bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Research on Span Direction Effect of Vortex-Induced Vibration of Bridge Main Girder Based on Symmetric Algorithm
Authors: Cao Xiaochuan , Luo Yuhu
Published: July 2022

Nowadays, the research of digital geometry is gradually shifting from low-level geometric attributes to high-level semantic attributes. The symmetry of the 3D geometric model is an important bridge between the underlying geometric information of the related model and the high-level semantic information. Symmetry analysis is an important issue in geometric processing. Bridge safety is a major issue related to the national economy and people’s livelihood. Therefore, the bridge structure health monitoring system is becoming a research focus in academic and technical circles. Wireless sensor network has been widely used in bridge health monitoring system due to its convenient installation, low maintenance cost, and flexible deployment. This article summarizes the existing bridge health monitoring system based on wireless sensor network. In the bridge control system of wireless sensor network, the basic principles and typical methods are introduced in turn, and the key technologies in the system are analyzed in combination with several specific examples. Finally, the existing problems and future research directions of the existing system are summarized. During the operation stage, vortex-induced resonance often occurs below the wind speed. The vortex excitation force of the main actuator along the axis of the bridge is related to the actual wind field and its own structural vibration. This article briefly introduces typical cases and control measures of vortex-induced vibration of long-span bridges, and summarizes the current research status of vortex-induced vibration of bridge main girder structures. At the same time, the vortex-induced vibration of bridge girder is experimentally studied by using symmetrical algorithm.
Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model
Authors: Chen Wenzheng , Yang Guanglei , Qi Dongkai
Published: July 2022

Nowadays, more and more college students’ physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information loss rate is 4.136%, and the accuracy of motion mode decision is 92.58%. The average relative time is 12.7, and the accuracy of physical health decision making is 90.02%. The above two values are not significant compared with the traditional ID3 algorithm. The experimental results show that the improved ID3 algorithm has significant optimization in the stability of information transmission and the accuracy of sports recommendation decision making, and can be applied to the physical health evaluation and sports recommendation of college students in a certain range.
VR-Based Innovation of the Ideological and Political Teaching Mode in Colleges
Authors: Chen Feng , Jiang Hongming
Published: July 2022

The current ideological and political (IAP) teaching has some disadvantages, such as emphasizing theoretical form over practical teaching, students’ differences in personality and cognition, and lack of interaction between teachers and students. Therefore, this paper first analyzes IAP teaching based on VR communication and summarizes the disadvantages of current IAP teaching and the application advantages of VR technology. Based on the principles of interactivity, practicality, and richness, the IAP teaching system is constructed based on the double-center teaching mode, and the teaching content are designed. In addition, in order to provide a carrier for VR IAP teaching, this paper designs the functional module, development process, and framework of the development of VR venues with red theme, combining with red historical events. The IAP education system proposed in this paper can help to explore the potential and advantages of VR technology that applied to IAP education in colleges and realize the modernization of IAP education.
Fault Diagnosis of Wind Turbine Gearbox Based on Multisensor Data Fusion
Authors: Zhang Ruimin , Zhou Tao
Published: July 2022

In order to improve the operation efficiency of wind turbine gearbox and reduce the operation and maintenance cost of wind farm, a fault diagnosis system for wind turbine gearbox based on multisensor data fusion was proposed. First, the different time-domain statistical characteristic parameters of the original vibration signal were calculated, and the information fusion of the feature level and the data level was carried out by means of parallel superposition to obtain the fused data set. Second, a fault classification and recognition model based on GMO-KELM was established by using the fusion data set. Finally, the proposed method was used to monitor the status of the measured data of the gearbox on the vibration test bed of rotating machinery. The experimental results showed that the average training accuracy and the average test accuracy of GMO-KELM method were 100% and 95.58%, respectively, which were much higher than those of other methods. Through experiments and analyses, it was shown that the proposed method was effective and feasible. Compared with other similar methods, the proposed method had the best classification performance.
Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment
Authors: Sun Jianzhao
Published: July 2022

With the rapid development of mobile communication technology, the data transmission rate of mobile communication has been significantly improved and emerging Internet of things applications need a lot of computing resources to meet their own computing needs. However, the existing intelligent terminals have limited computing power and cannot meet the low-latency computing requirements with limited energy consumption. Mobile edge computing technology is considered to be one of the technical solutions that can efficiently solve this problem. Through mobile edge computing technology, intelligent terminals can actively divert some computing tasks to the computing servers deployed in edge network nodes and return the computing results to intelligent terminals after the edge computing servers have finished computing, thus greatly reducing the computing delay of users. In this paper, aiming at multiple edge server networks, combined with dual-connection technology, based on the research of a single edge computing server network, it is further extended to two edge computing servers and a joint optimization problem of computing task allocation and security performance requirements of intelligent terminals and edge computing servers is proposed, so as to minimize the global time delay for completing the computing tasks of intelligent terminals. By layering the problem, this paper proposes a corresponding algorithm, which can efficiently obtain the optimal solution of the initial problem. Compared with the linear search algorithm, the algorithm proposed in this paper can significantly reduce the computation time.
COQ10B Knockdown Modulates Cell Proliferation, Invasion, Migration, and Apoptosis in Esophageal Squamous Cell Carcinoma
Authors: Wei Yu , Liu Juan , Gao Yan , Ma Xiaoli , Cao Leiyu , Maimaitiming Nuersimanguli , Qu Chengcheng , Zhang Li
Published: July 2022

Objective. Esophageal squamous-cell carcinoma (ESCC) is an aggressive malignant tumor, accounting for more than 90% of esophageal cancers. However, treatments such as surgical resection, radiotherapy, and chemotherapy are unable to achieve ideal clinical outcomes. The purpose of this study was to explore the effects of COQ10B on proliferation, apoptosis, migration, and invasion of esophageal squamous-cell carcinoma (ESCC) cells. Methods. Quantitative real-time PCR (qRT-PCR) was used to detect the expression of COQ10B in ESCC and normal tissues and in ESCC cell lines (KYSE-150 and TE-1). MTT assay and flow cytometry were applied to investigate the effects of COQ10B shRNA lentivirus (LV-shCOQ10B) on ESCC cell proliferation and apoptosis, respectively. The effect of COQ10B silencing on ESCC cell migration and invasion was determined by wound healing assay and transwell invasion assay, respectively. Results. The expression of COQ10B mRNA in ESCC tissues was higher than that in surrounding tissues. The decreased COQ10B level in KYSE-150 and TE-1 cells by LV-shCOQ10B could inhibit cell proliferation, promote cell apoptosis, and reduce the ability of invasion and migration (all

P
<
0.05

). Conclusion. COQ10B was highly expressed in human ESCC tissues. COQ10B silencing contributed to the inhibition of proliferation, invasion, and migration of ESCC cells and the promotion of cell apoptosis, suggesting COQ10B may be a potential molecular target for the diagnosis and treatment of ESCC.
The Application of Nurse Stratified Management in Nursing Management
Authors: Liu Qing , Guo Yujuan , Wei Pin , Cui Chao
Published: July 2022

Objective. To explore the application value of nurse stratified management in nursing management. Methods. One hundred nurses who worked in our hospital from June 2019 to September 2021 were recruited and assigned via the random number table method to an observation group (n = 50) and the control group (n = 50). The control group received routine nursing management, and the experimental group received stratified nursing management. The nursing quality, practice happiness, error rate, patient complaint, and patient satisfaction were compared between the two groups. Results. The experimental group showed a significantly higher nursing quality than the control group (

P
<
0.05

). The experimental group had significantly higher practice happiness among nurses than the control group (

P
<
0.05

). Stratified nursing management mode resulted in a significantly lower error rate and patient complaint versus routine nursing management mode (

P
<
0.05

). Patients were more satisfied with stratified nursing management than with the routine nursing management (

P
<
0.05

). Conclusion. The nurse stratified management method effectively improves the nursing quality and practice happiness of nurses, reduces the error rate of nursing and patient complaint, and increases nursing satisfaction.
Safety and Efficacy of Mild Moxibustion on Cancer-Related Fatigue in Non-Small-Cell Lung Cancer Patients Undergoing Chemotherapy
Authors: Xu Xiaomin , Gu Fen
Published: July 2022

Objective. Cancer-related fatigue (CRF) is a well-recognized issue for cancer patients undergoing chemotherapy; however, research on nonpharmacological alternatives have been underreported. This study is aimed at investigating the effect of mild moxibustion on CRF in patients with non-small-cell lung cancer (NSCLC) undergoing chemotherapy. Methods. A randomized controlled trial was performed on 126 NSCLC patients undergoing initial chemotherapy and were divided into the following three groups: mild moxibustion group, sham acupoint group, and control group. Moxibustion was performed on two groups of acupuncture points, Dazhui and Geshu acupoints in the prone position and Qihai, Guanyuan, and Zusanli acupoints in the supine position, during the chemotherapy for 30 min with one of the groups of acupuncture points per day. Moxibustion of the sham group was performed 1 cm away from the true acupoint, while only routine nursing care was given to the routine group. The efficacy and safety of the treatments were assessed based on the Chinese version of the Revised Piper Fatigue Scale (RPFS-CV), the quality of life (QoL) questionnaire for Chinese cancer patients receiving chemobiotherapy (QLQ-CCC), and liver and kidney function indexes. Results. A total of 118 cases completed all interventions. There was no significant difference in the baseline indicators among the three groups of NSCLC patients. Although all three groups demonstrated raised RPFS-CV scores over the chemotherapy cycle, comparatively, the mild moxibustion group had significantly lower RPFS-CV scores and better relief of CRF symptoms (

P
<
0.05

). The QLQ-CCC results indicated that the QoL of NSCLC patients dramatically decreased following chemotherapy, even with mild moxibustion. Further, mild moxibustion intervention did not show significantly different levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (Scr), and blood urea nitrogen (BUN) compared with the two other groups (

P
>
0.05

). Conclusion. Chemotherapy is accompanied by elevated fatigue scores and decreased life quality in NSCLC patients. Although mild moxibustion intervention could alleviate CRF in the patients and was not associated with any adverse events and liver and kidney toxicity when combined with chemotherapy, it could not improve their QoL.
Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network
Authors: Zhu Zijiang , Chen Hang , Xie Song , Hu Yi , Chang Jing
Published: July 2022

The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors’ clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients’ physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician’s diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.
Therapeutic Effect of Laparoscopic Cholecystectomy on Patients with Cholecystolithiasis Complicated with Chronic Cholecystitis and Postoperative Quality of Life
Authors: Song Jie , Chen Jie , Lin Chunyan
Published: July 2022

Objective. To assess the treatment outcome and postoperative quality of life of patients with gallbladder stones and chronic cholecystitis after open cholecystectomy and laparoscopic cholecystectomy. Methods. Between 2018 and 2020, 108 patients with gallbladder stones and chronic cholecystitis treated in our hospital were assessed for eligibility and randomly recruited. They were concurrently assigned (1 : 1) to receive either open cholecystectomy (control group) or laparoscopic cholecystectomy (study group). Outcome measures include surgical indices, inflammatory response, postoperative complications, and quality of life of patients. Results. Laparoscopic cholecystectomy was associated with a shorter duration of surgery, intraoperative bleeding, time to first postoperative bowel movement, and postoperative hospital stay versus open cholecystectomy (P<0.05). The levels of inflammatory factors of all eligible patients were comparable before cholecystectomy (P>0.05). The patients given laparoscopic cholecystectomy showed lower levels of C-reactive protein (CRP), interleukin (IL)-6, and IL-8 versus those given open cholecystectomy (P<0.05). Laparoscopic cholecystectomy resulted in a significantly lower incidence of complication (3.56%) versus open cholecystectomy (24.07%) (P<0.05). The patients had significantly higher physical, psychological, and social function scores after laparoscopic cholecystectomy versus open cholecystectomy (P<0.05). Conclusion. Laparoscopic cholecystectomy provides better surgical results, mitigates the inflammatory response, lowers the incidence of complications, and improves the quality of life of patients versus open cholecystectomy, so it is worthy of application in clinical treatment.
Certain Concepts of
Q
-Hesitant Fuzzy Ideals
Authors: Alleheb Lubna Abdul Aziz , Alsager Kholood Mohammad
Published: July 2022

The hesitant fuzzy set model has attracted the interest of scholars in various fields. The striking framework of hesitant fuzzy sets is keen to provide a larger domain of preference for fuzzy information modeling of deployment membership. Starting from the hybrid properties of hesitant fuzzy ideals (HFI), this paper constructs a new generalized hybrid structure

Q

-HFI. The concept of

Q

-hesitant fuzzy exchange ideal in

B
C
K

-algebra is considered. Lastly,

Q

-hesitant fuzzy exchange ideal features are described.
Numerical Simulation to Predict COVID-19 Cases in Punjab
Authors: Aggarwal Vanshika , Arora Geeta , Emadifar Homan , Hamasalh Faraidun K. , Khademi Masoumeh
Published: July 2022

Coronavirus disease 2019 is a novel disease caused by a newly identified virus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). India recorded its first case of COVID-19 on 30 January 2020. This work is an attempt to calculate the number of COVID-19 cases in Punjab by solving a partial differential equation using the modified cubic B-spline function and differential quadrature method. The real data of COVID-19 cases and Google Community Mobility Reports of Punjab districts were used to verify the numerical simulation of the model. The Google mobility data reflect the changes in social behavior in real time and therefore are an important factor in analyzing the spread of COVID-19 and the corresponding precautionary measures. To investigate the cross-border transmission of COVID-19 between the 23 districts of Punjab with an analysis of human activities as a factor, the 23 districts were divided into five regions. This paper is aimed at demonstrating the predictive ability of the model.
Efficacy of Respiratory Training in Relieving Postoperative Pain in Patients with Spinal Nerve Root Entrapment Syndrome
Authors: Hu Ling , Hu Liqiong , Xu Jinglin , Zhang Zhang , Wu Jun , Xiang Yun , He Qianqian , Zhang Ting , Li Jun
Published: July 2022

Background. Breathing exercise can help patients with dyspnea to change the wrong breathing pattern, improve the degree of freedom of respiratory muscles, increase alveolar ventilation, promote breathing, and relieve the symptoms of dyspnea patients. Therefore, this study is aimed at investigating the role of breathing training in alleviating postoperative pain in patients with spinal nerve root entrapment syndrome. Objective. To explore the effect of respiratory training in relieving postoperative pain in patients with spinal nerve root entrapment syndrome. Methods. Fifty-eight patients with spinal nerve root entrapment syndrome treated in our hospital from May 2020 to May 2021 were analyzed retrospectively. The patients were randomly divided into a control group (

n
=
29

) and an observation group (

n
=
29

). The control group was given routine postoperative pain nursing, and the observation group was given respiratory training on the basis of the control group. The scores of visual analogue scale (VAS), self-rating anxiety scale (SAS), Oswestry dysfunction index questionnaire (ODI), the dosage of postoperative analgesics, and the time of first out-of-bed activity were recorded before pain nursing intervention and 3 days and 7 days after intervention. Results. The VAS, SAS, and ODI scores of the observation group after 3 d and 7 d of intervention were lower than those of the control group. Compared with the same group, the scores of VAS, SAS, and ODI after 3 d and 7 d of intervention were lower than those before intervention, and those after 7 d of intervention were lower than those after 3 d of intervention (

P
<
0.05

). The dosage of postoperative analgesics and the time of first out-of-bed activity in the observation group were lower than those in the control group (

P
<
0.05

). Conclusion. Respiratory training can effectively relieve postoperative pain, reduce anxiety, and improve spinal function in patients with spinal nerve root entrapment syndrome, which is beneficial to the prognosis of patients and is worthy of promotion.
The Level of HbA1c Evaluates the Extent of Coronary Atherosclerosis Lesions and the Prognosis in Diabetes with Acute Coronary Syndrome
Authors: Xu Shaodong , Zhou Birong , Zhou Bingfeng , Zheng Jianfa , Xu Qinghe , Wang Bing , Fu Minmin , Meng Ying
Published: July 2022

Background. The level of HbA1c can reflect the average level of blood glucose over 3 months, which is the gold standard indicator for monitoring blood glucose. The relationship between the level of HbA1c and the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) remains poorly understood. Aims. To explore whether the level of HbA1c can evaluate the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) using the SYNTAX score, the Global Registry of Acute Coronary Events (GRACE) score, left ventricular function (LVEF), left ventricular end-diastolic volume (LVEDV), and major adverse cardiac events (MACEs) in the hospital and 12 months after discharge. Methods. This study was a prospective, randomized, open-label, and parallel group study. Patients with diabetes with ACS were recruited into this study indiscriminately, and all the participants were divided into two groups according to the level of HbA1c:

HbA
1
c
 
level
≤
7
%
group

and

HbA
1
c
 
level
>
7
%
group

. The followings were used as the evaluation indicators: SYNTAX score, GRACE score, LVEF, LVEDV, and MACEs in hospital and 12 months after discharge. Results. A total of 233 patients with diabetes and ACS were enrolled and assigned to two groups according to their level of HbA1c: the

HbA
1
c
≤
7
%
group

(

n
=
92

) and the

HbA
1
c
>
7
%
group

(

n
=
141

). The results showed that the proportion of STEMI was higher in the HbA1c ≤7% group (

p
<
0.05

), while the proportion of NSTEMI has not significantly higher in the HbA1c >7% group (

p
>
0.05

). Regression analysis indicated that HbA1c level was significantly positively correlated with GRACE score (

r
=
0.156

,

F
=
5.784

,

p
=
0.017

,

n
=
233

) and SYNTAX score (

r
=
0.237

,

F
=
13.788

,

p
<
0.001

,

n
=
233

), and there were no statistically significant differences in LVEDV and LVEF between the two groups (

p
>
0.05

). The total MACEs rate showed no significant difference between the two groups during hospitalization (

p
>
0.05

) but showed significant differences at 12 months after discharge (

p
<
0.05

). Conclusions. This study shows that HbA1c level was positively correlated with the extent of coronary atherosclerosis lesions and the prognosis in diabetes with ACS. The higher the HbA1c level is, the more severe the coronary atherosclerotic lesion and the worse the prognosis in diabetes with ACS are.
Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor
Authors: Dogan Daghan , Bogosyan Seta , Acarman Tankut
Published: July 2022

Advanced driver assistance systems and conditional automation (SAE level L3) bring full of challenges, central to the human role in such automated driving and are the transitions from automated to manual driving mode: driver monitoring systems should be able to adapt to the detected human state adjusting time given for takeover request. In this study, the human driver’s control authority during the transition from the automated system is evaluated by using a large set of sensors, including the galvanic skin response sensor, the speed, and the current sensor of the electric vehicle. The Local Outlier Factor (LOF) method detects the driver’s readiness to take control during the control authority transition transient stage. Eighteen drivers (8 females and 10 males) between 24 and 65 years of age participate in the experimental evaluation and data collection campaign. Takeover request time analysis evaluates each driver and three driver categories, such as experienced, semiexperienced, and inexperienced, and the questionnaire validates the takeover request time analysis. Galvanic skin response measurements and the LOF approach illustrate successful detection of the driver’s status about his/her readiness to take over and regulate the driving task when the transition of driving authority from the automated system is requested in a real road driving scenario. The takeover request time is personalized for each driver, which may improve the current conditional automated driving technologies’ penetration and acceptance.
3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing
Authors: Li Feng , Zhao Hui
Published: July 2022

Traditional digital geometry mainly focuses on local geometric features such as curvature and normal of 3D models. Although the curvature can describe the geometric curvature of the model surface, these local geometric properties cannot describe the global functional structure and associated properties of the 3D model. The purpose of this paper was to study the accurate splicing of cultural relic fragments based on the intrinsic structural features of the 3D model, such as geometry, texture, and function, whose method is the key to realizing the virtual reconstruction of damaged cultural relics. In this paper, a method for analyzing the intrinsic structure of 3D point cloud data is proposed, the feature representation method of 3D discrete curves and surfaces is studied, and a method for identifying geometric features of 3D point clouds based on the similarity measure of the principal curvature is proposed, which realizes the effective extraction of geometric features of the 3D point cloud model of cultural relics. By calculating the visual curvature distribution of the model under multiscale constraints, the effective extraction of the structural primitives of cultural relics with rich surface noise is realized. The experimental results in this paper show that the initial matching time is 1.416 seconds, the final matching time is 1.555 seconds, the average number of iterations is 13, the average stitching error is 1.7233 mm, and the standard deviation is 1.0265 mm. The experimental data show that the algorithm proposed in this chapter has good convergence characteristics and effectively avoids the divergence phenomenon that is easy to occur in the existing stitching algorithms.
Identification of Nine M6A-Related Long Noncoding RNAs as Prognostic Signatures Associated with Oxidative Stress in Oral Cancer Based on Data from The Cancer Genome Atlas
Authors: Shan Li , Lu Ye , Song Yihua , Zhu Xiaoli , Xiang Cheng-Cheng , Zuo Er-Dong , Cheng Xu
Published: July 2022

Objective. Although the expression of long noncoding RNAs (lncRNAs) and N6-methyladenosine (M6A) is correlated with different tumors, it remains unclear how M6A-related lncRNA functioning affects the initiation and progression of oral squamous cell carcinoma (OSCC). Materials and Methods. Gene expression and clinical data were retrieved from The Cancer Genome Atlas. The prognostic value of M6A-related lncRNAs was determined using univariate Cox regression analyses. Gene set enrichment analysis of OSCC patient clusters revealed the pathways that elucidate the mechanism. Furthermore, a risk-based model was established. The difference in the overall survival (OS) between groups, including low-/high-risk groups, was determined by Kaplan-Meier analysis. Relationships among immune cells, clusters, clinicopathological characteristics, and risk scores were explored. Results. Among 1,080 M6A-related lncRNAs, 36 were prognosis-related. Patients with OSCC were divided into two clusters. T stage and the pathological grade were noticeably lower in cluster 2 than in cluster 1. Epithelial-mesenchymal transition showed greater enrichment in cluster 1. Nine hub M6A-related lncRNAs were identified for the model of risk score for predicting OSCC prognosis. The OS was longer in patients with a low-risk score than in patients with a high-risk score. The risk score was an independent prognostic factor of OSCC and was associated with the infiltration of different immune cells. T stages and the American Joint Committee on Cancer (AJCC) stages were more advanced in the high-risk score group than in the low-risk score group. Finally, expression correlation analysis showed that the risk score is associated with the expression of oxidative stress markers. Conclusion. M6A-related lncRNAs play an important role in OSCC progression. Immune cell infiltration was related to the risk score model in OSCC and could accurately predict OSCC prognosis.
A Novel Microscopic Modeling Scheme for the Shape Memory Polymer Composites with respect to the Ambient Temperature
Authors: Li Yang , Ye Junjie , Liu Lu , Shi Baoquan , He Yumin
Published: July 2022

This paper is aimed at studying the effective mechanical property of shape memory polymer composites (SMPC) reinforced with natural short fibers. To this end, a novel modeling scheme was presented. The SMPC was firstly equivalent to the composite laminates, and the natural short fibers are also subtly equivalent to the ellipsoidal inclusions distributed in the matrix materials periodically. Moreover, a represented volume element along laminate thickness can be easily chosen, and its elastic constants are accurately acquired by employing a proper microscopic mechanical model. Herein, the high-fidelity generalized method of cells, which represents a good ability in predicting the effective mechanical behaviors of composites, was used. On this basis, the classic laminate theory was improved to suitable for describing the elastic constants and failure strength the SMPC with respect to ambient temperature. Numerical results show a good consistency to the experimental data. Moreover, a higher ambient temperature tends to sharply decrease their final failure strength. It is also revealed that the presented modeling method shows a great potential in calculating the effectively mechanical property of the natural short fiber-reinforced composites.
An Internet of Things (IoT) Based Block Chain Technology to Enhance the Quality of Supply Chain Management (SCM)
Authors: Rizwan Ali , Karras Dimitrios A. , Kumar Jitendra , Sánchez-Chero Manuel , Mogollón Taboada Marlon Martín , Altamirano Gilder Cieza
Published: July 2022

Recent technological developments indicate possible advancements in supply chain management (SCM). These innovations have attracted a lot of interest from industries including logistics, manufacturing, packaging, and transportation. The conventional systems, however, use centralised servers to control all operations, including the exchange of raw materials, making orders, dealing with buyers and sellers, and updating orders. The network’s supply chain may thus be insecure as a result of every activity being routed via centralised servers. The danger is additionally increased by a number of difficulties, including scalability, data integrity, security, and availability. Block chain technology may be used in these circumstances to decentralise transaction processing and eliminate the need for a centralised controller. In this approach, the performance of the resource-constrained supply chain network is improved by the effective use of edge computing and priority data access. The Intelligent K-Means (IKM) clustering algorithm is suggested across the edge nodes in the current research to categorise the priority level of each piece of data. This classifier determines if the edge node has received data that is high priority or low priority. Low priority data is recorded in the log files for future data analysis. Then, to allow safe data flow in the open block chain while excluding outside parties, the High Priority Access based Smart Contract (HPASC) technique is deployed. The whole experiment was conducted in a Python environment, and variables including scalability, reaction time, throughput, and accuracy were studied. Current systems’ constrained block sizes and fork creation lengthen the time transactions must wait before being processed. The suggested methodology is quicker and uses less storage space than current block chain systems. The results show that the suggested approach works better than current blockchain technology to raise the standard of supply chain management.
Advances in Tissue Engineering of the Temporomandibular Joint Disc: An Overview of Current Status and Future Directions
Authors: Singh Ashutosh Kumar , Khanal Nikita , Chaulagain Rajib
Published: July 2022

Advances in tissue engineering have progressed to potentially offer a solution to temporomandibular joint disc (TMJ) disorders not amenable to conservative therapies. Conclusive treatment options for patients with end-stage disc disorders requires discectomy and reconstruction of the articular disc with various materials. Tissue engineering TMJ disc is a promising alternative to the limited and sometimes inadequate clinical options in the management of such disorders. However, tissue engineering is far from completion for the TMJ disc regeneration. This review briefly discusses the properties of native disc, the mechanism by which TMJ disorders manifest, and how a tissue engineered disc could assuage the problems inherent in the management of such disorders. Furthermore, the review addresses and provides updates to relevant themes of tissue engineering in regards to the TMJ disc, namely, the scaffolds, cells and biomarkers, hurdles in tissue engineering of the disc, and its application in translation to the clinical practice and future directions.
[Retracted] Analysis of Research Status and Development Trend of Nanotoxicology of Liliaceae Medicinal Plants
Authors: Liu ChaoQun , Wang Yinquan , Liu DongLing , Yang Tao , Tang ZhanWen
Published: July 2022

The research status and development trend of nanotoxicology of Liliaceae medicinal plants were analyzed. In the research, the toxicology of Liliaceae medicinal plants was investigated by the preparation method of silver nanoparticles. By means of spectral curve experiment, the present situation of nanotoxicology of Liliaceae medicinal plants was analyzed, and then its subsequent development trend was analyzed. In this process, Liliaceae medicinal plants could be used effectively, which could create great economic benefits. In the application of the above scheme, the toxicological degradation of Liliaceae medicinal plants could be controlled at about 96%. The high‐dose silver nanoparticles could reach 100 μM, and the silver nitrate could reach 10 or 30 μM.
Application of the simplex method on profit maximization in Baker's Cottage
Authors: Mohd Azman Nur Zafira , Akmal Mohamed Nurul , Farihan Mohamed Nurul , Musa Muzirah
Published: July 2022

Linear programming is an operational research technique widely used to identify and optimize management decision. Its application encourages businesses to increase their output. Instead, however, many organizations most commonly adopt the trial-and-error method. As such, companies find it challenging to distribute scarce resources in a manner that maximizes profit. This study focuses on implementing linear programming to optimize the profit of a manufacturing sector based on the optimized (best possible, efficient) use of raw materials. Our study uses the data gathered on five market bread types from Baker's Cottage reports, i.e., chicken floss, spicy floss, Frank Cheese, Mexico bun, and doughnut. This attribute has been recognized as a linear programming problem mathematically built that was solved using Excel software. The result showed that the Baker’s Cottage unit had to produce 332 loaves of Chicken Floss and 196 loaves of Frank Cheese, as these products objectively contributed to the profit. In contrast, other types of bread did not have to be produced, as their value turned to zero to achieve the maximum monthly profit.
Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT
Authors: Grari Mounir , Idrissi Idriss , Boukabous Mohammed , Moussaoui Omar , Azizi Mostafa , Moussaoui Mimoun
Published: July 2022

The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.
An interpolation based steganographic technique with least-significant-bit and pixel value differencing in a pixel block
Authors: Majumder Jayeeta , Pradhan Chittaranjan
Published: July 2022

Over the past few years, in order to improve the hiding capacity and the peak signal-to-noise ratio (PSNR) value, several steganographic techniques have been developed. Steganography has become a popular technique to transmit secret data through any medium. In image steganography, the human eye cannot easily identify the hidden data which is embedded into the image. Small changes are also not detected by the human eye. High hidden capacity along with high visual quality is provided by the pixel value differencing (PVD) method. This paper first proposes the method of interpolation between the pixel blocks and then applies the least-significant-bit (LSB) substitution technique with the PVD method. At the starting phase, the original image is fixedto a 2x2 block, then the nearest neighbor interpolation (NNI) technique is implemented. In the next phase, the upper left pixel isembedded by the k-bit LSB substitution method along with hidden data. The newly generated neighbouring pixel value is measured. Thus, data is hidden from three directions. Through this paper using two different range tables, the new algorithm is proposed. We observed that in both cases, PSNR and the hiding capacity are improved.
Dual embedding with input embedding and output embedding for better word representation
Authors: Ahn Yoonjoo , Rhee Eugene , Lee Jihoon
Published: July 2022

Recent studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms of word analogy and word similarity with each input and output embeddings and various dual embeddings which are the combination of those two embeddings. Performance evaluation results show that the proposed dual embeddings outperform each single embedding, especially with the way of simply adding input and output embeddings. We figured out two things in this paper, i) not only input embedding but also output embedding has such meaning to represent the words and ii) combining input embedding and output embedding as dual embedding outperforms the single embedding when we use input embedding and output embedding individually.
Early disease prediction algorithm for hypertension-based diseases using data aware algorithms
Authors: Shaikh Yasmeen , Parvati Vasudev , Biradar Sangappa Ramachandra
Published: July 2022

This paper implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.
Thermodynamic properties in quantum dot nanocomposite for white light-emitted diodes
Authors: Huu Phuc Dang , Le Phan Xuan
Published: July 2022

For a colour conversion substance, we developed a new nanocomposite containing a CdSe/CdS/ZnS red-emitted quantum dot (QD), a green-emittedd Sr2SiO4:Eu phosphorus, and silicon resins. Regarding QD concentration and ingredients, the heat increase and optic features of the nanocomposite attributable to the QD inclusion were examined. According to the findings, a modest portion of QDs added to a photon converter at the emission wavelength of QD produced a considerable degree of heat. We used 0.2 wt% QDs over an InGaN blue-emitting light-emitted diodes (LED) chip to simulate a thermal increase in a nanocomposite. Consequently, we were able to produce a white-emitted LED module featuring a good 83.2 colour rendered index, an excellent 65.86 lm W-1 brightness, and a reasonable 94 °C thermal rise. The recently founded QD-phosphorus nanocomposite transformed white-emitted LED offers a lot of possibility of modern lighting.
Graphene-polyvinyl alcohol polymer based saturable absorption at 2000 nm region
Authors: Hussin Nabihah , Azmi Asrul Izam , Salim Mohd Rashidi , Noor Muhammad Yusof Mohd , Abdullah Ahmad Sharmi , David Michael , Ahmad Fauzan , Ibrahim Mohd Haniff
Published: July 2022

A graphene-polyvinyl alcohol (PVA) composite saturable absorption is demonstrated at 2000 nm region. Graphene suspension is produced using low-cost electrochemical exfoliation process. The suspension is mixed with PVA host polymer in 1:1 ratio and left evaporated at room temperature which finally produced graphene-PVA thin film. Thulium doped fiber (TDF) gain medium has been shown to produce a stable Q-switched pulse with a highest repetition rate of 54 kHz, a short pulse duration of 2.89 µs, a maximum peak power of 16 mW, and an estimated maximum pulse energy of 49 nJ. Apparently, at 2000 nm region, superior performances of graphene-PVA composite have been recorded which was largely contributed by meticulous composite preparation and homogenous mixture with PVA host.
Luminescence and transfer for power characteristics of Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ phosphor for WLEDs
Authors: Nguyen Thi My Hanh , Xuan Le Phan
Published: July 2022

We apply the high-heat solid-state technique to create a sequence of phosphors Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+. In Sr4La(PO4)3Othe luminescence characteristics, heat resistance, and transfer of energy between Ce3+ and Tb3+ as well as Mn2+ were thoroughly studied. The insertion of the sensitizer Ce3+ ions can significantly flatter the emission of the color green from Tb3+ and the emission of the color red from Mn2+ via energy transfer. By modifying the Ce3+/Tb3+ and Ce3+/Mn2+ ion ratios, it can change the emission color. White light, which has color coordinates of 0.3326, 0.3298, were produced by the Sr4La(PO4)3O: 0.12Ce3+, 0.3Mn2+ 0.12Ce3+, 0.3Mn2+ specimen, showing Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ might be profitable in white LEDs.
The luminescence efficiency of green phosphor Ca7(PO4)2(SiO4)2:Eu2+ for white light-emitting diode
Authors: Liem Bui Van , Xuan Le Phan
Published: July 2022

This study examines the green-emission phosphor composition of Eu2+ doped Ca7(PO4)2(SiO4)2 to serve the goal of efficiency enhancement for the white light emitting diode (LED). The process of preparation and photoluminescent investigation of proposed phosphor composition was monitored under near UV excitation wavelength of the LED die. The sample phase was determined using XRD. To explore Ca7(PO4)2(SiO4)2:Eu2+ capabilities, the diffuse reflectance and photoluminescence spectral figures were employed. The ultraviolet absorption of Ca7(PO4)2(SiO4)2:Eu2+ranged from 240 to 440 nm, with a wide band of green emission peaking at 522 nm. Besides the concentration quenching mechanism, we also focus on essential characteristics for white-light-emitting diode
(WLED) production like temperature-dependent lumen output and chromaticity coordinates.
Gender and race classification using geodesic distance measurement
Authors: Shahad Marzoog Zahraa , Dhannon Hasan Ashraf , Hassan Abbas Hawraa
Published: July 2022

Gender and ethnicity classifications are a long-standing challenge in the face recognition’s field. They are key-demographic traits of individuals and applied in real-world applications such as biometric and demographic research, human-computer interaction (HCI), law enforcement and online advertisements. Thus, many methods have been proposed to address gender or/and race classifications and achieved various accuracies. This research improves race and gender classification by employing a geodesic path algorithm to extract discriminative features of both gender and ethnicity. PCA is also utilized for dimensionality reduction of Gender-feature and race-feature matrices. KNN and SVM are used to classify the extracted feature. This research was tested on the face recognition technology (FERET) dataset, with classification results demonstrating high-level performance (100%) in distinguishing gender and ethnicity.