bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Incretin-Based Medications on Lipid Metabolism
Authors: Yaribeygi Habib , Maleki Mina , Butler Alexandra E. , Jamialahmadi Tannaz , Sahebkar Amirhossein
Published: December 2021

Pathophysiological pathways that are induced by chronic hyperglycemia negatively impact lipid metabolism. Thus, diabetes is commonly accompanied by varying degrees of dyslipidemia which is itself a major risk factor for further macro- and microvascular diabetes complications such as atherosclerosis and nephropathy. Therefore, normalizing lipid metabolism is an attractive goal for therapy in patients with diabetes. Incretin-based medications are a novel group of antidiabetic agents with potent hypoglycemic effects. While the impact of incretins on glucose metabolism is clear, recent evidence indicates their positive modulatory roles on various aspects of lipid metabolism. Therefore, incretins may offer additional beneficial effects beyond that of glucose normalization. In the current review, how these antidiabetic medications can regulate lipid homeostasis and the possible cellular pathways involved are discussed, incorporating related clinical evidence about incretin effects on lipid homeostasis.
Pelvic Hydatid Cyst with Hydroureteronephrosis: A Rare Case Report
Authors: Salehi Amir Mohammad , Salehi Hossain , Jenabi Ensiyeh
Published: December 2021

Hydatid cyst is a parasitic disease caused by Echinococcus granulosus or Echinococcus multilocularis. Humans are accidentally infected with the parasite. The cyst is usually found in the liver and lungs and rarely occurs in other body parts. The present article describes a rare case of pelvic hydatid cyst in a young man who presented with nausea, vomiting, and right abdominal pain. Two large cystic masses were discovered during a CT scan in the patient’s pelvic region, resulting in right urinary tract hydroureteronephrosis. Additionally, the antibody index was used to confirm the presence of a primary hydatid cyst.
Research on Video Quality Diagnosis Technology Based on Artificial Intelligence and Internet of Things
Authors: Sun Zhidong , Sun Jie , Li Xueqing
Published: December 2021

The remote video diagnosis system based on the Internet of Things is based on the Internet of Things and integrates advanced intelligent technology. To better promote a harmonious society, constructing a video surveillance system is accelerating in our country. Many enterprises and government agencies have invested much money to build video surveillance systems. The quality of video images is an important index to evaluate the video surveillance system. However, as the number of cameras continues to increase, the monitoring time continues to extend. In the face of many cameras, it is not realistic to rely on human eyes to diagnose video‐solely quality. Besides, due to human eyes’ subjectivity, there will be some deviation in diagnosis through human eyes, and these factors bring new challenges to system maintenance. Therefore, relying on artificial intelligence technology and digital image processing technology, the intelligent diagnosis system of monitoring video quality is born using the computer’s efficient mathematical operation ability. Based on artificial intelligence, this paper focuses on studying video quality diagnosis technology and establishes a video quality diagnosis system for video definition detection and noise detection. This article takes the artificial intelligence algorithm in the diagnosis of video quality effect. Compared with the improved algorithm, the improved video quality diagnosis algorithm has excellent improvement and can well finish video quality inspection work. The accuracy of the improved definition evaluation function for the definition detection of surveillance video and noise detection is as high as 95.56%.
Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition
Authors: Qin Bo , Luo Quanyi , Zhang Juanjuan , Li Zixian , Qin Yan
Published: December 2021

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.
Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning
Authors: Fan Bingqian , Xing Xuanxuan
Published: December 2021

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.
Analysis of Structural Response of Subway Shield Tunnel Lining under the Influence of Cavities
Authors: Shi Yufeng , Chen Zhaoyang , Wei Duqiang , Zhang Tao , Zhou Xuming , Zhao Xiushao , Hu Junhao , Zhou Yuhang
Published: December 2021

The existence of cavities behind the shield tunnel lining can cause cracking, broken pieces, water leakage, and other problems, which reduces the durability and safety of the shield tunnel segment structure. In order to clarify the mechanism of cavity damage, a more systematic study of the effects of cavities on the shield tunnel lining structure from the angle, depth, and the number of cavities is carried out using model tests and numerical simulations without considering the effects of the stiffness reduction effect at the tunnel segment joints and groundwater seepage in this paper. The findings show that the bending moment value and the cavity angle value are approximately linear with the increase of single cavity angle, and the bending moment at the vault arch is reversed when the angle of the cavity behind the arch is greater than 30°. With the increase of single cavity depth, the axial force and bending moment at the cavity increase, and the distribution of bending moment remains unchanged, and the bending moment tends to be stable and unchanged beyond a certain depth. With the increase of single cavity angle and depth, the structural safety coefficient of the segment decreases, and the degree of influence is angle value > depth value. The existence of multiple cavities intensifies the influence of each cavity on the segment, especially when there are cavities behind the top and bottom of the vault; the bending moment value of the top of the vault increases by 22.53% compared with that of the single cavity condition.
Mechanism of Coal Burst and Prevention Practice in Deep Asymmetric Isolated Coal Pillar: A Case Study from YaoQiao Coal Mine
Authors: Xue Chengchun , Cao Anye , Guo Wenhao , Wang Songwei , Liu Yaoqi , Shen Zhiping
Published: December 2021

Coal pillar bursts continue to be a severe dynamic hazard. Understanding its mechanism is of paramount importance and crucial in preventing and controlling its occurrence. The extreme roadway deformations from the asymmetric isolated coal pillars in the central mining district of YaoQiao Coal Mine have responded with frequent intense tremors, with risky isolated coal pillar bursts. The theoretical analysis, numerical simulation, and field measurements were done to research the impact of spatial overburden structure and stress distribution characteristics on the isolated coal pillar area, aiming to reveal the mechanism of coal pillar burst leading to the practice of prevention and control in the asymmetric isolated coal pillar area. The study shows that the overburden structure of the asymmetric is an asymmetric “T” structure in the strike‐profile, and the stress in the coal pillar is mostly asymmetric “saddle‐shaped” distribution, with the peak stress in the east side of the coal pillar, and the coal pillar is a “high stress serrated isolated coal pillar.” Numerical simulation results showed that the support pressure in the isolated coal pillar area on the strike profile was asymmetrically “saddle‐shaped” distribution. The peak vertical stress in the coal pillar area continued to rise and gradually shifted to the mining district′s deep part. As a result, the response of the roadway sides to the dynamic load disturbance was more pronounced. They developed a coal burst prevention and control program of deep‐hole blasting in the roof of asymmetrical isolated coal pillar roof and unloading pressure from coal seam borehole. Monitored data confirmed that the stress concentration was influential in the roadway’s surrounding rock in the asymmetric isolated coal pillar area, circumventing coal pillar burst accidents. The research outcomes reference the prevention and control of coal bursts at isolated working faces of coal pillars under similar conditions.
Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm
Authors: Talla Ouambo Steve Alan , Boum Alexandre Teplaira , Imano Adolphe Moukengue , Corriou Jean-Pierre
Published: December 2021

Although moving horizon estimation (MHE) is a very efficient technique for estimating parameters and states of constrained dynamical systems, however, the approximation of the arrival cost remains a major challenge and therefore a popular research topic. The importance of the arrival cost is such that it allows information from past measurements to be introduced into current estimates. In this paper, using an adaptive estimation algorithm, we approximate and update the parameters of the arrival cost of the moving horizon estimator. The proposed method is based on the least-squares algorithm but includes a variable forgetting factor which is based on the constant information principle and a dead zone which ensures robustness. We show by this method that a fairly good approximation of the arrival cost guarantees the convergence and stability of estimates. Some simulations are made to show and demonstrate the effectiveness of the proposed method and to compare it with the classical MHE.
Blade Serial Number Identification Based on Blade Tip Clearance without OPR Sensor
Authors: Zhang Liang , Wang Qidi , Yuan Huiqun , Li Xin
Published: December 2021

Blade serial number identification is one of the key issues in blade tip‐timing vibration measurement without once‐per‐revolution (OPR) sensor. In order to overcome the shortcomings of the existing blade serial number identification methods without OPR sensor, a new identification method of blade serial number based on blade tip clearance is proposed in this paper. The relationship between blade tip‐timing data and blade serial number can be identified by the matching relationship between blade tip clearance under static state and dynamic state. According to the finite element simulation and experimental data, the accuracy of the blade serial number identification method based on blade tip clearance is verified by using the OPR sensor method. The results show that in the nonresonant rotation speed region, the method can identify the blade serial number, and the identification result is consistent with the result of the OPR sensor method. In the resonance rotation speed region, when the blade tip clearance change caused by the blade circumferential bending vibration is less than the dispersion of initial blade tip clearance, the method in this paper can accurately identify the blade serial number. Otherwise, the inference method can be used. It provides theoretical support and technical basis for the engineering application of blade tip‐timing vibration measurement technology without OPR sensor.
The Research of Maneuverability Modeling and Environmental Monitoring Based on a Robotic Dolphin
Authors: Xue Zhibin , Li Liangliang , Song Yixiao
Published: December 2021

In this study, the C-turning, pitching, and flapping propulsion of a robotic dolphin during locomotion were explored. Considering the swimming action required of a three-dimensional (3D) robotic dolphin in the ocean, we propose a maneuverability model that can be applied to the flapping motion to provide precise and stable movements and function as the driving role in locomotion. Additionally, an added tail joint allows for the turning movement with efficient parameters obtained by a fluid-structure coupling method. To obtain a mathematical model, several disturbance signals were considered, including systematic uncertainties of the parameters, the perpetually changing environment, the interference from obstacles with effective fuzzy rules, and a sliding mode of control. Furthermore, a combined strategy of environment recognition was used for the positional control of the robotic dolphin, incorporating sonar, path planning with an artificial potential field, and trajectory tracking. The simulation results show satisfactory performance of the 3D robotic dolphin with respect to flexible movement and trajectory tracking under the observed interference factors.
[Retracted] Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks
Authors: He Hao , Yang Yong
Published: December 2021

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the protein sequence through the selected properties. The shorter windows reflect the characteristics of the central residue, and the longer windows reflect the characteristics of the surroundings around the central residue. Moreover, to highlight the specificity of sequence and evolutionary properties, they are preprocessed, respectively. After that, the preprocessed properties are combined into feature matrices as the input of the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict IDPs effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.
A Single-Central, Randomized, Double-Blinded, Placebo-Controlled, Crossover Trial Protocol: A Clinical Effect Evaluation Study on the TCM Comprehensive Intervention Program for Chronic Heart Failure
Authors: Li Xingxing , Li Dong , Cui Xiaoyun , Zhou Kun , Liu Jing , Fan Zongjing , Lu Jinjin , Wan Jie , Yan Rongkun , Xia Junyan , Li Xing , Wu Yang , Lin Qian , Li Yan
Published: December 2021

Background. It is known to all the doctors and patients that both the morality and incidence rate of chronic heart failure (CHF) are quite high among various heart diseases. Traditional Chinese medicine (TCM) comprehensive intervention becomes a rising prospective therapy for patients with CHF. Considering the efficacy of TCM, the study aims to test the safety as well as the validity of TCM comprehensive intervention in patients who are struggling with CHF. Methods. The study is an essentially randomized, single-central, placebo-controlled, double-blinded crossover trial. Eighty-two eligible subjects aged 18–75 years with CHF are supposed to be recruited. According to the subject plan, all the patients will be divided into group A and B. The patients in group A will receive oral Qishen Taohong granules (QTGs) combined with TCM external treatment and standard Western medicine for four weeks. After that, a 2-week washout would be proceeded; this group will be reallocated to another four weeks with oral placebo granules combined with TCM external treatment and standard Western medicine. In contrast, group B will perform the opposite protocol. The primary outcome conforms to the classification from the New York Heart Association (NYHA). Meanwhile, the secondary outcomes are echocardiogram, N-terminal pro-B-type natriuretic peptide (NT-proBNP), Chronic Heart Failure Quality of Life Scale of Integrated Chinese and Western Medicine (CHFQLS), TCM syndrome, symptom, sign, six-minute walk test (6MWT), Pittsburgh Sleep Quality Index (PSQI), Montreal cognitive assessment (MoCA), major adverse cardiovascular events (MACEs), and metabolomics. Discussion. Based on conventional treatments, TCM comprehensive intervention may further improve the patients’ cardiac function and then enhance their quality of life. The results will provide high-quality evidence of TCM comprehensive intervention in treating CHF.
Research and Analysis on the Influence of Small Clear Distance Drilling and Blasting Method on the Existing Tunnel Structure
Authors: Huang Haibin , Li Peng , Wang Chuang , Yuan Bingxiang , Chen Minjie , Feng Weimin
Published: December 2021

In order to study the vibration influence of tunnel drilling and blasting method on the built tunnel with small clear distance, taking the intersection of Zhuhai Dahengqinshan No. 1 tunnel and Zhuji urban rail tunnel as the engineering background, we used ABAQUS finite element software to conduct numerical simulation analysis on the influence of different blasting loads on existing tunnels with small clear distance in Zhuji tunnel construction. The following conclusions were drawn: the blasting construction of the tunnel under construction had the greatest impact on the vault of the existing tunnel; when the peak load was reduced by half, the stress value, vertical displacement, and resultant velocity of Mises were also reduced by half, which indicates that reducing the peak value of blasting load appropriately can ensure the safety of tunnel construction. When the peak load is 2.7 MPa, the measured and simulated values were less than the resultant velocity limit required by the specification. In addition, the relative error between the measured value and the simulated value was less than 5%, indicating the accuracy of the numerical simulation.
Synthesis, Anticancer Assessment, and Molecular Docking of Novel Chalcone‐Thienopyrimidine Derivatives in HepG2 and MCF‐7 Cell Lines
Authors: Safwat Ghada M. , Hassanin Kamel M. A. , Mohammed Eman T. , Ahmed Essam Kh. , Abdel Rheim Mahmoud R. , Ameen Mohamed A. , Abdel-Aziz Mohamed , Gouda Ahmed M. , Peluso Ilaria , Almeer Rafa , Abdel-Daim Mohamed M. , Abdel-Wahab Ahmed
Published: December 2021

Heterocycles containing thienopyrimidine moieties have attracted attention due to their interesting biological and pharmacological activities. In this research article, we reported the synthesis of a series of new hybrid molecules through merging the structural features of chalcones and pyridothienopyrimidinones. Our results indicated that the synthesis of chalcone‐thienopyrimidine derivatives from the corresponding thienopyrimidine and chalcones proceeded in a relatively short reaction time with good yields and high purity. Most of these novel compounds exhibited moderate to robust cytotoxicity against HepG2 and MCF‐7 cancer cells similar to that of 5‐fluorouracil (5‐FU). The results indicated that IC50 of the two compounds (3b and 3g) showed more potent anticancer activities against HepG2 and MCF‐7 than 5‐FU. An MTT assay and flow cytometry showed that only 3b and 3g had anticancer activity and antiproliferative activities at the G1 phase against MCF‐7 cells, while six compounds (3a‐e and 3g) had cytotoxicity and cell cycle arrest at different phases against HepG2 cells. Their cytotoxicity was achieved through downregulation of Bcl‐2 and upregulation of Bax, caspase‐3, and caspase‐9. Although all tested compounds increased oxidative stress via increment of MDA levels and decrement of glutathione reductase (GR) activities compared to control, the 3a, 3b, and 3g in HepG2 and 3b and 3g in MCF‐7 achieved the target results. Moreover, there was a positive correlation between cytotoxic efficacy of the compound and apoptosis in both HepG2 (R2 = 0.531; P = 0.001) and MCF‐7 (R2 = 0.219; P = 0.349) cell lines. The results of molecular docking analysis of 3a-g into the binding groove of Bcl‐2 revealed relatively moderate binding free energies compared to the selective Bcl‐2 inhibitor, DRO. Like venetoclax, compounds 3a-g showed 2 violations from Lipinski’s rule. However, the results of the ADME study also revealed higher drug‐likeness scores for compounds 3a-g than for venetoclax. In conclusion, the tested newly synthesized chalcone‐pyridothienopyrimidinone derivatives showed promising antiproliferative and apoptotic effects. Mechanistically, the compounds increased ROS production with concomitant cell cycle arrest and apoptosis. Therefore, regulation of the cell cycle and apoptosis are possible targets for anticancer therapy. The tested compounds could be potent anticancer agents to be tested in future clinical trials after extensive pharmacodynamic, pharmacokinetic, and toxicity profile investigations.
Efficacy of Sertraline Combined with Cognitive Behavioral Therapy for Adolescent Depression: A Systematic Review and Meta-Analysis
Authors: Liu Wenliang , Li Gongying , Wang Congjie , Wang Xiuzhen , Yang Lin
Published: December 2021

Objective. The efficacy of antidepressant drugs combined with psychotherapy is controversial; hence, this meta-analysis was conducted to assess the efficacy of the combination therapy. Methods. Relevant literature was searched in PubMed, Web of Science and Embase, Chinese databases CNKI, and WanFang Data. We included the literature on the comparison of the sertraline combined with cognitive behavioral therapy (CBT) and each treatment alone for adolescent depression published in 2000-2021. Meta-analysis was performed using Stata16.0 software. Results. A total of 421 relevant articles were retrieved, and 14 studies were finally included. In comparison with the control group (sertraline), sertraline combined with CBT achieved higher response rate (

OR
=
5.07

, 95% CI: 3.00, 8.58) and lower incidence of adverse reactions (

OR
=
0.43

, 95% CI: 0.24, 0.75). Before treatment, there were no significant differences in depression score, anxiety score, and symptom self-rating scale score between the two groups. After treatment, depression score (

SMD
=
−
2.79

, 95% CI: -3.64, -1.94), anxiety score (

SMD
=
−
1.22

, 95% CI: -1.96, -0.47), and symptom self-rating scale score (

SMD
=
−
1.73

, 95% CI: -3.19, -0.27) were significantly lower in the combined treatment group than in the control group. Conclusion. Although the number of comparative trials is small, this study shows that sertraline is effective for adolescent depression, but sertraline combined with CBT is more effective. The latter can significantly reduce the incidence of depressive symptoms, anxiety, and adverse reactions in patients. Therefore, this combination therapy is recommended for the clinical treatment of adolescent depression.
UV-Induced Skin Cancer Knowledge, Sun Exposure, and Tanning Behavior among University Students: Investigation of an Opportunity Sample of German University Students
Authors: Rocholl Marc , Hannappel Julia , Ludewig Michaela , John Swen Malte
Published: December 2021

Ultraviolet radiation (UVR) is the most important risk factor for developing skin cancer. University students can be considered as a particularly high-risk group for long- and short-term adverse effects of UVR due to intensive solar UVR exposure and high rates of sunburn. While validated questionnaires for assessing solar UVR exposure and sun protection behavior are available in German, a questionnaire for assessing the level of knowledge about this topic is still missing. We conducted a literature search for cross-sectional studies assessing skin cancer and sun protection knowledge among university students in Medline (via PubMed) and analyzed existing questionnaires and topics contained therein. We chose to translate the “Skin Cancer and Sun Knowledge Scale” referring to the TRAPD method into the German language and pilot-tested the translation with an opportunity sample of German students. The literature search revealed 36 eligible studies. Four major topics were identified within the studies: knowledge on skin cancer, risk factors, UVR, and sun protection measures. One hundred and seven German university students (86.0% female) with a mean age of 26.25 years (SD ± 4.58; range: 19–46) participated in our pilot study. The internal reliability of the scale was KR-20 = 0.624. We discovered an improvable level of knowledge in terms of skin cancer among the study population. Statistical analyses revealed no significant associations between the level of knowledge and UVR exposure or tanning behavior, respectively. The skin cancer and sun protection knowledge of German university students should be examined thoroughly. While the psychometric properties of the SCSK require further thorough investigation, first empirical experiences indicate the suitability of the tool to assess the level of knowledge regarding skin cancer and sun protection.
Performance Analysis of Iteratively Decoded Convergent Source Mapping with Sphere Packing‐Assisted Differential Space‐Time Spreading Technique for Efficient Video Transmission
Authors: Ahmed Ishtiaque , Minallah Nasru , Frnda Jaroslav , Nedoma Jan
Published: December 2021

With the substantial growth in number of wireless devices, future communication demands overarching research to design high‐throughput and efficient systems. We propose an intelligent Convergent Source Mapping (CSM) approach incorporating Differential Space‐Time Spreading (DSTS) technique with Sphere Packing (SP) modulation. The crux of CSM process is assured convergence by attaining an infinitesimal Bit‐Error Rate (BER). Data Partitioning (DP) H.264 video codec is deployed to gauge the performance of our intelligent and efficient system. For the purpose of efficient and higher data rates, we have incorporated compression efficient source encoding along with error resiliency and transmission robustness features. The proposed system follows the concept of iterations between the Soft‐Bit Source‐Decoder (SBSD) and Recursive Systematic Convolutional (RSC) decoder. Simulations of the DSTS‐SP‐assisted CSM system are presented for the correlated narrowband Rayleigh channel, using different CSM rates but constant overall bit‐rate budget. The SP‐assisted DSTS systems are mainly useful in decoding algorithms that operate without requiring Channel State Information (CSI). The effects of incorporating redundancy via different CSM schemes on the attainable performance and convergence of the proposed system are investigated using EXtrinsic Information Transfer (EXIT) charts. The effectiveness of the proposed system is demonstrated through IT++ based proof‐of‐concept simulations. The Peak Signal‐to‐Noise Ratio (PSNR) analysis shows that using Rate‐2/6 CSM with minimum Hamming distance (dH,min) of 4 offers about 5 dB gain, compared to an identical overall system code rate but with Rate‐2/3 CSM and dH,min of 2. Furthermore, for a consistent value of dH,min and overall rate, the Rate‐2/3 CSM scheme beats the Rate‐5/6 CSM by about 2 dB at the PSNR degradation point of 2 dB. Moreover, the proposed system with Rate‐2/3 CSM scheme furnishes an Eb/N0 gain of 20 dB when compared with the uniform‐rate benchmarker. Clearly, we can say that higher dH,min and lower CSM values are favourable for our proposed setup.
An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques
Authors: Kaur Ishleen , Doja M. N. , Ahmad Tanvir , Ahmad Musheer , Hussain Amir , Nadeem Ahmed , Abd El-Latif Ahmed A.
Published: December 2021

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
A Tent Marine Predators Algorithm with Estimation Distribution Algorithm and Gaussian Random Walk for Continuous Optimization Problems
Authors: Sun Chang-Jian , Gao Fang
Published: December 2021

The marine predators algorithm (MPA) is a novel population‐based optimization method that has been widely used in real‐world optimization applications. However, MPA can easily fall into a local optimum because of the lack of population diversity in the late stage of optimization. To overcome this shortcoming, this paper proposes an MPA variant with a hybrid estimation distribution algorithm (EDA) and a Gaussian random walk strategy, namely, HEGMPA. The initial population is constructed using cubic mapping to enhance the diversity of individuals in the population. Then, EDA is adapted into MPA to modify the evolutionary direction using the population distribution information, thus improving the convergence performance of the algorithm. In addition, a Gaussian random walk strategy with medium solution is used to help the algorithm get rid of stagnation. The proposed algorithm is verified by simulation using the CEC2014 test suite. Simulation results show that the performance of HEGMPA is more competitive than other comparative algorithms, with significant improvements in terms of convergence accuracy and convergence speed.
Mission Planning Issues of Imaging Satellites: Summary, Discussion, and Prospects
Authors: Zhang Guohui , Li Xinhong , Hu Gangxuan , Zhang Zhibin , An Jiping , Man Wanxin
Published: December 2021

Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus.
Preparation, Characteristics, and Controlled Release Efficiency of the Novel PCL-PEG/EM Rod Micelles
Authors: Song Huiling , Yin Yu , Peng Jiahui , Du Zixiu , Bao Wei
Published: December 2021

In order to achieve sustained and controlled release of the hydrophobic cargoes, improve the bioavailability, and reduce the side effects of antibiotics, the model drug erythromycin (EM) was used to prepare polycaprolactone-polyethylene glycol (PCL-PEG)/EM micelles. PCL-PEG, a biocompatible and biodegradable amphiphilic polymer, was used as carrier material of micelles to optimize the formulation and preparation process by orthogonal design. The morphology, stability, drug loading, and encapsulation efficiency and the in vitro release behavior of the micelles were investigated. In addition, activity assays of anti-Staphylococcus aureus were performed. The results indicated that PCL-PEG/EM were rod-like micelles with an average particle size of

220
±
2.6

 nm and a zeta potential of +19 mV. The average drug loading and encapsulation efficiency were approximately 6.5% and 97.0%, respectively. The micelles were stable in the serum within three days. At the effective concentration of the drug, the formulation indicated no apparent toxicity to the cells. The micelles were able to rapidly enter Staphylococcus aureus (S. aureus) and to provide sustained release cargoes that effectively inhibited S. aureus proliferation. The present study provided a new platform for the rational and effective use of hydrophobic antibiotics to treat infections.
Mixture of Lindley and Lognormal Distributions: Properties, Estimation, and
Application
Authors: Al-Moisheer A. S.
Published: December 2021

Finite mixture models provide a flexible tool for handling heterogeneous data. This paper
introduces a new mixture model which is the mixture of Lindley and lognormal distributions
(MLLND). First, the model is formulated, and some of its statistical properties are
studied. Next, maximum likelihood estimation of the parameters of the model is considered,
and the performance of the estimators of the parameters of the proposed models is
evaluated via simulation. Also, the flexibility of the proposed mixture distribution is
demonstrated by showing its superiority to fit a well-known real data set of 128 bladder
cancer patients compared to several mixture and nonmixture distributions. The Kolmogorov
Smirnov test and some information criteria are used to compare the fitted models to the
real dataset. Finally, the results are verified using several graphical methods.
DIAMOND: A Structured Coevolution Feature Optimization Method for LDDoS Detection in SDN‐IoT
Authors: Yin Wencheng , Cui Yunhe , Qian Qing , Shen Guowei , Guo Chun , Li Saifei
Published: December 2021

Software‐defined networking for IoT (SDN‐IoT) has become popular owing to its utility in smart applications. However, IoT devices are limited in computing resources, which makes them vulnerable to Low‐rate Distributed Denial of Service (LDDoS). It is worth noting that LDDoS attacks are extremely stealthy and can evade the monitoring of traditional detection methods. Therefore, how to choose the optimal features to improve the detection performance of LDDoS attack detection methods is a key problem. In this paper, we propose DIAMOND, a structured coevolution feature optimization method for LDDoS detection in SDN‐IoT. DIAMOND is consisted of a reachable count sorting clustering algorithm, a group structuring method, a comutation strategy, and a cocrossover strategy. By analysing the information of SDN‐IoT network features in the solution space, the relationship between different SDN‐IoT network features and the optimal solution is explored in DIAMOND. Then, the individuals with associated SDN‐IoT network features are divided into different subpopulations, and a structural tree is generated. Further, multiple structural trees evolve in concert with each other. The evaluation results show that DIAMOND can effectively select optimal low‐dimension feature sets and improve the performance of the LDDoS detection method, in terms of detection precision and response time.
Identification of Keratinocyte Differentiation-Involved Genes for Metastatic Melanoma by Gene Expression Profiles
Authors: Li Kezhu , Guo Shu , Tong Shuang , Sun Qiang , Jin Shifeng , Qi Bingran , Shao Yining , Xu Nan
Published: December 2021

Background. Melanoma is the deadliest type of skin cancer. Until now, its pathological mechanisms, particularly the mechanism of metastasis, remain largely unknown. Our study on the identification of genes in association with metastasis for melanoma provides a novel understanding of melanoma. Methods. From the Gene Expression Omnibus (GEO) database, the gene expression microarray datasets GSE46517, GSE7553, and GSE8401 were downloaded. We made use of R aiming at analyzing the differentially expressed genes (DEGs) between metastatic and nonmetastatic melanoma. R was also used in differentially expressed miRNA (DEM) data mining from GSE18509, GSE19387, GSE24996, GSE34460, GSE35579, GSE36236, and GSE54492 datasets referring to Li’s study. Based on the DEG and DEM data, we performed functional enrichment analysis through the application of the DAVID database. Furthermore, we constructed the protein-protein interaction (PPI) network and established functional modules by making use of the STRING database. Through making use of Cytoscape, the PPI results were visualized. We predicted the targets of the DEMs through applying TargetScan, miRanda, and PITA databases and identified the overlapping genes between DEGs and predicted targets, followed by the construction of DEM-DEG pair network. The expressions of these keratinocyte differentiation-involved genes in Module 1 were identified based on the data from TCGA. Results. 239 DEGs were screened out in all 3 datasets, which were inclusive of 21 positively regulated genes and 218 negatively regulated genes. Based on these 239 DEGs, we finished constructing the PPI network which was formed from 225 nodes and 846 edges. We finished establishing 3 functional modules. And we analyzed 92 overlapping genes and 26 miRNA, including 11 upregulated genes targeted by 11 negatively regulated DEMs and 81 downregulated genes targeted by 15 positively regulated DEMs. As proof of the differential expression of metastasis-associated genes, eleven keratinocyte differentiation-involved genes, including LOR, EVPL, SPRR1A, FLG, SPRR1B, SPRR2B, TGM1, DSP, CSTA, CDSN, and IVL in Module 1, were obviously downregulated in metastatic melanoma tissue in comparison with primary melanoma tissue based on the data from TCGA. Conclusion. 239 melanoma metastasis-associated genes and 26 differentially expressed miRNA were identified in our study. The keratinocyte differentiation-involved genes may take part in melanoma metastasis, providing a latent molecular mechanism for this disease.
Construction of a Multimedia-Based University Ideological and Political Big Data Cloud Service Teaching Resource Sharing Model
Authors: Feng Jian , Zhang Weiliang , Tsai Sang-Bing
Published: December 2021

In this paper, the intelligent education cloud service platform is first constructed in view of the high cost consumption in the process of resource sharing scheduling in colleges and universities. Secondly, the hierarchical education resource sharing grid model is proposed. Specifically, according to the characteristics of the educational resource grid, the key factors affecting the performance of copy creation strategy are analysed, and a dynamic copy creation strategy is proposed. A multiresource equity distribution mechanism based on the concept of resource sharing equity is further proposed. The mechanism establishes a planning model according to the limited task resource demand and the amount of resources shared by the user at different times so that the global cumulative share vector of superior resources meets the dictionary order optimally. The simulation experiment shows that the grid sharing model proposed in this paper has better performance on the educational cloud service platform. The proposed resource allocation mechanism has achieved good results in ensuring the fair distribution of resources and ensuring high resource utilization when resource sharing users put forward multiple groups of time-changing resource demands.
Local Knowledge and Use of Medicinal Plants in a Rural Community in the Agreste of Paraíba, Northeast Brazil
Authors: da Costa Ferreira Ezequiel , Anselmo Maria da Glória Vieira , Guerra Natan Medeiros , Marques de Lucena Camilla , Felix Cattleya do Monte Pessoa , Bussmann Rainer W. , Paniagua-Zambrana Narel Y , Paiva de Lucena Reinaldo Farias
Published: December 2021

The use of medicinal plants is an important source of therapeutic resources in rural communities and the wide versatility of some species may attract interest for prospecting studies. The aim of this study was to record and analyze local knowledge and the use of medicinal plants in the rural community of Malícia, municipality of Araçagi, Paraíba State, Northeastern Brazil, applying quantitative methods to calculate the Relative Importance (RI) and the Informant Consensus Factor (ICF). Semistructured interviews were conducted with 46 heads of households. The interviews addressed questions about the used parts of the plants, therapeutic indications, and form of use. Therapeutic indications were classified into categories of body systems. The Relative Importance Index (RI) was calculated to verify the species versatility, and the Informant Consensus Factor (ICF) was calculated to verify the consensus of use among informants regarding the body systems. A total of 111 plant species were recorded, inside 101 genera and 47 families. Fabaceae (16 spp.), Lamiaceae, and Myrtaceae (each one with 7 spp.) were the most representative families. Mentha arvensis, Aloe vera, and Myracrodruon urundeuva had the highest RI. A high consensus of use was observed among the informants for neoplasms, nervous system diseases, and infectious and parasitic diseases. Leaves were the part most cited for medicinal use. Regarding the method of preparation, the decoction and the oral administration route stood out. Neoplasms and respiratory system diseases had the highest ICF values. The results indicate a diversified knowledge of the local pharmacopeia and the need for in-depth studies to corroborate the effectiveness of medicinal plants and to understand the dynamics of local knowledge.
Improved Activation and Hemodynamic Response Function of Olfactory fMRI Using Simultaneous Multislice with Reduced TR Acquisition
Authors: Chen Hong , Yin Jianzhong , He Che , Wu Yalin , Long Miaomiao , Liu Guoping , Ni Hongyan , Jin Hua , Liu Yawu
Published: December 2021

Objectives. The respiration could decrease the time synchronization between odor stimulation and data acquisition, consequently deteriorating the functional activation and hemodynamic response function (HRF) in olfactory functional magnetic resonance imaging (fMRI) with a conventional repetition time (TR). In this study, we aimed to investigate whether simultaneous multislice (SMS) technology with reduced TR could improve the blood oxygen level‐dependent (BOLD) activation and optimize HRF modeling in olfactory fMRI. Methods. Sixteen young healthy subjects with normal olfaction underwent olfactory fMRI on a 3T MRI scanner using a 64 channel head coil. FMRI data were acquired using SMS acceleration at three different TRs: 3000 ms, 1000 ms, and 500 ms. Both metrics of BOLD activation (activated voxels, mean, and maximum t‐scores) and the HRF modeling (response height and time to peak) were calculated in the bilateral amygdalae, hippocampi, and insulae. Results. The 500 ms and 1000 ms TRs both significantly improved the number of activated voxels, mean, and maximum t‐score in the amygdalae and insulae, compared with a 3000 ms TR (all P < 0.05). But the increase of these metrics in the hippocampi did not reach a statistical significance (all P > 0.05). No significant difference in any BOLD activation metrics between TR 500 ms and 1000 ms was observed in all regions of interest (ROIs) (all P > 0.05). The HRF curves showed that higher response height and shorter time to peak in all ROIs were obtained at 500 ms and 1000 ms TRs compared to 3000 ms TR. TR 500 ms had a more significant effect on response height than TR 1000 ms in the amygdalae (P = 0.017), and there was no significant difference in time to peak between TR 500 ms and 1000 ms in all ROIs (all P > 0.05). Conclusions. The fast image acquisition technique of SMS with reduced TR could significantly improve the functional activation and HRF curve in olfactory fMRI.
Weak Fault Feature Extraction of Rolling Bearing Based on SVMD and Improved MOMEDA
Authors: Wang Xinyu , Ma Jie
Published: December 2021

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.