bluuebunny/crossref_metadata_2025 huggingface dataset:
Reversible Privacy Protection with the Capability of Antiforensics
Authors: Dou Liyun , Wang Zichi , Qian Zhenxing , Feng Guorui
Published: April 2021

In this paper, we propose a privacy protection scheme using image dual-inpainting and data hiding. In the proposed scheme, the privacy contents in the original image are concealed, which are reversible that the privacy content can be perfectly recovered. We use an interactive approach to select the areas to be protected, that is, the protection data. To address the disadvantage that single image inpainting is susceptible to forensic localization, we propose a dual-inpainting algorithm to implement the object removal task. The protection data is embedded into the image with object removed using a popular data hiding method. We further use the pattern noise forensic detection and the objective metrics to assess the proposed method. The results on different scenarios show that the proposed scheme can achieve better visual quality and antiforensic capability than the state-of-the-art works.
Prediction of Seepage Pressure Based on Memory Cells and Significance Analysis of Influencing Factors
Authors: Zhao Mengdie , Jiang Haifeng , Chen Shoukai , Bie Yajing
Published: April 2021

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS‐LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long‐ and short‐term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS‐LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction.
On a New Criterion for the Solvability of Non-Simple Finite Groups and m-Abelian Solvability
Authors: Sankari Hasan , Abobala Mohammad
Published: April 2021

This paper is devoted to introduce a sufficient condition for the solvability of finite groups. Also, it presents the concepts of m-abelian and m-cyclic solvability as new generalizations of solvability and polycyclicity, respectively. These new generalizations show a connection between prime powers of elements in a finite group G and its solvability.
Design and Implementation of Intelligent English Electronic Dictionary System Based on Internet of Things
Authors: Yong Wenming
Published: April 2021

In this paper, the intelligent English electronic dictionary system is studied to design and implement the electronic dictionary system according to the advantages of the Internet of Things. The software architecture, the design, and implementation of the client and server‐side and related technologies in the development process of the dictionary application are used as the research content to comprehensively discuss the development process of the electronic dictionary. The client and server‐side is based on C/S technology architecture, and the server‐side is a standard Maven Web project, which is managed by Maven and does not cause conflicts; the model‐view‐controller framework is built using Spring MVC to achieve the separation of user interface and application logic. Spring MVC is used to build a model‐view‐controller framework to separate user interface and application logic. Spring dependency injection is used to build a loosely coupled project, which helps to separate project components; Spring Data JPA is used to build a persistence layer to facilitate data access and maximize the developer’s ability to automatically realize logical operations on data. After the overall performance test of the system, the performance is good under the platform, and the intelligence of trilingual word query is achieved, and the quickness and ease of use meet the requirements that can be applied.
SIFT Feature‐Based Video Camera Boundary Detection Algorithm
Authors: Kong Lingqiang
Published: April 2021

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature‐based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.
Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model
Authors: Qin Rongyuan
Published: April 2021

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.
Multidirection Object Detection in Aerial View of Traffic Target under Complex Scenes
Authors: Zhang Zeqing , Lin Weiwei , Zheng Yuqiang
Published: April 2021

Focusing on DOTA, the multidirectional object dataset in aerial view of vehicles, CMDTD has been proposed. The reason why it is difficult for applying the general object detection algorithm in multidirectional object detection has been analyzed in this paper. Based on this, the detection principle of CMDTD including its backbone network and multidirectional multi‐information detection end module has been studied. In addition, in view of the complexity of the scene faced by aerial view of vehicles, a unique data expansion method is proposed. At last, three datasets have been experimented using the CMDTD algorithm, proving that the cascaded multidirectional object detection algorithm with high effectiveness is superior to other methods.
Vibration Images‐Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise
Authors: Fan Hongwei , Xue Ceyi , Zhang Xuhui , Cao Xiangang , Gao Shuoqi , Shao Sijie
Published: April 2021

Deep learning‐based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network‐ (CNN‐) and transfer learning‐ (TL‐) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition‐pseudo‐Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.
Can Circulating Cell-Free DNA or Circulating Tumor DNA Be a Promising Marker in Ovarian Cancer?
Authors: Yu Ming , Zhu Yu , Teng Lichen , Cui Jialin , Su Yajuan
Published: April 2021

In recent years, the studies on ovarian cancer have made great progress, but the morbidity and mortality of patients with ovarian cancer are still very high. Due to the lack of effective early screening and detecting tools, 70% of ovarian cancer patients are diagnosed at an advanced stage. The overall survival rate of ovarian cancer patients treated with surgical combined with chemotherapy has not been significantly improved, and they usually relapse or resist chemotherapy. Therefore, a novel tumor marker is beneficial for the diagnosis and prognosis of patients with ovarian cancer. As the index of “liquid biopsy,” circulating cell-free DNA/circulating tumor DNA (cfDNA/ctDNA) has attracted a lot of attention. It has more remarkable advantages than traditional methods and gives a wide range of clinical applications in kinds of solid tumors. This review attempts to illuminate the important value of cfDNA/ctDNA in ovarian cancer, including diagnosis, monitoring, and prognosis. Meanwhile, we will present future directions and challenges for detection of cfDNA/ctDNA.
Study on Electroosmosis Consolidation of Punctiform Electrode Unit
Authors: Yang Xiaoyu , Xie Yongbin , Dong Jianhua , Liu Guosheng , Zheng Yalin
Published: April 2021

In the electroosmosis method, when the distance between the opposite electrode and the same electrode is equal, the two‐dimensional effect of electroosmotic consolidation is significant, and the use of one‐dimensional model will overestimate the potential gradient, making the calculated pore pressure value too large. Aiming at this problem, according to the electrode arrangement rule and the minimum composition, a punctiform electrode unit model is proposed, and electroosmotic experiments are carried out on the symmetric and asymmetric unit models. The two‐dimensional electroosmotic consolidation governing equation of the punctiform electrode unit is established. The electric potential field of the electrode unit and the finite element form of the electroosmotic consolidation equation are given by the Galerkin method. The PyEcFem finite‐element numerical library is developed using Python programming to calculate. The research results show the following: (1) The two‐dimensional effect of the potential field distribution of the punctiform electrode unit is significant. The reduction of spacing of the same nature electrode in the symmetrical unit can make the potential distribution close to a uniform electric field. The asymmetry prevents the electric potential field distribution from being reduced to a one‐dimensional model. (2) The number of anodes will affect the electroosmosis effect of the soil. The more the anodes, the better the electroosmosis reinforcement effect of the soil, and the distribution of negative excess pore water pressure will be more uniform. (3) In the early stage of electroosmosis, the more the drainage boundaries, the faster the generation of negative pore pressure, but in the middle and late stages of electroosmosis, the potential value becomes the decisive factor, and the amplitude of negative pore water pressure in asymmetric units is higher than that in symmetric units. The potential distribution will not affect the degree of consolidation but will affect the extreme pore water pressure.
Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM
Authors: Meng Hao , Yuan Fei , Wu Yue , Yan Tianhao
Published: April 2021

In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.
Follistatin‐Like 1 Attenuation Suppresses Intervertebral Disc Degeneration in Mice through Interacting with TNF‐α and Smad Signaling Pathway
Authors: Wang Shaoyi , Wei Jianlu , Shi Jie , He Qiting , Zhou Xiaocong , Gao Ximei , Cheng Lei
Published: April 2021

Background. Inflammation plays an important role in intervertebral disc degeneration (IDD). The protein follistatin‐like 1 (FSTL1) plays a proinflammatory role in a variety of inflammatory diseases. Objectives. The purpose of this study was to investigate whether IDD could be delayed by inhibiting FSTL‐1 expression. Methods. We established a puncture‐induced IDD model in wild‐type and FSTL‐1+/‐ mice and collected intervertebral discs (IVDs) from the mice. Safranin O staining was used to detect cartilage loss of IVD tissue, and HE staining was used to detect morphological changes of IVD tissue. We measured the expression of FSTL‐1 and related inflammatory indicators in IVD tissues by immunohistochemical staining, real‐time PCR, and Western blotting. Results. In the age‐induced model of IDD, the level of FSTL‐1 increased with the exacerbation of degeneration. In the puncture‐induced IDD model, FSTL‐1‐knockdown mice showed a reduced degree of degeneration compared with that of wild‐type mice. Further experiments showed that FSTL‐1 knockdown also significantly reduced the level of related inflammatory factors in IVD. In vitro experiments showed that FSTL‐1 knockdown significantly reduced TNF‐α‐induced inflammation. Specifically, the expression levels of the inflammatory factors COX‐2, iNOS, MMP‐13, and ADAMTS‐5 were reduced. Knockdown of FSTL‐1 attenuated inflammation by inhibiting the expression of P‐Smad1/5/8, P‐Erk1/2, and P‐P65. Conclusion. Knockdown of FSTL‐1 attenuated inflammation by inhibiting the TNF‐α response and Smad pathway activity and ultimately delayed IDD.
Potential Predictors of Poor Prognosis among Severe COVID-19 Patients: A Single-Center Study
Authors: Ghaith Mazen M. , Albanghali Mohammad A. , Aldairi Abdullah F. , Iqbal Mohammad S. , Almaimani Riyad A. , AlQuthami Khalid , Alqasmi Mansour H. , Almaimani Wail , El-Readi Mahmoud Zaki , Alghamdi Ahmad , Almasmoum Hussain A
Published: April 2021

Background. Timely detection of the progression of the highly contagious coronavirus disease (COVID-19) is of utmost importance for management and intervention for patients in intensive care (ICU). Aim. This study aims to better understand this new infection and report the changes in the various laboratory tests identified in critically ill patients and associated with poor prognosis among COVID-19 patients admitted to the ICU. Methods. This was a retrospective study that included 160 confirmed SARS-CoV-2-positive patients. Results. Elevated serum ferritin, D-dimer, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and nonconjugated bilirubin levels were present in 139 (96%), 131 (96%), 107 (68%), 52 (34%), and 89 (70%) patients, respectively. Renal parameters were abnormal in a significant number of cases with elevated creatinine and blood urea nitrogen in 93 (62%) and 102 (68%) cases, respectively. Hematological profiles revealed lower red blood cell count, hemoglobin, eosinophils, basophils, monocytes, and lymphocytes in 90 (57%), 103 (65%), 89 (62%), 105 (73%), 35 (24%), and 119 (83%) cases, respectively. The neutrophil count was found to increase in 71.3% of the cases. There was significantly higher mortality (83%) among patients older than 60 years

p
=
0.001

and in female patients (75%)

p
=
0.012

. Patients with lung diseases had a poor outcome compared to patients with other comorbidities

p
=
0.002

. There was a significant association between elevated D-dimer levels and increased mortality

p

=

0.003

. Elevated levels of AST, creatinine, blood urea nitrogen, and bilirubin were significantly associated with unfavorable outcomes. Conclusion. Different parameters can be used to predict disease prognosis, especially the risk of poor prognosis. Accurate diagnosis and monitoring of disease progression from the early stages will help in reducing mortality and unfavorable outcomes.
TS‐PADM: Anomaly Detection Model of Wireless Sensors Based on Spatial‐Temporal Feature Points
Authors: Wang Fengjiao , Li Ruixing , Wang Hua , Zhu Hengliang , Xiong Naixue
Published: April 2021

In the practical application, sensor data collection is an essential means for the system to perceive the intrinsic features of data. The anomaly detection of data points can improve data quality and explore the potential information of data. The anomaly detection can be classified as two basic types, that is, classification and clustering. Those methods usually depend on the spatial correlation of data and have high computation complexity, so they are not suitable for the smart home and another mini‐Internet of Things (IoT) environment. To overcome these problems, we propose a novel method for anomaly detection. In this paper, we first define the temporal and spatial feature of data flows; then, a time series denoising autoencoder (TSDA) is proposed to extract the discriminative high‐dimensional characteristics to represent the data points. Moreover, a probability statistics‐based anomaly detection model (PADM) was proposed for identifying the abnormal data. Extensive experimental results demonstrated that our method has fewer parameters and is easy to adjust and optimize. More importantly, our approach has higher precision and recall rate than the gradient boosted decision tree and XGBoot.
ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling
Authors: Qin Xuebin , Ji ChenChen , Shen Yutong , Wang Pai , Li Mingqiao , Zhang Junle
Published: April 2021

The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two‐dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two‐phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two‐phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two‐phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two‐phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline.
Effects of Contact Angle on the Hysteresis Effect of Soil‐Water Characteristic Curves during Dry‐Wet Cycles
Authors: Tao Gaoliang , Li Ziyue , Liu Lisheng , Chen Yangyang , Gu Kai
Published: April 2021

The hysteresis characteristics of soil‐water characteristic curves (SWCCs) under dry‐wet cycling conditions are very important for understanding unsaturated soil properties, so it is crucial to propose an accurate and efficient method for predicting the hysteretic behaviors of SWCCs. To this end, this paper investigates the hysteresis characteristics of SWCCs in the full suction range of seven kinds of Hunan red clay with different initial dry densities by combination of the pressure plate method, the paper filter method, and the saturated salt solution method. It is found that there are, respectively, strong and weak hysteresis zones in the drying and wetting SWCCs under dry‐wet cycling conditions. By combining this feature and based on the drying curve, the soil volume and contact angle changes during the drying and wetting processes are employed to predict the hysteretic behaviors of SWCCs. To verify the validity of the prediction method, the predicted curves of the samples with different initial dry densities are compared with the measured curves. The results show that in the strong hysteresis zone, the hysteresis characteristics of the drying and wetting SWCCs are mainly resulted from the changes in the soil pore structure; in the weak hysteresis zone, the hysteresis characteristics are mainly influenced by the changes in the receding and advancing contact angles corresponding to the drying and wetting processes. The Young–Laplace theory is used to transform the changes of contact angle during the drying and wetting processes into the proportional relationship k of matric suction, and the corresponding wetting curve is obtained by smoothing the drying curve. It is found that the prediction effect in the high suction part (the strong hysteresis zone) is better than that in the weak hysteresis zone, which confirms that the hysteresis effect of SWCCs in the high suction part is influenced by the contact angle. Our proposed method can greatly reduce the test period and has a significant practical application value, which provides a new idea for the prediction of SWCCs under dry‐wet cycling conditions.
Dynamic Behavior Analysis and Robust Synchronization of a Novel Fractional-Order Chaotic System with Multiwing Attractors
Authors: Wang Chenhui
Published: April 2021

To enrich the types of multiwing chaotic attractors in fractional-order chaotic systems (FOCSs), a new type of 3-dimensional FOCSs is designed in this study. The most important contribution of this FOCS consists in the coexistence of multiple multiwing chaotic attractors, including 2-wing, 3-wing, and 4-wing attractors. It is also indicated that the minimum order that the system can exhibit chaotic behavior is 0.84. Then, based on certain fractional stability criteria, a robust synchronization controller is derived for this kind of FOCSs with multiwing chaotic attractors and parametric uncertainties, and the stability of the synchronization error is proven strictly. Meanwhile, the theoretical analysis is tested by simulation results.
Knowledge, Attitude, and Practice of Infection Control and Waste Management among Traditional Medicine Practitioners in Bhutan, 2019: A Nationwide Cross-Sectional Survey
Authors: Gyeltshen Dorji , Dorji Thinley , Choda Sonam , Gyeltshen Chencho , Dorji Sangay , Dorji Tashi , Tshering Ugyen , Wangmo Diki , Pongpirul Krit
Published: April 2021

Background. In Bhutan, Traditional Medicine (TM) is a part of the government-sponsored free healthcare system and Traditional Medicine Units (TMUs) are colocated with allopathic hospitals. Prevention of healthcare-associated infections and patient safety must apply to all institutionalized healthcare settings including TMUs. This study assessed the knowledge, attitude, and practice of TM practitioners in Bhutan in the field of infection control and waste management practices. Methods. This was a descriptive study among TM practitioners selected through simple random sampling. Data were collected using a structured pro forma and entered in EpiData 3.1 and analyzed in STATA 13.1. Results. There were 132 respondents (response rate 98%). The majority (64%) knew the seven steps of handwashing but their knowledge of WHO’s Five Moments for Handwashing was poor, especially handwashing after aseptic procedures (17%) and handwashing after touching patient surroundings (5%). Handwashing before palpation of the pulse (37%) and using gloves while dispensing medicines (9%) were poor; but the proportions of handwashing before performing moxibustion (96%), correct disposal of sharps (84%), and disinfection of cupping sets (78%) were high. The majority of participants hold a positive attitude towards the adoption of infection control and waste management practices for the benefit of patients. Only 23% had received preservice and 44% had received in-service training on infection control. Conclusions. The knowledge and practices of infection control and waste management are optimal only in select domains of practice. The practitioners hold a positive attitude towards the adoption of infection control and waste management standards.
Awareness on Teratogenic Effects of Isotretinoin and Compliance with Precautionary Measures among Women of Childbearing Age in Makkah Province, Saudi Arabia
Authors: Al-Mekhlafi Rawan , Attiyah Rawabi E. , Haddad Yara R. , Salah Louai A.
Published: April 2021

Acne vulgaris ranks among the most common dermatologic conditions encountered during adolescence up to adulthood. For moderate to severe cases of acne, isotretinoin is indicated as it is considered the most efficacious medication against acne. However, isotretinoin use is known to have its side effects and most importantly is the drug’s teratogenic potential. As a response, programs such as the Retinoid Pregnancy Prevention Program (PPP), System to Manage Accutane-Related Teratogenicity (SMART), and iPLEDGE were put into action as attempts to promote awareness on isotretinoin’s teratogenicity and reduce the incidence of exposed pregnancies. Such programs are lacking in Saudi Arabia. This study aimed, therefore, to evaluate the awareness of women of childbearing age in Makkah Province, Saudi Arabia, with regards to the side effects of the medication, specifically its teratogenicity. This study also intended to assess the compliance of both doctors and patients with the recommendations and precautions associated with isotretinoin. A cross-sectional study was conducted on 766 women participants using a previously validated questionnaire. Results showed that majority of the respondents (91%) are generally aware of the side effects of isotretinoin use, particularly its teratogenicity. However, lapses have been identified with regards to the compliance of both the treating physician and the patient. Three-fourths of sexually active women did not use any form of contraception while being on isotretinoin treatment. Two-thirds of the study participants responded that they were not issued approval forms indicating their understanding of the side effects of isotretinoin and the importance of compliance to the treating physician’s instructions; 11.5% claimed that their doctors did not perform any blood tests; and 67.7% claimed that no pregnancy test was performed at any time during the treatment. These findings strongly suggest a need for improvement when it comes to compliance of both doctors and patients. It is recommended that doctor-patient communication be more comprehensive and more efforts should be made to follow international guidelines in that regard.
FDA Approval Summary: Pembrolizumab for the First-line Treatment of Patients with MSI-H/dMMR Advanced Unresectable or Metastatic Colorectal Carcinoma
Authors: Casak Sandra J. , Marcus Leigh , Fashoyin-Aje Lola , Mushti Sirisha L. , Cheng Joyce , Shen Yuan-Li , Pierce William F. , Her Leah , Goldberg Kirsten B. , Theoret Marc R. , Kluetz Paul G. , Pazdur Richard , Lemery Steven J.
Published: April 2021

The FDA approved pembrolizumab on June 29, 2020, for the treatment of patients with unresectable or metastatic microsatellite instability-high (MSI-H) colorectal cancer with no prior systemic treatment for advanced disease. The approval was based on data from Study Keynote-177, which randomly allocated patients to receive either pembrolizumab or standard of care (SOC) with chemotherapy. Overall survival (OS) and independently assessed progression-free survival (PFS) were the primary endpoints. At the time of the final PFS analysis and second prespecified interim OS analysis, the estimated median PFS was 16.5 months (95% CI: 5.4–32.4) versus 8.2 months (95% CI: 6.1–10.2) in the pembrolizumab and SOC arms, respectively [HR: 0.60 (95% CI: 0.45–0.80); two-sided P = 0.0004]. FDA assessed unblinded OS data during the review of the application and identified no safety concerns that would preclude approval of this supplement. Adverse reactions occurring in >30% of patients receiving pembrolizumab were diarrhea, fatigue/asthenia, and nausea. Adverse reactions occurring in >30% of patients receiving SOC were diarrhea, nausea, fatigue/asthenia, neutropenia, decreased appetite, peripheral neuropathy (high-level term), vomiting, abdominal pain, constipation, and stomatitis. Duration of treatment in the pembrolizumab arm was almost double (median 11.1 months, range 0–30.6 months) than the duration of treatment in patients receiving SOC (median, 5.7 months). Approval of pembrolizumab is likely to change the treatment paradigm for first-line treatment with MSI-H advanced colorectal cancer given the study results and different safety profile.
Dianhydrogalactitol Overcomes Multiple Temozolomide Resistance Mechanisms in Glioblastoma
Authors: Jiménez-Alcázar Miguel , Curiel-García Álvaro , Nogales Paula , Perales-Patón Javier , Schuhmacher Alberto J. , Galán-Ganga Marcos , Zhu Lucía , Lowe Scott W. , Al-Shahrour Fátima , Squatrito Massimo
Published: April 2021

Glioblastoma (GBM) is the most frequent and aggressive primary tumor type in the central nervous system in adults. Resistance to chemotherapy remains one of the major obstacles in GBM treatment. Identifying and overcoming the mechanisms of therapy resistance is instrumental to develop novel therapeutic approaches for patients with GBM. To determine the major drivers of temozolomide (TMZ) sensitivity, we performed shRNA screenings in GBM lines with different O6-methylguanine-DNA methyl-transferase (MGMT) status. We then evaluated dianhydrogalactitol (Val-083), a small alkylating molecule that induces interstrand DNA crosslinking, as a potential treatment to bypass TMZ-resistance mechanisms. We found that loss of mismatch repair (MMR) components and MGMT expression are mutually exclusive mechanisms driving TMZ resistance in vitro. Treatment of established GBM cells and tumorsphere lines with Val-083 induces DNA damage and cell-cycle arrest in G2–M phase, independently of MGMT or MMR status, thus circumventing conventional resistance mechanisms to TMZ. Combination of TMZ and Val-083 shows a synergic cytotoxic effect in tumor cells in vitro, ex vivo, and in vivo. We propose this combinatorial treatment as a potential approach for patients with GBM.
Transient Clouding of a Sutureless Scleral Fixated Hydrophilic Intraocular Lens with Spontaneous Resolution: A Case Report and in vitro Experimental Test
Authors: Danese Carla , Calabresi Renzo , Lanzetta Paolo
Published: April 2021

A 83-year-old woman underwent phacoemulsification in the left eye, complicated by posterior capsular rupture and dislocation of nuclear fragments in the vitreous cavity. She was left aphakic and referred to the Clinica Oculistica, Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy. After 2 days, she underwent pars plana vitrectomy, with removal of the nuclear fragments, and secondary intraocular lens (IOL) implantation. The lens used was a sutureless scleral fixation Carlevale lens (I71 FIL SSF Carlevale lens™. Soleko IOL Division), which was delivered to the operating theatre on the morning of the operation. During the implantation, it appeared opaque, without any deposit on its surfaces. The lens was not removed. On the next day, the lens was transparent, and it remained clear at the following control visits. An in vitro experimental test demonstrated that 25% hydrophilic polymers are vulnerable to thermal shock, developing an opacification which resolves after temperature stabilization of the lens.
Necrotizing Streptococcus pyogenes Infiltrating Conjunctiva and Tenon’s Capsule: A Case Report
Authors: de Freitas Santoro Dalton , Polizelli Murilo Ubukata , Cervi Rosa Paulo Alberto , de Freitas Denise , de Sousa Luciene Barbosa , de Oliveira Lauro Augusto
Published: April 2021

We report a case of a patient with necrotizing infection of the conjunctiva and Tenon’s capsule caused by Streptococcus pyogenes, a rare and atypical ophthalmologic condition. A 50-years-old male patient with acute red-eye, purulent discharge, and pain diagnosed with post-septal cellulitis presented with a yellowish and dense membrane covering the ocular surface with necrotic Tenon’s capsule. Patient was hospitalized, and intravenous antibiotics were initiated (ceftriaxone and clindamycin). Topical antibiotics and corticosteroids were also administered, and the infection was eradicated in 2 weeks. Ancillary exams excluded rheumatologic involvement. Conjunctival culture confirmed Streptococcus pyogenes growth. Tenon’s biopsy revealed unspecific acute inflammatory necrosis. This is an uncommon condition in daily ophthalmological clinic. Literature review reported 3 cases associated with previous ocular surgery.
Treatment of Localized Vulvar Pain with Neural Therapy: A Case Series and Literature Review
Authors: Rey Novoa Modesto , Muñoz-Sellart Montserrat , Catalán Soriano Marta , Vinyes David
Published: April 2021

Background: Localized vulvar pain (LVP) is a common condition among fertile women, with physical and psychosexual implications. Treatment is complex with limited benefits. Neural therapy is a regulatory therapy that uses injections of local anesthetics in low concentrations in specific points to treat different conditions. Case Presentation: We present the cases of 5 women, ages 33–44 years, with LVP treated with procaine 0.5% injections in painful points. Complete relief from pain occurred in 2 patients, and significant improvement in 3. Only 1 or 2 sessions were required. Initial VAS score was ≥70 and decreased to ≤30 after the intervention. The improvement was maintained over time, with a minimum follow-up period of 6 months. None of the patients were able to have sex or use tampons due to pain, but they were able to resume after the intervention. Conclusions: In this case series, local injections of procaine showed a favorable outcome. Future randomized clinical trials could help elucidate the role of this intervention in LVP.
Increasing transmission control protocol speed by reducing the acknowledgement collision probability
Authors: Suherman Suherman , Hanafiah Rambe Ali , Haris Norshakila , Anhar Anhar
Published: April 2021

Transmission control protocol (TCP) employs acknowledgement (TCP-ACK) for every transmitted packets to ensure reliable transmission. As a result, it sends the next window packets after receiving the TCP-ACK packet of previous window. This means that the earlier the TCP-ACK packet arrives, the faster the TCP next window transmission and the better the TCP performances. To do so, there should be a special treatment to the transmitted TCP-ACK to fasten next window transmission. This paper proposes a collision probability reduction for the transmitted TCP-ACK packets so that the overall TCP delay reduces. Collision probability reduction can be implemented in many ways. Initially, mathematical analysis is provided to prove that method can work as expected. The mathematic analysis shows that when TCP-ACK collision probability is reduced, the overall TCP delay is also reduced. The proposed method is then implemented in 802.11, 802.16 and complex networks. The NS-2 simulations evaluations for the aforementioned networks and the proposed method proved that collision reductions on TCP-ACK exert average TCP delay reductions about 11.86%, 28.04% and 9.46% subsequently.The proposed method is also applicable for other TCP types.
Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques
Authors: Channegowda Arjun Benagatte , Prakash H N
Published: April 2021

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.
Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN
Authors: Darujati Cahyo , Susiki Nugroho Supeno Mardi , Kurniawan Deny , Hariadi Mochamad
Published: April 2021

Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.
Automated brain tumor classification using various deep learning models: a comparative study
Authors: Abbood Alaa Ahmed , Shallal Qahtan Makki , Fadhel Mohammed Abdulraheem
Published: April 2021

The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitations (i.e., precise quantitative measurements). Therefore, an automated classification system is necessary to avoid human mortality. The automatic categorization of brain tumors in the surrounding tumor region is a challenging task concerning space and structural variability. Four deep learning models: AlexNet, VGG16, GoogleNet, and RestNet50, are used in this comparative study to classify brain tumors. Based on accuracy, the results showed that RestNet50 is the best model with an accuracy of 95.8%, while AlexNet has the fast performance with a processing time of 1.2 seconds. In addition, a hardware parallel processing unit (GPU) is employed for real-time purposes, where AlexNet (the fastest model) has a processing time of only 8.3 msec.
Recently employed engineering techniques to reduce the spread of COVID-19 (corona virus disease 2019): a review study
Authors: Saman Bander , Eid Mahmoud M. A. , Eid Marwa M.
Published: April 2021

The main challenges of today’s global health care system are to reach to strong healthcare system, to provide effective methods to eliminate the increase in the number of dead and infected with virus of COVID-19. Therefore, during the last few months, the great importance and efficacy of a variety of engineering techniques that have greatly contributed in curbing the spread of the COVID-19, and evenly help to eliminate it according to recent scientific studies was highly prominent. Among these promising technologies in this field we mention, but not limited to, the use of ultraviolet (UV) rays to disinfection of air and surfaces. In addition, thermal imaging technology, which was employed using infrared radiation for monitoring people in crowded areas and human groups to determine who have abnormal temperatures, so that all preventive measures are taken. Robots have also been used and harnessed to perform many tasks that limit the spread of the virus and maintain the integrity of the human element. Last but not least, facial recognition techniques have also been used to limit the spread of this pandemic. Ultraviolet radiation is one of physical therapy modalities that can be used to increase the efficiency of human immune system to fight the virus. In conclusion UV radiation, infrared thermal imaging, robotics, AFR technologies are now widely used to reduce the spread of this virus and manage the outbreak.
Generation and collection of data for normal and conflicting flows in software defined network flow table
Authors: Khairi Mutaz Hamed Hussien , H. S. Ariffin Sharifah , M. Abdul Latiff N. , Mohamad Yusof Kamaludin
Published: April 2021

In terms of network simplification and regulation, Software Defined Networking (SDN) is a new form of infrastructure that offers greater adaptability and flexibility. SDN, however, is an invention that is logically centralized. In addition, the optimization of the control plane and data plane in SDN has become an area deserving of more attention. The flow in OpenFlow has been one of the essential parameters in the SDN standards, in which every individual flow includes packet matching fields, flow priority, separate counters, instructions for packet forwarding, flow timeouts and a cookie. This research work is conducted in order to produce and collect flows from the OpenFlow switch in two scenarios; in normal flows and when conflict policy rules are enforced in the network. In this article, the throughput is required to review and evaluate the conflict impact on two protocols as a performance metric; the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) that flows via a forwarded plane. During the simulation of the SDN OpenFlow network, the metrics are tested using MININET. The results demonstrate that the existence of SDN conflict rules allows TCP and UDP to have a significant average change in bandwidth that eventually affects the network and operations performance.
Mobility-prediction and energy optimization for multi-channel multi-interface ad hoc networks in the presence of location errors
Authors: Faouzi Hassan , Boutalline Mohammed
Published: April 2021

We present a mobility-prediction and energy optimization solution for multi-channel multi-interface (MCMI) ad hoc networks in the presence of location errors. This solution includes routing of the MCMI communication links that adapt to dynamic channel, traffic conditions, interference and mobility of nodes. We start first with implementing a novel cross-layer routing solution in order to share information between network and MAC layer, the benefit of this technique is to collect information about the channel quality and residual energy of the nodes and send them directly to the network layer. Next, we present a mobility-prediction model using Kalman filter to predict accurate locations and enhance routing performance, through estimating link duration and selecting reliable routes. The performance of proposed mechanism is measured using NS2.35 simulations with different scenarios and varying load in a network. Comparative analysis of simulation results shows better performance of our protocol (ME-MCMI AODV) in terms of reducing end-to-end delay, total dropped packets and increasing network lifetime and packet delivery ratio (PDR).