bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect Factor of Arsenite and Arsenate Removal by a Manufactured Material: Activated Carbon-Supported Nano-TiO2
Authors: Luo Qian , Li Gang , Chen Meifeng , Qin Fanxin , Li Haiyan , Qiang Yu
Published: September 2020

Carbon substrate-supported nano-TiO2 (a manufactured material) was prepared in this study for arsenic removal. The removal rates of arsenic were evaluated by batch tests under several simulation conditions including pH, ionic strength, and adsorbent dosage. Results showed that As(III) and As(V) adsorption reached equilibrium within 10 hours (pH = 8 and ionic strength 0.5 mol/L). At pH = 8, maximum adsorption efficiency was discovered for the adsorbent. Removal rate was proportional to the increase in ionic strength. The removal data were satisfactorily fitted to the pseudo-second-order kinetic model (R2 > 0.9990) and Freundlich equation (R2 ≥ 0.9600) for adsorption thermodynamic behaviors. New material showed more effective adsorption performance for As(V) than for As(III). It was found that 15.1800 mg/g As(V) and 13.3800 mg/g As(III) were adsorbed, respectively. In addition, material properties were studied including the structure of crystallinity, surface morphology, functional groups, and surface texture by XRD, TEM/SEM, FTIR, and BET methods, respectively. XRD result showed supported nano-TiO2 had the anatase phase. The size of the microparticle was around 52 nm. BET results indicated that material surface areas, pore volume, and pore size diameter were 371.17 m2/g, 0.35 cm3/g, and 11.70 nm, respectively. FTIR spectrum indicated that several functional groups (OH−, Ti-O) existing in supported nano-TiO2 may facilitate the adsorption efficiency. Mechanistically, supported nano-TiO2 played a key role in promoting adsorption efficiency and converting As(III) to As(V). Results indicated that the investigated adsorbents possessed an excellent arsenic removal capability.
Investigation of Mild Steel Corrosion in the Cement Production Associated with the Usage of Secondary Fuels
Authors: Thieme Michael , Bergmann Ute , Kiesewetter Anja , Wehry Tobias , Potzger Kay , Zarzycki Arkadiusz , Marszalek Marta , Worch Hartmut
Published: September 2020

The present work deals with the corrosion of mild steel (1.0037) used as the outer construction material of the preheater of a modern industrial cement production facility. The facility uses secondary fuels, which introduce considerable amounts of corrosive species. The situation at the examination sites in the preheater zone is tracked over a period of two years including operation and shut-down periods. The investigation is focused on (i) the acquisition of the underlying physicochemical conditions, such as moisture, temperature, and contamination data at the examination site of the preheater, (ii) the multianalytical identification of the formed corrosion products using scanning electron microscopy combined with energy-dispersive X-ray analysis, infrared spectrometry, Raman spectrometry, X-ray diffractometry, and Möβbauer spectrometry, and (iii) voltammetric and EIS laboratory investigations using model solutions. It was evidenced that corrosion takes place at a temperature level of about 100°C in the presence of moisture and oxygen as well as chloride ion as a consequence of the usage of secondary fuels. Typical hot-gas corrosion could be excluded under the current conditions. Appearance, structure, and nature of the corrosion products were found to be not mainly dependent on the varied length of exposure, but on the conditions of the hosting preheater intake. In addition to different FeOOH phases and hematite, magnetite was found, dependent on the oxygen concentration in the process gas. The decisive role of oxygen as key factor for the corrosion rate was electrochemically confirmed.
Isolation and Characterization of Human Synovial Fluid-Derived Mesenchymal Stromal Cells from Popliteal Cyst
Authors: Li Fang , Chen Jianglin , Gong Mengjia , Bi Yang , Hu Chengchen , Zhang Yuanyuan , Li Ming
Published: September 2020

Mesenchymal stem cells (MSCs) are multipotent progenitor cells in adult tissues. The aim of this study is to isolate and identify synovial fluid-derived mesenchymal stromal cells (SF-MSCs) from the popliteal cyst fluid of pediatric patients. SF-MSCs were collected from the popliteal cyst fluid of pediatric patients during cystectomy surgery. After cyst fluid extraction and adherent culturing, in vitro morphology, growth curve, and cell cycle were observed. The expression of stem cell surface markers was analyzed by flow cytometry, and expression of cell marker protein was detected by immunofluorescence. SF-MSCs were cultured in osteogenic, adipogenic, and chondrogenic differentiation medium. The differentiation potential of SF-MSCs was analyzed by alkaline phosphatase (Alizarin Red), Oil Red O, and Alcian blue. Antibody detection of human angiogenesis-related proteins was performed compared with bone marrow mesenchymal stem cells (BM-MSCs). The results show that SF-MSCs from the popliteal cyst fluid of pediatric patients showed a shuttle appearance and logarithmic growth. Flow cytometry analysis revealed that SF-MSCs were negative for hematopoietic lineage markers (CD34, CD45) and positive for MSC markers (CD44, CD73, CD90, and CD105). Interstitial cell marker (vimentin) and myofibroblast-like cell marker alpha-smooth muscle actin (α-SMA) were positive. These cells could differentiate into osteogenic, adipogenic, and chondrogenic lineages, respectively. Several types of human angiogenesis-related proteins were detected in the cell secretory fluid. These results show that we successfully obtained SF-MSCs from the popliteal cyst fluid of pediatric patients, which have the potential to be a valuable source of MSCs.
Quercetin Improving Lipid Metabolism by Regulating Lipid Metabolism Pathway of Ileum Mucosa in Broilers
Authors: Wang Mi , Mao Yanjun , Wang Bo , Wang Shanshan , Lu Han , Ying Linlin , Li Yao
Published: September 2020

This study is aimed at evaluating the regulatory mechanism of quercetin on lipid metabolism in the ileum of broilers to better understand these pathways decreasing abdominal fat. 480 chickens were randomly divided into 4 groups (control, 0.02% quercetin, 0.04% quercetin, and 0.06% quercetin). Breast muscle, thigh muscle, and abdominal fat pad were removed and weighed at 42 d of age. Serum was obtained by centrifuging blood samples from the jugular vein (10 ml) to determine high-density lipoprotein (HDL), total cholesterol (TC), low-density lipoprotein (LDL), triglyceride (TG), leptin, and adiponectin using ELISA. About 5 g of the ileum was harvested and immediately frozen in liquid nitrogen for RNA-seq. Then, the confirmation of RNA-seq results by the Real-Time Quantitative PCR (RT-qPCR) method was evaluated using Pearson’s correlation. Compared with control, abdominal fat percentage was significantly decreased with increasing quercetin supplementation, and the best result was obtained at 0.06% dietary quercetin supplementation (P<0.01). Breast muscle percentage was significantly decreased at 0.02% quercetin (P<0.01), and thigh muscle percentage tended to increase (P=0.078). Meanwhile, 0.04% and 0.06% quercetin significantly decreased TG (P<0.01), TC (P<0.01), and LDL content (P<0.05) in serum. Serum leptin and adiponectin contents were significantly increased by 0.04% and 0.06% dietary quercetin supplementation, compared with the control (P<0.01). Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to identify differently expressed genes and lipid metabolism pathways. Quercetin decreased abdominal fat percentage through regulating fat digestion and absorption, glycerophospholipid metabolism, AMPK signaling pathway, fatty acid degradation, and cholesterol metabolism.
A Stacked Deep Learning Approach for IoT Cyberattack Detection
Authors: Alotaibi Bandar , Alotaibi Munif
Published: September 2020

Internet of things (IoT) devices and applications are dramatically increasing worldwide, resulting in more cybersecurity challenges. Among these challenges are malicious activities that target IoT devices and cause serious damage, such as data leakage, phishing and spamming campaigns, distributed denial-of-service (DDoS) attacks, and security breaches. In this paper, a stacked deep learning method is proposed to detect malicious traffic data, particularly malicious attacks targeting IoT devices. The proposed stacked deep learning method is bundled with five pretrained residual networks (ResNets) to deeply learn the characteristics of the suspicious activities and distinguish them from normal traffic. Each pretrained ResNet model consists of 10 residual blocks. We used two large datasets to evaluate the performance of our detection method. We investigated two heterogeneous IoT environments to make our approach deployable in any IoT setting. Our proposed method has the ability to distinguish between benign and malicious traffic data and detect most IoT attacks. The experimental results show that our proposed stacked deep learning method can provide a higher detection rate in real time compared with existing classification techniques.
Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis
Authors: Fu Chao , Lv Qing , Lin Hsiung-Cheng
Published: September 2020

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).
Fuzzy Model-Based Asynchronous Control for Markov Switching Systems with Stochastic Fading Channels
Authors: Wu Fayuan , Tang Jinhui , Liu Zhuang , Xiao Qi , Zheng Xiaodong , Xue Shuangsi
Published: September 2020

This work investigates the asynchronous control for fuzzy Markov switching systems (MSSs) with randomly occurring fading channel. By resorting to a T-S fuzzy model, the nonlinear MSSs can be handled. Meanwhile, in the unreliable network, the Rice fading model is proposed to capture the randomly occurring channel fading, which covers packet dropouts and network-induced delays as special cases. In light of the hidden Markov model, the asynchronous phenomenon of controller is taken into consideration, and asynchronous fuzzy controller is obtained. In the end, a numerical example and a single-link robotic arm model are applied to demonstrate the validity of the derived results.
Touch: A Textual Programming Language for Developing APPs of Insect Intelligent Building
Authors: Chen Wenjie , Yang Qiliang , Jiang Ziyan , Xing Jianchun , Zhao Qianchuan , Zhou Qizhen , Han Deshuai
Published: September 2020

Insect intelligent building (I2B) is a novel decentralized, flat-structured intelligent building platform with excellent flexibility and scalability. I2B allows users to develop applications that include control strategies for efficiently managing and controlling buildings. However, developing I2B APPs (applications) is considered a challenging and complex task due to the complex structural features and parallel computing models of the I2B platform. Existing studies have been shown to encounter difficulty in supporting a high degree of abstraction and in allowing users to define control scenarios in a concise and comprehensible way. This paper aims to facilitate the development of such applications and to reduce the programming difficulty. We propose Touch, a textual domain-specific language (DSL) that provides a high-level abstraction of I2B APPs. Specifically, we first establish the conceptual programming architecture of the I2B APP, making the application more intuitive by abstracting different levels of physical entities in I2B. Then, we present special language elements to effectively support the parallel computing model of the I2B platform and provide a formal definition of the concrete Touch syntax. We also implement supporting tools for Touch, including a development environment as well as target code generation. Finally, we present experimental results to demonstrate the effectiveness and efficiency of Touch.
LCZ696 Ameliorates Oxidative Stress and Pressure Overload-Induced Pathological Cardiac Remodeling by Regulating the Sirt3/MnSOD Pathway
Authors: Peng Shi , Lu Xiao-feng , Qi Yi-ding , Li Jing , Xu Juan , Yuan Tian-you , Wu Xiao-yu , Ding Yu , Li Wen-hua , Zhou Gen-qing , Wei Yong , Li Jun , Chen Song-wen , Liu Shao-wen
Published: September 2020

Aims. We aimed to investigate whether LCZ696 protects against pathological cardiac hypertrophy by regulating the Sirt3/MnSOD pathway. Methods. In vivo, we established a transverse aortic constriction animal model to establish pressure overload-induced heart failure. Subsequently, the mice were given LCZ696 by oral gavage for 4 weeks. After that, the mice underwent transthoracic echocardiography before they were sacrificed. In vitro, we introduced phenylephrine to prime neonatal rat cardiomyocytes and small-interfering RNA to knock down Sirt3 expression. Results. Pathological hypertrophic stimuli caused cardiac hypertrophy and fibrosis and reduced the expression levels of Sirt3 and MnSOD. LCZ696 alleviated the accumulation of oxidative reactive oxygen species (ROS) and cardiomyocyte apoptosis. Furthermore, Sirt3 deficiency abolished the protective effect of LCZ696 on cardiomyocyte hypertrophy, indicating that LCZ696 induced the upregulation of MnSOD and phosphorylation of AMPK through a Sirt3-dependent pathway. Conclusions. LCZ696 may mitigate myocardium oxidative stress and apoptosis in pressure overload-induced heart failure by regulating the Sirt3/MnSOD pathway.
Weight Loss, Dietary Preferences, and Reduction in the Sense of Smell with the Use of a Novel Nasal Device
Authors: Dicker Dror , Beck Adva , Markel Arie , Marcovicu Dana , Mazzawi Salim , Sarid Miri , Greenberg Elhanan , Atkinson Richard L.
Published: September 2020

Introduction: Exposure to food odors are known to increase food intake. Olfaction declines from age 50 years. Objective: We examined changes in the sense of smell, body weight, food preferences, and parameters of metabolic status, following the use of a specially designed nasal device. Methods: This is a randomized, placebo-controlled study. Participants wore a nasal device (soft silicone insert) for 5–12 h daily (device group) or inserted 2 drops of normal saline into each nostril daily (control group). Follow-up visits occurred every 2 weeks. All participants were given a 500 kcal/day reduced diet and instructed not to change their regular physical activity. Weight, food preferences, olfactory sensitivity, and blood tests were performed at baseline and after 12 weeks. Results: Of 156 participants, 65 (42%) completed the study. Sense of smell decreased in the device group (from 6.4 ± 0.9 to 4.4 ± 1.5, on a scale of 0–7, p < 0.001), and did not change in the control group. Weight loss decreased by 6.6 ± 3.7% (p = 0.001) and by 5.7 ± 3.5% (p = 0.001) in the respective groups (between-group difference, p > 0.05). Among participants aged ≤50 years, weight loss was greater in the device than in the control group (7.7 ± 4.2% vs. 4.1 ± 2.9%, p = 0.02). Insulin level and the homeostatic model assessment of insulin resistance (HOMA-IR) were significantly reduced in the device group (p = 0.02 and p = 0.01, respectively), but not in the control group. Food preferences for sugar (p < 0.02), sweet beverages (p < 0.001), and artificial sweeteners (p < 0.02) were significantly reduced in the device group compared to the control group. Conclusions: The use of a novel self-administrated nasal device led to reduced olfactory sensitivity, improved insulin sensitivity, weight loss, and lesser preference for sweets in adults aged ≤50 years.
Anamnesis as a Prognostic Factor in Cochlear Implantation in Adults
Authors: Savvas Eleftherios , Heslinga Katharina , Spiekermann Christoph Otto , Stenner Markus , Rudack Claudia
Published: September 2020

Introduction: This study aims to analyze possible preoperative factors taken from the medical history that may assist the otolaryngologist in counseling an adult patient before cochlear implantation (CI). Objective: Analysis of preoperative factors taken during the initial patient presentation for a possible prognostic role in the auditory rehabilitation outcome. Methods: A cohort of 232 (272 CI implantations) postlingually deafened adults was evaluated in this study. Hearing results at 1, 2, and up to 3 years postoperatively were compared with various preoperative factors: living status, cause of deafness, gender, side of implantation, residual hearing, and duration of deafness. Postoperative hearing performance was measured based on the German Freiburg monosyllabic word test and the Oldenburg sentence test. Results: Duration of deafness showed a negative correlation to word recognition and a positive correlation to increased speech reception threshold in sentence testing. A significant decline in hearing outcome was shown starting around the second decade of deafness. Residual hearing as defined in our cohort and side of implantation showed limited benefit in speech understanding. Living status, gender, and cause of deafness did not show any prognostic value. Conclusion: In this retrospective review it could be shown that simple case history information can only provide limited prognostic insight before CI. The duration of deafness is the most reliable anamnestic factor present on initial patient evaluation.
A Case of Cervical Spondylotic Amyotrophy Mimicking Amyotrophic Lateral Sclerosis
Authors: Idiculla Pretty Sara , Govindarajan Raghav
Published: September 2020

Cervical spondylotic amyotrophy (CSA) is a rare clinical condition characterized by weakness and atrophy of the upper limb with minimal to no associated sensory deficits. The detection of the disease is based on clinical features at presentation, neurological examination, electrophysiological studies, and imaging. The proposed pathophysiological mechanisms include selective damage to the ventral root or anterior horn cells of the spinal cord. Depending on the muscle groups that are involved, CSA is broadly classified into a proximal type and a distal type. The clinical profiles of patients with CSA and ALS have a very close resemblance to each other, especially at the early stages of the disease. Cervical spine magnetic resonance imaging (MRI) may show T2 hyperintensity in both proximal and distal types. Electromyogram demonstrates denervation potentials and reduced motor unit potentials in the affected muscles. The conservative management is often the first-line modality, and those who fail to respond to conservative treatment have severe muscular atrophy and weakness, and distal-type CSA are considered potential candidates for surgery. We present the case of a 57-year-old female who presented with a 1-year history of left-hand weakness and wasting with no sensory deficits. She denied any involvement of her other hand or bilateral lower limbs, and she was referred to our clinic with the potential diagnosis of amyotrophic lateral sclerosis (ALS). An elaborate history, physical examination, electrophysiological studies, and imaging assisted us in reaching the diagnosis of CSA, 1 year after the onset of symptoms.
Motion artifacts reduction in cardiac pulse signal acquired from video imaging
Authors: Al-Yoonus Murthad , Alhabib Mustafa H. , Nayef Al-Dabagh Mustafa Zuhaer , Abdullah M. F. L.
Published: September 2020

This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments.
Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques
Authors: Fikri Meriem , Sabri Omar , Cheddadi Bouchra
Published: September 2020

In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using real electrical networks. To do that we used few significant discrepancies. These methods are characterized by giving results in real time. To make this comparison successful, we implemented these two methods, to predict the voltage magnitudes and the voltage phase angles, on two Moroccan electrical networks. The results of the comparison show that the method of Adaptive Neuro-Fuzzy Inference System have more advantages than the method of Artificial Neural Network.
A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms
Authors: Khoudry Elmahdi , Belfqih Abdelaziz , Ouaderhman Tayeb , Boukherouaa Jamal , Elmariami Faissal
Published: September 2020

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.
Implementation of a grid-tied emergency back-up power supply for medium and low power application
Authors: Chowdhury Dhiman , Miah Mohammad Sharif , Hossain Md. Feroz , Sarker Uzzal
Published: September 2020

Emergency back-up power supply units are necessary in case of grid power shortage, considerably poor regulation and costly establishment of a power system facility. In this regard, power electronic converters based systems emerge as consistent, = properly controlled and inexpensive electrical energy providers. This paper presents an implemented design of a grid-tied emergency back-up power supply for medium and low power applications. There are a rectifier-link boost derived DC-DC battery charging circuit and a 4-switch push-pull power inverter (DC-AC) circuit, which are controlled by pulse width modulation (PWM) signals. A changeover relay based transfer switch controls the power flow towards the utility loads. During off-grid situations, loads are fed power by the proposed system and during on-grid situations, battery is charged by an AC-link rectifier-fed boost converter. Charging phenomenon of the battery is controlled by a relay switched protection circuit. Laboratory experiments are carried out extensively for different loads. Power quality assessments along with back-up durations are recorded and analyzed. In addition, a cost allocation affirms the economic feasibility of the proposed framework in case of reasonable consumer applications. The test-bed results corroborate the reliability of the research work.
Object gripping algorithm for robotic assistance by means of deep learning
Authors: Jimenez-Moreno Robinson , Fonseca Astrid Rubiano , Ramirez Jose Luis
Published: September 2020

This paper exposes the use of recent deep learning techniques in the state of the art, little addressed in robotic applications, where a new algorithm based on Faster R-CNN and CNN regression is exposed. The machine vision systems implemented, tend to require multiple stages to locate an object and allow a robot to take it, increasing the noise in the system and the processing times. The convolutional networks based on regions allow one to solve this problem, it is used for it two convolutional architectures, one for classification and location of three types of objects and one to determine the grip angle for a robotic gripper. Under the establish virtual environment, the grip algorithm works up to 5 frames per second with a 100% object classification, and with the implementation of the Faster R-CNN, it allows obtain 100% accuracy in the classifications of the test database, and over a 97% of average precision locating the generated boxes in each element, gripping successfully the objects.
PID vs LQR controller for tilt rotor airplane
Authors: Houari Aoued , Bachir Imine , Mohame Della Krachai , Mohamed Mohamed Kara
Published: September 2020

The main thematic of this paper is controlling the main manoeuvers of a tilt rotor UAV airplane in several modes such as vertical takeoff and landing, longitudinal translation and the most important phase which deal with the transition from the helicopter mode to the airplane mode and visversa based on a new actuators combination technique for specially the yaw motion with not referring to rotor speed control strategy which is used in controlling the attitude of a huge number of vehicles nowadays. This new actuator combination is inspired from that the transient response of a trirotor using tilting motion dynamics provides a faster response than using rotor speed dynamics. In the literature, a lot of control technics are used for stabilizing and guarantee the necessary manoeuvers for executing such task, a multiple Attitude and Altitude PID controllers were chosen for a simple linear model of our tilt rotor airplane in order to fulfill the desired trajectory, for reasons of complexity of our model the multiple PID controller doesnt take into consideration all the coupling that exists between the degrees of freedom in our model, so an LQR controller is adopted for more feasible solution of complex manoeuvering, the both controllers need linearization of the model for an easy implementation.
Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system
Authors: Al-Gabalawy Mostafa A. , Hosny N. S. , Hussien Shimaa A.
Published: September 2020

Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion,

a suggestion of using a compination of each controller was introduced.
Automated smart hydroponics system using internet of things
Authors: Lakshmanan Ravi , Djama Mohamed , Perumal Sathish , Abdulla Raed
Published: September 2020

This paper presents a design and implementation of an automated smart hydroponics system using internet of things. The challenges to be solved with this system are the increasing food demand in the world, the need of market of new sustainable method of farming using the Internet of Things. The design was implemented using NodeMcu, Node Red, MQTT and sensors that were chosen during component selection based on required parameters and sending it to the cloud to monitor and be processed. Investigation on previous works done and a review of Internet of Things and Hydroponic systems were done. First the prototype was constructed, programmed and tested, as well as sensors data between two different environments were taken and monitored on cloud-based web page with mobile application. Moreover, a bot has been introduced to control the supply chain and for notification purposes. The system improved its performance and allows it to successfully achieve the aim of the entire system implemented. There are some limitations which can be improved as future work such as including data science with the usage of the artificial intelligence to further improve the crops and get better outcome. Lastly to design end user platform to ease user interaction by using attractive design with no technical configuration involved.
Development in building fire detection and evacuation system-a comprehensive review
Authors: Birajdar Gajanand S. , Singh Rajesh , Gehlot Anita , Thakur Amit Kumar
Published: September 2020

Fire is both beneficial to man and his environment as well as destructive and deadly among all the natural disasters. A fire Accident occurs very rarely, but once it crops up its consequences will be devastating. The early detection of fire will help to avoid further consequences and saves the life of people. During the fire accidents, it is also important to guide people within the building to exit safely. Because of this, the paper gives a review of literature related to recent advancements in building fire detection and emergency evacuation system. It is intended to provide details about fire simulation tools with features, suitable hardware, communication methods, and effective user interface.
Input switched closed-loop single phase SEPIC controlled rectifier with improved performances
Authors: Arifin Md. Shamsul , Alam Mohammad Jahangir
Published: September 2020

DC power supply has become the driving source for some essential modern applications. Thereby, DC power conditioning has become a significant issue for engineers. Typically used rectifiers associated with a bridge structure is nonlinear in nature. Thereby, the current at input side of the rectifier contains harmonics, which also flow through the power line. The presence of harmonics causes several interruptions and reduce power quality. In this regard, a new close loop SEPIC controlled rectifier is proposed in this paper. The conventional scheme is arranged with a rectifier connected to a DC-DC converter, which is an open loop system. Consequently, such system cannot regulate voltage at load varying condition. The proposed SEPIC controlled rectifier can regulate voltage under load varying condition for a wide range. Additionally, the performance in terms of total harmonic distortion (THD) of input current and power factor at AC side are also within satisfactory range for the closed loop configuration. The controlled rectifier has four operating phases associated with switching states and input voltage polarity. The close loop configuration also incorporates a current and a voltage loop at the feedback path. The comparative studies have been performed among the proposed closed loop construction, the open-loop structure as well as the conventional construction. The effectiveness of the proposed controlled rectifier is verified in terms of THD and input power factor considering the results obtained from simulation.
Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm
Authors: Majdoubi Rania , Masmoudi Lhoussaine , Bakhti Mohammed , Elharif Abderrahmane , Jabri Bouazza
Published: September 2020

Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software.
Detection and tracking of moving object using modified background subtraction and Kalman filter
Authors: S. H. Jeevith , S. Lakshmikanth
Published: September 2020

Moving object detection and tracking (MODT) is the major challenging issue in computer vision, which plays a vital role in many applications like robotics, surveillance, navigation systems, militaries, environmental monitoring etc. There are several existing techniques, which has been used to detect and track the moving object in Surveillance system. Therefore it is necessary to develop new algorithm or modified algorithm which is robust to work in both day and night time. In this paper, modified BGS technique is proposed. The video is first converted to number of frames, then these frame are applied to modified background subtraction technique with adaptive threshold which gives detected object. Kalman filter technique is used for tracking the detected object. The experimental results shows this proposed method can efficiently and correctly detect and track the moving objects with less processing time which is compared with existing techniques.
Fully symbolic-based technique for solving complex state-space control systems
Authors: Abd-Alrahem Amera M. , Elhadidy Hala M. , Elserafi Kamel A. , Dorrah Hassen T.
Published: September 2020

Despite the superiority of symbolic approaches over the purely numerical approaches in many aspects, it does not receive the proper attention due to its significant complexity, high resources requirement and long drawn time which even grows significantly with the increase of model dimensions. However, its merits deserve every attempt to overcome the difficulties being faced. In this paper, a fully generic symbolic-based technique is proposed to deal with complex state space control problems. In this technique, depending on the model dimension if exceeds a predefined limit, the state space is solved using the partitioned matrices theory and block wise inversion formula. Experimental results demonstrate that the proposed technique overcomes all the previously mentioned barriers and gives the same results when compared to numerical methods (Simulink). Moreover, it can be used to gain useful information about the system itself, provides an indication of which parameters are more important and reveals the sensitivity of system model to single parameter variations.
Recognition of corona virus disease (COVID-19) using deep learning network
Authors: Abdulmunem Ashwan A. , Abutiheen Zinah Abdulridha , Aleqabie Hiba J.
Published: September 2020

Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
A novel population-based local search for nurse rostering problem
Authors: Abuhamdah Anmar , Boulila Wadii , Jaradat Ghaith M. , Quteishat Anas M. , Alsmadi Mutasem K. , Almarashdeh Ibrahim A.
Published: September 2020

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.
APMorph: finite-state transducer for Amazigh pronominal morphology
Authors: Ammari Rachid , Zenkoua Ahbib
Published: September 2020

Our work aims to present an amazigh pronominal morphological analyzer (APMorph) based on xerox’s finite-state transducer (XFST). Our system revolves around a large lexicon named “APlex” including the affixed pronoun to the noun and to the verb and the characteristics relating to each lemma. A set of rules are added to define the inflectional behavior and morphosyntactic links of each entry as well as the relationship between the different lexical units. The implementation and the evaluation of our approach will be detailed within this article. The use of XFST remains a relevant choice in the sense that this platform allows both analysis and generation. The robustness of our system makes it able to be integrated in other applications of natural language processing (NLP) especially spellchecking, machine translation, and machine learning. This paper presents a continuation of our previous works on the automatic processing of Amazigh nouns and verbs.
Performance analysis of grid-tied photovoltaic system under varying weather condition and load
Authors: Adebiyi Abayomi A. , Lazarus Ian J. , Saha Akshay K. , Ojo Evans E.
Published: September 2020

Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250W/m2 and 1000W/m2. The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load.
Dynamic Queue Synchronization for Congestion Control Over Wireless Sensor Networks
Authors: Ghai Shilpy , Kumar Vijay , Vaid Rohit
Published: September 2020

Congestion control over resource constrained sensor network is a critical challenge and it is also quite hard to fulfill the QoS constraints for end users. In this paper, a dynamic queue synchronization (DQS) scheme is introduced that can alter the data rate dynamically as per the current
buffer length. Its performance is compared with existing schemes i.e., Dynamic Round Robin (DRR) and Random Early Discard (RED) using different routing protocols i.e., LEACH/PEGASIS/TEEN under various constraints (Throughput/End-to-End Delay/Routing Load/Energy).
Simulation of Cloud Platform Supporting Wireless Sensor Network
Authors: Sharma Shweta , Kaur Amandeep
Published: September 2020

WSN has been an area of interest for a lot of researchers as well as the industry specialist due to its high usage in healthcare, military applications and in robotics. The focus area of WSN has been energy consumption. Integration of Cloud Computing in WSN has been observed by couple
of research scholars as Cloud has been observed to be struggling against the power management issue. This paper presents a novel solution of power management in Cloud computing which uses the concept of clustering borrowed from WSN. The jobs are grouped or Clustered as per the algorithmic
architecture of WSN and the minimization of migration is attempted using Genetic Algorithm (GA). Artificial Neural Network (ANN) is used as a conjunction to GA for energy efficiency. MATLAB is used as a simulation tool and consumed energy has been evaluated as the major evaluation parameter.
Impact of the Timing of Foot Tissue Resection on Outcomes in Patients Undergoing Revascularization for Chronic Limb-Threatening Ischemia
Authors: Shannon Alexander H. , de Grijs Derek P. , Goudreau Bernadette J. , Mehaffey J. Hunter , Cullen J. Michael , Williams Carlin , Robinson William P.
Published: September 2020

The objective of this study is to describe utilization of revascularization and tissue resection in patients with chronic limb-threatening ischemia (CLTI) and determine whether the timing of resection impacts outcomes. Revascularizations for CLTI were queried (ACS-NSQIP 2011-2015). Outcomes included 30-day major adverse limb events (MALE), major adverse cardiac events (MACE), length of stay (LOS), operative time, 30-day readmissions, and wound infections. Groups included revascularization alone, revascularization/tissue resection during the same procedure (concurrent), or revascularization/delayed tissue resection (delayed). Resections were debridement or transmetatarsal amputations. Multivariate logistic regression determined risk-adjusted effects of tissue resection on outcomes. There was no difference in overall 30-day MACE or MALE between groups ( P = .70 and P = .35, respectively). Length of stay (6.1 days revascularization alone vs 7.8 days concurrent vs 8.7 days delayed, P < .0001) was longer in patients who underwent any tissue resection. Highest 30-day readmission and operative time was the concurrent group ( P = .02 and P < .0001, respectively). Wound infection was highest in the delayed group (1.4% revascularization alone vs 1.3% concurrent vs 6.2% delayed, P < .0001). After risk adjustment, timing of resection did not impact LOS for concurrent and delayed groups compared to revascularization alone (both P < .0001). Debridement and minor amputations can be done concurrently in patients undergoing revascularization for CLTI.
Permissive Winners? The Quality of Democracy and the Winner–Loser Gap in the Perception of Freedoms
Authors: Monsiváis-Carrillo Alejandro
Published: September 2020

Voters usually differ in their assessment of the regime’s legitimacy, depending on their status as winners or losers. However, how wide or narrow the winner–loser gap is also depends on the quality of democratic institutions. Using survey data from 18 Latin American countries, this research provides evidence that winners and losers respond differently to the quality of democracy. While most research is concerned with the losers’ consent, this study shows that the winners express more favorable assessments of the supply of freedoms, even in regimes where democracy is weak or undermined by the deliberate efforts of the political authorities. Instead, in their perception of freedoms, losers are more willing to acknowledge if the quality of democracy improves or declines. These results suggest that the potential consequences of the winner–loser gap for regime stability are highly dependent on the democratic attributes of the political context.
Numerical modeling and experimental testing analysis of Assembled Rubber Metal Isolator
Authors: Wu Jida , Liu Chusheng , Jiang Haishen , Wang Zhenqian
Published: September 2020

In order to analyze the stiffness characteristics of Assembled Rubber Metal Isolator (ARMI) more accurately, the present work elaborates on the detailed numerical modeling and analysis process of the ARMI, considering prestressing condition. By comparing the applicability of different constitutive models, the reasonable parameters of the proposed Money-Revlon constitutive model were determined by rubber compression test and least square method. Considering the structural characteristics and complex constraints of the isolator, a step-by-step analysis method is described, based on the rigid-flexible coupling theory and the contact cutting algorithm. The full Newton-Raphson algorithm is used to simulate the mechanical behavior of elastic components in ARMI, during the whole compression-torsion deformation process, while the results are verified by theoretical calculation and practical experiments, respectively. For the assembly process, the maximum relative error between numerical results and empirical formulas is 3.97%. The derived torsional curve, under the simulated pre-stress conditions, is in good agreement with the experimental results, and the maximum error is less than 8.43%. The achieved accuracy is significantly improved, compared to the existing simulation model that does not consider the pre-compression process. The proposed approach provides an effective method for the analysis of same type vibration isolator.