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Abstract

In the paper, we are analyzing and proposing an improvement to current tools and solutions for supporting fighting with COVID-19. We analyzed the most popular anti-covid tools and COVID prediction models. We addressed issues of secure data collection, prediction accuracy based on COVID models. What is most important, we proposed a solution for improving the prediction and contract tracing element in these applications. The proof of concept solution to support the fight against a global pandemic is presented, and the future possibilities for its development are discussed.
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Bibliography

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  2.  J. Stanley and J.S. Granick, “Aclu white paper: The limits of location tracking in an epidemic,” Am. Civ. Lib. Union, April 2020.
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  18.  N. Crepaz, T. Tang, G. Marks, M.J. Mugavero, L. Espinoza, and H.I. Hall, “Durable viral suppression and transmission risk potential among persons with diagnosed hiv infection: United states, 2012–2013,” Clin. Infect. Dis., vol. 63, no. 7, pp. 976– 983, 2016.
  19.  A.A. Hussain, O. Bouachir, F. Al-Turjman, and M. Aloqaily, “AI techniques for COVID-19,” IEEE Access, vol. 8, pp. 128776‒128795, 2020.
  20.  R. Vaishya, M. Javaid, I.H. Khan, and A. Haleem, “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr.: Clin. Res. Rev., 2020.
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  25.  J.A. Quiña-Mera, E.R. Saransig-Perugachi, D.J. Trejo- España, M.E. Naranjo-Toro, and C.P. Guevara-Vega, “Automation of the barter exchange management in ecuador applying Google V3 API for geolocation,” in International Conference on Information Technology & Systems. Springer, 2019, pp. 210‒219.
  26.  N. Ahmed, R.A. Michelin, W. Xue, S. Ruj, R. Malaney, S.S. Kanhere, A. Seneviratne, W. Hu, H. Janicke, and S.K. Jha, “A survey of COVID-19 contact tracing apps,” IEEE Access, vol. 8, pp. 134577–134601, 2020.
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Authors and Affiliations

Martyna Gruda
1
Michal Kedziora
1

  1. Wroclaw University of Science and Technology, ul. Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wroclaw, Poland
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Abstract

The strip yield model from the NASGRO computer software has been applied to predict fatigue crack growth in two different aircraft aluminium alloys under constant amplitude loading and programmed and random variable amplitude load histories. The computation options realized included either of the two different strip yield model implementations available in NASGRO and two types of the input material data description. The model performance has been evaluated based on comparisons between the predicted and observed results. It is concluded that altogether unsatisfactory prediction quality stems from an inadequate constraint factor conception incorporated in the NASGRO models.
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Authors and Affiliations

Małgorzata Skorupa
Tomasz Machniewicz
Andrzej Skorupa
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Abstract

This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.

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Authors and Affiliations

Alireza Bagheri
Negin Zargarian
Farzad Mondani
Iraj Nosratti
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Abstract

The objective of the present work was to evaluate the hydrodynamic behaviour of a stratified bed filtration column consisting of 4 cm of sand and 2 cm of limestone to remove turbidity and measuring the head loss through the filter in several runs. In this study, two types of sand were used as filtering bed material, one fine and one medium. Crushed limestone was also available. These materials were characterized to determine the average particle diameter, porosity, and permeability coefficient. These were respectively 1.7∙10 –4 m, 336.96 and 0.68 m∙day –1 for fine sand, 3.3∙10 –4 m, 654.24 and 2.59 m∙day –1 for the medium sand and 1.26∙10–3 m, 388.8 and 8.64 m∙day–1 for crushed limestone. Using these materials, hydrodynamic analyses were carried out using clean water under rapid filtration conditions. In these analyses, different filtration rates were determined to be used in each experiment. Once the filtration rates were determined, the filtration analysis was performed with synthetic turbid water prepared at 8 NTU using tap water and bentonite. From the results obtained, a predictive model was developed based on total head losses for the evaluated filter, maintaining the rapid filtration condition. As a result, a turbidity removal efficiency of 97.7% was obtained with a total head loss of 17.8 cm at a filtration rate of 153 m·day –1. The developed model predicted head loss as a function of operating time, filtration rate, and filter depth to maximise turbidity removal. The model showed excellent prediction accuracy with R2 of 0.9999, which indicates that the model predictions are not biased. It was concluded that, due to the porosity of these materials, a stratified bed of sedimentary rocks has a great potential to be used in surface water filtration processes, which implies that it could be used at the rural community level as a form of water treatment, since the
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Authors and Affiliations

Candelaria N. Tejada-Tovar
1
ORCID: ORCID
Ángel Villabona-Ortíz
1
ORCID: ORCID
David López-Barbosa
1
ORCID: ORCID

  1. Universidad de Cartagena, Faculty of Engineering, Chemical Engineering Department, Avenida del Consulado St. #30 No. 48 152, 130015, Cartagena, Colombia
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Abstract

A study was carried to assess the effect of traffic noise pollution on the work efficiency of shopkeepers in Indian urban areas. For this, an extensive literature survey was done on previous research done on similar topics. It was found that personal characteristics, noise levels in an area, working conditions of shopkeepers, type of task they are performing are the most significant factors to study effects on work efficiency. Noise monitoring, as well as a questionnaire survey, was done in Surat city to collect desired data. A total of 17 parameters were considered for assessing work efficiency under the influence of traffic noise. It is recommended that not more than 6 parameters should be considered for ANFIS modeling hence, before opting for the ANFIS modeling, most affecting parameters to work efficiency under the influence of traffic noise, was chosen by Structural Equation Model (SEM). As a result of the SEM model, two ANFIS prediction models were developed to predict the effect on work efficiency under the influence of traffic noise. R squared for model 1, for training data was 0.829 and for testing data, it was 0.727 and R squared for model 2 for training data was 0.828 and for testing data, it was 0.728. These two models can be used satisfactorily for predicting work efficiency under traffic noise environment for open shutter shopkeepers in tier II Indian cities.
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Bibliography

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Authors and Affiliations

Manoj Yadav
1
ORCID: ORCID
Bhaven Tandel
1

  1. Civil Engineering Department, S. V. National Institute of Technology, Surat, India
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Abstract

Natural fibres are attractive as the raw material for developing sound absorber, as they are green, eco-friendly, and health friendly. In this paper, pineapple leaf fibre/epoxy composite is considered in sound absorber development where several values of mechanical pressures were introduced during the fabrication of absorber composite. The results show that the composite can absorb incoming sound wave, where sound absorption coefficients α _n > 0.5 are pronounced at mid and high frequencies. It is also found that 23.15 kN/m^2 mechanical pressure in composite fabrication is preferred, while higher pressure leads to solid panel rather than sound absorber so that the absorption capability reduces. To extend the absorption towards lower frequency, the composite absorber requires thickness higher than 3 cm, while a thinner absorber is only effective at 1 kHz and above. Additionally, it is confirmed that the Delany-Bazley formulation fails to predict associated absorption behavior of pineapple leaf fibre-based absorber. Meanwhile, a modified Delany-Bazley model discussed in this paper is more useful. It is expected that the model can assist further development of the pineapple leaf composite sound absorber.

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Authors and Affiliations

Damar Rastri Adhika
Iwan Prasetiyo
Abiyoga Noeriman
Nurul Hidayah
Widayani
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Abstract

As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.
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Authors and Affiliations

Siming Wang
1
Zhao Kaikai
1

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China
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Abstract

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
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Authors and Affiliations

Ping He
1
ORCID: ORCID
Jie Dong
1
ORCID: ORCID
Xiaopeng Wu
1
ORCID: ORCID
Lei Yun
1
ORCID: ORCID
Hua Yang
1
ORCID: ORCID

  1. Zhengzhou University of Light Industry, College of Electrical and Information Engineering, China
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Abstract

Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
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Authors and Affiliations

Dariusz Puchala
1
ORCID: ORCID
Kamil Stokfiszewski
1
ORCID: ORCID
Kamil Wieloch
1

  1. Institute of Information Technology, Łódź University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland

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