bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program
Authors: Handayani Nining , Louis Claudio Michael , Erwin Alva , Aprilliana Tri , Polim Arie A , Sirait Batara , Boediono Arief , Sini Ivan
Published: August 2022

Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning-based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF.

Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables.

Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each prediction model was shown to work optimally with different combinations of variables, with some variables being consistently included, such as female age, and some consistently excluded, which provides an insight into the relationship between the variables and each prediction model.

Conclusion: Machine learning algorithm remains effective for the purpose of data mining and knowledge extraction in IVF clinical datasets through which a relatively reliable prediction system for clinical pregnancy could be constructed, provided the available data is sufficient.
Quantum Chemical and Experimental Insight into Structure, Physicochemical Properties and Dissolving Behavior of Deep Eutectic Solvents
Authors: Zhang Yuling , Ren Hongwei , Liu Yize , Li Meiyu , Rasheed Adnan Hameed , Alias Hajar , Duan Erhong
Published: August 2022

During the past decade, deep eutectic solvents (DESs), emerging as green, versatile and adjustable alternatives to traditional solvents, have attracted intensive interests and immense research in various research fields. Especially DESs show quite a remarkable potential dissolving capability, due to their ability to donate as well as accept protons and electrons. A thorough and deep understanding of the microstructure, interactions and dissolving mechanism within DESs plays a key role in acquiring a task-specific DES. Quantum chemistry (QC) calculations provide structure-property-function relationship based on the molecular description and make up for the limitations of the current experimental techniques, showing promises for DESs screening and design. This paper summarizes the current research involving QC calculations and combined experiments, on investigating the structure, physicochemical properties and dissolving capability of DESs from the macroscopic and microcosmic perspective. This paper highlights and discusses the dissolving mechanism of various compounds in DESs, covering the recent successes in applying QC calculations to select the appropriate DESs as dissolving media. Furthermore, a brief analysis of perspectives and challenges for the future research in this field is presented. It is expected that this paper will inspire the future development of DESs from synthesis design to various designated applications.
Understanding International Credibility and Its Changes: Taking China as an Example
Authors: Jiang Fangfei
Published: August 2022

This paper defines international credibility as the comprehensive evaluation of the trustworthiness of a country’s capabilities and intentions by other countries in the international community. It is not only a reflection of the country’s national capabilities and behavior choices, but also a kind of national reputation based on the evaluation of other countries. The establishment, maintenance and improvement of a country’s international credibility depends on the following five necessary conditions: hard power, comprehensive governance and contractual capabilities, government credibility and social cohesion domestically, trustworthy foreign policies and behaviors, international communication capabilities and public opinion influence. This article demonstrates that China’s international credibility has suffered severe damage since the 2008 financial crisis, despite of its rapid enhancement in the early post-Cold War period. The outbreak of the COVID-19 pandemic in 2020 has provided China with a precious opportunity to consolidate its domestic social foundation and enhance its international credibility. However, it seems that the epidemic has been witnessing greater divergence in other countries’ evaluation of China’s capabilities and intentions.
Training PPA Models for Embedded Memories on a Low-data Diet
Authors: Last Felix , Schlichtmann Ulf
Published: August 2022

Supervised machine learning requires large amounts of labeled data for training. In power, performance, and area (PPA) estimation of embedded memories, every new memory compiler version is considered independently of previous compiler versions. Since the data of different memory compilers originate from similar domains, transfer learning may reduce the amount of supervised data required by pre-training PPA estimation neural networks on related domains. We show that provisioning times of PPA models for new compiler versions can be reduced significantly by exploiting similarities among different compilers, versions, and technology nodes. Through transfer learning, we shorten the time to provision PPA models for new compiler versions, which speeds up time-critical periods of the design cycle. Using only 901 training samples (10%) is sufficient to achieve an almost worst-case (98th percentile) estimation error of 2.67% and allows us to shorten model provisioning times from 40 days to less than one week without sacrificing accuracy. To enable a diverse set of source domains for transfer learning, we devise a new, application-independent method for overcoming structural domain differences through domain equalization that attains competitive results when compared to domain-free transfer. A high degree of automation necessitates the efficient assessment of the best source domains. We propose using various metrics to accurately identify four of the five best among 45 datasets with low computational effort.
Networks and Economic Fragility
Authors: Elliott Matthew , Golub Benjamin
Published: August 2022

Many firms, banks, or other economic agents embedded in a network of codependencies may experience a contemporaneous, sharp drop in functionality or productivity following a shock—even if that shock is localized or moderate in magnitude. We offer an extended review of motivating evidence that such fragility is a live concern in supply networks and in financial systems. We then discuss network models of fragility, focusing on the forces that make aggregate functionality especially sensitive to the economic environment. The key structural features of networks that determine their fragility are reviewed, with an emphasis on the importance of phase transitions. We then turn to endogenous decisions, both by market participants (e.g., firms investing in network formation and robustness) and by planners (e.g., authorities undertaking macroprudential regulation). Fragility has some distinctive implications for such decisions.
Stress Management Interventions to Facilitate Psychological and Physiological Adaptation and Optimal Health Outcomes in Cancer Patients and Survivors
Authors: Antoni Michael H. , Moreno Patricia I. , Penedo Frank J.
Published: August 2022

Cancer diagnosis and treatment constitute profoundly stressful experiences involving unique and common challenges that generate uncertainty, fear, and emotional distress. Individuals with cancer must cope with multiple stressors, from the point of diagnosis through surgical and adjuvant treatments and into survivorship, that require substantial psychological and physiological adaptation. This can take a toll on quality of life and well-being and may also promote cellular and molecular changes that can exacerbate physical symptoms and facilitate tumor growth and metastasis, thereby contributing to negative long-term health outcomes. Since modifying responses tostressors might improve psychological and physiological adaptation, quality of life, and clinical health outcomes, several randomized controlled trials have tested interventions that aim to facilitate stress management. We review evidence for the effects of stress management interventions on psychological and physiological adaptation and health outcomes in cancer patients and survivors and summarize emerging research in the field to address unanswered questions.
An Ultrasensitive Glypican‑3 Electrochemical Aptasensor Based on Reduced Graphene Oxide-Carboxymethylchitosan-Hemin/Palladium Nanoparticles
Authors: Li Shengnan , Li Xinhao , Cao Liping , Wang Chaoxian , Liang Jintao , Zhou Zhide , Li Guiyin
Published: August 2022

Glypican-3 (GPC3) is a serum marker for hepatocellular carcinoma (HCC) and has excellent specificity and sensitivity. Herein, the construction of an electrochemical aptasensor based on reduced graphene oxide-carboxymethyl chitosan-hemin-palladium nanoparticles (RGO-CMCS-Hemin/Pd NPs) and GPC3 aptamer (GPC3apt) has the function of GPC3 identification and analysis. RGO-CMCS-Hemin/Pd NPs has excellent biocompatibility, large specific surface area and good electrical conductivity, which is not only used as advanced sensing materials to modify the electrode surface to reduce the resistance and increase the signal amplitude, but also used as an electroactive substance to provide electrical signals. GPC3apt as a biometric element makes the aptasensor has good specificity and sensitivity. The GPC3apt can combine with GPC3 and form GPC3-aptamer conjugation, which increases the electron transfer impedance and changes the electrical signal. Under the optimum conditions, the aptasensor had a good linear relationship with the GPC3 concentration in the range of 1.0–100.0 ng ml−1 with R2 of 0.9573. The lower limit of detection was 1.0 ng ml−1. When the aptasensor was applied to GPC3 detection in human serum samples, the recovery was 93.73%–118.83%. In addition, the GPC3 aptasensor has excellent specificity, reproducibility and stability, which is anticipated to be applied for the clinical detection of GPC3.
Influence of Gold Nano Particles on Dielectric Features A.C. Conductivity and Dielectric Breakdown Strength of PbO-B2O3-SeO2:Ho2O3 Glass Ceramics
Authors: Siva Sesha Reddy A. , Kostrzewa M. , Purnachand N. , Ingram A. , Sahaya Baskaran G. , Venkatramaiah N. , Ravi Kumar V. , Veeraiah N.
Published: August 2022

The objective of this study is to report the results of dielectric properties and dielectric breakdown strength (DBS) of PbO-B2O3-SeO2:Ho2O3 (PBSH) glass ceramics as functions of Au2O3 content and to correlate the results with spectroscopic properties. PBSH glass ceramics with traces of Au2O3 were fabricated and characterized with different techniques that include positron annihilation spectroscopy (PAS). Several dielectric parameters and DBS were studied as functions of Au2O3 concentration. Observed increase of ε′ with Au2O3 content was attributed to a hike in the space charge polarization (scp) due to the increased concentration of structural imperfections. Dipolar relaxation phenomenon exhibited by electric moduli was analyzed using Cole-Cole plots. σ

ac
(a c, conductivity) showed an increasing tendency with Au2O3 content. Such increase was attributed to the polaronic exchange between two structural groups of SeO2. Inferences drawn from dielectric and DBS studies were found to be in agreement with different spectroscopic properties of these glass ceramics.
Investigation of Common Source Amplifier Circuit using Gate Stack-Based GAA Dopingless Nanowire Field Effect Transistor
Authors: Solay Leo Raj , Kumar Pradeep , Amin S. Intekhab , Anand Sunny
Published: August 2022

Reported work demonstrates the application of common source amplifier circuit using the proposed Gate Stack based Gate All Around Dopingless Nanowire Field Effect Transistor (GS GAA DL NW—FET) structure. Primarily, impact of the gate stack (GS) technique on the conventional Gate All Around Dopingless Nanowire Field Effect Transistor (GAA DL NW—FET) structure is explored. The proposed FET structure resulted in excellent electrostatic control over the channel by incorporating the advantages of GAA architectures and dopingless technique. As transfer characteristics of conventional GAA DL NW—FET have been enhanced with gate stack (SiO2 + high k) technique when employed at dielectric region. A contrast is drawn between both structures in terms of analog parametric analysis which resulted in improved ION of 30.6 (μA), reduced IOFF of 10−7 (μA) and enhanced ION/IOFF of 6.7 × 107. Linearity analysis were made to examine the distortion less digital communication and a fair comparison is depicted between the structures. CS amplifier circuit application with proposed GS GAA DL NW—FET resulted in improved VOUT with 15.2 dB of gain when compared with GAA DL NW—FET based CS amplifier which stood at 13.9 dB which proves the promising candidature for forthcoming nanoscale circuit applications.
Temperature-Dependent Polarization Switching and Endurance Cycling Properties of HfAlO Ferroelectric Thin Film
Authors: Cheng Chun-Hu , Chen Wei-Ting , Lin Kuan-Hsiang , Chen Hsuan-Han , Liao Ruo-Yin , Huang Ching-Chien , Lin Shih-Hao , Hsu Hsiao-Hsuan
Published: August 2022

In this work, we studied the temperature dependences of endurance cycling properties on atomic layer deposition (ALD) HfAlO metal-ferroelectric-metal (MFM) capacitor in the range from 25 °C, 40 °C, 50 °C and 75 °C. Base on experiment results, it is found the reduction percentage of the ferroelectric memory window (2Pr) from 6.5 μC cm−2 (25 °C) to 6.3 μC cm−2 (75 °C) is only 3%, indicating that the ferroelectric HfAlO film has a robust operating temperature stability. The excellent high temperature endurance properties show around 30% of the original 2Pr value (6.3 μC cm−2) can be held after being fatigued up to 108 endurance cycles at 75 °C without breakdown. Additionally, using Arrhenius plot fitting (ln(J/E) vs 1/kT) before and after endurance cycles was extracted the changes of trapping energy level to better understand the relationship between leakage current, oxygen vacancies or defects tapping of polarization-switching behavior in HfAlO ferroelectric film.
Lessons from SARS-CoV, MERS-CoV, and SARS-CoV-2 Infections: What We Know So Far
Authors: Alsafi Radi Taha
Published: August 2022

Within past decades, human infections with emerging and reemerging zoonotic viral pathogens have raised the eminent public health concern. Since November 2002, three highly pathogenic and major deadly human coronaviruses of the βετα-genera (β-hCoVs), namely, severe acute respiratory distress syndrome-coronavirus (SARS-CoV), middle east respiratory syndrome-coronavirus (MERS-CoV), and SARS-CoV-2, have been globally emerged and culminated in the occurrence of SARS epidemic, MERS outbreak, and coronavirus disease 19 (COVID-19) pandemic, respectively. The global emergence and spread of these three major deadly β-hCoVs have extremely dreadful impacts on human health and become an economic burden. Unfortunately, clear specific and highly efficient medical countermeasures for these three β-hCoVs and their underlying fatal illnesses remain under development. Although they belong to the same family and share many features and convergent evolution, these three deadly β-hCoVs have some important and obvious differences. By utilizing their lessons and gaining a deeper understanding of these β-hCoVs, we can identify areas of improvement and provide preparedness plans for fighting and controlling the future reemerging human infections that might arise from them or from other potential pathogenic hCoVs. Therefore, this review summarizes the state-of-the-art information and compares the similarities and dissimilarities between SARS-CoV, MERS-CoV, and SARS-CoV-2, in terms of their evolution trait, genome organization, host cell entry mechanisms, tissue infectivity tropisms, transmission routes and contagiousness, and the clinical characteristics, laboratory features, and immunological abnormalities of their related illnesses. It also provides an overview of the emerging SARS-CoV-2 variants. Additionally, it discusses the challenges of the most proposed treatment options for SARS-CoV-2 infections.
Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine
Authors: Wahid Fazli , Azhar Sania , Ali Sikandar , Zia Muhammad Sultan , Abdulaziz Almisned Faisal , Gumaei Abdu
Published: August 2022

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.
Application and the Effect of the Triple Prerehabilitation Nursing Model in the Perioperative Period of Knee Arthroplasty in Diabetic Patients
Authors: Zhao Sisi , Peng Lingjun , Mo Tingting , Ruan Qianzi
Published: August 2022

Objective. The aim of the study was to explore the application and the effect of the triple prerehabilitation nursing model in the perioperative period of knee arthroplasty in diabetic patients. Methods. The prospectively included 60 patients with diabetes who underwent total knee replacement were admitted from August 2021 to April 2022 and were divided into 2 groups according to the (1 : 1) ratio. The control group was mainly given routine nursing care. On the basis of the control group, the observation group received triple prerehabilitation nursing. The postoperative knee flexion, hospital for the special surgery knee score (HSS), the daily living ability (Barthel) score, the modified fall efficacy scale (MFES) score, the recovery of the lower-limb muscle strength, and the incidence of complications were compared between the two groups. Results. The knee flexion degree and lower-limb muscle recovery of the observation group were better than those of the control group at 3 d, 7 d, and 14 d after operation (

P
<
0.05

). The HSS score, Barthel score, and MFES score of the observation group were higher than those of the control group (

P
<
0.05

). There was no significant difference in postoperative complications between the two groups (

P
>
0.05

). Conclusion. The triple prerehabilitation nursing care for diabetic patients undergoing total knee replacement can promote the recovery of limb function.
Extraction and Classification of the Supervised Coastal Objects Based on HSRIs and a Novel Lightweight Fully Connected Spatial Dropout Network
Authors: Chen Yan , Wan Jiahua , Xi Yantao , Jiang Wenxiang , Wang Mengyuan , Kang Menglei
Published: August 2022

For the protection and management of coastal ecosystems, it is crucial to monitor typical coastal objects and examine their characteristics of spatial and temporal variation. There are limitations to the conventional object-oriented and spectrum-based approaches to HSRIs interpretation. The majority of recently conducted studies on semantic segmentation based on DCNNs concentrate on improving the accuracy of single objects at local scales. The completeness, generalization, and edge accuracy of the extraction and classification of multiple objects with the complex background at regional scales still need to be improved. We created a benchmark dataset CSRSD for coastal supervision using HSRIs and GIS in this study to address the aforementioned problems. In the meantime, by combining the traditional U-Net and DeepLabV3+ feature fusion strategies, we propose a novel fully connected fusion pattern by switching to deepwise separable convolution from conventional convolution and introducing spatial dropout to create a brand new CBS module. The LFCSDN, a new lightweight fully connected spatial dropout network, has been suggested. The findings demonstrate that our constructed semantic segmentation dataset, which has produced reliable results on U-Net and DeepLabV3+, can be used as a benchmark for applications based on DCNNs for coastal scenes. While maintaining high accuracy, LFCSDN can significantly reduce the number of parameters. Our suggested CBS module can increase the model’s generalization by reducing overfitting. In order to analyze the spatiotemporal characteristics of target changes in the study area, tests on expansive remote sensing imagery were also conducted. The findings can be applied to ecological restoration, coastal area mapping, and integrated management. Additionally, it serves as a resource for studies on multiscale semantic segmentation in computer vision.
Ultrasonic Image Features under the Intelligent Algorithm in the Diagnosis of Severe Sepsis Complicated with Renal Injury
Authors: Xu Leiming , Wang Xin , Pu Pu , Li Suhui , Shao Yongzheng , Li Yong
Published: August 2022

This research was aimed at analyzing the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse-coupled neural network (PCNN) algorithm and at improving the diagnostic accuracy and efficiency of clinical severe sepsis complicated with AKI. In this research, 50 patients with sepsis complicated with AKI were collected as the observation group and 50 patients with sepsis as the control group. All patients underwent ultrasound examination. The clinical data of the two groups were collected, and the scores of acute physiology and chronic health assessment (APACHE II) and sequential organ failure assessment (SOFA) were compared. The ultrasonic image information enhancement algorithm based on artificial intelligence PCNN is constructed and simulated and is compared with the maximum between-class variance (OSTU) algorithm and the maximum entropy algorithm. The results showed that the PCNN algorithm was superior to the OSTU algorithm and maximum entropy algorithm in the segmentation results of severe sepsis combined with AKI in terms of regional consistency (UM), regional contrast (CM), and shape measure (SM). The acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) scores in the observation group were substantially higher than those in the control group (

P
<
0.05

). The interlobular artery resistance index (RI) in the observation group was substantially higher than that in the control group (

P
<
0.05

). Moreover, the mean transit time (mTT) in the observation group was significantly higher than that in the control group (

4.85
±
1.27

vs.

3.42
±
1.04

), and the perfusion index (PI) was significantly lower than that in the control group (

134.46
±
17.29

vs.

168.37
±
19.28

), with statistical significance (

P
<
0.05

). In summary, it can substantially increase ultrasonic image information based on the artificial intelligence PCNN algorithm. The RI, mTT, and PI of the renal interlobular artery level in ultrasound images can be used as indexes for the diagnosis of severe sepsis complicated with AKI.
Porous Blasting Morphology and Resistivity Response Characteristics of Soft Coal Seam
Authors: Yang Wenwang , He Sanlin , Zhao Taotao , Ge Chenying
Published: August 2022

The fracturing characteristics of multi-holes blasting in soft coal seam were analyzed quantitatively by using the multi-holes model blasting experiment of soft coal seam and digital image processing (DIP) and electrical resistivity tomography (ERT) techniques, and the electrical resistivity response rules before and after blasting were obtained. The results show the following: (1) At the same time as the formation of blasting-induced cracks, the coal inside the control hole presents two kinds of displacement phenomena: crack and noncrack, partially offsetting the extrusion effect caused by the expansion of the blasting hole. (2) The comprehensive value (S) of blasting-induced cracks, control hole, and blasting hole area change is proposed to analyze the blasting effect. When single-hole blasting is performed, S < 0, the fractal dimension change rate of coal seam crack is only 5%, the control area presents a compaction phenomenon, and the resistivity decreases by about 20%. When multi-hole blasting is performed, the blasting cracks are abundant and the coal displaces sufficiently. The fractal dimension of cracks reaches 21.28%, the negative effect of the blast holes cavity is offset (S > 0), and the resistivity in the controlled area generally increases by 1 to 30 times. (3) Compared with the single-hole blasting model, the multi-hole blasting is more in line with the coal mine site, and can effectively increase the permeability in the control area, and the resistivity generally increases.
Sports Deep Learning Method Based on Cognitive Human Behavior Recognition
Authors: Liu Xiwei
Published: August 2022

An in-depth learning-based approach is designed to develop the ability to recognize human behavior on the move. We introduce 3D residual structures and create 3D residual models. In order to get the most out of the data relationship of several consecutive frames, this study introduces 3D techniques for assigning different values to the existing frames. Experiments show that both structures improve recognition performance. For the 3D residual model, 3D attention model, and 3D attention residual model, this study proposes two model fusion strategies: average and weighted. Among them, the weighted fusion is to give a higher fusion proportion to the high accuracy model by using the model weight calculation method designed in this study. The experimental results show that the additive fusion strategy based on feature contribution has an obvious improvement effect on the test results of the two benchmark datasets, with an increase of more than 2% points, including an increase of 2.69% on HMDB51. The effect of splicing and fusion strategy has also increased by more than 1% point, including 1.34% on UCF101 dataset and about 1.9% on HMDB51. It is proven that deep learning can effectively recognize human behavior in sports.
Educational Data Mining by Optimally Fusing Shallow and Deep Features
Authors: Xiao Bo-An
Published: August 2022

In order to meet the requirement of designing the educational curriculum and extract the relevant data toward students, we in this paper utilize the deep learning architecture to study the personalized understanding and educational feature extraction algorithm of the high school and college students. Aiming at the insufficiency of deep collaborative filtering recommendation algorithm that can only discover the paths that have attracted students’ attention, the requirements of intelligent course are proposed by us. First, the word vector permutation discourse poke model is extracted to produce audible vectors of message data, a well-designed learning algorithm is proposed subsequently to extract and fuse multiple heterogeneous educational data. Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. The method of combining hand-extracted low-level features with high-level human gesture features (image visual gate features and image deep features) automatically extracted by our adopted deep network is used to identify students’ various emotions. The conclusive product is a flat semantic description that effectively detects copying and objection. Experiments are performed on a generic dataset, and we use the old-fashioned manual birth method and two well-known data sets VGG16 and fine-tune AlexNet.
The Application of a Multidimensional Prediction Model in the Recurrence of Female Pelvic Organ Prolapse after Surgery
Authors: Zhang Ruirui , Wang Liming , Shao Yawei
Published: August 2022

Objective. The relationship between multiple indicators of women and postoperative recurrence of pelvic organ prolapse was analyzed to establish a model for predicting postoperative recurrence of female pelvic organ prolapse. Methods. Three hundred patients with pelvic organ prolapse who underwent pelvic organ prolapse surgery at our hospital were monitored for 1-2 years to determine their prognosis. Whether there was a postoperative recurrence, they were divided into two groups. We collected the relevant data from the two groups of patients before and after surgery. Through single factor and logistic multivariate analysis, we selected the risk factors that may affect the recurrence of patients to construct a prediction model. We verified the identification ability, proofreading ability, and clinical applicability of the model. Results. Eighty-four patients with pelvic organ prolapse who had postoperative recurrence were assigned to the recurrence group, and 216 patients were included in the nonrecurrence group. Based on the logistic multivariate analysis results, we constructed a nomogram model containing 5 dimensions of age, BMI, degree of prolapse, pubic fissure, and serum calcium to predict postoperative recurrence. The tests revealed that the model had an excellent identification ability (

AUC
=
0.910

), and the expected recurrence rate was significantly in agreement with the actual recurrence rate (

U
=
−
0.007

,

Brief
=
0.087

). The Hosmer-Lemeshow goodness-of-fit test demonstrated that the model had good calibration (

c
2
=
29.352

,

P
=
0.522

), and the decision curve showed that the threshold probability was in the range of ~12% to 100%, having a high net benefit value. Conclusion. Based on the present study findings, we concluded that the constructed nomogram model has suitable identification, calibration, and clinical applicability.
Fracture Characteristics and Disaster-Causing Mechanism of Rock Strata Based on Arch Mechanical Model of Plane Contact Block
Authors: Xin-chao Zhang , Feng Du , Jun-hua Tang , Qin-ting Wang , Fei-fei Lu
Published: August 2022

The 52307 working face with large mining height in Daliuta coal mine is chosen as the research background, where there is the special mining condition in the overburden structure with a buried depth of more than 150 m and only one layer of key structure in Shendong mining area. Using the methods of similarity simulation experiment and theoretical analysis, the overburden movement law based on the arch mechanical model of plane contact block is systematically researched. The research results indicate that before the initial weight, the main key layer appears layered fracture in the mining process, and its lower rock layer becomes a part of the collapse zone. Before the initial weight, the upper key layer forms a “fixed supported beam” structure. When the beam reaches the limit length, shear failure occurs in the middle position; during the periodic weight, the upper key layer breaks into the plane contact block arch structure close to the same size. Relying on the friction between the contact planes, it has a certain displacement but does not collapse completely. The load transmitted from overburden is transmitted to the rear of the gob through the semiarch characteristics of the structure; the sliding instability and rotary deformation instability of the plane contact block arch structure are analyzed. The fracture of the key layer lags behind the mining position of the working face by a certain distance, due to the influence of the fracture angle of collapse zone in the advancing process of working face. The research results provide an important guiding significance for prediction and prevention for dynamic disaster of rock strata in working face with large mining height.
Application of Teaching Administrating Informatization in Universities Based on the Era of Big Data
Authors: Xi Difei , Sonbull Dick
Published: August 2022

The arrival of the era of big data has brought a great impact on China’s education industry and prompted the education industry to launch a series of educational reforms. Further use of information tools is an inevitable requirement of higher education reform, and many colleges and universities regard information construction as the foundation of higher education reform. In the process of informatization construction, there are bound to be various problems. By introducing the big data analysis system, this paper analyzes and summarizes the data collected by information systems such as the teaching resource database and the square educational administration system.

K

-MEANS clustering algorithm and association rule mining algorithm are designed. Aiming at the low time efficiency of Apriori algorithm of association rules, an improved

I
+
+

algorithm is proposed, and the association rules are applied to students’ behavior analysis, and experimental analysis is carried out. Experiments show that the running time of the improved algorithm is obviously shortened, and the time complexity is small. Because the algorithm uses Boolean matrix and stores Boolean values “0” or “1,” the space complexity is also small. Therefore, the improved Apriori algorithm

I
+
+

is faster and more efficient.
A Network Traffic Network Prediction Model with K-Means Optimization Algorithm
Authors: Wei Zhen , Sun Jingwei
Published: August 2022

Data storage and computation on cloud servers can handle many gigabytes of data, but their network traffic will also be heavy. Researchers have developed several models for predicting network traffic in order to reduce the communication pressure on cloud servers. However, the existing models are not accurate enough to be applied to the cloud server. To deal with this problem, a network traffic prediction model (NTPM) with the K-means optimization algorithm is presented in this paper, which clusters network traffic data from cloud servers by the K-means optimization algorithm, and then SVM is used to train the model. Our study shows that compared with a recent NTPM that predicted network traffic accurately, the proposed model provides better network traffic prediction and is better suited to cloud servers.
Three-Dimensional Simulation Garden Landscape Design Method Based on Virtual Simulation Technology
Authors: Chen Shasha , Wang Xiaolan
Published: August 2022

In order to further promote the design and development of 3D simulated garden landscape, the author proposes a method of 3D simulated garden landscape design based on virtual simulation technology. By combining virtual simulation technology and traditional 3D simulation garden landscape design method, a 3D garden landscape simulation system is constructed, thereby realizing the improvement of 3D simulation garden landscape design method and further promoting the development of 3D simulation garden landscape design. Experimental results show that with the gradual expansion of the scope, the accuracy of the method used by the author gradually improved, reaching a maximum of about 89%, while the traditional method was always maintained at about 40%, and the judgment rate was doubled compared with the traditional method. Conclusion. By combining virtual simulation technology and traditional 3D simulation garden landscape design method, the development of 3D simulation garden landscape design can be effectively promoted.
A Clinical Profile of Pediatric COVID-19 Testing in the Emergency Department, Dubai, United Arab Emirates
Authors: Mir Fatima Farid , Saleh Maysa
Published: August 2022

Background. The COVID-19 pandemic marked a health and economic crisis of massive proportions. In its early months, literature was centered on adult medical and critical care. As time progressed, international reports of COVID-19 infection in children steadily grew; however, data on disease features in the United Arab Emirates’ pediatric population was noticeably lagging. Method. The presented research was conducted at Latifa Women and Children Hospital Emergency Department to ascertain an association between a child’s presenting features and basic investigations to a subsequent positive COVID-19 test result. Data was collected via electronic medical records and statistical analysis performed with SPSS version 22.0. Results. A total of four hundred and five (405) patients were analyzed, with 32 (8%) being COVID-19 positive on initial testing in emergency department. There is a statistically significant correlation (

p
<
0.05

) between testing positive for COVID-19 infection and history of exposure to COVID-19-positive individuals; the presence of runny nose, cough, poor feeding, and abdominal pain with reassuring physical examination findings; and predominantly normal reports of basic blood investigations and chest X-ray images. Conclusion. This research demonstrates that a minority of children tested for COVID-19 in the initial wave of the pandemic tested positive. A significant proportion of COVID-19-positive pediatric patients exhibit history of exposure to COVID-19-positive individuals; the presence of runny nose, cough, poor feeding, and abdominal pain; normal physical examination; normal basic blood investigations and chest X-ray findings.
In Silico Study of Mercury Resistance Genes Extracted from Pseudomonas spp. Involved in Bioremediation: Understanding the Promoter Regions and Regulatory Elements
Authors: Dibbisa Duguma , Wagari Gobena
Published: August 2022

Microbial genes and their product were diverse and beneficial for heavy metal bioremediation from the contaminated sites. Screening of genes and gene products plays a significant role in the detoxification of pollutants. Understanding of the promoter region and its regulatory elements is a vital implication of microbial genes. To the best of our knowledge, there is no in silico study reported so far on mer gene families used for heavy metal bioremediation. The motif distribution was observed densely upstream of the TSSs (transcription start sites) between +1 and -350 bp and sparsely distributed above -350 bp, according to the current study. MEME identified the best common candidate motifs of TFs (transcription factors) binding with the lowest e value (7.2

e

-033) and is the most statistically significant candidate motif. The EXPREG output of the 11 TFs with varying degrees of function such as activation, repression, transcription, and dual purposes was thoroughly examined. Data revealed that transcriptional gene regulation in terms of activation and repression was observed at 36.4% and 54.56%, respectively. This shows that most TFs are involved in transcription gene repression rather than activation. Likewise, EXPREG output revealed that transcriptional conformational modes, such as monomers, dimers, tetramers, and other factors, were also analyzed. The data indicated that most of the transcriptional conformation mode was dual, which accounts for 96%. CpG island analysis using online and offline tools revealed that the gene body had fewer CpG islands compared to the promoter regions. Understanding the common candidate motifs, transcriptional factors, and regulatory elements of the mer operon gene cluster using a machine learning approach could help us better understand gene expression patterns in heavy metal bioremediation.
Exploration of the Crucial Genes and Molecular Mechanisms Mediating Atherosclerosis and Abnormal Endothelial Shear Stress
Authors: Zhu Guoqi , Lai Yan , Chen Fei , Qian Jun , Lin Hao , Yuan Deqiang , Yao Tongqing , Liu XueBo
Published: August 2022

Background. Abnormal endothelial shear stress (ESS) is a significant risk factor for atherosclerosis (AS); however, the genes and pathways between ESS and AS are poorly understood. Here, we screened hub genes and potential regulatory targets linked to the progression of AS induced by abnormal ESS. Methods. The microarray data of ESS and AS were downloaded from the Gene Expression Omnibus (GEO) database. The coexpression modules related to shear stress and AS were identified with weighted gene coexpression network analysis (WGCNA). Coexpression genes in modules obtained from GSE28829 and GSE160611 were considered as SET1. The results were validated in validation set by differential gene analysis. The limma package in R was used to identify differentially expressed genes (DEGs). The common DEGs of GSE100927 and GSE103672 were regarded as SET2. Next, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted. Protein-protein interaction (PPI) enrichment analysis was assembled, and hub genes were identified using MCODE and ClueGO in Cytoscape. ROC curve analyses were conducted to assess the ability of common hub genes to distinguish samples of atherosclerotic plaque from normal arterial. The expression of common hub gene was verified in ox-LDL-induced foam cells and GSE41571. Results. We identified three gene modules (the blue, tan, and cyan modules) related to AS and three shear stress-related modules (the brown, red, and pink modules). A total of 129 genes in SET1 and 476 genes in SET2 were identified. CCRL2, LGALS9, and PLCB2 were identified as common hub genes and validated in the GSE100927, GSE28829, and GSE41571. ROC analysis indicates the expression of CCRL2, LGALS9, and PLCB2 could effectively distinguish the atherosclerotic plaque and normal arterial. The expression level of CCRL2, LGALS9, and PLCB2 increases with the accumulation of lipid increased. Conclusion. We identified CCRL2, LGALS9, and PLCB2 as key genes associated with abnormal ESS and AS and may provide potential prevention and treatment target of AS induced by abnormal ESS.
[Retracted] Multiresolution‐Based Singular Value Decomposition Approach for Breast Cancer Image Classification
Authors: Mann Suman , Bindal Amit Kumar , Balyan Archana , Shukla Vijay , Gupta Zatin , Tomar Vivek , Miah Shahajan
Published: August 2022

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z‐moments for extracting features from the NSCT‐decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state‐of‐the‐art approaches.
Insights into the Anti-inflammatory and Antiviral Mechanisms of Resveratrol
Authors: Chen Xiangxiu , Song Xu , Zhao Xinghong , Zhang Yu , Wang Yiming , Jia Renyong , Zou Yuanfeng , Li Lixia , Yin Zhongqiong
Published: August 2022

Resveratrol is a naturally occurring stilbene phytoalexin phenolic compound, which has been extensively studied on its biological activity. It has been widely accepted that resveratrol possesses anti-inflammatory and antiviral activities. In this review, we summarize the anti-inflammatory dosages and mechanism and antiviral mechanism of resveratrol. Since viral infections are often accompanied by inflammation, we propose that the NF-κB signaling pathway is a key and common molecular mechanism of resveratrol to exert anti-inflammatory and antiviral effects. For future studies, we believe that resveratrol’s anti-inflammatory and antiviral mechanisms can consider the upstream signaling molecules of the NF-κB signaling pathway. For resveratrol antivirus, future studies can be conducted on the interaction of resveratrol with key proteins or important enzymes of the virus. In addition, we also think that the clinical application of resveratrol is very important. In short, resveratrol is a promising anti-inflammatory and antiviral drug, and research on it needs to be expanded.
Application of a Wind Environment Simulation Generation Technology in Urban Planning
Authors: Chen Yang
Published: August 2022

With the acceleration of urbanization, cities have shown a development trend of high density, high height, and high intensity, which significantly weakens the airflow in the city, resulting in the accumulation of heat and pollutants, and in the urban environment, the problem is getting worse. Therefore, it is particularly necessary to strengthen urban ventilation in the urban environment. However, in the real urban environment, the building layouts are diverse, the building forms are different, and the interaction of the building wind environment is complex due to the lack of relevant research on the complex building spatial form in the urban mesoscale area on the outdoor pedestrian. The research results are difficult to be applied in actual urban construction, and it is difficult to guide the optimization of the urban wind environment. To solve this problem, this paper adopts the CFD method in a real-scale (1 : 1) urban mesoscale regional wind environment simulation study. On this basis, the influencing factors of the urban pedestrian wind environment are analyzed, and the optimization strategy of the wind environment is proposed, which can provide practical guidance for the planning practice of enhancing urban wind comfort.