bluuebunny/crossref_metadata_2025 huggingface dataset:
[Retracted] Machine Learning and IoT‐Based Waste Management Model
Authors: Khan Rijwan , Kumar Santosh , Srivastava Akhilesh Kumar , Dhingra Niharika , Gupta Mahima , Bhati Neha , Kumari Pallavi
Published: August 2021

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste‐collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution‐free cities.
Occupational Health and Safety Risk Assessment of Cruise Ship Construction Based on Improved Intuitionistic Fuzzy TOPSIS Decision Model
Authors: Ziquan Xiang , Jiaqi Yang , Naseem Muhammad Hamza , Zuquan Xiang
Published: August 2021

The logistics of cruise ship construction are complex, with an extended period, many participants, and a stringent node control. The resulting occupational health and safety risks have more risks and uncertainties. If it is not identified and controlled, it will lead to low efficiency of logistics management in the construction stage, leading to chain breaking and going out of control. In response to this situation, this article proposes an improved intuitionistic fuzzy TOPSIS decision model to evaluate occupational health and safety risks in cruise ship construction. First, based on field research and interviews, risk sources and risk evaluation criteria are determined, and experts are given different weights based on the relative importance of decision-making experts. Then, to reduce the ambiguity and uncertainty of the risk source information, the intuitionistic fuzzy weighted arithmetic average operator is used to aggregate the judicial opinions of experts to obtain the intuitionistic fuzzy aggregate decision risk matrix. To improve the accuracy of risk assessment results, subjective weight and objective weight are comprehensively considered. The stepwise weight assessment ratio analysis (SWARA) method is used to determine the subjective weight of the evaluation criteria, and the normal distribution method is used to determine the objective weight of the evaluation criteria. According to the relative closeness coefficient of each risk source to the positive-ideal solution, the ranking of the risk sources is obtained. Finally, taking a cruise ship construction as an example, the risk sources of occupational health and safety are sorted, sensitivity analysis is carried out and compared with other methods, and the targeted risk control measures are put forward, which verifies the effectiveness and correctness of the improved intuitionistic fuzzy TOPSIS decision model.
Evaluating Drug Risk Using GAN and SMOTE Based on CFDA’s Spontaneous Reporting Data
Authors: Wei Jianxiang , Feng Guanzhong , Lu Zhiqiang , Han Pu , Zhu Yunxia , Huang Weidong
Published: August 2021

Adverse drug reactions (ADRs) pose health threats to humans. Therefore, the risk re-evaluation of post-marketing drugs has become an important part of the pharmacovigilance work of various countries. In China, drugs are mainly divided into three categories, from high-risk to low-risk drugs, namely, prescription drugs (Rx), over-the-counter drugs A (OTC-A), and over-the-counter drugs B (OTC-B). Until now, there has been a lack of automated evaluation methods for the three status switch of drugs. Based on China Food and Drug Administration’s (CFDA) spontaneous reporting database (CSRD), we proposed a classification model to predict risk level of drugs by using feature enhancement based on Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE). A total of 985,960 spontaneous reports from 2011 to 2018 were selected from CSRD in Jiangsu Province as experimental data. After data preprocessing, a class-imbalance data set was obtained, which contained 887 Rx (accounting for 84.72%), 113 OTC-A (10.79%), and 47 OTC-B (4.49%). Taking drugs as the samples, ADRs as the features, and signal detection results obtained by proportional reporting ratio (PRR) method as the feature values, we constructed the original data matrix, where the last column represents the category label of each drug. Our proposed model expands the ADR data from both the sample space and the feature space. In terms of feature space, we use feature selection (FS) to screen ADR symptoms with higher importance scores. Then, we use GAN to generate artificial data, which are added to the feature space to achieve feature enhancement. In terms of sample space, we use SMOTE technology to expand the minority samples to balance three categories of drugs and minimize the classification deviation caused by the gap in the sample size. Finally, we use random forest (RF) algorithm to classify the feature-enhanced and balanced data set. The experimental results show that the accuracy of the proposed classification model reaches 98%. Our proposed model can well evaluate drug risk levels and provide automated methods for status switch of post-marketing drugs.
A High-Dimensional Modeling System Based on Analytical Hierarchy Process and Information Criteria
Authors: Koç Tuba
Published: August 2021

High-dimensional data sets frequently occur in several scientific areas, and special techniques are required to analyze these types of data sets. Especially, it becomes important to apply a suitable model in classification problems. In this study, a novel approach is proposed to estimate a statistical model for high-dimensional data sets. The proposed method uses analytical hierarchical process (AHP) and information criteria for determining the optimal PCs for the classification model. The high-dimensional “colon” and “gravier” datasets were used in evaluation part. Application results demonstrate that the proposed approach can be successfully used for modeling purposes.
Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain
Authors: Sadiq Muhammad Tariq , Akbari Hesam , Rehman Ateeq Ur , Nishtar Zuhaib , Masood Bilal , Ghazvini Mahdieh , Too Jingwei , Hamedi Nastaran , Kaabar Mohammed K. A.
Published: August 2021

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.
Cloud Computing and Internet of Things in the Evaluation of Ecological Environment Quality in Rural Tourist Areas in Smart Cities
Authors: Ding Ling , Liang Yezhang
Published: August 2021

With the increase of tourist environment, the real-time monitoring of ecological environment has become a concern. This study mainly discusses the application of cloud computing and Internet of things in the evaluation of ecological environment quality of rural tourism areas in a smart city. In this study, the real-time monitoring of the atmosphere, water, and meteorological data is collected through the GPRS data transmission module and then sent back to the local server by the GPRS network, and the obtained non-real-time and real-time data are used to establish the ecological monitoring database, the database analysis of its information, and get real-time data, monthly data, and longer cycle data. In the cloud GIS platform, there are multiple subnodes. The split tasks can be processed by each subnode through a map, and the results after processing can be summarized through reduce, which completes the implementation process of the whole idea of map reduce. Monitoring station management is mainly to establish monitoring stations in rural tourism areas and collect first-hand environmental monitoring data by using temperature, humidity, infrared, ultrasonic, and other sensors and cameras. The monitoring objects are the air quality, water quality, meteorology, etc. of the scenic area, mainly showing the location of monitoring stations and the placement of sensors. At the same time, an LED screen is set at the monitoring station to display the air quality data of the scenic spot. The data content is introduced into the DPSIR model, combined with social and economic data; according to the ecological health grading evaluation standard, the evaluation score and health grade are obtained and the ecological health status of rural tourism area is judged and evaluated. When the amount of data is less than 500 MB, there is little difference between the storage speed of the cloud GIS platform and single machine, but with the continuous increase of the amount of data, the storage speed of the cloud GIS platform is significantly higher than that of a single machine. This study is helpful in improving the ecological environment quality of rural tourism areas.
Effects of Dapagliflozin Adjunct to Insulin on Glycemic Variations in Patients with Newly Diagnosed Type 2 Diabetes: A Randomized, Controlled, Open‐Labeled Trial
Authors: Jiang Lan-lan , Zhang Peng , Liu Bing-li , Yan Reng-na , Ye Lei , Ma Jian-hua , Li Feng-fei
Published: August 2021

Background. This study is aimed at investigating whether dapagliflozin adjunct to insulin therapy further improves glycemic control compared to insulin therapy alone in patients with newly diagnosed type 2 diabetes (T2D). Methods. This single‐centre, randomized, controlled, open‐labeled trial recruited newly diagnosed T2D patients. Subjects were randomized 1 : 1 to the dapagliflozin add‐on to continuous subcutaneous insulin infusion (CSII) group (DAPA) or the CSII therapy group for 5 weeks. Standard meal tests were performed 3 times at days ‐3, 7, and 35 for glucose, C‐peptide, and insulin level determination. Two‐time continuous glucose monitoring (CGM) was performed at baseline and at the end of the study. The primary endpoint was the difference in the mean amplitude of glycemic excursions (MAGEs) between the groups. Results. A total of 66 subjects completed the study, with 34 and 32 patients in the DAPA and CSII groups, respectively. Patients in the DAPA group exhibited significant decreases in MAGE levels at the endpoint. We also observed that patients in the DAPA group had a lower homoeostasis model assessment insulin resistance (HOMA‐IR) and a higher homoeostasis model assessment B (HOMA‐B) value at 1 week and 5 weeks compared to those with insulin therapy, respectively. In addition, our data showed that patients in the DAPA group showed a significantly lower insulin dose (0.07 U/kg) and weighed less than those in the CSII group. Conclusion. Our data indicate that dapagliflozin adjunct to insulin is a safe and effective therapy for improving glycemic variations, insulin sensitivity, and weight loss in newly diagnosed T2D patients.
Two Lip Carcinomas following Allogeneic Hematopoietic Stem Cell Transplantation: A Case Report and Literature Review
Authors: Dammak Nouha , Berrezouga Latifa , Njima Manel , Lahouel Ines , Khemiss Mehdi , Ben Khelifa Mohamed
Published: August 2021

Background. Secondary solid cancers are severe complications in patients who have undergone allogeneic hematopoietic stem cell transplantation (AHSCT) for malignant and nonmalignant lymphohematopoietic diseases. Objective. The aim of this work was to report a case of two lip carcinomas following AHSCT and to warn doctors about the importance of regular check-ups of patients who have received HSCT. Observation. A 57-year-old man was referred by the dermatology department for the management of exophytic budding lesions on the lower lip evolving since 5 months. The patient was in complete remission following allogeneic bone marrow transplantation for acute myeloid leukemia since five years. Clinical and histological findings confirmed the diagnosis of a squamous cell carcinoma of the two lesions. Conclusion. It is of paramount importance to seek an oral squamous cell carcinoma in the presence of persistent lesions in HSCT recipients.
Przewalski’s Horses (Equus ferus przewalskii) Responses to Unmanned Aerial Vehicles Flights under Semireserve Conditions: Conservation Implication
Authors: Lu Vanessa , Xu Feng , Turghan Mardan Aghabey
Published: August 2021

Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (also known as drones and hereafter referred to as UAVs)—aircraft remotely operated from the ground—which are increasingly popular tools for ecological research, and the question of this study concerns the extent to which wildlife responses might allow aerial wildlife monitoring (AWM) by UAVs. Our experiment tests the hypothesis that the wildlife-UAVs interaction depends strongly on flight altitude that there may be a lowest altitude range for which the ungulates are not exceedingly disturbed, dictating a practically achievable level of discernibility in flight observation, for this question might influence the future viability of the UAVs in the study and protection of the other wildlife in China’s semiarid ecosystem. We examined the behavioral responses of a group of enclosed Przewalski’s horses (Equus ferus przewalskii) to the presence of different in-flight UAVs models by conducting flights at altitudes ranging from 1 to 52 meters and recorded the heights at which each horse reacted to (noticed and fled) the UAVs. All horses exhibited a stress response to UAVs flights as evidenced by running away. The results suggest strong correlations between flight altitude and response across the different subjects that adults generally noticed the UAVs at the larger heights (20.58 ± 10.46 m) than the immature (4.67 ± 0.87 m). Meanwhile, reaction heights of females (15.85 ± 6.01 m) are smaller than that of males (26.85 ± 18.52 m). Supported by their biological roles in herds (e.g., males must give protection to his entire herd while females are purely responsible for their offspring), our results also show that age, closely followed by gender, are the two most significant elements that determine a horse’s level of alertness to the UAVs. This research will help future scientists to better gauge the appropriate height to use a drone for animal observation in order to minimize disturbance and best preserve their natural behavior.
Incidence of Diabetic Nephropathy and Its Predictors among Type 2 Diabetes Mellitus Patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia
Authors: Kebede Sewnet Adem , Tusa Biruk Shalmeno , Weldesenbet Adisu Birhanu , Tessema Zemenu Tadesse , Ayele Tadesse Awoke
Published: August 2021

Background. Although the rate of diabetic nephropathy which is the leading cause of end-stage renal disease (ESRD) continues to rise, there is limited information about the problem. This study aimed to assess the incidence and predictors of diabetic nephropathy among type 2 DM patients. Methods. Institution-based retrospective follow-up study was conducted at UGCSH with 462 newly diagnosed type 2 DM patients from January 2001 to February 2016, and the data were collected by reviewing their records. The Schoenfeld residuals test was used to check proportional hazard assumption. The best model was selected by using Akaike information criteria (AIC). Hazard ratios (HR) with its respective 95% confidence interval were reported to show significance and strength of association. Results. The incidence rate of diabetic nephropathy was 14 (95% CI 10.8–17.7) cases per 10,000 patient-month observation. In addition, 63 (13.6%) DM patients developed diabetic nephropathy. The median time to develop diabetic nephropathy was 94.9 months with interquartile range (IOR) of (64.1–127.4) months. Type 2 DM patients who had coronary heart disease (AHR = 2.69, 95% CI 1.42–5.13) and anemia (AHR = 1.94, 95% CI 0.97–3.87) were at higher hazard for developing diabetic nephropathy. Besides this, having a long duration (>10 years) (AHR = 0.24, 95% CI 0.11–0.56) and being female (AHR = 0.44, 95% CI 0.26–0.73) was found to be protective against diabetic nephropathy. Conclusion. The incidence of diabetic nephropathy among type 2 diabetes patients remains a significant public health problem. Duration of diabetes >10 years and female sex reduced the risk of diabetic nephropathy. Coronary heart disease and anemia increased the risk of diabetic nephropathy among type 2 DM patients. In light of these findings, early screening for diabetes complication is needed, and health professionals should give targeted intervention for type 2 DM patients with coronary heart disease comorbidity and anemia.
Study of the Structure and Properties of ZnS Utilized in a Fluorescence Biosensor
Authors: Ren Y. , Zhou H. , Wang X. , Liu Q. W. , Hou X. D. , Zhang G. F.
Published: August 2021

ZnS materials have been widely used in fluorescence biosensors to characterize different types of stem cells due to their excellent fluorescence effect. In this study, ZnS was prepared by vulcanizing nano-Zn particles synthesized using a DC arc plasma. The composition and structure of the ZnS materials were studied by X-ray diffraction (XRD), and their functional group information and optical properties were investigated by using IR spectrophotometry and UV-vis spectrophotometry. It has been found that the synthesized materials consist of Zn, cubic ZnS, and hexagonal ZnS according to the vulcanization parameters. Crystalline ZnS was gradually transformed from a cubic to a hexagonal structure, and the cycling properties first increase, then decrease with increasing sulfurization temperature. There is an optimal curing temperature giving the best cycling performance and specific capacity: the material sulfurized thereat mainly consists of cubic β-ZnS phase with a small quantity of Zn and hexagonal α-ZnS. The cubic phase ZnS has better conductivity than hexagonal ZnS, as evinced by electrochemical impedance spectroscopy (EIS). The ZnS (as prepared) shows board absorption, which can be used in fluorescence biosensors in cell imaging systems.
The Effect of Statins on C-Reactive Protein in Stroke Patients: A Systematic Review of Clinical Trials
Authors: Alikiaii Babak , Heidari Zahra , Bagherniya Mohammad , Askari Gholamreza , Sathyapalan Thozhukat , Sahebkar Amirhossein
Published: August 2021

Background. Statins reportedly have anti-inflammatory effects aside from their lipid-lowering impact. We investigated the effects of statin therapy on the level of C-reactive protein (CRP) or highly sensitive CRP (hs-CRP), a liver-derived marker of systemic inflammation, among stroke patients. Methods. An online search was performed in Scopus, PubMed/MEDLINE, ISI Web of Science, and Google Scholar up to November 2020 to recognize clinical trials investigating the effects of statins on the CRP level in stroke patients. Results. Overall, nine studies (11 treatment arms) with 1659 participants met the inclusion criteria. Six out of 9 studies (8 out of 11 arms) were categorized as studies with a high-quality methodological approach using the Cochrane Collaboration’s tool. Data from 5 treatment arms indicated a significant decrease in CRP concentration, and in one treatment arm, CRP concentration did not suggest any considerable alteration following statin therapy. Moreover, two treatment arms showed a significant reduction in hs-CRP concentration and three treatment arms revealed no significant alteration in hs-CRP concentration following statin therapy. Generally, results were heterogeneous and independent of the type of statin, statin dose, treatment duration, and changes in plasma low-density lipoprotein cholesterol concentration. Conclusion. The results suggest that statin therapy could reduce and, therefore, could be considered in these patients as potential anti-inflammatory agents.
An Improved CNN Architecture to Diagnose Skin Cancer in Dermoscopic Images Based on Wildebeest Herd Optimization Algorithm
Authors: Zhou Biying , Arandian Behdad
Published: August 2021

Skin cancer is one of the most common types of cancers that is sometimes difficult for doctors and experts to diagnose. The noninvasive dermatoscopic method is a popular method for observing and diagnosing skin cancer. Because this method is based on ocular inference, the skin cancer diagnosis by the dermatologists is difficult, especially in the early stages of the disease. Artificial intelligence is a proper complementary tool that can be used alongside the experts to increase the accuracy of the diagnosis. In the present study, a new computer‐aided method has been introduced for the diagnosis of the skin cancer. The method is designed based on combination of deep learning and a newly introduced metaheuristic algorithm, namely, Wildebeest Herd Optimization (WHO) Algorithm. The method uses an Inception convolutional neural network for the initial features’ extraction. Afterward, the WHO algorithm has been employed for selecting the useful features to decrease the analysis time complexity. The method is then performed to an ISIC‐2008 skin cancer dataset. Final results of the feature selection based on the proposed WHO are compared with three other algorithms, and the results have indicated good results for the system. Finally, the total diagnosis system has been compared with five other methods to indicate its effectiveness against the studied methods. Final results showed that the proposed method has the best results than the comparative methods.
The Role of Dexmedetomidine in Pediatric Patients Presenting with an Anticholinergic Toxidrome
Authors: Zekhtser Mitchell , Carroll Erin , Boyd Molly , Ambati Shashikanth
Published: August 2021

Background. We report two pediatric cases of anticholinergic toxidrome, including the youngest reported to date, in which standard therapeutic strategies were either contraindicated or ineffective, while treatment with dexmedetomidine was rapidly efficacious with no adverse effects. Moreover, with the recent shortage of physostigmine, we highlight an alternative treatment in this clinical setting. Case Summaries. In case 1, a two-year-old had an overdose presenting with an anticholinergic toxidrome. However, his hypopnea precluded the use of benzodiazepines due to the high likelihood of intubation. In case 2, a 14-year-old had a polypharmacy overdose inducing agitated delirium that was refractory to high-dose benzodiazepines. Due to the unknown ingestion, physostigmine was avoided. In both cases, dexmedetomidine helped the patient remain calm and metabolize the ingestions. Conclusion. Our experience suggests that dexmedetomidine may be a useful adjunct in anticholinergic presentations in the setting of polypharmacy, when standard therapy is proven ineffective, contraindicated, or unavailable.
Artificial Intelligence-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Quantitative Evaluation of the Efficacy of Targeted Therapy for Liver Metastasis from Colon Cancer and Nursing
Authors: Zheng Yanling , Cao Yongqing , Liu Jiheng , Chen Hui , Peng Liming
Published: August 2021

The purpose of this study was to quantitatively evaluate the targeted treatment and nursing effect of patients with liver metastasis of colon cancer by enhanced magnetic resonance imaging. First, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) images were restored. 35 patients with liver metastases from colon cancer were selected as research subjects and treated by bevacizumab, and high-quality nursing methods were used to improve the psychophysiology of the patients. Enhanced CT  examination and DCE-MRI scan were performed before and after the treatment. After the image was processed by the artificial intelligence algorithm, apparent diffusion coefficient (ADC), measurement transmission constant (Ktrans), reflux constant (Kep), extravascular extracellular volume fraction (Ve), and other data on the image were recorded. Two months after the treatment, 35 patients were divided into effective and ineffective groups. It was found that, after treatment, the Kep value and Ktrans value of the effective group were significantly reduced, showing statistically significant differences, and the

P

value was less than 0.05, and the Kep value and Ktrans value of the ineffective group showed no significant changes. The Ve value of the two groups gradually decreased with the progress of the treatment. In the effective group, the Ve values before the treatment, on the third day after the treatment, and on the seventh day after treatment were (0.235 

±

 0.134), (0.338 

±

 0.116), and (0.457 

±

 0.097), respectively. Compared with the Ve values of the ineffective group, there was a statistical difference, F = 3.592,

P
=
0.0245

; the ADC value was negatively correlated with the Ktrans and Kep values, and the results were statistically significant. It was concluded that the above indicators are effective in evaluating the efficacy of the targeted therapy of liver metastasis from colon cancer, and DCE-MRI is of great significance in predicting the efficacy of targeted therapy of liver metastasis from colon cancer.
EGb in the Treatment for Patients with VCI: A Systematic Review and Meta‐Analysis
Authors: Zhan Min , Sun Linjuan , Liu Jianxun , Zeng Zixiu , Shen Wei , Li Huimin , Wang Ying , Han Fuhua , Shi Jingzi , Zeng Xinyun , Lu Xiyue , Zhang Yunling , Liao Xing
Published: August 2021

Background. Ginkgo biloba extract (EGb) is widely used to treat impairments in memory, cognition, activities of daily living, inflammation, edema, stroke, Alzheimer’s dementia, and aging. Aim. We aimed to evaluate the safety and efficacy of EGb in treating vascular cognitive impairment (VCI). Methods. The systematic review was performed using the latest guidelines. We searched for EGb‐related trials up to March 1, 2021, in four Chinese databases, three English databases, and clinical trial registry platforms. Randomized controlled trials (RCTs) were included if the study enrolled participants with VCI. Two reviewers independently extracted the data and critically appraised the study quality. Heterogeneity was quantified with I2. Both sensitivity and subgroup analyses were used to identify the sources of heterogeneity. Publication bias was assessed with funnel plots. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to rate the evidence quality. Outcomes included assessments using the Activities of Daily Living (ADL), Montreal Cognitive Assessment (MoCA), Mini‐Mental State Examination (MMSE), Hasegawa Dementia Scale (HDS), Barthel Index (BI), Functional Activity Questionnaire (FAQ), and adverse events. Results. In this study, a total of 2019 patients in 23 RCTs were included. EGb appeared to be more effective than control conditions as assessed by the results of cognitive function evaluation, including MMSE (MDMMSE,EGb vs.blank = 3.04, 95% CI: 0.10‐5.98; MDMMSE,EGb vs.drugs for VCI = 2.70, 95% CI: 1.39‐4.01; MDMMSE,EGb+drugs for VCI vs.blank = 5.90, 95% CI: 4.21‐7.59; and MDMMSE,EGb+drugs for VCI vs.drugs for VCI = 3.14, 95% CI: 2.14‐4.15), MoCA (MDMoCA,EGb vs.blank = 5.30, 95% CI: 2.15‐8.46; MDMoCA,EGb+drugs for VCI vs.blank = 2.66, 95% CI: 1.82‐3.50; and MDMoCA,EGb+drugs for VCI vs.drugs for VCI = 2.56, 95% CI: 1.85‐3.27), HDS (MDHDS,EGb vs.blank = 6.50; 95% CI: 4.86‐8.14; MDHDS,EGb+drugs for VCI vs.drugs for VCI = 3.60, 95% CI: 2.50‐4.70), ADL (MDADL,EGb vs.blank = 7.20, 95% CI: 3.28‐11.12; MDADL,EGb+drugs for VCI vs.blank = 10.00, 95% CI: 7.51‐12.49; and MDADL,EGb+drugs for VCI vs.drugs for VCI = 9.20, 95% CI: 7.26‐11.14), BI (MDBI,EGb+drugs for VCI vs.drugs for VCI = 5.71, 95% CI: 2.99‐8.43; MDFAQ,EGb vs.drugs for VCI = −1.43, 95% CI: ‐2.78 to 0.08), and FAQ (MDFAQ,EGb+drugs for VCI vs.drugs for VCI = −2.17, 95% CI: ‐4.13 to 0.21). Evidence of certainty ranged from medium certainty to very low certainty. Conclusion. This meta‐analysis showed that EGb may be an effective and safe treatment in improving MMSE, MOCA, ADL, and BI for VCI patients within three months of diagnosis. However, given the quality of the included RCTs, more preregistered trials are needed that explicitly examine the efficacy of EGb. This systematic review has been registered on PROSPERO, with the registration number CRD42021232967.
Comparison of the Effects of Articaine and Lidocaine Anesthetics on Blood Pressure after Maxillary Infiltration Technique: A Triple-Blind Randomized Clinical Trial
Authors: Moaddabi Amirhossein , Soltani Parisa , Zamanzadeh Maryam , Nosrati Kamran , Mollamirzaei Mojtaba , Cernera Mariangela , Spagnuolo Gianrico
Published: August 2021

Background. Many dental procedures begin with local anesthesia. Subsequent increase in blood pressure in healthy individuals commonly occurs and may be affected by several factors such as mental and physical stress, painful stimuli, and action of catecholamines present in local anesthetic solutions. The aim of the present study is to compare the effects of 4% articaine with 1 : 100000 epinephrine and 2% lidocaine with 1 : 80000 epinephrine on blood pressure after maxillary infiltration technique. Materials and Methods. In this randomized clinical trial, 102 patients were randomly assigned into two groups. One group received 4% articaine with 1 : 100000 epinephrine and the other group received 2% lidocaine with 1 : 80000 epinephrine for local maxillary infiltration. Systolic and diastolic blood pressure of both groups was determined twice: once before anesthetic injection and once 10 minutes after injection. The data were statistically analyzed using descriptive statistics, Shapiro–Wilks test, Levene test, chi-square test, independent t-test, and paired t-test. Results. The mean systolic blood pressure after anesthetic injection in the articaine and lidocaine groups was 125.00 ± 5.67 and 123.16 ± 6.417 mmHg, respectively, showing no statistically significant difference (p=0.127). The mean diastolic blood pressure after injection was 85.02 ± 7.331 in the articaine group and 81.35 ± 12.815 mmHg in the lidocaine group. These values show no statistically significant difference (p=0.080). In both groups, the mean systolic and diastolic blood pressures have increased significantly (p<0.001). Conclusion. Articaine can be regarded as a suitable alternative for lidocaine for maxillary local infiltration, as no significant difference was observed between the effects of the two anesthetic solutions on blood pressure.
Mechanism and Stability Analysis of Deformation Failure of a Slope
Authors: Lu Yingfa , Liu Gan , Cui Kai , Zheng Jie
Published: August 2021

Force distribution during progressive slope failure is an important element in slope stability analysis. In this study, five mechanical failure modes are proposed for thrust‐ and pull‐type slopes, respectively, and five field forms of thrust‐type slopes are described. The properties of progressive failure are evaluated quantitatively: the failure mode of slope obeys the geo‐material rule under the peak stress state, and the instability range is gradually developed. The critical stress state zone is in the process of dynamic change with the development of deformation. It appears that the driving sliding force is greater than the frictional resistance along the sliding surface. When rock or soil stabilizing stresses are at maximum, the vector sum of the driving sliding stress and stabilizing stress is equal to zero at the critical state. The frictional resistance is equal to the driving sliding force in the stable and less‐stable regions, and the normal pressure is wherever equal to the counterpressure. Rigid, flexible, and rigid‐flexible design theories are proposed for slope control. New terms are defined and used to evaluate the stability. The conventional local and surplus stability factors of slopes and their calculation are explained. The force distribution rule is analyzed during progressive failure, and the conventional stability factor definition is discussed. The geological settings and monitoring data of landslides are used to analyse changes in the critical stress state. An example is given to illustrate the failure process analysis. The results show that progressive failure can be well represented and the safety factor can be well described by the main thrust method (MTM), comprehensive displacement method (CDM), and surplus displacement method (SDM), which can be used to feasibly evaluate slope stability.
Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data
Authors: Cao Yang , Zhou Xiaokang , Yan Ke
Published: August 2021

Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India
Authors: Tran Trung-Hieu , Dam Nguyen Duc , Jalal Fazal E. , Al-Ansari Nadhir , Ho Lanh Si , Phong Tran Van , Iqbal Mudassir , Le Hiep Van , Nguyen Hanh Bich Thi , Prakash Indra , Pham Binh Thai
Published: August 2021

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.
Strategies to Improve the Efficiency of Transplantation with Mesenchymal Stem Cells for the Treatment of Ischemic Stroke: A Review of Recent Progress
Authors: Li Yongchang , Zhong Wei , Tang Xiangqi
Published: August 2021

Cerebral ischemia is a common global disease that is characterized by a loss of neurological function and a poor prognosis in many patients. However, only a limited number of treatments are available for this condition at present. Given that the efficacies of these treatments tend to be poor, cerebral ischemia can create a significant burden on patients, families, and society. Mesenchymal stem cell (MSC) transplantation treatment has shown significant potential in animal models of ischemic stroke; however, the specific mechanisms underlying this effect have yet to be elucidated. Furthermore, clinical trials have yet to yield promising results. Consequently, there is an urgent need to identify new methods to improve the efficiency of MSC transplantation as an optimal treatment for ischemic stroke. In this review, we provide an overview of recent scientific reports concerning novel strategies that promote MSC transplantation as an effective therapeutic approach, including physical approaches, chemical agents, traditional Chinese medicines and extracts, and genetic modification. Our analyses showed that two key factors need to be considered if we are to improve the efficacy of MSC transplantation treatments: survival ability and homing ability. We also highlight the importance of other significant mechanisms, including the enhanced activation of MSCs to promote neurogenesis and angiogenesis, and the regulation of permeability in the blood-brain barrier. Further in-depth investigations of the specific mechanisms underlying MSC transplantation treatment will help us to identify effective methods that improve the efficiency of MSC transplantation for ischemic stroke. The development of safer and more effective methods will facilitate the application of MSC transplantation as a promising adjuvant therapy for the treatment of poststroke brain damage.
Gene Expression Analysis Reveals Key Genes and Signalings Associated with the Prognosis of Prostate Cancer
Authors: Shen Hao , Guo Yong-Lian , Li Guo-Hao , Zhao Wei , Zhang Ling
Published: August 2021

It is urgent to identify novel biomarkers for prostate cancer (PCa) prognosis and to understand the mechanisms regulating the tumorigenesis for PCa treatment. In this study, GSE17951 and TCGA were used to identify the differentially expressed genes (DEGs). Our study demonstrated that 1533 genes with increased expression and 2301 genes with decreased expression in PCa. Bioinformatics analysis data indicated that these up-regulated genes had an association with the modulation of mitotic nuclear division, sister chromatid cohesion, cell division, and cell cycle. Additionally, our results revealed downregulated genes took part in modulating extracellular matrix organization, angiogenesis, signal transduction, and Ras signaling pathway. Hub upregulated and downregulated PPI networks were identified by protein-protein interaction (PPI) network analysis and MCODE analysis. Of note, 12 cell cycle regulators, comprising CCNB1, CCNB2, PLK1, TTK, AURKA, CDC20, BUB1, PTTG1, CDC45, CDC25C, CCNA2, and BUB1B, were demonstrated to function crucially in PCa development. By detecting their expression in PCa cell lines, we confirmed that these cell cycle regulator expressions were heightened in PCa cells. GEPIA databases analysis showed that higher expression of these cell cycle regulators was correlated to shorter disease-free survival (DFS) time in PCa samples. Our findings collectively suggested targeting cell cycle pathways may offer novel prognosis and treatment biomarkers for PCa.
Comprehensive Analysis of the Systemic Transcriptomic Alternations and Inflammatory Response during the Occurrence and Progress of COVID‐19
Authors: Mo Shaocong , Dai Leijie , Wang Yulin , Song Biao , Yang Zongcheng , Gu Wenchao
Published: August 2021

The pandemic of the coronavirus disease 2019 (COVID‐19) has posed huge threats to healthcare systems and the global economy. However, the host response towards COVID‐19 on the molecular and cellular levels still lacks full understanding and effective therapies are in urgent need. Here, we integrate three datasets, GSE152641, GSE161777, and GSE157103. Compared to healthy people, 314 differentially expressed genes were identified, which were mostly involved in neutrophil degranulation and cell division. The protein‐protein network was established and two significant subsets were filtered by MCODE: ssGSEA and CIBERSORT, which comprehensively revealed the alternation of immune cell abundance. Weighted gene coexpression network analysis (WGCNA) as well as GO and KEGG analyses unveiled the role of neutrophils and T cells during the progress of the disease. Based on the hospital‐free days after 45 days of follow‐up and statistical methods such as nonnegative matrix factorization (NMF), submap, and linear correlation analysis, 31 genes were regarded as the signature of the peripheral blood of COVID‐19. Various immune cells were identified to be related to the prognosis of the patients. Drugs were predicted for the genes in the signature by DGIdb. Overall, our study comprehensively revealed the relationship between the inflammatory response and the disease course, which provided strategies for the treatment of COVID‐19.
An efﬁcient forward error correction code for wireless sensor networks
Authors: Alabady Salah A.
Published: August 2021

The main requirements in the design of wireless sensor network applications are to minimize energy consumption and maximize battery lifetime. Power is primarily consumed during wireless transmission and reception. Automatic repeat request (ARQ) and forward error correction (FEC) are the two basic methods to recover erroneous packets. As energy conservation is a major issue of concern in wireless sensor networks, repeat transmission because the error in the data received is not an option, and FEC would be preferred over ARQ. FEC is applied in situations where retransmissions are relatively costly or impossible. A successful data transmission means a higher energy saving and a long-life network. This paper presents a novel linear block forward error correction code for wireless sensor network applications called Low Complexity Parity Check (LCPC). The LCPC code offers lower encoding and decoding complexity than other types of codes. To validate the performance of the LCPC code, the proposed coding scheme was investigated at different values of data transmission with different types of modulations over Additive white Gaussian noise (AWGN) and Rayleigh fading channels. The simulation results show that the proposed code outperforms the renowned LDPC (8, 4), (255,175), and (576, 288) codes.
Service-aware LSP selection with fuzzy based packet scheduling scheme for non-real time traffics
Authors: Mustapha Oba Zubair , Ali Muhammad , Hu Yim Fun , Abd-Alhameed Raed A.
Published: August 2021

An essential solution is available in Multi-protocol label switching (MPLS), which solve the problems faced by present-day networks: speed, scalability, quality-of-service (QoS) management, and traffic engineering. This paper is an extension of work on Fuzzy based Packet Scheduling Algorithm (FPSA) combined with Packets Processing Algorithm (PPA) in an Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) networks. This will make provision for an intelligent service to the Label Switched Path (LSP) in MPLS networks. Several research work have been proposed on the MPLS Traffic Engineering. However, it is still imperative to further research on the effect of bandwidth increment on the core network using different mechanisms such as the analytical model of MPLS, expert-based packet scheduling algorithm for MPLS QoS support. Since MPLS is not able to provide intelligent routing, it is necessary to propose an intelligent expert system of FPSA combined with PPA. And analytical model of packet forwarding in the MPLS network would be given to provide suitable solution to traffic congestion and reliable services. Furthermore, the network model created using Network Simulator (NS 2), which carries non-real time application such as File Transfer Protocol (FTP) with bandwidth variations. The results obtained from trace files are interpreted by AWK script and used for the further analysis.
Adopting task technology fit model on e-voting technology
Authors: Ratnasari Asti , Harahap Avrillaila Akbar , Anshori Adib Arsyad , Alam Mumtazul
Published: August 2021

E-voting is a tool to support the voting process starting from recording, voting, and counting votes using electronic devices. E-voting promises a faster voting process, reduced budget costs, lost votes due to damaged ballot papers during the voting process, and others. E-voting is also used in high school student council elections. Young people are more computer literate (computer literacy) and interested in using new technology (e-voting) than adults and older people. This study aims to determine the suitability of e-voting in the SMA OSIS election to the user's task support using the task-technology fit (TTF) model. The data analysis used is multiple regression analysis. This analysis is used to determine the effect of the independent variables on the dependent variable with a significance level of 5%. The results of this study indicate that the 4 formulated hypotheses can be accepted and the students of SMK Muhammadiyah 1 Bantul feel the suitability of technology support when voting using e-voting in the selection of the high school student council.