bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Respiratory Training Combined with Core Muscle Training on the Overall Motor Function and Activities of Daily Living of Patients with Early and Midterm Stroke
Authors: Li Ruichun , Li Long , Chen Qiuju
Published: January 2022

Stroke is a cerebral ischemic or hemorrhagic disease with sudden onset and rapid progress. To analyze the effect of respiratory training combined with core muscle training on the overall motor function and activities of daily living of patients with early and midterm stroke, 90 cases with early and midterm stroke admitted to the neurological department of our hospital from April 2018 to April 2019 were chosen as the research objects. According to the odd or even hospitalization numbers, they were equally divided into the study group and the reference group. Both groups received basic drug treatment. On this basis, the reference group was given routine rehabilitation training, while the study group was given respiratory training combined with core muscle training. The clinical indexes of both groups before and after intervention were evaluated to analyze the effect of different training methods on the rehabilitation of patients with early and midterm stroke. There was no significant difference in gender ratio, average age, average BMI, average course of disease, stroke types, MAS grading, location of limb dysfunction, and combined disease between the two groups (

P
<
0.05

). The total clinical effective rate of the study group after intervention was obviously higher than that of the reference group (

P
<
0.05

). The MoCA scores of both groups after intervention were obviously higher than those before intervention, and the score of the study group after intervention was obviously higher than that of the reference group. The scores of limb motor function, activities of daily living, and balance function at T2, T3, and T4 in the study group were obviously higher than those in the reference group (

P
<
0.001

). At 4 and 8 weeks after intervention, the 10 m MWS of the study group was obviously higher than that of the reference group (

P
<
0.001

), while the TUGT was obviously lower (

P
<
0.001

). Respiratory training combined with core muscle training can obviously improve the activities of daily living, cognitive function, and limb motor function of patients with early and midterm stroke, which is worth popularizing and using.
Changes in Metabolic Regulation and the Microbiota Composition after Supplementation with Different Fatty Acids in db/db Mice
Authors: Martínez-Carrillo Beatriz Elina , Mondragón-Velásquez Talia , Ramírez-Durán Ninfa , Aguirre-Garrido José Félix , Valdés-Ramos Roxana , Guadarrama-López Ana Laura , Castillo-Cardiel Arturo
Published: January 2022

Introduction. The effects of fatty acids on health vary and depend on the type, amount, and route of consumption. EPA and DHA have a defined role in health, unlike coconut oil. Objective. The aim was to investigate the changes in metabolic regulation and the composition of the culture-dependent microbiota after supplementation with different fatty acids in db/db mice. Material and Methods. We were using 32 8-week-old db/db mice, supplemented for eight weeks with EPA/DHA derived from microalgae as well as coconut oil. The lipid, hormonal profiles, and composition of the culture-dependent microbiota and the phylogenetic analysis based on the 16S rRNA gene sequencing were determined for identification of the intestinal microbiota. Results. Enriched diet with EPA/DHA reduced TNF-α, C-peptide, insulin resistance, resistin, and the plasma atherogenic index, but increased TC, LDL-c, VLDL-c, and TG without changes in HDL-c. Coconut oil raised the HDL-c, GIP, and TNF-α, with TG, insulin resistance, adiponectin, and C-peptide reduced. Conclusion. The most abundant microbial populations were Firmicutes and the least Proteobacteria. EPA/DHA derived from microalgae contributes to improving the systemic inflammatory status, but depressed the diversity of the small intestine microbiota. Coconut oil only decreased the C-peptide, raising TNF-α, with an unfavorable hormonal and lipid profile.
Application of Smart Healthcare in LTCI, Outpatient Mutual-Aid Guarantee Mechanism, and Sustainability of Medical Insurance Fund for Urban Employees
Authors: Ren Yating , Yang Zhe
Published: January 2022

With the aggravation of population aging and the increase of life expectancy, long-term care insurance (LTCI) system has been established to meet the medical and long-term care needs of the increasing elderly population. In China, LTCI system is currently not a stand-alone insurance, but it is attached to the national basic medical insurance fund for urban employees (MIUE). As a result, the expenditure of LTCI is a part of the expenditure of the MIUE, which has an impact on the sustainability of the MIUE. By modeling the income and expenditure of MIUE, especially including the expenditure of LTCI, this study optimized an LTCI system with a higher individual out-of-pocket payment ratio of LTCI and implementation of the outpatient mutual-aid guarantee mechanism (OMAGM), which could improve the sustainability of the MIUE. The study also reveals the following: (i) solely increasing individual out-of-pocket payment ratio of LTCI to 20%–50% can only postpone the deficit on Social Pooling Accounts (SPAs) by 1 or 2 years, and the effect is very limited. (ii) Besides a higher individual out-of-pocket payment ratio, further implementation of a partial OMAGM from 2022 will postpone the deficit on SPAs by 7–9 years, and the implementation of a complete OMAGM from 2022 will postpone the deficit by 14–18 years. Accordingly, China should implement OMAGM as soon as possible to enhance the solvency of MIUE fund, and, in the long run, an independent LTCI scheme should be established to ensure the stability and sustainability of the LTCI fund and the MIUE fund.
Healthcare’s Sustainable Resource Planning Using Neutrosophic Goal Programming
Authors: Hezam Ibrahim M. , Taher Sarah A. H. , Foul Abdelaziz , Alrasheedi Adel Fahad
Published: January 2022

We develop neutrosophic goal programming models for sustainable resource planning in a healthcare organization. The neutrosophic approach can help examine the imprecise aspiration levels of resources. For deneutrosophication, the neutrosophic value is transformed into three intervals based on the truth, falsity, and indeterminacy-membership functions. Then, a crisp value is derived. Moreover, multi-choice goal programming is also used to get a crisp value. The proposed models seek to draw a strategic plan and long-term vision for a healthcare organization. Accordingly, the specific aims of the proposed flexible models are meant to evaluate hospital service performance and to establish an optimal plan to meet the growing patient needs. As a result, sustainability’s economic and social goals will be achieved so that the total cost would be optimized, patients’ waiting time would be reduced, high-quality services would be offered, and appropriate medical drugs would be provided. The simplicity and feasibility of the proposed models are validated using real data collected from the Al-Amal Center for Oncology, Aden, Yemen. The results obtained indicate the robustness of the proposed models, which would be valuable for planners who could guide healthcare staff in providing the necessary resources for optimal annual planning.
RNA Sequencing Analysis of Gene Expression by Electroacupuncture in Guinea Pig Gallstone Models
Authors: Hao Mingyao , Dou Zhiqiang , Xu Luyao , Shao Zongchen , Sun Hongwei , Li Zhaofeng
Published: January 2022

Background. Clinical studies have shown that electroacupuncture (EA) promotes gallbladder motility and alleviates gallstone. However, the mechanism underlying the effects of EA on gallstone is poorly understood. In this study, the mRNA transcriptome analysis was used to study the possible therapeutic targets of EA. Methods. Hartley SPF guinea pigs were employed for the gallstone models. Illumina NovaSeq 6000 platform was used for the RNA sequencing of guinea pig gallbladders in the normal group (Normal), gallstone model group (Model), and EA-treated group (EA). Differently expressed genes (DEGs) were examined separately in Model vs. Normal and EA vs. Model. DEGs reversed by EA were selected by comparing the DEGs of Model vs. Normal and EA vs. Model. Biological functions were enriched by gene ontology (GO) analysis. The protein-protein interaction (PPI) network was analyzed. Results. After 2 weeks of EA, 257 DEGs in Model vs. Normal and 1704 DEGs in EA vs. Model were identified. 94 DEGs reversed by EA were identified among these DEGs, including 28 reversed upregulated DEGs and 66 reversed downregulated DEGs. By PPI network analysis, 10 hub genes were found by Cytohubba plugin of Cytoscape. Quantitative real-time PCR (qRT-PCR) verified the changes. Conclusion. We identified a few GOs and genes that might play key roles in the treatment of gallstone. This study may help understand the therapeutic mechanism of EA for gallstone.
BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma
Authors: Zhou Jin , Liu Zheming , Zhang Huibo , Lei Tianyu , Liu Jiahui , Zhao Yaqi , Yao Yi , Song Qibin
Published: January 2022

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and

t

-AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.
The Service Quality Evaluation of Agricultural E‐Commerce Based on Interval‐Valued Intuitionistic Fuzzy GRA Method
Authors: Zhang Haibin , Wang Lei
Published: January 2022

The service quality evaluation of agricultural business‐to‐customer (B2C) e‐commerce is viewed as a multiattribute group decision‐making (MAGDM) activity. Thus, a useful MAGDM process is required. Based on the grey relational analysis (GRA) process and the interval‐valued intuitionistic fuzzy set (IVIFS), this study defines an interval‐valued intuitionistic fuzzy (IVIF) GRA process to depict the service quality of agricultural B2C e‐commerce. This is important to agricultural B2C e‐commerce because this industry increases rapidly and many new services are innovated. In this article, some necessary definitions related to IVIFSs are reviewed. Additionally, criteria weights are derived using the Criteria Importance Through Intercriteria Correlation method (CRITIC). Subsequently, the GRA method is extended to incorporate IVIFs to obtain a final service alternative. All alternatives can then be ranked, and the best service quality option can be identified and promulgated. Finally, a numerical example and some useful comparative studies are obtained. The analysis results show that the defined algorithm is effective for identifying the service qualities of agricultural B2C e‐commerce, which provide a new assessment method for MAGDM.
Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm
Authors: Wang Qiang , Liu Dong , Liu Guangheng
Published: January 2022

This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and

F
1

-score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant (

P
<
0.05

). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid (

P
<
0.05

). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.
Apoptosis Activation and Autophagy Inhibition of Chondrocytes by Leptin by the Upregulation of LOXL3 in Osteoarthritis Pathogenesis
Authors: Wei Qianhao
Published: January 2022

Background. Osteoarthritis is one of the usual chronic musculoskeletal dysfunctions. It is one of the primary leading causes which leads to limitation of movement and absenteeism in the working adult population. Chondrocytes are the singlecellular-based component found in the cartilage which has an important role in the degradation of the cartilage. In recent studies, autophagy is observed to protect the human chondrocytes from stress.Leptin an adipokine managing food consumption and energy outlay. Chondrocytes indicate prolonged isoform of the leptin receptor where inside these cells theleptin signals individually or combine with the remaining molecules and promptthe indication of the pro-inflammatory molecules and cartilage disintegration enzymes. Materials and Methods. mRNA expressions of Lysyl oxidase-like 3 in tissues of cartilage and concentration of leptin from synovial fluidwere measured from all samples from disease-induced groups, sham group, and RAPA-treated groups via RT-PCR and immunoassays. Histopathological analysis was also performed post-induction of the rat osteoarthritis model by the anterior cruciate ligament transection method. Western blot analysis was done, and expressions were analyzed by autophagy and apoptosis regulatory markers. Cell apoptosis and cell survival were evaluated with the help of flow cytometry, respectively, in all groups. Result. mRNA of LOXL3 was increased in osteoarthritis models which were directly related to leptin concentration in SF. ACLT surgery caused an increase in cleaved caspase 3 protein levels, while a significant reduction in Bcl-2, Beclin1, and LC3 I was noted (figure 4,5). When LOXL3 was silenced in the ACLT group and leptin-treated group apoptosis was inhibited and autophagy, cell proliferation was promoted in primary chondrocytes. A significant increase in LOXL3 caused inhibition of autophagy in chondrocytes. Conclusion. LOXL3 has stimulated apoptosis while inhibited autophagy in chondrocytes; hence LOXL3 is a prominent target for treating osteoarthritis. Keywords:chondrocytes, LOXL3, Leptin, osteoarthritis, qRT-PCR, ACLT, mRNA.
Animation Character Detection Algorithm Based on Clustering and Cascaded SSD
Authors: Wang Yuan
Published: January 2022

With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms.
The Application Value of ceMDCT in the Diagnosis of Gastric Cancer Extramural Vascular Invasion and Its Influencing Factors
Authors: Yang Shifeng , Zou Xiaoming , Li Jiacheng , Zhang Ange , Zhu Lei , Hu Xiaolong , Li Changjian
Published: January 2022

Objective. To investigate the value of enhanced multislice spiral CT (ceMDCT) in the diagnosis of extramural vascular invasion of gastric cancer and the influencing factors of extramural vascular invasion. There are different methods used in this paper. Method. 131 patients with primary gastric cancer treated in our hospital from January 2017 to May 2019 were selected. All patients underwent surgical resection and ceMDCT examination before operation. Result. There were 40 cases with extramural vascular invasion of gastric cancer by surgical pathological diagnosis. The kappa value of ceMDCT in diagnosing extramural vascular invasion of gastric cancer was 0.947, and the consistency was excellent. The diagnostic sensitivity, specificity, positive predictive value, and negative predictive value were 100.00%, 96.70%, 93.02%, and 100.00%, respectively. The proportions of T3-T4, tumour diameter ≥5.0 cm, and growth pattern of proximal nodular + diffuse type in patients with gastric cancer extramural vascular invasion were 92.50%, 85.00%, and 65.00%, respectively, which were significantly higher than those in patients without extramural vascular invasion (

P
<
0.05

). The logistic regression analysis results showed that T3-T4, tumour diameter ≥5.0 cm, proximal nodular + diffuse growth pattern were the risk factors for extrahepatic vascular invasion in gastric cancer (OR = 3.751, 2.901, and 3.367,

P
<
0.05

). Conclusion. ceMDCT has good application value in diagnosing gastric cancer extramural vascular invasion. The occurrence of gastric cancer extramural vascular invasion is affected by T staging, tumour diameter, and tumour growth pattern.
A Novel Financial Risk Early Warning Strategy Based on Decision Tree Algorithm
Authors: Tong Lili , Tong Guoliang
Published: January 2022

This paper requires a lot of assumptions for financial risk, which cannot use all of the data and is often limited to financial data; and in the past, most early warning models for financial crises did not, so they could not track the fluctuation and change trend of financial indicators. A decision tree algorithm model is used to propose a financial risk early warning method. Enterprises have suffered as a result of the financial crisis, and some have even gone bankrupt. Any financial crisis, on the other hand, has a gradual and deteriorating course. As a result, it is critical to track and monitor the company's financial operations so that early warning signs of a financial crisis can be identified and effective measures taken to mitigate the company’s business risk. This paper establishes a financial early warning system to predict financial operations using the decision tree algorithm in big data. Operators can take measures to improve their enterprise’s operation and prevent the failure of the embryonic stage of the financial crisis, to avoid greater losses after discovering the bud of the enterprise’s financial crisis, and to avoid greater losses after discovering the bud of the enterprise’s financial crisis. This prediction can be used by banks and other financial institutions to help them make loan decisions and keep track of their loans. Relevant businesses can use this signal to make credit decisions and effectively manage accounts receivable; CPAs can use this early warning information to determine their audit procedures, assess the enterprise's prospects, and reduce audit risk. As a result, the principle of steady operation should guide modern enterprise management. Prepare emergency plans in advance of a business risk or financial crisis to resolve the financial crisis and reduce the financial risk.
Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
Authors: Almarzouki Hatim Z
Published: January 2022

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method’s main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure’s training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.
Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices
Authors: Qiao Jian
Published: January 2022

In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team’s winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players’ pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.
Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study
Authors: Li Wei , Wang Dalin , Zhou Wei , Wang Yimeng , Shen Chao
Published: January 2022

The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.
Group Relationship Mining of College Students Based on Predictive Social Network
Authors: Liu Huazhang
Published: January 2022

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.
Construction of Moral Education Evaluation Model Based on Quality Cultivation of College Students
Authors: Yuan Xiaolin
Published: January 2022

Contemporary young college students are greatly impacted in the aspects of moral cognition and moral choice, which results in the weak moral will of some college students, vague moral concepts, and weak ideals and beliefs, which seriously affect the formation and development of college students’ moral quality. Therefore, the moral education evaluation model based on college students’ quality cultivation is constructed. Firstly, the present situation and defects of college students’ quality training are analyzed. Based on this, association rules in data mining method are constructed and introduced to extract valuable knowledge hidden in the data to assist education managers to make effective decisions and improve management level. Finally, the evaluation index is selected and the weighted principal component TOP-SIS model is constructed to realize the evaluation of moral education based on college students’ quality cultivation. The experimental results show that the evaluation results of the model are consistent with the actual situation, high degree of fit and freedom, and good practical performance.
A Longitudinal-Bending Hybrid Linear Ultrasonic Motor and Its Driving Characteristic
Authors: Qu Huajie , Liu Chendong , Zhang Lei , Qu Jianjun , Song Baoyu
Published: January 2022

As a new type of driver, linear ultrasonic motor (LUSM) is widely used in the high-tech field because of its low speed, high thrust, low noise, and no electromagnetic interference. However, as an actuator used in microdevices, most of the existing LUSMs are large in size and not compact in structure. In order to overcome these limitations, a new structure of linear ultrasonic motor’s stator is developed in this paper. The stator is similar to a tuning fork structure, which is divided into three parts: two driving feet, two driving legs, and the driving body. By using the first-order longitudinal vibration mode of the whole stator and the unique partial second-order bending vibration mode of the driving legs to achieve vibration mode degeneracy, a mode hybrid linear ultrasonic motor that is easy to miniaturize is proposed. Its working principle is analyzed. The dynamic analysis of the stator is carried out by using finite element software. The structure dimension of the stator and the driving frequency under the working mode are determined. At the same time, the feasibility of driving feet synthesizing elliptical motion is verified theoretically and experimentally. In addition, the LUSM test setup is built. The effects of driving frequency and Vpp on stator stall force and average velocity are studied. The results show that the maximum stall force can reach 99 mN, and the average velocity of the motor is 88.67 mm/s with Vpp = 320 V and driving frequency 80.2 kHz. The proposed LUSM is appropriate for use in occasions with quick return characteristics, like the controlling valve or nozzle of the printer. The research results provide guidance for the stator design of the linear ultrasonic motor.
Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis
Authors: Zhou Fei
Published: January 2022

With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.
Quantitative Evaluation of Real-Time Shear-Wave Elastography under Deep Learning in Children with Chronic Kidney Disease
Authors: Zhang Jie , Duan Cuirong , Duan Xingxing , Hu Yuan , Liu Jinqiao , Chen Wenjuan
Published: January 2022

Objective. This research was to study the application value of real-time shear wave elastography (SWE) quantitative evaluation based on deep learning (DL) in the diagnosis of chronic kidney disease (CKD) in children. Methods. 60 children with pathological diagnoses of CKD were selected as a CKD group. During the same period, 45 healthy children for physical examination were selected as the control group. The application value of real-time shear-wave elastography based on DL in the evaluation of CKD in children was explored by comparing the differences between the two groups. Results. It was found that the elastic modulus values of the middle and lower parenchyma of the left kidney and right kidney in the case group were (22.02 ± 10.98) kPa and (21.99 ± 11.87) kPa, respectively, which were substantially higher compared with (4.61 ± 0.47) kPa and (4.50 ± 0.59) kPa in the control group. Young’s modulus (YM) of the middle and lower parenchyma of the left kidney in patients with CKD stages 3 to 5 was 13.27 ± 0.83, 24.21 ± 5.69, and 31.67 ± 3.82, respectively, and that of the right kidney was 17.26 ± 0.98, 26.76 ± 7.22, and 32.37 ± 4.27, respectively, and the difference was significant (

P

 < 0.05). In patients with moderate and severe CKD, the YM values of the middle and lower parenchyma of the left kidney were 17.27 ± 0.83, 27.93 ± 6.49, and those of the right kidney were 17.26 ± 0.98, 29.56 ± 6.49, respectively, and the difference was statistically significant (

P

 < 0.05). The serum creatinine (Scr) of the CKD group was substantially higher than that of the control group, and the estimated glomerular filtration rate (eGFR) level of the former was lower than that of the latter. However, there was no statistical difference between the YM values of the middle and lower parts of the left and right kidneys of the CKD group and the control group. Conclusion. The DL-based SWE is a new noninvasive, real-time, and quantitative detection method, which can effectively evaluate the stiffness of the kidney and help to better detect the progress of CKD as a clinical reference.
Prevalence, Severity, and Predictors of Poststroke Depression in a Prospective Cohort of Jordanian Patients
Authors: Al Qawasmeh Majdi , Aldabbour Belal , Abuabada Amal , Abdelrahman Khalid , Elamassie Samah , Khweileh Mays , Zahran Mohammad , El-Salem Khalid
Published: January 2022

Poststroke depression (PSD) is common and remains a significant risk factor for poor outcomes. This prospective study is aimed at assessing the prevalence, severity, and predictors of PSD among Jordanian stroke survivors. A total of 151 patients who were consequently admitted to a tertiary teaching hospital with ischemic or hemorrhagic strokes were enrolled. Participants were screened on admission for premorbid depression using the PHQ-9 questionnaire; then, screening for PSD was repeated one and three months after stroke using the same tool. Depression prevalence at each screening was reported, and logistic regression analysis was conducted to evaluate for significant predictors. PHQ-9 scores suggestive of depression were reported by 15%, 24.83%, and 17.39% of respondents on admission and after one and three months, respectively. Scores suggesting severe depression were reported by 0.71%, 2.13%, and 6.52% of respondents, respectively. Significant predictors of PSD were having chronic kidney disease, current smoking status, moderate or severe disability (mRS score) at stroke onset, and severe dependence (BI) after one month (

p

values 0.007, 0,002, 0.014, and 0.031, respectively). Patients with secondary and high school education levels were less likely to get depression compared with illiterate patients (

p

0.042). This study showed that nearly one in four Jordanian stroke survivors experienced PSD after one month. In contrast, while the overall PSD prevalence declined towards the end of follow-up period, patients who remained depressed showed a tendency towards higher PSD severity.
Occult Untreated Alcohol Use Disorder in a Patient with Recurrent Pituitary Macroadenoma
Authors: Rowe Mckenzie P. , Balasanova Alëna A.
Published: January 2022

Alcohol use disorder (AUD) is a chronic relapsing and remitting psychiatric condition associated with adverse health outcomes. Although common, AUD is underdiagnosed, and treatment is often overlooked. At times of increased risk, such as the postoperative period, it is imperative to screen for and treat AUD to improve patient outcomes. Psychiatrists can play an important role in addressing AUD in this patient population through addiction psychiatry consultation services. We present the case of a patient with occult alcohol use disorder (AUD) leading to hospitalization in the setting of depressive mood symptoms and personality changes after a repeat pituitary macroadenoma resection and radiation five months earlier. AUD was noted months prior to hospitalization but was not addressed despite regular interactions with the healthcare system. Evaluation by addiction psychiatry specialists during hospitalization prompted recognition and treatment of AUD, resulting in cessation of alcohol use and resolution of mood symptoms and personality changes. The patient was discharged 3 days after admission and maintained abstinence from alcohol at two months postdischarge without recurrence of psychiatric symptoms.
The Ulam Stability of Fractional Differential Equation with the Caputo-Fabrizio Derivative
Authors: Wang Shuyi
Published: January 2022

The aim of this paper is to establish the Ulam stability of the Caputo-Fabrizio fractional differential equation with integral boundary condition. We also present the existence and uniqueness results of the solution for the Caputo-Fabrizio fractional differential equation by Krasnoselskii’s fixed point theorem and Banach fixed point theorem. Some examples are provided to illustrate our theorems.
Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System
Authors: Yang Yizhe , Cui Hongjun , Ma Xinwei , Fan Wei , Zhu Minqing , Yao Sheng
Published: January 2022

With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing.
U-Shape Relationship between Plasma Leucine Level and Mortality in the Intensive Care Unit
Authors: Wang Mei-Ying , Wang Chao-Hung , Chen Wei-Siang , Chu Chien-Ming , Wu Huang-Ping , Liu Min-Hui , Lin Yi-Tsen , Kao Kuo-Chin , Liang Chung-Yu , Chen Wen-Hsin , Wang Huei-Jen , Lee Shu-Chiu
Published: January 2022

Patients in the intensive care unit (ICU) are at high risk of mortality which is not well predicted. Previous studies noted that leucine has prognostic value in a variety of diseases. This study investigated whether leucine concentration was a useful biomarker of metabolic and nutritional status and 6-month mortality in ICU. We recruited 454 subjects admitted to ICU (348 and 106 in the initiation and validation cohorts, respectively) with an acute physiology and chronic health evaluation (APACHE II)

score
≥
15

. We measured plasma leucine concentrations, traditional biomarkers, and calculated APACHE II and sequential organ failure assessment (SOFA) scores. Leucine levels were weakly correlated with albumin, prealbumin, and transferrin levels (

r
=
0.30

, 0.12, and 0.15,

p
=
0.001

, 0.029, and 0.007, respectively). During follow-up, 116 (33.3%) patients died. Compared to patients with leucine levels between 109 and 174 μM, patients with

leucine
>
174
 
μ
M

or <109 μM had a lower cumulative survival rate. Death was also associated with age, higher APACHE II and SOFA scores, C-reactive protein, and longer stays in the ICU, but with lower albumin, prealbumin, and transferrin. Patients with leucine

levels
>
174
 
μ
M

had higher alanine aminotransferase levels, but no significant differences in other variables; patients with leucine

levels
<
109
 
μ
M

had higher APACHE II and SOFA scores, higher incidence of using inotropic agents, longer ICU and hospital stays, but lower albumin and transferrin levels. Multivariable analysis demonstrated that

leucine
>
174
 
μ
M

was an independent predictor of mortality, especially early mortality. However, among patients who stayed in ICU longer than two weeks,

leucine
<
109
 
μ
M

was an independent predictor of mortality. In addition,

leucine
<
109
 
μ
M

was associated with worse ventilator weaning profiles. These findings were similar in the validation cohort. Our study demonstrated a U-shape relationship between leucine levels and mortality rate in ICU.
To Analyze the Influencing Factors of Senile Coronary Heart Disease Patients Complicated with Frailty Syndrome
Authors: Qin Tian , Sheng Wang , Hu Guoheng
Published: January 2022

To analyze the influencing factors of senile coronary heart disease patients complicated with frailty syndrome. A total of 80 elderly patients with coronary heart disease admitted to our hospital from March 2020 to March 2021 were selected as the research subjects. The Fried Frailty Symptom Scale was used to evaluate whether the 80 patients were complicated with frailty syndrome. According to the evaluation results, the patients were divided into a nonfrailty syndrome group (52 cases in total) and frailty syndrome group (28 cases in total). Clinical data of two groups of patients were collected, and multivariate logistic regression was used to analyze the influencing factors of senile coronary heart disease patients complicated with frailty syndrome. Among 80 patients, the incidence of frailty syndrome was 35.00% (28/80), including 18 cases in early frailty and 10 cases in frailty stage. Univariate analysis showed that age, body mass (BMI), diabetes mellitus, congestive heart failure, chronic renal insufficiency, chronic obstructive pulmonary disease (COPD), tumor, high uric acid hematic disease, arrhythmia, interleukin-6 (IL-6), c-reactive protein (CRP), fibrinogen (FIB), brain natriuretic peptide (BNP), uric acid (UA), serum creatinine (Scr), serum protein (ALB), white blood cell count (WBC), and neutrophil count were the possible risk factors for senile coronary heart disease complicated with frailty syndrome (

P

 < 0.05). Multivariate logistic regression analysis showed that combined COPD, combined tumor, IL-6, BNP, UA, SCR, ALB, and neutrophil count were independent risk factors for senile CHD complicated with frailty syndrome (

P

 < 0.05). Combined with COPD, combined with tumor, IL-6, BNP, UA, SCR, ALB, and neutron cell count are the influencing factors for senile coronary heart disease patients complicated with frailty syndrome. These factors can be used as the basis for the diagnosis of frailty syndrome and guide the clinical development of targeted diagnosis and treatment plan.
The Clinical Efficacy of Chemotherapy Combined with Traditional Chinese Medicine in the Treatment of Cervical Cancer and Its Influence on Cellular Immunity, Serum CEA, and TNF-α
Authors: Zhao Qiannan , Liu Juanjuan , Wang Sai , Wang Xiuqin , Jiang Xiufang
Published: January 2022

Background. This study aims to investigate the clinical efficacy of chemotherapy combined with traditional Chinese medicine in patients with cervical cancer and its effect on cellular immunoglobulin, serum sugar chain antigen 125 (CA125), carcinoembryonic antigen (CEA), and tumor necrosis factor-α (TNF-α). Methods. Conventional chemotherapy was performed in control and observation groups. Meantime, the observation group received traditional Chinese medicine. Finally, the clinical efficacy, immunoglobulin, serum tumor markers, and serum TNF-α of the two groups were compared. Results. Compared with the control group, total effective rate in the observation group was increased. After treatment, serum CD8+, TNF-α, CA125, and CEA levels were reduced in the two groups, and the observation group was higher. In the two groups, CD3+ and CD4+ levels were enhanced after treatment, and the observation group was also higher. Compared with the control group, the immunoglobulin IgG, IgA, and IgM levels increased in the observation group. The incidence of adverse reactions in the observation group was reduced compared to the control group. Conclusion. Chemotherapy combined with traditional Chinese can help improve the clinical efficacy and immunity in patients with cervical cancer. Moreover, the safety and feasibility of the treatment method are relatively high.