bluuebunny/crossref_metadata_2025 huggingface dataset:
Failure Mechanism and Mitigation Measures of the G1002 Electricity Pylon Landslide at the Jinping I Hydropower Station
Authors: Luo Gang , Zhong Yutian , Yang Yuanxiang
Published: September 2020

On August 29 and 30, 2012, local extreme rainfalls struck the construction area of the Jinping I Hydropower Station, Xichang, China, and triggered many geohazards. The upper region of the left valley slope 200 m downstream of the dam failed and slid, exposing the D‐pile of the G1002 electricity pylon and threatening the entire power transmission line. Therefore, guaranteeing the stability of the residual soil masses in the rear area of the main scarp and the safety of the G1002 electricity pylon became a primary emergency task. Geological field surveys, topographical mapping, study of the failure mechanisms, and stability evaluations were carried out from October 12, 2012, to November 7, 2013. It is revealed that the failure mechanism of the G1002 electricity pylon landslide is flood‐induced tractive sliding along the interlayer between the colluvium and the bedrock, significantly influenced by heavy precipitation and frequent blasting activities during the dam construction. The residual soil masses around the G1002 electricity pylon foundation are unstable under rainfall conditions. In order to ensure the stability of the residual soil masses and pylon foundation, a mitigation measure of the anchor cables combined with lattice frame beams was proposed and applied in practice. This paper provides insights into the problems associated with the selection of the locations of electricity pylons in ravine regions as well as mitigation strategies for similar landslides.
p130 And pRb in the Maintenance of Transient Quiescence of Mesenchymal Stem Cells
Authors: Popov Boris , Petrov Nikolai , Ryabov Vladimir , Evsyukov Igor
Published: September 2020

An effective regulation of quiescence plays a key role in the differentiation, plasticity, and prevention of stem cells from becoming malignant. The state of quiescence is being controlled by the pRb family proteins which show overlapping functions in cell cycle regulation; however, their roles in controlling the proliferation of mesenchymal stem cells (MSCs) remain to be understood. This study investigated the regulation of transient quiescence using growth curves, proliferation assay, the cytometric evaluation of cell cycle, Western blotting, and the electromobility gel shift assay (EMSA) on synchronized MSCs of the C3H10Т1/2 and control cells with different statuses of pRb proteins. It has been found that functional steady-state level of p130 but not pRb plays a critical role for entering, exiting, and maintenance of transient quiescence in multipotent mesenchymal stem cells.
Study on Model and Tests of Sheet Pile Wall with a Relieving Platform
Authors: Zhang Ming , Wang Wei , Hu Ronghua , Wang Ziyi
Published: September 2020

A new type of retaining wall, the sheet pile wall with a relieving platform, is introduced in this paper. Based on the prototype retaining wall with a height of 12 meters, the model tests with a geometric similarity ratio of 7 are designed and we focus on the model production and analysis on the test results. Some comparative analyses between the measured values and the calculation values by using the theoretical calculation method and finite difference method are carried out, including Earth pressure behind the wall, prepile resistance force, the bending moment, and the deformation of rib pillars in the retaining wall. The results show that Earth pressure behind the wall has a linear increase with the depth and lies between Rankine Earth pressure and Earth pressure at rest. Moreover, the prepile resistance force can be approximated by the m method, and the bending moment can be also used for approximate calculation by the m method and is larger than the results calculated by the finite difference method. It is also observed that there is a zero‐displacement point on the pile bottom, and the Earth pressure above the point behind the pile develops from Earth pressure at rest to the active Earth pressure; the Earth pressure under the point behind the pile develops from Earth pressure at rest to the passive Earth pressure. Therefore, the Earth pressure behind the bottom wall is larger than the calculated value by the Rankine theory. Finally, the displacement of the rib pillars is greater than the calculated results using the finite difference method and exceeds the standard requirements, owing to the failure of the retaining wall, and the unloading board needs to be constructed to improve the retaining wall’s behaviour. These findings verify the model’s credibility and provide an underpinning for studying the behavior of the retaining wall.
Weibo Rumor Recognition Based on Communication and Stacking Ensemble Learning
Authors: Wu Yu , Zeng Yan , Yang Jie , Zhao Zhenni
Published: September 2020

Research on the identification of rumors in cyberspace helps to discover social issues that are of concern to the public and are not easily found, and it also can help to purify cyberspace and to maintain social stability. However, the real complexity of rumors makes it difficult for its recognition technology to bridge the semantic gap between qualitative description and quantitative calculation of rumors. Firstly, the existing rumor definitions are mostly qualitative descriptions, so we propose a technical definition of Internet rumors to facilitate quantitative calculations. Secondly, since the feature set used in rumor recognition research is not effective, by combining with communication, we construct a more suitable feature set for rumor recognition. Thirdly, aiming at the problem that traditional classification algorithms are not suitable for complex rumor information recognition, a rumor recognition method based on Stacking ensemble learning is proposed. Our experiment results show that the proposed method has higher accuracy, less algorithm execution time, and better practical application effect.
Association between Receipt of Guideline-Concordant Lung Cancer Treatment and Individual- and Area-Level Factors: A Spatio-Temporal Analysis
Authors: Wah Win , Stirling Rob G. , Ahern Susannah , Earnest Arul
Published: September 2020

Background:

Guideline-concordant treatment (GCT) of lung cancer has been observed to vary across geographic regions over the years. However, there is little evidence as to what extent this variation is explained by differences in patients’ clinical characteristics versus contextual factors, including socioeconomic inequalities.

Methods:

This study evaluated the independent effects of individual- and area-level risk factors on geographic and temporal variation in receipt of GCT among patients with lung cancer. Receipt of GCT was defined on the basis of the National Comprehensive Cancer Network guidelines. We used Bayesian spatial-temporal multilevel models to combine individual and areal predictors and outcomes while accounting for geographically structured and unstructured correlation and linear and nonlinear trends.

Results:

Our study included 4,854 non–small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cases, reported to the Victorian Lung Cancer Registry between 2011 and 2018. Area-level data comprised socioeconomic disadvantage and remoteness data at the local government area level in Victoria, Australia. Around 60.36% of patients received GCT, and the rates varied across geographic areas over time. This variation was mainly associated with poor performance status, advanced clinical stages, NSCLC types, public hospital insurance, area-level deprivation, and comorbidities.

Conclusions:

This study highlights the need to address disparities in receipt of GCT among patients with lung cancer with poor performance status, NSCLC, advanced clinical stage, stage I–III SCLC, stage III NSCLC, public hospital insurance, and comorbidities, and living in socioeconomically disadvantaged areas.

Impact:

Two-year mortality outcomes significantly improved with GCT. Interventions aimed at reducing these inequalities could help to improve lung cancer outcomes.
Atypical Hemolytic Uremic Syndrome: New Challenges in the Complement Blockage Era
Authors: Avila Bernabeu Ana Isabel , Cavero Escribano Teresa , Cao Vilarino Mercedes
Published: September 2020

Atypical hemolytic uremic syndrome (aHUS) is a rare cause of thrombotic microangiopathy (TMA), characterized by microangiopathic hemolytic anemia, consumptive thrombocytopenia, and multisystem end organ involvement, most commonly affecting the kidney. Diagnosis is clinical, after exclusion of other TMA causes. Primary aHUS arises from genetic abnormalities, resulting in uncontrolled complement activity, while a variety of clinical scenarios cause secondary aHUS, including infection, pregnancy, malignancy, autoimmune disease, and medications. They can also induce a temporary complement deregulation with an overlap between both scenarios, which can make differential diagnosis difficult. Primary aHUS can be sporadic or familial and is associated with a high rate of progression to ESRD. Many aHUS patients relapse in the native or transplanted kidneys, leading to kidney failure. The introduction of eculizumab has changed the prognosis of aHUS, by inducing hematologic remission, improving or stabilizing kidney functions, and preventing graft failure. The early institution of appropriate therapy can prevent multiorgan damage, so is essential to recognize and differentiate the TMA syndromes. Eculizumab is considered now the first-line treatment, and it is recommended lifelong therapy. However, the high cost of therapy has led to make efforts to develop precise complement functional and genetic studies that help physicians to determine the appropriate duration of eculizumab therapy. Nowadays, more studies are needed to select candidates to adjustment of therapy.
Circulating Tumour DNA to Guide Treatment of Gastrointestinal Malignancies
Authors: To Yat Hang , Lee Belinda , Wong Hui-Li , Gibbs Peter , Tie Jeanne
Published: September 2020

Background: Gastrointestinal cancers are among the most common cancers worldwide and account for a high proportion of cancer-related mortality. Advancements to improve outcomes are constrained by the lack of biomarkers that can offer early diagnostic and prognostic information as traditional serological tumour markers and conventional imaging approaches are not able to provide early information regarding disease recurrence and treatment outcomes. Recent advances in technology have allowed the detection of circulating tumour DNA (ctDNA) in plasma, nucleic acid fragments released into the circulation from primary or metastatic lesions undergoing apoptosis and necrosis. A growing body of evidence has emerged supporting the use of ctDNA in many aspects of cancer care. Summary: This review focuses on the potential role of ctDNA in the management of patients with gastrointestinal cancers including colorectal, pancreatic, and upper gastrointestinal cancers. In this review, we discuss its possible utility in screening, detection of minimal residual disease and prognostication, longitudinal surveillance, and identification of therapeutic targets and resistance incorporating recent literature and ongoing randomised clinical trials. Key Messages: ctDNA has substantial potential as a clinically useful marker in the management of gastrointestinal cancers from cancer screening through to treatment of advanced disease.
When Therapeutic IgA Antibodies Might Come of Age
Authors: Sterlin Delphine , Gorochov Guy
Published: September 2020

Background: Extensive efforts have been made in optimizing monoclonal immunoglobulin (Ig)G antibodies for use in clinical practice. Accumulating evidence suggests that IgA or anti-FcαRI could also represent an exciting avenue toward novel therapeutic strategies. Summary: Here, we underline that IgA is more effective in recruiting neutrophils for tumor cell killing and is potently active against several pathogens, including rotavirus, poliovirus, influenza virus, and SARS-CoV-2. IgA could also be used to modulate excessive immune responses in inflammatory diseases. Furthermore, secretory IgA is emerging as a major regulator of gut microbiota, which impacts intestinal homeostasis and global health as well. As such, IgA could be used to promote a healthy microbiota in a therapeutic setting. Key messages: IgA combines multifaceted functions that can be desirable for immunotherapy.
Efficacy and Safety of Capecitabine Alone or in Combination in Advanced Metastatic Breast Cancer Patients Previously Treated with Anthracycline and Taxane: A Systematic Review and Meta-Analysis
Authors: Alsaloumi Louai , Shawagfeh Shaima , Abdi Abdikarim , Basgut Bilgen
Published: September 2020

Background: Capecitabine is frequently used alone or combined with other chemotherapy agents for the treatment of metastatic breast cancer in relapsed patients. Objective: The objective of this meta-analysis is to evaluate the effectiveness and safety of capecitabine monotherapy versus combination in the treatment of metastatic breast cancer patients pretreated with anthracycline and taxane. Methods: Eligible randomized controlled trials examining the efficacy and safety of capecitabine alone compared to capecitabine combination were systematically searched. Progression-free survival (PFS), overall survival (OS), overall response rate (ORR), and grades 3–4 drug-related adverse events were the outcomes assessed. Results: A total of 6,714 patients of 9 trials were involved in the pooled analysis. Our findings demonstrated that capecitabine combination is significantly superior to capecitabine monotherapy in improving PFS (hazard ratio [HR] 1.32, 95% CI 1.13–1.54, p < 0.0001) and ORR (risk ratio [RR] 0.67, 95% CI 0.54–0.83, p < 0.001), but it was insignificant in OS (HR 1.09, 95% CI 0.98–1.22, p = 0.12). On the other hand, the incidence of non-hematological adverse events such as hand-foot syndrome and diarrhea was lower in capecitabine combination compared to capecitabine monotherapy. Conclusion: Capecitabine-based combination chemotherapy showed superiority over capecitabine monotherapy in terms of PFS and ORR, with no significant difference in OS. Non-hematological adverse effects such as hand-foot syndrome were fewer with a combination regimen. However, hematological adverse events were fewer with capecitabine monotherapy regimen.
Switching Host Metabolism as an Approach to Dampen SARS-CoV-2 Infection
Authors: Soliman Sameh , Faris MoezAlIslam E. , Ratemi Zakaria , Halwani Rabih
Published: September 2020

Background: COVID-19 pandemic, a global threat, adversely affects all daily lives, altered governmental plans around the world, and urges the development of therapeutics and prophylactics to avoid the expansion of the viral infection. With the recent gradual opening after long lockdown, several recommendations have been placed, with dietary modification as one of the most important approaches that have been appraised. Summary: Here, we are reviewing how changing the host metabolism, particularly changing the host metabolic state from the carbohydrate-dependent glycolytic state to a fat-dependent ketogenic state, may affect viral replication. Furthermore, the impact of intermittent fasting (IF) in triggering metabolic switch along with the impact of supplementation with medium-chain triglycerides (MCTs) such as lauric acid in repressing the envelope formation and viral replication is also addressed. The amalgamation of IF and a ketogenic diet rich in MCTs is thought to work as a prophylactic measure for normal people and adjunct therapy for infected persons. Key Message: A diet regimen of ketogenic breakfast along with supplementation with two doses of lauric acid-rich MCTs at breakfast and lunch times, followed by 8–12-h IF and a dinner rich with fruits and vegetables, could be a potential prophylactic strategy and adjuvant therapy to combat SARS-CoV-2 infections.
Time-domain harmonic extraction algorithms for three-level inverter-based shunt active power filter under steady-state and dynamic-state conditions-an evaluation study
Authors: Al-Ogaili Ali Saadon , Ramasamy Agileswari , Hoon Yap , Verayiah Renuga , Marsadek Marayati , Juhana Tengku , Rahmat Nur Azzammudin
Published: September 2020

Power quality is a major consideration in all office equipment, manufacturies and residential home appliances. Harmonic distortion is one of the crucial power quality issues. In order to mitigate the harmonic distortion, the performance of shunt active power filter (SAPF) is judged in terms of the accuracy and response time of its designed controller. In this context, the controller consists of three parts: harmonic extraction, switching control, and DC-link capacitor. The harmonic extraction technique serves the major role of deriving the required reference current to ensure successful mitigation of current harmonics by SAPF. Among the existing techniques, harmonic extraction algorithms based on time-domain approaches are most widely applied as they offer simple implementation features with increased speed and reduced computational burden. This paper presents detailed investigation and analysis regarding the performance of two famous time-domain harmonic extraction techniques namely, synchronous reference frame (SRF) and instantaneous power (PQ) theory. Extensive simulation work is conducted in MATLAB-Simulink platform under two conditions, which are, steady-state conditions and dynamic-state conditions, considering various highly nonlinear loads. For evaluation purposes, each control algorithm is incorporated into the controller of a three-phase SAPF, developed using a three-level neutral-point-clamped (NPC) inverter. Comprehensive results are provided to confirm mitigation performance of the SAPF utilizing each harmonic extraction algorithm.
Design and implementation of 4 bit binary weighted current steering DAC
Authors: Patel Jayeshkumar J. , Naik Amisha P.
Published: September 2020

A compact current-mode Digital-to-Analog converter (DAC) suitable for biomedical application is repesented in this paper .The designed DAC is binary weighted in 180nm CMOS technology with 1.8V supply voltage. In this implementation, authors have focused on calculaton of Non linearity error say INL and DNL for 4 bit DAC having various type of switches: NMOS, PMOS and Transmission Gate. The implemented DAC uses lower area and power compared to unary architecture due to absence of digital decoders. The desired value of Integrated non linearity (INL) and Differential non linearity (DNL) for DAC for are within a range of +0.5LSB. Result obtained in this works for INL and DNL for the case DAC using Transmission Gate is +0.34LSB and +0.38 LSB respectively with 22mW power dissipation.
Color image encryption based on chaotic shit keying with lossless compression
Authors: Hashim Ashwaq T. , Jalil Bahaa D.
Published: September 2020

In order to protect valuable data from undesirable readers or against illegal reproduction and modifications, there have been various data encryption techniques. Many methods are developed to perform image encryption. The use of chaotic map for image encryption is very effective, since it increase the security, due to its random behavior. The most attractive feature of deterministic chaotic systems is he extremely unexpected and random-look nature of chaotic signals that may lead to novel applications. A novel image encryption algorithm based on compression and hyper chaotic map techniques is proposed. Firstly the image is decomposed into three subbands R, G, and B then each band is compressed using lossless technique. The generated chaotic sequences from the 3D chaotic system are employed to code the compressed results by employing the idea of chaotic shift encoding (CSK) modulation to encode the three bands to generate the encrypted image. The experiments show that the proposed method give good results in term of security, feasibility, and robustness.
Internet of things–based vital sign monitoring system
Authors: Alamsyah Alamsyah , Subito Mery , Ikhlayel Mohammad , Setijadi Eko
Published: September 2020

Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%.
An efficient data masking for securing medical data using DNA encoding and chaotic system
Authors: K. Siddartha B. , K. Ravikumar G.
Published: September 2020

Data security is utmost important for ubiquitous computing of medical/diagnostic data or images. Along with must consider preserving privacy of patients. Recently, deoxyribose nucleic acid (DNA) sequences and chaotic sequence are jointly used for building efficient data masking model. However, the state-of-art model are not robust against noise and cropping attack (CA). Since in existing model most digits of each pixel are not altered. This work present efficient data masking (EDM) method using chaos and DNA based encryption method for securing health care data. For overcoming research challenges effective bit scrambling method is required. Firstly, this work present an efficient bit scrambling using logistic sine map and pseudorandom sequence using chaotic system. Then, DNA substitution is performed among them to resist against differential attack (DA), statistical attack (SA) and CA. Experiment are conducted on standard considering diverse images. The outcome achieved shows proposed model efficient when compared to existing models.
Examining relationship between service quality, user satisfaction, and performance impact in the context of smart government in UAE
Authors: Ameen Ali , Al-Ali Dawoud , Isaac Osama , Mohammed Fathey
Published: September 2020

Governments attempt to use all forms of information technologies including Internet and mobile computing to be able to transform relationships with citizens. However, there is a clear gap between the indicator of the impact of technology innovation output and government’s vision in UAE (United Arab Emirates). In this regard, investigating the relationship between service quality, user satisfaction, and performance impact may help the government to mark its current progress and milestone achievement. This research proposed a model based on Delone & McLean IS success model by considering the research context. The modeling of structural equations via PLS (Partial least squares) regression was applied to evaluate the model within the context of public sector in the UAE. The data was collected from a sample of 147 employees in public organizations using a questionnaire. Results demonstrated that the quality of service has a significant effect on user satisfaction. In addition, quality of service and user satisfaction positively influences the staff performance. The outcome of this research helps to enhance the understanding of the impact of smart government applications.
Fuel enhancement of parallel hybrid electric two-wheeler motorcycle
Authors: Krithika V. , Subramani C.
Published: September 2020

In this paper, design and simulation of a parallel hybrid electric two-wheeler motorcycle (PHETM) by means of continuous variable transmission (CVT) is illustrated. For simulation, the parallel hybrid electric power train model type in MATLAB/ADVISOR is customized. The internal combustion engine (ICE) be supposed to drive at elevated efficiency areas, in order to attain enhanced fuel economy and a reduced amount of emission. Simultaneously, the ICE must not activate at values of low torque areas. For that reason, get better it whilst ICE is ON, a new energy control strategy is proposed. In the new strategy, the electrical machine absorbs the extra torque of the ICE. This article proposes a PHETM system to propel the vehicle efficiently with reduced amounts of emission on comparing witha conventional vehicle. This system includes two modes of operations for achieving the better results known as motoring mode and generating mode. The switching from one mode to other is based on the vehicle speed which is sensed in real time. A drive cycle is generated by running the vehicle in normal and slightly gradient condition and finally the results are compared.
A stage-structured delayed advection reaction-diffusion model for single species
Authors: Alkhasawneh Raed Ali
Published: September 2020

In this paper, we derived a delay advection reaction-diffusion equation with linear advection term from a stage-structured model, then the derived equation is used under the homogeneous Dirichlet boundary conditions u_m (0,t)=0, u_m (L,t)=0, and the initial condition u_m (x,0)=u_m^0 (x)>0,x∈[-τ,0] with u_m^0 (0)>0 in order to find the minimum value of domain L that prevents extinction of the species under the effect of advection reaction diffusion equation. Finally, for the measurement the time lengths from birth to the development of the species population, time delays are integrated.
A new hybrid text encryption approach over mobile ad hoc network
Authors: Almaiah Mohammed Amin , Dawahdeh Ziad , Almomani Omar , Alsaaidah Adeeb , Al-Khasawneh Ahmad , Khawatreh Saleh
Published: September 2020

Data exchange has been rapidly increased recently by increasing the use of mobile networks. Sharing information (text, image, audio and video) over unsecured mobile network channels is liable for attacking and stealing. Encryption techniques are the most suitable methods to protect information from hackers. Hill cipher algorithm is one of symmetric techniques, it has a simple structure and fast computations, but weak security because sender and receiver need to use and share the same private key within a non-secure channel. Therefore, a novel hybrid encryption approach between elliptic curve cryptosystem and hill cipher (ECCHC) is proposed in this paper to convert Hill Cipher from symmetric technique (private key) to asymmetric one (public key) and increase its security and efficiency and resist the hackers. Thus, no need to share the secret key between sender and receiver and both can generate it from the private and public keys. Therefore, the proposed approach presents a new contribution by its ability to encrypt every character in the 128 ASCII table by using its ASCII value direct without needing to assign a numerical value for each character. The main advantages of the proposed method are represented in the computation simplicity, security efficiency and faster computation.
An effective identification of crop diseases using faster region based convolutional neural network and expert systems
Authors: Chandana P. , Pradeep Ghantasala G. S. , Virgil Jeny J. Rethna , Sekaran Kaushik , N. Deepika , Nam Yunyoung , Kadry Seifedine
Published: September 2020

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
Parallel implementation of pulse compression method on a multi-core digital signal processor
Authors: Klilou Abdessamad , Arsalane Assia
Published: September 2020

Pulse compression algorithm is widely used in radar applications. It requires a huge processing power in order to be executed in real time. Therefore, its processing must be distributed along multiple processing units. The present paper proposes a real time platform based on the multi-core digital signal processor (DSP) C6678 from Texas Instruments (TI). The objective of this paper is the optimization of the parallel implementation of pulse compression algorithm over the eight cores of the C6678 DSP. Two parallelization approaches were implemented. The first approach is based on the open multi processing (OpenMP) programming interface, which is a software interface that helps to execute different sections of a program on a multi core processor. The second approach is an optimized method that we have proposed in order to distribute the processing and to synchronize the eight cores of the C6678 DSP. The proposed method gives the best performance. Indeed, a parallel efficiency of 94% was obtained when the eight cores were activated.
A systematic review of text classification research based on deep learning models in Arabic language
Authors: Wahdan Ahlam , AL Hantoobi Sendeyah , Salloum Said A. , Shaalan Khaled
Published: September 2020

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.
A comparison of activation functions in multilayer neural network for predicting the production and consumption of electricity power
Authors: Salam Abdulwahed , Hibaoui Abdelaaziz El , Saif Abdulgabbar
Published: September 2020

Predicting electricity power is an important task, which helps power utilities in improving their systems’ performance in terms of effectiveness, productivity, management and control. Several researches had introduced this task using three main models: engineering, statistical and artificial intelligence. Based on the experiments, which used artificial intelligence models, multilayer neural networks model has proven its success in predicting many evaluation datasets. However, the performance of this model depends mainly on the type of activation function. Therefore, this paper introduces an experimental study for investigating the performance of the multilayer neural networks model with respect to different activation functions and different depths of hidden layers. The experiments in this paper cover the comparison among eleven activation functions using four benchmark electricity datasets. The activation functions under examination are sigmoid, hyperbolic tangent, SoftSign, SoftPlus, ReLU, Leak ReLU, Gaussian, ELU, SELU, Swish and Adjust-Swish. Experimental results show that ReLU and Leak ReLU activation functions outperform their counterparts in all datasets.
Relationship of drain induced barrier lowering and top/bottom gate oxide thickness in asymmetric junctionless double gate MOSFET
Authors: Jung Hakkee
Published: September 2020

The relationship of drain induced barrier lowering (DIBL) phenomenon and channel length, silicon thickness, and thicknesses of top and bottom gate oxide films is derived for asymmetric junctionless double gate (JLDG) MOSFETs. The characteristics between the drain current and the gate voltage is derived by using the potential distribution model to propose in this paper. In this case, the threshold voltage is defined as the corresponding gate voltage when the drain current is (W/L) × 10-7 A, and the DIBL representing the change in the threshold voltage with respect to the drain voltage is obtained. As a result, we observe the DIBL is proportional to the negative third power of the channel length and the second power of the silicon thickness and linearly proportional to the geometric mean of the top and bottom gate oxide thicknesses, and derive a relation such as DIBL =25.15ηL_g^(-3) t_si^2 √(t_ox1∙t_ox2 ), where η is a static feedback coefficients between 0 and 1. The η is found to be between 0.5 and 1.0 in this model. The DIBL model of this paper has been observed to be in good agreement with the result of other paper, so it can be used in circuit simulation such as SPICE.
Analysis of subthreshold swing in junctionless double gate MOSFET using stacked high-k gate oxide
Authors: Jung Hakkee
Published: September 2020

In this paper, the subthreshold swing was observed when the stacked high-k gate oxide was used for a junctionless double gate (JLDG) MOSFET. For this purpose, a subthreshold swing model was presented using the series-type potential model derived from the Poisson equation. The results of the model presented in this paper were in good agreement with the two-dimensional numerical values and those from other papers. Using this model, the variation of the subthreshold swing for the channel length, silicon thickness, gate oxide thickness, and dielectric constant of the stacked high-k material was observed using the dielectric constant as a parameter. As a result, the subthreshold swing was reduced when the high-k materials were used as the stacked gate oxide film. In the case of the asymmetric structure, the subthreshold swing can be reduced than that of the symmetric JLDG MOSFET when the dielectric constant of the bottom stacked oxide film was greater than that of the top stacked oxide film. In the case of the asymmetric structure, the subthreshold swing could be also reduced by applying the bottom gate voltage lower than the top gate voltage.
FPGA based synchronous multi-channel PWM generator for humanoid robot
Authors: Muttaqin Adharul , Finnadi Stefanus Dion , Abidin Zainul , Araki Kakeru
Published: September 2020

In this paper, synchronous multi-channel pulse width modulation (PWM) generator for driving servo motors of humanoid robot was proposed. In an application, the humanoid robot requires smooth and beautiful movement, therefore the PWM signal for each servo motor must be synchronized. Since microcontroller (slave) has no enough channels to generate synchronous PWMs for 32 servo motors, field programmable gate array (FPGA) was used as slave for the humanoid robot. The FPGA was controlled by microcontroller (master) using serial communication. Simulation results show the system can perform serial communication, synchronize, and convert data well. The system can also generate PWM simultaneously with accurate duty cycle and fix period of 20ms.
Benchmarking data mining approaches for traveler segmentation
Authors: Uçar Tamer , Karahoca Adem
Published: September 2020

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.
IoT based implemented comparison analysis of two well-known network platforms for smart home automation
Authors: Alani Sameer , Mahmood Sarmad Nozad , Attaallah Sarah Zaeead , Mhmood Haneen Sameer , Khudhur Zeena Abdulsattar , Dhannoon Azzam Amer
Published: September 2020

The developments of the internet of things (IoT) technologies fascinated the universe and provided great opportunities to introduce these innovations in smart house networks. Smart home automation is highly required these days. Smart home automation is a collection of electronic devices connected to monitor and control in the market home appliance remotely. However, it is still needed to design a friendly and reliable system since the system mainly depends on the devices used and the environment of the network. NETPI and BLYNK are IoT frameworks used for hardware-agnostic with smartphones, websites, private clouds, system security, data mining, and deep learning. The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework. The proposed system shows its applicability in monitoring and controlling home appliances remotely.
Secured node detection technique based on artificial neural network for wireless sensor network
Authors: Hasan Bassam , Alani Sameer , Saad Mohammed Ayad
Published: September 2020

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.
Multi-user media streaming service for e-learning based web real-time communication technology
Authors: Edan Naktal , Mahmood Sanabil A.
Published: September 2020

Web real-time communication (WebRTC) standards do not define precisely how two browsers establish and control their communication. Therefore, a signalling mechanism/protocol has not specified in WebRTC. The essential goal of this research is to create and apply a WebRTC bi-directional video conferencing based on mesh topology (many-to-many) using Google Chrome, Firefox, Opera, and Explorer. This experiment involved through Ethernet and Wireless of the Internet and 4G networks in e-learning. The signalling mechanism of this experiment has been created and implemented using JavaScript language along with MultiConnection libraries. In addition, an evaluation of quality of experience (QoE), resources, such as bandwidth consumption, and CPU performance was done. In this paper, a novel implementation was accomplished over e-learning using different networks, different browsers, many peers, opening one or many rooms concurrently, defining room initiator, sharing the information of the new user with participants, using user identification (user-id), and so on. Moreover, the paper also highlights the advantages and disadvantages of using WebRTC video conferencing.
A space-structure based dissimilarity measure for categorical data
Authors: Hernández Kevin Alejandro , Peña D. Cárdenas , Orozco Álvaro A.
Published: September 2020

The development of analysis methods for categorical data begun in 90's decade, and it has been booming in the last years. On the other hand, the performance of many of these methods depends on the used metric. Therefore, determining a dissimilarity measure for categorical data is one of the most attractive and recent challenges in data mining problems. However, several similarity/dissimilarity measures proposed in the literature have drawbacks due to high computational cost, or poor performance. For this reason, we propose a new distance metric for categorical data. We call it: Weighted pairing (W-P) based on feature space-structure, where the weights are understood like a degree of contribution of an attribute to the compact cluster structure. The performance of W-P metric was evaluated in the unsupervised learning framework in terms of cluster quality index. We test the W-P in six real categorical datasets downloaded from the public UCI repository, and we make a comparison with the distance metric (DM3) method and hamming metric (H-SBI). Results show that our proposal outperforms DM3 and H-SBI in different experimental configurations. Also, the W-P achieves highest rand index values and a better clustering discriminant than the other methods.
Optimization of energy consumption in cloud computing datacenters
Authors: Osman Ahmed , Sagahyroon Assim , Aburukba Raafat , Aloul Fadi
Published: September 2020

Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
A hybrid feature selection on AIRS method for identifying breast cancer diseases
Authors: Ridok Achmad , Widodo Nashi , Mahmudy Wayan Firdaus , Rifa’i Muhaimin
Published: September 2020

Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.
Parameters estimation of solar photovoltaic module using camel behavior search algorithm
Authors: Hassan Kadhim H. , Rashid Abdulmuttalib T. , Jasim Basil H.
Published: September 2020

Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.
Particle swarm optimization-based stator resistance observer for speed sensorless induction motor drive
Authors: Ho Sang Dang , Palacky Petr , Kuchar Martin , Brandstetter Pavel , Tran Cuong Dinh
Published: September 2020

This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.