Primary cells are essential in cellular experiments because of their features, such as high biological relevance, original genome, and the best experimental models for in vivo studies. The primary culture originated with Wilhelm Roux's efforts, but it began in 1907 with Ross Harrison's experiments on frogs and the growing of neuron fibers. Enzymatic DISAGGREGATION is a helpful tool to separate cells from organs or tissues. This method is one of the three main DISAGGREGATION methods (fine dissection, enzymatic DISAGGREGATION, and mechanical DISAGGREGATION) that are used in primary culture. This study established a new enzymatic DISAGGREGATION method to harvest murine prostate cells by collagenase enzyme.

In this study, a modified “probabilistic seismic hazard assessment” (PSHA) method is used to estimate the level of the potential seismic ground motion in Firoozkouh. A problem that may be encountered in probabilistic studies of seismic hazard for a specific site, for engineering purposes, is the selection of design earthquakes corresponding to a given hazard value. In order to derive a seismic scenario consistent with the results of PSHA for a site and determine the relative contribution of events to the overall seismic hazard, the concept of DISAGGREGATION was introduced. The DISAGGREGATION of seismic hazard is an effective way to identify the scenario events that contribute to a selected seismic-hazard level. In other words, the DISAGGREGATION process separates the contributions to the mean annual rate of exceedance (MRE) of a specific ground motion value at a site due to scenarios of given magnitude M, distance R, and the ground motion error term, e. DISAGGREGATION results could change with the spectral ordinate and return period, thus more than one single event may dominate the hazard especially if multiple sources affect the hazard at the site. These results can provide useful information for better defining the design scenario and selecting corresponding time histories for seismic design. In most cases, as the probability decreases, the hazard sources closer to the site dominate. Larger, more distant earthquakes contribute more significantly to hazard for longer periods than shorter periods. In this study, the seismic hazard DISAGGREGATION process is performed to identify dominant scenarios in “peak ground acceleration” (PGA) and 5% damped 0.2 and 2.0 s spectral accelerations corresponding to mean return periods (MRPs) of 50 yr, and 475 yr (hazard levels of 63% and 10% probability of exceedance in 50 yr, respectively) in Firoozkouh city. In this regard, potential seismic sources and their seismicity parameters have been estimated based on the concept of spatial distribution function in 34o–37oN latitudes and 52o–55o E longitudes in grid intervals of 0.1º. For each point using proper attenuation relationships, PGA, 0.2 and 2.0 s spectral acceleration values with 63% and 10% probabilities of exceedance in 50 yr have been calculated using the EZ-FRISK (version 7.43) code. The hazard can be simultaneously disaggregated in different types of bin. The result of seismic hazard DISAGGREGATION are presented in terms of 1-D M, R and ε bins and 2-D M-R bins. Bins of width 0.4 in magnitude, 10 km in distance, and 0.2 in e are selected. The DISAGGREGATION results in terms of probability density function (PDF) are reported, which is obtained by dividing the probability mass function (PMF) contribution of each bin by the bin’s size, thus the PDF representation is independent of the bin’s amplitude. The results identify the distribution of the earthquake scenarios that contribute to exceedance of PGA and 5% damped 0.2 and 2. s spectral accelerations for 50 yr and 475 yr MRPs, in terms of magnitude and distance (M-R). Dominant scenarios are identified for interest hazard levels in Firoozkouh city.

Selection of appropriate ground motion is a key element within any seismic assessment of structures. Many methodologies have been developed in order to select and scale ground motion records for the purpose of response history analysis. UHS is one of those developments and has been widely used in performance based earthquake engineering. However, implementation of UHS is somewhat conservative when used as a proxy for ground motion selection processes, since it is very unlikely to find are cord which has a spectrum as high as the UHS.Spectral shape indicators, including Epsilon and Eta, have been recently proposed in order to take a wide range of spectra into account. Additionally, conditional mean spectra have been obtained by the mentioned indicators which can be used as the target design spectra, e.g.; Conditional Mean Spectrum (CMS) and Eta-based Conditional Mean Spectrum (ECMS).The Eta indicator uses the conventional epsilon in combination with the peak ground velocity epsilon. As the peak ground velocity epsilon is not convenient in seismic hazard DISAGGREGATION, a simple formula was proposed by Mousavi et al. to approximate the target peak ground velocity epsilon based on the conventional epsilon. However, this simple formula needs to be investigated more precisely. Therefore, a comprehensive seismic hazard DISAGGREGATION is proposed, in this paper, to explicitly obtain the target peak ground velocity epsilon. An ideal site, with a single linear fault, was assumed in order to obtain the target peak ground velocity epsilon. The results show that the exact target peak ground velocity epsilon is meaningfully different from the simple formula in this issue, and the resulted conditional spectra are sensitive to this refinement in the low period range when using the exact target peak ground velocity epsilon.

Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.Preference DISAGGREGATION approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all, ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.

Estimation of protein stability is important for many reasons: first providing an understanding of the basic thermodynamics of the process of folding, protein engineering, and protein stability plays important role in biotechnology especially in food and protein drug design. Today, proteins are used in many branches, including industrial processes, pharmaceutical industry, and medical fields. Activity and stability of proteins are essential for providing healthy condition or required during their production, storage and use in their applications. Through the first part of this review, we aim to define the protein stability terms and factors. Any factor induces stabilizing conformation and/or aggregation of proteins might be of importance in etiology of the conformational diseases. In the second part we are going to clarify a comprehensive definition of protein stability issues with special emphasis on the advantages of these concepts in protein conformational diseases and biotechnology with a short insight to protein engineering approaches.

Background and Objectives: Digital soil data with high spatial resolution and enough accuracy and precision are necessary for management of global challenges such as food security, environment problems. Generally, soil data are available in small scale. Nevertheless, in the last decades, with the advent of soil digital mapping and modeling approaches, it is possible to disaggregate soil map units. The spatial DISAGGREGATION of soil map units is a method for modeling the spatial distribution of individual soil classes. During this process, the soil map data from a small scale (coarse resolution) is converted to a large scale (fine resolution). The statistical and data mining methods are used for its implementation. The purpose of this research was to predict the spatial distribution of soil classes by disaggregating the soil map units of a semi detailed soil map using disaggregating and harmonizing soil map units through resampled classification trees algorithm (DSMART method). Materials and Methods: The study area is located in Kermanshah province. The total area of the study was approximately 14083. 9 ha. Soil polygon map include 5 map units and 4 soil subgroups. In this study, elevation, slope, aspect, convexity, direct duration, sediment index, topographic wetness index, valley depth and vertical distance to channel network as covariates produced using DEM 10 m. Grain size index, clay index and NDVI were also calculated using Landsat 7 ETM+ imagery. Geological map at scale of 1: 100, 000 were also used as a qualitative covariate. Then, dsmart method is run as a novel approach for DISAGGREGATION soil maps. DSMART samples randomly within the soil map units and uses classification trees (C5. 0 algorithm) to produce probability surface maps of soil class distribution. External validation was performed using 82 profiles. The validation dataset was intersected with the corresponding probability surface maps and validation quantified by overall accuracy, producer’ s accuracy, user’ s accuracy and kappa coefficients. Furthermore, confusion index was calculated between the most probable and second-most-probable soil class. The CI expresses concisely degree of confusion of given soil classes. Results: The most important predictive variables in the tree classification model were Vertical distance to channel network, Elevation, lithology, grain size index and MRVBF. The confusion index close to 1 has a large extent in the study area. It shows that occurrence probability of soil subgroups is near equal in each location in both the most probable soil class and second probable soil class maps. Validation of probability surfaces showed that overall accuracy of the most probable soil class, second probable soil class and third probable soil class are 44%, 28% and 11%, respectively. These results indicated the relatively good performance of dsmart method for generating digital individual soil class map. However, kappa coefficients for first, second and third probable surfaces soil maps were obtained 0. 04, 0. 02,-0. 08, respectively. Low kappa coefficients can be attributed to the actual nature of the data, i. e the dominance of the Typic Calcixerepts subgroup as compared to other subgroups of the soil in the traditional soil map and the dsmart model prediction map and validation data. Conclusion: Dsmart method can predict the probability of occurrence of all soil classes which their distribution is unclear in soil map unit. This provides the opportunity to produced digital soil class maps when legacy soil data and covariate information are available. Such outputs may help to understand the relationship between landscape and soil.

With the emergence of new urban development plans in different countries as well as the necessity of proper land use, land allocation has always been one of the most controversial issues in the context of new developments. The main objective of urban land segregation is to provide maximum efficiency of land and space and to maintain access between urban areas, thus creating a favorable urban environment. Several factors are effective in determining the size of the segments. Of these, the socio-economic characteristics of the households that are expected to reside on the horizon in the developed area are of great importance. Accordingly, in the present study four factors including household size, income level, land price and access to urban centers were selected as independent variables and land area as dependent variable and the results obtained from the importance of each Independent variables in explaining the dependent variable are presented in a regression equation. Accordingly, in the present study four factors including household size, income level, land price and access to urban centers were selected as independent variables and land area as dependent variable and the results obtained from the importance of each Independent variables in explaining the dependent variable are presented in a regression equation. The case study area is Semnan city which requires 86 Hectares based on forecasting urban development process to master plan horizon (1405). Land segmentation modeling process is done in six steps and basic tool used for land segmentation model. Designed by Dahal and Cho (2014), based on size, shape, and direction, it provides a fully automated large-scale segmentation scheme. Using this model, the plot of land is subdivided into 16 primary blocks. Each block has a different place or value to households depending on the price of land and their access to urban centers. By calculating the average of accessibility index and forecasting the price of land and income in the horizon and using the regression equation, the required land parcels for different income levels are obtained. On the other hand, by calculating the location value for each block and assuming that higher-income households tend to live in higher-value blocks, the obtained areas are allocated to the considered blocks. Finally, in order to achieve better results, the limitations of the automated land segmentation model as well as suggestions for achieving more accurate results are presented.

Nonlinear MUSA is an extension of MUSA, which employs a derived approach to analyze customer satisfaction and its determinants. It is a preference DISAGGREGATION approach, widely welcomed by scholars since 2002, following the principles of ordinal regression analysis. N-MUSA as a goal programing model, evaluates the level of satisfaction among some groups including customers, employees, etcetera according to their values and expressed preferences. Using simple satisfaction survey data, N-MUSA aggregates the different preferences in a unique satisfaction function. The main advantage of this approach is to consider and convert the qualitative form of customer judgments and preferences in an ordinal scale based on a simple questionnaire to an interval scale, in the first place, and to develop various fruitful analytical indices in order to get more knowledge of customers in the second place. In spite of the abovementioned strengths, this paper tackles some computational shortcomings within MUSA and leads to the development of nonlinear form (N-MUSA), which is more effective and efficient in practice. This paper takes MUSA and its drawbacks into account, to introduce N-MUSA as a more efficient alternative, then, deploys it in numerical examples and a real case for more insights.

Due to the need to simulate rainfall time series at different time scales for engineering purposes on one hand and lack of recordings for these parameters in small scales caused by the administrative and financial problems, on the other hand, DISAGGREGATION of rainfall time series to the desired scale is an essential topic in water resources engineering. In this study, to disaggregate Tabriz and Sahand rain gauges time series, the wavelet-artificial neural network (WANN) hybrid model is proposed according to nonlinear characteristics of the time scales. For this purpose, ten years of daily data from four rain gauges and monthly data from six rain gauges in Urmia Lake Basin were decomposed with wavelet transform. Then using mutual information and correlation coefficient criteria, the subseries were ranked and dominant subseries were used as input to ANN model for disaggregating the monthly rainfall time series into daily time series. Results obtained by the WANN DISAGGREGATION model were also compared with the results of ANN and conventional multiple linear regression models. The efficiency of WANN model at validation stage for Tabriz rain gauge showed an increase of up to 8. 5% and 33% with regards to ANN and multiple linear regression models. For Sahand rain gauge a respectively increase of up to 13. 7% and 26% were remarked. It was concluded that WANN hybrid model can be considered as an accurate model for DISAGGREGATION of the hydro-climatological time series.

Rain gauge stations that measure fine scale rainfall are mainly limited in number or in the length of the recorded data. Therefore, temporal DISAGGREGATION models have been considered because of their ability in generating fine scale data from coarse scale measurements of rainfall. In this study, microcanonical cascade model, which is based on the scaling properties of rainfall and constant volume of rainfall was used for the DISAGGREGATION purposes of rainfall. To this end, a software package was primarily developed based on the existing theory and then applied to rainfall data from two weather stations located in the Zanjan Province. In order to evaluate the performance of the microcanonical cascade model, comparisons were conducted between the observations and model results using some statistics including percentage of zero-values, volume of individual values, duration of events, volume of events and length of dry periods between events of rainfall. It was concluded that the use of microcanonical cascade model for DISAGGREGATION of daily rainfall to 12, 6, 3 and 1. 5-hour rainfall will bring favorable results.