bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach
Authors: He Zhaoliang , Li Hongshan , Wang Zhi , Xia Shutao , Zhu Wenwu
Published: November 2021

With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the
de facto
compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an
explore-exploit
mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.
Unsupervised Domain Expansion for Visual Categorization
Authors: Wang Jie , Tian Kaibin , Ding Dayong , Yang Gang , Li Xirong
Published: November 2021

Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA seeks for a deep representation that is both discriminative and domain-invariant. While UDA focuses on the target domain, we argue that the performance on both source and target domains matters, as in practice which domain a test example comes from is unknown. In this article, we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model’s performance on the source domain. We propose Knowledge Distillation Domain Expansion (KDDE) as a general method for the UDE task. Its domain-adaptation module can be instantiated with any existing model. We develop a knowledge distillation-based learning mechanism, enabling KDDE to optimize a single objective wherein the source and target domains are equally treated. Extensive experiments on two major benchmarks, i.e., Office-Home and DomainNet, show that KDDE compares favorably against four competitive baselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our study also reveals that the current UDA models improve their performance on the target domain at the cost of noticeable performance loss on the source domain.
Task-independent Recognition of Communication Skills in Group Interaction Using Time-series Modeling
Authors: Mawalim Candy Olivia , Okada Shogo , Nakano Yukiko I.
Published: November 2021

Case studies of group discussions are considered an effective way to assess communication skills (CS). This method can help researchers evaluate participants’ engagement with each other in a specific realistic context. In this article, multimodal analysis was performed to estimate CS indices using a three-task-type group discussion dataset, the MATRICS corpus. The current research investigated the effectiveness of engaging both static and time-series modeling, especially in task-independent settings. This investigation aimed to understand three main points: first, the effectiveness of time-series modeling compared to nonsequential modeling; second, multimodal analysis in a task-independent setting; and third, important differences to consider when dealing with task-dependent and task-independent settings, specifically in terms of modalities and prediction models. Several modalities were extracted (e.g., acoustics, speaking turns, linguistic-related movement, dialog tags, head motions, and face feature sets) for inferring the CS indices as a regression task. Three predictive models, including support vector regression (SVR), long short-term memory (LSTM), and an enhanced time-series model (an LSTM model with a combination of static and time-series features), were taken into account in this study. Our evaluation was conducted by using the
R
2
score in a cross-validation scheme. The experimental results suggested that time-series modeling can improve the performance of multimodal analysis significantly in the task-dependent setting (with the best
R
2
= 0.797 for the total CS index), with word2vec being the most prominent feature. Unfortunately, highly context-related features did not fit well with the task-independent setting. Thus, we propose an enhanced LSTM model for dealing with task-independent settings, and we successfully obtained better performance with the enhanced model than with the conventional SVR and LSTM models (the best
R
2
= 0.602 for the total CS index). In other words, our study shows that a particular time-series modeling can outperform traditional nonsequential modeling for automatically estimating the CS indices of a participant in a group discussion with regard to task dependency.
Perceptual Quality Assessment of Low-light Image Enhancement
Authors: Zhai Guangtao , Sun Wei , Min Xiongkuo , Zhou Jiantao
Published: November 2021

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.
Using Multisensory Content to Impact the Quality of Experience of Reading Digital Books
Authors: Silva Ellen P. , Vieira Natália , Amorim Glauco , Mousinho Renata , Guedes Gustavo , Ghinea Gheorghita , Dos Santos Joel A. F.
Published: November 2021

Multisensorial books enrich a story with either traditional multimedia content or sensorial effects. The main idea is to increase children’s interest in reading by enhancing their QoE while reading. Studies on enriched and/or augmented e-books also propose synchronizing additional content with text. However, they usually focus on audio, vídeo, images, or haptic feedback. In this work, we presentMBook, a tool for presenting multisensorial books. It decouples the book’s textual content from the additional content, as well as its synchronization, and rendering. Thus, a change in the additional content or its synchronization does not require changes to the book’s content. To enable fine-grained synchronization,MBookcaptures the reading position using an eye-tracker. Experimental results with students within the 13- to 19-year-old age group point toMBookbeing able to provide good usability.
Dissimilarity-Based Regularized Learning of Charts
Authors: Mishra Prerna , Kumar Santosh , Chaube Mithilesh Kumar
Published: November 2021

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.
A Fast View Synthesis Implementation Method for Light Field Applications
Authors: Gao Wei , Zhou Linjie , Tao Lvfang
Published: November 2021

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.
Residual-guided In-loop Filter Using Convolution Neural Network
Authors: Jia Wei , Li Li , Li Zhu , Zhang Xiang , Liu Shan
Published: November 2021

The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, and so on. To compensate for those artifacts, extensive filtering techniques were proposed in the loop of video codecs, which are capable of boosting the subjective and objective qualities of reconstructed videos. Recently, neural network-based filters were presented with the power of deep learning from a large magnitude of data. Though the coding efficiency has been improved from traditional methods in High-Efficiency Video Coding (HEVC), the rich features and information generated by the compression pipeline have not been fully utilized in the design of neural networks. Therefore, in this article, we propose the Residual-Reconstruction-based Convolutional Neural Network (RRNet) to further improve the coding efficiency to its full extent, where the compression features induced from bitstream in form of prediction residual are fed into the network as an additional input to the reconstructed frame. In essence, the residual signal can provide valuable information about block partitions and can aid reconstruction of edge and texture regions in a picture. Thus, more adaptive parameters can be trained to handle different texture characteristics. The experimental results show that our proposed RRNet approach presents significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based schemes, indicating that residual signal plays a significant role in enhancing video frame reconstruction.
Y-Net: Dual-branch Joint Network for Semantic Segmentation
Authors: Chen Yizhen , Hu Haifeng
Published: November 2021

Most existing segmentation networks are built upon a “
U
-shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “
U
-shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “
U
-shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.
The Evaluation of Mg–Ga Compounds as Electrode Materials for Mg-Ion Batteries via Ab Initio Simulation
Authors: Song Chao , Yuan Yuan , Gu Dachong , Chen Tao , Liu Yuping , Tang Aitao , Wu Liang , Li Dajian , Pan Fusheng
Published: November 2021

The Mg–Ga alloy-type electrode is one of the potential anode materials for Magnesium-ion batteries (MIBs). In this work, the thermodynamic, electrochemical and kinetic properties of Mg–Ga compounds, i.e. Mg2Ga5, MgGa2, MgGa, Mg2Ga and Mg5Ga2, have been systematically studied. Combining the first-principles calculations and charge-discharge experimental results, the structure evolution and voltage curves of Mg–Ga compounds are presented, where the Mg–Ga compounds show low voltages and high capacity up to 1922 mAh·g−1 with Mg5Ga2. Additionally, the diffusion barriers of Mg in Mg–Ga alloys are low, which is favorable for the fast ion-transmission and then good rate performance as being anodes of MIBs.
Enhanced Oxygen Evolution Reaction Electrocatalysis on Co(OH)2@MnO2Decorated Carbon Nanoarrays: Effect of Heterostructure, Conductivity and Charge Storgae Capability
Authors: Zheng Tianlong , He Jing , Cai Pingwei , Liu Xi , Wu Duojie , Song Lutao , He Qinggang , Tang Yizhao , Wang Guangjin , Gu Meng , Lu Jianguo , Hu Yongfeng , Wen Zhenhai
Published: November 2021

Self-supporting three-dimensional (3D) transition metal electrodes have been considered for designing high-performance non-noble metal oxygen evolution reaction (OER) catalysts owing to their advantages such as binder-free, good mass transfer, and large specific surface area. However, the poor conductivity of ((oxy)hydr)oxides and the difficulty in adjusting their electronic structure limit their application. As an alternative strategy, instead of constituting the array electrode by the active components themselves, we herein report 3D Co(OH)2@MnO2heterostructure decorated carbon nanoarrays grown directly on carbon paper (Co(OH)2@MnO2-CNAs). This unique structure can not only enhance electrical conductivity but also provide a larger specific surface area, and facilitate electrolyte diffusion and ion transport. The heterostructured Co(OH)2@MnO2formed via incorporation with MnO2facilitates the transition of CoIIto CoIIIin Co(OH)2and it increases the storage of oxidative charge in the catalyst, leading to an OER activity matching with benchmark RuO2and good stability. Density functional theory calculations suggest that the improved OER performance can be attributed to the formation of the heterojunction structure, resulting in the modulation of the electronic structure of Co atoms and the reduction of the free energy barrier of the rate-determining step for the OER.
Time-Resolved X-ray Operando Observations of Lithiation Gradients across the Cathode Matrix and Individual Oxide Particles during Fast Cycling of a Li-Ion Cell
Authors: Okasinski John S. , Shkrob Ilya A. , Rodrigues Marco-Tulio F. , Raj Abhi , Prado Andressa Y. R. , Chuang Andrew C. , Pidaparthy Saran S. , Abraham Daniel P.
Published: November 2021

Lithiated transition metal oxides serve as active materials in the positive electrode (cathode) of lithium-ion cells. During electrochemical cycling, lithium ions intercalate and deintercalate into these oxide particles. This behavior causes two types of lithiation gradients to emerge: (i) a bulk gradient across the depth of the cathode matrix (averaged over individual oxide particles) and (ii) a microscopic gradient across the particles themselves, which also depends on their location in the electrode. Here we show how both gradients can be studied using operando X-ray diffraction during 4C charge and 4C discharge. The oxide (de)lithiation is estimated from the unit cell parameters by indexing the X-ray diffraction spectra. By fitting the lithiation profiles with orthogonal polynomials, the bulk gradients across the electrode thickness are quantified. These gradients develop as the current flows through the cell and dissipate during open-circuit and potentiostatic-hold periods. Further details of lithiation dynamics can be obtained through shape analysis of the Bragg peaks. In particular, from electrochemical model simulations, we show that the width and skewness of the (003) peak track (de)lithiation fronts moving across the individual oxide particles.
Polymorphisms in Genes of Lipid Metabolism Are Associated with Type 2 Diabetes Mellitus and Periodontitis, as Comorbidities, and with the Subjects’ Periodontal, Glycemic, and Lipid Profiles
Authors: Nicchio Ingra G. , Cirelli Thamiris , Nepomuceno Rafael , Hidalgo Marco A. R. , Rossa Carlos , Cirelli Joni A. , Orrico Silvana R. P. , Barros Silvana P. , Theodoro Letícia H. , Scarel-Caminaga Raquel M.
Published: November 2021

Background. Type 2 diabetes mellitus (T2DM) and periodontitis (P) commonly occur as comorbidities, but the commonalities in the genetic makeup of affected individuals is largely unknown. Since dyslipidemia is a frequent condition in these individuals, we investigate the association of genomic variations in genes involved in lipid metabolism with periodontal, glycemic, lipid profiles, and the association with periodontitis and T2DM (as comorbidities). Methods. Based on clinical periodontal examination and biochemical evaluation, 893 subjects were divided into T2DM+P (T2DM subjects also affected by periodontitis,

n
=
205

), periodontitis (

n
=
345

), and healthy (

n
=
343

). Fourteen single-nucleotide polymorphisms (SNPs) were investigated: LDLR gene (rs5925 and rs688), APOB (rs676210, rs1042031, and rs693), ABCC8 (rs6544718 and 6544713), LPL (rs28524, rs3735964, and rs1370225), HNF1A (rs2650000), APOE (rs429358 and rs7412), and HNF4A (rs1800961). Multiple linear and logistic regressions (adjusted for covariates) were made for all populations and stratified by sex and smoking habits. Results. Individuals carrying APOB-rs1042031-CT (mainly women and never smokers) had a lower risk of developing periodontitis and T2DM (T2DM+P); altogether, this genotype was related with healthier glycemic, lipid, and periodontal parameters. Significant disease-phenotype associations with gene-sex interaction were also found for carriers of APOB-rs1676210-AG, HNF4A-rs1800961-CT, ABCC8-rs6544718-CT, LPL-rs13702-CC, and LPL-rs285-CT. Conclusions. Polymorphisms in lipid metabolism genes are associated with susceptibility to T2DM-periodontitis comorbidities, demonstrating gene-sex interaction. The APOB-rs1042031 was the most relevant gene marker related to glucose and lipid metabolism profiles, as well as with obesity and periodontitis.
Scalp Acupuncture and Treadmill Training Inhibits Neuronal Apoptosis through Activating cIAP1 in Cerebral Ischemia Rats
Authors: Tang Qiang , Ye Tao , Liang Runyu , Wang Yan , Li Hongyu , Zhang Jiyao , Yuan Xingxing , Zhu Luwen
Published: November 2021

Stroke is the leading cause of long-term disability in developed countries. Multitudinous evidence suggests that treadmill training treatment is beneficial for balance and stroke rehabilitation; however, the need for stroke therapy remains unmet. In the present study, a cerebral ischemia rat model was established by permanent middle cerebral artery occlusion (pMCAO) to explore the therapeutic effect and mechanism of scalp acupuncture combined with treadmill training on ischemic stroke. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling and neuronal nuclear protein (NeuN) double staining and cellular inhibitor of apoptosis protein-1 (cIAP1) and NeuN immunofluorescence double staining were used to detect the short-term and long-term neuroprotective effects of scalp acupuncture combined with treadmill training on pMCAO rats. In addition, the antiapoptotic effect of the combined treatment was evaluated in pMCAO rats transfected with cIAP1 shRNA. Western blotting was used to detect the relative protein expression in the caspase-8/-9/-3 activation pathway downstream of cIAP1 to further clarify its regulatory mechanism. Our results showed that scalp acupuncture combined with treadmill training successfully achieved short-term and long-term functional improvement within 14 days after stroke, significantly inhibited neuronal apoptosis, and upregulated the expression of cIAP1 protein in the ischemic penumbra area of the ischemic brain. However, no significant functional improvement and antiapoptotic effect were found in pMCAO rats transfected with cIAP1 shRNA. Western blotting results showed that the combined therapy markedly inhibited the activation of the caspase-8/-9/-3 pathway. These findings indicate that scalp acupuncture combined with treadmill training therapy may serve as a more effective alternative modality in the treatment of ischemic stroke, playing an antiapoptotic role by upregulating the expression of cIAP1 and inhibiting the activation of the caspase-8/-9/-3 pathway.
A Game-Based Scheme for Resource Purchasing and Pricing in MEC for Internet of Things
Authors: Leng Yajing , Wang Ming , Ma Bowen , Chen Ying , Huang Jiwei
Published: November 2021

Mobile edge computing (MEC) is emerging as a promising paradigm to support the applications of Internet of Things (IoT). The edge servers bring computing resources to the edge of the network, so as to meet the delay requirements of the IoT devices’ service requests. At the same time, the edge servers can gain profit by leasing computing resources to IoT users and realize the allocation of computing resources. How to determine a reasonable resource leasing price for the edge servers and how to determine the number of resource purchased by users with different needs is a challenging problem. In order to solve the problem, this paper proposes a game-based scheme for resource purchasing and pricing aiming at maximizing user utility and server profit. The interaction between users and the edge servers is modeled based on Stackelberg game theory. The properties of incentive compatibility and envy freeness are theoretically proved, and the existence of Stackelberg equilibrium is also proved. A game-based user resource purchasing algorithm called GURP and a game-based server resource pricing algorithm called GSRP are proposed. It is theoretically proven that solutions of the proposed algorithms satisfy the individual rationality property. Finally, simulation experiments are carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can quickly converge to the optimal solutions. Comparison experiments with the benchmark algorithms are also carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can maximize user utility and server profit.
Acute Mesenteric Ischemia in a COVID-19 Patient: Delay in Referral and Recommendation for Surgery
Authors: Bagheripour Mohammad Hossein , Zakeri Mohammad Ali
Published: November 2021

Coronavirus (COVID-19) is more common with symptoms such as fever, dry cough, and shortness of breath. However, it may be associated with COVID-19-induced gastrointestinal (GI) symptoms including acute mesenteric ischemia (AMI). These conditions make the diagnosis of AMI challenging. Timely referral with correct diagnosis and attention to the uncommon symptoms of COVID-19 can play an important role in the management and treatment of AMI in COVID-19 patients. We present a patient with AMI due to thrombotic complications of COVID-19, who referred to the hospital too late and ignored the recommendation for abdominal surgery.
Strength Behavior of Flat and Folded Fly Ash‐Based Geopolymer Ferrocement Panels under Flexure and Impact
Authors: Sakkarai Dharmar , Soundarapandian Nagan
Published: November 2021

This paper presents the impact of fiber and wire mesh layers on the strength behavior of flat as well as folded fly ash‐based geopolymer ferrocement panels. The behaviors, namely, flexural strength, impact strength, ductility, stiffness, and cracking patterns, are observed. With the objective of decreasing CO2 emissions, concrete utilizes wastes such as fly ash disposed by industrial sectors. Six panels (three flat and three folded) were cast utilizing a fly ash‐dependent geopolymer mortar of size 1000 mm × 400 mm × 30 mm in addition to two panels of each type for the impact study. Heat curing in a temperature‐controlled chamber maintaining 75°C to 80°C for 24 hours was done after 24 hours of the resting period. The experimental results indicated that the flexural strength got enhanced by 33 percent by increasing the quantity of wire mesh layers, but the ductility got decreased by 30 percent for the flat panels; however, there was no noticeable impact in the case of folded panels. The flexural strength of the folded panel was found to be three times greater than that of the flat panels. In addition, it is noticed that the behavior of the fiber‐reinforced flat as well as folded panels of single layer mesh is stronger than the double layer wire mesh panels regarding cracking and ultimate load. Furthermore, the impact strength of the folded panels was found to be 90% greater than that of flat panels, and the energy absorbed at failure was directly proportional to the volume of reinforcement provided in the panels. Moreover, the failure pattern of the impact tested specimens showed punching shear as the predominant factor.
[Retracted] Clinical Efficacy of Methotrexate Combined with Iguratimod on Patients with Rheumatoid Arthritis and Its Influence on the Expression Levels of HOTAIR in Serum
Authors: Tan Jingya , Dan Jiaqiang , Liu Yi
Published: November 2021

Objective. This study was designed to explore the clinical efficacy of methotrexate combined with iguratimod on patients with rheumatoid arthritis (RA) and its influence on the expression levels of HOTAIR in serum. Methods. A total of 268 RA patients were selected as research objects, 145 patients received methotrexate alone were used as a control group (CG), 123 patients received methotrexate combined with iguratimod were taken as a research group (RG), and serum of 60 healthy people undergoing physical examination was selected as a healthy control group (HCG). The therapeutic value of two therapeutic methods for RA was compared, and the HOTAIR expression in serum was detected by qRT‐PCR. Results. Compared with methotrexate used alone, the joint use of methotrexate and iguratimod could provide better clinical efficacy for RA patients and would not increase the incidence of adverse events. HOTAIR was highly expressed in the serum of RA patients, and its expression decreased after treatment. Conclusion. Combination therapy of methotrexate and iguratimod is a safe and effective way to treat RA patients, which can be popularized clinically.
Pharmacological Approaches to Attenuate Inflammation and Obesity with Natural Products Formulations by Regulating the Associated Promoting Molecular Signaling Pathways
Authors: Khan Muhammad Imran , Khan Muhammad Zubair , Shin Jin Hyuk , Shin Tia Sun , Lee Young Bok , Kim Min Yung , Kim Jong Deog
Published: November 2021

Obesity is a public health problem characterized by increased body weight due to abnormal adipose tissue expansion. Bioactive compound consumption from the diet or intake of dietary supplements is one of the possible ways to control obesity. Natural products with adipogenesis‐regulating potential act as obesity treatments. We evaluated the synergistic antiangiogenesis, antiadipogenic and antilipogenic efficacy of standardized rebaudioside A, sativoside, and theasaponin E1 formulations (RASE1) in vitro in human umbilical vein endothelial cells (HUVECs), 3T3‐L1 preadipocytes respectively, and in vivo using a high‐fat and carbohydrate diet‐induced obesity mouse model. Orlistat was used as a positive control, while untreated cells and animals were normal controls (NCs). Adipose tissue, liver, and blood were analyzed after dissection. Extracted stevia compounds and green tea seed saponin E1 exhibited pronounced antiobesity effects when combined. RASE1 inhibited HUVEC proliferation and tube formation by suppressing VEGFR2, NF‐κB, PIK3, and‐catenin beta‐1 expression levels. RASE1 inhibited 3T3‐L1 adipocyte differentiation and lipid accumulation by downregulating adipogenesis‐ and lipogenesis‐promoting genes. RASE1 oral administration reduced mouse body and body fat pad weight and blood cholesterol, TG, ALT, AST, glucose, insulin, and adipokine levels. RASE1 suppressed adipogenic and lipid metabolism gene expression in mouse adipose and liver tissues and enhanced AMP‐activated protein kinase levels in liver and adipose tissues and in serum adiponectin. RASE1 suppressed the NF‐κB pathway and proinflammatory cytokines IL‐10, IL‐6, and TNF‐α levels in mice which involve inflammation and progression of obesity. The overall results indicate RASE1 is a potential therapeutic formulation and functional food for treating or preventing obesity and inflammation.
Clinical Characteristics and Empirical Research Model of Infectious Mononucleosis Complicated with Mycoplasma pneumoniae or/and Cytomegalovirus Infection
Authors: Cai Jie , Yuan Liping , Gao Hui , Hu Bo , Gui Ming
Published: November 2021

To study the clinical features of infectious mononucleosis (IM) caused by Epstein-Barr virus (EBV) mixed with Mycoplasma pneumonia (MP) or/and cytomegalovirus (CMV)infection, collected 201 hospitalized children who met the IM diagnostic criteria, the clinical manifestations, laboratory tests, complications, treatment, and outcome were compared among EBV infection alone and EBV mixed with MP or/and CMV infection. Most of the children with IM were preschoolers, more frequently occurred in boys than girls. EBV patients with MP had the longest duration of fever. When mixed pathogen infections were involved, the white blood cell count of preschool children was significantly increased, while splenomegaly was more common in older children. In the cases of EBV infection alone, abnormal liver function was positively correlated with age (

P
=
0.044

). Mixed pathogen infections were more common in children with IM, occurring in all age groups, and some clinical characteristics were related to the age of onset and the pathogen of the infection.
Intelligent Application of Artificial Intelligence Internet of Things Technology in the Economic and Legal Fields
Authors: Tan Tingting
Published: November 2021

In today’s globalized situation, people on the one hand enjoy the great convenience brought by the Internet and artificial intelligence Internet of Things (IoT) technology, and, on the other hand, they are also inevitably subject to a series of harms brought by network technology. Internet economic crime is a new type of crime based on Internet technology. Criminals use Internet technology to conduct illegal visits and Trojan horse program attacks, steal user information, and defraud victims of money. This has resulted in the people’s personal and property safety and social harmony and stability. Strictly cracking down on cyber economic crimes in accordance with the law is of great significance to safeguarding the interests of the people and maintaining social stability. However, as the methods and forms of cyber economic crimes emerge endlessly, it is very important to collect intelligence information on such crimes. This paper proposes using the sensor technology, embedded system technology, radio frequency automatic identification technology, and cloud computing technology in artificial intelligence Internet of Things technology to design and build a data-mining-based network economic crime intelligent information aggregation collection system to realize network economic crime intelligence of aggregation and analyze and help combat cyber economic crimes. This article takes cyber economic crime cases in various cities in our province as an example, selects 9 cyber economic criminals’ intelligence information as sample data, and tests and applies the designed cyber economic crime intelligence information system. The final results show that the numbers of cyber economic crime cases in four cities A, B, C, and D in four provinces are roughly the same, but city A has the largest number; the minimum confidence of the 9 criminals is above 0.60, indicating that the economic crimes of cyber economic criminals are related to their academic background and family status and criminal history are related to a certain extent; illegal fund-raising fraud and online credit card fraud account for the largest proportion of the four cities and are currently the main forms of online economic crime.
A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
Authors: Tung Chun-Liang , Wang Ching-Hsin , Peng Bo-Syuan
Published: November 2021

Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.
The Effect of Curcuma longa on Inflammatory Mediators and Immunological, Oxidant, and Antioxidant Biomarkers in Asthmatic Rats
Authors: Boskabady Mohammad Hossein , Amin Fatemeh , Shakeri Farzaneh
Published: November 2021

The effects of Curcuma longa (C. longa) on total and differential WBC, inflammatory and immunologic mediators, and oxidant and antioxidant biomarkers in bronchoalveolar lavage fluid (BALF) of rats model of asthma were assessed. Animals were divided to 5 groups including control (C), asthma (sensitized to ovalbumin), and asthmatic groups treated with 0.75, 1.50, and 3.00 mg/ml C. longa (CL) and 1.25 μg/ml dexamethasone (D) (8 rats in each group). Total and differential WBC count, concentrations of phospholipase A2 (PLA2), total protein (TP), interferon-gamma (IFN-γ), interleukin-4 (IL-4), immunoglobulin E (IgE), NO2, NO3, malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and thiol in BALF were assessed. Total and most differential WBC counts and BALF levels of PLA2, TP, IgE, IL-4, and oxidants in asthma group were higher but antioxidants and IFN-γ levels as well as IFN-γ/IL-4 ratio were lower than control group (

p

 < 0.001 for all cases). Total WBC and levels of PLA2, IgE, NO2, and NO3 were significantly reduced following treatment with C. longa, compared to asthma group (

p

 < 0.001 for all cases). In groups treated with dexamethasone and two higher concentrations of C. longa, neutrophil and eosinophil counts as well as TP, IL-4, and MDA levels were significantly decreased but IFN-γ, IFN-γ/IL-4 ratio, and antioxidants were increased (except IFN-γ/IL-4 ratio), compared to asthma group (

p

 < 0.05 to

p

 < 0.001). Compared to dexamethasone, C. longa exerted more pronounced effects on lung inflammation, oxidative stress, and immune system in asthmatic rats.
Review of Methane Seepages in the Okinawa Trough: Progress and Outlook
Authors: Li Ang , Li Qing , Xu Cuiling , Cai Feng , Wang Hongbin
Published: November 2021

It has been two decades since the cold seeps were firstly found in the Okinawa Trough (OT). The scientific cruises and the geological surveys since then have unveiled the currently active submarine methane seeps and significantly improved the understanding of methane seeps in the back-arc basin of the OT. In this paper, we review the up-to-date progress of the research of methane seepages then put forward the promising, yet challenging, outlook by listing the unsolved questions of the cold seeps in the OT. Multiple approaches and techniques, including seismic and echo-sounder recording, dredging, gravity-piston and ROV coring, seafloor drilling, and isotopic and microarray-based genomic analysis, have been used to reveal the geological processes responsible for the seeping activities and the biogeochemical processes related to them. The geophysical signature associated with gas seeps mainly includes the acoustic turbidity in the subsurface, the anomaly of the backscattering intensity at the seabed, and the gas plumes observed in the water column. Pore water and methane-derived authigenic carbonate archive the intensification of methane seepage and the paleoenvironment changes at different time scales. The methane feeding of the seeps in the OT was generated mostly via the microbially mediated process and has an origin mixed by thermogenic hydrocarbon gas in the middle OT. Sulfate-driven and Fe-driven anaerobic oxidations of methane are suggested to be the key biogeochemical processes, which would shape the material cycling in the seeping environment. The future research on the cold seeps in the OT is worth looking forward to due to its geographic and potential geologic links with the nearby hydrothermal activities. Multidisciplinary studies are expected to concentrate on their link with the undiscovered gas hydrates, the amount of methane transferring into the oceans and its impact on the climatic change, and the evolution of the seeping activities accompanied by the biogeochemical processes.
Deriving Design Knowledge Graph for Complex Sociotechnical Systems Using the AIA Design Thinking
Authors: Shen Tao , Gao Chan , Nagai Yukari , Ou Wei
Published: November 2021

The AIA design thinking has been validated in complex design tasks, which includes three overlapping design thinking fields and uses the knowledge field theory as a theoretical mechanism of knowledge flow among design thinking fields. Meanwhile, the design of complex sociotechnical systems highly relies on multidisciplinary knowledge and design methods. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), designers continue to store knowledge in unstructured ways. To facilitate the integration of knowledge graph and design thinking, we introduce an integrated approach to structure design knowledge graph with the AIA design thinking, which organizes existing design knowledge through Agent (concept)-Interaction (relation)-Adaptation (concept) framework. The approach uses an optimized convolutional neural network to accomplish two tasks: building concept graph from text and stimulating design thinking information processing for complex sociotechnical system tasks. Based on our knowledge graph, the validation experiment demonstrates the advantages of promoting the designer’s extension of idea space and idea quality.
Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing
Authors: Wu Xintao , Gan Jie , Chen Shiyong , Zhao Xu , Wu Yucheng
Published: November 2021

Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi‐distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi‐distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.
Speckle Tracking Algorithm-Based Cardiac Color Ultrasound in Diagnosis of Patients with Atrial Fibrillation Combined with Heart Failure
Authors: Liao Chunfeng , Luo Hui , Yang Jianqing , Wu Xianliang , Zhao Min
Published: November 2021

The study focused on the application of speckle tracking algorithm in the segmentation of cardiac color ultrasound images of patients with atrial fibrillation combined with heart failure. First, the optical flow method and block matching method were introduced on the basis of multiphase level set algorithm. Then, the pyramid block matching method was applied to build a pyramid model from bottom to top according to each image, and thus a new segmentation algorithm of cardiac color ultrasound image was constructed. The speckle tracking algorithm based on the pyramid block matching method was applied to segment cardiac color ultrasound images of 136 patients with atrial fibrillation and heart failure and compared with the traditional diagnosis for the sensitivity, specificity, and accuracy. It was found that the curve smoothness and accuracy of the algorithm in this study were better than the traditional level set algorithm, and it made up for the shortcomings of the traditional method. The proportion of patients of class III-IV cardiac function was significantly higher than that of non-atrial fibrillation patients, and the difference was statistically significant (

P
<
0.05

); patients of classes III-IV showed better left ventricular ejection fraction (LVEF) (42.4 ± 2.8%), left ventricular end-diastolic diameter (LVED) (58.7 ± 7.4 mm), left ventricular end-systolic diameter (LVSD) (49.3 ± 5.6 mm), and left atrial inner diameter (LAD) (55.0 ± 1.4 mm) versus those of classes I-II, of whom the corresponding indexes were 58.8 ± 3.3%, 48.5 ± 5.9 mm, 33.5 ± 4.5 mm, and 45.2 ± 2.0 mm. The accuracy of diagnosis based on the algorithm of this study (93.22%) was significantly higher than that of traditional method (79.23%), and the differences were statistically significant (

P
<
0.05

). In conclusion, the algorithm in this study improves the segmentation accuracy and smoothness of the curve, which is suggested in clinic.
NFIB-Mediated lncRNA PVT1 Aggravates Laryngeal Squamous Cell Carcinoma Progression via the miR-1301-3p/MBNL1 Axis
Authors: Tang Tian , Zeng Feng
Published: November 2021

Laryngeal squamous cell carcinoma (LSCC) is one of the most common malignant tumors of head and neck cancers. In the past decades, although the therapy strategies of LSCC have made considerable improvement, the terrible outcomes of LSCC still bring an enormous burden to the world health care system. Novel therapeutic targets for LSCC are urgently needed. lncRNAs exert important roles in various biological progressions, including LSCC. Here, we aimed to investigate the function of lncRNA PVT1 in LSCC progression and its underlying molecular mechanisms. By conducting multiple experiments, our results showed that lncRNA PVT1 was upregulated in LSCC cell lines and regulated LSCC cell proliferation, apoptosis, and its cell susceptibility to natural killer (NK) cells. Moreover, it was found that lncRNA PVT1 promotes MBNL1 expression to regulate LSCC cellular progression through sponging miR-1301-3p. Our study might provide novel targets for LSCC basic research or clinical management.