bluuebunny/crossref_metadata_2025_split huggingface dataset:
The afterglow emission from a stratified jet in GRB 170817A
Authors: Cheng Kang-Fa , Zhao Xiao-Hong , Zhang Bin-Bin , Bai Jin-Ming 
Published: August 2021

Abstract
The afterglow of GRB 170817A has been detected for more than three years, but the origin of the multi-band afterglow light curves remains under debate. A classical top-hat jet model is faced with difficulties in producing a shallow rise of the afterglow light curves as observed (Fν
∝ T
0.8). Here we reconsider the model of stratified ejecta with an energy profile of E(>Γ β) = E
0(Γ β)−k
as the origin of the afterglow light curves of the burst, where Γ and β are the Lorentz factor and speed of the ejecta, respectively. k is the power-law slope of the energy profile. We consider that the ejecta are collimated into jets. Two kinds of jet evolutions are investigated, including a lateral-spreading jet and a non-lateral-spreading jet. We fit the multi-band afterglow light curves, including the X-ray data at one thousand days post-burst, and find that both the models of the spreading and non-spreading jets can fit the light curves well, but the observed angular size of the source and the apparent velocity of the flux centroid for the spreading jet model are beyond the observation limits, while the non-spreading jet model meets the observation limits. Some of the best-fit parameters for the non-spreading jet model, such as the number density of the circumburst medium ∼10−2 cm−3 and the total jet kinetic energy E ∼ 4.8 × 1051 erg, also appear plausible. The best-fit slope of the jet energy profile is k ∼ 7.1. Our results suggest that the afterglow of GRB 170817A may arise from the stratified jet and that the lateral spreading of the jet is not significant.
Recurrence Analysis Applied to Ultrasonic Absorptive Coating
Authors: Martellini Edoardo , Candeloro Paolo , Peng Lv , Pagliaroli Tiziano 
Published: August 2021

Abstract
Metasurfaces are, at the actual state-of-the-art, a constant topic for scientific community as they find widespread applications in several fields, including acoustics and aeroacoustics. In this paper, the potential of recurrence analysis was evaluated by applying it to some metasurfaces. These devices effectiveness has been previously demonstrated with a reflection coefficients analysis in the Fourier and Wavelet domains. A novel strategy based on the Recurrence Analysis, which is a no man’s land applied on the metasurfaces, was performed. The evaluation of the recurrence plot and phase space attractors led to interesting results. The results show an introduction of non linearities after the forcing wave interacts with the metasurfaces. In light of that, there is the evidence that the chaotic analysis principles may be a powerful tool to characterize the performance of metasurfaces in particular through the calculation of the chaotic indexes. This method could be integrated with previous ones for the evaluation of more complex and multiscale geometries that nowadays show huge potential in too many fields of application.
Analisys of particles transportation applied to a ship’s ballast system by using fastcam recorded images
Authors: Amoresano A. , Iodice P. , Langella G. , Mocerino L. , Quaranta F. 
Published: August 2021

Abstract
High-performance imaging techniques represent the present of academic and industrial research. The use of these technologies in the maritime sector sees different applications in different phases of the life of a ship: photogrammetry in the design phase and the use of Lidar sensors to measure emissions are just a few examples. The novelty of this study is the application of these acquisition techniques to ship ballast systems. The new ballast water management regulations aimed at limiting the alteration of the marine ecosystem has overturned the canonical use of the ballast systems. In recent years, studies have shown how the spillage of ballast water has generated the proliferation of invasive species in seas all over the world. For this reason, the IMO has tried to regulate these operations. In this work, transportation phenomena of sediments through the duct, suctioned by a pump have been analysed. The inspected duct has been exposed to a CMOS MINI FASTCAM AX100. This camera was used to acquire images that allow studying the behaviour of the particles. With the use of a Matlab toolbox, a post processing of the images has been realized in order to estimate the speed and accelerations of the particles.
Application of the Gas-Coupled Laser Acoustic Detection technique to non-destructive monitoring of mechanical components
Authors: Gulino Michelangelo-Santo , Bruzzi Mara , Vangi Dario 
Published: August 2021

Abstract
Non-contact ultrasonic techniques are fundamental to devise online monitoring systems for moving or difficult to access structures. Gas-Coupled Laser Acoustic Detection (GCLAD) is an unestablished, non-contact detection technology which relies on measuring the deviation affecting a laser beam when travelling across an ultrasonic wavefront propagating in a fluid. The aim of the work is to provide in-depth highlights on the principles on which the technique leverages, with a view towards how several laser beam and ultrasonic wave features reflects on the signal acquired by the GCLAD device. By numerical and experimental approaches, parameters needing to be specifically addressed and suitably set during the investigation phase are highlighted, which enable amplitude maximization of the acquired signal. Specifically, effect of the probe laser beam spot size is thoroughly analyzed, as well as the mutual orientation between the beam and the ultrasonic propagation directions. Three test configurations are lastly proposed, providing different results in terms of GCLAD sensitivity to the acoustic waves; such differences are highlighted by applying the technique to a railway axle on which an artificial crack has been machined, providing a first assessment of the GCLAD capabilities in the non-destructive testing field.
Evolution of flow structures in twin-rotors wakes in drones by time-resolved PIV
Authors: Nargi Ranieri Emanuele , Gregorio Fabrizio De , Candeloro Paolo , Ceglia Giuseppe , Pagliaroli Tiziano 
Published: August 2021

AbstractAn experimental investigation of the noise emissions of a twin-rotor together with the evolution and spatial organization of the flow structures wakes has been carried out by means of aeroacoustics and time-resolved PIV (TR-PIV) measurements. Each rotor is characterized by three bladed propellers with diameter D = 393.7 mm running at four different rotational speeds (2620, 3500, 4360, 5200 RPM). Intricate flow patterns characterized by periodic vortical structures are formed in the wake of the rotors in twin configuration at the rotor-to-rotor distance of 1.02D. Their interaction determines a strong impact on the aerodynamic performances as well as on the noise generation. Hence, the need for TR-PIV measurements relies on what is the role of these instantaneous flow patterns to unveil the spatial organization and the dynamic behaviour inspected by imaging the region between the rotors. It is found that the flow organization and the interaction between evolution of vorticity intensity of tip vortices characterize the twin-rotors wakes.
Preliminary analysis of the flow field in a transparent model of a SRF cavity by PIV
Authors: Cozzi F , Pianello G , Ambros A D’ , Pagani C , Sertore D 
Published: August 2021

Abstract
Niobium superconductive radio frequency cavities (SRF cavities) are commonly employed in linear accelerator; very pure, clean, and smooth internal walls are required to make the cavity working properly and with a high Q factor. Typically, to achieve a suitable surface quality the SRF cavities are subjected to a surface treatment called Buffered Chemical Polish (BCP), the latter uses a strong concentrated acids mixture flowing through the cavity to chemically etch a thin niobium layer (∼250 μm). A viable way to understand and optimize the BCP process is by using computational fluid dynamic (CFD); however, the experimental validation of the numerical and physical models is required to confidently use CFD results. The aim of our work is to investigate the fluid dynamics of the BCP process by using the Particle Image Velocimetry (PIV) and the refractive index matching (RIM) techniques, and to provide data to validate CFD simulations. The paper describes and briefly analyse the experimental setup and the preliminary results achieved so far.
Preliminary Analysis on Environmental and Intrinsic Factors on FBG-Based Vibration Sensors
Authors: Quattrocchi Gaetano , Dalla Vedova Matteo Davide Lorenzo , Berri Pier Carlo , Maggiore Paolo 
Published: August 2021

Abstract
In recent years, optical-based sensors have sparked interest for the many advantages over traditional, electrical-based sensors, such as EMI insensitivity, ease of multiplexing on a single line, resilience to hostile environment and very compact size and global weight saving due to signal cables reduction. Considering said properties, optical sensors offer a compelling alternative to traditional sensing elements. One type of optical sensor is the Fiber Braggs Gratings sensors (FBG), which is a type of sensor that reflects a very narrow band of wavelengths, called Bragg wavelength, while being transparent for others; this behavior is achieved by local variations of the core refractive index. The Bragg wavelength can be easily correlated with physical changes in the sensor itself, due to either physical strain or temperature variation. It should be noted that the achievable measurement accuracy is thus comparable to the Bragg wavelength. However, for any practical application, FBGs need to be bonded to a support or surface; in this case, there is a lack of understanding of the effects of temperature and humidity variations on the combined sensor-glue system. In this work, a setup, intended to characterize the sensitivity of the fiber-glue combination to humidity and temperature will be presented.
Green Channel and Top Hat based Image Enhancement for Diabetic Retinopathy Screening
Authors: Sharif N A M , Azhar A S M , Harun N H , Bakar J A , Abdullah A A , Chong Y F 
Published: August 2021

Abstract
Diabetic Retinopathy remains one of the most feared diabetes complications that could lead to blindness. Image processing techniques have been widely used all around the world for early detection of diabetic retinopathy. However, most techniques used do not focus on the low visual quality problems in the fundus image. Low visual quality of fundus image may lead to difficulty in evaluation by ophthalmologist before reading it out to the patients. Hence, Automated Screening for Diabetic Retinopathy was created to focus on image enhancement of the fundus image. In this study, two main algorithms for image processing have been used which are green channel conversion and top-hat filters. Green channel in fundus image is selected due to better contrast of the features and background compared to the red and blue channel. While Top-hat filter used to details out small features in the fundus image. The evaluation result of the techniques is compared by using Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Entropy calculations to measure quality of the enhanced fundus images. Results of image enhancement techniques implemented has proved that quality of the fundus image is improved.
Analyzing the Factors Affecting the Tollgate Entry/Exit Experience
Authors: Magsino Elmer R. 
Published: August 2021

Abstract
When goods and commodities are transported from source to market, producers/sellers utilize highways and expressways to reduce travel time and avoid damage to perishable goods. One unavoidable experience is when these people reach the tollgate to pay toll fees for using this convenience, especially during peak hours. In this study, the five factors affecting tollgate entry/exit experience are analyzed. The effects of varying one factor at a time are presented and relevant findings are suggested to improve the vehicular entry/exit experience. Extensive simulation runs show that the main contributors of prolonged queue waiting time and increased server utilization are the heavy arrival rate and slow service times. Results also verify that there is an unbalanced formed queues and server utilization among all active tollgates.
Development of a Predictive Analytic Tool for Detection of Alzheimer’s Disease
Authors: Yee Aw Hui , Abdullah Azian Azamimi , Bakar Juhaida Abu 
Published: August 2021

Abstract
Alzheimer’s disease (AD) is the common neurodegenerative disease that causes the impairment of the brain tissue with a gradual decline in the memory and thinking skills among the elderly population. However, there is a lack of reliable biomarker and effective medical treatment to detect and cure the AD respectively. Since there is a commitment agrees that AD should be the focus on the early diagnosis stage due to the great improvement on the treatment efficacy, hence with the help of imaging modality such as Magnetic Resonance Imaging (MRI), it can provide a detailed brain’s features information for analysis and interpretation purposes. Besides that, this project is focusing on the implementation of the supervised machine learning algorithms on the prediction of AD based on the Graphical User Interface (GUI). By referring to the results obtained from classifiers’ performance evaluation, Random Forest achieved the best performance in classification of AD with the highest accuracy of 0.7825 and AUC score of 0.8314 along with Lasso Regularization, followed by Gradient Boosting Machine (GBM) with accuracy of 0.7352 and Deep Neural Network (DNN) with accuracy of 0.6201 only. The machine learning with feature selection, Lasso Regularization is performed much better than those without feature selection. Thus, the GUI is then developed to provide convenience for the user to accurately predict their current AD’s status.
COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms
Authors: Ing Soon Hwai , Abdullah Azian Azamimi , Harun Nor Hazlyna , Kanaya Shigehiko 
Published: August 2021

Abstract
The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.
Taxi Hotspots Identification through Origin and Destination Analysis of Taxi Trips using K-means Clustering and H-indexing
Authors: Magsino Elmer R. , Abello Ara Jyllian , Lalusin Joaquin Miguel 
Published: August 2021

Abstract
It is apparent that the taxi industry has grown and developed over the years. In addition to that, it will presumably continue to grow as time goes on due to the increasing popularity of taxi-hailing applications. However, taxi origin and destination (O-D) locations are not clearly established since taxis are very flexible in terms of where they can pick up and drop off passengers. In this study, the taxi origin and destination hotspots are determined by first clustering the available O-D pairs from empirical mobility traces. The validity of these formed clusters is determined by utilizing the silhouette analysis. Finally, hotspots are located by measuring the cluster’s h-index. Simulation results reveal that more clusters tend to provide unreliable silhouette values due to the fact that origin/destination GPS points are very close to each other. For a given number of clusters, the h-index tend to locate clusters that can be considered as hotspots.
Harris Hawks Optimizer for Solving Multiple Sequence Alignment
Authors: Ibrahim Mohammed K. , Yusof Umi Kalsom , Abdullah Rosni 
Published: August 2021

Abstract
In the field of bioinformatics, sequence alignment is crucial for the genes study and drug development. Multiple Sequence Alignment (MSA) is an NP-hard problem in the sequence alignment. In the last three decades, the MSA problem has drawn a great deal of interest from biologists and scientists. It is an essential method to identify the structural and behavioural attributes of biomolecules present in organisms and vital to biological processes. Nature-inspired algorithms are one of the approaches to solve the MSA problem of aligning multiple sequences at a time. They construct powerful tools to overpower traditional optimization techniques to find an accurate solution. This investigative work (HHO-MSA) brings a new nature-inspired algorithm known as the Harris Hawks Optimizer (HHO) Algorithm to solve the MSA problem. HHO’s key influence is the cooperative behaviour and hunting style of the Harris hawks in nature called the surprise pounce. The computational results across five references of the BAliBASE dataset proven the proposed HHO-MSA method’s effectiveness by reaching great results (up to 83.5% optimum solutions). Findings were statistically supported while comparing it with other established literature works to solve the MSA problem.
i-DetectBC: Intelligence detection of breast cancer
Authors: Suradi Saifullah Harith , Abdullah Kamarul Amin , Isa Nor Ashidi Mat 
Published: August 2021

Abstract
Detecting breast cancer lesions at an early stage can help to improve the patients' survival rates. Digital mammograms can be used to detect breast cancer lesions. However, mammographic images suffer from low image quality due to the low exposure factors used. This paper proposes an interactive way of enhancing mammographic images while improving the detection of breast cancer lesions. The Intelligence Detection of Breast Cancer (i-DetectBC) allows the radiologist or clinicians to enhance the original mammographic images by using appropriate algorithms automatically. The i-DetectBC consists of two digital image processing techniques: Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) enhancement and Multilevel Otsu Thresholding segmentation technique. The interface of i-DetectBC can be considered user-friendly with low computational methods to provide fast results, especially when identifying breast cancer types such as benign or malignant. The i-DetectBC has been performed on 322 mammographic images, which were retrieved from the MIAS database. The efficiency of the i-DetectBC is 95.7%, and the error rate is 4.3%. In summary, this i-DetectBC can be helpful in the detection and categorization of breast cancer lesions.
Design, Optimizing and Improvement of Microstrip 5G Patch Antenna Parameters Using Keysight Advanced Designed System (ADS)
Authors: Oo Tun Tun , Africa Aaron Don M. , Navea Roy Francis 
Published: August 2021

Abstract
This paper described the microstrip Patch antenna of 5G mobile phone with calculation, simulation, and optimization method by using the ADS Simulation software. The patch antenna sized could be applied for various applications and designed wideband, multiband frequency operation. Bandwidth improvement of 3500 MHz is progressed after optimization and the patch can operate for wideband application. In this paper, the calculation method of inset feed patch antenna, designed with simulation by using the calculation method. However, some of the calculations are matched the designed frequency. Therefore, we optimized the design with length only. Moreover, the author implemented the designs that the MLIN (Microstrip Line) is based on the patch distributive line and Asymmetric Edge Coupled Microstrip Lines is designed as the MACLIN3 and MLOC mean microstrip location design.
Data Forwarding System with Error Detection and Leader Selection Features
Authors: Obias Karl , Magsino Elmer R. , Abad Alexander , Arada Gerald 
Published: August 2021

Abstract
The concept of vehicular networks utilizes vehicular nodes and roadside units to exchange data to achieve travel comfort and convenience. However, as the number of vehicles on the road increases exponentially, the data dissemination becomes inefficient and lossy due to communication factors, one of which is the availability of too much sources of information. In this paper, a data forwarding system (DFS) is proposed to address communication failure due to bit error during data transmission and in the presence of data collision. The proposed system enables the receiver to detect errors in the received data using Hamming codes and utilizes a retransmission scheme to correct bit errors. DFS also selects a limited number of vehicles, called leaders, that can communicate with the infrastructure to minimize data collision and flooding. DFS, under various number of vehicles, is tested in additive white gaussian noise (AWGN) and Rayleigh channels. Simulation results show that DFS reduces bit error rate (BER) to approximately 54% in the AWGN and 62% in the Rayleigh channel for the maximum number of retransmissions simulated, respectively. Also, with DFS, data upload to infrastructures is reduced by at most 99.5% depending on the maximum allowable number of clusters and vehicles inside the RSU coverage.
Lossless ECG Encoder with Multi-Channel Adaptive Fuzzy Predictor and Enhanced Entropy Encoding in Wireless Body Sensor Networks
Authors: Principe Genesis Marr N. , Arada Gerald P. , Abad Alexander C. , Magsino Elmer R. 
Published: August 2021

Abstract
With the advancement of wireless technology, Wireless Body Sensor Networks, such as Electrocardiograms (ECGs) will serve as state-of-the-art method for electronic-healthcare systems and applications. Like other digital communications, however, ECGs highlight power consumption as the main design constraint and bottleneck as it affects device lifespan and data accuracy. Hence, power reduction and power management techniques and schemes have been developed to eliminate this constraint such as hardware optimization, source and channel coding, signal conditioning, and resolution control. This paper proposes a lossless ECG encoder that combines existing data compression techniques specifically the adaptive fuzzy predictor based on fuzzy decision making and an enhanced entropy encoding that utilizes algorithms in both run length encoding (RLE) and incremental prefix encoding. Simulation results that the proposed scheme outperforms the entropy encoding using Huffman, RLE, and predictive encoding schemes in compression ratio (CR), with the enhanced entropy encoding leading the pre-existing compression techniques by 24.0907 on RLE and 23.6580 on Huffman.