Due to the growing increase of generated images via cameras and various instruments, image processing has found an important role in most of practical usages including medical, security and driving. However, most of the available models has no considerable performance and in some usages the amount of error is very effective. The main cause of this failure in most of available models is the distribution mismatch across the source and target domains. In fact, the made model has no generalization to test data with different properties and distribution compared to the source data, and its performance degrades dramatically to face with new data. In this paper, we propose a novel approach entitled Sparse coding and ADAptive classification (SADA) which is robust against data drift across domains. The proposed model reduces the distribution difference across domains via generating a common subspace between the source and target domains and increases the performance of model. Also, SADA reduces the distribution mismatch across domains via the selection of the source samples which are related to target samples. Moreover, SADA adapts the model parameters to build an adaptive model to encounter with data drift. Our variety of experiments demonstrate that the proposed approach outperforms all stat-of-the-art domain adaptation methods.

ABSTRACT Considering the concept of quality of life in cities to be the same, urban planners do not have a precise understanding of the factors affecting the quality of life. Therefore, according to the size of the city, different indicators should be considered to measure the quality of life. Thus, according to the main goal of the research, which is to recognize, identify and compile the factors affecting the improvement of the quality of life in middle and small cities on an intra-provincial scale, Qazvin and Zia Abad cities in Qazvin province were selected as study samples. In order to achieve the goal of the research, exploratory factor analysis and regression analysis were exerted. The findings show that the homogenization of the indicators affecting the quality of life in all cities is incorrect. Some concepts, such as the size of the city, are effective in compiling and selecting the indicators affecting the quality of life. So, the indicators affecting the quality of life in each city can be different according to some concepts, such as the city's characteristics or the city's size. Examining study samples also shows that in small cities, social indicators are more important due to strong social solidarity among people and the high importance of social and cultural concepts. Moreover, in middle cities, due to the importance of finding economic concepts and urban services in people's lives and people's less communication with each other and the weakening of solidarity and social connections, indicators of urban services and facilities and economic factors have the most importance in explaining the quality of life; this shows that the Quality of life indicators are different in all cities Extended Abstract Introduction Quality of life is a multifaceted and dynamic concept that can be different from city to city and region to region and can be defined by various indicators. Not paying attention to this issue and considering the concept of quality of life equally in all cities has caused the level of quality of life to decrease in many cities, especially middle and small cities, in recent years. On the other hand, considering the concept of quality of life in cities to be the same, urban planners do not have a precise understanding of the factors affecting the quality of life. Following this, the main goal of this research is to identify, identify and compile effective factors for improving the quality of life in middle and small cities on an intra-provincial scale. In order to achieve this goal, the cities of Qazvin and Zia Abad in Qazvin province were selected as study samples, so that according to the size of the city, in order to achieve the goal of the research, the factors affecting the quality of life are analyzed on an intra-provincial scale.   Methodology The research method is applied research based on quantitative methods. In line with data analysis, after extracting 60 indicators of quality of life, first, the indicators were separated into separate and structured factors using the exploratory factor analysis method in SPSS software. And then, step by step regression method was used to measure the effective factors in improving the quality of life in cities.   Results and discussion The research findings show that based on exploratory factor analysis, 16 factors were extracted in Zia Abad city and 15 factors in Qazvin city as effective factors on the quality of life in small and middle cities. In order to present and explain the factors affecting the improvement of the quality of life in small and middle cities, the factors extracted from the factor analysis were analyzed by multivariate regression method and step-by-step method. Furthermore, finally, (13) factors were introduced in Zia   Abad and (13) factors in Qazvin as the main factors influencing the quality of life. The analysis of the findings indicates that in Zia Abad, factors such as life expectancy and social relations, security, the state of urban services and access to urban facilities, living expenses, the state of urban furniture, urban traffic, the state of offices and housing have more effects in explaining and improving the quality of life. In Qazvin, factors such as the state of urban services, the state of green and public spaces in the city, the state of security and economic opportunities in the city, the density and hope of urban, economic, and cultural life, the state of roads and leisure in the city are effective in explaining the quality of life. Following this, according to the above findings, quality of life is a multidimensional and dynamic concept that directly relates to the type of cities and the specific characteristics of cities. Therefore, the quality of life and its indicators can differ from city to city; because every city has its characteristics and characteristics with citizens with different cultures and thinking, which causes the factors affecting the quality of life to be different. Therefore, considering the quality of life and its indicators to be the same in all cities is wrong, which can lead to the decline of the quality of life and the failure of quality of life improvement programs.   Conclusion Finally, the research results indicate that quality of life is a simple concept covering various urban life aspects. This concept can be defined as a determining factor in the excellence and progress of cities and residents' satisfaction with their urban life. It is multifaceted and dependent on place and time. As a result, the perception of people in different cities and communities about the concept of quality of life and the factors affecting it can be different. Following this, factors and indicators should be selected according to the characteristics and conditions of that city in urban planning to improve the quality of life for each city. Thus, this research, using the classification of cities based on the size of the city, has presented factors specifically for middle and small cities, which in small cities due to strong social solidarity among people and the great importance of social and cultural concepts, social indicators are more important. Moreover, in middle cities, due to the importance of finding economic concepts and urban services in people's lives and people's less communication with each other and the weakening of solidarity and social connections, indicators of urban services and facilities and economic factors have the most importance in explaining the quality of life; this shows that the quality of life indicators are different in all cities.   Funding There is no funding support.   Authors’ Contribution All of the authors approved the content of the manuscript and agreed on all aspects of the work.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments We are grateful to all the scientific.

Research, with its’,all ethical dimensions, is trusteeship with the researcher to explore, reveal and disseminate facts to bounce it to the owner. To do this, introducing the right and reliable source of research data (Statistical population) is the prim discipline to consider although unfortunately this initial regulation is considered less in many researches. Therefore, invalid and unreliable source of date is bothering abundant researches to invalidate their findings regarding facts investigation. Vague research population, sample size, sample selection, and even sample assignment procedure were the most predominant insufficiency biasing research to unveil facts in over 1500 research articles, considerable number of research reports, graduate theses and even Ph.d. dissertations that reviewed in this analytical documentary investigation article. Providing accurate and adequate research proposal,proper and right data,sufficient statistical procedures, realistic analysis, deduction and generalization are four fundamental research principles stressed and enlightened in this article to assist researchers to avoid research obstacles. Because, the more blemished these research principles, the more inaccurate data, and hence the more unreliable results. Thus, recent precise and applicable on-line calculation procedures to determine the sample size based on the characteristics of the confidence level, confidence interval, size, and mos t importantly, the effect size of the research population introduced in this article.

Information on the size dependence of the concentration of pollutants in fine-grained residues is required for the design of classification processes for their processing. For such an analysis, the samples have to be classified prior to chemical analysis. Depending on the particle size range of the residue, a classification method has to be selected. For very fine material, air classification is a suitable method. In this work, the results of the classification of fly ash from a biomass combustion plant with an air classifier are discussed with respect to the observed sample contamination. The chemical analysis of the produced size classes for heavy metals yielded unexpected results. For most heavy metals, the mass balance resulted in a recovery rate of about 100 %; however, for Cr and Ni, the recovery rate was way above 100 %. A more detailed analysis of the data revealed that the ratio of the excess of Cr to Ni in the fly ash was nearly the same as the ratio of Cr to Ni in the material of parts of the classifier. Therefore, erosion of some material from the classifier can be assumed to have caused the contamination of the sample. For the classification of samples, which have to be analysed for Cr and Ni, a classifier has to be used which is made of a material other than stainless steel.

Dear Editor, Prior to initiating interventional clinical studies in humans, researchers usually conduct in-silico (computer simulation) in vitro and in vivo studies in order to evaluate the initial efficacy, dose determination and toxicity, pharmacokinetics, and pharmacodynamics of the interventions. Accurate determination of sample size in animal studies is essential both in terms of the validity of scientific inference and animal rights. Insufficient sample size reduces the probability of detecting the relationship between variables. Furthermore, excessive sample size imposes unnecessary costs and kills more animals (1). According to the paragraph 12 of the Helsinki Declaration the rights of laboratory animals in research need to be respected (2).