Just like any other process, vaccine manufacturing procedures are defined as a series of interrelated functions and activities using a variety of specified actions and equipment designed to produce a defined product. To assure the reproducibility and consistency of such processes, they must be carried out using validated equipment and under the established procedures that meet all the acceptance criteria, at least 3 times. In many cases, “worst case” conditions are used for the validation purposes to ensure that the process would be acceptable in extreme cases. Therefore, the validation concept in vaccine production facilities is a key element of the quality assurance goals which may reduce the dependence upon intensive in-process and finished products testing. Nevertheless, the concept of validation has expanded through the years to embrace a wide range of activities such as analytical methods used for quality control of drugs, the computerized systems for the clinical trials, the labeling and the process control. To perform such validation activities properly, the updated knowledge of the current regulations are needed. Therefore, the present article focuses on the recommendations in the related guidelines addressing different aspects of validation procedures related to the vaccine production facilities as a part of the product’s life-cycle.

There are many foundations in the word of the jurists for expert witness validation. Acceptance of them has specific effects, especially on the scope of expert witness validation and its circumstances. In this research, while criticizing the basics of the credibility of the expert evidence, such as accuracy because of witness validation, unique news, obstruction, assurance and judges awareness, prove that the correct basis in the validation of expert witness is wise way.Based on this expert opinion on the external issue of the subject, because of existence wise way, it is an independent reason for proving issues and its not just way for the judge awareness, but in proving crime and citation, according to lack of the same way between the Imams and wise men, in referring to the expert opinion, the wise is not an independent reason but merely a sign of based on suspicion that along with other signs can be a documentary for judges awareness. In matters of judgment – such as determine Arsh- expert evidence is not validation, but it can not described as an argument.

Land surface temperature (LST) is one of the key parameters in a wide range of geosciences and environmental studies. Remote sensing technology, can monitor the space and time variations of this quantity in large areas. But evaluating the accuracy of this parameter is the most challenging issues. On the one hand, rapid changes in temperature occur in space and time and the mismatch between the spatial scale of satellite and ground-based sensors, validation of LST is difficult by ground based temperature data. Validation is a process of independently assessing the uncertainty of the data derived from the system outputs. Without validation, no methods, algorithms, or parameters derived from remotely sensed data can be used with confidence. As the retrieved LSTs from satellite TIR data involve corrections to the satellite-observed radiances to account for atmospheric effects and non-unity LSEs, it is necessary to assess the accuracy of the retrieval to provide potential LST users with reliable information regarding the quality of the LST product. However, validation of satellite-derived products is often a challenge in itself. This is true for the sensors having a coarse resolution and even more with the thermal bands over land. Although many algorithms have been proposed and developed over recent decades to retrieve the LST from satellite TIR data, validation of satellite-derived LSTs remains far behind the development of new LST retrieval algorithms due to the difficulties. The important difficulties including: (i) it is difficult to obtain representative LST data at the scale of a pixel, which usually covers an area of about several square kilometers, due to the spatial variability of landscape and the large spatial–temporal variations in the LST itself. (ii) The other difficulty concerns the temporal sampling. Surface temperature is characterized by a very high temporal frequency. In a matter of seconds, the surface skin temperature may change by several degrees due to wind, shadow, etc. Generally, ground radiometers often integrate the temperature over several minutes, but the sensor onboard the satellite acquires the data in a fraction of a second. (iii) In addition, it is difficult to conduct in situ LST measurements at local scale, because LST, LSE, and the atmospheric and environmental radiances are coupled. Therefore, to obtain LST, one has to measure simultaneously the atmospheric and environmental radiances and the LSE, which is time-consuming and hard to monitor. Although there exist many problems and difficulties in the validation of the LST derived from satellite TIR data, in recent years, many efforts and studies have been performed to validate the derived LSTs from different satellite sensors. The methods utilized to validate LST values retrieved from space may be roughly grouped into four categories: the temperature-based method (T-based), the radiance-based method (R-based), the cross-validation method, and the indirect validation method. Among these methods, due to development of thermal sensors, cross-validation procedure is the most common method to evaluate the accuracy of surface temperature. Due to the large spatial and temporal variations in the LST, geographic coordinate matching, time matching, and view zenith angle matching are basic requirements of the cross-validation. In this paper, an alternative scaling method was proposed for cross-validation between LST product of LDCM and MODIS sensors. Because of the wide coverage and taken LST product twice per day by each of the MODIS sensors (Terra and Aqua), this product was selected as the reference temperature. The LST products of MODIS sensors have been validated with in situ measurements and by various methods in more than 50 clear-sky cases taking into account the higher accuracies within 1 °K for both Terra and Aqua. The results of the proposed method showed that evaluation of LST accuracy in areas with high homogeneity and in the first examined LDCM scene, in terms of mean differences and mean absolute differences measures, is 0.6 °K and 1.63 °K, respectively. For the second examined LDCM scene, the accuracy measures were 0.94 °K and 1.27 °K in the LST, respectively. Generally, the experimental results on two LDCM data demonstrated that the proposed cross-validation method not only is a robust and accurate LST validation approach, but also it is applicable for each thermal sensor and every time and place.

Due to the historical domination of the positivist-quantitative approach over the major part of theories and methods in social sciences, it has been often assumed that the qualitative research is based an unstructured and unorganized methodology. As a result, the qualitative arguments and findings were considered to be not so confident and well-documented. In addition, it is simply supposed that the qualitative methodology has no standards and strategies for evaluation and validation. Therefore, qualitative methodologists have recently attempted to provide a large numbers of validation and evaluation standards and strategies for qualitative research which have more variation and precision in comparison with its quantitative counterpart.In other words, the aim of this paper is to investigate the evaluation of quality in qualitative research and to introduce the strategies and principles of validation and generalization in this methodology. In this respect, while introducing a variety of validation and generalization criterions, some of the most important ones have been discussed.

Vegetation is an important component of global ecosystems. Determining of the Earth''s vegetation cover is important to understand land-atmosphere interactions and their effects on climate، soil erosion، drought and natural resources management. The main objective of this study is to estimate vegetation fraction (Fv) using various vegetation indices. First، vegetation fraction was collected in 52 random sample plots in July 2009. Some vegetation indices (ARVI، DVI، EVI، SAVI، SAVI2، TSAVI، SARVI، WDVI،NDVI، PVI، IPVI، GEMI، MSAVI1، MSAVI2، OSAVI، SR، MSR) and ALOS (AVNIR2) imagery (June 2009) were used to find the most suitable index for estimating of Fv. Validation of the obtained results have been done using two methods of Cross-Validation and، dividing measured samples to calibration and validation data. Result revealed that selecting the calibration and validation data randomly، leads to different results. Generally، among all studied vegetation indices، those VIs use soil line coefficient، have higher R2 (>0. 63) and lower RMSE (<3%).

Metal-induced artifacts are known to degrade CT image quality and deteriorate the quantitative value of the images. Therefore, numerous metal artifact reduction techniques have been proposed and their performances have been evaluated using different qualitative or quantitative approaches. Various approaches and measures have been applied for the validation process visual assessment of the corrected images being one of the most commonly applied techniques. A high proportion of the presented techniques are not properly validated in the clinical environment, which hampers an unbiased comparison of the techniques and as such the clinical acceptability of the techniques remains questionable. Accurate quantitative evaluation of the processed images guarantees the reliability of the correction method. The main motivation of this work was to present the qualitative and quantitative validation approaches and metrics used in various metal artifact reduction studies in both phantom and clinical experiments. Considering the challenging task of validation of the clinical studies, where the gold standard is not present, having a proper knowledge about the potential solutions would assist the researchers to apply the right validation approaches.

The aim of this study is the calibration and validation of the physical-empirical SLSM model for simulation of the snow depth in the Saghez synoptic station. For this purpose, essential daily meteorological data of the model for the time period of 1992-2014 were prepared from the Iranian Meteorological Organization and by applying the Generalized Likelihood Uncertainty Estimation (GLUE) method, that performed by considering 15 parameters and performing 15000 simulations and using both Coefficient of Determination and Nash-Sutcliffe Efficiency indices for evaluating the accuracy of the model, Calibration and validation of the model were performed for the 1992-2009 and 2010-2014 time periods, respectively. Calibration process of the model was led to extracting 108 behavioral simulations and for performing validation of the model, the median of the 108 behavioral simulations was considered as the final output of the model and compared with the measured snow depth data. The results of the model validation showed that in most of the cases, the calibrated SLSM model has an acceptable efficiency to following the fluctuations of the measured snow depth data but it partly underestimates high values of the snow depth.

Application of continuous hydrological models and its calibration methods development using geographic information systems are increasingly expand. The purpose of this study is calibration and validation of SMA model and simulation of continuously river flow in Neka basin. In this study, firstly the temperature and precipitation variables prepared for the model and then SMA parameters were calculated for different period of calibration. The parameter estimates needed analyzed in GIS for the different period and finally validation and its accuracy was estimated. Generated hydrograph from the scenarios set of model parameters was compared in Gelvard observation station. Annual calibration and validation results showed that the observed and simulated correlation coefficient was 0.80 and 0.75, respectively. Also, for the validation period the objective function of AEPM (10.17) had a better matching than the calibration period (18.26). By simulating the flow river using climatic events which continuously over a long period of time, the daily discharge is obtained and showed the satisfactory results. Using this model for estimating of stream flow, the study of climate change, changes in land use and therefore better management of water resources is possible.

In this paper the daily runoff of Ravansar Sanjabi basin (1260 Km^2), in Kermanshah Province of Iran is simulated through SRM hydrological model. The Snow Cover Area variable which a main and necessary variable in SRM model is calculated from MOD10A2 product of MODIS which separate snow cover maps with 8-day intervals. The runoff is simulated in a period of 2 years from 2002 to 2003, where 2002 (January 1 to December 31, 2002) is selected as a calibration period and 2003 (January 1 to 31 December, 2003) as a validation period of model. The results of simulations during the calibration and validation periods were evaluated through three statistical indices namely Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and volume difference (Dv). According to compared simulated and observed daily flow hydrographs and also calculated statistical coefficients in 2002 and 2003, the SRM shows appropriate and acceptable results in simulating daily runoff during both calibration and validation periods, so that the NSE coefficients are 0.9 and 0.85 for calibration and validation periods, respectively.

For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical analysis. Among the various methods of modelling and statistical prediction, and in a new approach, we modeled the data using data mining techniques consisting of decision tree methods, associative rules, and Bayesian law. Then, prediction, validation, and standardization of the accuracy of the validation are performed on them. Results of data validation in the urban and national area and the results of the standardization of the accuracy of validation in the urban and national area are presented with the desired accuracy.