bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Dynamical Circuit Based on CCII+, Physical Implementation and Synchronization
Authors: Özçelik Ahmet Can , Taşkiran Zehra Gülru Çam
Published: June 2021

In this study, a second-generation positive current conveyor (CCII+)-based analog circuit is proposed for the electronic implementation of a different dynamical system which is an adaptation of the chaotic Lorenz differential equation set. The proposed circuit is more cost-effective and contains less active and passive elements than the circuit obtained by applying the classical parallel synthesis method with opamps. Mathematical analyses and SPICE simulations are performed for chaotic phase portraits and bifurcation diagrams. The proposed dynamical circuit is implemented on the board by using commercially available active and passive elements on the market and an experimental study is conducted. In order to demonstrate the usability of this proposed circuit in secure communication studies, three different synchronization methods are applied and one of them is implemented. The obtained experimental results are in good agreement with the mathematical analysis and simulation results.
A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture
Authors: Huang Neng , Zhu Junxing , Guo Chaonian , Cheng Shuhan , Li Xiaoyong
Published: June 2021

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.
Multistability Regions and Related Basins of Multiattractors in Piecewise Linear Discontinuous Map from Financial Markets
Authors: Gu En-Guo
Published: June 2021

By adding trend followers, we extend the model given by Tramontana et al. from one-dimensional ([Formula: see text]D) piecewise linear discontinuous (PWLD) map to a new 2D PWLD map. Using this map in financial markets, we describe the bifurcation mechanisms associated with the appearance/disappearance of cycles, which may be related to several cases: border collision bifurcations; Poincaré equator collision bifurcations; degenerate flip bifurcations in both supercritical and subcritical cases. We investigate the multistability regions in the parameter plane and related basins of multiattractors to uncover the reason for the unpredictability of the internal law of price fluctuations in financial market.
Degrees of faithful irreducible representations of metabelian groups
Authors: Kitture Rahul Dattatraya , Pradhan Soham Swadhin
Published: June 2021

In 1993, Sim proved that all the faithful irreducible representations of a finite metacyclic group over any field of positive characteristic have the same degree. In this paper, we restrict our attention to non-modular representations and generalize this result for — (1) finite metabelian groups, over fields of positive characteristic coprime to the order of groups, and (2) finite groups having a cyclic quotient by an abelian normal subgroup, over number fields.
On the projectivity of proper normal curves over valuation domains
Authors: Knaf Hagen
Published: June 2021

A theorem of Lichtenbaum states, that every proper, regular curve [Formula: see text] over a discrete valuation domain [Formula: see text] is projective. This theorem is generalized to the case of an arbitrary valuation domain [Formula: see text] using the following notion of regularity for non-noetherian rings introduced by Bertin: the local ring [Formula: see text] of a point [Formula: see text] is called regular, if every finitely generated ideal [Formula: see text] has finite projective dimension. The generalization is a particular case of a projectivity criterion for proper, normal [Formula: see text]-curves: such a curve [Formula: see text] is projective if for every irreducible component [Formula: see text] of its closed fiber [Formula: see text] there exists a closed point [Formula: see text] of the generic fiber of [Formula: see text] such that the Zariski closure [Formula: see text] meets [Formula: see text] and meets [Formula: see text] in regular points only.
Kraus operators and symmetric groups
Authors: Cattabriga Alessia , Ercolessi Elisa , Gozzi Riccardo , Meucci Erika
Published: June 2021

In the contest of open quantum systems, we study a class of Kraus operators whose definition relies on the defining representation of the symmetric groups. We analyze the induced orbits as well as the limit set and the degenerate cases.
Double barrier American put option pricing under uncertain volatility model
Authors: Zaineb El Kharrazi , Sahar Saoud , Zouhir Mahani
Published: June 2021

This paper aims to study the asymptotic behavior of double barrier American-style put option prices under an uncertain volatility model, which degenerates to a single point. We give an approximation of the double barrier American-style option prices with a small volatility interval, expressed by the Black–Scholes–Barenblatt equation. Then, we propose a novel representation for the early exercise boundary of American-style double barrier options in terms of the optimal stopping boundary of a single barrier contract.
Using Mobile Device Batteries as Thermometers
Authors: He Liang , Lee Youngmoon , Shin Kang
Published: June 2021

Sensing the ambient temperature is the key to many applications, such as smart homes/buildings/ communities/cities [1, 2]. However, the ability to sense the ambient temperature pervasively is still deficient, causing various problems in people's daily lives, including but not limited to:
Technical Perspective
Authors: Cormode Graham
Published: June 2021

Over the past two decades the data management community has devoted particular attention to handling data that arrives as a stream of updates. This captures a number of "big data" scenarios, ranging from monitoring networks to processing high volumes of transactions in commerce and finance. This has led to data streams becoming a mainstream data management topic, with many systems offering explicit support for handling such inputs. Within these systems, streaming algorithms are used to approximate various statistical and modeling queries, which would traditionally require random access to the full data to compute exactly.
A Framework for Adversarially Robust Streaming Algorithms
Authors: Ben-Eliezer Omri , Jayaram Rajesh , Woodruff David P. , Yogev Eylon
Published: June 2021

We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chosen adaptively by an adversary that observes the outputs of the algorithm along the stream and can react in an online manner. While deterministic streaming algorithms are inherently robust, many central problems in the streaming literature do not admit sublinear-space deterministic algorithms; on the other hand, classical space-efficient randomized algorithms for these problems are generally not adversarially robust. This raises the natural question of whether there exist efficient adversarially robust (randomized) streaming algorithms for these problems.
Chiller
Authors: Fekete Alan D.
Published: June 2021

Many computing researchers and practitioners may be surprised to find a "research highlight" which innovates on the way to process database transactions. Work in the early 1970s, by Turing winner Jim Gray and others, established a standard set of techniques for transaction management. These remain the basis of most commercial and open-source platforms [1], and they are still taught in university database classes. So why is important research still needed in this topic? The technology environment keeps evolving, and new performance characteristics mean that new algorithms and system designs become appropriate. This perspective will summarise the early work, and point to how the field has continued to progress.
Technical Perspective
Authors: Howe Bill
Published: June 2021

Visualization enables effective data exploration by harnessing the higher bandwidth interactivity of the human visual cortex. But the space of possible visualizations is immense, such that general abstractions for creating (i.e., finding) the right visualization are elusive, despite recent progressin systems like vega [2] and Draco [1].
A Hybrid Neutrosophic-Grey Analytic Hierarchy Process Method: Decision-Making Modelling in Uncertain Environments
Authors: Vafadarnikjoo Amin , Scherz Marco
Published: June 2021

The analytic hierarchy process (AHP) is recognised as one of the most commonly applied methods in the multiple attribute decision-making (MADM) literature. In the AHP, encompassing uncertainty feature necessitates using suitable uncertainty theories, since dealing efficiently with uncertainty in subjective judgements is of great importance in real-world decision-making problems. The neutrosophic set (NS) theory and grey systems are two reliable uncertainty theories which can bring considerable benefits to uncertain decision-making. The aim of this study is to improve uncertain decision-making by incorporating advantages of the NS and grey systems theories with the AHP in investigating sustainability through agility readiness evaluation in large manufacturing plants. This study pioneers a combined neutrosophic-grey AHP (NG-AHP) method for uncertain decision-making modelling. The applicability of the hybrid NG-AHP method is shown in an illustrative real-case study for agility evaluations in the Iranian steel industry. The computational results indicate the effectiveness of the proposed method in adequately capturing uncertainty in the subjective judgements of decision makers. In addition, the results verify the significance of the research in group decision-making under uncertainty. The practical outcome reveals that, to become a more sustainable agile steel producer in the case country, they should first focus on the “organisation management agility” as the most significant criterion in the assessment followed by “manufacturing process agility,” “product design agility,” “integration of information system,” and “partnership formation capability,” respectively.
Further Results on Bifurcation for a Fractional-Order Predator-Prey System concerning Mixed Time Delays
Authors: Yao Zhenjiang , Tang Bingnan
Published: June 2021

In the present work, we mainly focus on a new established fractional-order predator-prey system concerning both types of time delays. Exploiting an advisable change of variable, we set up an isovalent fractional-order predator-prey model concerning a single delay. Taking advantage of the stability criterion and bifurcation theory of fractional-order dynamical system and regarding time delay as bifurcation parameter, we establish a new delay-independent stability and bifurcation criterion for the involved fractional-order predator-prey system. The numerical simulation figures and bifurcation plots successfully support the correctness of the established key conclusions.
Optimal Solutions for Constrained Bimatrix Games with Payoffs Represented by Single-Valued Trapezoidal Neutrosophic Numbers
Authors: Gaber Mohamed , Alharbi Majed G. , Dagestani Abd Alwahed , Ammar El-Saeed
Published: June 2021

Single-valued neutrosophic set (SVNS) is considered as generalization and extension of fuzzy set, intuitionistic fuzzy set (IFS), and crisp set for expressing the imprecise, incomplete, and indeterminate information about real-life decision-oriented models. The theme of this research is to develop a solution approach to solve constrained bimatrix games with payoffs of single-valued trapezoidal neutrosophic numbers (SVTNNs). In this approach, the concepts and suitable ranking function of SVTNNs are defined. Hereby, the equilibrium optimal strategies and equilibrium values for both players can be determined by solving the parameterized mathematical programming problems, which are obtained from two novel auxiliary SVTNNs programming problems based on the proposed ranking approach of SVTNNs. Moreover, an application example is examined to verify the effectiveness and superiority of the developed algorithm. Finally, a comparison analysis between the proposed and the existing approaches is conducted to expose the advantages of our work.
Downregulation of Long Noncoding RNA LINC00261 Attenuates Myocardial Infarction through the miR-522-3p/Trinucleotide Repeat-Containing Gene 6a (TNRC6A) Axis
Authors: Jiang Chaoxin , Zhao Qing , Wang Chenlong , Peng Minyan , Hao Guoqing , Liu Zhifeng , Fu Wenjin , Zhao Kewei
Published: June 2021

Background. Myocardial infarction (MI) is cardiac tissue necrosis caused by acute and persistent ischemic hypoxia of the coronary arteries. This study is aimed at investigating the expression of long noncoding RNA (lncRNA) LINC00261 in MI and its effect on myocardial cells. Methods. qRT-PCR was performed to detect the expression levels of LINC00261, miR-522-3p, and TNRC6A in normal and MI cells. Western blotting analysis was performed to detect the expression of TNRC6A protein. Viability and apoptosis of myocardial cells after MI with the knockout of LINC00261 or TNRC6A were detected. The relationships among miR-522-3p, LINC00261, and TNRC6A in cardiomyocytes were evaluated using a double luciferase reporter gene assay. Hypoxic preconditioning in normal cells was used to construct a simulated MI environment to investigate the effect of LINC00261 on apoptosis of cardiac cells. Results. LINC00261 and TNRC6A were upregulated, while miR-522-3p was downregulated in coronary heart disease tissues with MI. Knockout of LINC00261 can increase the viability of cardiomyocytes and inhibit cell apoptosis. LINC00261 targets miR-522-3p in cardiomyocytes. In addition, miR-522-3p targets TNRC6A in cardiomyocytes. TNRC6A regulates cell viability and apoptosis of cardiomyocytes after MI, and TNRC6A-induced MI can be reversed by overexpression of miR-522-3p. Conclusions. LINC00261 downregulated miR-522-3p in cardiomyocytes after MI by directly targeting miR-522-3p. TNRC6A is the direct target of miR-522-3p. Our results indicated that LINC00261 might serve as a therapeutic target for the treatment of MI.
Wound Management Property of a Hydroethanolic Leaf Extract of Cnestis ferruginea DC
Authors: Yakubu Jibira , Koffuor G. A. , Atsu-Nunyawu Talent , Aboagye Jeremiah , Aabeinir Irene Aasam , Alhassan Fasilatu , Ocran Lord Christian , Entsie Philomena
Published: June 2021

Context. Nature assumes imperative part in managing both acute and chronic ailments. Cnestis ferruginea is one of the sources of healing prowlers in traditional medicine practice for the treatment of wounds. Nonetheless, there are no scientific reports archived so far on the wound management activity of this plant. Objective. To establish the wound management property of a hydroethanolic Cnestis ferruginea leaf extract (CFHE). Materials and Methods. The wound area was measured after excision at the dorsal part of the Albino rats, and after treatment with 5–15%

w
/
w

CFHE ointments for 14 days. Absorbances of platelet-rich plasma treated with 0.8–100 mg/mL CFHE and an aggregating agent were spectrophotometrically determined in an in vitro platelet aggregation test. Wound tissue histopathology of CFHE ointment-treated animals revealed angiogenesis, reepithelialization, deposition of collagen, and granular tissue formation in wound tissues. Reduction in thigh oedema and pain threshold, in 7-day-old chicks, were assessed by carrageenan-induced oedema and Randall-Sellito pressure models, respectively. By the Agar diffusion method, bacterial growth inhibition by a 15%

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CFHE ointment was investigated on Salmonella typhi, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus. Aureus, and Streptococcus pyogenes. Results. All concentrations of CFHE ointment significantly reduced (

p
<
0.0001

) wound area by 29–41% posttreatment. CFHE (1.6–100 mg/ml) promoted platelet aggregation (

p
≤
0.0001

) by 37–67% (IC50: 3.1–6.2 mg/ml). There were improved wound tissue reepithelization, fibroblast proliferation, angiogenesis, and collagen deposition with 15% CFHE ointment treatment. CFHE ointment significantly (

p
≤
0.0001

) and dose-dependently reduced thigh oedema and showed a significant (

p
≤
0.05

) analgesic effect. In vitro, 15% CFHE ointment caused >100% growth inhibition of selected bacteria. Conclusion. The hydroethanolic leaf extract of Cnestis ferruginea possesses wound healing, platelet aggregation, anti-inflammatory, analgesic, and antimicrobial properties and, hence, could be effective in the management of open and some closed wounds.
A numerical algorithm to find a root of non-linearequations using householder’s method
Authors: Thota Srinivasarao
Published: June 2021

In this paper, we propose a three step algorithm using Householder’s method for find-ing an approximate root of the given non-linear equations in one variable. Severalnumerical examples are presented to illustrate and validation of the proposed method.Implementation of the proposed algorithm in Maple is also discussed with samplecomputations.
Real power loss diminution by rain drop optimization algorithm
Authors: Kanagasabai Lenin
Published: June 2021

In this work Rain Drop Optimization (RDO) Algorithm is projected to reduce power loss. Proceedings of Rain drop have been imitated to model the RDO algorithm. Natural action of rain drop is flowing downwardsform the peak and it may form small streams during the headway from the mountain or hill. As by gravitation principle raindrop flow as stream then as river form the peak of mountains or hill then it reach the sea as global optimum. Proposed Rain Drop Optimization (RDO) Algorithm evaluated in IEEE 30, bus test system. Power loss reduction, voltage deviation minimization, and voltage stability improvementhas been achieved
Groundwater suitability for irrigated agriculture in Alluvial Bengal delta plain: Areview
Authors: Islam Md. Shajedul , Mostafa M. G.
Published: June 2021

Groundwater is a vital source of irrigation and domestic purposes in Bangladesh, and hence, it must satisfy the water quality guidelines. The study has selected eleven (11) Districts of Bangladesh and collect secondary data regarding the irrigation water quality of groundwater. Several irrigation water indices, such as soluble sodium percentage (SSP), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), residual sodium carbonate (RSC), Kelly’s ratio (KR), permeability index (PI), and irrigation water quality (IWQ) index are applied to evaluate irrigation water quality. According to IWQindex, the results showed 25.5% of water samples fell in highly suitability water type, whereas 33.5% exhibited low suitability type, and the remaining 41% were showed medium suitability for irrigation purposes. The values of SAR, SSP, RSBC, and MAR indices showed that about 31-64% of water samples were very good, whereas 5-20% were very poor for the same purpose. The results of TDS, EC, and total hardness showed good results as 88-93% of water samples fell in fair to excellent quality. The salinity hazard was found in the groundwater of coastal areas and completely unfit for irrigation. The study findings would help for improving the management of the groundwater resources for agricultural purposes in Bangladesh.
Implementation of modular MPPT algorithm for energy harvesting embedded and IoT applications
Authors: Kommuri Krishnaveni , Kolluru Venkata Ratnam
Published: June 2021

The establishment of the latest IoT systems available today such as smart cities, smart buildings, and smart homes and wireless sensor networks (WSNs) are let the main design restriction on the inadequate supply of battery power. Hence proposing a solar-based photovoltaic (PV) system which is designed DC-DC buck-boost converter with an improved modular maximum power point tracking (MPPT) algorithm. The output voltage depends on the inductor, capacitor values, metal oxide semiconductor field effect transistor (MOSFET) switching frequency, and duty cycle. This paper focuses on the design and simulation of min ripple current/voltage and improved efficiency at PV array output, to store DC power. The stored DC power will be used for smart IoT systems. From the simulation results, the current ripples are observed to be minimized from 0.062 A to 0.02 A maintaining the duty cycle at 61.09 for switching frequencies ranges from 300 kHz to 10 MHz at the input voltage 48 V and the output voltage in buck mode 24 V, boost mode 100 V by maintaining constant 99.7 efficiencies. The improvised approach is compared to various existed techniques. It is noticed that the results are more useful for the self-powered Embedded & Internet of Things systems.
Improving power theft detection using efficient clustering and ensemble classification
Authors: Ghaedi Hassan , Tabbakh Farizani Seyed Reza Kamel , Ghaemi Reza
Published: June 2021

One of the main concerns of power generation systems around the world is power theft. This research proposes a framework that merges clustering and classification together in order to power theft detection. Due to the fact that most datasets do not have abnormal samples or are few, we have added abnormal samples to the original datasets using artificial attacks to create balance in the datasets and increase the correct detection rate. We improved the crow search algorithm (CSA) and used the weight feature of Crows to improve performance of clustering phase. Also, to create balance between diversification and intensification, we calculated the awareness probability parameter (AP) dynamically at iterations of the algorithm. To evaluate the performance, we used the cross validation technique have used the stacking technique in its training phase. The results of extensive experiments on three reference datasets showed high performance to detect power theft. The evaluation results showed that if the data is collected correctly and sufficiently, this framework can effectively detect power theft in any actual power grid. Also, for new attacks, if their patterns can be detected from the data, it is easily possible to implement these types of attacks.
Bipolar and unipolar schemes for confined band variable switching frequency PWM based inverter
Authors: Attia Hussain , Che Hang Seng , Suan Freddy Tan Kheng , Elkhateb Ahmad
Published: June 2021

The single phase inverter performance through the unipolar and bipolar strategies has been previously analyzed based on the constant switching frequency pulse width modulation (CSFPWM). However, the confined band variable switching frequency PWM (CB-VSFPWM) is currently proposed as a new variable switching frequency PWM technique through unipolar strategy to facilitate the design of high order filter, to reduce the switching losses, and to reduce the current total harmonics distortion (THD) as well. To evaluate the performance of a single phase inverter based on the CBVSFPWM through bipolar strategy, this paper presents a comparative study of the CB-VSFPWM based inverter performance using the unipolar PWM and the bipolar PWM strategies. The study adopts MATLAB/Simulink to simulate the inverter and to analyze the simulation results in terms of harmonics spectrum, total harmonic distortion (THD), and fundamental components. The analysis of the study results gives an indication about the appropriate type of CB-VSFPWM strategy (unipolar PWM or bipolar PWM) to guarantee the desired performance of the connected inverter in terms of the electrical grid standards like THD, and harmonics spectrum of the inverter current.
Three-dimensional kidney’s stones segmentation and chemical composition detection
Authors: Alquran Hiam , Alslity Mohammed , Qasmieh Isam Abu , Alawneh Khaled Z. , Alqudah Ali Mohammad , Al-Rasheed Ahmed , Al-Hawari Mohammed
Published: June 2021

Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this paper, we design an automated system for 3D kidney segmentation and stones detection in addition to their number and size evaluation. The proposed system is built based on CT kidney image series of 10 subjects, four healthy subjects (with no stones) and the rest have stones based on medical doctor diagnosis, and its performance is tested based on 32 CT kidney series images. The designed system shows its ability to extract kidney either in abdominal or pelvis non-contrast series CT images, and it distinguishes the stones from the surrounding tissues in the kidney image, besides to its ability to analyze the stones and classify them in vivo for further medical treatment. The result agreed with medical doctor's diagnosis. The system can be improved by analyzing the stones in the laboratory and using a large CT dataset. The present method is not limited to extract stones but, also a new approach is proposed to extract the 3D kidneys as well with accuracy 99%.
Lifting dual tree complex wavelets transform
Authors: Naimi Hilal , Adamou-Mitiche Amelbahahouda , Mitiche Lahcène
Published: June 2021

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.
A new feature extraction approach based on non linear source separation
Authors: Elmannai Hela , Naceur Mohamed Saber , Loghmari Mohamed Anis , AlGarni Abeer
Published: June 2021

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations.
LHCP four patches stack triangular truncated antenna using corporate feed microstrip-line for CP-SAR sensor
Authors: Edy Purnomo Muhammad Fauzan , Kusumasari Vita , Yuwono Rudy , Rahmadwati Rahmadwati , Romadhoni Rakhmad , Rafli Azizurrahman , Wahyu Yuyu , Kitagawa Akio
Published: June 2021

In this paper, we acquire the configuration of the left-hand circular polarization (LHCP) array four patches stack triangular truncated microstrip antenna. This construction use the basic corporate feed microstrip-line with modified lossless T-junction power divider on radiating patch for circularly polarized-synthetic aperture radar (CP-SAR) sensor embedded on unmanned aerial vehicle (UAV) with compact, small, and simple configuration. The design of circular polarization (CP) is realized by truncating the whole three tips and adjusting the parameters of antenna at the target frequency, f = 5.2 GHz. The results of characteristic performance and S-parameter for the LHCP array four patches stack antenna at the target frequency show successively about 9.74 dBic of gain, 2.89 dB of axial ratio (Ar), and -10.91 dB of S-parameter. Moreover, the impedance bandwidth and the 3 dB-Ar bandwidth of this antenna are around 410 MHz (7.89%) and 100 MHz (1.92%), respectively.
Incorporate ACO routing algorithm and mobile sink in wireless sensor networks
Authors: Hung Tran Cong , The Phan Thi
Published: June 2021

Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.
New 2-D interleaving grouping LBC applied on image transmission
Authors: Mohamed Wurod Qasim , Al–Sultani Marwa , Hatem Haraa Raheem
Published: June 2021

The modern technologies of the image transmission look for ultra-reducing of the error transmission in addition to enhancing the security over a wireless communication channel. This paper is applied and discussed two different techniques to achieve these requirements, which are linear block code (LBC) and two-dimensions (2-D) interleaving approach. We investigate a new approach of 2-D interleaving that increases the security of the image transmission and helps to diminution the bit error probability (BER). Using an investigated 2-D interleaving grouping LBC approach on image transmission, the system achieves a higher-security information and a better BER comparing with the other systems. It was done by means of peak signal to noise ratio (PSNR) and histogram analysis tests. Simulation results state these enhancements.
BER performance study for optical OFDM of optical camera communication
Authors: Jihad Noor J. , Abdul Satar Sinan M.
Published: June 2021

In this article, different forms of optical orthogonal frequency division multiplexing (OFDM) were observed which were suitable for optical camera communication (OCC) systems. This research aims to establish the bit error rate (BER) versus signal-to-noise ratio (SNR) of the OCC system. This research will focus on OCC systems and the design that produces the noise of the clipping but will gain SNR as a whole if an optimum clipping factor is chosen. The BER versus SNR analysis was investigated for the different clipping factors 0.7, 1.4, and 2.6. The BER performance of the asymmetrically clipped optical OFDM (ACO-OFDM) was also compared with the direct current optical OFDM (DCO-OFDM) to show the suitable effectiveness of the proposed approach. ACO-OFDM was considered to be better due to lower bit loading, but DCO-OFDM was efficient for higher SNR values. This was because the DC bias used was inefficient in terms of optical capacity, while ACO-OFDM used only half of the subcarriers to transmit the information. Moreover, ACO-OFDM two-dimensional half-subcarriers of mapping rule would introduce the clipping noise to its unused 2D subcarriers, although further data can be provided by the 2D DCO-OFDM mapping rule.
Predicting stock price movement using effective Thai financial probabilistic lexicon
Authors: Sakphoowadon Surinthip , Wisitpongphan Nawaporn , Haruechaiyasak Choochart
Published: June 2021

Predicting stock price fluctuation during critical events remains a big challenge for many researchers because the stock market is extremely vulnerable and sensitive during such time. Most existing works rely on various numerical data of related factors which can impact the stock price direction. However, very few research papers analyzed the effect of information appearing in financial news articles. In this paper, a novel probabilistic lexicon based stock market prediction (PLSP) algorithm is proposed to predict the direction of stock price movement. Our approach used the proposed thai financial probabilistic lexicon (ThaiFinLex) derived from Thai financial news and stock market historical prices. The PLSP development consists of three steps. Firstly, we constructed ThaiFinLex by extracting event terms from news articles and calculating their associated probability of increasing/decreasing values of stock prices. Then, event terms with bad prediction performance were filtered out. Finally, the stock price directions were predicted using the PLSP and the remaining effective event terms. Our results indicated that the proposed model can be used for predicting stock price movement. The performance is as high as 83.33% when PLSP is used to predict stocks from the financial sector.
Forecasting number of vulnerabilities using long short-term neural memory network
Authors: Hoque Mohammad Shamsul , Jamil Norziana , Amin Nowshad , Abdul Rahim Azril Azam , Jidin Razali  B.
Published: June 2021

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
ASR-FANET: An adaptive SDN-based routing framework for FANET
Authors: Albu-slaih Alaa Taima , Khudhair Hayder Ayad
Published: June 2021

Flying ad hoc network (FANET) is widely used in many military, commercial and civilian applications. Compared with mobile adhoc network (MANET) and vehicular ad hoc network (VANET), FANET holds unique characteristics such as high mobility, intermittent links and frequent topology changes, which cause a challenging task in the design of routing protocols. A novel adaptive software defined networking (SDN)-based routing framework for FANET called ASR-FANET is proposed in this article to solve the above challenges. The ASR-FANET framework is mainly composed of three important parts, which are the topology discovery mechanism, statistics gathering mechanism and route computation mechanism. In topology discovery mechanism, the periodic information about network topology is collected, including nodes and links. In statistics gathering mechanism, the status of the wireless network connection and flight statistics are collected. In route computation mechanism, the optimal path is calculated based on link costs. The performance of ASR-FANET framework is also has been evaluated by comprehensive simulations. The simulation results show that proposed framework is much better than other traditional protocols in packet delivery fraction, average end to end delay, normalized routing load, packet loss and throughput.
AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI
Authors: Kadam Kalyani , Ahirrao Swati , Kotecha Ketan
Published: June 2021

Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
The role of family support and internal locus of control in entrepreneurial intention of vocational high school students
Authors: Annisa Dewi Niki , Tentama Fatwa , Bashori Khoiruddin
Published: June 2021

This study examined the relationship between family support and internal locus of control with entrepreneurial intention among grade XII students attending Muhammadiyah 3 Vocational High School. From 172 grade XI students attending the school, 93 were selected to participate in the study. This study used a cluster-sampling technique. The data was collected using the entrepreneurial intention scale, the family support scale, and the internal locus of control scale. Multiple regression techniques were used to analyze the data on SPSS 19.0. The results indicated very significant positive relationship between family support and internal locus of control with entrepreneurial intention, family support with entrepreneurial intention, and internal locus of control with entrepreneurship intention. Thus, it can be inferred that the contribution of internal locus of control to entrepreneurial intention is more dominant than family support's contribution.
Religiosity and emotional intelligence on Muslim student learning achievement
Authors: Farhan Fikri , Rofi’ulmuiz M. Abdul
Published: June 2021

Learning achievement was one of the indicators often used to measure student success in learning. A comprehensive understanding of this topic requires contributions from a variety of disciplines. Recently, researchers are interested in examining the impact of religiosity and emotional intelligence on learning achievement. However, the study on this topic is inconclusive. This study aimed to examine the influence of religiosity and emotional intelligence on learning achievement by learning motivation as a mediating variable. This research was conducted with a quantitative approach. The data was collected through a questionnaire distributed to 122 students of the Management study program, Faculty of Business and Economics, Islamic University of Indonesia. The data was processed using SPSS analysis tools with hierarchical regression analysis techniques. The results demonstrated religiosity and emotional intelligence had a positive impact on learning motivation; learning motivation positively impacted learning achievement. Furthermore, learning motivation mediated the correlation between religiosity and emotional intelligence on learning achievement. The results offer practical implications for campus leaders; Campus leaders should instill religiosity and emotional intelligence in Muslim students to improve their learning achievement.
 
Performance analysis and enhancement of direct power control of DFIG based wind system
Authors: Beniss Mohamed Amine , El Moussaoui Hassan , Lamhamdi Tijani , El Markhi Hassane
Published: June 2021

The paper proposes a complete modeling and control technique of variable speed wind turbine system (WTS) based on the doubly fed induction generator (DFIG). Two levels back-to-back converter is used to ensure the energy transfer between the DFIG rotor and the grid. The wind turbine to operate efficiently, a maximum power point tracking (MPPT) algorithm is implemented. Then, direct power control (DPC) strategy has been combined with the MPPT technique in order to guarantee the selection of the appropriate rotor voltage vectors and to minimize the active and reactive power errors. Finally, the simulation is performed by using MATLAB/simulink platform basing on 7.5KW DFIG wind generation system, and the results prove the effectiveness of our proposed control technique.
Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools
Authors: Badawi Ahmed Samir , Yusoff Siti Hajar , Zyoud Alhareth Mohammed , Khan Sheroz , Hashim Aisha , Uyaroğlu Yılmaz , Ismail Mahmoud
Published: June 2021

This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X2), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.
Output voltage regulation of synchronous generator using PSO algorithm-based PI controller
Authors: Habbi Fatiha , Gabour Nour El Houda , Boudissa El Ghalia , Bounekhla M’hamed
Published: June 2021

In this paper a regulation of the terminal voltage of synchronous generator (SG) has been developed. Here, the nonlinear model of the SG is used directly without requirement for a linearized mathematical model of the generator. A proportional integral PI-controller is used to adjust the duty cycle of the DC chopper of step-down type for controlling the field voltage and consequently the output voltage of the generator. Furthermore, Particle swarm optimization (PSO) algorithm is employed as an optimization technique for tuning the optimal parameters of the PI controller (Kp and Ki). This is achieved by the minimization of the quadratic output error between the reference voltage and the output voltage calculated from the adopted model at the same time. In order to test the performance of the PSO-PI controller, results are compared with the genetic algorithm (GA). Moreover, to reduce the overshoot resulting in the response of the terminal voltage, a varied reference voltage is adopted. Results obtained show the superiority of the varied reference voltage to decrease the overshoot versus the fixed reference voltage.
Adaptive fuzzy sliding mode controller design for PMLSM position control
Authors: Makhloufi Khadidja , Bousserhane Ismail Khalil , Zegnoun Si Ahmed
Published: June 2021

We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.