bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond blur
Authors: Walton David R. , Anjos Rafael Kuffner Dos , Friston Sebastian , Swapp David , Akşit Kaan , Steed Anthony , Ritschel Tobias
Published: July 2021

To peripheral vision, a pair of physically different images can look the same. Such pairs are
metamers
relative to each other, just as physically-different spectra of light are perceived as the same color. We propose a real-time method to compute such ventral metamers for foveated rendering where, in particular for near-eye displays, the largest part of the framebuffer maps to the periphery. This improves in quality over state-of-the-art foveation methods which blur the periphery. Work in Vision Science has established how peripheral stimuli are ventral metamers if their statistics are similar. Existing methods, however, require a costly optimization process to find such metamers. To this end, we propose a novel type of statistics particularly well-suited for practical real-time rendering: smooth moments of steerable filter responses. These can be extracted from images in time constant in the number of pixels and in parallel over all pixels using a GPU. Further, we show that they can be compressed effectively and transmitted at low bandwidth. Finally, computing realizations of those statistics can again be performed in constant time and in parallel. This enables a new level of quality for foveated applications such as such as remote rendering, level-of-detail and Monte-Carlo denoising. In a user study, we finally show how human task performance increases and foveation artifacts are less suspicious, when using our method compared to common blurring.
Bayesian Additive Matrix Approximation for Social Recommendation
Authors: Liu Huafeng , Jing Liping , Wen Jingxuan , Xu Pengyu , Yu Jian , Ng Michael K.
Published: July 2021

Social relations between users have been proven to be a good type of auxiliary information to improve the recommendation performance. However, it is a challenging issue to sufficiently exploit the social relations and correctly determine the user preference from both social and rating information. In this article, we propose a unified Bayesian Additive Matrix Approximation model (BAMA), which takes advantage of rating preference and social network to provide high-quality recommendation. The basic idea of BAMA is to extract social influence from social networks, integrate them to Bayesian additive co-clustering for effectively determining the user clusters and item clusters, and provide an accurate rating prediction. In addition, an efficient algorithm with collapsed Gibbs Sampling is designed to inference the proposed model. A series of experiments were conducted on six real-world social datasets. The results demonstrate the superiority of the proposed BAMA by comparing with the state-of-the-art methods from three views, all users, cold-start users, and users with few social relations. With the aid of social information, furthermore, BAMA has ability to provide the explainable recommendation.
A Joint Passenger Flow Inference and Path Recommender System for Deploying New Routes and Stations of Mass Transit Transportation
Authors: Lin Fandel , Hsieh Hsun-Ping
Published: July 2021

In this work, a novel decision assistant system for urban transportation, called Route Scheme Assistant (RSA), is proposed to address two crucial issues that few former researches have focused on: route-based passenger flow (PF) inference and multivariant high-PF route recommendation. First, RSA can estimate the PF of arbitrary user-designated routes effectively by utilizing Deep Neural Network (DNN) for regression based on geographical information and spatial-temporal urban informatics. Second, our proposed Bidirectional Prioritized Spanning Tree (BDPST) intelligently combines the parallel computing concept and Gaussian mixture model (GMM) for route recommendation under users’ constraints running in a timely manner. We did experiments on bus-ticket data of Tainan and Chicago and the experimental results show that the PF inference model outperforms baseline and comparative methods from 41% to 57%. Moreover, the proposed BDPST algorithm's performance is not far away from the optimal PF and outperforms other comparative methods from 39% to 71% in large-scale route recommendations.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
Authors: Gao Jianliang , Ying Xiaoting , Xu Cong , Wang Jianxin , Zhang Shichao , Li Zhao
Published: July 2021

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.
Distributed Latent Dirichlet Allocation on Streams
Authors: Guo Yunyan , Li Jianzhong
Published: July 2021

Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge and are gathered in a streaming fashion. As the state-of-the-art LDA algorithm on streams,
Streaming Variational Bayes
(SVB) introduced
Bayesian updating
to provide a streaming procedure. However, the utility of SVB is limited in applications since it ignored three challenges of processing real-world streams:
topic evolution
,
data turbulence
, and
real-time inference
. In this article, we propose a novel distributed LDA algorithm—referred to as
StreamFed-LDA—
to deal with challenges on streams. For topic modeling of streaming data, the ability to capture evolving topics is essential for practical online inference. To achieve this goal,
StreamFed-LDA
is based on a specialized framework that supports lifelong (continual) learning of evolving topics. On the other hand, data turbulence is commonly present in streams due to real-life events. In that case, the design of
StreamFed-LDA
allows the model to learn new characteristics from the most recent data while maintaining the historical information. On massive streaming data, it is difficult and crucial to provide real-time inference results. To increase the throughput and reduce the latency,
StreamFed-LDA
introduces additional techniques that substantially reduce both computation and communication costs in distributed systems. Experiments on four real-world datasets show that the proposed framework achieves significantly better performance of online inference compared with the baselines. At the same time,
StreamFed-LDA
also reduces the latency by orders of magnitudes in real-world datasets.
Explainable Artificial Intelligence-Based Competitive Factor Identification
Authors: Han Juhee , Lee Younghoon
Published: July 2021

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.
Recurrent Coupled Topic Modeling over Sequential Documents
Authors: Guo Jinjin , Cao Longbing , Gong Zhiguo
Published: July 2021

The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the
multi-topic-thread evolution
. Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward–forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.
Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks
Authors: Liu Haobing , Zhu Yanmin , Zang Tianzi , Xu Yanan , Yu Jiadi , Tang Feilong
Published: July 2021

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints that encode heterogeneous behaviors continuously. In this article, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of
Long-Short Term Memory (LSTM)
and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.
New Multi-View Classification Method with Uncertain Data
Authors: Liu Bo , Zhong Haowen , Xiao Yanshan
Published: July 2021

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.
BiLabel-Specific Features for Multi-Label Classification
Authors: Zhang Min-Ling , Fang Jun-Peng , Wang Yi-Bo
Published: July 2021

In multi-label classification, the task is to induce predictive models which can assign
a set of
relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its
tailored
features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.
Welcome to AI Matters 7(1)
Authors: Leite Iolanda , Karpatne Anuj
Published: July 2021

Welcome to the first issue of this year's AI Matters Newsletter!

We start with a report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis, our conference coordination officers. In our regular Education column, Duri Long, Jonathan Moon, and Brian Magerko introduce two "unplugged" activities (i.e., no technology needed) to learn about AI focussed on K-12 AI Education. We then bring you our regular Policy column, where Larry Medsker covers several topics on AI policy, including the role of Big Tech on AI Ethics and an interview with Dr. Eric Daimler who is the CEO of the MIT-spinout Conexus.com.

Finally, we close with four article contributions. The first article discusses emerging applications of AI in analyzing source code and its implications to several industries. The second article discusses topics in the area of physical scene understanding that are necessary for machines to perceive, interact, and reason about the physical world. The third article presents novel practices and highlights from the Fourth Workshop on Mechanism Design for Social Good. The fourth article provides a report on the "Decoding AI" event that was conducted online by ViSER for high school students and adults sponsored by ACM SIGAI.
AI policy matters
Authors: Medsker Larry
Published: July 2021

AI Policy Matters is a regular column in AI Matters featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog (https://sigai.acm.org/aimatters/blog/). We welcome everyone to make blog comments so we can develop a rich knowledge base of information and ideas representing the SIGAI members.
Novel practices and highlights from the Fourth Workshop on Mechanism Design for Social Good
Authors: Marmolejo Cossio Francisco J. , Monachou Faidra
Published: July 2021

The Fourth Workshop on Mechanism Design for Social Good was held virtually in August 2020, with a focus on work bridging research and policy. This article represents the experience of the chairs and discusses novel conference-organizing practices aimed at promoting multi-disciplinary research for social good and increasing racial, linguistic, and geographic diversity and inclusion.
LDA-LFM
Authors: Aslanyan Tatev Karen , Frasincar Flavius
Published: July 2021

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modeling technique (i.e., Latent Factor Model) with a topic modeling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models, we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.
Deep learning-based semantic classification of EMF-related scientific literature
Authors: Won Kwanghee , Choi Hyung-do , Shin Sung
Published: July 2021

Semantic classification of scientific literature using machine learning approaches is challenging due to the difficulties in labeling data and the length of the texts [2, 7]. Most of the work has been done for keyword-based categorization tasks, which take care of occurrence of important terms, whereas semantic classification requires understanding of terms and the meaning of sentences in a context. In this study, we have evaluated neural network models on a semantic classification task using 1091 labeled EMF-related scientific papers listed in the Powerwatch study. The EMF-related papers are labeled into three categories: positive, null finding, and neither. We have conducted neural architecture and hyperparameter search to find the most suitable model for the task. In experiments, we compared the performance of several neural network models in terms of classification accuracy. In addition, we have tested two different types of attention mechanisms. First, a Fully Convolutional Neural Network (FCN) has been used to identify important sentences in the text for the semantic classification. Second, the Transformer, a self-attention-based model, has been tested on the dataset. The experimental result showed that the BiLSTM performed best on both unbalanced and balanced data and the FCN was able to identify important parts in input texts.
Detailed black-box monitoring of distributed systems
Authors: Neves Francisco , Vilaça Ricardo , Pereira José
Published: July 2021

Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, interapplication traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms.

In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).
Toward accurate platform-aware performance modeling for deep neural networks
Authors: Wang Chuan-Chi , Liao Ying-Chiao , Kao Ming-Chang , Liang Wen-Yew , Hung Shih-Hao
Published: July 2021

In this paper, we provide a fine-grain machine learning-based method, PerfNetV2, which improves the accuracy of our previous work for modeling the neural network performance on a variety of GPU accelerators. Given an application, the proposed method can be used to predict the inference time and training time of the convolutional neural networks used in the application, which enables the system developer to optimize the performance by choosing the neural networks and/or incorporating the hardware accelerators to deliver satisfactory results in time. Furthermore, the proposed method is capable of predicting the performance of an unseen or non-existing device, e.g. a new GPU which has a higher operating frequency with less processor cores, but more memory capacity. This allows a system developer to quickly search the hardware design space and/or fine-tune the system configuration. Compared to the previous works, PerfNetV2 delivers more accurate results by modeling detailed host-accelerator interactions in executing the full neural networks and improving the architecture of the machine learning model used in the predictor. Our case studies show that PerfNetV2 yields a mean absolute percentage error within 13.1% on LeNet, AlexNet, and VGG16 on NVIDIA GTX-1080Ti, while the error rate on a previous work published in ICBD 2018 could be as large as 200%.
Nutritional Interventions and the Gut Microbiome in Children
Authors: Mehta Saurabh , Huey Samantha L. , McDonald Daniel , Knight Rob , Finkelstein Julia L.
Published: July 2021

The gut microbiome plays an integral role in health and disease, and diet is a major driver of its composition, diversity, and functional capacity. Given the dynamic development of the gut microbiome in infants and children, it is critical to address two major questions: ( a) Can diet modify the composition, diversity, or function of the gut microbiome, and ( b) will such modification affect functional/clinical outcomes including immune function, cognitive development, and overall health? We synthesize the evidence on the effect of nutritional interventions on the gut microbiome in infants and children across 26 studies. Findings indicate the need to study older children, assess the whole intestinal tract, and harmonize methods and interpretation of findings, which are critical for informing meaningful clinical and public health practice. These findings are relevant for precision health, may help identify windows of opportunity for intervention, and may inform the design and delivery of such interventions.
First Principles Study of Oxygen Adsorption on Li-MO2 (M = Mn, Ti and V) (110) Surface
Authors: Maenetja Khomotso P. , Ngoepe Phuti E.
Published: July 2021

Metal-air batteries are attractive for any application where weight is a primary concern, such as in mobile devices. Since oxygen doesn’t need to be stored in the battery, the cathode is much lighter than that of a lithium-ion battery, which gives lithium-air batteries their high energy density. Density functional theory study (DFT) is employed in order to investigate the surfaces of, β-MnO2, β-TiO2 and β-VO2 (β-MO2) which act as catalysts in metal-air batteries. Adsorption of oxygen at (110) Li-MO2 is investigated, which is important in the discharging and charging of Li–air batteries. Oxygen adsorption on Li/MO2 was simulated and we found that in all the metal oxides (MnO2, TiO2 and VO2) comprises most stable orientation is the dissociated composition where there is an oxygen atom on the “bulk-like” positions on top of each of the M cations. The surface lithium peroxide for MO2 simulated produces clusters with oxygen - oxygen bond lengths that are comparable to the calculated bulk and monomer discharge products reported in literature.
Characterization of a functional V1B vasopressin receptor in the male rat kidney: evidence for cross talk between V1B and V2 receptor signaling pathways
Authors: Hus-Citharel Annette , Bouby Nadine , Corbani Maithé , Mion Julie , Mendre Christiane , Darusi Judit , Tomboly Csaba , Trueba Miguel , Serradeil-Le Gal Claudine , Llorens-Cortes Catherine , Guillon Gilles
Published: July 2021

Although V1BR mRNA has been detected in the kidney, the precise renal localization as well as pharmacological and physiological properties of this receptor remain unknown. Using original pharmaceutical tools, this study brings new data on the localization and signaling pathways of V1BR, highlights a cross talk between V1BR and V2 receptor (V2R) in the inner medullary collecting duct, and suggests that V1BR may counterbalance in some pathophysiological conditions the antidiuretic effect triggered by V2R activation.
Diagnostic Accuracy of CBCT for Detection of Second Canal of Permanent Teeth: A Systematic Review and Meta-Analysis
Authors: Aung Nyan M. , Myint Kyaw K.
Published: July 2021

Introduction. Missed canal is one of the common reasons for nonsurgical endodontic retreatments. The missed canals were frequently associated with periapical pathology. The aim of this systematic review was to find the diagnostic accuracy of CBCT for detection of the second canal of the root canal system of permanent teeth. Materials and Methods. The articles were selected from seven electronic databases according to selection criteria. All eligible studies were judged by the reviewers. The selected studies were checked with the QUADAS-2 tool for risk of bias and applicability concerns. Finally, 12 studies were selected for qualitative and quantitative analyses. The summary estimates of sensitivities and specificities and SROC curves were calculated and drawn by RevMan 5.3 and MetaDTA software. Results. Summary estimates of CBCT for detection of second canal anatomy in permanent teeth were 94% sensitivity and 93.1% specificity. 96.6% sensitivity of MB2 was followed by 88.8% sensitivity of maxillary and mandibular premolars and 81% that of mandibular molars. The specificity of 97.6% for premolars was trialed by 85% specificity of mandibular molars and MB2. For permanent mandibular canines, 67% sensitivity and 100% specificity were estimated. CBCT showed more agreement with detecting the second canal with micro-CT, estimating 100% sensitivity and 95.6% specificity. The highest prevalence of the second canal comprised the highest sensitivity of 99.1% and lowest specificity of 77.5%. After the exclusion of case-control studies, a 3% drop of sensitivity from the summary estimate was observed. Multiple spectrum of the second canal had 8.6% higher sensitivity and 4.4% lower specificity than single spectrum. Conclusion. CBCT is informative for detecting the second canal. Clinicians should keep in mind that the accuracy can vary in different types of teeth, with the prevalence of second canal across different populations, and with the spectrum of second canal anatomy in spite of the reviewers having postulated overestimation of the findings.
Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform
Authors: Lv Chen , Li Jiahan , Kou Qiqi , Zhuang Huandong , Tang Shoufeng
Published: July 2021

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.
A Novel Bond Anchor for Unidirectional FRP Member: Conceptual Design and Experimental Investigation
Authors: Liu Yue , Wei Qing , Wang Ze-Yu , Xiang Peng , Du Long-Ji , Fu Bai-Yong
Published: July 2021

Fiber‐reinforced polymer (FRP) is an advanced composite material with many advantages including light weight, high strength, and high fatigue and corrosion resistance, which makes unidirectional FRP suitable for tension members, such as cables, prestressing tendons, and tie rods. However, the unidirectional FRP is a typical isotropic material, which is difficult to be anchored and hence unable to give full play to the advantages of FRP. To solve the anchoring problem of unidirectional FRP member, a novel bond anchor, i.e., dissolution‐rebond anchor, is proposed in this paper. In this novel anchorage system, the polymer matrix of two ends of the unidirectional FRP member is dissolved by solvent and the fibers in the anchorage length are directly bonded by the binder. Theoretical analysis was performed to illustrate the high anchorage bearing capacity of this dissolution‐rebond anchor. Static tensile test was conducted to verify this novel anchor design and compare with traditional bond anchor. Results show that the novel dissolution‐rebond anchor is feasible and its anchorage efficiency is significantly higher than the traditional bond anchor.
The Auxiliary Diagnosis and Imaging Characteristics of MRI Combined with CT in Patients with Cholangiocarcinoma
Authors: Li Jing , Yu Yuanlin , He Qing
Published: July 2021

Background. Patients with cholangiocarcinoma (CCA) have poor prognosis and high mortality. Therefore, early detection and early diagnosis are extremely important to control the development of CCA. This study aims to explore the diagnostic effect in patients with CCA and imaging characteristics of MRI combined with CT. Methods. 109 patients with suspected CCA underwent CT and MRI before diagnosis. The examination results were compared with the “gold standard.” ROC curve was drawn to analyze the diagnostic efficacy of MRI combined with CT for CCA patients. Results. The diagnosis rate of suspected CCA patients was 95.41%. The diagnostic coincidence rate of CT and MRI examination was 89.42% and 92.31%, respectively. The diagnostic coincidence rate of MRI combined with CT examination was 100.00%. The number of CT delayed enhancement, peripheral bile duct dilatation, and hepatic capsular depression were more than those of MRI. The number of circular enhancement cases in the CT group was less than that in the MRI group. ROC curve results showed that the sensitivity and specificity of MRI combined with CT for the diagnosis of CCA patients were higher than those of MRI or CT alone. Conclusion. MRI combined with CT has high diagnostic sensitivity and specificity and can provide imaging evidence for the clinical diagnosis and treatment of CCA.
Intuitionistic Fuzzy Dombi Hybrid Decision-Making Method and Their Applications to Enterprise Financial Performance Evaluation
Authors: Jana Chiranjibe , Muhiuddin G. , Pal Madhumangal , Al-Kadi D.
Published: July 2021

In the era of the knowledge-based economy, the active branch of information technology plays a crucial role. The enterprise administration covers efficient changes, and it has been entered in the age of reasonable management argument. The standard enterprise financial review evaluation centers on the importance of bondholders. The investor takes operational data as an issue and pays surveillance to the study of material attraction and the result. Otherwise, it is not intelligent to adjust in a modern marketplace period. Therefore, enterprise financial directing the interests of shareholders and business policies that are taking into account stakeholders’ needs is continually investigated in the future lively competition. In that view, accumulating data is an essential research tool to draw the researchers’ recent attention during the knowledge investigation. In this research, multiple attribute decision-making (MADM) approach has been proposed for the enterprise financial performance evaluation. To this view, financial performance evaluation has been done with intuitionistic fuzzy arguments. We apply new Dombi hybrid operators such as the intuitionistic fuzzy Dombi hybrid average (IFDHWA) operator and intuitionistic fuzzy Dombi hybrid geometric (IFDHWG) operator. These operators have a good advantage of adaptability in the working parameter. Finally, a realistic instance for enterprise financial performance is reported following comment on the benefit or utility of the recommended output.
Comparison of the Effect of Four Different Abutment Screw Torques on Screw Loosening in Single Implant-Supported Prosthesis after the Application of Mechanical Loading
Authors: Ebadian Behnaz , Fathi Amirhossein , Khodadad Saba
Published: July 2021

Background. The complications of implant-supported prostheses can be classified into mechanical and biological ones, one part of which is associated with screw loosening. This study was aimed to compare the effect of four different abutment screw torque techniques on screw loosening in single implant-supported prostheses following the application of mechanical loading. Materials and Methods. In this experimental study, a total of 40 implants in acrylic blocks (6 × 10 × 20 mm) were mounted perpendicular to the surface. They were then randomly divided into four groups: (1) torquing once with 30 Ncm, (2) torquing three times with 30 Ncm and 5-minute intervals, (3) torquing once with 30 Ncm, opening the screw, and retorquing with 30 Ncm, and (4) torquing once with 35 Ncm. The torque values were confirmed by using a digital torque meter. Then, the samples underwent a force (2 cps, 0.453–11.793 kg) for three hours before the measurement of detorque values. The screw loosening force (torque) was then measured and recorded. The obtained data were analyzed by SPSS (version 22) software using one-way ANOVA and Tukey post hoc test at a 5% error level. Results. The maximum mean detorque values of the abutment screws in single implant-supported prostheses were reported for groups 4 (27.8 ± 1.3), 1 (26.8 ± 1.3), and 3 (25.1 ± 1.3), and the minimum mean detorque value was found in group 2 (24.9 ± 1.2). Moreover, no significant difference was observed between groups 2 and 3 (p>0.05), but a significant difference was found between groups 1 and 3 and other groups (p<0.05). Conclusion. The increase in the torque value increased the torque loss. However, the detorque value in group 4 showed the least difference with the value recommended by the manufacturer (30 Ncm).
Treatment Technology of Microbial Landscape Aquatic Plants for Water Pollution
Authors: Hu Ming , Li Lei
Published: July 2021

With the rapid development of industrial and agricultural production, the rapid growth of population, and the acceleration of urbanization, the problem of water pollution is becoming more and more serious. Water shortages and pollution disrupt the balance of ecosystems and seriously limit people’s health and rapid economic development. Nowadays, the method of repairing sewage bodies using microbial landscape aquatic plants is attracting more and more attention, and it is a big challenge to maintain the sustainable development of human beings and nature. This paper uses floating rafts to combine microorganisms and landscape aquatic plants to conduct sewage treatment experiments. According to microorganisms, landscape aquatic plants absorb nutrients in the water body, examine the changes in water quality during the restoration of microorganisms’ landscape aquatic plants, and establish the growth of microorganisms’ landscape aquatic plants. The relationship with changes in water quality aims to provide a theoretical basis for the treatment of slow‐flowing water bodies such as lakes, reservoirs, large artificial ponds, and rivers. In this paper, the experiments are divided into four groups (A (experimental sewage + microbial inoculant), B (experimental sewage + plant), C (experimental sewage + microbial inoculant + plant), and D (experimental sewage)). It can be divided into the total nitrogen content, total phosphorus content, and COD value data, and chromaticity detection of each group of the test is continuously monitored weekly to comprehensively detect and observe the repair effect on contaminated water bodies. The experiment proved that the water quality of the three treatment groups was significantly clearer than that of the blank control group, and its clarity: microorganism + plant > microorganism > plant > blank control group. This shows that the combination of microorganisms and landscape aquatic plants can effectively reduce the various pollutants contained in sewage and reduce the color of sewage. Treating sewage using plant technology that combines microorganisms is feasible and promising.
Protective Effect of Mitochondrial ND2 C5178A Gene Mutation on Cell and Mitochondrial Functions
Authors: Tian Liuyang , Zhu Chao , Yang Huanwan , Li Yang , Liu Yuqi
Published: July 2021

Background. Mitochondrial NADH dehydrogenase subunit 2 (MT‐ND2) m. 5178C>A gene mutation has protective effects against various diseases, but the molecular mechanism is still unclear. In previous study, we found a heteroplasmy level of MT‐ND2 m. 5178C>A mutation in normotensive controls. Peripheral blood samples were obtained from essential hypertension individuals carrying the mutation and healthy controls without gene mutation to establish immortalized lymphocyte lines. To investigate the effect of the MT‐ND2 m. 5178C>A gene mutation, comparative analyses of the two group cell lines were performed, including measurements of cell proliferation, viability, ATP synthesis, mitochondrial oxidative stress, and oxidative phosphorylation. Results. The cell proliferation rate and viability of the MT‐ND2 m. 5178C>A mutant lymphocyte line were higher than those of the control group. Mitochondrial functions of the MT‐ND2 m. 5178C>A mutant lymphocyte were increased, including increased ATP synthesis, decreased ROS production, increased mitochondrial membrane potential and Bcl‐2 gene transcription and protein translation, decreased Caspase 3/7 activity, and decreased early apoptosis and late apoptosis. The oxygen consumption rate (OCR) of the mutant lymphocyte line was higher than that of the control group, including basal OCR, ATP‐linked OCR, maximal OCR, proton leak OCR, and reserve OCR, and there was no significant difference in nonmitochondrial OCR. The activity of Mitochondrial Complex I of the mutant group was increased than that of the control group. Conclusions. The MT‐ND2 m. 5178C>A mutation is a protective mutation that may be related to improvement of mitochondrial functions and decrease in apoptosis.
Genome‐Wide MicroRNA Analysis of Peripheral Blood Mononuclear Cells Reveals Elevated miR‐142‐3p Expression as a Potential Biomarker for Secondary Syphilis
Authors: Yang Zhusheng , Yang Xiumin , Gan Zhaoyan , Yuan Liang , Liu Wei , Si Chaozeng
Published: July 2021

Background. Treponema pallidum subspecies pallidum (T. pallidum) infection induces significant immune responses, resulting in tissue damage. Gene expression plays an essential role in regulating the progression of syphilis infection. However, little is known about the regulatory role of microRNAs (miRNAs) in the immune response to T. pallidum infection. Here, we analyze the differential expression of miRNAs in peripheral blood mononuclear cells (PBMCs) between untreated secondary syphilis patients and healthy controls and study the correlation between miRNA expression and clinical features with bioinformatics. Methods. The expression profile of miRNAs was measured by microarray analysis in PBMCs of untreated secondary syphilis patients and healthy controls. Weighted Gene Coexpression Network Analysis (WGCNA) was used to construct the expression of miRNAs and the clinical data of secondary syphilis patients. Gene ontology (GO) and KEGG enrichment analyses were performed on target genes of miR‐142‐3p. Results. 244 miRNAs exhibited at least 1.0‐fold differential expression between secondary syphilis patients and healthy controls. The miRNAs were divided into three modules by WGCNA. The blue module was positively correlated with TPHA, TRUST, duration of disease, and erythema. And in the blue module, the expression of miR‐142‐3p was significantly higher in secondary syphilis patients than in healthy controls (p = 0.02), which is also close to the clinical features of secondary syphilis. GO and KEGG pathway analyses showed that these target genes of miR‐142‐3p are correlated with endocytosis and positive regulation of the apoptotic process. Conclusion. The elevated miR‐142‐3p expression in PBMCs may play an important role in the immune response to T. pallidum infection and may be a potential biomarker for secondary syphilis.
Laparoscopic Repair of Morgagni Hernia Combined with Right Hemicolectomy for Bleeding Ascending Colon Carcinoma Lodged within the Chest: A Case Report and Review of the Literature
Authors: Onafowokan Oluwatobi O , Khosa Kiran , Bonatti Hugo
Published: July 2021

Background. Morgagni hernias are rare in adults and may be asymptomatic but, nevertheless, require surgical repair, with laparoscopy offering an excellent option. The colon dislodged into the chest through diaphragmatic hernias may be affected by various disorders, including malignancies. Case Report. A 70-year-old obese male presented with fatigue and shortness of breath. CT scan showed the right colon lodged in the chest through a Morgagni hernia. He was anaemic, and colonoscopy revealed a colon cancer. He underwent combined laparoscopic hernia repair with bioabsorbable mesh and right hemicolectomy. Recovery was uneventful, but the patient died 5 months later from chemotherapy-associated cardiac failure. Literature review revealed eight similar published cases, and including ours, there were seven Morgagni hernias, one traumatic hernia, and one Bochdalek hernia. Median age of the five men and four women was 66 (range 49-85) years. Surgical approach was thoracotomy (2), laparotomy (5), and laparoscopy (2). Conclusion. Outcome of the rare condition is determined by the course of the colon cancer. Hernia repair was successful in ours and all other published cases. A combined laparoscopic approach can be safely done.
[Retracted] Modified Prehospital Acute Stroke Severity (mPASS) Scale to Predict Emergent Large Arterial Occlusion
Authors: Si Xiaoli , Ruan Jie , Li Lingfei , Lu Shan , Huang Huan , Xia Wenqing , Liu Keqin , Chen Tianwen , Jiang Lin , Yin Congguo
Published: July 2021

Introduction. To date, identifying emergent large vessel occlusion (ELVO) patients in the prehospital stage is important but still challenging. In this present study, we aimed to design a modified prehospital acute stroke severity (mPASS) scale to identify ELVO patients and compared the scale to the PASS scale which has been published. Methods. We retrospectively evaluated a consecutive cohort of acute ischemic stroke (AIS) in our stroke unit who visited the emergercy department. These patients underwent CT angiography (CTA), MR angiography (MRA), or digital subtraction angiography (DSA) at admission. The mPASS scale was calculated based on the National Institutes of Health Stroke Scale (NIHSS) items retrospectively, including the level of consciousness commands, gaze, arm weakness, and aphasia/dysarthria. Receiver operating characteristic (ROC) analysis was used to obtain the area under the curve (AUC) of the mPASS scale, NIHSS, and PASS scale. U‐statistics was used to compare the AUC of the mPASS scale to the NIHSS and PASS scale. Results. A total of 382 AIS patients were enrolled. The AUC and specificity of the mPASS scale (0.92, 84.4) were all higher than those of the PASS scale. Cortical symptoms such as gaze palsy and consciousness disorder were more specific indicators for ELVO than motor deficits. Conclusions. The mPASS scale had a better discrimination for identifying ELVO than the PASS scale in our retrospective cohort. It might predict ELVO in an effective and simple way for paramedics in the prehospital triage stage or emergency stage. Moreover, cortical symptoms might have relatively high specificities to predict ELVO on their own.
Nonlinear Flutter Analysis for a Long‐Span Suspension Bridge
Authors: Liu Jieshan , Wang Fan , Yang Yang , Liu Renhuai
Published: July 2021

The wind‐induced flutter of the long‐span suspension bridge structure is extremely harmful to the bridge. Therefore, it is necessary to study the nonlinear problem of wind‐induced flutter. Here, the nonlinear flutter problem of a long‐span suspension bridge with cubic torsional stiffness is analyzed by the equivalent linearization method. The system has Hopf bifurcation and limit cycle oscillations (LCOs) under critical wind speed. Replacing the nonlinear stiffness term of the original nonlinear equation with the equivalent linear stiffness, we can obtain the equivalent linearized equation of the nonlinear flutter system and the solution, critical wind speed, and flutter frequency of the suspension bridge flutter system. At the same time, the system has a limit cycle vibration, and the Hopf bifurcation point is obtained. Compared with the numerical method, the calculation results are consistent. The influence of the damping ratio on the flutter system is analyzed. Increasing the system damping ratio can increase the flutter critical wind speed and reduce the amplitude of LCOs. The influence of cubic torsional stiffness on the flutter system is analyzed. The increase of the cubic stiffness coefficient does not change the critical state of flutter, but reduces the amplitude of LCOs.