bluuebunny/crossref_metadata_2025 huggingface dataset:
Partial Information Stochastic Differential Games for Backward Stochastic Systems Driven by Lévy Processes
Authors: Zhang Fu , Meng QingXin , Tang MaoNing
Published: September 2020

In this paper, we consider a partial information two-person zero-sum stochastic differential game problem, where the system is governed by a backward stochastic differential equation driven by Teugels martingales and an independent Brownian motion. A sufficient condition and a necessary one for the existence of the saddle point for the game are proved. As an application, a linear quadratic stochastic differential game problem is discussed.
Strength and Solidification Mechanism of Silt Solidified by Polyurethane
Authors: Li Fengyuan , Wang Chaojie , Xia Yangyang , Hao Yanjie , Zhao Peng , Shi Mingsheng
Published: September 2020

To determine the mechanism and strength characteristics of solidification of silt by a permeable polyurethane grouting material, the effects of polymer content, soil moisture, and immersion time on the unconfined compressive strength (UCS) of the silt have been studied. The results showed that the permeable polymer grouting material can significantly improve the performance of silt: (1) A higher amount of polymer produced a greater strength in the solidified soil. (2) The strength of the solidified soil increased as the immersion time was increased. (3) Moisture in the soil was not conducive to improving the strength of the solidified soil. The X‐ray diffraction (XRD) and energy‐dispersive spectroscopy (EDS) have proven that polyurethane does not react with the silt, but they could improve the strength of the silt through physical action. Mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) were performed to find that polymers can reduce soil porosity, and the addition of polyurethane improved the strength of the silt mainly through adhesion, wrapping, filling, and bridging.
Conservative Treatment of Postinfarction Left Ventricular Free Wall Rupture
Authors: Vo Anh Tuan , Nguyen Thu Trang , Tran Thanh Thuy , Nguyen Dinh Hoang
Published: September 2020

Left ventricular free wall rupture is a fatal complication of acute myocardial infarction. Emergency surgical repair is usually indicated to treat this condition. However, in very high surgical risk cohort, conservative treatment can also be considered. We report a case of left ventricle pseudoaneurysm that was successfully treated conservatively in a 4-year period.
Mechanical Behavior and Calculation Method for RC Fifteen‐Pile Cap of Mixed Passenger and Freight Railway Bridge
Authors: Huang Hongmeng , Cui Lu , Lu Wei
Published: September 2020

The thickness, reinforcement, and concrete strength grade of railway caps in China are generally determined according to the force, yet the method for calculating the force is unclear. To date, there is no desirable calculation method for analyzing the caps. Based on the fifteen‐pile thick cap of mixed passenger and freight railway, the influencing factors on cap bearing capacity were analyzed using finite element method (FEM). The variations of load‐bearing capacity and mechanical behavior of thick cap were characterized by introducing rigid angle α. Results indicated that ultimate load‐bearing value of the cap increased linearly with the increase of concrete strength grade, and an increasing load‐bearing capacity of the reinforcement distributed in the pile diameter range was larger than that of the uniform reinforcement; when the reinforcement ratio was 0.15%, it increased by 9.3%. The cap showed punching failure when α < 45°. The reaction force at each pile top under vertical load was not equal; thereby, the cap was not absolutely rigid. The principal compressive stress trajectories in the concrete were distributed in the range of connecting the pile and the outer edge of the pier, and the effective tensile stresses in the reinforcement were mainly distributed in the diameter range of pile and pile connection, which is in accord with the stress mode of the ordinary spatial truss model. Based on this, a spatial truss model applicable to the design of railway caps is proposed, and a method for calculating reaction force at pile top and formulas for calculating the bearing capacity of strut and tie were presented. The feasibility of the proposed method was also verified by comparison with FEM results.
Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia
Authors: Yue Zhenjia , Ma Liangping , Zhang Runfeng
Published: September 2020

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor’s clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet’s network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet’s network could also extend to other areas for application.
Assessment of Stiffness and Strength Parameters for the Soft Soil Model of Clays of Cameroon
Authors: Ambassa Zoa , Amba Jean Chills
Published: September 2020

This paper focuses on the advanced modelling of soft Cameroon clays for the global prediction of the behaviour of geotechnical structures. A comprehensive set of experimental data on Cameroon subsoils from the oedometer and triaxial tests are analyzed in this paper in order to determine the stiffness and strength parameters for the Soft Soil model. It is based on 71 soil samples taken from both sides of the construction sites of several major structures across the territory. At the first approach, the soil samples taken were analyzed in a geotechnical laboratory to obtain physical and mechanical identification parameters specific to each soil type. The results obtained reveal that the analyzed soils are generally compressible clays. In the second phase, the law of behaviour of the Soft Soil model was used to characterize these Cameroon soils. Its parameters were obtained after calibration of the results of the laboratory tests obtained in first approach. The results obtained in this article can be compared to the different models obtained on clays soils around the world. The parameters are of the same order of magnitude as other clays modelled around the world.
Understanding the Mechanism and Selectivities of the Reaction of Meta-Chloroperbenzoic Acid and Dibromocarbene with β-Himachalene: A DFT Study
Authors: El Hamidi Sana , Khnifira Malika , Lemdek El Mokhtar , Hammal Redouan , Barka Noureddine , Sadiq M’hamed , Benharref Ahmed , Chekroun Ahmed , Zgou Hssaine , Abdennouri Mohamed
Published: September 2020

This study was performed to understand the site selectivity in the reaction between β-himachalene and meta-chloroperbenzoic acid (m-CPBA) in the first step followed by the addition of dibromocarbene (CBr2) to the main monoepoxidation product Pα formed in the first reaction. Calculations were performed using the Becke three-parameter hybrid exchange functional and the Lee–Yang–Parr correlation functional (B3LYP) with the 6-311 + G (d, p) basis set. Transition states were located by QST2, and their highlighting was validated by the existence of only one imaginary frequency in the Hessian matrix. The action of m-CPBA on β-himachalene was analyzed on the two double bonds of β-himachalene whose theoretical calculations show that the attack affects the most substituted double bond on α side containing hydrogen of ring junction. The obtained Pα product thereafter treated with dibromocarbene leads via an exothermic reaction to the six-membered ring double bond position of α-monoepoxide. The major products Pαα are kinetically and thermodynamically favored with a high stereoselectivity in perfect correlation with the experimental observations.
SOR-Like New Iterative Method for Solving the Epidemic Model and the Prey and Predator Problem
Authors: Radid Atika , Rhofir Karim
Published: September 2020

Our aim in this paper is to propose an SOR-like new iterative method by introducing a relaxation parameterωto improve the new iterative method proposed by Daftardar-Gejji and Jafari (NIM) [J. Math. Anal. Appl. 316 (2006) 753–763] in order to solve two problems. The first one is the problem of the spread of a nonfatal disease in a population which is assumed to have constant size over the period of the epidemic, and the other one is the problem of prey and predator. The proposed method is not limited to these two problems but can be applicable to a wide range of systems of nonlinear functional problem. The results, for different values ofω, show that we found some known methods and our method compared to methods using the calculation of special polynomials and derivatives like the Adomian decomposition method (ADM), the calculation of the Lagrange multiplier as in the variational iterative method (VIM), or the construction of a homotopy as in the homotopy perturbation method (HPM) has several advantages, such as very effective and very simple to implement. Unfortunately, these methods do not guarantee a valid approximation in large time interval. To overcome this, we applied our method for approximating the solution of the problems in a sequence of time intervals as a multistage approach. Some numerical results are presented with plots according to the parameterω.
Curcumin Attenuates Chronic Unpredictable Mild Stress-Induced Depressive-Like Behaviors via Restoring Changes in Oxidative Stress and the Activation of Nrf2 Signaling Pathway in Rats
Authors: Liao Dehua , Lv Chuanfeng , Cao Lizhi , Yao Dunwu , Wu Yi , Long Minghui , Liu Ni , Jiang Pei
Published: September 2020

Accumulating evidence has demonstrated that oxidative stress is associated with depression. Our present study aimed at investigating the antidepressant effect and the possible mechanisms of curcumin (CUR) in chronic unpredictable mild stress- (CUMS-) induced depression model in rats. After exposure to CUMS for four weeks, the rats showed depressive-like behavior, and the depressive-like behaviors in CUMS-treated rats were successfully corrected after administration of CUR. In addition, CUR could effectively decrease protein expression of oxidative stress markers (Nox2, 4-HNE, and MDA) and increase the activity of CAT. CUR treatment also reversed CUMS-induced inhibition of Nrf2-ARE signaling pathway, along with increasing the mRNA expression of NQO-1 and HO-1. Furthermore, the supplementation of CUR also increased the ratio of pCREB/CREB and synaptic-related protein (BDNF, PSD-95, and synaptophysin). In addition, CUR could effectively reverse CUMS-induced reduction of spine density and total dendritic length. In conclusion, the study revealed that CUR relieves depressive-like state through the mitigation of oxidative stress and the activation of Nrf2-ARE signaling pathway.
A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition
Authors: Matindife Liston , Sun Yanxia , Wang Zenghui
Published: September 2020

In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.
Approximation by Szász-Jakimovski-Leviatan-Type Operators via Aid of Appell Polynomials
Authors: Nasiruzzaman Md. , Aljohani A. F.
Published: September 2020

The main purpose of the present article is to construct a newly Szász-Jakimovski-Leviatan-type positive linear operators in the Dunkl analogue by the aid of Appell polynomials. In order to investigate the approximation properties of these operators, first we estimate the moments and obtain the basic results. Further, we study the approximation by the use of modulus of continuity in the spaces of the Lipschitz functions, Peetres K-functional, and weighted modulus of continuity. Moreover, we study A-statistical convergence of operators and approximation properties of the bivariate case.
Apolipoprotein B Level and the Apolipoprotein B/Apolipoprotein A-I Ratio as a Harbinger of Ischemic Stroke: A Prospective Observation in Taiwan
Authors: Chou Yu-Ching , Chan Po-Chi , Yang Tsan , You San-Lin , Bai Chyi-Huey , Sun Chien-An
Published: September 2020

Aim: Prospective studies indicate that apolipoprotein (apo) measurements predict coronary heart disease risk. However, few population-based follow-up studies have addressed the predictive value of apo measurements in stroke risk. The aims of the present study were to analyze the predictive ability of apo measurements in the risk of ischemic stroke. Methods: Serum apo A-I and apo B levels and calculated apo B/apo A-I ratio were measured at baseline in 2002 in a cohort of 4,204 participants who were followed for a mean of 4.61 years for a stroke event. Results: After adjustment for potential confounders, a significantly stepwise increase in the incidence rate of stroke across quartiles of both apo B and the apo B/apo A-I ratio was evident in both genders and across age-groups. The predictive ability of apo B to detect ischemic stroke was comparable with that of the apo B/apo A-I ratio. Furthermore, both apo B and the apo B/apo A-I ratio were better predictors of the risk of ischemic stroke than total cholesterol (TC), low-density lipoprotein cholesterol, and the TC/high-density lipoprotein cholesterol ratio. Conclusions: This cohort study demonstrates that apo B and the apo B/apo A-I ratio were a significant risk predictor of stroke. Furthermore, the predictive ability of apo B and the apo B/apo A-I ratio in stroke risk was better than routine clinical lipid measurements. Thus, measurements of apolipoproteins have superior clinical utility over traditional lipid measurements in identifying subjects at risk for ischemic stroke.
The Influence of Pregnancy on the Clinical Evolution and Prognosis of Pre-Existing Inflammatory and Autoimmune Skin Disorders and Their Management
Authors: Nasca Maria Rita , Giuffrida Giorgia , Micali Giuseppe
Published: September 2020

Background: Although the possible occurrence of typical dermatoses during pregnancy is well recognized, little is known about the influence of pregnancy on the clinical evolution and prognosis of different pre-existing chronic dermatological disorders. Summary: In this study a comprehensive search of the available literature and reviews has been undertaken in order to collect and analyze articles reporting pre-existing chronic skin disorders in pregnant women and report current knowledge on their particular clinical and therapeutic aspects.
Expositionstherapie bei chronischen Rückenschmerzen: Nicht ohne Verhaltensexperimente
Authors: Schemer Lea , Riecke Jenny , Glombiewski Julia Anna
Published: September 2020

Expositionstherapie stellt mittlerweile eine anerkannte Behandlungsmethode für chronische Rückenschmerzen dar – insbesondere wenn Patientinnen und Patienten zusätzlich unter erhöhten schmerzbezogenen Ängsten sowie Ver­meidungsverhalten leiden. Ziel dieses Verfahrens ist, bestehende Ängste und Vermeidung im Hinblick auf spezifische Bewegungen abzubauen, um dadurch langfristig schmerzbedingte Beeinträchtigungen zu reduzieren. Der übliche Ablauf dieses Therapieverfahrens wird anhand eines konkreten Patientenbeispiels verdeutlicht. Zudem werden die Effekte und lerntheoretischen Grundlagen von Expositionen und Verhaltensexperimenten dargestellt. Es wird diskutiert, inwiefern eine Kombination beider Interventionen die spezifischen Besonderheiten schmerzbezogener Ängste optimaler adressiert. Bezüglich dieser Frage werden erste Ergebnisse aus laborexperimentellen Studien vorgestellt sowie ein Überblick über aktuelle Wirksamkeitsbelege und Wei­terentwicklungen der Expositionstherapie bei chronischen Schmerzen gegeben.
Characteristics and Contributing Factors Related to Nonsteroidal Anti-Inflammatory Drugs Hypersensitivity
Authors: Yuenyongviwat Araya , Chantaravisarut Nisarat , Phattarapongdilok Wassamon , Koosakulchai Vanlaya , Jessadapakorn Wipa , Sangsupawanich Pasuree
Published: September 2020

Introduction: Hypersensitivity to nonsteroidal anti-inflammatory drugs (NSAIDs) is reported to be the most common drug hypersensitivity. The aim of this study was to evaluate the characteristics of self-reported NSAID hypersensitivity and identify patients at high risk of NSAID hypersensitivity. Methods: Patients who presented at a single tertiary care hospital between January–December 2017 with reported NSAID hypersensitivity were evaluated. Clinical information obtained from a review of medical records was further supplemented with data gained from a telephone-administered questionnaire. Results: From a total of 535 patients with reported NSAID hypersensitivity, 301 were included in the study. The mean age of onset of NSAID hypersensitivity reaction was 30.3 ± 14.9 years old. A total of 84 patients (27.9%) were hypersensitive to 2 or more chemically unrelated NSAIDs. The leading NSAID hypersensitivity was to propionic acid derivatives (73%) followed by acetic acid derivatives (28.9%). Immediate reaction (≤1 h) was identified in 171 patients (57.8%), and angioedema was the most frequently reported symptom (179 patients, 59.5%), followed by urticaria and anaphylaxis in 85 (28.2%) and 62 (20.6%) patients, respectively. A drug provocation test was performed on 53 patients, and NSAID hypersensitivity was confirmed in 38 patients (71.6%). The independent factors identified, which could predict NSAID hypersensitivity, were personal history of allergic rhinitis/chronic rhinosinusitis (AR/CRS), onset of NSAID hypersensitivity over 15 years old, and immediate reaction. Conclusion: Angioedema was the most typical symptom, and propionic acid derivatives were the most frequently reported culprit drugs. The significant risk factors predicting NSAID hypersensitivity were personal history of AR/CRS, onset of NSAID hypersensitivity reaction over 15 years old, and immediate reaction.
Understanding the Treatment Algorithm of Patients with Metastatic Pancreatic Neuroendocrine Neoplasms: A Single-Institution Retrospective Analysis Comparing Outcomes of Chemotherapy, Molecular Targeted Therapy, and Peptide Receptor Radionuclide Therapy in 255 Patients
Authors: Hayes Aimee R. , Mak Ingrid Y.F. , Evans Nicholas , Naik Rishi , Crawford Alexander , Khoo Bernard , Grossman Ashley B. , Navalkissoor Shaunak , Watkins Jennifer , Luong Tu Vinh , Mandair Dalvinder , Toumpanakis Christos , Thirlwell Christina , Caplin Martyn E. , Meyer Tim
Published: September 2020

Background: The number of therapeutic options for patients with pancreatic neuroendocrine neoplasms (PNEN) has increased, but the optimal therapeutic algorithm has not been defined due to lack of randomised trials comparing different modalities. Methods: We performed a retrospective study in patients with metastatic PNEN treated with ≥1 line of systemic therapy. The relationship between baseline characteristics, treatment type, and time to treatment failure (TTF), time to progression (TTP), and overall survival (OS) was analysed using the Kaplan-Meier method. Univariate and multivariate analyses were performed using the Cox proportional hazards model. Results: Two hundred and fifty-five patients with metastatic PNEN had 491 evaluable lines of therapy. Independent predictors of TTF included treatment type, Ki-67, tumour grade, and chromogranin A. To reduce selection bias, a subgroup of 114 patients with grade 2 (G2) metastatic pancreatic neuroendocrine tumours (PNET) was analysed separately. These patients had received 234 lines of treatment (105 chemotherapy, 82 molecular targeted therapy, and 47 peptide receptor radionuclide therapy [PRRT]). In the G2 cohort, TTF and TTP were superior for PRRT compared with both chemotherapy and molecular targeted therapy. OS in the G2 cohort was also superior for those that had received PRRT compared with those that had not (median 84 vs. 56 months; HR 0.55, 95% CI: 0.31–0.98, p = 0.04). Conclusions: This study suggests that PRRT is associated with superior clinical outcomes relative to other systemic therapies for G2 metastatic PNET. Prospective studies are required to confirm these observations.
Survey on Deep Learning applied to predictive maintenance
Authors: Maher Youssef , Danouj Boujemaa
Published: September 2020

Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
A novel algorithm for detection of tuberculosis bacilli in sputum smear fluorescence images
Authors: Dianderas Erwin , Del Carpio Christian , Zimic Mirko , Sheen Patricia , Coronel Jorge , Lavarello Roberto , Kemper Guillermo
Published: September 2020

This work proposes an algorithm aimed at recognizing and accounting Koch bacilli in digital images of microbiological sputum samples stained with auramine, in order to determine the degree of concentration and the state of the disease (tuberculosis). The algorithm was developed with the main objective of maximizing the sensitivity and specificity of the analysis of microbiological samples (recognition and counting of bacilli) according to each preparation method (direct and diluted pellets) in order to reduce the subjectivity of the visual inspection applied by the specialist at the time of analyzing the samples. The proposed algorithm consists of a background removal, an image improvement stage based on consecutive morphological closing operations, a segmentation stage of objects of interest based on thresholdization and a classification stage based on SVM. Each algorithmic stage was developed taking into account the method of preparation of the sample to be processed, being this aspect the main contribution of the proposed work, since it was possible to achieve very satisfactory results in terms of specificity and sensitivity. In this context, sensitivity levels of 91.24% and 93.79% were obtained. Specificity levels of 90.33% and 94.85% were also achieved for direct and diluted pellet methods respectively.
A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation
Authors: Abd H. J. , Abdullah Ahmad S. , Sadiq Alkafaji Muhammed Salah
Published: September 2020

Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage.
Short-term wind speed forecasting system using deep learning for wind turbine applications
Authors: Erdemir Gokhan , Zengin Aydin Tarik , Akinci Tahir Cetin
Published: September 2020

It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.
Design and implement a smart system to detect intruders and firing using IOT
Authors: Kadhim Hussam Jawad , MohammedAmeen Mohammed Jabbar
Published: September 2020

The security system is essential for occupants' convenience and protection from intruders and fire. Theft and fire are the most important requirement for the security system. The advancement of wireless sensor networks using IOTs increased the features in a security system and play an important role in daily life. In this paper, the proposed system is divided into two units. The first one about security which use to take snapshots by a camera whenever there is fire or intruders in the security zone and mail it to the owner every three seconds by using Arduino configured with MATLAB program. MATLAB program plays the main role to coordinate between sensors and to turn on/off the cameras. The second unit is about controlling the appliances and also the main door by using AVR microcontroller configured by CVAVR software that connected with Bluetooth sensor and controlled by a smartphone by using the implementation software built-up in the smartphone. To arrival of the control unit, the user should send code from the software implementation to the framework that use to turn on /off the devices or open/close the door. This proposed system is designed and implemented in details in this paper.
Video content analysis and retrieval system using video storytelling and indexing techniques
Authors: Jacob Jaimon , Elayidom M. Sudheep , Devassia V. P.
Published: September 2020

Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.
Automated server-side model for recognition of security vulnerabilities in scripting languages
Authors: Abdel-Kader Rabab F. , Nashaat Mona , Habib Mohamed I. , Mahdi Hani M. K.
Published: September 2020

With the increase of global accessibility of web applications, maintaining a reasonable security level for both user data and server resources has become an extremely challenging issue. Therefore, static code analysis systems can help web developers to reduce time and cost. In this paper, a new static analysis model is proposed. This model is designed to discover the security problems in scripting languages. The proposed model is implemented in a prototype SCAT, which is a static code analysis Tool. SCAT applies the phases of the proposed model to catch security vulnerabilities in PHP 5.3. Empirical results attest that the proposed prototype is feasible and is able to contribute to the security of real-world web applications. SCAT managed to detect 94% of security vulnerabilities found in the testing benchmarks; this clearly indicates that the proposed model is able to provide an effective solution to complicated web systems by offering benefits of securing private data for users and maintaining web application stability for web applications providers.
Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance
Authors: Neda Omar Muhammed
Published: September 2020

Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB.
MPPT oscillations minimization in PV system by controlling non-linear dynamics in SEPIC DC-DC converter
Authors: Vaigundamoorthi M. , Ramesh R. , Prabhu V. Vasan , Kumar K. Arul
Published: September 2020

Solar PV power generation has achieved rapid growth in developing countries which has many merits such as absence of noise, longer life, no pollution, less time for installation, and ease of grid interface. A maximum power point tracking circuit (MPPT) consists of DC-DC power electronics converters that are used to improve the energy attainment from solar PV array. This paper presents a detailed analysis to control of chaos, a non-linear dynamic in SEPIC DC-DC converter interfaced solar PV system, to minimize the oscillations near to MPP. In SEPIC DC-DC converter, the input inductor current is continuous and capable of sweeping the whole I-V curve of a PV module from open circuit voltage (Voc) to short circuit current (Isc) operating points. To trace the true maximum power point and to nullify the oscillations near to MPP, the yield output voltage needs to ensure period-1 operation.
A native enhanced elastic extension tables multi-tenant database
Authors: El Banhawy Magy , Saber Walaa , Amer Fathy
Published: September 2020

A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
Physical layer security and energy efficiency over different error correcting codes in wireless sensor networks
Authors: Magzoub Mohammed Ahmed , Aziz Azlan Abd , Salem Mohammed Ahmed , Ghani Hadhrami Ab , Aziz Azlina Abdul , Mahmud Azwan
Published: September 2020

Despite the rapid growth in the market demanding for wireless sensor networks (WSNs), they are far from being secured or efficient. WSNs are vulnerable to malicious attacks and utilize too much power. At the same time, there is a significant increment of the security threats due to the growth of the several applications that employ wireless sensor networks. Therefore, introducing physical layer security is considered to be a promising solution to mitigate the threats. This paper evaluates popular coding techniques like Reed solomon (RS) techniques and scrambled error correcting codes specifically in terms of security gap. The difference between the signal to nose ratio (SNR) of the eavesdropper and the legitimate receiver nodes is defined as the security gap. We investigate the security gap, energy efficiency, and bit error rate between RS and scrambled t-error correcting codes for wireless sensor networks. Lastly, energy efficiency in RS and Bose-Chaudhuri-Hocquenghem (BCH) is also studied. The results of the simulation emphasize that RS technique achieves similar security gap as scrambled error correcting codes. However, the analysis concludes that the computational complexities of the RS is less compared to the scrambled error correcting codes. We also found that BCH code is more energy-efficient than RS.
Bio-signals compression using auto-encoder
Authors: N. Sunilkumar K. , Shivashankar Shivashankar , Keshavamurthy Keshavamurthy
Published: September 2020

Latest developments in wearable devices permits un-damageable and cheapest way for gathering of medical data such as bio-signals like ECG, Respiration, Blood pressure etc. Gathering and analysis of various biomarkers are considered to provide anticipatory healthcare through customized applications for medical purpose. Wearable devices will rely on size, resources and battery capacity; we need a novel algorithm to robustly control memory and the energy of the device. The rapid growth of the technology has led to numerous auto encoders that guarantee the results by extracting feature selection from time and frequency domain in an efficient way. The main aim is to train the hidden layer to reconstruct the data similar to that of input. In the previous works, to accomplish the compression all features were needed but in our proposed framework bio-signals compression using auto-encoder (BCAE) will perform task by taking only important features and compress it. By doing this it can reduce power consumption at the source end and hence increases battery life. The performance of the result comparison is done for the 3 parameters compression ratio, reconstruction error and power consumption. Our proposed work outperforms with respect to the SURF method.
Optimized architecture for SNOW 3G
Authors: Hulle N. B. , B. Prathiba , Khope Sarika R. , Anuradha K. , Borole Yogini , Kotambkar D.
Published: September 2020

SNOW 3G is a synchronous, word-oriented stream cipher used by the 3GPP standards as a confidentiality and integrity algorithms. It is used as first set in long term evolution (LTE) and as a second set in universal mobile telecommunications system (UMTS) networks. The cipher uses 128-bit key and 128 bit IV to produce 32-bit ciphertext. The paper presents two techniques for performance enhancement. The first technique uses novel CLA architecture to minimize the propagation delay of the 232 modulo adders. The second technique uses novel architecture for S-box to minimize the chip area. The presented work uses VHDL language for coding. The same is implemented on the FPGA device Virtex xc5vfx100e manufactured by Xilinx. The presented architecture achieved a maximum frequency of 254.9 MHz and throughput of 7.2235 Gbps.
Target-based test path prioritization for UML activity diagram using weight assignment methods
Authors: Sornkliang Walaiporn , Phetkaew Thimaporn
Published: September 2020

The benefit of exploratory testing and ad hoc testing by tester’s experience is that crucial bugs are found quickly. Regression testing and test case prioritization are important processes of software testing when software functions have been changed. We propose a test path prioritization method to generate a sequence of test paths that would match the testers’ interests and focuses on the target area of interest or on the changed area. We generate test paths form the activity diagrams and survey the test path prioritization from testers. We define node and edge weight to the symbols of activity diagrams by applying Time management, Pareto, Buffett, Binary, and Bipolar method. Then we propose a test path score equation to prioritize test paths. We also propose evaluation methods i.e., the difference and the similarity of test path prioritization to testers’ interests. Our proposed method had the least average of the difference and the most average of the similarity compare with the tester’s prioritization of test paths. The Bipolar method was the most suitable for assigning weights to match test path rank by the tester. Our proposed method also has given the affected path by changing area higher priority than the other test path.
Demilitarized network to secure the data stored in industrial networks
Authors: Nuñez Alvarez José R. , Zamora Yelena Pérez , Pina Israel Benítez , Angarita Eliana Noriega
Published: September 2020

Currently, the data and variables of a control system are the most important elements to be safeguarded in an industrial network, so it is vitally important to ensure their safety. This paper presents the design and simulation of a demilitarized network (DMZ) using firewalls to control access to all the information that is stored in the servers of the industrial network of the Hermanos Díaz Refinery in Santiago de Cuba, Cuba. In addition, the characteristics, configurations, methods, and rules of DMZs and firewalls are shown, select the configuration with three multi-legged firewalls as the most appropriate for our application, since it allows efficient exchange of data guaranteeing security and avoiding the violation of the control system. Finally, the simulation of the proposed network is carried out.
A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory
Authors: Ferdoush Zannatul , Mahmud Booshra Nazifa , Chakrabarty Amitabha , Uddin Jia
Published: September 2020

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.
A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images
Authors: Kamil Mohammed Y.
Published: September 2020

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.
Face recognition using fractional coefficients and discrete cosine transform tool
Authors: Moussa Mourad , Hmila Maha , Douik Ali
Published: September 2020

Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.