bluuebunny/crossref_metadata_2025 huggingface dataset:
Dispersion of light traveling through the interstellar space, induced and intrinsic Lorentz invariance violation
Authors: Brevik I. , Chaichian M. , Oksanen M.
Published: October 2021

Theoretical models and experimental observations suggest that gamma-ray bursts (GRB) and high-energy neutrino bursts travelling through the interstellar space may reach the Earth at different speeds. We propose and study in details the mechanism (i), which always exists, where GRB are slowed down due to the dispersion of light in the interstellar medium. In addition to the standard media such as electrons and photons as CMB, we consider the medium with invisible axions. The amount of GRB delays in different media are calculated in details utilizing a novel technique in QFT by using the hitherto known or estimated densities of particles in the space without introducing any arbitrary parameter. Previously, the GRB delays have been interpreted as a sign of Lorentz invariance violation by modifying the dispersion relation of Special Relativity, which relates the energy, the momentum and the mass of a particle, based on different mechanisms (ii), such as a stringy spacetime foam, coming from a quantum gravity effect and using an adjustable parameter. Obviously, all the above-mentioned mechanisms (i) and (ii) are induced (seeming) Lorentz invariance violations but not an intrinsic (genuine) one. The amount of GRB delay due to the two aforementioned interpretations can be distinguished by observing the time of arrival of light with different frequencies. Namely, dispersion of light (i) predicts that the higher energy GRB arrive the Earth earlier, while in the other interpretations (ii), they arrive later. We notice that the needed amount for delay due to the dispersion of light shall have the potential power to shed additional light on the microstructure of interstellar media with respect to the densities of constituent particles and the origins of their sources. Finally, we indicate the ways to detect the intrinsic Lorentz invariance violation and to interpret them theoretically.
Interacting ω(q) dark energy model with phase space analysis
Authors: Yerzhanov Koblandy , Myrzakul Shynaray , Kenzhalin Duman , Khurshudyan Martiros
Published: October 2021

The phase space analysis has been used to probe the accelerated expansion of the Universe when [Formula: see text] dark energy interacts with cold dark matter. Non-gravitational interactions [Formula: see text] and [Formula: see text] considered in this work are one of the first models of sign changing interactions that appeared in the literature. Specific [Formula: see text] dark energy model with [Formula: see text] has been assumed and all late time scaling attractors have been found. This is a two-parameter model with [Formula: see text] and [Formula: see text] parameters to be determined, while [Formula: see text] is the deceleration parameter. In general the motivation to consider similar fluid models is directly related to the attempts to unify dark energy and dark matter involving the properties of the deceleration parameter. The previous study using similar dark energy model showed that the BOSS result for the expansion rate at [Formula: see text] can be explained without interaction with cold dark matter. In this way, the previous result provides a reasonable basis to organize future studies in this direction. This study is one of the first attempts in this direction. It should be mentioned that the full comparison of the models with observation data and the classification of future singularities have been left as a subject of a forthcoming paper. There are several ways that the model can be extended which also has been left as a subject of a forthcoming paper.
AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS
Authors: WANG SHUJUAN , CHENG JUNFEN , LI FANCHUANG , WANG YANZHONG , LIU WANG , SHEN JIHONG , QU SHENGRU , YE CHUNTONG , XIE WANQING
Published: October 2021

Efficient [Formula: see text] peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective [Formula: see text] peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel [Formula: see text] peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true [Formula: see text] peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity [Formula: see text], Positive predictivity [Formula: see text] and Accuracy [Formula: see text]. On Fantasia Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. On MIT-BIH Normal Sinus Rhythm Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. Compared with other [Formula: see text] peaks detection methods, the proposed algorithm is simple, efficient and robust.
Nominalization and Natural Language Ontology
Authors: Grimm Scott , McNally Louise
Published: October 2021

Nominalization (e.g., sleeping, that we slept, sleepiness) allows speakers to refer and ascribe properties to whatever sorts of entities clauses, verbs, or adjectives typically denote. Characterizing these relatively abstract entities has challenged semanticists and philosophers of language for over 50 years, thanks especially to Zeno Vendler's early work. Vendler took different kinds of English nominalization constructions to support positing facts, propositions, and events as distinct ontological objects. However, his conclusions remain controversial. The research on nominalization and natural language ontology has never been brought together or put into perspective; in this article, we clarify the complex variety of subsequent ontological positions that build on Vendler's work, identifying points of consensus and disagreement. We also reflect on the consequences and challenges of focusing on the English data and offer a glimpse of how the landscape might change with greater attention to nominalization in other languages.
Progress in the Management of Pancreatic Neuroendocrine Tumors
Authors: Chang Amy , Sherman Scott K. , Howe James R. , Sahai Vaibhav
Published: October 2021

Pancreatic neuroendocrine tumors (PNETs) are a heterogeneous and orphan group of neoplasms that vary in their histology, clinical features, prognosis, and management. The treatment of PNETs is highly dependent on the stage at presentation, tumor grade and differentiation, presence of symptoms from hormonal overproduction or from local growth, tumor burden, and rate of progression. The US Food and Drug Administration has recently approved many novel treatments, which have altered decision making and positively impacted the care and prognosis of these patients. In this review, we focus on the significant progress made in the management of PNETs over the past decade, as well as the active areas of research.
The Gut–Brain Axis
Authors: Mayer Emeran A. , Nance Karina , Chen Shelley
Published: October 2021

Preclinical evidence has firmly established bidirectional interactions among the brain, the gut, and the gut microbiome. Candidate signaling molecules and at least three communication channels have been identified. Communication within this system is nonlinear, is bidirectional with multiple feedback loops, and likely involves interactions between different channels. Alterations in gut–brain–microbiome interactions have been identified in rodent models of several digestive, psychiatric, and neurological disorders. While alterations in gut–brain interactions have clearly been established in irritable bowel syndrome, a causative role of the microbiome in irritable bowel syndrome remains to be determined. In the absence of specific microbial targets for more effective therapies, current approaches are limited to dietary interventions and centrally targeted pharmacological and behavioral approaches. A more comprehensive understanding of causative influences within the gut–brain–microbiome system and well-designed randomized controlled trials are needed to translate these exciting preclinical findings into effective therapies.
Understanding the Effects of Metabolites on the Gut Microbiome and Severe Acute Pancreatitis
Authors: Ye Shijie , Si Chenli , Deng Jie , Chen Xiaohu , Kong Lingming , Zhou Xiang , Wang Weiming
Published: October 2021

Acute pancreatitis (AP) is an inflammatory disease of the pancreas. The severity is classified as mild (MAP), moderately severe (MSAP), or severe (SAP). In patients with SAP, organ dysfunction can occur in the early stage of the disease course, accompanied by secondary infection, with a mortality rate of 36%–50%. In the late stage SAP, infection‐related complications caused by pancreatic necrotic tissue and peripancreatic effusion are the main causes of death in patients. Dysbacteriosis of intestinal microflora, barrier dysfunction of intestinal mucosa, and translocation of enteric bacteria are considered to be the main causes of infection of pancreatic necrotic tissue and peripancreatic effusion. During the past few years, increasing attention has been paid to the metabolic activities of intestinal microflora in SAP, which plays an important role in the metabolic activities of the human body. This review is aimed at bringing together the most recent findings and advances regarding the gut microbial community and associated gut microbial community metabolites and illustrating the role of these metabolites in disease progression in severe acute pancreatitis. We hope that this review will provide new ideas and schemes for the treatment of SAP in the clinical settings.
Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network
Authors: Zhao Fang , Liang Ziyi , Zhang Qiyan , Seng Dewen , Chen Xiyuan
Published: October 2021

Accurate monitoring of air quality can no longer meet people’s needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short‐term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time‐series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long‐ and short‐term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models.
COVID‐19 Diagnosis Using Capsule Network and Fuzzy C‐Means and Mayfly Optimization Algorithm
Authors: Farki Ali , Salekshahrezaee Zahra , Tofigh Arash Mohammadi , Ghanavati Reza , Arandian Behdad , Chapnevis Amirahmad
Published: October 2021

The COVID‐19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer‐aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID‐19. Here, a method based on fuzzy C‐ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X‐ray COVID‐19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state‐of‐the‐art methods, including FOMPA, MID, and 4S‐DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1‐score rate gives the uppermost value compared to the others.
Modified Look-Locker Inverse-Recovery (MOLLI) Sequence of Quantitative Imaging in Dirty Magnetic Resonance Longitudinal Relaxation Time Diagnostic Value of GE Combined with Longitudinal Relaxation Time Quantitative Imaging for Myocardial Amyloidosis
Authors: Lao Qun , Xia Wenping , Jin Jing , Jia Yuzhu , Feng Jianju
Published: October 2021

The pathological changes of myocarditis include degeneration and necrosis of myocardial cells and infiltration of inflammatory cells in the myocardial interstitium, accompanied by obvious myocardial fibrosis. Myocardial fibrosis is a determinant of ventricular remodeling and an important indicator of the classification of clinical risk factors and has an important value in evaluating the prognosis of heart disease. Cardiac magnetic resonance (CMR) is the “gold standard” for evaluating the shape and function of the heart, and it can show the characteristic pathological changes of myocardial tissue. The traditional gadolinium imaging agent delays the enhanced sequence images to visually show the extent of the affected myocardial fibrosis, but it cannot effectively identify small focal fibrosis or widespread diffuse fibrosis. The CMR longitudinal relaxation time quantitative technique can directly measure the relaxation time (T1) determined by the myocardial tissue and does not depend on the signal strength of the reference tissue and can quantitatively analyze the affected myocardium. In this study, the initial and enhanced quantitative imaging techniques of CMR were used to measure the magnetic value of the myocardium in patients with myocarditis, to explore the diagnostic value of myocardial fibrosis, and to analyze the correlation between cardiac fibrosis and cardiac function.
Calorie Restriction Protects against Contrast‐Induced Nephropathy via SIRT1/GPX4 Activation
Authors: Fang Dandong , Wang Yongbin , Zhang Ziyue , Yang Donghai , Gu Daqian , He Bo , Zhang Xiaoqun , He Duofen , Wang HongYong , Jose Pedro A. , Han Yu , Zeng Chunyu
Published: October 2021

Calorie restriction (CR) extends lifespan and increases resistance to multiple forms of stress, including renal ischemia‐reperfusion (I/R) injury. However, whether CR has protective effects on contrast‐induced nephropathy (CIN) remains to be determined. In this study, we evaluated the therapeutic effects of CR on CIN and investigated the potential mechanisms. CIN was induced by the intravenous injection of iodinated contrast medium (CM) iopromide (1.8 g/kg) into Sprague Dawley rats with normal food intake or 40% reduced food intake, 4 weeks prior to iopromide administration. We found that CR was protective of CIN, assessed by renal structure and function. CM increased apoptosis, reactive oxygen species (ROS), and inflammation in the renal outer medulla, which were decreased by CR. The silent information regulator 1 (SIRT1) participated in the protective effect of CR on CIN, by upregulating glutathione peroxidase 4 (GPX4), a regulator of ferroptosis, because this protective effect was reversed by EX527, a specific SIRT1 antagonist. Our study showed that CR protected CIN via SIRT1/GPX4 activation. CR may be used to mitigate CIN.
PF : Website Fingerprinting Attack Using Probabilistic Topic Model
Authors: Zou Hongcheng , Wei Ziling , Su Jinshu , Zhao Baokang , Xia Yusheng , Zhao Na
Published: October 2021

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.
Ethnicity Differences in the Association of UCP1‐3826A/G, UCP2‐866G/A and Ala55Val, and UCP3‐55C/T Polymorphisms with Type 2 Diabetes Mellitus Susceptibility: An Updated Meta‐Analysis
Authors: Huang Rong , Cai Tingting , Zhou Yunting , Wang Yuming , Wang Huiying , Shen Ziyang , Xia Wenqing , Liu Xiaomei , Ding Bo , Luo Yong , Yan Rengna , Li Huiqin , Wu Jindan , Ma Jianhua
Published: October 2021

Background. The relationship between uncoupling protein (UCP) 1‐3 polymorphisms and susceptibility to type 2 diabetes mellitus (T2DM) has been extensively studied, while conclusions remain contradictory. Thus, we performed this meta‐analysis to elucidate whether the UCP1‐3826A/G, UCP2‐866G/A, Ala55Val, and UCP3‐55C/T polymorphisms are associated with T2DM. Methods. Eligible studies were searched from PubMed, Cochrane Library, and Web of Science database before 12 July 2020. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the strength of the association. Heterogeneity analysis, subgroup analysis, sensitivity analysis, and publication bias were also performed. Results. A total of 38 case‐control studies were included in this meta‐analysis. The overall results revealed significant association between T2DM and the UCP2 Ala55Val polymorphism (recessive model: OR = 1.25, 95% CI 1.12‐1.40, P < 0.01; homozygous model: OR = 1.33, 95% CI 1.03‐1.72, P = 0.029, respectively). In subgroup analysis stratified by ethnicity, T2DM risk was increased with the UCP2 Ala55Val polymorphism (allele model: OR = 1.17, 95% CI 1.02‐1.34, P = 0.023; recessive model: OR = 1.28, 95% CI 1.13‐1.45, P < 0.01; homozygous model: OR = 1.39, 95% CI 1.05‐1.86, P = 0.023, respectively), while decreased with the UCP2‐866G/A polymorphism in Asians (dominant model: OR = 0.86, 95% CI 0.74‐1.00, P = 0.045). Conclusions. Our results demonstrate that the UCP2‐866G/A polymorphism is protective against T2DM, while the UCP2 Ala55Val polymorphism is susceptible to T2DM in Asians.
Truncating Regular Vine Copula Based on Mutual Information: An Efficient Parsimonious Model for High-Dimensional Data
Authors: Alanazi Fadhah Amer
Published: October 2021

Based on (different) bivariate copulas as simple building blocks to model complex multivariate dependency patterns, vine copulas provide flexible multivariate models. They, however, lose their flexibility with dimensions. Attempts have been existing to reduce the model complexity by searching for a subclass of truncation vine copulas, of which only a limited number of vine trees are estimated. However, they are either time-consuming or model-dependent or require additional computational efforts. Inspired by the relationship between copula’s parameters (and the corresponding Kendall’s tau) and the mutual information on the one side and the mutual information and copula entropy on another side, this study proposed a novel truncation vine copula model using only mutual information values among variables. This newly proposed truncation method is evaluated in simulation studies and a real application of financial returns dataset. The simulated and real studies show that the model is sufficiently good to find the most appropriate truncation level with a good fit of a given data.
Towards 5G Security Analysis against Null Security Algorithms Used in Normal Communication
Authors: Zhang Run , Zhou WenAn , Hu Huamiao
Published: October 2021

mailto: 5G network makes our lives delicate and more pleasant, and its security will impact the operation of the entire society. Compared with the LTE network, 5G brings up many new security features and possesses more sophisticated and robust security mechanisms, while there are still many potential security issues with the 5G network. Therefore, the security analysis of the 5G network is highly crucial. Null security algorithm (i.e., NEA0 and NIA0) is used in normal communication, a security vulnerability that exists and has not been fully addressed in the LTE network, but in the 5G network, no studies have been performed to demonstrate whether this security vulnerability still exists so far. Therefore, in this paper, we apply a systematic approach based on the principle of model checking to verify. We conduct an in-depth analysis of the signaling interaction and security mechanism for the attach procedure in the 5G network. And then, we model UE and AMF into two synchronous communication finite-state machines, extract the desired properties from 3GPP relevant specifications, and construct an adversary model to test the system’s security. By observing the operation of state machines and analyzing relevant protocol behavior, we discover that faulty security algorithm selection could result in the acceptance of the null security algorithm (i.e., NEA0 and NIA0) on the side of the core network, and attackers can exploit this to trigger IP spoofing attacks and SUPI catching attacks on the victim UE. We analyze the root cause of these network attacks and propose an anomaly detection method to avoid these network attacks from being launched effectively.
Study on Vibration and Bifurcation of an Aeroengine Rotor System with Elastic Ring Squeeze Film Damper
Authors: Pang Guoying , Cao Shuqian , Chen Yushu , Chen Huizheng
Published: October 2021

To analyze the problem of vibration and bifurcation in the rotor system of the aeroengine with the elastic ring squeeze film damper (ERSFD) and elastic supports, the theoretical equation of the dynamic rotor system is developed in this paper, based on the rotor system, elastic ring squeeze film damper (ERSFD), and three elastic supports. The estimated analytical solution of the oil film force is solved using the short bearing approximation theory and the semi‐oil film inference theory in the suspension and the inner and outer boss contact. Considering the oil film stiffness and damping of rolling bearings, the rolling bearing force model is established based on the elastohydrodynamic lubrication (EHL) theory. By the average method, the vibration and bifurcation modes are obtained concerning the bearing coefficient and parameters. The range optimization of parameters can be appropriately improved to enhance the dynamic characteristics of the device given different parameters of the hole of oil seepage, the stiffness, the position of elastic supports, and other structural parameters.
Production-Distribution of Perishable Food considering Customer Time Window
Authors: Feng Weibin , Du Shangcheng , Xiao Yanli , Ren Xiangyang
Published: October 2021

Aiming at the production-distribution problem of perishable food, considering the interference caused by the change of the customer’s time window in actual distribution process, using the ideas of disruption management, analyzing the disturbance of the interference event to the production-distribution system, and constructing the perishable food production-distribution problem disturbance identification and disturbance measurement, and with the minimum deviation between the new scheme and the original scheme as the goal, a mathematical model of disturbance recovery is established. An improved ant colony algorithm-mixed ant colony algorithm based on the change of customer time window for perishable food production-distribution problem was designed to solve the problem. Finally, the simulation experiments are carried out by examples, and compared with the rescheduling results, the effectiveness of the disruption management model and the algorithm-mixed ant colony algorithm are also verified. The research results show that the disruption management can effectively reduce the degree of program deviation and control the cost reasonably.
Risky Alcohol Use and Comorbidity in a Swedish Adolescent Emergency Psychiatric Inpatient Population
Authors: Eberhard Sophia , Rask Olof , Höglund Peter , Råstam Maria , Johansson Björn Axel
Published: October 2021

Introduction. Underage drinking is associated with poor mental health. Early detection for risky alcohol use is recommended, although less implemented in adolescent care. The objective of this study was to investigate the prevalence of risky alcohol use and psychiatric comorbidity. Methods. Over a nine-month period, 145 eligible adolescents admitted to the child and adolescent emergency unit in Malmö, Sweden, were offered computerized screening with Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) and Drug Use Disorders Identification Test (DUDIT). Results. Ninety-six patients (73 girls and 23 boys) agreed to participate. The most common diagnoses were affective disorder (41%) and anxiety disorder (27%). Risky alcohol use was found among 33% of the girls and 22% of the boys (

p
=
0.45

) and did not differ between diagnostic categories. There was a positive correlation between AUDIT-C and DUDIT scores (

p
=
0.019

). Among adolescents with risky alcohol use, 33% of the girls and 60% of the boys also had a risky drug use. Conversely, 47% of the girls and 60% of the boys with risky drug use also had a risky alcohol use. Conclusion. In view of the poor prognosis of risky alcohol use in adolescents, alcohol and drug habits should be assessed when adolescents seek psychiatric emergency care.
A Study of Bipolar Fuzzy Soft Sets and Its Application in Decision-Making Problems
Authors: Mustafa Saima , Safdar Neelofar , Bibi Murrium , Sayed A.F. , Khan Muhammad Ghaffar , Salleh Zabidin
Published: October 2021

We live in a society where we have to deal with so many social issues daily, and making a right choice is everyone’s main concern. This study is based on the selection of right university while getting admission which is the core issue for students nowadays. We are concerned with bipolar fuzzy multicriteria decision-making methods. The main purpose is to provide guidance to the students for determining the best university and evaluating the factors that are affecting while getting admission. We thus combined bipolar fuzzy and soft expert sets to give multicriteria decision-making approach that overcomes the issues that arise while taking decisions. This study involves the development of structural hierarchical models of parameters and the implementation of soft expert sets to make the decision-making problem much more precise by introducing a new algorithm. Thus, this model is helpful for multicriteria decision making and can be used for university selection and thus suitable for education sector as well.
The Kinematic and Dynamic Modeling and Numerical Calculation of Robots with Complex Mechanisms Based on Lie Group Theory
Authors: Ma Lu-Han , Zhong Yong-Bo , Wang Gong-Dong , Li Nan
Published: October 2021

The kinematic and dynamic models of robots with complex mechanisms such as the closed-chain mechanism and the branch mechanism are often very complex and difficult to be calculated. Aiming at this issue, in this paper, the pose of the component in robots is represented by the Euclidean group and its subgroups with the proposed method. The component’s velocity is derived using the relationship between the Lie group and Lie algebra, and the acceleration and Jacobian matrix are then derived on this basis. The Lagrange equation is expressed by the obtained kinematic parameter expressions. Establishing the model with this method can obtain clear physical meaning and make the expressions uniform and easy to program, which is convenient for computer-aided calculation and parameterization. Calculating by the properties of the Lie group can reduce the calculation and model complexity, especially for calculating the velocity and acceleration, which reduces the calculation error and eases the calculation. Therefore, the proposed modeling and calculation method of kinematics and dynamics of robots is especially suitable for robots with complex mechanisms. As an example, the kinematic and dynamic model of the manipulator developed in our laboratory is established and a working process of it is numerically calculated. Then, the results of the numerical calculation are compared with the results of virtual prototype simulation in ADAMS to verify the correctness.
Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network
Authors: Liu Xiancheng , Tian Congxiang
Published: October 2021

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.
Haptoglobin Gene Polymorphism among Sickle Cell Patients in West Cameroon: Hematological and Clinical Implications
Authors: Bernard Kengne Fotsing Christian , Anatole Pieme Constant , Cabral Biapa Nya Prosper , Paul Chedjou Jean , Ashusong Samuel , Njindam Gisele , Nengom Jocelyn Tony , Teto Georges , Nguemeni Carine , Fon Mbacham Wilfred , Gatsing Donatien
Published: October 2021

Haptoglobin is a protein involved in protecting the body from the harmful effects of free hemoglobin. The haptoglobin gene exhibits a polymorphism, and the different genotypes do not have the same capacity to combat the free hemoglobin effects. The present study aimed at determining the polymorphic distribution of haptoglobin in sickle cell patients (SCPs) from West Cameroon and their impact on the hematological parameters, as well as clinical manifestations of the disease severity. Haptoglobin genotype of 102 SCPs (SS) and 115 healthy individuals (60 AA and 55 AS) was determined by allele-specific polymerase chain reaction, and the complete blood count was determined using the AutoAnalyser. Results showed that the genotype Hp2-2 was significantly (

p

 < 0.05) represented in SS patients (54%) than in controls AA and AS (27% and 29%, respectively), while Hp2-1 was mostly found (

p

 < 0.05) in AS (42%) and AA (38%), against 15% in SS. The allelic distribution in SS patients was Hp2: 0.613, Hp1S: 0.304, and Hp1F: 0.084. In AA and AS controls, the proportions of the Hp1 and Hp2 alleles were similar (around 0.5 each), with 0.282 for Hp1S and 0.218 for Hp1F in AS and 0.283 for Hp1S and 0.258 for Hp1F in AA. The distribution of the haptoglobin genotypes did not reveal any significant difference across hematological parameters and clinical manifestations of disease severity in SCP and controls. SCP with Hp1S-1F genotype presented the highest level of hemoglobin. Although Hp2-2 was more frequent in SS patients, it appeared not to be related to the hematological parameters and to the disease’s severity. Further investigations are necessary to explore the impact of Hp polymorphism such as antioxidant, lipid profile, and functionality of some tissues in SCP in Cameroon.
Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning
Authors: Chen Lingjing
Published: October 2021

Facial features are an effective representation of students’ fatigue state, and the eye is more closely related to fatigue state. However, there are three main problems in the existing research: (1) the positioning of the eye is vulnerable to the external environment; (2) the ocular features need to be artificially defined and extracted for state judgment; and (3) although the student fatigue state detection based on convolutional neural network has a high accuracy, it is difficult to apply in the terminal side in real time. In view of the above problems, a method of student fatigue state judgment is proposed which combines face detection and lightweight depth learning technology. First, the AdaBoost algorithm is used to detect the human face from the input images, and the images marked with human face regions are saved to the local folder, which is used as the sample dataset of the open‐close judgment part. Second, a novel reconstructed pyramid structure is proposed to improve the MobileNetV2‐SSD to improve the accuracy of target detection. Then, the feature enhancement suppression mechanism based on SE‐Net module is introduced to effectively improve the feature expression ability. The final experimental results show that, compared with the current commonly used target detection network, the proposed method has better classification ability for eye state and is improved in real‐time performance and accuracy.
An Unconditionally Stable Positivity-Preserving Scheme for the One-Dimensional Fisher–Kolmogorov–Petrovsky–Piskunov Equation
Authors: Kim Sangkwon , Lee Chaeyoung , Lee Hyun Geun , Kim Hyundong , Kwak Soobin , Hwang Youngjin , Kang Seungyoon , Ham Seokjun , Kim Junseok
Published: October 2021

In this study, we present an unconditionally stable positivity-preserving numerical method for the Fisher–Kolmogorov–Petrovsky–Piskunov (Fisher–KPP) equation in the one-dimensional space. The Fisher–KPP equation is a reaction-diffusion system that can be used to model population growth and wave propagation. The proposed method is based on the operator splitting method and an interpolation method. We perform several characteristic numerical experiments. The computational results demonstrate the unconditional stability, boundedness, and positivity-preserving properties of the proposed scheme.
Changes in the Quality of Life, Psychological Status, Medication Compliance, and Prognosis of Patients with Acute Myocardial Infarction after PCI by Applying PDCA Cycle Management Model
Authors: Li Yan , Hong Mei , Liu Guohui
Published: October 2021

Objective. To discuss the changes in the quality of life, psychological status, medication compliance, and prognosis of patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) by applying plan-do-check-action (PDCA) cycle management model. Methods. A total of 125 patients with AMI who underwent PCI in our hospital from June 2018 to June 2020 were selected and divided into control group (n = 62) and research group (n = 63) by the random number method. The conventional nursing measures were used in the control group, and the PDCA cycle management model on the basis of the control group was used in the research group. The changes in the quality of life, psychological status, medication compliance, and prognosis were observed. Results. After intervention, the Generic Quality of Life Inventory-74 scores and the self-made medication compliance questionnaire score of the research group were higher than the control group (

P
<
0.05

). After intervention, the self-rating anxiety scale score and self-rating depression scale score of the research group were lower than those of the control group (

P
<
0.05

). The total incidence of adverse events in the research group (7.94%) was lower than that in the control group (20.97%) (

P
<
0.05

). Conclusion. After the application of PDCA cycle management model, the quality of life, psychological status, medication compliance, and prognosis of AMI patients who underwent PCI were improved.
Extended Epidemiological Models for Weak Economic Region: Case Studies of the Spreading of COVID‐19 in the South Asian Subcontinental Countries
Authors: Mugdha Shafayat Bin Shabbir , Uddin Mahtab , Islam Md. Toriqul
Published: October 2021

The ascendancy of coronavirus has become widespread all around the world. For the prevention of viral transmission, the pattern of disease is explored. Epidemiological modeling is a vital component of the research. These models assist in studying various aspects of infectious diseases, such as death, recovery, and infection rates. Coronavirus trends across several countries may analyze sufficiently using SIR, SEIR, and SIQR models. Across this study, we propose two modified versions of the SEIRD method for evaluating the transmission of this infectious disease in the South Asian countries, more precisely, in the south Asian subcontinent. The SEIRD model is updated further by fusing some new factors, namely, isolation for the suspected people and recovery and death of the people who are not under the coverage of healthcare schemes or reluctant to receive treatment for various catastrophes. We will investigate the influences of those ingredients on public health‐related issues. Finally, we will predict and display the infection scenario and relevant elements with the concluding remarks through the statistical analysis.
Affective Episodic Memory System for Virtual Creatures: The First Step of Emotion‐Oriented Memory
Authors: Martin Luis , Rosales Jonathan H. , Jaime Karina , Ramos Felix
Published: October 2021

Episodic memory and emotions are considered essential functions in human cognition. Both allow us to acquire new knowledge from the environment, ranging from the objects around us to how we feel towards them. These qualities make them crucial functions for systems trying to create human‐like behaviour. In the field of cognitive architectures (CAs), there are multiple studies covering memory and emotions. However, most of them treat these subjects in an isolated manner, considering emotions only as a reward signal unrelated to a retrieved experience. To address this lack of direct interaction, we propose a computational model that covers the common processes that are related to memory and emotions. Specifically, this proposal focuses on affective evaluations of episodic memories. Neurosciences and psychology are the bases of this model. That is, the model’s components and the processes that they carry out on the information they receive are designed based on evidence from these cognitive sciences. The proposed model is a part of Cuáyóllótl, a cognitive architecture for cybernetic entities such as virtual creatures and robots. Case studies validate our proposal. They show the relevance of the integration of emotions and memory in a virtual creature. The virtual creature endowed with our emotional episodic model improves its learning and modifies its behaviour according to planning and decision‐making processes.
Computationally Efficient Optimal Control for Unstable Power System Models
Authors: Uddin Mahtab , Uddin M. Monir , Hakim Khan Md. Abdul
Published: October 2021

In this article, the focus is mainly on gaining the optimal control for the unstable power system models and stabilizing them through the Riccati-based feedback stabilization process with sparsity-preserving techniques. We are to find the solution of the Continuous-time Algebraic Riccati Equations (CAREs) governed from the unstable power system models derived from the Brazilian Inter-Connected Power System (BIPS) models, which are large-scale sparse index-1 descriptor systems. We propose the projection-based Rational Krylov Subspace Method (RKSM) for the iterative computation of the solution of the CAREs. The novelties of RKSM are sparsity-preserving computations and the implementation of time-convenient adaptive shift parameters. We modify the Low-Rank Cholesky-Factor integrated Alternating Direction Implicit (LRCF-ADI) technique-based nested iterative Kleinman–Newton (KN) method to a sparse form and adjust this to solve the desired CAREs. We compare the results achieved by the Kleinman–Newton method with that of using the RKSM. The applicability and adaptability of the proposed techniques are justified numerically with MATLAB simulations. Transient behaviors of the target models are investigated for comparative analysis through the tabular and graphical approaches.
Design and Implementation of Data Processing Software for Internet of Things Based on Virtual Reality
Authors: Liu Yuwang , Liu Xiaoming , Xu Shaotong
Published: October 2021

With the rapid development of the times, the application of the Internet of Things also develops rapidly, resulting in a linear increase in the data generated by the application. Nowadays, people’s needs for the forms and types of data are becoming more and more diversified, and the demand for the analysis and processing of Internet of Things data is only increasing. In order to meet the demand for data analysis of the Internet of Things, this article mainly introduces the design and implementation of the data analysis and processing software of the Internet of Things based on virtual reality. Realistic data analysis algorithms are applied to the data analysis function modules of the Internet of Things data analysis and processing software, and then the design of the data collection, data capture, and data analysis and processing functions of the Internet of Things data analysis and processing system is improved. Finally. The realization of the data analysis and processing software of the Internet of Things is carried out through different environments for and various aspects of software testing. The test results show that the realization rate of the software function is as high as 90%, and the data loss rate is reduced to 5%.
Mobile Phones: Vital Addiction or Lethal Addiction? Mobile Phone Usage Patterns and Assessment of Mobile Addiction among Undergraduate Medical Students in Telangana, India
Authors: Jahagirdar Vinay , Rama Kaanthi , Soppari Pranavi , Kumar M. Vijay
Published: October 2021

Background. Increased mobile phone usage among undergraduate medical students causes a detrimental effect on their health. The main focus of this study is to determine the pattern of mobile phone usage among undergraduate medical students in Hyderabad, India, and the detrimental effect on their health due to excess mobile use. Materials and Methods. A cross-sectional study was conducted among undergraduate medical students from various medical colleges in Hyderabad, India, from September 2020 to January 2021. Data were collected from 626 respondents using a semistructured, pretested questionnaire. Smartphone Addiction Scale-Short Version (SAS-SV) was used to assess the risk of smartphone addiction. Microsoft Excel and SAS were employed to analyze the data. Associations were examined using Fisher’s exact test. Results. 100% of the respondents were using mobiles, with 83.2% spending more than 4 hours on them. Only 22% reported that no mobile use during classes. Half (51.6%) admitted to keeping their mobiles close by while sleeping. 84.3% used social networking apps via their mobiles. Common symptoms arising from prolonged mobile usage included eye strain (67.9%), blurred vision (31.4%), and numbness or tingling in palms (30.9%). 52.70% of the respondents were at high risk of mobile addiction according to SAS-SV. Screen time more than 4 hours was associated with high risk of mobile addiction (

p
<
0.0001

). Significant association was found between high risk of mobile addiction and eye strain (

p
<
0.0001

), blurry vision (

p
=
0.0115

), numbness/tingling in palms (

p
<
0.0001

), and heat/tingling in the auditory area (

p
<
0.0001

). Conclusion. The study shows the alarming rate of risk of smartphone addiction among medical students. Students can be encouraged to assess their mobile addiction status and become aware of the issue. More research may be performed to develop standardized tools for early identification of mobile addiction and appropriate therapies for its rectification.
Effect and Mechanism of LRP6 on Cardiac Myocyte Ferroptosis in Myocardial Infarction
Authors: Li Rui-lin , Fan Cheng-hui , Gong Shi-yu , Kang Sheng
Published: October 2021

Background. This study was aimed at exploring the biological function and molecular mechanism of ferroptosis of LRP6 modulation in cardiomyocytes of myocardial infarction (MI). Method. We established the ferroptosis model of MI in vivo and in vitro and constructed the modulation network of circRNA‐miRNA‐LRP6 by bioinformatics analysis; then, we focused on exploring the regulatory relationship of LRP6 and its upstream genes circRNA1615 and miR‐152‐3p in the RIP experiments and the double luciferase reporter gene assay. Also, we tested the LRP6‐mediated autophagy‐related ferroptosis in MI. Results. Ferroptosis was found in cardiomyocytes of MI, and ferroptosis inhibitor Ferrostatin‐1 (Fer‐1) could improve the pathological process of MI. LRP6 was involved in the process of ferroptosis in cardiomyocytes, and LRP6 deletion regulated ferroptosis in cardiomyocytes through autophagy. Screening and identification of the upstream gene circRNA1615 would target LRP6. circRNA1615 inhibited ferroptosis in cardiomyocytes, and circRNA1615 could regulate the expression of LRP6 through sponge adsorption of miR‐152‐3p, prevent LRP6‐mediated autophagy‐related ferroptosis in cardiomyocytes, and finally control the pathological process of MI. Conclusions. circRNA1615 inhibits ferroptosis via modulation of autophagy by the miRNA152‐3p/LRP6 molecular axis in cardiomyocytes of myocardial infarction.