bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploiting Papers’ Reference’s Section for Multi-Label Computer Science Research Papers’ Classification
Authors: Sajid Naseer Ahmed , Ahmad Munir , Afzal Muhammad Tanvir , Atta-ur-Rahman
Published: March 2021

The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.
Bidirectional Encoding Contextual Approach for Identification of Relevant Document in Corpus
Authors: Prasad K. M. Shiva , Reddy T. Hanumantha
Published: March 2021

With the increasing advance of computer and information technologies, numerous documents have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding their interesting documents. These documents can be in the form of blogs, research papers, and thesis. There is a heterogeneous set of documents which has information linked with each other. Traditional search is about taking an input of the query text from the user and checking if the subsequence is a part of any sentence in the set of documents and showing the set to the user. In this paper, we have proposed a Bidiection Encoding Contextual algorithm that can be applied to different types of documents and do a semantic search across the corpus. The algorithm used to understand the meaning of the word, their relative relationship between other words and provide the user with the documents that not just has the textual reference but also contain the relative meaning of the query. On the COVID-19 dataset, test been performed on the reliability of the interpretation through the function of linguistic similarities. The experimental findings demonstrate the strong association between the conceptual term interpretation of human consciousness in the role of measuring the similarity. Experiments show that the Bidirectional Encoding Contextual model has the best accuracy of 85.6% when compared with other traditional models like RNN, CNN and LSTM models.
Light-induced energy transfer followed by electron transfer in axially co-ordinated benzothiazole tethered zinc porphyrin-fullero[C60/C70]pyrrolidine triads
Authors: Chitta Raghu , Badgurjar Deepak , Reddy Govind , Jain Kanika , Karla Vijendar Reddy , Boligorla Anjaiah , Giribabu Lingamallu
Published: March 2021

Benzothiazole (BTZ)-zinc porphyrin (ZnP) dyads, Dyad-1 and Dyad-2 connected together with two different spacers, ester and ethoxy esters, were synthesized and light induced energy and electron transfer events were investigated. Within these dyads, due to the spectral overlap of the BTZ emission with the ZnP absorption, a selective photoexcitation of BTZ at 325 nm resulted in the photo-induced energy transfer (PEnT) from 1BTZ* to ZnP displaying the quenching of the BTZ emission followed by the concurrent appearance of the ZnP emission at 600 and 650 nm suggesting the formation of the 1ZnP* [Formula: see text]. 1BTZ*-ZnP [Formula: see text] BTZ-1ZnP*. When the dyads are titrated with imidazole appended fullero[C[Formula: see text]/C[Formula: see text]]pyrrolidines, four supramolecular triads, involving the axial co-ordination of the imidazole to the zinc center of the ZnP, were formed and the assembly formation was systematically monitored by the optical absorption technique. Cyclic voltammetry and the density functional theory calculations have revealed that, in these triads, the zinc porphyrin acts as an electron donor and fullerene moiety as the electron acceptor. Steady state fluorescence studies revealed that, upon selective excitation of the ZnP moiety at 550 nm, the emission of ZnP at 600 and 650 nm was quenched revealing the occurrence of photo-induced electron transfer (PET) from 1ZnP* to fullerene moiety leading to the formation of charge separated state [Formula: see text]. BTZ-1ZnP* : (ImC[Formula: see text] BTZ-ZnP[Formula: see text]:(ImC[Formula: see text]. More importantly, when the supramolecular triads were excited at 325 nm, the wavelength at which the BTZ absorbs predominantly, the emission of the BTZ moiety which was quenched due to PEnT from 1BTZ* to ZnP followed by the PET from 1ZnP* to fullerene indicates the probability of occurrence of 1BTZ*-ZnP:(ImC[Formula: see text] [Formula: see text] BTZ-1ZnP*[Formula: see text]: (ImC[Formula: see text] BTZ-ZnP[Formula: see text]:(ImC[Formula: see text].
UNDERSTANDING THE SPECIFIC CHARACTERISTICS AND DETERMINANTS OF OPEN INNOVATION IN SMALL AND MEDIUM-SIZED ENTERPRISES: A SYSTEMATIC LITERATURE REVIEW
Authors: TCHOUWO CARENE TCHUINOU , POULIN DIANE , VEILLEUX SOPHIE
Published: March 2021

Open Innovation (OI) assumes that businesses combine external and internal ideas as the primary means to accelerate internal innovation or access the market to commercialise their technologies. In the last decade, research interest has increased towards understanding OI in small and medium-sized enterprises (SMEs). Given the fast pace with which the literature has been developing in this area, there is a strong need to revisit the literature to investigate nuances, ambiguities and differences of opinion. This paper aims to systematically review OI in SMEs and survey the current state of his characteristics and determinants. 130 papers published in peer-reviewed journals are analysed. Findings suggest OI in SME differs considerably from OI in a large business, in terms of characteristics and determinants. The originality of the paper is rooted in the conceptual framework that illustrates how characteristics and determinants of OI in SME relate to each other in terms of input-output.
Traditional Chinese Medicine in the United States: Current state, regulations, challenges, and the way forward
Authors: Jin Louis Lei , Zheng Jin , Honarvar Niyaz M. , Chen Xiqun
Published: March 2021

In the United States, there has been a steady presence and growth of Traditional Medicine (interchangeable in this paper with Complementary or Alternative Medicine) over the past few decades. The costs for such practices are relatively low along with minimal-to-no obvious side effects. Amongst a variety of traditional medical systems, Traditional Chinese Medicine is one of the most popular alternatives to help manage chronic health conditions or to improve the overall quality of life. While not exhaustive, this paper provides a snapshot of Traditional Chinese Medicine in the United States with insights into its current state, regulations, challenges, and the way forward.
Neural Dependency Parser for Tibetan Sentences
Authors: An Bo , Long Congjun
Published: March 2021

The research of Tibetan dependency analysis is mainly limited to two challenges: lack of a dataset and reliance on expert knowledge. To resolve the preceding challenges, we first introduce a new Tibetan dependency analysis dataset, and then propose a neural-based framework that resolves the reliance on the expert knowledge issue by automatically extracting feature vectors of words and predicts their head words and type of dependency arcs. Specifically, we convert the words in the sentence into distributional vectors and employ a sequence to vector network to extract feature words. Furthermore, we introduce a head classifier and type classifier to predict the head word and type of dependency arc, respectively. Experiments demonstrate that our model achieves promising performance on the Tibetan dependency analysis task.
A complexity chasm for solving sparse polynomial equations over
p
-adic fields
Authors: Rojas J. Maurice , Zhu Yuyu
Published: March 2021

The applications of solving systems of polynomial equations are legion: The real case permeates all of non-linear optimization as well as numerous problems in engineering. The
p
-adic case leads to many classical questions in number theory, and is close to many applications in cryptography, coding theory, and computational number theory. As such, it is important to understand the complexity of solving systems of polynomial equations over local fields. Furthermore, the complexity of solving
structured
systems --- such as those with a fixed number of monomial terms or invariance with respect to a group action --- arises naturally in many computational geometric applications and is closely related to a deeper understanding of circuit complexity (see, e.g., [8]). Clearly, if we are to fully understand the complexity of solving sparse polynomial systems, then we should at least be able to settle the univariate case, e.g., classify when it is possible to separate and approximate roots in deterministic time polynomial in the input size.
Improved divisor arithmetic on generic hyperelliptic curves
Authors: Lindner Sebastian , Imbert Laurent , Jacobson Michael J.
Published: March 2021

The divisor class group of a hyperelliptic curve defined over a finite field is a finite abelian group at the center of a number of important open questions in algebraic geometry, number theory and cryptography. Many of these problems lend themselves to numerical investigation, and as emphasized by Sutherland [14, 13], fast arithmetic in the divisor class group is crucial for their efficiency. Besides, implementations of these fundamental operations are at the core of the algebraic geometry packages of widely-used computer algebra systems such as Magma and Sage.
Optimization Scheme of Fine Toll and Bus Departure Quantity for Bottleneck Congestion Management
Authors: Wu Jianhui , Ji Yuanfa , Sun Xiyan , Xu Yan
Published: March 2021

This paper chooses car travel and bus travel as the research objects, establishes a dual‐mode equilibrium model based on the bottleneck model, and compares the travel characteristics of the no‐toll and fine‐toll schemes. We find that the fine‐toll scheme can eliminate the queuing time at the bottleneck, but it also increases the congestion risk cost of bus travel. In order to eliminate the queuing time at the bottleneck and reduce the congestion risk cost of bus travel without increasing the car travel cost and bus travel cost, we propose an optimization scheme of fine toll and bus departure quantity and analyze its travel characteristics theoretically. Through the numerical example, we calculate and analyze the equilibrium results of no‐toll scheme, fine‐toll scheme, and optimization scheme of fine toll and bus departure quantity. The results indicate that the optimization scheme of fine toll and bus departure quantity can help travelers to choose a reasonable travel mode and travel time to travel in the rush hour.
Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence
Authors: Du Cong
Published: March 2021

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.
Education Data-Driven Online Course Optimization Mechanism for College Student
Authors: Wang Ziqiao , Yu Ningning
Published: March 2021

During the recent epidemic period of COVID-19, online courses have become an important learning form for college students. However, online learning cannot communicate face to face in class and position students’ abilities accurately, and there are many problems and limitations such as one-way evaluation, for example, neglecting of process evaluation and simple evaluation form. Therefore, how to construct the evaluation system of online course teaching and make effective use of the evaluation mechanism becomes an urgent problem. Based on the big data mining of online course evaluation data, the online course evaluation optimization architecture based on process evaluation is proposed. The optimization of online course evaluation is analyzed from online course evaluation data and student comments using deep learning and collaborative filtering technology. This includes improving teacher teaching and improving student learning efficiency. Data experiment proves that the proposed algorithm can provide an optimal evaluation strategy, guarantee the students’ learning quality, and improve the efficiency of online course.
Astragali Radix Contributes to the Inhibition of Liver Fibrosis via High-Mobility Group Box 1-Mediated Inflammatory Signaling Pathway
Authors: Wen Jianxia , Wang Dan , Wang Jian , Wang Ruilin , Wei Shizhang , Zhao Yanling
Published: March 2021

Astragali Radix (AR), the dried root of Astragali Radix membranaceus (Fisch.) Bge. or Astragali Radix membranaceus (Fisch.) Bge. var. mongholicus (Bge) Hsiao, is a commonly used traditional Chinese medicine for the treatment of liver diseases. This study aimed to comprehensively evaluate the pharmacological action and explore the potential mechanism of AR on liver fibrosis. Rats were administered with carbon tetrachloride for eight weeks, followed by oral treatment with AR for six weeks. The efficacy was confirmed by measuring liver function and liver fibrosis levels. The underlying mechanisms were explored by detecting the expression of related proteins. AR significantly decreased the serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), collagen IV (COL-IV), hyaluronic acid (HA), laminin (LN), and precollagen type III (PCIII). In addition, AR inhibited the deposition of collagen and the activation of hepatic stellate cells. Those data strongly demonstrated that AR alleviated liver fibrosis by CCl4. In order to illustrate the potential inflammatory, the mRNA levels of IL-6, TNF-α, and IL-1β were detected. Subsequently, immunohistochemistry analysis was performed to further verify the expression of type I collagen. Finally, the expression of key proteins in the inflammatory signaling pathway was detected. AR significantly reduced the expression of high-mobility group box 1 (HMGB1), TLR4, Myd88, RAGE, and NF-κ B p65 genes and proteins. In addition, western blotting showed AR decreased the protein expression of RAGE, p-MEK1/2, p-ERK1/2, and p-c-Jun. Taken together, our data reveal that AR significantly inhibits liver fibrosis by intervening in the HMGB1-mediated inflammatory signaling pathway and secretion signaling pathway. This study will provide valuable references for the in-depth research and development of Astragali Radix against liver fibrosis.
Assessing the Industrial Effluent Effect on Irrigation Water Quality and Farm Soil near Kombolcha Town, Ethiopia
Authors: Awol Mohammed Jemal , Adugna Amare Tiruneh , M Kamaraj
Published: March 2021

This study evaluated various industrial effluents’ effect on irrigation water quality and farm soil near Kombolcha town. Several industries such as brewery, steel iron, textile, and tannery have been installed near the Borkena River that crosses Kombolcha town. Representative samples of irrigation water and farm soil were collected from the upper and down part of Borkena river. The upper site was used as a control as it was not contaminated by industrial effluents. The analysis for selected parameters showed that the downstream irrigation water quality had mean concentrations of pH = 8.54, magnesium (Mg+2) = 5.27 mg/l, carbonate (CO3−2) = 1.25 mg/l, bicarbonate (HCO3−) = 9.10 mg/l, copper (Cu) = 0.21 mg/l, chromium (Cr) = 0.31 mg/l, and cadmium (Cd) = 0.03 mg/l which were above the permissible limit of the Food and Agriculture Organization’s (FAO’s) irrigation water quality standard. The mean concentrations of electric conductivity (EC) = 0.96 ds/m, sodium (Na+) = 3.35 mg/l, chloride (Cl−) = 7.67 mg/l, and total dissolved solids (TDS) = 612.98 mg/l were slightly and moderately restricted for irrigation. Moreover, the concentration of heavy metals, calcium (Ca+2) = 16.61 mg/l, iron (Fe) = 4.25 mg/l, manganese (Mn) = 0.18 mg/l, and lead (Pb) = 0.47 mg/l, was below the permissible limit of the FAO and nonrestricted. However, the mean concentration of EC, HCO3−, Cu, Cr, Cd, and TDS for downstream-irrigated farm soil samples was above the permissible limit of the FAO. The concentration of most selected parameters in downstream farm soil was also decreasing along with depth except pH, CO3−2, and HCO3-. Generally, there is a significant quality difference (at P ≤ 0.05) between the upstream and downstream irrigation water quality on the parameters of Mg+2, Cl−, Pb, and Cu.
A Family of Optimal Eighth Order Iteration Functions for Multiple Roots and Its Dynamics
Authors: Akram Saima , Akram Faiza , Junjua Moin-ud-Din , Arshad Misbah , Afzal Tariq
Published: March 2021

In this manuscript, we present a new general family of optimal iterative methods for finding multiple roots of nonlinear equations with known multiplicity using weight functions. An extensive convergence analysis is presented to verify the optimal eighth order convergence of the new family. Some special cases of the family are also presented which require only three functions and one derivative evaluation at each iteration to reach optimal eighth order convergence. A variety of numerical test functions along with some real-world problems such as beam designing model and Van der Waals’ equation of state are presented to ensure that the newly developed family efficiently competes with the other existing methods. The dynamical analysis of the proposed methods is also presented to validate the theoretical results by using graphical tools, termed as the basins of attraction.
Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method
Authors: Lu Shan , Xing Zhiheng , Zhao Shiyu , Meng Xianglu , Yang Juhong , Ding Wenlong , Wang Jigang , Huang Chencui , Xu Jingxu , Chang Baocheng , Shen Jun
Published: March 2021

COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, n = 15) and group B (NDM COVID-19 pneumonia group, n = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (

P
=
0.031

and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups (

P
>
0.05

). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients.
Interleukin-22 Plays a Protective Role by Regulating the JAK2-STAT3 Pathway to Improve Inflammation, Oxidative Stress, and Neuronal Apoptosis following Cerebral Ischemia-Reperfusion Injury
Authors: Dong Yongfei , Hu Chengyun , Huang Chunxia , Gao Jie , Niu Wanxiang , Wang Di , Wang Yang , Niu Chaoshi
Published: March 2021

The interleukins (ILs) are a pluripotent cytokine family that have been reported to regulate ischemic stroke and cerebral ischemia/reperfusion (I/R) injury. IL-22 is a member of the IL-10 superfamily and plays important roles in tissue injury and repair. However, the effects of IL-22 on ischemic stroke and cerebral I/R injury remain unclear. In the current study, we provided direct evidence that IL-22 treatment decreased infarct size, neurological deficits, and brain water content in mice subjected to cerebral I/R injury. IL-22 treatment remarkably reduced the expression of inflammatory cytokines, including IL-1β, monocyte chemotactic protein- (MCP-) 1, and tumor necrosis factor- (TNF-) α, both in serum and the ischemic cerebral cortex. In addition, IL-22 treatment also decreased oxidative stress and neuronal apoptosis in mice after cerebral I/R injury. Moreover, IL-22 treatment significantly increased Janus tyrosine kinase (JAK) 2 and signal transducer and activator of transcription (STAT) 3 phosphorylation levels in mice and PC12 cells, and STAT3 knockdown abolished the IL-22-mediated neuroprotective function. These findings suggest that IL-22 might be exploited as a potential therapeutic agent for ischemic stroke and cerebral I/R injury.
The Emotional Status, Attitudes in Decision-Making Process, and Their Impact on Surgical Choices in Korean Breast Cancer Patients
Authors: Jeon Sook Young , Kim Kyoung-Eun , Kim Eun-Kyu , Han Hyunhee , Lee Han-Byoel , Han Wonshik , Noh Dong-Young , Moon Hyeong-Gon
Published: March 2021

Purpose. We examined the incidence of emotional distress in women with newly diagnosed breast cancer to determine whether the degree of emotional distress affected their choice of breast-conserving surgery (BCS) or mastectomy and evaluated how the patient’s preferred role in decision-making influenced her choice of surgical method. Methods. This prospective study included 85 patients newly diagnosed with in situ or invasive breast cancer eligible for BCS. Their degree of depression/anxiety and attitude toward the decision-making process were measured using the Hospital Anxiety and Depression Scale (HADS) and Control Preference Scale (CPS), respectively. After receiving information on both surgical methods, the patients indicated their preferred surgical method and completed the CPS at their initial and second visits before surgery. Results. After the diagnosis of breast cancer, 75.3% of patients showed abnormal or borderline HADS scores for depression and 41.2% for anxiety. Patients with borderline or abnormal degrees of depression were more likely to have coexisting abnormal degrees of anxiety (

p
<
0.001

). However, the presence of depression or anxiety was not associated with patients’ surgical choices (

p
=
0.394

and 0.530, respectively). Patients who preferred a more active role in the decision-making process were more likely to choose mastectomy over BCS, while those who were passive or collaborative chose BCS more frequently (

p
=
0.001

). Conclusion. Although many patients with newly diagnosed breast cancer experience depression and anxiety before surgery, these do not affect the choice of surgical method; however, their attitudes toward the decision-making process do.
Near-Coincidence Point Results in Norm Interval Spaces via Simulation Functions
Authors: Ullah Misbah , Sarwar Muhammad , Aydi Hassen , Gaba Yaé Ulrich
Published: March 2021

Recently, Wu in 2018 established interesting results in the framework of interval spaces. He initiated the idea of near-fixed points and proved some related basic results in metric interval, norm interval, and hyperspaces. In 2015, Khojasteh et al. gave the concept of simulation functions and studied some fixed-point results in metric spaces. Motivated by this work, we give some near-coincidence point results in norm interval spaces using the concept given by Khojasteh et al. Examples are also provided for the validation of the results.
Research on Control Strategy of the Electric Power Steering System for All-Terrain Vehicles Based on Model Predictive Current Control
Authors: Jie Chen , Yanling Guo
Published: March 2021

Aiming at the high demand for the torque response speed and torque pulsation of the all-terrain vehicle (ATV) Electric Power Steering (EPS) system, this paper proposes to apply the Model Predictive Current Control to the all-terrain vehicle EPS system. A Novel Three-Vector Model Predictive Current Control (N3V-MPCC) is proposed in this paper to reduce the current ripple and reduce the calculation load. Two effective voltage vectors and a zero vector are selected in the control period through only six times of prediction and application of the Sector Vector Selection method. The duration of each voltage is calculated and output to the Voltage Source Inverter (VSI). Simulation and experimental results show that, compared with PID cascade Field Oriented Control (PI-FOC), N3C-MPCC can effectively reduce the ripple current of the d-axis and the q-axis. In the simulated electric power mode, the q-axis current ripple of the N3V-MPCC is reduced by 66.67%. Experimental results show that the current ripple of the motor is reduced by 60%, and the torque pulsation is reduced by 62.5%. Therefore, N3V-MPCC has a faster current response speed and smooth steering torque.
Image Recognition Technology in Texture Identification of Marine Sediment Sonar Image
Authors: Sun Chao , Wang Li , Wang Nan , Jin Shaohua
Published: March 2021

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side‐scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K‐means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.
A Sampling‐Based Method for Highly Efficient Privacy‐Preserving Data Publication
Authors: Lu Guoming , Zheng Xu , Duan Jingyuan , Tian Ling , Wang Xia
Published: March 2021

The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains. It has been treated as one prominent prerequisite for enabling AI techniques in wireless networks. With the emergence of diversified smart devices and applications, data held by individuals becomes more pervasive and nontrivial for publication. First, the data are more private and sensitive, as they cover every aspect of daily life, from the incoming data to the fitness data. Second, the publication of such data is also bandwidth‐consuming, as they are likely to be stored on mobile devices. The local differential privacy has been considered a novel paradigm for such distributed data publication. However, existing works mostly request the encoding of contents into vector space for publication, which is still costly in network resources. Therefore, this work proposes a novel framework for highly efficient privacy‐preserving data publication. Specifically, two sampling‐based algorithms are proposed for the histogram publication, which is an important statistic for data analysis. The first algorithm applies a bit‐level sampling strategy to both reduce the overall bandwidth and balance the cost among contributors. The second algorithm allows consumers to adjust their focus on different intervals and can properly allocate the sampling ratios to optimize the overall performance. Both the analysis and the validation of real‐world data traces have demonstrated the advancement of our work.
Hepatocardiac or Cardiohepatic Interaction: From Traditional Chinese Medicine to Western Medicine
Authors: Zhang Yaxing , Fang Xian-Ming
Published: March 2021

There is a close relationship between the liver and heart based on “zang-xiang theory,” “five-element theory,” and “five-zang/five-viscus/five-organ correlation theory” in the theoretical system of Traditional Chinese Medicine (TCM). Moreover, with the development of molecular biology, genetics, immunology, and others, the Modern Medicine indicates the existence of the essential interorgan communication between the liver and heart (the heart and liver). Anatomically and physiologically, the liver and heart are connected with each other primarily via “blood circulation.” Pathologically, liver diseases can affect the heart; for example, patients with end-stage liver disease (liver failure/cirrhosis) may develop into “cirrhotic cardiomyopathy,” and nonalcoholic fatty liver disease (NAFLD) may promote the development of cardiovascular diseases via multiple molecular mechanisms. In contrast, heart diseases can affect the liver, heart failure may lead to cardiogenic hypoxic hepatitis and cardiac cirrhosis, and atrial fibrillation (AF) markedly alters the hepatic gene expression profile and induces AF-related hypercoagulation. The heart can also influence liver metabolism via certain nonsecretory cardiac gene-mediated multiple signals. Moreover, organokines are essential mediators of organ crosstalk, e.g., cardiomyokines link the heart to the liver, while hepatokines link the liver to the heart. Therefore, both TCM and Western Medicine, and both the basic research studies and the clinical practices, all indicate that there exist essential “heart-liver axes” and “liver-heart axes.” To investigate the organ interactions between the liver and heart (the heart and liver) will help us broaden and deepen our understanding of the pathogenesis of both liver and heart diseases, thus improving the strategies of prevention and treatment in the future.
Effect of Bushen Huoxue Prescription on Cognitive Dysfunction of KK-Ay Type 2 Diabetic Mice
Authors: Zhao Shao-Yang , Zhao Huan-Huan , Hao Ting-Ting , Li Wei-Wei , Guo Hao-
Published: March 2021

Diabetic cognitive impairment is one of the common complications of type 2 diabetes, which can cause neurological and microvascular damage in the brain. Bushen Huoxue prescription (BSHX), a compound Chinese medicine, has been used clinically to treat diabetes-induced cognitive impairment. However, its underlying mechanisms remain unclear. In this study, KK-Ay diabetic model mouse was administered BSHX daily for 12 weeks. Bodyweight, random blood glucose (RBG), and fasting blood glucose (FBG) were measured every 4 weeks. Triglycerides (TG), cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting serum insulin (FINS), and Morris water maze were tested after 12 weeks of administration. On the day of sacrifice, the hippocampus was collected for pathological staining and advanced glycation end products (AGEs) analysis to evaluate the neuroprotective effect of BSHX. Our results showed that BSHX treatment significantly ameliorated the T2DM related insults, including the increased bodyweight, blood glucose, TG, insulin levels, AGEs, the reduced HDL-C, the impaired spatial memory, and the neurological impairment. Moreover, Western blot analysis showed that increased expression of receptors of AGEs (RAGEs), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and activation of nuclear factor-κB (NF-κB) in the hippocampus were significantly inhibited by BSHX treatment. These results indicate that BSHX can significantly ameliorate glucose and lipid metabolism dysfunction, reduce the morphological changes in hippocampus tissues, and improve the cognitive function of KK-Ay mice. These protective effects of BSHX may involve regulation of the AGEs/RAGE/NF-κB signaling pathway.
Penile Glans Necrosis following Prostatic Artery Embolization for the Treatment of Benign Prostatic Hyperplasia: A Rare but Serious Complication
Authors: Chung Eric
Published: March 2021

Background. Prostate artery embolization (PAE) is a novel endovascular procedure to treat men with benign prostatic hyperplasia (BPH) symptoms who wish to maintain sexual potency postoperatively. However, serious treatment-related adverse events (TRAEs) of PAE such as penile glans necrosis (PGN) can be devastating and require urgent attention. Case presentation. Mr GM is a 65-year-old sexually active Anglo-Saxon man who have long-standing BPH symptoms unresponsive to medical therapy. While he had an uneventful bilateral superselective PAE using gel foam, there were signs to suggest of PGN, and this was treated conservatively. The patient presented to the emergency department 5 days later with a painful, dark penile glans and accompanying urinary dysuria and hematuria. Clinical examination confirmed evolving PGN. He received 10 courses of hyperbaric oxygen therapy (HBOT) with complete resolution of his PGN. Conclusion. While superselective embolization is usually always performed, nontarget embolization may occur, as intravascular particles may reflux into adjacent vessels. In this unique and extremely rare case report of PGN following PAE, complete resolution was achieved with HBOT. Proposed benefits of HBOT include anti-inflammation, promotion of neovascularization, and induced rate of collagen deposition, resulting in a faster and more effective resolution of PGN.
Onboard Trajectory Generation of Hypersonic Morphing Aircraft
Authors: Zhang Chenxin , Zhang Yankun , Wei Changzhu
Published: March 2021

In this paper, a trajectory optimization strategy for the hypersonic morphing aircraft is proposed, and the AMPI method is used to generate the online trajectory with initial state errors. Firstly, the aerodynamic model and propulsion model of the hypersonic morphing aircraft were established considering the wingspan and the scramjet. Secondly, the optimization strategy was proposed via Gauss pseudospectral method considering the control variables including angle of attack (AOA) and wingspan. The optimized trajectory met the final constraints and path constraints with the objective to minimize the time of the ascent phase. Then, the AMPI method was used to generate online trajectory without solving OCP or NLP on the base of trajectory database calculated by the optimization strategy. The simulation results indicate high accuracy of AMPI method and the final errors corresponding to different initial errors were acceptable. The mean value of the CPU time of the method was about 0.1 second, which shows real-time capability.
Anti-Inflammatory Effect Fraction of Bletilla striata and Its Protective Effect on LPS-Induced Acute Lung Injury
Authors: Zhang Chunchun , Ning Dandan , Pan Jieli , Chen Cheng , Gao Chengxian , Ding Zhishan , Jiang Fusheng , Li Meiya
Published: March 2021

Bletilla striata is a well-known traditional Chinese herb with anti-inflammatory properties that is widely used in the treatment of lung conditions such as silicosis, tuberculosis, and pneumogastric hemorrhage. However, little information on the anti-inflammatory ingredients and their activities is available. In this study, an effect fraction of Bletilla striata (EFBS) was enriched, and its anti-inflammatory activities and underlying mechanisms were investigated. EFBS was enriched by polyamide column chromatography and characterized by HPLC; an LPS-induced acute lung injury model was used to evaluate the anti-inflammatory activities of EFBS. Meanwhile, the main anti-inflammation-contributing ingredients and possible molecular mechanism of anti-inflammatory activity in EFBS were verified by component-knockout method combined with LPS-induced RAW264.7 cell model. The EFBS mainly consisted of coelonin (15.88%), batatasin III (32.49%), 3

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-O-methylbatatasin III (6.96%), and 3-hydroxy-5-methoxy bibenzyl (2.51%). Pretreatment with the EFBS (20 mg/kg and 60 mg/kg) for five days prior to the administration of LPS resulted in decreases in wet-to-dry lung weight ratio, neutrophil number, MPO activity, total protein concentration, NO level, and MDA level, as well as IL-1β, IL-6, MCP-1, and TNF-α concentrations in the bronchoalveolar lavage fluid. Western blot analysis demonstrated the increased expressions of iNOS, COX-2, and NF-κB p65 in the LPS treatment group, all of which were ameliorated by EFBS pretreatment. Histological examination confirmed the protective effect of the EFBS. Additionally, component-knockout assay confirmed that these four quantitative components contributed significantly to the anti-inflammatory effect of EFBS. Coelonin, batatasin III, 3

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-O-methylbatatasin III and 3-hydroxy-5-methoxy bibenzyl were the main anti-inflammatory components of EFBS and could regulate the expression of downstream inflammatory cytokines by inhibiting p65 nuclear translocation. These findings uncover, in part, the molecular basis underlying the anti-inflammatory activity of Bletilla striata.
[Retracted] Effect of Platelet‐Rich Plasma on Bone Healing in Immediate Implants Analyzed by Cone Beam Computerized Tomography: A Randomized Controlled Trial
Authors: Khan Aleshba S. , Zaheer Naauman , Zaigham Abdul M. , Shahbaz Maliha , Zaheer Usman , Alam Mohammad Khursheed
Published: March 2021

The possibility of platelet‐rich plasma (PRP) on the improvement of bone and adjacent tissue recovery has previously been validated. However, there is insufficient data supporting the use of platelet‐rich plasma to improve the healing of bone and adjacent tissues around an implant in the oral cavity. The purpose of this randomized controlled trial was to observe the effect of platelet‐rich plasma (PRP) concentrate on marginal bone loss and bone density around immediate implant placement using Cone Beam Computerized Tomography (CBCT). This clinical study was conducted over a period of six months on 12 subjects, who were equally categorized into two groups. Group I was the control, whereas the subjects in Group II received PRP therapy at the surgical site. All subjects were given a standard treatment with a single implant system (DIO UFII hybrid sandblasted acid‐etched implants). Inserted implants were analyzed through CBCT, and records were registered at baseline, at the 12th week before functional loading and the 26th week after functional loading. The bone loss was calculated at the proximal (mesial and distal) side of the implant and bone density at baseline, 12th week, and 26th week after implant placement. SPSS version 23.0 was used for statistical analysis of data. The changes in bone levels were measured and compared between the two groups using the Mann–Whitney U test, with no significant difference. Bone density was analyzed by an independent sample t‐test, p value ≤ 0.05 was considered statistically significant. Again, no significant difference in bone density was observed between both groups at all three instances. Therefore, it can be concluded that local injection of PRP after immediate implant placement did not show any decrease in marginal bone loss or improvement in bone density. This trial is registered with NCT04650763.
Clinical Prediction Score for Ruptured Appendicitis in ED
Authors: Prachanukool Thidathit , Yuksen Chaiyaporn , Tienpratarn Welawat , Savatmongkorngul Sorravit , Tangkulpanich Panvilai , Jenpanitpong Chetsadakon , Phootothum Yuranun , Phontabtim Malivan , Nuanprom Promphet
Published: March 2021

Background. Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to develop the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods. This study was a diagnostic, retrospective cross-sectional study in the Emergency Medicine Department of Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN) > 75%, and pain duration before presentation. The predictive model and prediction score for ruptured appendicitis were developed by multivariable logistic regression analysis. Result. During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score >6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion. Using the Ramathibodi Welawat Ruptured Appendicitis Score (RAMA WeRA Score) developed in this study, a score of >6 was associated with ruptured appendicitis.
Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors
Authors: Pan Derun , Liu Renyi , Zheng Bowen , Yuan Jianxiang , Zeng Hui , He Zilong , Luo Zhendong , Qin Genggeng , Chen Weiguo
Published: March 2021

Objectives. To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients’ clinical characteristics, and identify the most essential features for the classification of bone tumors. Materials and Methods. In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong‐Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves. Results. The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686‐0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high‐density components, and cortex involvement. Conclusions. This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.