bluuebunny/crossref_metadata_2025 huggingface dataset:
Transient Responses of Sandwich Plates with a Functionally Graded Porous Core: Jacobi–Ritz Method
Authors: Wattanasakulpong Nuttawit , Eiadtrong Suppakit
Published: August 2022

This study examined the transient or dynamic response of sandwich plates with a functionally graded porous core under the action of time-dependent loads. The plates had two isotropic faces at the top and bottom layers, and the middle layer was made of an open-cell material with functionally graded internal pores. By using the first-order shear deformation theory, the equations of motion used to describe the dynamic behavior of the plates were applied to generate accurate results with less computational effort. To solve the equations of motion, the Ritz method based on the Jacobi polynomials for the admissible displacements, cooperating with the time integration of Newmark, was used to find out the dynamic response of the plates. The results of the numerical experiments revealed that the plates carrying a larger number of internal pores at the middle zone of the core had a great improvement in flexural stiffness, providing less deflection under dynamic loads. The observed results of the plates’ dynamic behavior related to the effects of the porosity coefficient, plate’s geometrical ratio, dynamic loading types, porous distributions of the core, etc. are shown in the form of graphs and tables, which can be used as a benchmark for future research.
Identification of Potent ABL Inhibitors from Coumestrol: An Integrative In Silico Approach
Authors: Raghi Kottoly R. , Sherin Daisy R. , Archana Thottukara M. , Saumya Maria J. , Sajesha Karakkad P. , Manojkumar Thanathu K.
Published: August 2022

Chronic Myeloid Leukemia (CML) is known as clonal myeloproliferative disorder resulted by the proliferative and deregulated expression of the BCR-ABL gene. Although targeting the BCR-ABL gene is an effective treatment for CML, the development of drug intolerance and drug resistance is still an issue for BCR-ABL targeted therapy. With an aim to develop novel BCR-ABL inhibitors with fewer side effects, we started with the anti-cancerous natural compound coumestrol as the lead. Chalcone and pyrazole are the well-exploited scaffolds in the anticancer domain; hence, in this work, we designed novel coumestrol derivatives with these structures. In order to quantify the structural changes, we employed in silico methods. The new drug leads were compared with the FDA-approved CML drug bosutinib. The molecular orbital analysis of the selected lead compounds was assessed by Density Functional Theory (DFT) approach. Molecular Dynamic (MD) computations were performed on the most promising leads to find the stability. Following which, the pharmacokinetics parameters of the screened compounds were also analyzed to check the drug-like property.
QPPAL: A Two-phase Proximal Augmented Lagrangian Method for High-dimensional Convex Quadratic Programming Problems
Authors: Liang Ling , Li Xudong , Sun Defeng , Toh Kim-Chuan
Published: August 2022

In this article, we aim to solve high-dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality, and inequality constraints. To solve the targeted QP problem to a desired accuracy efficiently, we consider the restricted-Wolfe dual problem and develop a two-phase Proximal Augmented Lagrangian method (QPPAL), with Phase I to generate a reasonably good initial point to warm start Phase II to obtain an accurate solution efficiently. More specifically, in Phase I, based on the recently developed symmetric Gauss-Seidel (sGS) decomposition technique, we design a novel sGS-based semi-proximal augmented Lagrangian method for the purpose of finding a solution of low to medium accuracy. Then, in Phase II, a proximal augmented Lagrangian algorithm is proposed to obtain a more accurate solution efficiently. Extensive numerical results evaluating the performance of QPPAL against existing state-of-the-art solvers Gurobi, OSQP, and QPALM are presented to demonstrate the high efficiency and robustness of our proposed algorithm for solving various classes of large-scale convex QP problems. The MATLAB implementation of the software package QPPAL is available at
https://blog.nus.edu.sg/mattohkc/softwares/qppal/
.
Compiler Support for Sparse Tensor Computations in MLIR
Authors: Bik Aart , Koanantakool Penporn , Shpeisman Tatiana , Vasilache Nicolas , Zheng Bixia , Kjolstad Fredrik
Published: August 2022

Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse software by hand, however, is a complex and error-prone task. Therefore, we propose treating sparsity as a property of tensors, not a tedious implementation task, and letting a sparse compiler generate sparse code automatically from a sparsity-agnostic definition of the computation. This article discusses integrating this idea into MLIR.
OD1NF1ST: True Skip Intrusion Detection and Avionics Network Cyber-attack Simulation
Authors: Wrana Michael Maximilian , Elsayed Marwa , Lounis Karim , Mansour Ziad , Ding Steven , Zulkernine Mohammad
Published: August 2022

MIL-STD-1553 is a communication bus that has been used by many military avionics platforms, such as the F-15 and F-35 fighter jets, for almost 50 years. Recently, it has become clear that the lack of security on MIL-STD-1553 and the requirement for internet communication between planes has revealed numerous potential attack vectors for malicious parties. Prevention of these attacks by modernizing the MIL-STD-1553 is not practical due to the military applications and existing far-reaching installations of the bus. We present a software system that can simulate bus transmissions to create easy, replicable, and large datasets of MIL-STD-1553 communications. We also propose an intrusion detection system (IDS) that can identify anomalies and the precise type of attack using recurrent neural networks with a reinforcement learning true-skip data selection algorithm. Our IDS outperforms existing algorithms designed for MIL-STD-1553 in binary anomaly detection tasks while also performing attack classification and minimizing computational resource cost. Our simulator can generate more data with higher fidelity than existing methods and integrate attack scenarios with greater detail. Furthermore, the simulator and IDS can be combined to form a web-based attack-defense game.
The S-BAN: Insights into the Perception of Shape-Changing Haptic Interfaces via Virtual Pedestrian Navigation
Authors: Spiers Adam , Young Eric , Kuchenbecker Katherine J.
Published: August 2022

Screen-based pedestrian navigation assistance can be distracting or inaccessible to users. Shape-changing haptic interfaces can overcome these concerns. The S-BAN is a new handheld haptic interface that utilizes a parallel kinematic structure to deliver 2-DOF spatial information over a continuous workspace, with a form factor suited to integration with other travel aids. The ability to pivot, extend and retract its body opens possibilities and questions around spatial data representation. We present a static study to understand user perception of absolute pose and relative motion for two spatial mappings, showing the highest sensitivity to relative motions in the cardinal directions. We then present an embodied navigation experiment in virtual reality (VR). User motion efficiency when guided by the S-BAN was statistically equivalent to using a vision-based tool (a smartphone proxy). Although haptic trials were slower than visual trials, participants’ heads were more elevated with the S-BAN, allowing greater visual focus on the environment.
pylspack
: Parallel Algorithms and Data Structures for Sketching, Column Subset Selection, Regression, and Leverage Scores
Authors: Sobczyk Aleksandros , Gallopoulos Efstratios
Published: August 2022

We present parallel algorithms and data structures for three fundamental operations in Numerical Linear Algebra: (i) Gaussian and CountSketch random projections and their combination, (ii) computation of the Gram matrix, and (iii) computation of the squared row norms of the product of two matrices, with a special focus on “tall-and-skinny” matrices, which arise in many applications. We provide a detailed analysis of the ubiquitous CountSketch transform and its combination with Gaussian random projections, accounting for memory requirements, computational complexity and workload balancing. We also demonstrate how these results can be applied to column subset selection, least squares regression and leverage scores computation. These tools have been implemented in
pylspack
, a publicly available Python package

1

whose core is written in C++ and parallelized with OpenMP and that is compatible with standard matrix data structures of SciPy and NumPy. Extensive numerical experiments indicate that the proposed algorithms scale well and significantly outperform existing libraries for tall-and-skinny matrices.
Decentralized Collaborative Learning Framework for Next POI Recommendation
Authors: Long Jing , Chen Tong , Nguyen Quoc Viet Hung , Yin Hongzhi
Published: August 2022

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel
d
ecentralized
c
ollaborative
l
earning framework for POI
r
ecommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models’ dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.
Evolutionary Transitions Between Hermaphroditism and Dioecy in Animals and Plants
Authors: Pannell John R. , Jordan Crispin Y.
Published: August 2022

We review transitions between hermaphroditism and dioecy in animals and (mainly flowering) plants. Although hermaphroditism and dioecy represent two end states in a sex-allocation continuum, both vary in major ways among clades. However, drawing on sex-allocation theory and distinguishing between self-fertilization and outcrossing, we recognize five broad paths to dioecy and two broad paths to hermaphroditism. Which path is taken likely depends on the starting state (especially in terms of the mating system), as well as the ecological setting or genetic context of the transition. Androdioecy may have been more important in some transitions to dioecy than current theory would suggest, and gynodioecy may often be an evolutionary end point in itself rather than a step between hermaphroditism and dioecy. Transitions between environmental and genetic sex determination may also play an important role in sexual-system evolution. Further theory is required to address these possibilities. Detailed empirical work is also greatly needed, especially in animal clades that vary in their sexual system.
Analysis of Electrochemical Behavior of (LiF–CaF2)eut.–Yb2O3 System with (Ni2+/Ni) Internal Reference Electrode
Authors: Cai Boqing , Zhang Yongjian , Wang Xu , Liao Chunfa , Sun Kailei , Shi Zhongning
Published: August 2022

The transient electrochemical behavior of the Yb (III) ion (Yb3+) on the surface of a Ni working electrode in a (LiF–CaF2)eut.–Yb2O3 molten salt system was analyzed using a three-electrode system with a reversible (Ni2+/Ni) internal reference electrode and Pt external reference electrode. The (Ni2+/Ni) internal reference electrode exhibited good stability and reliability, and provided results that were consistent with those of the Pt external reference electrode. The (Ni2+/Ni) electrode also had a wider measurement window for analysis of the electrochemical process than that of the Ni working electrode. Cyclic voltammetry, square wave voltammetry, chronopotentiometry, and chronoamperometry were used to analyze the Yb3+ ion in the (LiF–CaF2)eut.–Yb2O3 (0.03 mol%) system. It was confirmed that the Yb3+ ion was reduced according to Yb3+ + e → Yb2+ and Yb2+ + 2e → Yb, and the electrode reaction process was quasi-reversible and controlled by diffusion. Moreover, the Yb3+ ion alloyed with the Ni electrode to form a Ni–Yb alloy.
Electrodeposition of High Entropy Alloy of Ni-Co-Cu-Mo-W from an Aqueous Bath
Authors: Ahmadkhaniha D. , Kruemmling J. , Zanella C.
Published: August 2022

This study aimed to deposit high entropy alloy (HEA) coatings with five different elements, Ni, Co, Cu, Mo, and W, from a single aqueous bath. The influence of pH, current density, and complex agent on the composition of deposited coating was examined. It was shown that Mo and W were codeposited mainly with Ni and Co. pH had the most impact on the codeposition of reluctant elements like Mo and W, while current density had the minimum effect. The deposited coating had a metallic, dense, and nodular morphology with configurational entropy of around 1.6 R.
Electrodeposition onto Conductive Additive-Impregnated 3D Printed Polylactic Acid Electrodes
Authors: O’Hanlon Sally , O’Dwyer Colm
Published: August 2022

Conductive additive-impregnated polylactic acid can be coated with vanadium oxide (V2O5) by electrodeposition. By thermal pre-treatment of the thermoset printed electrode structure comprising a graphite-PLA composite, the conductivity of the composite material is improved by exposing the graphite at the outer surface by surface segregation, that is ordinarily buried within the plastic. This promotes quite effective electrodeposition under potentiostatic conditions, allowing conformal coating a complex electrode surface and structure with V2O5. The surface coating and electrode were analysed using Raman scattering spectroscopy, X-ray diffraction, energy dispersive X-ray analysis and scanning electron microscopy, and details the relationship between electrodeposition parameters and the quality of the deposit on the PLA electrode.
Influence of Humic Acid on the Strength of Cement‐Soil and Analysis of Its Microscopic Mechanism
Authors: Cao Jing , Liu Fangyi , Huang Siyang , Kong Cheng , Sun Huafeng , Gao Yue , Liu Fuhua , Li Jianyun , Liu Guoshou
Published: August 2022

The disposal of soil containing humic acid (HA) poses an increasingly difficult problem for geotechnical environmental engineering. In this study, the unconfined compressive strength (UCS) test of cement‐soil with different HA contents is conducted to analyze the effect of HA on the strength of cement‐soil. Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and X‐ray diffraction (XRD) experiments reveal the effective mechanism of HA on the strength of cement‐soil from the microscopic level. The results show that the strength of cement‐soil decreases gradually with the increase of HA content. The failure mode of cement‐soil slightly changed from brittle failure to plastic failure. The microscopic test shows that the incorporation of HA will lead to the apparent enlargement of the pores in the cement‐soil. The large pores in the sample increase, the small micropores decrease, and the structure tends to be loose and overhead. The increased HA content added will significantly reduce the cement hydration products and destroy the cementation of the hydration products.
Leaderless Consensus of Semilinear Hyperbolic Multiagent Systems with Semipositive or Seminegative Definite Convection
Authors: Dai Jiashu , Yang Chengdong , Xu Dong , Wen Shiping , Jian Muwei , Yang Dongliang
Published: August 2022

This paper deals with a leaderless consensus of semilinear first‐order hyperbolic partial differential equation‐based multiagent systems (HPDEMASs). A consensus controller under an undirected graph is designed. Dealing with different convection assumptions, two different boundary conditions are presented, one right endpoint and the other left endpoint. Two sufficient conditions for leaderless consensus of HPDEMAS are presented by giving the gain range in the case of the symmetric seminegative definite convection coefficient and the semipositive definite convection coefficient, respectively. Two examples are presented to show the effectiveness of the control methods.
A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks
Authors: Naveed Muhammad , Arif Fahim , Usman Syed Muhammad , Anwar Aamir , Hadjouni Myriam , Elmannai Hela , Hussain Saddam , Ullah Syed Sajid , Umar Fazlullah
Published: August 2022

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches.
Bioinorganic Synthesis of Sodium Polytungstate/Polyoxometalate in Microbial Kombucha Media for Precise Detection of Doxorubicin
Authors: Mousavi Seyyed Mojtaba , Hashemi Seyyed Alireza , Bahrani Sonia , Sadrmousavi-Dizaj Asma , Arjmand Omid , Omidifar Navid , Lai Chin Wei , Chiang Wei-Hung , Gholami Ahmad
Published: August 2022

In this study, we have developed a new platform of polyoxometalate as a biocompatible and electrosensitive polymeric biosensor for the accurate detection of doxorubicin. For this purpose, we used a green synthesis approach using tartaric acid, glutamic acid, and kombucha solvent. Thanks to its bioinorganic components, the biogenic approach can chemically modify and improve the performance of the biosensor, which was experimentally confirmed. Our results showed excellent sensitivity (175.72 μA·μM−1·cm−2), low detection limit (DL, 8.12 nM), and low quantification limit (QL, 0.056 μM) when the newly developed biosensor was used. The results also show that the biosynthesized biosensor has improved performance in detecting DOX in the biological fluid with an accuracy of more than 99% depending on the components used, which underlines the high efficiency of the biosensor produced. Considering the body’s physiological condition, the biosensor fabricated as a biocompatible component can show high efficiency. Therefore, its applicability for clinical use still needs to be studied in detail.
Artificial Intelligence Development and Music Education System Reform in the Context of 5G Network
Authors: Zhang Zi , Li Zhuolin , Pan Jian , Chen Wenli , Bai Qiulin
Published: August 2022

The application of 5G communication technology will help our lives in many ways because the biggest advantage of 5G technology is the ubiquitous wireless connectivity, coupled with the standardization of IP as a data protocol, the availability of low-cost and powerful computing resources in the cloud, and the increased speed of transmitted data, all of which help shape the vision of 5G. Many application scenarios can use lower-cost 5G networks. Based on advances in various communication technologies, several new application scenarios, including robotics and human-robot interaction, are beginning to emerge. Most of the new applications can be realized through 4G networks. To maximize its potential, 5G networks will need to meet a variety of requirements and support the allocation of resources to different applications in an intelligent manner, which is a major technical challenge. As a hot technology in 5G communications, high-density heterogeneous networks have attracted a lot of attention worldwide. This paper firstly discusses the current status of 5G technology research and analyzes the necessity and main features of 5G technology development, so as to gain a deeper understanding of the main features of 5G. This paper then follows a comprehensive discussion of the development of artificial intelligence in the context of 5G networks and its contribution to the reform of the music education system and designs a method for reforming the music teaching system based on artificial intelligence algorithms in the context of 5G networks. The experimental results show that the algorithm of this paper has a good promotion effect on the music education system reform and verifies the effectiveness of the model.
The Practical Application of Agricultural Genetic Breeding Technology in Elm Cultivation Based on Big Data Analysis
Authors: Huang Haiguang , Yang Rong , Hao Lei , Zhang Guosheng
Published: August 2022

In order to explore the role of agricultural genetic breeding technology in elm cultivation, this paper improves the big data technology. According to the test data verification requirements, based on the Monte Carlo idea, a TDSTI statistical test method is proposed, and a set of judgment criteria for the significance of spatio-temporal autocorrelation is given. Moreover, in order to make the observed variables of the time lag series have a clearer geographical significance, the obtained spatio-temporal weight matrix is subjected to row standardization. In addition, this paper combines big data technology and agricultural genetic breeding technology to study the cultivation of elm trees. Finally, this paper conducts a correlation analysis between the distance matrix generated by cluster analysis of leaf shape traits and the distance matrix generated by cluster analysis of SSR molecular markers. The research results show that agricultural genetic breeding technology can play an important role in the cultivation of elm.
[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis
Authors: Li Yan , Li Zhan
Published: August 2022

The nonlinear time‐series analysis method, based on the recurrence plot theory, has received great attention from researchers and has been successfully used in multiple fields. However, traditional recurrence plots that use Heaviside step functions to determine the recursive behavior of a point in the phase space have two problems: (1) Heaviside step functions produce a rigid boundary, resulting in information loss; and (2) the selection of the critical distance,ε, is crucial; if the selection is inappropriate, it will result in a low‐dimensional dynamics error, and as of now, there exists no unified method for selecting this parameter. With regard to the problems described above, the novelty of this article lies in the following: (1) when determining the state‐phase point recursiveness, a Gaussian function is used to replace the Heaviside function, thereby solving the rigidity and binary value problems of the recursive analysis results caused by the Heaviside step function; and (2) texture analysis is performed on a recurrence plot, new ways of studying complex system dynamics features are proposed, and a system of complex system dynamic‐like measurement methods is built.
Function Projective Synchronization of Two Complex Networks with Unknown Sector Nonlinear Input and Multiple Time-Varying Delay Couplings
Authors: Fang Na , Wei Da , Yin Nan-nan , Xu Dan-ying , Liu Hua , Fang Jie
Published: August 2022

This paper deals with the function projective synchronization of two complex dynamic networks with unknown sector nonlinear input, multiple time-varying delay couplings, model uncertainty, and external interferences. Based on Lyapunov stability theory and inequality transformation method, the robust adaptive synchronization controller is designed, by which the drive and response systems can achieve synchronization according to the function scaling factor. Different from some existing studies on nonlinear system with sector nonlinear input, this paper studies the synchronization of two complex dynamic networks when the boundary of sector nonlinear input is unknown. The controller does not include the boundary value of the sector nonlinear input and the time delay term, so it is more practical and relatively easy to implement. The corresponding simulation examples demonstrate the effectiveness of the proposed scheme.
Determination of Mycotoxins and Veterinary Medicines in Duck Flesh and Viscera and Assessment of Their Exposure
Authors: Zheng Yi , Wu Yuxin , Zhang Huanxin , Chen Xiaolan , Lou Zaixiang , Jing Huijuan , Wan Chunpeng (Craig)
Published: August 2022

Mycotoxins can accumulate in various feeds and thus may get in duck meat, which may have severe food safety and public health implications. This study examined mycotoxins and veterinary medications in duck meat marketed in eight marketplaces around China. For the determination of mycotoxins, including the mycotoxins aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin M1 (AFM1), T-2 toxin, zearalenone (ZEN), and ochratoxin A (OTA), a liquid-chromatography tandem mass spectrometry (LC-MS/MS) method was validated. Overall, 13 out of 48 samples (27%) presented AFB1, and AFB2 was present in 14 out of 48 samples with positive levels ranging from 0.5 μg/kg (gizzard) to 4.1 μg/kg (lung). Eleven samples were contaminated with AFM1. T-2 was also found in three parts of duck samples (duck gizzard, neck, and lung), and the 5th and 48th samples were contaminated with T-2. ZEN was found in 5 of 48 analyzed samples (10%), and OTA was present in 21 out of 48 samples. The maximum kinds of mycotoxins found simultaneously in duck samples were six in duck lungs. High co-occurrence of mycotoxins was verified in several samples. The detection rate of various veterinary drugs was 0–12.5% in duck meat samples, and the over standard rate was 2.1%. Co-occurrence of veterinary drugs was verified in several samples.
[Retracted] Application of CT Imaging in Differential Diagnosis and Nursing of Endocrine Tumors
Authors: Jiang Xue , Xu Weiwei , Zhao Ying
Published: August 2022

In order to investigate the value of preoperative X‐ray computed tomography (CT) in predicting the pathological grade of pancreatic neuroendocrine tumors. This paper retrospectively analyzed the CT image examination of pancreatic neuroendocrine tumors, the image characteristics of G‐NEC detected by CT image, and the detection of GST by spiral CT. In order to clearly diagnose and evaluate the size and scope of the focus, whether there is adjacent tissue invasion, metastasis, and treatment effect, CT, MR, PET‐CT, nuclide specific imaging, and other imaging methods are widely used in the medical treatment of pNEN patients. These imaging methods have the advantages of noninvasive, rapid imaging, objective image medium, and strong repeatability. If the pathological grade of pNEN patients can be obtained by imaging examination before operation, it will be of great benefit to the formulation of treatment strategies and the prediction of clinical outcomes. Combining CT image performance with imaging omics characteristics to establish a prediction model that can develop a better auxiliary decision‐making tool for clinical practice. Different pathological grades prompt clinicians to provide personalized and accurate medical treatment for patients, and reduce excessive medical treatment or wrong judgment caused by unclear preoperative diagnostic information.
Effect of Aronia Extract on Collagen Synthesis in Human Skin Cell and Dermal Equivalent
Authors: Lee Hwa-Rim , Ryu Hye Guk , Lee Yunji , Park Ju An , Kim Seongju , Lee Chang Eon , Jung Sungjune , Lee Kyung-Ha
Published: August 2022

The regulation of collagen synthesis, which occurs in fibroblasts in the dermal layer, is a key process in dermis regeneration and skin reconstruction. Herein, we investigated whether Aronia melanocarpa extract affects the human skin condition. We focused on type I collagen synthesis using two different types of model systems: a monolayer of cells and a bioprinted 3D dermal equivalent. The Aronia extract showed no cytotoxicity and increased cell proliferation in neonatal human dermal fibroblasts. Treatment with Aronia extract increased the transcription of COL1A1 mRNA in direct proportion to the extract concentration without causing a decrease in COL1A1 mRNA degradation. Additionally, the Aronia extract inhibited the expression of MMP1 and MMP3, and an increase in type I collagen was observed along with a decrease in MMP1 protein. We also fabricated dermal equivalents from type I collagen (the major component of the dermis) and dermal fibroblasts by bioprinting. In the 3D dermis model, the compressive modulus directly affected by collagen synthesis increased in direct proportion to the Aronia extract concentration, and expression levels of MMP1 and MMP3 decreased in exactly inverse proportion to its concentration. The findings that the Aronia extract increases synthesis of type I collagen and decreases MMP1 and MMP3 expression suggest that this extract may be useful for the treatment of damaged or aged skin.
The Teaching Effectiveness of Civics Class Based on the Optimization of Integrated Teaching and Deep Learning
Authors: Liu Lingjuan
Published: August 2022

Due to the impact of the new crown pneumonia outbreak, offline teaching was conducted to varying degrees in schools and universities nationwide in spring 2020. After the epidemic was effectively controlled, students in various schools around the country returned to school one after another, and offline teaching was resumed. In order to deeply promote it, the School of Marxism of Guangdong Second Normal College developed and built an on-campus online open course on “Outline of Modern Chinese History” and carried out the reform and practice of a hybrid teaching on the basis of abandoning traditional teaching. The reform and practice of online teaching go hand in hand. Information technology is a high-tech product; hybrid teaching is a new thing; reform of Civics and Political Science class should be combined with the actual front-line teaching, for students’ ideas and cognitive characteristics of continuous improvement and student growth. Due to some shortcomings of the process of the current teaching mode of college teaching, such as large evaluation errors and long time, the paper proposed the evaluation method of college teaching mode with the goal of improving the accuracy of college teaching mode evaluation. Firstly, we analyze the current research status of college teaching mode evaluation and find the reasons for the poor results of current college teaching mode evaluation; then, we collect the college teaching mode evaluation data, adopt deep learning algorithm to learn the college teaching mode evaluation data, and establish the college teaching mode evaluation model; finally, we conduct the application example test of college teaching mode evaluation.
Intelligent Interactive English Teaching System for Engineering Education
Authors: Shi Hui , Shi Cong
Published: August 2022

In order to improve the effect of English teaching, this paper combines with the intelligent algorithm to construct an intelligent interactive English teaching system for engineering education. In the process of interactive data processing, this paper specifies the time that a single pulse needs to be maintained and controls a counter to maintain a certain number of FPGA clocks. Moreover, data processing is completed in the digital domain, the modulation method is digital quadrature amplitude modulation, data transmission is generally in the form of a real signal, and the modulated IQ signal is converted back to a real signal. After constructing an intelligent interactive English teaching system for engineering education, this paper verifies that the intelligent interactive English teaching system for engineering education has certain effects through simulation teaching combined with the expert evaluation method.
A Case of Listeria monocytogenes Infective Endocarditis
Authors: Badar Faraz , Bhuiyan Refayat , Nabeel Shaha
Published: August 2022

Listeriamonocytogenes (L. monocytogenes) is a Gram-positive facultative intracellular aerobic bacterium. We report the case of a 77-year-old male presenting with focal neurological symptoms found secondary to embolization from L. monocytogenes infective endocarditis (IE) involving the mitral valve. Blood cultures and echocardiogram were positive. Meningitis was ruled out via lumbar puncture. Treatment was done successfully with a prolonged course of ceftazidime. IE from L. monocytogenes is a rare but life-threatening complication and therefore prompt identification of infectious process is paramount. The mainstay of therapy is use of beta-lactams.
[Retracted] Effects of Psychological Well‐Being and Depression on Menopausal Symptoms in Middle‐Aged South Korean Women and the Necessity of Exercise
Authors: Lee Sungsim , Jeong Sookyoung , Choi Youngsoon
Published: August 2022

The purpose of this study was to provide basic data on the health by examining the effects of psychological well‐being and depression on menopausal symptoms and the necessity of exercise in middle‐aged women. This study is a cross‐sectional study using a structured questionnaire. The respondent’s consent was obtained prior to data collection, and the investigation period runs from November 1, 2021 to November 30, 2021. The final analysis included 150 as respondents to the study. The tool developed by Yang was used for psychological well‐being, the Korean integrated CES‐D tool was used for depression, and the Menopause Symptom Index was used for menopausal symptoms. The differences and levels of psychological well‐being, depression, and menopausal symptoms according to demographic and sociological characteristics were analyzed using descriptive statistics, t‐test, and ANOVA. The correlation between psychological well‐being, depression, and menopausal symptoms was analyzed by Pearson’s correlation, and the effect on menopausal symptoms was analyzed by multiple regression. Because of this study, significant results were found in marital status, religion, economic level, regular exercise, hobbies, personality, and subjective health in the difference in psychological well‐being according to general characteristics. In depression, significant results were found in marital status, education level, monthly income, living status, regular exercise, hobbies, stress, personality, and subjective health status. Moreover, age, frequent exercise, hobby, and subjective health all had a significant impact on menopausal symptoms. According to the findings of the regression analysis, the factors influencing menopausal symptoms were, in that order, depression and psychological well‐being. Among the factors that showed significant results for menopausal symptoms, exercise is recommended as a factor that can alleviate menopausal symptoms due to individual lifestyle changes, so it is expected to contribute to reducing menopausal symptoms and improving quality of life.
Anatomic Risk Factors for Osteochondral Fracture of Acute First-Time Patellar Dislocation in Adolescents: A Retrospective Magnetic Resonance Imaging Study
Authors: Li Junran , Chen Chuanjie , Zhou Hongyan , Zhai Jingxiu , Zhao Hongbo , Li Ligeng
Published: August 2022

Objective. To analyze the risk factors for osteochondral fracture (OCF) of first-time acute patellar dislocation (APD) through measurements of patellofemoral anatomy in adolescents. Methods. In this prospective study, all patients were divided into two groups according to whether OCF was detected on magnetic resonance imaging (MRI): Group A (associated with OCF) and Group B (without OCF). Patellofemoral anatomy was evaluated with four aspects including trochlear/patellar dysplasia, patella location, patellofemoral matching, and morphologic classification. On MRI scans, trochlear facet asymmetry ratio (TFAR), lateral trochlear inclination (LTI), sulcus angle (SA), trochlear depth (TD), and patellar depth (PD) were measured to assess trochlear/patellar dysplasia. Insall–Salvati index (ISI), Caton–Deschamps index (CDI), Blackburne–Peel index (BPI), lateral patellofemoral angle (LPFA), patellar tilt angle (PTA), and lateral patellar displacement (LPD) were measured to show the location of patella. Patellofemoral matching was analyzed through the measurements of patellofemoral congruence angle (PFCA), patellofemoral index (PFI), and patellotrochlear index (PTI). Results. A total of ninety-four adolescents from 49 boys and 45 girls (mean age, 15 years; range, 10–18 years) with first-time APD were recruited and included in Group A (65) and Group B (29). The PFI (2.62 ± 0.51 vs. 2.10 ± 0.44) and PTI (0.28 ± 0.05 vs. 0.22 ± 0.07) were significantly higher in Group B than Group A

P
<
0.05

. There were no significant differences in other quantitative outcomes of the two groups

P
>
0.05

. The distribution of Dejour/Wiberg classification was statistically similar between the two groups

P
>
0.05

. Conclusions. Adolescent patients with first-time APD complicating OCF have closer morphologic features of patellofemoral dysplasia and patella location when compared to adolescents without OCF. Abnormal patellofemoral matching increases the risk of OCF after first-time APD in adolescents.
Adjustment Method of “FAST” Active Reflector Based on Optimal Fitting Strategy
Authors: Xia Yu-Chen
Published: August 2022

The adjustment method of the fast active reflector of “Chinese heavenly eye” is studied. Firstly, this paper studies the offset characteristics of fast main cable node position, combined with the position of the celestial body to be measured, the follow-up coordinate system is established, and the optimal fitting model of active reflector adjustment based on discrete main cable node is established by using the idea of optimal fitting of the ideal surface. Secondly, the genetic algorithm is used to solve the minimum root mean square error (RMSE). The offset of the triangular reflector is driven by the offset of the main cable node, so that the working area is closer to the ideal paraboloid, and the maximum electromagnetic wave signal receiving ratio is achieved. Finally, rotate the coordinate data of each main cable node according to the known celestial body angle and use the Lagrange operator method to obtain the shortest distance from the actual offset point to the ideal paraboloid as the objective function, with the radial expansion range of the actuator and the variation range of the distance between adjacent points as constraints, and the size of the electromagnetic wave contact area is the basis for judging the amount of information, and a reflector adjustment model for the optimal displacement strategy of discrete main cable nodes is established. Through the test, concluded that the optimal fitting strategy is ideal, the model can accurately obtain the fast active reflector adjustment method under the constraint conditions. Through the test, the conclusion that the optimal fitting strategy is ideal is obtained, and the fast active reflector adjustment method that the model can accurately obtain under the constraints is verified.
IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques
Authors: Praveenchandar J. , Vetrithangam D. , Kaliappan S. , Karthick M. , Pegada Naresh Kumar , Patil Pravin P. , Rao S. Govinda , Umar Syed
Published: August 2022

One of the main reasons for accidents among workers is harmful gas leakage. Many people die in chemical industries and their surrounding areas. The present invention is responsible for monitoring and controlling hazardous toxic gases like nitrogen dioxide (NO2), carbon monoxide, ozone (O3), sulfur dioxide (SO2), LPG, hydrocarbon gases, silicones, hydrocarbons, alcohol, CH4, hexane, benzine, as well as environmental conditions, such as temperature and relative humidity to prevent industrial accidents. The Arduino UNO R3 board is used as the central microcontroller. It is connected to the Cloud via AQ3 sensor, Minipid 2 HS PID sensor, IR5500 open path infrared gas detector, DHT11 Temperature and Humidity Sensor, MQ3 sensor, and ESP8266 and WIFI Module, which can store real-time sensor data and send alert messages to the industry’s safety control board. Machine learning and artificial intelligence will be used to make an intelligent prediction (AI). The information gathered will be examined in real-time. The real-time data provided through the sensor can be accessed worldwide. Sensor data quality is critical in the Internet of Things (IoT) applications because poor data quality renders them useless. Error detection in sensor data improves the IoT-based toxic gas monitoring, controlling, and prediction system. Live data from sensors or datasets should be analyzed properly using appropriate techniques. Hence, hybrid hidden Markov and artificial intelligence models are applied as an error detection technique in the sensor dataset. This technique outperformed the dataset gas sensor array under dynamic gas mixtures and lived data. Our method outperformed harmful gas monitoring and error detection in sensor datasets compared to other existing technologies. The hybrid HMM and ANN fault detection methods performed well on the datasets and produced 0.01% false positive rate.
Construction and Realization Path Analysis of Cross-Border E-Commerce Logistics Collaboration Model
Authors: Ma Meng
Published: August 2022

In order to improve the synergy effect of cross-border e-commerce slogistics, this paper combines intelligent logistics communication technology to construct a cross-border e-commerce collaborative logistics model. Moreover, this paper proposes a parallel energy-efficient routing algorithm based on 3D cell space to improve the system operation effect. In addition, this paper deeply analyzes the data fusion function performed by MA when accessing nodes and proposes a data fusion criterion to judge the necessity of data fusion on nodes. Finally, this paper balances the energy consumption of e-commerce logistics data fusion and data transmission so that the sum of the two is minimized. The data simulation analysis shows that the cross-border e-commerce logistics collaboration model proposed in this paper has certain effects, and it can be applied to the actual operation of cross-border e-commerce.
Semirigid Ureteroscopy Therapy against Childhood Kidney Stones
Authors: Mu Xiao-Nan , Guo Long-Fei , Wang Peng-Xin , Zhang Long-Yang , Wang Si-Jun
Published: August 2022

Objective. The purpose of the current study was to assess the effectiveness of semirigid ureterorenoscopy (URS) as first-line therapy for early childhood patients with <20 mm stones in the pelvic, middle, or upper calices. Methods. In all, 61 pediatric kidney stone patients who had flexible ureteroscopy (fURS) between January 1, 2010, and December 31, 2019, were included in this study. Before fURS, semirigid URS employed the UreTron or holmium: YAG (Ho : YAG) laser was conducted. When semirigid URS was unsuccessful, fURS was used for retrograde intrarenal surgery (RIRS). All participants were monitored clinically for a minimum of three months after each procedure. Results. The patient’s mean age was

4.52
±
1.53

years, and 52 (83.61%) participants underwent semirigid URS successfully. Mean procedural duration of semirigid URS was

36.49
±
7.72

 min. The stone-free rate after semirigid URS was 92.16% (47/51). During the postprocedural medical observation, there were no serious adverse effects. Conclusions. Based on the present study’s findings, semirigid URS is a low-risk, effective therapy for kidney stones in selected pediatric patients.
Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev
Authors: Wang Song , Zeng Cheng , Song Bailing , Huang Xuxiang , Zhou Sicheng
Published: August 2022

To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect.