Publications by authors named "Amir Mosavi"

29 Publications

  • Page 1 of 1

Mechanical and Fracture Parameters of Ultra-High Performance Fiber Reinforcement Concrete Cured via Steam and Water: Optimization of Binder Content.

Materials (Basel) 2021 Apr 16;14(8). Epub 2021 Apr 16.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

An investigational study is conducted to examine the effects of different amounts of binders and curing methods on the mechanical behavior and ductility of Ultra-High Performance Fiber Reinforced Concretes (UHPFRCs) that contain 2% of Micro Steel Fiber (MSF). The aim is to find an optimum binder content for the UHPFRC mixes. The same water-to-binder ratio (w/b) of 0.12 was used for both water curing (WC) and steam curing (SC). Based on the curing methods, two series of eight mixes of UHPFRCs containing different binder contents ranging from 850 to 1200 kg/m with an increment of 50 kg/m were produced. Mechanical properties such as compressive strength, splitting tensile strength, static elastic module, flexural tensile strength and the ductility behavior were investigated. This study revealed that the mixture of 1150 kg/m binder content exhibited the highest values of the experimental results such as a compressive strength greater than 190 MPa, a splitting tensile strength greater than 12.5 MPa, and a modulus of elasticity higher than 45 GPa. The results also show that all of the improvements began to slightly decrease at 1200 kg/m of the binder content. On the other hand, it was concluded that SC resulted in higher mechanical performance and ductility behavior than WC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ma14082016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074175PMC
April 2021

Factor analysis approach to classify COVID-19 datasets in several regions.

Results Phys 2021 Jun 22;25:104071. Epub 2021 Mar 22.

John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.

The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.rinp.2021.104071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982653PMC
June 2021

Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers.

Environ Sci Pollut Res Int 2021 Mar 8. Epub 2021 Mar 8.

Faculty of Informatics, Selye Janos University, Komarom, 94501, Slovakia.

The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC's estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon's complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation's precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R (0.8), Willmott's index of agreement (0.93), Nash-Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (P) when the value of the failure state containing 50 to 600 m/s is increasing for the P determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R = 0.98 compared with linear and exponential functions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11356-021-12651-0DOI Listing
March 2021

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete.

Materials (Basel) 2021 Feb 26;14(5). Epub 2021 Feb 26.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO solids to water ratio () in sodium silicate (NaSiO) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (NaO) to water ratio () for preparing NaSiO solution, and the NaSiO to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ma14051106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956343PMC
February 2021

Design of Adaptive-Robust Controller for Multi-State Synchronization of Chaotic Systems with Unknown and Time-Varying Delays and Its Application in Secure Communication.

Sensors (Basel) 2021 Jan 2;21(1). Epub 2021 Jan 2.

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia.

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/s21010254DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795752PMC
January 2021

Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System.

Molecules 2020 Dec 31;26(1). Epub 2020 Dec 31.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring's absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring's theory's results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/molecules26010156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795042PMC
December 2020

Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach.

Entropy (Basel) 2020 Dec 16;22(12). Epub 2020 Dec 16.

John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers' entropy, worker gyration, dwellers' work distance, and workers' home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott's index (WI). The proposed model showed promising results revealing that the workers' entropy and the dwellers' work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers' gyration, and the workers' home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/e22121421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766813PMC
December 2020

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction.

Entropy (Basel) 2020 Oct 31;22(11). Epub 2020 Oct 31.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs' direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron-genetic algorithms (MLP-GA) and multilayer perceptron-particle swarm optimization (MLP-PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP-PSO with population size 125, followed by MLP-GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/e22111239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712111PMC
October 2020

A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle.

Entropy (Basel) 2020 Oct 26;22(11). Epub 2020 Oct 26.

Department of Civil Engineering, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada.

This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier () as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope (St¯) value comprehensively using a Gene Expression Programming (GEP) by knowing the initial information (discharge () and mean sediment size ()) related to the intended problem. An explicit and simple equation of the St¯ of banks and the geometric and hydraulic parameters of flow is introduced based on the GEP in combination with the previous shape profile equation related to previous researchers. Therefore, a reliable numerical hybrid model is designed, namely Entropy-based Design Model of Threshold Channels (EDMTC) based on entropy theory combined with the evolutionary algorithm of the GEP model, for estimating the bank profile shape and also dimensions of threshold channels. A wide range of laboratory and field data are utilized to verify the proposed EDMTC. The results demonstrate that the used Shannon entropy model is accurate with a lower average value of Mean Absolute Relative Error (MARE) equal to 0.317 than a previous model proposed by Cao and Knight (1997) (MARE = 0.98) in estimating the bank profile shape of threshold channels based on entropy for the first time. Furthermore, the EDMTC proposed in this paper has acceptable accuracy in predicting the shape profile and consequently, the dimensions of threshold channel banks with a wide range of laboratory and field data when only the channel hydraulic characteristics (e.g., and ) are known. Thus, EDMTC can be used in threshold channel design and implementation applications in cases when the channel characteristics are unknown. Furthermore, the uncertainty analysis of the EDMTC supports the model's high reliability with a Width of Uncertainty Bound () of ±0.03 and standard deviation (Sd) of 0.24.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/e22111218DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712950PMC
October 2020

Social Capital Contributions to Food Security: A Comprehensive Literature Review.

Foods 2020 Nov 12;9(11). Epub 2020 Nov 12.

Department of Public Management and Information Technology, National University of Public Services, 1083 Budapest, Hungary.

Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state of the art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families, and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members' resilience and decreases the community's vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study's findings to provide solutions to address food insecurity problems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/foods9111650DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698312PMC
November 2020

Urban views and their impacts on citizens: A grounded theory study of Sanandaj city.

Heliyon 2020 Oct 8;6(10):e05157. Epub 2020 Oct 8.

School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway.

This research deals with urban views and their impacts on citizens, as well as to identify the factors that create and influence urban views and their impacts. The research method was adopted as a grounded theory, in which open coding, axial coding, and selective coding analysis were performed based on the Strauss and Corbin procedures. Data were collected from field studies, interviews and semi-structured questionnaires. The participants included 48 citizens and 12 experts. The researchers spent a lot of time on purposeful roaming in the city to explore the vibrant city views, and enough time was spent interviewing citizens and research samples in the city of Sanandaj. Regarding urban views, the terms in the literature became more complete in new categories included Spot View, Focal View, Continuous View, Tunnel View, Planar View, Blocked View, and Layered View. Regarding the reasons for desirability or undesirability of views, five main categories identified included Natural Elements, Visual Harmony, Spatial Proportions, Identity, and Visual Disturbance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.heliyon.2020.e05157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550913PMC
October 2020

Calculating Filament Feed in the Fused Deposition Modeling Process to Correctly Print Continuous Fiber Composites in Curved Paths.

Materials (Basel) 2020 Oct 9;13(20). Epub 2020 Oct 9.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Fused deposition modeling (FDM) is a popular additive manufacturing (AM) method that has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the filament feed presented in the production G-code. The filament feed is calculated according to the layer height, the extrusion width, and the length of the printing path. All required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs placed next to each other at a fixed distance. In special curved paths, the distance of adjacent paths is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. The creation of a special computer code that can be used to make various functions of curved paths was investigated in this study. The filament feed parameter was also studied in detail. Next, by introducing a correction technique, the filament feed was changed on the curved path to uniformly distribute the polymer material. Variable-stiffness composite samples consisting of curved fibers can be produced with the proposed method. The high quality of the printed samples confirms the suggested code and technique.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ma13204480DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600913PMC
October 2020

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment.

Nanomaterials (Basel) 2020 Sep 7;10(9). Epub 2020 Sep 7.

Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/nano10091767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558292PMC
September 2020

The Effect of Incorporating Silica Stone Waste on the Mechanical Properties of Sustainable Concretes.

Materials (Basel) 2020 Aug 30;13(17). Epub 2020 Aug 30.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Incorporating various industrial waste materials into concrete has recently gained attention for sustainable construction. This paper, for the first time, studies the effects of silica stone waste (SSW) powder on concrete. The cement of concrete was replaced with 5, 10, 15, and 20% of the SSW powder. The mechanical properties of concrete, such as compressive and tensile strength, were studied. Furthermore, the microstructure of concrete was studied by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy analysis (EDX), Fourier transformed infrared spectroscopy (FTIR), and X-Ray diffraction (XRD) tests. Compressive and tensile strength of samples with 5% SSW powder was improved up to 18.8% and 10.46%, respectively. As can be observed in the SEM images, a reduced number of pores and higher density in the matrix can explain the better compressive strength of samples with 5% SSW powder.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ma13173832DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503760PMC
August 2020

Improving Aviation Safety through Modeling Accident Risk Assessment of Runway.

Int J Environ Res Public Health 2020 08 21;17(17). Epub 2020 Aug 21.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

The exponential increase in aviation activity and air traffic in recent decades has raised several public health issues. One of the critical public health concerns is runway safety and the increasing demand for airports without accidents. In addition to threatening human lives, runway accidents are often associated with severe environmental and pollution consequences. In this study, a three-step approach is used for runway risk assessment considering probability, location, and consequences of accidents through advanced statistical methods. This study proposes novel models for the implementation of these three steps in Iran. Data on runway excursion accidents were collected from several countries with similar air accident rates. The proposed models empower engineers to advance an accurate assessment of the accident probability and safety assessment of airports. For in-service airports, it is possible to assess existing runways to remove obstacles close to runways if necessary. Also, the proposed models can be used for preliminary evaluations of developing existing airports and the construction of new runways.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijerph17176085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7504454PMC
August 2020

Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process.

Int J Environ Res Public Health 2020 08 17;17(16). Epub 2020 Aug 17.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Road transportation poses one of the significant public health risks. Several contributors and factors strongly link public health and road safety. The design and advancement of higher-quality roads can significantly contribute to safer roads and save lives. In this article, the safety aspect of the roads' horizontal curves under the standard of the American Association of State Highway Transportation Officials (AASHTO) is evaluated. Several factors, including vehicle weight, vehicle dimensions, longitudinal grades, and vehicle speed in the geometric design of the horizontal curves, are investigated through a multi-body dynamic simulation process. According to the AASHTO, a combination of simple circular and clothoid transition curves with various longitudinal upgrades and downgrades was designed. Three vehicles were used in this simulation, including a sedan, a bus, and a 3-axle truck. The analysis was based on the lateral friction between the tire and the pavement and also the safety margin parameter. The results showed that designers must differentiate between light and heavy vehicles, especially in curves with a high radius. Evaluation of longitudinal grade impacts indicated that the safety margin decreases when the vehicle is entering the curve. Safety margin reduction on the clothoid curve takes place with a lower grade toward the simple circular curve. By increasing the speed, the difference between lateral friction demand obtained from simulation and lateral friction demand proposed by AASHTO grows. The proposed novel methodology can be used for evaluating road safety.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijerph17165975DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459981PMC
August 2020

The Impact of Natural Elements on Environmental Comfort in the Iranian-Islamic Historical City of Isfahan.

Int J Environ Res Public Health 2020 08 10;17(16). Epub 2020 Aug 10.

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.

Cities directly influence microclimates. As the urbanization expands, and the green spaces diminish, the heat islands begin to emerge. An old technique used during the past centuries-in both hot and dry climates of the central cities of Iran-was the moderation of microclimates via water and plants. With a diachronic approach to the study of the historical Chahar Bagh Street in Isfahan, this paper investigates the impact of the structural changes on its microclimate in three different scenarios, i.e., the street with its features during the Safavid Era (from 1501 to 1736); the street in its current status; and finally a probable critical condition resulting from complete elimination of natural elements from the environment. The mixed strategy used in this study relies on logical reasoning and software-assisted evaluation for comparing the three scenarios. The predicted mean vote (PMV) model was used for measuring thermal comfort. The results indicate that the evaluated comfort-providing area in the Safavid scenario is 7-17 times more favorable than the others. Moreover, the temperature in the contemporary era was found to be 1.5 degrees Celsius cooler than that of the critical status scenario.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijerph17165776DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460087PMC
August 2020

Insight into the antiviral activity of synthesized schizonepetin derivatives: A theoretical investigation.

Sci Rep 2020 05 25;10(1):8599. Epub 2020 May 25.

Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.

The antiviral activity of schizonepetin derivatives 1A-1C were investigated via theoretical methods and results are compared with experimental results. The derivatives 1 A and 1 C have the highest and the lowest antiviral activity, respectively. The interactions of derivatives 1A-1C and BN-nanotube are examined. Results show that, derivatives 1A-1C can effectively interact with BN-nanotube (9, 9) and their adsorptions are favorable. The energy of derivative 1 A is higher than derivatives 1B and 1 C. The derivative 1 A has highest absolute µ, ω and ∆N values and it has lowest absolute ƞ value. Results show that, theoretical and experimental trends of antiviral activity of derivatives 1A-1C were similar, successfully.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-65866-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248107PMC
May 2020

Insights into the Effects of Pore Size Distribution on the Flowing Behavior of Carbonate Rocks: Linking a Nano-Based Enhanced Oil Recovery Method to Rock Typing.

Nanomaterials (Basel) 2020 May 18;10(5). Epub 2020 May 18.

Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

As a fixed reservoir rock property, pore throat size distribution (PSD) is known to affect the distribution of reservoir fluid saturation strongly. This study aims to investigate the relations between the PSD and the oil-water relative permeabilities of reservoir rock with a focus on the efficiency of surfactant-nanofluid flooding as an enhanced oil recovery (EOR) technique. For this purpose, mercury injection capillary pressure (MICP) tests were conducted on two core plugs with similar rock types (in respect to their flow zone index (FZI) values), which were selected among more than 20 core plugs, to examine the effectiveness of a surfactant-nanoparticle EOR method for reducing the amount of oil left behind after secondary core flooding experiments. Thus, interfacial tension (IFT) and contact angle measurements were carried out to determine the optimum concentrations of an anionic surfactant and silica nanoparticles (NPs) for core flooding experiments. Results of relative permeability tests showed that the PSDs could significantly affect the endpoints of the relative permeability curves, and a large amount of unswept oil could be recovered by flooding a mixture of the alpha olefin sulfonate (AOS) surfactant + silica NPs as an EOR solution. Results of core flooding tests indicated that the injection of AOS + NPs solution in tertiary mode could increase the post-water flooding oil recovery by up to 2.5% and 8.6% for the carbonate core plugs with homogeneous and heterogeneous PSDs, respectively.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/nano10050972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712098PMC
May 2020

Forecasting shear stress parameters in rectangular channels using new soft computing methods.

PLoS One 2020 9;15(4):e0229731. Epub 2020 Apr 9.

School of the Built Environment, Oxford Brookes University, Oxford, United Kingdom.

Shear stress comprises basic information for predicting the average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw, non-dimension wall shear stress ([Formula: see text]) and non-dimension bed shear stress ([Formula: see text]) in smooth rectangular channels were predicted by a three methods, the Bayesian Regularized Neural Network (BRNN), the Radial Basis Function (RBF), and the Modified Structure-Radial Basis Function (MS-RBF). For this aim, eight data series of research experimental results in smooth rectangular channels were used. The results of the new method of MS-RBF were compared with those of a simple RBF and BRNN methods and the best model was selected for modeling each predicted parameters. The MS-RBF model with RMSE of 3.073, 0.0366 and 0.0354 for %SFw, [Formula: see text] and [Formula: see text] respectively, demonstrated better performance than those of the RBF and BRNN models. The results of MS-RBF model were compared with three other proposed equations by researchers for trapezoidal channels and rectangular ducts. The results showed that the MS-RBF model performance in estimating %SFw, [Formula: see text] and [Formula: see text] is superior than those of presented equations by researchers.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229731PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145149PMC
July 2020

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health.

Int J Environ Res Public Health 2020 03 31;17(7). Epub 2020 Mar 31.

Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany.

Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijerph17072359DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178257PMC
March 2020

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.

Int J Environ Res Public Health 2020 01 23;17(3). Epub 2020 Jan 23.

Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijerph17030731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037941PMC
January 2020

Integrated machine learning methods with resampling algorithms for flood susceptibility prediction.

Sci Total Environ 2020 Feb 6;705:135983. Epub 2019 Dec 6.

Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary; School of the Built Environment, Oxford Brookes University, Oxford OX30BP, UK.

Flood susceptibility projections relying on standalone models, with one-time train-test data splitting for model calibration, yields biased results. This study proposed novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling (RS) and bootstrapping (BT) algorithms, integrated with machine learning models: generalized additive model (GAM), boosted regression tree (BTR) and multivariate adaptive regression splines (MARS). RS and BT algorithms provided 10 runs of data resampling for learning and validation of the models. Then the mean of 10 runs of predictions is used to produce the flood susceptibility maps (FSM). This methodology was applied to Ardabil Province on coastal margins of the Caspian Sea which faced destructive floods. The area under curve (AUC) of receiver operating characteristic (ROC) and true skill statistic (TSS) and correlation coefficient (COR) were utilized to evaluate the predictive accuracy of the proposed models. Results demonstrated that resampling algorithms improved the performance of Standalone GAM, MARS and BRT models. Results also revealed that Standalone models had better performance with the BT algorithm compared to the RS algorithm. BT-GAM model attained superior performance in terms of statistical measures (AUC = 0.98, TSS = 0.93, COR = 0.91), followed by BT-MARS (AUC = 0.97, TSS = 0.91, COR = 0.91) and BT-BRT model (AUC = 0.95, TSS = 0.79, COR = 0.79). Results demonstrated that the proposed models outperformed the benchmark models such as Standalone GAM, MARS, BRT, multilayer perceptron (MLP) and support vector machine (SVM). Given the admirable performance of the proposed models in a large scale area, the promising results can be expected from these models for other regions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2019.135983DOI Listing
February 2020

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

Sci Total Environ 2020 Apr 21;711:135161. Epub 2019 Nov 21.

Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland.

Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the mostdevastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy = 90-92%, Kappa = 79-84%, Success ratio = 94-96%, Threat score = 80-84%, and Heidke skill score = 79-84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2019.135161DOI Listing
April 2020

Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain.

Sci Total Environ 2020 Jan 4;701:134474. Epub 2019 Oct 4.

Exploration Devision, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.

Air pollution, and especially atmospheric particulate matter (PM), has a profound impact on human mortality and morbidity, environment, and ecological system. Accordingly, it is very relevant predicting air quality. Although the application of the machine learning (ML) models for predicting air quality parameters, such as PM concentrations, has been evaluated in previous studies, those on the spatial hazard modeling of them are very limited. Due to the high potential of the ML models, the spatial modeling of PM can help managers to identify the pollution hotspots. Accordingly, this study aims at developing new ML models, such as Random Forest (RF), Bagged Classification and Regression Trees (Bagged CART), and Mixture Discriminate Analysis (MDA) for the hazard prediction of PM10 (particles with a diameter less than 10 µm) in the Barcelona Province, Spain. According to the annual PM10 concentration in 75 stations, the healthy and unhealthy locations are determined, and a ratio 70/30 (53/22 stations) is applied for calibrating and validating the ML models to predict the most hazardous areas for PM10. In order to identify the influential variables of PM modeling, the simulated annealing (SA) feature selection method is used. Seven features, among the thirteen features, are selected as critical features. According to the results, all the three-machine learning (ML) models achieve an excellent performance (Accuracy > 87% and precision > 86%). However, the Bagged CART and RF models have the same performance and higher than the MDA model. Spatial hazard maps predicted by the three models indicate that the high hazardous areas are located in the middle of the Barcelona Province more than in the Barcelona's Metropolitan Area.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2019.134474DOI Listing
January 2020

Securing IoT-Based RFID Systems: A Robust Authentication Protocol Using Symmetric Cryptography.

Sensors (Basel) 2019 Nov 1;19(21). Epub 2019 Nov 1.

Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, QLD 4059, Australia.

Despite the many conveniences of Radio Frequency Identification (RFID) systems, the underlying open architecture for communication between the RFID devices may lead to various security threats. Recently, many solutions were proposed to secure RFID systems and many such systems are based on only lightweight primitives, including symmetric encryption, hash functions, and exclusive operation. Many solutions based on only lightweight primitives were proved insecure, whereas, due to resource-constrained nature of RFID devices, the public key-based cryptographic solutions are unenviable for RFID systems. Very recently, Gope and Hwang proposed an authentication protocol for RFID systems based on only lightweight primitives and claimed their protocol can withstand all known attacks. However, as per the analysis in this article, their protocol is infeasible and is vulnerable to collision, denial-of-service (DoS), and stolen verifier attacks. This article then presents an improved realistic and lightweight authentication protocol to ensure protection against known attacks. The security of the proposed protocol is formally analyzed using Burrows Abadi-Needham (BAN) logic and under the attack model of automated security verification tool ProVerif. Moreover, the security features are also well analyzed, although informally. The proposed protocol outperforms the competing protocols in terms of security.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/s19214752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864817PMC
November 2019

Earth fissure hazard prediction using machine learning models.

Environ Res 2019 12 23;179(Pt A):108770. Epub 2019 Sep 23.

Exploration Devision, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany.

Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations' major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.envres.2019.108770DOI Listing
December 2019

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks.

Sensors (Basel) 2019 Aug 15;19(16). Epub 2019 Aug 15.

Oxford Institute for Sustainable Development, School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/s19163556DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720984PMC
August 2019

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines.

Sci Total Environ 2019 Feb 6;651(Pt 2):2087-2096. Epub 2018 Oct 6.

Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary; Institute of Advanced Studies Koszeg, IASK, Koszeg, Hungary.

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2018.10.064DOI Listing
February 2019