bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Investigation on Metal Matrix Nanocomposite: Aluminium Alloy 6061 and 7075 with SiC and Fly Ash
Authors: Kotteda Tarun Kumar , Eshwar D. , Balakrishna G. , Kuchampudi Sandeep Varma , Prasad B. Durga , Sadasivam Sasivaradhan
Published: September 2022

Aluminium metal matrix nanocomposites (AMMNCs) have been recognized as a promising material with excellent structural and functional characteristics that can be suggestively personalized to satisfy industrial demands as well as design requirements for a wide range of applications, including those in defense, marine, automobiles, and aerospace. Particularly, these industries are using an increasing number of components manufactured by AMMNCs. When compared to monolithic materials, components manufactured from aluminium‐based composites have better qualities such as tribological behaviors, hardness, stiffness, strength, and higher strength to weight ratio. Therefore, this article addresses the experimental investigation on aluminium‐based metal matrix nanocomposites (MMNCs) with silicon carbide (SiC) and fly ash (FA) as a reinforcement material with distinct ratios. Here aluminium alloy 6061 and 7075, hereafter referred as AA6061 and AA7075, are considered as base material. The final workpieces obtained using the process of stir casting and machining are tested to compute the output response parameters like yield strength (YS), wear rate (WR), hardness, impact strength (IS), and ultimate tensile strength (UTS). In addition, the scanning electron microscopy (SEM) images also studied for examining the distribution of reinforcement material particles in base metal.
Ecological Concept of Basic Computer Vision and Visual Communication of Advertising under the Concept of Ecological Design
Authors: Zhang Ao , Wenyue Jin
Published: September 2022

With the importance people attach to the harmony of ecosystem in modern society, the concept of ecological design has been adopted in some industries and is gradually being valued and recognized by people. In real life, advertising design and production is an important field of art design. With the rapid development of society and the continuous updating of network information technology, network advertising and visual communication design inevitably collide. As a new form of advertising, network advertising had become a part of people’s network life. This paper analyzes the technical characteristics of image transformation, image enhancement, image restoration, image coding, and image recognition in computer vision processing. Through the image processing algorithm and program of computer, the advertising image can be processed to realize the functions of recognition, restoration, coding, enhancement, and transformation of advertising image. This thesis had studied the visual communication design in modern online advertising under the concept of ecological design. Using computer vision processing technology, this thesis had studied how to process the ecological concept and advertising visual communication content through computer vision technology, which will help to better improve the configuration of online advertising and create a more pleasant visual environment. It is of great significance to promote the rapid development of online advertising industry.
Identification of Vibration Signal for Residual Pressure Utilization Hydraulic Unit Using MRFO-BP Neural Network
Authors: Cao Qingjiao , Wang Liying , Zhang Jiajie , Guo Tengfei , Liu Xiyuan
Published: September 2022

Combined with wavelet threshold denoising and Ensemble Empirical Mode Decomposition (EEMD) decomposition, an identification method based on Manta Ray Foraging Optimization-BP (MRFO-BP) neural network for vibration signals of residual pressure utilization hydraulic units is proposed to distinguish the vibration signal of each unit. The feature vectors of vibration signals are extracted by wavelet denoising and EEMD decomposition. The weights and thresholds in BP neural network are optimized by the MRFO algorithm. The feature vectors are input into the optimized BP neural network to realize the identification and classification of vibration signals. Compared with Particle Swarm Optimization-BP (PSO-BP) neural network, Bat Algorithm-BP (BA-BP) neural network, and BP neural network, the results show that the identification rate of each measuring point from the MRFO-BP neural network is greatly improved. The average identification rate of other measuring points is 98.514%, except the identification rate of the generator, which is 85.389%. Therefore, the MRFO-BP neural network has better stability and higher identification accuracy and can identify and classify vibration signals more accurately. The conclusions can provide theoretical basis for vibration signals identification of residual pressure utilization hydraulic unit. When the vibration signal of each unit cannot be clearly distinguished, the vibration signals of the units are identified by the method proposed in this paper. According to the obtained results, a feasible classification method can be provided for the vibration signals belonging to different units.
Research and Application of Prefilled Concrete Wall to Reduce the Size of Stopping Mining Coal Pillar
Authors: Zhang Jiao , Sun Shuwei , Wang Zhiqiang , Li Jingkai
Published: September 2022

The long-term stability and width of stopping mining coal pillar have an important influence on mine safety production. In view of the “┤” type spatial structure of stop mining position of 6105 working face in Hongshuliang Coal Mine, the mechanical equilibrium, the distribution law of stress concentration factor, and the distribution characteristics of elastic zone of “filling body-remaining stopping mining “ structure are studied by means of theoretical calculation, FLAC3D numerical simulation software, and field measurement. The research shows that (1) the concrete filling body can protect the remaining stopping mining coal pillar, the surrounding rock of the retracement channel, and the surrounding rock of the main roadway, and the width formula of “ filling body - remaining stopping mining coal pillar “ structure is deduced; (2) in the retracement channel, increasing the internal friction angle and cohesion of coal can eliminate the spalling and large deformation of surrounding rock; (3) according to the comprehensive analysis, the width of the “filling body-remaining coal pillar” structure is 25 m, in which the width of the concrete filling body with uniaxial compressive strength of 50 MPa is 6 m, and the recovery rate of stopping mining coal pillar is increased by 52.5%; and (4) field measurement shows that in the retracement channel, there is no spalling and large deformation. In the auxiliary main haulage roadway, the maximum deformations of roof, floor, solid coal side, and stopping mining coal pillar side are 46 mm, 18.4 mm, 55.9 mm, and 74.8 mm, respectively.
Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection
Authors: Sumathi S. , Rajesh R. , Lim Sangsoon
Published: September 2022

Distributed denial of service (DDoS) attack is a subclass of denial of service attack that performs severe attack in a cloud computing environment. It makes a malicious attempt to disturb the usual services of any network or server by using botnets. Hence, an efficient intrusion detection system (IDS) is essential to detect this attack. Some limitations in the existing IDS models for DDoS attack detection are delayed convergence, local stagnation issues, and local and global optimal trapping issues. These limitations are met by the recurrent neural network (RNN) and deep learning- (DL-) based proposed models that can utilize the previous states of the hidden neuron. The proposed research has used a long short-term memory (LSTM) recurrent neural network and autoencoder- and decoder-based deep learning strategy with gradient descent learning rule. The network parameters like weight vectors and bias coefficient are tuned optimally by employing the proposed a hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithm. The proposed hybrid optimization algorithm selects the essential attributes, and the results obtained confirmed that the proposed LSTM and deep learning model outperformed all other models developed in the literature.
[Retracted] Teaching of Dance Choreography Course Based on Multimedia Network Environment
Authors: Sun Yue
Published: September 2022

The growth of society has been significantly impacted by the introduction of the new multimedia era. A new task will present itself to the choreographer in light of this context. This paper makes the argument that multimedia technology should be incorporated with dance art so that dance works can give audiences a deeper feeling and impression and promote the diversified development of dance art by outlining the characteristics of multimedia and its application advantages in choreography. A multimedia network classroom contains a variety of features, including real‐time educational feedback, an interactive learning environment, and a diverse instructional content. Based on this, this article builds and integrates the instructional resources for the dance choreography course as well as the instructional management system for the course. In order to precisely and effectively teach choreography, it is necessary to give the proper instructional materials. According to experimental findings, the recall and precision of the suggested method can both approach 95.71% and 96.84%, respectively. It partially resolves the issues of users having trouble locating necessary educational resources and instructional resources being underutilised. The dance choreography course is being taught using this way in a multimedia network setting.
On the Design of Assistant Teaching Using Network Multimedia Database in Volleyball Teaching
Authors: Yin Juan , Song Qingyao
Published: September 2022

Physical education is an important part of school’s physical education and practical teaching activity. There are many restricting factors in physical education, such as the type of equipment and environmental conditions. The standardization of volleyball teaching technology has become the primary factor affecting teaching efficiency. Teachers demonstrate that the action is not standard and will lead students to learn that the technical action is not in place, and teachers can show students standardized technical action through multimedia technology, for example, through the video of athletes. The spatiotemporal nature presented in volleyball technology teaching affects students’ understanding and mastering of technology, using network multimedia system-assisted volleyball training teaching, using the advantages of network multimedia, volleyball teaching students difficult to understand the technical action of the intuitive display, improve students’ independent learning ability. This thesis studies the application of multimedia technology in the training and teaching of physical volleyball in colleges and universities. It analyzes the application of multimedia technology in the training and teaching of physical volleyball in colleges and universities, so as to provide useful references for the teaching reform of physical volleyball in colleges and universities.
The Effect of Simulated Chewing on the Surface Roughness of Direct and Indirect Resin‐Composites Opposed by Zirconia: An In Vitro Study
Authors: AlSuwaidi Ghadeer S. , Alshali Ruwaida Z. , Salim Nesreen A. , Satterthwaite Julian D. , Silikas Nick
Published: September 2022

Purpose. To assess the surface roughness of two different light‐cured resin‐composites when opposed by monolithic zirconia after simulated mastication. Materials and Methods. Materials included a direct restorative nanohybrid (n = 10) and an indirect microhybrid (n = 10) resin‐composite (Tetric EvoCeram and Sinfony, respectively). The antagonist material was 3 mol% yttria‐stabilized tetragonal zirconia polycrystalline ceramic. Each material was subjected to in vitro chewing against zirconia using a chewing simulator for 250,000 cycles. A 3D profilometer was used to assess the surface roughness parameters of each resin‐composite before and after the simulated chewing. Independent t‐test and paired sample t‐test were performed to compare roughness values for both materials and to compare baseline and after chewing simulation values (p = 0.05). Results. Sinfony showed significantly greater roughness values compared to Tetric EvoCeram (p ≤ 0.025) before and after simulated chewing, except for Sa and Sv parameters after simulated chewing where the difference between the two materials was insignificant (p = 0.06 and 0.89, respectively). Surface roughness increased for both materials after simulated chewing compared to baseline values, but the difference was insignificant (p ≥ 0.065). However, Sa (p = 0.04) and Sv (p = 0.012) for Tetric EvoCeram were significantly higher after compared to before chewing simulation. Conclusion. Tetric EvoCeram had a smoother surface than Sinfony before and after simulated chewing. Surface roughness for both materials was higher after simulated chewing compared to baseline values which represent surface damage that was significant for Tetric EvoCeram while Sinfony showed better resistance.
Parallel Big Bang-Big Crunch-LSTM Approach for Developing a Marathi Speech Recognition System
Authors: Sharma Ashok , Bachate Ravindra Parshuram , Singh Parveen , Kumar Vinod , Kumar Ravi Kant , Singh Amar , Kadariya Madan
Published: September 2022

The Voice User Interface (VUI) for human-computer interaction has received wide acceptance, due to which the systems for speech recognition in regional languages are now being developed, taking into account all of the dialects. Because of the limited availability of the speech corpus (SC) of regional languages for doing research, designing a speech recognition system is challenging. This contribution provides a Parallel Big Bang-Big Crunch (PB3C)-based mechanism to automatically evolve the optimal architecture of LSTM (Long Short-Term Memory). To decide the optimal architecture, we evolved a number of neurons and hidden layers of LSTM model. We validated the proposed approach on Marathi speech recognition system. In this research work, the performance comparisons of the proposed method are done with BBBC based LSTM and manually configured LSTM. The results indicate that the proposed approach is better than two other approaches.
A Water-Soluble Polysaccharide from the Fibrous Root of Anemarrhena asphodeloides Bge. and Its Immune Enhancement Effect in Vivo and in Vitro
Authors: Huang Qi , Wang Jingwen , Zong Rumin , Wu Deling , Jin Chuanshan
Published: September 2022

Background. The fibrous roots of Anemarrhena asphodeloides Bge. (FRAAB) are byproducts of the rhizome of Anemarrhena asphodeloides. Some studies have revealed secondary metabolic small molecules in FRAAB, but there are few reports on the polysaccharides of FRAAB (PFRAAB). Aim of the Study. The present study aimed to investigate the preliminary characterization and underlying mechanism of immune stimulation of PFRAAB. Materials and Methods. The crude polysaccharide of FRAAB was obtained by hot water extraction and alcohol precipitation, and PFRAAB was purified by a diethylaminoethyl-52 (DEAE-52) cellulose chromatographic column and graphene dialysis membrane. The preliminary characterization of PFRAAB was studied by ultraviolet (UV) scanning and Fourier Transform Infrared Reflection (FTIR). The molecular weight and composition of PFRAAB were analysed by high-performance gel permeation chromatography (HPGPC) and high-performance liquid chromatography (HPLC), respectively. The immune stimulation of PFRAAB was investigated by using cyclophosphamide- (CCP-) treated mice and RAW264.7 cells. Results. A water-soluble PFRAAB was obtained with a molecular weight of 115 kDa and was mainly composed of arabinose (ara), galactose (gal), glucose (glc), and mannose (man). Compared with CCP-induced mice, PFRAAB significantly (

p
<
0.05

or

p
<
0.01

) increased the spleen and thymus index, ameliorated injury to the spleen and thymus, and evaluated immunoglobulin levels. In addition, PFRAAB also increased the secretion of nitric oxide (NO), interleukin-1β (IL-1β), tumour necrosis factor-α (TNF-α), and IL-6 in RAW264.7 cells and upregulated the expression of toll-like receptor 4 (TLR4), Myd88, nuclear factor kappa-B (NF-κB) P65, p–NF–κB P65, IKB-α, and p-IKB-α. Conclusion. PFRAAB possesses immune stimulation activity and can be used as a potential resource for immune-enhancing drugs. Our present study provides a scientific basis for the comprehensive development of Anemarrhena asphodeloides medicinal plant resources.
Research on the Impact of “Internet+” on China’s Manufacturing Industry Agglomeration --An Empirical Research Based on the Mediating Effect Model of Provincial Panel Data
Authors: Li Zhaoyang , Yang Guoliang
Published: September 2022

With the continuous deepening of the “Internet +” action plan, the deep integration of the Internet and the manufacturing industry has an increasing impact on the degree of agglomeration of the manufacturing industry. This paper mainly studies the impact of Internet development on the degree of manufacturing agglomeration and empirically examines the above effects using the interprovincial panel data from 2006 to 2017, and further uses the mediation effect model to study the mechanism through which Internet development affects the manufacturing agglomeration effect. The research results show that the development of the Internet has improved the degree of agglomeration of the manufacturing industry, and the development of the Internet has promoted the degree of manufacturing agglomeration by promoting the level of manufacturing infrastructure investment and knowledge spillovers. Therefore, it is necessary to accelerate the deep integration of the Internet and the manufacturing industry, use the Internet to transform the manufacturing industry, and create new business forms and new models.
Psychological Consultation and Health Analysis Method for Artificial Intelligence Multidecision Support
Authors: Jin Ye , Liu Aheng
Published: September 2022

With the increase in market economy and life competition, mental health problems are particularly prominent in today’s society. This is also an important reason for the sudden increase in extreme events. The frequent occurrence of violent incidents and depressive incidents is mainly caused by major problems in mental health. They accumulate greater pressure in life or study, which can easily lead to mental health problems. However, mental health problems are difficult to detect up front. When the mental health problem is more serious, they will seek a psychological counselor or a way to release stress to solve the mental health problem. Only a small number of people will take extreme measures, which will cause social or family losses. Artificial intelligence methods have better advantages in dealing with nonlinear relationships of features. This study uses a convolutional neural network (CNN) and long short-term memory (LSTM) methods in artificial intelligence methods to study the characteristics of psychological counseling and health. The research results show that CNN and LSTM methods can accurately predict the characteristics of psychological counseling and health, which can do certain forecasting work for discovering people’s mental health problems. The maximum prediction error of CNN and LSTM methods in predicting the characteristics of psychological counseling and health is only 2.64%.
Successful Treatment of Whole Coronary Artery Spasm after Off-Pump Coronary Artery Bypass Grafting
Authors: Kobayashi Akihiro , Araki Yoshimori , Terada Takafumi , Kawaguchi Osamu
Published: September 2022

Coronary artery spasm after coronary artery bypass grafting is a rare but life-threatening condition. Herein, we report the case of a 77-year-old man who received off-pump coronary artery bypass grafting. An hour after surgery, there was a sudden hemodynamic compromise due to coronary artery spasm, prompting emergent coronary angiography with extracorporeal membrane oxygenation support. Because the angiography results showed diffuse severe spasm of the entire native coronary artery, the patient was treated with an intracoronary injection of vasodilators. The patient recovered in 7 days with mechanical support, catecholamines, and vasodilators, and he was discharged on postoperative day 30. Although coronary artery spasm after off-pump coronary artery bypass surgery is a rare condition, it must be suspected when sudden circulatory collapse occurs.
Near-Field Electromagnetic Scatterings and Imaging of a Ship Based on High-Frequency Techniques
Authors: Qi Conghui , Yang Wei , Zhu Xiaozhang
Published: September 2022

In radar detection, it is important to investigate the near-field scattering characteristics since the far-field condition is not easily satisfied for the distance between the radar and the electrically large ship on the sea surface. A high-frequency techniques of the physical optics (PO) and shooting and bouncing rays (SBR) with local expansions based on the facets of the target are proposed to take the electromagnetic scatterings in the near zone. Therefore, it is a more straightforward method to modify the computation from traditional far-field problem to near-field problem with the singularity-free characteristic. Simulation results show that it has high accuracy but requires very little increase in computational costs in the near-field problem. Moreover, the 1-D high-resolution range profile (HRRP) demonstrates that the near-field scattering mechanism is much different from the cases in the traditional far-field problem.
Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods
Authors: Zhang Jinbo , Wu Xiaozhi , Song Qingqing
Published: September 2022

Background and Purpose. Fetal overgrowth can pose a serious threat to the safety of a mother and child. Early identification of high-risk pregnant women and timely pregnancy intervention and guidance are of great value in preventing the development of giant babies and improving adverse maternal and infant outcomes. The current clinical methods for predicting macrosomia mainly rely on obstetric examination and imaging, but their accuracy is controversial. And there is no accepted method for accurately predicting macrosomia. We investigated the risk factors influencing the occurrence of macrosomia and established a prediction model for the occurrence of macrosomia to provide a reference basis for interventions to prevent macrosomia. Method. A retrospective selection of 93 women who were hospitalized in our hospital from March 2019 to May 2022 with a singleton pregnancy and delivered at term with macrosomia were the study group. And 356 women who delivered a normal size baby during the same period were the control group. The variables that were associated with the onset of macrosomia were screened from maternal medical records. Logistic regression models, random forest, and CART decision tree models were developed using the screened variables as input variables and whether they were macrosomia as outcome variables, respectively. The performance of the three models was evaluated by accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC). Result. The risk prediction models for the onset of macrosomia, logistic regression model, random forest model, and decision tree, were successfully developed, with accuracies of 0.904, 1.000, and 0.901 in the training set and 0.926, 0.582, and 0.852 in the validation set, respectively. The AUC in the training set were 0.898, 1.000, and 0.789, and in the validation set were 0.906, 0.913, and 0.731, respectively. In general, the logistic regression model has the highest diagnostic efficiency, followed by the random forest model. Conclusion. Logistic regression models have high application value in the assessment of predicting the risk of macrosomia, and it is suggested that the advantages of logistic regression models and random forest models should be combined in future studies and applications to make them work better in the prediction of the risk of macrosomia.
The Prediction of English Online Network Performance Based on the XGBoost Algorithm
Authors: Chen Xiaohua
Published: September 2022

The demand of C language informatization for English learning has increased greatly, and strengthening the application of informatization in teaching has become a current trend, while deep learning algorithms have been applied in various tasks due to their obvious advantages. In this paper, an English score prediction method based on the XGBoost algorithm is proposed. In order to verify the effectiveness of the model in English score prediction, the principle of the XGBoost algorithm is firstly analyzed as a basis. The English test scores of a university for 2019–2021 were used as the basic data source, and the output probabilities of the proposed model were used to compare the results under different conditions. The experimental results show that the predicted scores are basically consistent with the actual scores. From a scientific point of view, the ability to predict unknown data with low error suggests that it enables students and teachers to identify the underlying factors that make it difficult for students to answer questions. Understanding these causes is useful for designing high-quality courses and lesson plans.
A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis
Authors: Begum Almas , Dhilip Kumar V. , Asghar Junaid , Hemalatha D. , Arulkumaran G.
Published: September 2022

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer‐aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short‐term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1‐score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.
Fault Diagnosis Method of Rotor Magnetic Field Local Demagnetization in Permanent Magnet Synchronous Motor Model Predictive Current Control System
Authors: Chen Tao , Chen Bing
Published: September 2022

In order to realize the reliable diagnosis of the rotor magnetic field local demagnetization fault of permanent magnet synchronous motor (PMSM) model predictive current control system, based on the establishment of the mathematical model of PMSM considering local demagnetization of rotor magnetic field and the simulation model of its model predictive current control system, a diagnosis method combining the adaptive signal extraction algorithm and Hilbert transform is proposed in this study. This method is first based on the adaptive signal extraction algorithm to extract the fault characteristic harmonics of rotor magnetic field local demagnetization fault in PMSM model predictive current control system under stationary and nonstationary operating conditions, and then, we use Hilbert transform to realize the time-frequency transformation of the extracted fault characteristic harmonics. Based on solving the problem that the weak fault characteristic signal near the fundamental wave component existing in the Hilbert–Huang transform is difficult to effectively decompose, the reliable diagnosis of the rotor magnetic field local demagnetization fault of the PMSM model predictive current control system is realized with the calculation amount less than Hilbert–Huang transform. The simulation and experimental results show that the proposed method is accurate and useful.
Diversification Benefits between Stock Returns from Ghana and Jamaica: Insights from Time-Frequency and VMD-Based Asymmetric Quantile-on-Quantile Analysis
Authors: Agyei Samuel Kwaku
Published: September 2022

Due to the susceptibility of assets to the dynamism in financial markets, the emergence of new asset classes induces empirical assessments of their risk-reduction abilities. This issue is envisaged from the perspective of new investment combinations that emerge from the new market alliance between Ghana and Jamaica. This study investigates the heterogeneous and asymmetric co-movements between stock market returns from Ghana and Jamaica with data from 04 April 2011 to 17 March 2022. The wavelet analysis is carried out, followed by causality in quantiles and quantile-on-quantile regression (QQR) analysis with decomposed return series using the variational mode decomposition (VMD) approach. The findings from the bi-wavelet analysis divulge low connectedness between stock returns from Ghana and Jamaica. The cone of influence from the coherence plot does not cover the entire spectrum, particularly beyond the annual scale. Hence, co-movements between GSECI and JSEIND beyond a year may be less significant for portfolio management. Findings from the causality test evidenced bi-directional asymmetric causality between the two markets. From the VMD-based QQR analysis, it is revealed that stock returns from Ghana and Jamaica are safe-havens, hedges, and diversifiers for each other. The significant diversification prospects between the two markets signal that the two stock markets could facilitate the inflow of capital assets for extended growth and development of their overall economies. Policymakers and regulators could attract international investors and promote the flow of funds between the two economies through effective regulation of stock markets. Specific implications for market participants and policymakers are discussed.
Human-Machine Interaction Methods for Minimally Invasive Surgical Robotic Arms
Authors: Jiang Fenglin , Jia Ruiyao , Jiang Xiujuan , Cao Fang , Lei Tingting , Luo Li
Published: September 2022

Minimally invasive surgery has a smaller incision area than traditional open surgery, which can greatly reduce damage to the human body and improve the utilization of medical devices. However, minimally invasive surgery also has disadvantages such as limited flexibility and operational characteristics. The interactive minimally invasive surgical robot system not only improves the stability, safety, and accuracy of minimally invasive surgery but also introduces force feedback in controlling the surgical robot, which is a new development direction in the field of minimally invasive surgery. This paper reviews the development status of interactive minimally invasive surgical robotic systems and key technologies to achieve human-robot interaction and finally provides an outlook and summary of its development. Fuzzy theory and reinforcement learning are introduced into the parameter adjustment process of the variable guide control model, and a human-robot interaction method for minimally invasive surgical robot posture adjustment is proposed.
Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things
Authors: Rong Li
Published: September 2022

More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.
[Retracted] Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory
Authors: Li Hailong
Published: September 2022

College English has almost always been a required course, and a college student’s level of English proficiency is one of the factors used to assess their learning capacity. The quality of students’ English learning is largely influenced by the level of English instruction provided in colleges. However, there are still a lot of issues with college English instruction today, the most glaring of which is that English instruction is being overly simplified, and that the methods, modes, and purposes of instruction are also very narrow. Due to this, it is challenging for most colleges and universities’ English teaching levels to satisfy the requirements of high‐level education. The PSO‐BP neural network model, which optimizes the BP neural network (BPNN), is used in this study to build a high‐precision and diversified English teaching evaluation model in order to address the aforementioned issues. According to the experimental findings, the PSO‐BPNN algorithm has a relative error of just 0.29 percent and an average accuracy rate of 97.02 percent. Overall performance is superior to that of the conventional BPNN algorithm, and it is the most adaptable in terms of creating various evaluation modes.
CREB1 Transcriptionally Activates LTBR to Promote the NF-κB Pathway and Apoptosis in Lung Epithelial Cells
Authors: Hu Zhengyun , Zhou Guoping
Published: September 2022

Bronchopulmonary dysplasia (BPD) is a prevalent chronic pediatric lung disease. Aberrant proliferation and apoptosis of lung epithelial cells are important in the pathogenesis of BPD. Lymphotoxin beta receptor (LTBR) is expressed in lung epithelial cells. Blocking LTBR induces regeneration of lung tissue and reverts airway fibrosis in young and aged mice. This study is aimed at revealing the role of LTBR in BPD. A mouse model of BPD and two in vitro models of BPD using A549 cells and type II alveolar epithelial (ATII) cells were established by exposure to hyperoxia. We found that LTBR and CREB1 exhibited a significant upregulation in lungs of mouse model of BPD. LTBR and CREB1 expression were also increased by hyperoxia in A549 and ATII cells. According to results of cell counting kit-8 assay and flow cytometry analysis, silencing of LTBR rescued the suppressive effect of hyperoxia on cell viability and its promotive effect on cell apoptosis of A549 and ATII cells. Bioinformatics revealed CREB1 as a transcriptional factor for LTBR, and the luciferase reporter assay and ChIP assay subsequently confirmed it. The NF-κB pathway was regulated by LTBR. CREB1 induced LTBR expression at the transcriptional level to regulate NF-κB pathway and further modulate A549 and ATII cells viability and apoptosis. In conclusion, this study revealed the CREB1/LTBR/NF-κB pathway in BPD and supported the beneficial role of LTBR silence in BPD by promoting viability and decreasing apoptosis of lung epithelial cells.
Robot Trajectory Planning Based on the Energy Management Strategy
Authors: Li Mingkai
Published: September 2022

With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.
A Homotopy Bayesian Approach for Inverse Problems
Authors: Yang Xiao-Mei , Deng Zhi-Liang
Published: September 2022

In solving Bayesian inverse problems, it is often desirable to use a common density parameterization to characterize the prior and posterior. Typically, we seek an optimal approximation of the true posterior from the same distribution family as the prior. As one of the most important classes of distributions in statistics, the exponential family is considered as the parameterization. The optimal parameter values for representing the approximated posterior are achieved by minimizing the deviation between the parameterized density and a homotopy that deforms the prior density into the posterior density. Instead of the usual moment parameters, we introduce a new parameter system in the process, by which we reduce the dimension of the parameter and therefore the computational cost. The parameters are ruled by a system of explicit first-order differential equations. Solving this system over finite “time” interval yields the desired optimal density parameters. This method is proven to be effective using some numerical examples.
Medical Data Classification Assisted by Machine Learning Strategy
Authors: Wang Lei , Zuo Keqiang
Published: September 2022

With the development of science and technology, data plays an increasingly important role in our daily life. Therefore, much attention has been paid to the field of data mining. Data classification is the premise of data mining, and how well the data is classified directly affects the performance of subsequent models. In particular, in the medical field, data classification can help accurately determine the location of patients’ lesions and reduce the workload of doctors in the treatment process. However, medical data has the characteristics of high noise, strong correlation, and high data dimension, which brings great challenges to the traditional classification model. Therefore, it is very important to design an advanced model to improve the effect of medical data classification. In this context, this paper first introduces the structure and characteristics of the convolutional neural network (CNN) model and then demonstrates its unique advantages in medical data processing, especially in data classification. Secondly, we design a new kind of medical data classification model based on the CNN model. Finally, the simulation results show that the proposed method achieves higher classification accuracy with faster model convergence speed and the lower training error when compared with conventional machine leaning methods, which has demonstrated the effectiveness of the new method in respect to medical data classification.
Exploration of Hub Genes in Retinopathy of Prematurity Based on Bioinformatics Analysis of the Oxygen-Induced Retinopathy Model
Authors: Xiong Qi , Li Zhiliang , Zhang Jing , Yang Lin , Chen Xiaomin , Gong Yan , Cai Xiaojun , Ke Min
Published: September 2022

Retinopathy of prematurity (ROP) is a major blindness-causing disease that is characterized by an arrest of normal vascular development and neovascularization of the retina. Previous studies have shown that genetic factors may be associated with the development and severity of ROP. However, the genes and mechanisms underlying ROP remain unclear. We aimed to identify hub genes in ROP and drugs related to these genes by integrative analysis. The expression profiles of GSE158799 and GSE135844 were acquired from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Then, an integrative analysis was performed including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, transcription factor (TF)-gene, and miRNA-gene networks analysis. Moreover, we verified hub genes and identified potential drugs. 225 common DEGs were identified. Biological function analysis indicated that angiogenesis, cell surface, cell adhesion, extracellular matrix, and focal adhesion genes were enriched among DEGs. The PI3K/Akt signalingpathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction were markedly enriched in the KEGG pathway analysis. Finally, 5 hub genes related to the nosogenesis of ROP were identified and found to be targeted by VEGFA inhibitors, TLR4 antagonists, and sunitinib. The present study showed that VEGFA, ACTA2, MKI67, CD68, and TLR4 are potential hub genes involved in the pathogenesis of ROP. Moreover, TLR4 antagonists and sunitinib may be new candidate drugs for ROP therapy, in addition to VEGFA inhibitors.
A Multistage Stochastic Programming Model with Multiple Objectives for the Optimal Issuance of Corporate Bonds
Authors: Yang Ruicheng , Hu Zinan
Published: September 2022

Large corporations usually cover their capital and operating expenses by issuing bonds with fixed rates and different maturities. This paper proposes a multistage stochastic programming (MSP) model with multiple objectives to optimize bond issuance by satisfying the three common objectives of corporate managers, as follows: (i) Minimizing expected discounted cost under cash liquidity and financial leverage risk constraints. (ii) Minimizing financial leverage risk under expected discounted cost and cash liquidity risk constraints. (iii) Minimizing cash liquidity risk under expected discounted cost and financial leverage risk constraints. We measure liquidity risk as conditional payment‐at‐risk (CPaR), according to the corporation’s financial characteristics. Financial leverage risk is captured by conditional financial leverage‐at‐risk (CFLaR), which we design based on conditional value‐at‐risk (CVaR). Through empirical analysis of a company in China, we explore the efficient frontier curves for the three above objectives and provide the corresponding issuance compositions of an optimal bond portfolio. Our MSP model offers guidance for corporations on achieving a trade‐off between cost and risk when issuing corporate bonds.
[Retracted] Clinical Effect of Ear Endoscopic Intervention on CMEC Patients and Analysis of the Relationship between ROS, P‐Akt, and HIF‐1α Expression and the Degree of Bone Destruction
Authors: Yang Ming , Yang Longqiu , Wan Lang , Sun Qiuping
Published: September 2022

The clinical efficacy of ear endoscopic intervention in patients with congenital middle ear cholesteatoma (CMEC) is explored, and the relationship between the expression of reactive oxygen species (ROS), phosphorylated protein kinase B (P‐Akt), hypoxia‐inducible factor‐1 α (HIF‐1α) and the degree of bone damage are analyzed. A total of 72 CMEC patients admitted to the otolaryngology department of our hospital from 2019 to January 2021 for surgical treatment are selected. According to the different intervention methods, the microscope group and the otolaryngology intervention group are established, respectively, with 36 patients in each group. The patients in the microscope group are treated with a microscope for middle ear cholesteatoma surgery, and the patients in the otoscope intervention group are treated with an otoscope for middle ear cholesteatoma surgery. The experimental results show that ear endoscopic intervention has better clinical efficacy for CMEC patients, which can effectively shorten the operation time, reduce the incidence of postoperative complications, and effectively improve the hearing of patients.
Infrared Optical Functions of Water Retrieved Using Attenuated Total Reflection Spectroscopy
Authors: Vieira Luis G.
Published: September 2022

Comprehensive modeling of non-polarized infrared attenuated total reflection spectrum based on Fresnel equations and wavenumber-dependent dielectric function models of isotropic materials is shown to be a suitable and easy methodology to retrieve optical functions. The scheme is completely general and can be used even for strong dispersion and absorption resonances. Attenuated total reflection spectra in liquid water, measured in the spectral region 100–4400 cm−1 with diamond and germanium as internal reflection elements, were used to illustrate and evaluate the method. The refractive index of water computed from the dispersion analysis is critically compared with literature data.