In this study, in order to evaluate the performance of neural network, two models of MLP neural network and RBF neural network were used to predict flexural, tensile and compressive strengths. The data used are taken from the results of models fitted to the results of tests performed on roller concrete samples containing different amounts of recycled crumb rubber, fly ash and nanosilica based on compressive, flexural and tensile strength tests. Different types of artificial neural networks have been used to predict the types of concrete strength. In each section, the structure of the neural network used is given along with the table of input information and output results of that network. In each type of neural network, the number of layers and the number of different neurons have been used for modeling. In the tables, the green rows represent the best structure that has been able to predict the strength of concrete well. Also, the best result (lowest error and highest correlation coefficient) has been selected by considering the network performance in simultaneous prediction of resistance types. The results of compressive and tensile strengths are in the same direction and generally in the same direction, but flexural strength usually shows different results.

When the density current smashes into the lower dense fluid mass a plunge region will occur. Since the plunge depth bounds between density current and ambient fluid, determining of this point is important. In this research, the hydraulic parameters of density current and bed slope of stagnant fluid were investigated. To achieve the purpose of this research a physical model was built in the hydraulic laboratory of Shahid Chamran University (SCU) and various experiments were performed. In the experiments, the plunge depth at the different discharge of density current and density difference 6, 9, 13 and 16 kg/m3 at three slopes 8, 12 and 16% was measured. Then using Flow-3D under RNG turbulence model, plunge depth was simulated for the same experimental condition. The comparison of results obtained showed that the predicted plunge depth with Flow-3D under RNG turbulence model had a high and satisfactory accuracy. Finding showed that for the 8% bed slope, the results of RNG turbulence model 14% was more than experimental data. While, for the entire slope, the results of RNG turbulence model was predicted 10.5% in average more than measured data. Generally, the statistical investigations showed that the RNG turbulence model with satisfactory precision estimated the plunge point depth.

The aim of the paper is to study normalized null hypersurfaces in Lorentzian manifolds equipped with an almost contact structure using rigging technique. We show that the leaves of integrable screen distribution admit a Sasakian structure. Some theorems for the normalized hypersurfaces are obtained when the ambient manifold is endowed with a Sasakian structure. We also investigate the integrability conditions of various distributions evolving the hypersurface.

In this paper, we introduce the notion of normalized distance Laplacian matrices for signed graphs corresponding to the two signed distances defined for signed graphs. We characterize balance in signed graphs using these matrices and compare the normalized distance Laplacian spectral radius of signed graphs with that of all-negative signed graphs. Also we characterize the signed graphs having maximum normalized distance Laplacian spectral radius.

Let $G$ be a simple connected graph of order $n$ with $m$ edges. Denote by $% \gamma _{1}^{+}\geq \gamma _{2}^{+}\geq \cdots \geq \gamma _{n}^{+}\geq 0$ the normalized signless Laplacian eigenvalues of $G$. In this work, we define the normalized signless Laplacian Estrada index of $G$ as $NSEE\left(G\right) =\sum_{i=1}^{n}e^{\gamma _{i}^{+}}.$ Some lower bounds on $%NSEE\left( G\right) $ are also established.

Facilities built in areas affected by earthquake activity, such as tunnels, which have always been an integral part of human life, must withstand both dynamic and static loading. It has led to the need for practical studies on the effects of earthquakes on underground structures and the factors affecting their destruction. For this purpose, in this research, at first different patterns of tunnel’s excavation were investigated and by using Plaxis 2D software and based on Tabas earthquake in Iran, sensitivity analysis on geotechnical parameters of the soil surrounding tunnel such as cohesion, friction angle, unit weight and modulus of elasticity was carried out, and the parameters whose changes have the greatest and least effects on the bending moment changes on the tunnel lining are introduced. The results show that tunnel excavation patterns significantly affect the bending moment, axial forces, displacements, and surface settlement of the tunnel. Often, by dividing tunnel excavation area to small parts, the values of bending moment, axial forces, displacements, and surface settlement of the tunnel decreases in static analysis. Also, outputs of sensitivity analysis on geotechnical parameters of the soil surrounding tunnel showed that modulus of elasticity of the soil surrounding tunnel has the most effect and cohesion changes have the least effect on bending moment induced on tunnel lining..

Urban heat island phenomenon is generally caused by a reduction in latent heat and a rise in sensible heat in urban areas. Today, this is one of the major problems of the large cities which has attracted the attention of many researchers and experts in various fields. This study investigates heat island in Tehran metropolitan as the most densely populated city of Iran. This paper aims to use satellite imageries to compares the normalized difference vegetation index (NDVI) and impervious surface (ISA) as representative parameters that of surface urban heat island (SUHI) by examining their relationship with land surface temperature indices (LST) and land-use map. For this purpose, LANDSAT 5 TM imageries and Tehran1: 2000 land use map and subpixels model has been used. The results show that there are a linear and strong relationship between LST and ISA, while the relationship between LST and NDVI is much weaker and in order to quantitative investigation of LST in urban heat island we used thermal remote sensing. Results indicated that ISA indicator is suitable than NDVI. Also, the investigation on percentage of impervious surfaces in each land-use represents that residential land uses has the highest percentage of impervious surfaces because having the surfaces like asphalt and concrete and vegetation is the lowest one.

This study aimed to estimate the genetic parameters of body weight traits in Markhoz goats, using B-spline random regression models. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the models and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.

This study aims to investigate the analytical scheme of both the bi-normalized acceleration response spectrum and the bi-normalized tripartite response spectrum according to an ensemble of near-field strong ground motion records in the forward directivity conditions. For this purpose, the horizontal components of the ground acceleration time history were considered and a group of 16 ground motion pairs were selected, which have been recorded during two great earthquakes occurred in California, namely the Northridge (1994) and the Imperial Valley (1979). Also, the analytical scheme of the normalized acceleration response spectrum and the normalized tripartite response spectrum corresponding to a symbolic single-degree-of-freedom system have been compared with the related bi-normalized acceleration response spectra. It should be noted that the identification of near-field strong ground motion records is usually recognized by their physical characteristics such as peak ground acceleration (PGA), peak ground velocity (PGV), and the relatively short period process of energy release. Many strong ground motions have been recorded in less than 20 km from the fault rupture surface during intensive powerful earthquakes. In this case, the most famous earthquake tremors are the Imperial Valley (1979), Loma Prieta (1989), Landers (1992), and Northridge (1994) in California, plus the Kobe (1995) in Japan, Erzincan (1992), as well as Kocaeli (1999) in Turkey. Moreover, there are two very strong records, which were related to the Tabas (1978) and Bam (2003) earthquakes in Iran. The ensemble of the selected earthquake records in this research includes near-field ones that contain various tectonic occurrences. The main physical characteristics of the chosen records cover a wide range of the frequency content and strong ground motions duration as well as various high seismological amplitudes. The related values of the peak ground acceleration and velocity are numerically high. Generally, the peak ground velocity (PGV) is often viewed as a better indicator of the earthquake record damage potential than the peak ground acceleration (PGA). The large velocity pulses evident in the related plots can be viewed as damaging features. Moreover, the earthquake record damage potential also depends on how much dynamic ground displacement occurs during these velocity pulses. Referred to apparent physical influences caused by strong faulting mechanisms, the presence of high-amplitude velocity pulses is one of the most important characteristics of near-field records, which can be registered in a time-history domain. In general, these pulses appear as wave-shaped features with high amplitudes and long periods, which have a compound and continuous shape. Distinct powerful velocity pulses are resulted corresponding to high-amplitude acceleration pulses and spikes usually with a less than 1.5 second time-domain and also high-amplitude and short-time spikes with 0.2 to 0.3 second time-step in the spectral windows of the horizontal parallel and perpendicular components with respect to the fault rupture couture. These processes would essentially cause an enormous amount of the kinetic energy of strong ground motions to get released in the time-range of compound coherent and long-term velocity pulses in the related time history. In order to investigate these effects on the configuration of the aimed response spectrum, the velocity pulse time step, the ratio of corresponding kinematic energy of both two horizontal components, peak ground acceleration (PGA), and peak ground velocity (PGV) were considered.According to the performed computational assessments in this research, it was resulted that the bi-normalized response spectrum by the basic criterion of predominant period has a monotonous configuration and would get fewer effects due to variation of the notified spectral parameters.

BACKGROUND AND OBJECTIVES: Urban heat island is characterized by higher temperatures in urban areas compared to their surroundings. Vegetation, quantified by the normalized difference vegetation index, is key in mitigating urban heat island effects and influencing land surface temperature. With the rise of Machine Learning techniques, particularly random forest, land surface temperature predictions have become more accurate. This study explores alternative normalized difference vegetation index adjustments to understand their impact on urban heat island classification in Chiang Mai, Thailand. It investigates how changes to the normalized difference vegetation index can help to be part of practical urban planning measures, such as prioritizing vegetation type and location for cooling strategies in urban areas. Furthermore, the study aims to highlight the importance of vegetation as a sustainable solution for mitigating the adverse effects of urban heat island and enhancing urban livability.METHODS: Satellite data from Sentinel-2 and Landsat 8 for 2016–2022 were used to develop a 20-meters grid resolution dataset, resulting in approximately 2 million points. Random Forest was employed to predict land surface temperature, followed by systematically adjusting normalized difference vegetation index values from -100 percent to +100 percent in 10 percent increments. Urban heat island was classified based on standard deviation thresholds. The results were analyzed and compared visually using geographic information system, incorporating spatial variations and heat intensity patterns to better understand the urban landscape.FINDINGS: Adjusting normalized difference vegetation index values showed a nonlinear relationship with Land Surface Temperature predictions, where certain thresholds caused unexpected decreases in Land Surface Temperature. Urban heat island classifications identified distinct urban regions with varying heat intensities. The visual comparison highlighted significant differences between the base case and alternative scenarios, revealing the sensitivity of land surface temperature to vegetation density. the results also emphasized the role of high normalized difference vegetation index values in cooling urban regions and reducing urban heat island intensity, while extreme reductions in vegetation led to potential misclassification of water bodies, creating anomalies in cooling patterns. The results of this research provide information on important variables that affect the changes in the urban heat islands, focusing on changes in vegetation, which can be a part of decision-making to improve urban planning in the future.CONCLUSION: The study demonstrates the influence of normalized difference vegetation index on urban heat island classification and its potential in urban planning strategies. By highlighting nonlinear trends, the research underscores further the need to explore vegetation dynamics in land surface temperature predictions. The findings contribute to a deeper understanding of urban heat island effects and provide a basis for enhancing machine Learning models and urban planning frameworks. Future studies could expand to other urban areas, incorporate additional variables, and refine predictive algorithms for broader applications. This study will serve as a foundation for the development of future real-time monitoring tools that will enable proactive and sustainable solutions to UHI problems.