bluuebunny/crossref_metadata_2025 huggingface dataset:
Unexpectedness as a Measure of Semantic Learning When Training Transformer Models
Authors: Calix Ricardo A. , Javadpour Leili
Published: October 2022

Many problems in NLP such as language translation and sentiment analysis have shown a lot of improvement in recent years. As simpler language problems are solved or better understood, the focus shifts to more complex problems such as semantic analysis and understanding. Unfortunately, a lot of studies in the literature suffer from a too much specificity problem. The algorithms and datasets are too domain specific. In this study, we analyze and elaborate on this notion of generality. Instead of selecting a highly specialized data set for semantic analysis, we take a generic and possibly dry data set, and we study how a plain vanilla Transformer performs in learning higher level semantic patterns beyond what was obvious or expected. We tune our Transformer model on a classic language task to ensure correct performance. Once tuned, the goal is to select sentences with specific key words and study whether higher level semantic patterns may have been learned by our model. We believe that we obtained promising results. The average BLEU score for sentences less than 25 words is equal to 39.79. Our initial qualitative analysis of possible semantic content of interest shows a 17 percent rate in finding interesting semantic patterns. We provide discussion of data driven results of unexpectedness as a measure of semantic learning.
Novel Sparse Feature Regression Method for Traffic Forecasting
Authors: Salamanis Athanasios I. , Gravvanis George A. , Kotsiantis Sotiris B. , Vrahatis Michael N.
Published: October 2022

Traffic forecasting is an integral part of modern intelligent transportation systems. Although many techniques have been proposed in the literature to address the problem, most of them focus almost exclusively on forecasting accuracy and ignore other important aspects of the problem. In the paper at hand, a new method for both accurate and fast large-scale traffic forecasting, named “sparse feature regression”, is presented. Initially, a set of carefully selected features is extracted from the available traffic data. Then, some of the initial features are sparsified, namely they are transformed into sets of sparse features. Finally, a linear regression model is designed using the sparse feature set, which is trained by solving an optimization problem using a sparse approximate pseudoinverse as a preconditioner. We evaluated the proposed method by conducting experiments on two real-world traffic datasets, and the experimental results showed that the method presents the best balance between accuracy of predictions and time required for achieving them, in comparison with a set of benchmark models.
Double-inputs Illumination Pattern Recognizing Model with Automatic Shadow Detection Network in a Single Face Image
Authors: Liu Jiaqi , Yi Jizheng , Chen Aibin
Published: October 2022

Illumination pattern recognition of face image has always been a hot research topic in the field of human-computer interaction, and has been widely used in lighting recovery, virtual scene construction and other multimedia fields. Most of the traditional methods achieve this task by analyzing the illumination components from the image texture and structure information, which is often considered to be indirect, complex and time-consuming. This paper introduces a Double-inputs Illumination Pattern Recognizing Model (DIPRM) with Automatic Shadow Detection Network (ASDN) based on Convolution Neural Networks (CNNs). In the proposed coherent system framework, we first annotate the shadow regions of face images with uneven illumination to obtain the face shadow detection dataset. Second, an ASDN which has the encoder-decoder structure is designed. The main architecture of the ASDN is based on the nested U-Net, and for this nesting, the attention mechanism is applied to fuse the output of each sublayer. Third, a double-inputs Facial Illumination Pattern Recognizing Network (FIPRN) following the ASDN is organized, which consists of AlexNet and the attention module. As the double-inputs, the binary image after the shadow segmentation from the ASDN and the original image are input into the FIPRN to make the whole network converge to a good collaboration state eventually. For shadow detection task, the ASDN was evaluated in comparisons with U-Net and UNet++. Experimental results demonstrated that the ASDN achieved an average IoU and Dice gains of 1.9 and 1.2 points over the base-line model with best results. Moreover, the FIPRN was tested in comparison with some baseline models in illumination pattern recognizing task, where the results demonstrated that it achieved an accuracy rate gain of 1.0 points than the AlexNet with a signal-input.
MAPPING THE NETWORKED CONTEXT OF COPERNICUS, MICHELANGELO, AND DELLA MIRANDOLA IN WIKIPEDIA
Authors: MICCIO LUIS A. , GÁMEZ-PÉREZ CARLOS , SUÁREZ JUAN LUIS , SCHWARTZ GUSTAVO A.
Published: October 2022

To discern the role social and cultural networks play in the emergence of preeminent historical figures and ideas in History, we use a method based on complex networks analysis to reveal emergent interactions in Wikipedia. We built a network constituted by derivative links, where nodes are connected if they are co-linked by other papers or co-link other papers within Wikipedia. We apply this method, focused on the structural distance, to three significant individuals associated with the Italian Renaissance: Copernicus, Michelangelo, and Pico della Mirandola. The results point to the effectiveness of this approach for discovering new knowledge about the interdisciplinary transactions between people and ideas coming from artistic, scientific and philosophical domains during this period. The emergent network reflects the apparently strong network-level interactions between Michelangelo and Mirandola’s clusters; the importance of Hermeticism across the three clusters; and how the so-called “knowledge dealers” related to Neoplatonism contribute to the depiction of the period by future historians. Finally, we advance the notion of “focus reading”, in which complex networks analysis allows us to build bridges between close and distant forms of reading historical evidence.
Polymer materials based on metal-free hydroxy- and aminophenyl porphyrin as potential catalysts: Preparation and properties
Authors: Kuzmin Sergey M. , Chulovskaya Svetlana A. , Parfenyuk Vladimir I.
Published: October 2022

Hydroxy- and aminophenylporphyrin polymer films have been obtained by a superoxide-assisted electrochemical deposition method. This paper demonstrates the effect of the porphyrin structure on the film morphology and electrocatalytic properties of the prepared materials. A catalytic activity series of the synthesized metal-free electropolymers has been defined for the oxygen electroreduction reaction in an aqueous alkaline medium. It is shown that the samples prepared from 2H-5,10,15,20-tetrakis(3-aminophenyl)porphyrin possess the highest catalytic activity.
Photophysical properties and aggregation behavior of diethylene glycol substituted pyropheophorbide-a in cationic surfactant solutions
Authors: Gradova Margarita A. , Gradov Oleg V. , Lobanov Anton V. , Movchan Tamara G. , Plotnikova Elena V. , Chernyadyev Andrey Yu. , Startseva Olga M. , Belykh Dmitry V.
Published: October 2022

The aggregation behavior of a novel amphiphilic photosensitizer - pyropheophorbide-[Formula: see text] 17-diethylene glycol ester 1 was studied in aqueous and aqueous-organic mixtures by means of steady-state absorption and fluorescence emission spectroscopy. The formation of [Formula: see text]-aggregates with a bathochromic shift of the absorption bands, weak fluorescence and resonance enhancement of the light scattering was observed in the mixed water-organic solvents. Solubilization studies in micellar cationic surfactant solutions of alkyl trimethylammonium bromides (C[Formula: see text]TAB) and alkyl triphenylphosphonium bromides (C[Formula: see text]TPPB) resulted in the most effective stabilization of the fluorescent monomolecular form of 1 in C[Formula: see text]TPPB micelles. It was shown that [Formula: see text]-aggregate formation in submicellar surfactant solutions is the initial stage of the solubilization process, which significantly increases in phosphate buffer medium at pH 7.4. Fluorescence lifetime ([Formula: see text] of 1 in DMSO was found to be 7.4 ns, fluorescence quantum yield - 34% and singlet oxygen quantum yield - 62%. The results obtained indicate the need for further studies of compound 1 solubilized in C[Formula: see text]TPPB micelles as the most promising prototype of the drug formulation for mitochondria-targeted photodynamic therapy of cancer.
Microwave-induced thermoacoustic imaging with functional nanoparticles
Authors: Tang Xiaoyu , Fu Jia , Qin Huan
Published: October 2022

As an emerging hybrid imaging modality, microwave-induced thermoacoustic imaging (MTAI), using microwaves as the excitation source and ultrasonic signals as the information carrier for combining the characteristics of high contrast of electromagnetic imaging and high resolution of ultrasound imaging, has shown broad prospects in biomedical and clinical applications. The imaging contrast depends on the microwave-absorption coefficient of the endogenous imaged tissue and the injected MTAI contrast agents. With systemically introduced functional nanoparticles, MTAI contrast and sensitivity can be further improved, and enables visualization of biological processes in vivo. In recent years, functional nanoparticles for MTAI have been developed to improve the performance and application range of MTAI in biomedical applications. This paper reviews the recent progress of functional nanoparticles for MTAI and their biomedical applications. The challenges and future directions of microwave thermoacoustic imaging with functional nanoparticles in the field of translational medicine are discussed.
Nonenveloped Virus-Like Particles: A Promising Antigen-Delivery Strategy for the Induction of Antitumor Immune Responses
Authors: Jiménez-Chávez Ángel de Jesús , Moreno-Fierros Leticia
Published: October 2022

The use of antigen delivery systems is essential for inducing antitumor immune responses. Among these, virus-like particles (VLPs) increase the immunogenicity of coupled antigens to stimulate cellular and humoral immune responses. In particular, non-enveloped or capsid VLPs (cVLPs) are a promising antigen delivery system option due to their additional advantages of high production yields and low cost. This review summarizes the latest works on cVLPs in cancer vaccines, supporting cVLPs as a tumor antigen delivery system for immunotherapy and addressing some critical aspects of cVLP use such as production, assembly, decoration and immunization strategies, which can improve the effectiveness of cancer vaccines based on cVLPs.
Electrical and Structural Optimization of Nickel Sulfide Nano-Flower by Mn Modulation as Robust Bifunctional Electrocatalyst for Overall Water Splitting
Authors: Hou Zhuoran , Xiang Dan , Yang Di , Wen LiangYing , Luo Han , Li Lili , Yang Qi , Hu Liwen
Published: October 2022

Designing an efficient and low-cost bifunctional electrocatalyst for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline electrolyte is still very important and challenging. Herein, using a simple hydrothermal and sulfidation annealing method, nanoflower-like Mn-NiS with regular morphology was grown in situ on the surface of nickel foam. Through the detailed analysis of X-ray photoelectron spectroscopy results, it is found that the successful doping of Mn2+ can effectively adjust the electronic structure of NiS. As we envisioned, the prepared Mn-NiS shows only 99 mV overpotential and 64.8 mV dec−1 Tafel slope in HER at 10 mA cm−2 and has only 320 mV and 65.5 mV dec−1 in OER at 50 mA cm−2. In addition, the alkaline electrolytic cell composed of Mn-NiS double electrodes has a cell-voltage of only 1.54 V at 10 mA cm−2, and maintains good stability. More importantly, the results of density functional theory calculations show that after Mn2+ successfully changes the electronic structure on the NiS surface, it not only promotes the HER kinetics, but also greatly increases the OER activity.
Corrosion Behavior of Cu in Accelerated Hydrogen Peroxide-Based Disinfectants
Authors: Nakhaie Davood , Clifford Amanda M. , Asselin Edouard
Published: October 2022

Cu ion release due to corrosion plays a critical role in the antimicrobial efficacy of Cu. There are, however, concerns regarding surface passivation, and thus, decreased Cu ion release after routine cleaning with hospital-grade disinfectants. In this study, immersion corrosion rate measurements up to 7 d coupled with SEM and XPS were employed to evaluate the corrosion behavior of pure Cu in 0.5% H2O2 and two commercially available hospital-grade H2O2-based disinfectants. Results show considerably higher corrosion rates in the commercially-available cleansers compared to H2O2 alone, meaning that Cu ion release occurs readily during routine disinfection with these products.
A mechanistic model of calcium homeostasis leading to occurrence and propagation of secondary brain injury
Authors: Song Jiang-Ling , Westover M. Brandon , Zhang Rui
Published: October 2022

We build a mechanistic model SBI-CH for calcium homeostasis (CH) to study how alterations of Ca2+ levels following PBI affect the occurrence and propagation of SBI. Specifically, we investigate how the opening of VGCCs, decreasing of PMCA, and reversal of NCX disrupt CH, and further induce the occurrence of SBI. We also present a coupled SBI-CH model to show how disrupted CH causes synaptic dysfunction, and further promotes the propagation of SBI between neurons.
Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm
Authors: Tu Dan , Yun Liang , Chen Lan , Yang Yue , Zeng Qiaoqiong , Chen Lin
Published: October 2022

In order to solve the modeling problem of travel as a service (MaaS) collaborative dispatching system of railway passenger transport hub based on neural network algorithm, meet people’s needs, make up for the lack of high traffic travel pressure, and improve people’s living standards, through 30 random questionnaires, it is found that 28 people think that travel convenience improves their quality of life, and 2 people think that owning a car has little effect on travel convenience. Travel has always been people’s basic living needs. In recent years, with the improvement of people’s living standards, there are more and more urban vehicles. At the same time, the increase of vehicles also directly leads to the increasing pressure of urban traffic travel, and the problems of vehicle emission pollution and traffic congestion are becoming more and more obvious. With the national low-carbon environmental protection policies, green transportation has become the theme of the times. With the continuous development of shared transportation mode and intelligent information technology, the new transportation concept of “Mobility as a Service” based on this technical mode will highly integrate the existing transportation modes and travel services. With the support of MaaS system, people’s travel modes will change greatly. Starting with the MaaS cooperative dispatching system of railway passenger transport hub, a multiparameter fuzzy neural network control system dispatching algorithm is proposed to better help the modeling of MaaS cooperative dispatching system of railway passenger transport hub.
Comparative Genomic Characterization of Relaxin Peptide Family in Cattle and Buffalo
Authors: Rehman Muhammad Saif-ur , Hassan Faiz-ul , Rehman Zia-ur , Hussain Hafiz Noubahar , Shahid Muhammad Adnan , Mushahid Muhammad , Shokrollahi Borhan
Published: October 2022

Relaxin family peptides significantly regulate reproduction, nutrient metabolism, and immune response in mammals. The present study aimed to identify and characterize the relaxin family peptides in cattle and buffalo at the genome level. The genomic and proteomic sequences of cattle, buffalo, goat, sheep, horse, and camel were accessed through the NCBI database, and BLAST was performed. We identified four relaxin peptides genes (RLN3, INSL3, INSL5, and INSL6) in Bos taurus, whereas three relaxin genes (RLN3, INSL3, and INSL6) in Bubalus bubalis. Evolutionary analysis revealed the conserved nature of relaxin family peptides in buffalo and cattle. Physicochemical properties revealed that relaxin proteins were thermostable, hydrophilic, and basic peptides except for INSL5 which was an acidic peptide. Three nonsynonymous mutations (two in RLN3 at positions A16 > T and P29 > A, and one in INSL6 at position R32 > Q) in Bos taurus, whereas two nonsynonymous mutations (one in RLN3 at positions G105 > w and one in INSL3 at position G22 > R) in Bubalus bubalis, were identified. INSL3 had one indel (insertion) at position 55 in Bos taurus. Gene duplication analysis revealed predominantly segmental duplications (INSL5/RLN3 and INSL6/INSL3 gene pairs) that helped expand this gene family, whereas Bubalus bubalis showed primarily tandem duplication (INSL3/RLN3). Our study concluded that relaxin family peptides remained conserved during the evolution, and gene duplications might help to adapt and enrich specific functions like reproduction, nutrient metabolism, and immune response. Further, the nonsynonymous mutations identified potentially affect these functions in buffalo.
Poisson Incorporated Credibility Regression Modelling of Systematic Mortality Risk for Populations with Finite Data
Authors: Odhiambo Joab , Weke Patrick , Ngare Philip , Naryongo Raphael , Sewe Stanley
Published: October 2022

This study considered the modeling of systematic mortality risk for populations with finite data using the Poisson incorporated Credibility regression model. For novelty, we have included the credibility regression approach to modelling mortality by assuming the number of annual deaths follow a Poisson distribution. Our model shows improvement in precision levels when estimating mortality risk compared to classical models used in European countries. We have illustrated that our model works optimally when using Kenyan mortality data, comparing male and female lives under the different strategies, thus making better predictions than the classical Lee–Carter (LC) and Cairns–Blake–Dowd (CBD) models. The mean absolute forecast error (MAFE), mean absolute percentage forecast error (MAPFE), root mean square error (RMSE), and root mean square forecast error (RMSFE) under the incorporated credibility regression model are much lower than the values obtained without incorporation of the Buhlmann credibility approach. The findings of this research will help insurance companies, pension firms, and government agencies in sub-Saharan countries model and forecast systematic mortality risks accurately. Finally, the results are essential in actuarial modelling and pricing, thus making life assurance products affordable for most people in low-income African countries.
[Retracted] Analysis of Nostalgia Emotional Experience, Rural Tourism Environment Cognition, and Consumption Willingness Based on Big Data Technology
Authors: Cao Wei , Meng Fanhui
Published: October 2022

As the primary research vehicle, rural tourism is used in this paper to examine urban visitors’ “homesickness.” This research explores the effects of homesickness on tourists’ perceptions of travel destinations and consumption intentions through qualitative analysis, logistic empirical analysis, and questionnaire survey. This work also explores the behavioral traits of customers’ purchase intentions from the perspective of a “big data” environment, and it constructs the relevant consumption behavior model for rural visitors in accordance with those behavioral traits. Through questionnaire design and investigation, this paper makes statistical analysis on tourists’ basic information, expected value, and actual experience and explores the related factors that affect their consumption behavior, so as to understand the consumption behavior law of tourists in rural tourism consumption. The research shows that the MSE (mean squared error) of this algorithm is about 4.16, and the RMSE (root mean square error) of this algorithm is about 0.508. Finally, combining the influence of tourists’ consumption behavior, this paper tries to put forward some countermeasures and suggestions on rural tourism. This paper aims to study the development and planning of rural tourist destinations, and provide corresponding basis for the destination selection of tourists with homesickness.
Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy
Authors: Deng Zhibo , Wang Zhe , Shen Xuehong
Published: October 2022

Surface integrity has a very significant effect on surface roughness and surface microhardness. These are the main characteristics of surface integrity. The present study investigated the influence of the cutting depth (ap), the cutting speed (

v

c

), and the feed rate (f) on the surface roughness (Ra) and surface microhardness (HV) in turning TC17 titanium alloy. Data obtained from the Box-Behnken design experiments were used to develop the response surface methodology (RSM) and artificial neural network (ANN) models. Through analysis of variance (ANOVA), the relative effects of each cutting parameter on the responses have been determined. To examine the interaction effects of cutting parameters, 3D surface plots were generated. The desirability function approach (DFA) was used to optimize cutting parameters to achieve the lowest surface roughness and highest surface microhardness. The results show that ANN response prediction models have higher prediction accuracy and lower error than RSM prediction models. The optimization parameters are 60 m/min cutting speed, 0.06 mm/r feed rate, and 0.2 mm cutting depth for the minimum surface roughness and maximum surface microhardness with a maximum error of 2.83%.
A Simulation Study: Population Distribution Function Estimation Using Dual Auxiliary Information under Stratified Sampling Scheme
Authors: Ahmad Sohaib , Hussain Sardar , Zahid Erum , Iftikhar Anum , Hussain Saddam , Shabbir Javid , Aamir Muhammad
Published: October 2022

In survey sampling, information on auxiliary variables related to the main variable is often available in many practical problems. Since the mid-twentieth century, researchers have taken a keen interest in the use of auxiliary information, due to its usefulness in estimation methods. In this article, our main objective is to discover the problem associated with estimation of the finite population distribution function, using the known auxiliary variable, which occurs as the sample distribution function and the rank of the auxiliary variable. A new family of the finite population distribution function estimators is proposed in the stratified sampling scheme. The mathematical equations for the bias and mean square error have been obtained for each proposed estimator, along with the eﬃciency conditions. Besides theoretical eﬃciency comparison, an empirical study has also been conducted to analyze the performance of estimators. A simulation study is also performed to observe the efficiency of the proposed estimators. The implementation of the proposed sampling scheme is illustrated by a practical example.
The Potential Role of Voltage-Dependent Anion Channel in the Treatment of Parkinson’s Disease
Authors: He Yajie , Wang Wenjun , Yang Ting , Thomas Elizabeth Rosalind , Dai Rongyang , Li Xiang
Published: October 2022

Parkinson’s disease (PD) is a neurodegenerative disease second only to Alzheimer’s disease in terms of prevalence. Previous studies have indicated that the occurrence and progression of PD are associated with mitochondrial dysfunction. Mitochondrial dysfunction is one of the most important causes for apoptosis of dopaminergic neurons. Therefore, maintaining the stability of mitochondrial functioning is a potential strategy in the treatment of PD. Voltage-dependent anion channel (VDAC) is the main component in the outer mitochondrial membrane, and it participates in a variety of biological processes. In this review, we focus on the potential roles of VDACs in the treatment of PD. We found that VDACs are involved in PD by regulating apoptosis, autophagy, and ferroptosis. VDAC1 oligomerization, VDACs ubiquitination, regulation of mitochondrial permeability transition pore (mPTP) by VDACs, and interaction between VDACs and α-synuclein (α-syn) are all promising methods for the treatment of PD. We proposed that inhibition of VDAC1 oligomerization and promotion of VDAC1 ubiquitination as an effective approach for the treatment of PD. Previous studies have proven that the expression of VDAC1 has a significant change in PD models. The expression levels of VDAC1 are decreased in the substantia nigra (SN) of patients suffering from PD compared with the control group consisting of normal individuals by using bioinformatics tools. VDAC2 is involved in PD mainly through the regulation of apoptosis. VDAC3 may have a similar function to VDAC1. It can be concluded that the functional roles of VDACs contribute to the therapeutic strategy of PD.
Effect of Enteral and Parenteral Nutrition Support on Pulmonary Function in Elderly Patients with Chronic Obstructive Pulmonary Disease Complicated by Respiratory Failure
Authors: Wang Liangge , Rui Wenxiu , Chen Si , Li Yazhou , Ren Minhuan
Published: October 2022

Objective. To investigate the effect of enteral and parenteral nutrition support (EPNS) on pulmonary function in elderly patients with chronic obstructive pulmonary disease (COPD) complicated by respiratory failure (RF). Methods. A total of 127 patients who underwent treatment for elderly patients with COPD complicated by RF in our hospital from February 2020 to May 2022 were collected for a retrospective analysis. There were 41 patients with enteral nutrition support (group A), 46 with parenteral nutrition support (group B), and 40 with EPNS (group C). The levels of serum albumin (ALB), prealbumin (PA), serum hemoglobin (Hb), and serum transferrin (TRF) were measured before and after nutritional support in the three groups, and the changes in pulmonary function of patients were compared. The changes in the levels of inflammatory factors and markers of oxidative stress (OS) in serum were also detected, and the incidence of adverse reactions and length of stay (LOS) were counted. Results. ALB, PA, Hb, and TRF levels were increased in all 3 groups after nutritional support, with the highest in group C (

P
<
0.05

). Similarly, lung function was improved in all 3 groups and inflammatory factor levels and OS were suppressed, also most dramatically in group C (

P
<
0.05

). There was no difference in the incidence of adverse reactions among the 3 groups, and the LOS in group C was shorter than those in groups A and B (

P
<
0.05

). Conclusion. EPNS can effectively improve the lung function of patients with COPD combined with RF and reduce the inflammation and OS damage. It can effectively improve the therapeutic effect of patients and has great application prospects in the treatment of COPD combined with RH in the future.
Numerical Simulation of the Stability of a Cutting Slope and Study on Its Reinforcement Scheme
Authors: Chen Song , Yang Zhao , Zhang Wenpeng , Li Liufang , Zheng Yibo , Yuan Ying
Published: October 2022

Analyzing the stability of the slope and studying the reinforcement scheme can reduce ecological disasters, improve the ecological environment, and protect people’s lives and property. In this paper, taking the left side slope of ZK100 + 860∼+975 cutting of the Huizhou‐Qingyuan section of the Shantou‐Zhanjiang Expressway as an example, based on the strength reduction method (SRM), the stability of the slope under different conditions (natural conditions, unsupported excavation conditions, rainstorm conditions, and reinforcement conditions) is analyzed by using finite element software. The results show that the slope was almost stable before excavation, and the safety factor of slope stability decreases due to excavation, so it is in an unstable state. Rainwater has a great influence on the stability of the slope. The damage to the slope is deepened, and the stability safety factor is decreased under the rainstorm condition. The stability safety factor of the slope has increased by 24.3% after the reinforcement of the anchor rod and anchor cable, and the slope is in a stable state. It can be seen that the anchor rod and anchor cable can effectively control the deformation of the slope and have a good reinforcement effect.
Clinical Effect of Catgut Embedding plus Warm Needle Moxibustion on Improving Inflammation and Quality of Life of Knee Osteoarthritis Patients
Authors: Zhang Di , Song Shuanglin , Bian Zhenyu , Huang Zhen
Published: October 2022

Objective. To clarify influence of catgut embedding plus warm needle moxibustion on improving inflammation and quality of life of knee osteoarthritis patients. Materials and Methods. The data of 120 patients with knee osteoarthritis admitted to our hospital from June 2018 to June 2021 were used for retrospective analysis. Patients were divided into control group and observation group in a random manner, with 60 cases each. The control group was orally administrated with one diclofenac sodium sustained-release tablet (100 mg) once a day, for 8 courses with 7 days for each course. The observation group underwent catgut embedding plus warm needle moxibustion. Results. The Western Ontario and McMaster Universities Arthritis Index and gonitis Lequesne scores in two groups were decreased after treatment and were lower in observation group than control group (

P
<
0.05

). Quality of life scores presented elevation after treatment and were higher in observation group than control group (

P
<
0.05

). Curative efficacy presented no difference between two groups (

P
>
0.05

), while clinical cure rate was higher in observation group than control group (

X
2
=
8.257

,

P
=
0.006

). Conclusion. Warm needle moxibustion plus functional exercise can effectively improve clinical symptoms of knee osteoarthritis patients, and improve their knee joint function and inflammatory response.
A Flexible Extension of Reduced Kies Distribution: Properties, Inference, and Applications in Biology
Authors: Almuqrin Muqrin A. , Gemeay Ahmed M. , El-Raouf M. M. Abd , Kilai Mutua , Aldallal Ramy , Hussam Eslam
Published: October 2022

The extended reduced Kies distribution (ExRKD), which is an asymmetric flexible extension of the reduced Kies distribution, is the subject of this research. Some of its most basic mathematical properties are deduced from its formal definitions. We computed the ExRKD parameters using eight well‐known methods. A full simulation analysis was done that allows the study of these estimators’ asymptotic behavior. The efficiency and applicability of the ExRKD are investigated via the modeling of COVID‐19 and milk data sets, which demonstrates that the ExRKD delivers a better match to the data sets when compared to competing models.
Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining
Authors: Qu Yansong , Li Zhenlong , Liu Qin , Pan Mengniu , Zhang Zihao
Published: October 2022

This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.
A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM
Authors: Yao Chengpeng , Yang Yu , Yang Jinwei , Yin Kun
Published: October 2022

The rapid development of information technology has brought much convenience to human life, but more network threats have also come one after another. Network security situation prediction technology is an effective means to protect against network threats. Currently, the network environment is characterized by high data traffic and complex features, making it difficult to maintain the accuracy of the situation prediction. In this study, a network security situation prediction model based on attention mechanism (AM) improved temporal convolutional network (ATCN) combined with bidirectional long short-term memory (BiDLSTM) network is proposed. The TCN is improved by AM to extract the input temporal features, which has a more stable feature extraction capability compared with the traditional TCN and BiDLSTM, which is more capable of processing temporal data, and is used to perform the situation prediction. Finally, by validating on a real network traffic dataset, the proposed method has better performance on multiple loss functions and has more accurate and stable prediction results than TCN, BiDLSTM, TCN-LSTM, and other time-series prediction methods.
[Retracted] Psychological Adaptability and Intervention Strategies of College Students’ English Learning under the Mixed Foreign Language Teaching Environment Monitoring
Authors: Hu Keli
Published: October 2022

The issues with traditional teaching methods in current English education are becoming more and more evident in college classrooms, which significantly impedes the innovative growth of college English education and does not promote the development of college students’ all‐around English proficiency. This essay focuses on the English learning adaptability and intervention tactics of college students using BFLTM (Blended Foreign Language Teaching Model). This study employs the individual‐centered approach to choose college students as its primary subjects and conducts a cluster analysis of the adaptability of various college students’ English learning based on the classification indicators. The study makes the following deductions: the general state of students’ adaptability to studying English is average (M = 2.17). On the internal dimension, learning easy adaptation (M = 2.165) is average, and cognitive strategy adaptation (M = 2.642) is average, whether the average learning adaptability of English majors is significant (P = 0.001 < 0.05), and it is significant in the dimensions of learning attitude, learning interaction, and learning environment (P < 0.05). In view of the improvement of college students’ English learning adaptability under BFLTM, this paper puts forward some strategies to further cultivate students’ English autonomous learning ability by creating a suitable classroom atmosphere for autonomous learning, establishing an English autonomous learning center and a virtual community.
An Empirical Study on the Influence of College Students’ Physical Fitness on the Level of Public Health
Authors: Zhang Yinghong
Published: October 2022

As a compulsory course in universities, physical fitness is an important part of public health. At present, research on physical fitness lacks a comprehensive evaluation method, which cannot accurately guide public health. Based on this, this paper compares the key factors affecting public health, aiming to better guide public health and improve the level of public health. In this paper, 219 college students were selected as the research object, using statistical analysis methods, to find out the significant differences in influencing factors. Then, regression analysis is carried out on different influencing factors to determine the key factors affecting physical fitness. The results show that physical fitness level, exercise frequency, and physical injury are the key factors affecting physical fitness and have a serious impact on public health, with an impact level of about 70~80%. Therefore, colleges and sports departments should set out from the above factors, formulate a development plan for physical fitness, and promote the improvement of public health.
Innovation and Entrepreneurship Coupling Based on Simultaneous Equation Model and Its Energy Economic Effect Measurement
Authors: Chen Wei
Published: October 2022

In order to quantitatively analyze the synergistic relationship between the digital economy and high-quality development, the author proposes a measure of the coupling and coordination of China’s digital economy and high-quality development. Based on the data of 271 prefecture-level and above cities in China from 2016 to 2021, the coupling coordination degree model is used to examine the coupling relationship between the digital economy and high-quality development. The result shows that China’s digital economy and high-quality development are highly coupled, with an average coupling value of 0.9227. Conclusion. This study reveals the characteristics and facts of the coupling and coordination relationship between China's digital economy and high-quality development and provides useful policy inspiration for the central and regional formulation of differentiated digital economy development policies and high-quality development strategies.
Designing of Fiber Bragg Gratings for Long-Distance Optical Fiber Sensing Networks
Authors: Braunfelds Janis , Haritonovs Elvis , Senkans Ugis , Kurbatska Inna , Murans Ints , Porins Jurgis , Spolitis Sandis
Published: October 2022

Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15 dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine, and raised sine apodizations are studied to achieve the main requirements, which are maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm) and FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.
[Retracted] The Anti‐inflammatory Effect of Chitosan Oligosaccharide on Heart Failure in Mice
Authors: Zhang Yubiao , Wang Yu , Liu Yunen , Gong Tianxing , Hou Mingxiao
Published: October 2022

Heart failure is currently one of the leading causes of death worldwide, and the inflammatory factors play an important role in its development. Chitosan oligosaccharide (COS), a low‐molecular‐weight form of chitosan, has many specific biological activities. In this study, COS effects on heart failure were studied for the first time by performing transverse arch constriction (TAC) surgery in mice, as an animal model of heart failure. Our findings revealed that COS administration (in both 40 mg/kg and 80 mg/kg doses) significantly ameliorated TCA‐induced left ventricular (LV) hypertrophy as well as the increase in lung and heart weight in mice, while improving TAC‐induced LV dysfunction. Both doses effectively attenuated LV cardiomyocyte hypertrophy, while decreasing heart inflammation after heart failure in mice. In conclusion, our results indicated that the supplementation of COS in normal diet might be an effective way to prevent further myocardial tissue damage in patients suffering from heart failure.
A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning
Authors: Chen XiaoGan , Liu Yu
Published: October 2022

Objective. There are more and more basketball competitions, to propose a classification method of thoracolumbar fractures to assist in the diagnosis of basketball injuries, to analyze the feasibility of its clinical application, and to improve the recovery rate. Methods. From February 2015 to May 2022, 1130 CT images of thoracolumbar fractures admitted to our hospital and affiliated hospital units due to basketball injuries were collected, and the image labeling system uniformly labeled them. All CT images were classified according to the AO spine classification of thoracolumbar injuries. In the ABC-type classification, 935 CT images were used for training and validation to optimize the deep learning system, including 815 training sets and 120 validation sets; the remaining 198 CT images were used as test sets for comparing the deep learning system and clinician’s diagnosis. In the classification of subtype A, a total of 523 CT scans can be performed for training and validation to optimize the deep learning system, including 500 training sets and 23 validation sets; the remaining 94 CT images are used as test sets for comparing depth learning systems and clinicians’ diagnostic results. Results. The deep learning system had a correct rate of ABC classification of fractures in 86.4%, with a kappa coefficient of 0.850 (

P
<
0.001

); the correct rate of subtype A was 85.3%, with a kappa coefficient of 0.815 (

P
<
0.001

). Conclusion. The classification accuracy of thoracolumbar fractures based on deep learning is high. The method can assist in diagnosing CT images of thoracolumbar fractures and improve the current manual and complex diagnosis process.
The Limited Aperture Sparse Array for DOA Estimation of Coherent Signals: A Mutual-Coupling-Optimized Array and Coherent DOA Estimation Algorithm
Authors: Zhang Yu , Yang Meng , Zhang Xiaofei , Han Shengxinlai , Cao Jinke
Published: October 2022

The paper investigates DOA estimation of coherent Signals with the limited aperture sparse array. Mutual coupling between the sensors of the array cannot be ignored in practical radar with a limited aperture of array sensors, which will result in a degradation in the performance of Direction of Arrival (DOA) estimation. This paper proposes a Mutual-coupling-optimized array (MCOA) with a limited aperture in this scenario to reduce the mutual coupling effect. Firstly, we prove the sparse uniform linear array (SULA) has the smallest mutual coupling leakage when the array aperture and the number of sensors is determined. Secondly, we modify the spacing of the array sensors in SULA to make sure that the spacing between all array sensors and the reference sensor are coprime aiming to estimate DOA without spatial aliasing. Thirdly, we give an expression for the array element spacing arrangement with reduced mutual coupling leakage. Finally, the coherent signals are well resolved by the Sparse Bayesian Learning (SBL) algorithm. Numerous simulations are conducted to validate the advantages of the proposed array compared to several sparse arrays for estimating coherent signals in the presence of mutual coupling.
Design of Residents’ Sports Nutrition Data Monitoring System Based on Genetic Algorithm
Authors: Zhou Jianwei
Published: October 2022

With the development of modern Internet technology, the health assessment model based on computer technology has gradually become a research hotspot. In the process of studying the health level of residents, exercise status and diet nutrition are important factors affecting their health. Therefore, based on the idea of the genetic algorithm, this paper establishes a resident sports nutrition data monitoring system. In this system, the data feature selection method of the genetic algorithm, which simulates the process of biological evolution, is used in the key technology of real-time motion data processing of residents, and it is improved to interfere with the data cross process of the genetic algorithm, to seek the local optimal solution. Two different types of data sets and different data classifiers are selected to verify the system’s performance. It is proved that compared with the traditional filter class feature selection method, this method can achieve more effective data feature recognition. In addition, some samples are selected to test the residents’ sports nutrition data monitoring system, mainly through the analysis and quantification of the exercise process, eating habits, physique, and other data of the athletes; to evaluate the impact of their sports and eating habits on physical health; and to obtain the best sports guidance scheme, to guide the adjustment and improvement of their later sports and eating plans. Through the analysis of the existing residents’ health status, the monitoring and management strategies of residents’ health status are summarized, which provides a certain reference for the improvement of residents’ physical health under the background of artificial intelligence.
[Retracted] Phytochemical Analysis, Total Phenolic, Flavonoid Contents, and Anticancer Evaluations of Solvent Extracts and Saponins of H. digitata
Authors: Alshehri Osama M. , Alshamrani Saleh , Mahnashi Mater H. , Alshahrani Mohammed Merae , Khan Jalwa Ali , Shah Muhammad , Alshehri Mohammed Ali , Zafar Rehman , Zahoor Muhammad , Jan Muhammad Saeed , Hassan Syed Shams ul , Sadiq Abdul
Published: October 2022

Cancer is one of the most challenging diseases in the modern era for the researchers and investigators. Extensive research worldwide is underway to find novel therapeutics for prevention and treatment of diseases. The extracted natural sources have shown to be one of the best and effective treatments for cell proliferation and angiogenesis. Different approaches including disc potato model, brine shrimp, and chorioallantoic membrane (CAM) assay were adopted to analyze the anticancer effects. Habenaria digitata was also evaluated for MTT activity against NIH/3T3 cell line. The dexamethasone, etoposide, and vincristine sulfate were used as a positive control in these assays. All of the extracts including crude extracts (Hd.Cr), saponin (Hd.Sp), n‐hexane (Hd.Hx), chloroform (Hd.Chf), ethyl acetate (Hd.EA), and aqueous fraction (Hd.Aq) were shown excellent results by using various assays. For example, saponin and chloroform have displayed decent antitumor and angiogenic activity by using potato tumor assay. The saponin fraction and chloroform were shown to be the most efficient in potato tumor experiment, demonstrating 87.5 and 93.7% tumor suppression at concentration of 1000 μg/ml, respectively, with IC50 values of 25.5 and 18.3 μg/ml. Additionally, the two samples, chloroform and saponins, outperformed the rest of the test samples in terms of antiangiogenic activity, with IC50 28.63 μg/ml and 16.20 μg/ml, respectively. In characterizing all solvent fractions, the chloroform (Hd.Chf) and saponin (Hd.Sp) appeared to display good effectiveness against tumor and angiogenesis but very minimal activity against A. tumefaciens. The Hd.Chf and Hd.Sp have been prospective candidates in the isolation of natural products with antineoplastic properties.