bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Transcutaneous Acupoint Electrical Stimulation on Cancer‐related Fatigue and Negative Emotions in Cancer Patients: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Authors: He Yubo , Yuan Minchi , He Chun , Zhu Danwei , Wang Feida
Published: July 2022

Transcutaneous electrical acupoint stimulation (TEAS) is a noninvasive and therapeutic technique that stimulated the acupoint by delivering electricity. Whether TEAS could relieve cancer‐related fatigue (CRF), anxiety, and depression and improve the quality of life in cancer patients remains controversial. Thus, we conducted a thorough literature search of electronic Chinese and English databases for randomized controlled trials (RCTs) reporting the effect of CRF, anxiety, depression, and quality of life in cancer patients from inception to July 1st, 2021. The Cochrane Collaboration Risk of Bias criteria were used to assess the risk of bias for each included RCT. Continuous variables were analyzed using standardized mean difference (SMD) and 95% confidence interval (CI). A fixed‐effects model was used for the meta‐analysis of all outcomes. A total of nine RCTs with 924 cancer patients were included in this analysis, including 460 patients in the interventional group and 464 patients in the control group. We found that TEAS could significantly reduce CRF, depression, and anxiety (SWD = −0.83, 95% CI: −0.99 to −0.66, P < 0.05) and improve the quality of life (SWD = −1.37, 95% CI: −2.34 to −0.40, P < 0.05). The funnel plot analysis revealed no significant publication bias. We conclude that TEAS is beneficial for reducing CRF, depression, and anxiety and improving the quality of life of cancer patients, but additional high‐quality evidence in the future is entailed to support this.
Optimization of Physical Education Model Based on Cluster Analysis in the Context of Big Data
Authors: Yang Hanxue
Published: July 2022

In recent years, many foreign advanced teaching concepts have been introduced into China, and teaching reforms have been deepened, increasing the demand for physical education in China. Due to the various influences on the physical education curriculum and changes in the way it operates, curriculum standards provide only theoretical guidance and are often too general and abstract to play a concrete role in physical education. The diversity and inclusiveness of educational goals have led to the emergence of new areas of physical education. The repetitive nature of all physical education content settings is generally high and is detrimental to physical education reform. Based on the method of optimization of physical education models by cluster analysis, this paper proposes to classify and analyze the characteristics of the sample of students. We propose teaching principles and various teaching methods based on education and students’ abilities. The experimental results show that among the 200 data samples, the highest percentage of student performance sample data by grade level was 90–50, indicating that the optimization of the movement model under cluster analysis was more successful. Therefore, the research results of this project provide a theoretical basis for the establishment and research of physical education in Chinese schools and further promote the reform of physical education teaching methods in China.
Biologic Agents in Crohn’s Patients Reduce CD4+ T Cells Activation and Are Inversely Related to Treg Cells
Authors: Rosseto-Welter Eliane Aparecida , D’argenio-Garcia Leticia , Blasco Tavares Pereira Lopes Filipa , Zulim Carvalho Ana Eduarda , Flaquer Fernando , Severo-Lemos Vanessa , Viero Nora Claudia Concer , Steinwurz Flavio , Pires Garcia Oliveria Lucas , Aloia Thiago , Rizzo Luiz Vicente , Pitangueira Mangueira Cristóvão Luis , Carvalho Karina Inacio
Published: July 2022

Crohn’s disease (CD) is a chronic inflammatory disease with a complex interface of broad factors. There are two main treatments for Chron’s disease: biological therapy and nonbiological therapy. Biological agent therapy (e.g., anti-TNF) is the most frequently prescribed treatment; however, it is not universally accessible. In fact, in Brazil, many patients are only given the option of receiving nonbiological therapy. This approach prolongs the subsequent clinical relapse; however, this procedure could be implicated in the immune response and enhance disease severity. Our purpose was to assess the effects of different treatments on CD4+ T cells in a cohort of patients with Crohn’s disease compared with healthy individuals. To examine the immune status in a Brazilian cohort, we analyzed CD4+ T cells, activation status, cytokine production, and Treg cells in blood of Crohn’s patients. Patients that underwent biological therapy can recover the percentage of CD4+CD73+ T cells, decrease the CD4+ T cell activation/effector functions, and maintain the peripheral percentage of regulatory T cells. These results show that anti-TNF agents can improve CD4+ T cell subsets, thereby inducing Crohn’s patients to relapse and remission rates.
Practice of Multidisciplinary Collaborative Chain Management Model in Constructing Nursing Path for Acute Trauma Treatment
Authors: Xiang Shuangqiong , Tang Weiping , Shang Xiaoyuan , Ni Hongyan
Published: July 2022

Prompt and effective treatment is the key to improve the prognosis of patients with acute trauma, and nursing plays an important role. However, conventional nursing has many limitations. Some studies have pointed out that the multidisciplinary collaborative chain management model can optimize the emergency procedures, ensure the continuity of the emergency treatment process, and optimize the treatment details. This study analyzed the practice of constructing an acute trauma care pathway based on a multidisciplinary collaborative chain management model. The results showed that the application of the multidisciplinary collaborative chain management model in the construction of acute trauma treatment nursing paths can enhance the emergency efficiency and nursing treatment, prevent the occurrence of adverse events, and improve the prognosis of patients.
Intracytoplasmic Sperm Injection Improves Normal Fertilization Rate and Clinical Pregnancy Rate in Male Infertility
Authors: Xu Jiqun , Yu Yi , Xue Mingyue , Lv Xiangyang
Published: July 2022

The purpose of this study was to investigate the effect of intracytoplasmic sperm injection (ICSI) on the normal fertilization rate of oocytes and the clinical pregnancy rate of cycles in male infertility. Eighty cases of male infertility patients attending our hospital from March 2020 to March 2021 were selected and divided into observation group and control group using the random number table method, with 40 cases in each group. The control group was treated with in vitro fertilization (IVF), and the observation group was treated with ICSI. The normal fertilization rate of oocytes and the clinical pregnancy rate of the cycle were compared between the two groups, and the adverse pregnancy outcome and obstetric and perinatal complications were compared between the two groups The rate of normal fertilization and clinical pregnancy in the cycle was higher in the observation group (P < 0.05). The differences were not statistically significant (P > 0.05) when comparing adverse pregnancy outcomes between the two groups. The difference was not statistically significant (P > 0.05) when comparing obstetrics (5.41%, 10.34%) and perinatal complications (8.33%, 14.81%) between the two groups. ICSI in male infertility is significantly effective in improving the rate of normal oocyte fertilization and the clinical pregnancy rate of the cycle. It also has a low impact on adverse pregnancy outcomes and obstetric and perinatal complications and has a high safety profile.
Dithiocarbazate Ligand‐Based Cu(II), Ni(II), and Zn(II) Complexes: Synthesis, Structural Investigations, Cytotoxicity, DNA Binding, and Molecular Docking Studies
Authors: Yusof Enis Nadia Md , Azam Mohammad , Sirat Siti Syaida , Ravoof Thahira B. S. A. , Page Alister J. , Veerakumarasivam Abhi , Karunakaran Thiruventhan , Razali Mohd Rizal
Published: July 2022

S‐4‐methylbenzyl‐β‐N‐(2‐methoxybenzylmethylene)dithiocarbazate ligand, 1, prepared from S‐(4‐methylbenzyl)dithiocarbazate, was used to produce a novel series of transition metal complexes of the type, [M (L)2] [M = Cu(II) (2), Ni(II) (3), and Zn(II) (4), L = 1]. The ligand and its complexes were investigated by elemental analysis, FTIR, 1H and 13C‐NMR, MS spectrometry, and molar conductivity. In addition, single X‐ray crystallography was also performed for ligand, 1, and complex 3. The Hirshfeld surface analyses were also performed to know about various bonding interactions in the ligand, 1, and complex 3. The investigated compounds were also tested to evaluate their cytotoxic behaviour. However, complex 2 showed promising results against MCF‐7 and MDA‐MB‐213 cancer cell lines. Furthermore, the interaction of CT‐DNA with ligand, 1, and complex 2 was also studied using the electronic absorption method, revealing that the compounds have potential DNA‐binding ability via hydrogen bonding and hydrophobic and van der Waals interactions. A molecular docking study of complex 2 was also carried out, which revealed that free binding free energy value was −7.39 kcal mol−1.
Repetitive Transcranial Magnetic Stimulation for Neuropathic Pain and Neuropsychiatric Symptoms in Traumatic Brain Injury: A Systematic Review and Meta-Analysis
Authors: Li Xin , Lu Tijiang , Yu Hong , Shen Jie , Chen Zhengquan , Yang Xiaoyan , Huang Zefan , Yang Yuqi , Feng Yufei , Zhou Xuan , Du Qing
Published: July 2022

Neuropathic pain and neuropsychiatric symptoms are common complications reported by the traumatic brain injury (TBI) population. Although a growing body of research has indicated the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for the management of neurological and psychiatric disorders, little evidence has been presented to support the effects of rTMS on neuropathic pain and neuropsychiatric symptoms in patients with TBI in all age groups. In addition, a better understanding of the potential factors that might influence the therapeutic effect of rTMS is necessary. The objective of this preregistered systematic review and meta-analysis was to quantify the effects of rTMS on physical and psychological symptoms in individuals with TBI. We systematically searched six databases for randomized controlled trials (RCTs) of rTMS in TBI patients reporting pain and neuropsychiatric outcomes published until March 20, 2022. The mean difference (MD) with 95% confidence intervals (CIs) was estimated separately for outcomes to understand the mean effect size. Twelve RCTs with 276 TBI patients were ultimately selected from 1605 records for systematic review, and 11 of the studies were included in the meta-analysis. Overall, five of the included studies showed a low risk of bias. The effects of rTMS on neuropathic pain were statistically significant (

MD
=
−
1.00

, 95% CI -1.76 to -0.25,

P
=
0.009

), with high heterogeneity (

I

2

=
76
%

). A significant advantage of 1 Hz rTMS over the right dorsolateral prefrontal cortex (DLPFC) in improving depression (

MD
=
−
6.52

, 95% CI -11.58 to -1.46,

P
=
0.01

) was shown, and a significant improvement was noted in the Rivermead Post-Concussion Symptoms Questionnaire-13 (RPQ-13) scores of mild TBI patients after rTMS (

MD
=
−
5.87

, 95% CI -10.63 to -1.11,

P
=
0.02

). However, no significance was found in cognition measurement. No major adverse events related to rTMS were reported. Moderate evidence suggests that rTMS can effectively and safely improve neuropathic pain, while its effectiveness on depression, postconcussion symptoms, and cognition is limited. More trials with a larger number of participants are needed to draw firm conclusions. This trial is registered with PROSPERO (PROSPERO registration number: CRD42021242364.
Nursing Process Design of Intravenous Thrombolysis in Elderly Patients with Acute Cerebral Infarction
Authors: Xia Yuanyuan , Chen Yuan , Xia Rucui , Chen Yunyun , Gu Qian , Lu Qing
Published: July 2022

The economy of contemporary society is developing rapidly and the society is making continuous progress. The population is aging and diet structure is also adjusted. Acute cerebral infarction(ACI) in the elderly has become a common disease that seriously threatens the health of the elderly. Therefore, research on nursing of senile ACI is of great significance. Nursing is essential in the thrombolytic therapy of cerebral infarction. This article aims to study the process design of intravenous thrombolytic nursing for elderly patients with ACI and the conclusions are as follows: the nursing process has a good nursing effect. This increased the Barthel index of elderly patients with ACI after intravenous thrombolysis care by 15.8 compared with that before care. Compared with the traditional nursing mode, the nursing course and cost are significantly reduced.
Voice Quality Evaluation of Singing Art Based on 1DCNN Model
Authors: Liusong Yang , Hui Du
Published: July 2022

Traditional speech recognition still has the problems of poor robustness and low signal-to-noise ratio, which makes the accuracy of speech recognition not ideal. Combining the idea of one-dimensional convolutional neural network with objective evaluation, an improved CNN speech recognition method is proposed in this paper. The simulation experiment is carried out with MATLAB. The effectiveness and feasibility of this method are verified by simulation. This new method is based on one-dimensional convolutional neural network. The traditional 1DNN algorithm is optimized by using the fractional processing node theory, and the corresponding parameters are set. Establish an objective evaluation system based on improved 1DCNN. Through the comparison with other neural networks, the results show that the evaluation method based on the improved 1DCNN has high stability, and the error between subjective score and evaluation method is the smallest.
ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices
Authors: Shukla Neha , Pandey Anand , Shukla Anand Prakash , Neupane Sanjeev Chandra
Published: July 2022

The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.
Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion
Authors: Kshirsagar Pravin R. , Manoharan Hariprasath , Siva Nagaraju V , Alqahtani Hamed , Noorulhasan Quadri , Islam Saiful , Thangamani M. , Sahni Varsha , Adigo Amsalu Gosu
Published: July 2022

A neurological disorder is a problem with the neural system of the body, as a brain tumor is one of the deadliest neurological conditions and it requires an early and effective detection procedure. The existing detection and diagnosis methods for image evaluation are based on the judgment of the radiologist and neurospecialist, where a risk of human mistakes can be found. Therefore, a new flanged method and methodology for detecting brain tumors using magnetic resonance imaging and the artificial neural network (ANN) technique are applied. The research is based on an artificial neural network-based behavioral examination of neurological disorders. In this study, an artificial neural network is used to detect a brain tumor as early as possible. The current work develops an effective approach for detecting cancer from a given brain MRI and recognizing the retrieved data for further use. To obtain the desired result, the following three procedures are used: preprocessing, feature extraction, training, and detection or classification. A Gaussian filter is also incorporated to eliminate noise from the image, and for texture feature extraction, GLCM is considered in this study. Further entropy, contrast, energy, homogeneity, and other GLCM texture properties of tumor categorization are measured using the ANFIS approach, which determines if the tumor is normal, benign, or malignant. Future research will focus on applying advanced texture analysis to classify brain tumors into distinct classes in order to improve the accuracy of brain tumor diagnosis. In the future, MRI brain imaging will be used to classify metastatic brain tumors.
Research on Education Teaching Quality Analysis Based on the Neural Network Model
Authors: Zhu Ying
Published: July 2022

Universities emphasize quality review and monitoring. To effectively evaluate classroom teaching effectiveness, a trustworthy model of teaching quality evaluation is required. Due to the fact that teaching is a dynamic process, numerous elements influence teaching quality, and the link between assessment index and teaching effect is complex and nonlinear. There are numerous methods for measuring the quality of classroom instruction, but the vast majority of it relies on a single machine learning algorithm, making it difficult to construct an accurate and reliable mathematical model. In this paper, we employ the AdaBoost’s multicore neural network learning algorithm to learn several weak classifiers and combine them into a single strong classifier. We also transfer the classification probabilities into teaching quality outcomes to obtain the final teaching quality results. Our model offers a new, effective way for evaluating the quality of classroom instruction, and it can serve as a solid theoretical resource for reforming classroom instruction.
Heterogeneous Group Risk Decision Behavior Simulation Based on Particle Swarm Optimization Algorithm
Authors: Lu Na
Published: July 2022

This paper studies the general SoC design AMBA bus and proposes an automatic generation method of software structure test data based on adaptive data optimization algorithm. By simplifying the basic particle expansion equation and eliminating the particle velocity term, an adaptive scheme based on inertia weight is proposed. In the research process, this article fully considers heterogeneous groups. Investment companies are divided into three types of decision-makers: reciprocal, intelligent, and leveraged, and their investment behavior is modeled. Given that factors such as technical level will affect the future of the project, and swarm simulation software is used to simulate and analyze the impact of the profitability of smart community microgrid construction projects on decision-making and to conduct dynamic research on market modeling risks. This article first outlines the construction of the renewable energy macro- and micro-market risk decision-making behavior model and clarifies the logic process of the market to promote the consumption of renewable energy. Then, it analyzes the causes of market risks based on dynamic models, first examines the relationship between key risks and risk factors, forms risks that affect returns (bilateral random risks and market efficiency risks), and then an analysis framework for the impact of renewable energy. This paper applies it to the analysis of data-based algorithms, thereby promoting the development of data-based algorithms.
Enhanced Ciprofloxacin Removal from Aqueous Solution Using a Chemically Modified Biochar Derived from Bamboo Sawdust: Adsorption Process Optimization with Response Surface Methodology
Authors: Wakejo Wondimu K. , Meshasha Beteley T. , Kang Joon W. , Chebude Yonas
Published: July 2022

Contamination of water by ciprofloxacin has become a significant concern due to its adverse health effects and growing evidence of antimicrobial-resistant gene evolution. To this end, a chemically modified bamboo biochar was prepared from bamboo sawdust to effectively remove ciprofloxacin (CIP) from an aqueous solution. Under similar adsorption conditions, the modified bamboo biochar (MBC) has an excellent CIP removal efficiency (96%) compared to unmodified bamboo biochar (UBC) efficiency (45%). Thus, MBC was used in batch adsorption experiments, and the process was optimized with the central composite design (CCD) framework of response surface methodology (RSM). Sorption process parameters such as initial CIP concentration, pH, adsorbent dose, and contact time were studied and found to have a significant effect on CIP removal. The optimal CIP removal (96%) was obtained at MBC dose (0.5 g L-1), CIP initial concentration (20 mg L-1), pH (7.5), and contact time (46 min). The adsorption kinetic data were well described by the pseudo-second-order model ([Formula: see text]), and both Langmuir ([Formula: see text]) and Freundlich ([Formula: see text]) models gave the best fit in CIP adsorption isotherm analysis. The maximum monolayer adsorption capacity of the MBC was 78.43 mg g-1 based on the Langmuir isotherm model. These results suggest that CIP adsorption was mainly controlled by chemisorption. Moreover, the CIP adsorption process was endothermic and spontaneous. Overall, MBC is a low-cost, efficient, and recyclable adsorbent for eliminating emerging contaminants such as ciprofloxacin from an aqueous solution.
[Retracted] Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data
Authors: Jiang Shujie
Published: July 2022

Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co‐word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.
Genetic Algorithm Integrated Fuzzy AHP-VIKOR Approach for the Investigation of W-Cut Insert Heat Exchanger for Cooling of Dielectric Fluid Used in Ultra-High Voltage Transformer
Authors: Gowri N. Vasantha , Isaac J. Samson , Muralikrishna T. , Babu G. Suresh , Depoures Melvin Victor , Sekar S. , Sasirekha P. , Ramesh M. , Prabhakar S.
Published: July 2022

This study proposes a novel W-cut twisted tape to evaluate and enhance the effectiveness of novel grooved tube heat exchanger. The experimental investigations are carried out with W-cut inserts, which possess two main control factors, namely, twist tape ratio (y = 3.5 to 6.5) and width tape ratio (WR = 0.43 to 0.6). The investigation results prove that in all cases the energy transmission and friction factor of W-cut insert along with the first law and second law efficiency are considerably higher than the conventional tube arrangement. The empirical model equations for friction factor, performance enhancement ratio, Nusselt number, and rational efficiency are developed, and they show good affinity with experimental data. Since the abovementioned factors have conflicting terms, it is essential to select the proper operating configuration of the heat exchanger in order to increase the thermal energy transfer rate with reduced consumption of pumping energy. The multi-objective genetic algorithm (GA) tool is used to identify the optimum operating conditions. Further, the hybrid multi-criteria decision-making model, FAHP-VIKOR, is applied to select the perfect model from the set of non-dominated solution. The ranking of alternatives is as follows: A4 > A11 > A2 > A9 > A21 > A17 > A1 > A24 > A11 > A23 > A7 > A18 > A13 > A10 > A20 > A5 > A19 > A14 > A16 > A22 > A25 > A6 > A15 > A3 > A8. The optimized configuration of W-cut insert is compared with the former inserts, and the optimal configuration exhibits supreme performance than other inserts.
MicroRNA-147a Targets SLC40A1 to Induce Ferroptosis in Human Glioblastoma
Authors: Xu Peng , Ge Fei-Hang , Li Wen-Xin , Xu Zhen , Wang Xue-Li , Shen Jing-Lan , Xu Ai-Bo , Hao Rong-Rong
Published: July 2022

Objective. Glioblastoma is one of the most common malignant tumors in the brain, and these glioblastoma patients have very poor prognosis. Ferroptosis is involved in the progression of various tumors, including the glioblastoma. This study aims to determine the involvement of microRNA (miR)-147a in regulating ferroptosis of glioblastoma in vitro. Methods. Human glioblastoma cell lines were transfected with the inhibitor, mimic and matched negative controls of miR-147a in the presence or absence of ferroptotic inducers. To knock down the endogenous solute carrier family 40 member 1 (SLC40A1), cells were transfected with the small interfering RNA against SLC40A1. In addition, cells with or without the miR-147a mimic treatment were also incubated with temozolomide (TMZ) to investigate whether miR-147a overexpression could sensitize human glioblastoma cells to TMZ chemotherapy in vitro. Results. We found that miR-147a level was decreased in human glioblastoma tissues and cell lines and that the miR-147a mimic significantly suppressed the growth of glioblastoma cells in vitro. In addition, miR-147a expression was elevated in human glioblastoma cells upon erastin or RSL3 stimulation. Treatment with the miR-147a mimic significantly induced ferroptosis of glioblastoma cells, and the ferroptotic inhibitors could block the miR-147a mimic-mediated tumor suppression in vitro. Conversely, the miR-147a inhibitor prevented erastin- or RSL3-induced ferroptosis and increased the viability of glioblastoma cells in vitro. Mechanistically, we determined that miR-147a directly bound to the 3

′

-untranslated region of SLC40A1 and inhibited SLC40A1-mediated iron export, thereby facilitating iron overload, lipid peroxidation, and ferroptosis. Furthermore, miR-147a mimic-treated human glioblastoma cells exhibited higher sensitivity to TMZ chemotherapy than those treated with the mimic control in vitro. Conclusion. We for the first time determine that miR-147a targets SLC40A1 to induce ferroptosis in human glioblastoma in vitro.
Classification of Housing Submarkets considering Human Preference: A Case Study in Shenyang, China
Authors: Liu Xinrui , Guo Rongfei , Lei Guoping , Liu Na
Published: July 2022

Housing submarket is affected by the quality of public facilities in neighborhood. In this article, a three-step framework is proposed to classify the housing market in Shenyang city, China. The saliences of point of interest, POI, are used to quantify the degree of activities using social media data and merged into the model of density-field hotspot detector to extract high-quality of urban facilities, aiming at exploring the influences on the housing price. The multiscale geographically weighted regression model, MGWR, is applied to investigate relations between the housing price and attribute variables geographically and to identify multiscale impacts on the housing price with flexible bandwidths. The spatial “k”luster analysis by the tree edge removal method, SKATER, is applied to delimit urban housing submarkets based on the MGWR betas, where ten groups of the housing submarkets are recognized. The proposed classification method captures multiscale spatial heterogeneity of the housing price and predominant influencing factors for each housing submarket in Shenyang, China. It also offers suggestions for urban planners to restrain the unbalanced development of the housing market.
Investigation on the Identity Construction of Young Foreign Language Teachers in Colleges and Universities Based on Feature Selection Algorithm
Authors: Yao Lingzhi
Published: July 2022

With the gradual implementation of the strategic goal of strengthening the country in education, people’s attention to students’ learning is also increasing day by day. At the same time, the teaching effect of individual teachers depends to a large extent on the identity of teachers themselves, so it is necessary to improve the construction of teachers’ identity. Most of the research on teachers’ identity construction starts from the theory of identity, which does not play a significant role in the critical application in practical teaching activities. In response to these problems, this paper will use the ReliefF algorithm and the Spark algorithm in the feature selection algorithm to scientifically process the identity construction, and implement the application of the improved ReliefF feature selection algorithm and the feature selection application steps based on the Spark algorithm respectively. The experimental results show that the improved ReliefF algorithm has better feature selection accuracy when the feature range is 30% to 40%. It shows that the construction of teacher identity based on feature selection algorithm can provide an objective basis for the realization of teacher identity.
X-Ray Fluorescence for Laminated Silty Shale Reservoirs in Ordos Basin, China: Implications for Lithology Identification
Authors: Cheng Wei , Cheng Hao , Yu Hongyan , Zhang Shaohua , Wang Zhenliang , Li Xiaolong , Johnson Lukman
Published: July 2022

Shale lithofacies were an important key for the exploration and development of unconventional oil and gas reservoirs. Previous studies usually use logging data, X-ray diffraction analysis (XRD), and scanning electron microscopy (SEM) to identify shale lithofacies, but these methods cannot accurately and quickly identify geochemical characteristics. Portable X-ray fluorescence (XRF) analysis can identify geochemical characteristics. However, few publications reported the feasibility of XRF identifying lithology. We tested the major elements of this formation systematically by X-ray fluorescence and compared the characteristics of these elements in the silty laminated shales against the calcareous laminated shale and the argillaceous shales. We analyzed the geochemical characteristics of shale using portable XRF and proposed a method to identify argillaceous shale, silty laminated shale, and calcareous laminated shale in the Chang 7 of Well Y1 (in Xiasiwan area). Furthermore, we established a lithoface identification standard. We also found that Ca/Mg of calcareous laminated shale was the highest which indicated a dry depositional environment; in contrast, lowest Ca/Mg of silty laminated shale indicated a humid depositional environment. Argillaceous shale was deposited in a strongly reducing environment because Fe content of argillaceous shale was the highest. Finally, this criterion is tested against the data from the Well Y2, and the results are in good agreement with the thin section analysis and core data.
Analysis and Construction of Software Engineering OBE Talent Training System Structure Based on Big Data
Authors: Jie Zhang
Published: July 2022

Software engineering is one of the most active fields of entrepreneurship and innovation in the world, and it is also the core field of the information technology industry. Software talents as the foundation and support are an important weight to determine the future direction of my country’s software engineering. How to make colleges and universities cultivate compound software talents with innovative ability and engineering ability and how to guide students to closely combine innovative thinking with social practice are a major challenge faced by the current software process education in colleges and universities in my country. At present, the overall quality of software engineers is poor, which cannot meet the needs of enterprises and training objectives. This paper puts forward the application of the OBE (outcome-based education) model in the training of software talents, which can effectively solve the current problems of talent quality and social demand. The analysis shows that there is a high correlation between collaborative education and satisfaction, and the collaborative education model can effectively improve satisfaction. The investment of scientific research funds can effectively improve the overall quality of scientific research team members. The OBE talent training mode can effectively improve the overall test effect, whether in the experimental set or the test set, the test result of the OBE talent training structure is still the highest, the accuracy rate can reach 94.23%, the recall rate can reach 94.51%, and the F1 value can reach 95.13%. It is fully explained that the identification accuracy is the highest when the OBE talent-training structure is adopted.
Clinical Efficacy and Safety of Different Dental Prosthetic Membranes in Guided Bone Regeneration during Dental Implants: A Meta-Analysis
Authors: Guo Yan , Su Linghan , Chen Caidi , Liu Yan , Li Jianxue
Published: July 2022

Objective. To evaluate clinical efficacy and safety of absorbable and non-absorbable dental restorative membranes in guided bone regeneration (GBR). Articles concerning absorbable and non-absorbable prosthetic membrane-related studies of GBR were screened from multiple databases. In the end, 526 postoperative patients who met eligibility criteria were screened for the study from eight trials. The results showed that the repair success rate of the experimental group (absorbable dental restorative membrane) was higher than that of the control group (non-absorbable dental restorative membrane) (RR = 1.18, 95% CI [1.11,1.26], and the total physical therapy effect was P < 0.0001, I2 = 0%), and the height of bone graft in the experimental group was higher than that in the control group (MD = 0.67, 95% CI [0.11, 1.23]). The thickness of bone graft in the experimental group was higher than that in the control group (MD = 0.43, 95% CI [0.30,0.56], P < 0.00001, I2 = 61%), and the adverse events in the experimental group were less than those in the control group (RR = 0.31, 95% CI [0.18, 0.51], P < 0.00001, I2 = 13%). Absorbable prosthetic membrane is superior to non-absorbable prosthetic membrane in clinical efficacy and safety.
Detection of Weak Pulse Signal in Chaotic Noise Based on Improved Brain Emotional Learning Model and PSO-AGA
Authors: Dan Chen , Zhao Shengli , Li Hua , Su Liyun
Published: July 2022

A model for detecting weak pulse signals in chaotic noise was proposed. Firstly, based on the short-term predictability of chaotic signals, according to Takens’s theorem, the phase space of observed signal was reconstructed. Then, an improved brain emotional learning (BEL) model combined with PSO-AGA was proposed to predict chaotic signals, and the one-step prediction error was obtained. In order to optimize the parameters of the BEL model, an algorithm named PSO-AGA combined with particle swarm optimization and adaptive genetic algorithm was adopted to achieve the balance of global search and local search capabilities. Finally, the hypothesis testing method was used to detect whether there existed the pulse signal from the one-step prediction error. The experiments simulated the Lorenz system and the magnetic storm loop current system. In the Lorenz system, the MAD of BEL-PSO-AGA, BP-NN-PSO-AGA, and Wavelet-NN-PSO-AGA were 0.0022, 0.0142, and 0.0076; the MSE were

8.95
×
1

0

−
6

, 0.00034, and 0.00016; the RMSE were 0.0029, 0.0187, and 0.0128; the running times were 410 s, 792 s, and 721 s; the ACC were 0.999, 0.972, and 0.997; the F1 were 0.984, 0.423, and 0.878. It could be seen that the BEL model had better performance, shorter running time and higher values of the ACC and F1, indicated that the BEL model ran faster and had a better predictive effect. The MAD of BEL-PSO-AGA, BEL-WOA, BEL-AGA, and BEL-PSO were 0.0022, 0.0065, 0.0135, and 0.0071; the MSE were

8.95
×
1

0

−
6

, 0.00013, 0.00029, and 0.00014; the RMSE were 0.0029, 0.0115, 0.0173, and 0.0119; the ACC were 0.999, 0.992, 0.990, and 0.997; the F1 were 0.984, 0.733, 0.451, and 0.878. This indicated that the PSO-AGA also had better performance and higher prediction accuracy. In the magnetic storm loop current system, the experimental results were similar to the Lorenz experiment, which also indicated that the BEL-PSO-AGA model was better. To sum up, the detection results of simulations showed that the proposed model and algorithm could effectively detect weak pulse signals from the chaotic noise.
Determination of the Content of Phenolic Compounds and the Changes of Polyphenol Oxidase and Each Index during Browning of Phyllanthus emblica at Different Storage Temperatures
Authors: Li Rong , Guo Shuang , Wu Wenjun , Lin Aihe , Xia Zongxiao , Long Xiaomei , Liu Tao , Fan Yuan , Chen Zukun
Published: July 2022

In order to further study the early browning of P. emblica marker and mechanism, it is necessary for us to measure the changes in the content of some substances to find quality markers during the browning process of P. emblica. So, we simulated the storage conditions under different temperatures, including 5°C, 25°C, and 35°C. And, the contents of some of its phenolic compounds were determined by RP-HPLC. Polyphenol oxidase (PPO) activity was determined by PPO reagent kits, and pH was determined with a pH measuring instrument, etc. The experimental results showed that 1-galloyl-glucose was the smoothest at 5°C among the three storage temperature conditions. It fluctuates and decreases in a curve at 35°C and 25°C. The content changes of gallic acid (GA) fluctuated more obviously at 35°C, and it showed a maximum value on the 7th day. The content changes of 1,3,6-tri-O-galloyl-β-D-glucose (TGG) all showed a peak on the 5th day and then showed a gradual decrease. The content changes of ellagic acid (EA) fluctuated more at 35°C. The peak of the content of rutin at both 35°C and 25°C appeared on the 3rd day, and that showed a gradually decreasing trend. The PPO activity varied more significantly at 25°C and 35°C. The browning index increased with time at both 35°C and 25°C. The complete browning of P. emblica was already observed on the 13th day. Tips for experimental results 5°C are effective in preventing browning of P. emblica. The contents of TGG, 1-galloyl-glucose, and rutin in P. emblica are greatly affected by temperature and time, and its contents had a linear relationship with time and temperature and can be used as one of the indicators of early browning, which provides a reference for the storage and transportation of fresh fruit in Chinese herbals.
Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy
Authors: Wang Qinan , Jiang Weiwei
Published: July 2022

Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy.
Research on Blended Teaching of Flipped Classroom Based on CNN-SSA-Bi-LSTM Deep Learning Model Computer Media
Authors: Lu Feng
Published: July 2022

Aiming at the problem that the influencing factors of computer media flipped classroom hybrid teaching lead to the teaching effect not reaching the expected, this study proposes an ultra-short-term prediction model based on CNN-SSA-Bi-LSTM. CNN-SSA-Bi-LSTM is used to flip the study of mixed teaching in the classroom. This method constructs a one-dimensional convolutional neural network, performs data fusion and feature transformation on multiple key variables, and then constructs a two-way long-term short-term memory network prediction model, which realizes a 45-minute classroom for ultra-short-term prediction of the future. In addition, data optimization is performed through SSA to improve the predictive effect of the CNN-Bi-LSTM model. Experimental results show that compared with the traditional machine learning method, the proposed prediction model can effectively improve the prediction accuracy of the ultra-short-term classroom effect, and the relative variance of the continuous model is increased by 16.22%. High prediction accuracy and low error prove that CNN-SSA-Bi-LSTM deep learning model has strong application prospects in the research of flipped classroom hybrid teaching.
A Statistical English Syntax Analysis Model Based on Linguistic Evaluation Information
Authors: Shu Peng , Cuiqin Sun
Published: July 2022

Language evaluation research currently focuses on the analysis of scholars from various native language backgrounds, whereas the local grammatical characteristics of other groups, particularly English language learners, are discussed less frequently. Local grammar offers a new perspective for analyzing the meaning characteristics of evaluation languages from the point of view of the people who employ them. In order to provide context for this paper, past research on local syntax is reviewed. The language model generates text that can be analyzed to determine the model’s aggressiveness when perturbed. To evaluate the method’s precision and efficacy, we compared the aggressiveness of pretrained models under various conditions using an English database. The results demonstrate that the method is capable of automatically and effectively evaluating the aggressiveness of language models. We then examine the scales of model parameters and the relationships between words in the training corpus.
Influence of Early Multidisciplinary Collaboration on Prevention of ICU-Acquired Weakness in Critically Ill Patients
Authors: Wang Bolan , He Xiqiang , Tian Shujun , Feng Can , Feng Wenbin , Song Limin
Published: July 2022

Objective. This study focused on elucidating the influence of early multidisciplinary collaboration on preventing intensive care unit- (ICU-) acquired weakness (AW) in critically ill patients (CIPs). Methods. Ninety-five CIPs admitted between December 2018 and December 2021 were selected and assigned to the following two groups according to the intervention pattern: the control group (the Con;

n
=
40

) treated with routine early rehabilitation intervention, and the research group (the Res;

n
=
55

) intervened by early multidisciplinary collaborative intervention. The incidence of complications (ICU-AW, deep vein thrombosis (DVT), and pressure ulcers (PSs)) and recovery indices (days of ventilator use, ICU treatment time, and length of hospital stay (LOS)) were recorded. Besides, patients’ activity function and quality of life (QoL) were evaluated and compared, among which the former was evaluated by the Barthel Index (BI), ICU Mobility Scale (IMS), and Medical Research Council (MRC) Scale, and the latter was assessed by the World Health Organization Quality of Life Assessment (100-item version) (WHOQOL-100). Results. The data identified statistically a lower incidence of complications (ICU-AW, DVT, and PSs) and shorter time of ventilator use, ICU residence, and LOS in the Res compared with the Con. In addition, BI, IMS, MRC, and WHOQOL-100 scores in the Res elevated statistically after treatment and were higher than those of the Con. Conclusions. Early multidisciplinary collaboration can validly prevent ICU-AW in CIPs, reduce the incidence of DVT and PSs, and promote patients’ rehabilitation, mobility, and QoL.
Jiedu-Yizhi Formula Alleviates Neuroinflammation in AD Rats by Modulating the Gut Microbiota
Authors: Wang Jiale , Zhu Xiaoting , Li Yuhui , Guo Wenping , Li Mingquan
Published: July 2022

Background. The Jiedu-Yizhi formula (JDYZF) is a Chinese herbal prescription used to treat Alzheimer’s disease (AD). It was previously confirmed that JDYZF can inhibit the expression of pyroptosis-related proteins in the hippocampus of AD rats and inhibit gut inflammation in AD rats. Therefore, it is hypothesized that JDYZF has a regulatory effect on the gut microbiota. Methods. In this study, an AD rat model was prepared by bilateral hippocampal injection of Aβ25-35 and AD rats received high, medium, and low doses of JDYZF orally for 8 weeks. The body weights of the AD rats were observed to assess the effect of JDYZF. The 16S rRNA sequencing technique was used to study the regulation of the gut microbiota by JDYZF in AD rats. Immunohistochemical staining was used to observe the expression levels of Caspase-1 and Caspase-11 in the hippocampus. Results. JDYZF reduced body weight in AD rats, and this effect may be related to JDYZF regulating body-weight-related gut microbes. The 16S rRNA analysis showed that JDYZF increased the diversity of the gut microbiota in AD rats. At the phylum level, JDYZF increased the abundances of Bacteroidota and Actinobacteriota and decreased the abundances of Firmicutes, Campilobacterota, and Desulfobacterota. At the genus level, the abundances of Lactobacillus, Prevotella, Bacteroides, Christensenellaceae_R-7_group, Rikenellaceae_RC9_gut_group, and Blautia were increased and the abundances of Lachnospiraceae-NK4A136-group, Anaerobiospirillum, Turicibacter, Oscillibacter, Desulfovibrio, Helicobacter, and Intestinimonas were decreased. At the species level, the abundances of Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus faecis were increased and the abundances of Helicobacter rodentium and Ruminococcus_sp_N15.MGS-57 were decreased. Immunohistochemistry showed that JDYZF reduced the levels of Caspase-1- and Caspase-11-positive staining. Conclusion. JDYZF has a regulatory effect on the gut microbiota of AD rats, which may represent the basis for the anti-inflammatory effect of JDYZF.