0.000; 5-year CSS was 98% vs 80%, 10-year CSS 96% was 62%, p = 0.000). Similar survival trends were also observed in different sub-group analyses, including disease with different stages (p = 0.000), different histology subgroups (p = 0.000), and different sites (p = 0.000). Age has an important effect on overall survival and cancer-specific survival. The addition of age to the primary site, histology, and stage improves predicting long-term outcomes in cutaneous B-cell lymphoma.

Clothing transfer is a challenging task in computer vision where the goal is to transfer the human clothing style in an input image conditioned on a given language description. However, existing approaches have limited ability in delicate colorization and texture synthesis with a conventional fully convolutional generator. To tackle this problem, we propose a novel semantic-based Fused Attention model for Clothing Transfer (FACT), which allows fine-grained synthesis, high global consistency and plausible hallucination in images. Towards this end, we incorporate two attention modules based on spatial levels: (i) soft attention that searches for the most related positions in sentences, and (ii) self-attention modeling long-range dependencies on feature maps. Furthermore, we also develop a stylized channel-wise attention module to capture correlations on feature levels. We effectively fuse these attention modules in the generator and achieve better performances than the state-of-the-art method on the DeepFashion dataset. Qualitative and quantitative comparisons against the baselines demonstrate the effectiveness of our approach.

Background and Aims: The World Health Organization (WHO) Western Pacific Region set a target of eliminating mother-to-child transmission (MTCT) of hepatitis B virus (HBV) by 2030. To assess the feasibility of this target in China, we carried out an epidemiological study to investigate the status quo of MTCT in the real-world setting. Methods: One thousand and eight hepatitis B surface antigen-positive pregnant women were enrolled at 10 hospitals. Immunoprophylaxis was administered to infants. In addition, mothers with HBV DNA level >2,000,000 IU/mL were advised to initiate antiviral therapy during late pregnancy. A health application called SHIELD was used to manage the study. Results: Nine hundred and five of the enrolled mothers, with 924 infants, completed the follow-up. Birth-dose hepatitis B vaccine and hepatitis B immunoglobulin were received by 99.7% and 99.7% of infants, respectively, within 24 h after birth. There were 446 mothers who received antiviral therapy, including 72.3% of the mothers with HBV DNA level >2,000,000 IU/mL and 21.0% of the mothers with HBV DNA level

Bile duct injury remains the most serious complication of laparoscopic cholecystectomy (LC), the main cause was misidentification of cystic duct (CD). The aim of this study was to evaluate the effectiveness and security of retrograde tracing along "cystic duct" (RTACD) method for the prevention of biliary misidentification injury in LC. The conception of RTACD method was first described and then illustrated by simulation dissection with extrahepatic biliary structure charts. A total of 840 patients undergoing LC were selected. After the "CD" was separated during operation, its authenticity was identified by RTACD method according to its course and origin. The "CD" can be clipped/divided only when it was identified to be true CD. Among 840 patients, the initially separated "CD" was identified as actual CD in 831 cases, common hepatic (bile) duct in six cases, accessory right posterior sectoral duct in two cases, and right haptic duct in one case. LCs were successfully finished in 837 patients, and converted to open cholecystectomy in three cases. The average operation time was 64.23 min (range 25-225 min), and the average blood loss was 8.07 ml (range 2-200 ml). No biliary misidentification injury was found. All patients recovered smoothly. No jaundice or abdominal pain was noted in the patients during 1-19 months follow-up. RTACD method is a safe and effective new technique of preventing biliary misidentification injury.

SUMMARY This study examines the association between the office size of engagement auditors and their clients' future stock price crash risk, a consequence of managerial bad news hoarding. Using a sample of U.S. public firms with Big 4 auditors, we find robust evidence that local audit office size is significantly and negatively related to future stock price crash risk. The evidence is consistent with the view that large audit offices effectively detect and deter bad news hoarding activities in comparison with their smaller counterparts. We further explore two possible explanations for these findings, the Auditor Incentive Channel and the Auditor Competency Channel. Our empirical tests offer support for both channels. JEL Classifications: G12; G34; M49.

The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of power distribution networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief network (DBN) simulation model for power distribution network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of power distribution network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural network to the reliability analysis of distribution network.

In social media, images and texts are used to convey individuals’ attitudes and feelings; thus, social media has become an indispensable part of people’s lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions.

Hole transporting materials (HTMs) play a critical role in ameliorating the performance of perovskite solar cells (PSCs). Dedicated HTMs can improve not only the hole extraction and efficiency but also the stability. Herein, PFDT-COOH and its fluorinated derivative, PFDT-2F-COOH, are introduced as dopant-free HTMs for inverted PSCs. Compared to PFDT-COOH, PFDT-2F-COOH exhibits a deeper highest occupied molecular orbital (HOMO) level, a higher work function on indium tin oxide electrodes, and an elevated built-in potential in the device. The PFDT-COOH device based on FA1−xMAxPbI3 mixed-cation perovskite exhibits a champion power conversion efficiency (PCE) of 20.64%, while the PFDT-2F-COOH device exhibits a champion PCE of 21.68%, which is close to the highest value (21.7%) attained in inverted single-junction PSCs. The elevated efficiency is attributed to reduction of carrier recombination and enhancement of carrier extraction via the fluorination strategy. In addition, the two devices also show excellent operational and thermal stabilities. Therefore, our work offers a feasible strategy for preparing high efficiency and stable inverted PSCs.

Viewport-Dependent Streaming (VDS) is a preferred way in practice to deliver the omnidirectional videos, of which a High-Quality (HQ) scale is applied for the content in current viewport but a Low-Quality (LQ) scale elsewhere. Quality adaptation or refinement happens after users stabilize their fixations to a new viewport. In this article, we formulate this as a perceptually optimized quality adaptation problem to maximize the Quality of Experience (QoE) for the refinement from a LQ scale to another HQ level within a specific duration under the given network constraint. With our developed perceptual model considering the adaptation quality for VDS of omnidirectional videos, we first provide baseline solutions numerically, demonstrating the noticeable subjective improvements of model-driven solution against the heuristic selection based approach. We also propose a novel viewport prediction algorithm based on the Hidden Markov Model (HMM), and experimental results show that it significantly outperforms the relevant methods with better prediction accuracy. We then improve the adaptation strategy with proposed viewport prediction-based data prefetching, leading to better visual perception than the baseline system at the same bandwidth constraint. Generally, prefetching the content of predicted next viewport one second ahead of its playback time, would lead to more than 8% Bjontegaard Delta Rate (BD-Rate) gain.

Patients with diabetes are more likely to be infected with Coronavirus disease 2019 (COVID-19), and the risk of death is significantly higher than ordinary patients. Dipeptidyl peptidase-4 (DPP4) is one of the functional receptor of human coronavirus. Exploring the relationship between diabetes mellitus targets and DPP4 is particularly important for the management of patients with diabetes and COVID-19. We intend to study the protein interaction through the protein interaction network in order to find a new clue for the management of patients with diabetes with COVID-19. Diabetes mellitus targets were obtained from GeneCards database. Targets with a relevance score exceeding 20 were included, and DPP4 protein was added manually. The initial protein interaction network was obtained through String. The targets directly related to DPP4 were selected as the final analysis targets. Importing them into String again to obtain the protein interaction network. Module identification, gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were carried out respectively. The impact of DPP4 on the whole network was analyzed by scoring the module where it located. 43 DPP4-related proteins were finally selected from the diabetes mellitus targets and three functional modules were found by the cluster analysis. Module 1 was involved in insulin secretion and glucagon signaling pathway, module 2 and module 3 were involved in signaling receptor binding. The scoring results showed that LEP and apoB in module 1 were the highest, and the scores of INS, IL6 and ALB of cross module associated proteins of module 1 were the highest. DPP4 is widely associated with key proteins in diabetes mellitus. COVID-19 may affect DPP4 in patients with diabetes mellitus, leading to high mortality of diabetes mellitus combined with COVID-19. DPP4 inhibitors and IL-6 antagonists can be considered to reduce the effect of COVID-19 infection on patients with diabetes.