Dental decay is the most common chronic disease in children. Fluoride varnish (FV) is a preventive oral health service with proven effectiveness at reducing dental caries in dental and primary care settings. The objective of this study was to determine how long it takes to apply FV treatments during primary care well visits to address one of the most common barriers as reported by pediatricians - lack of time. FV treatment videos were collected at six clinics in Georgia with rigorous time studies conducted on each video to determine the Standard Time for the FV treatment process as well as the FV Application Component of the process and reasons for delays. Median Standard Times varied by clinic, ranging from 67.7 seconds to 166.9 seconds with an overall median of 109.7 seconds. This results in per FV application labor costs of approximately $2.38 for pediatricians, $1.16 for registered nurses, and $0.53 for medical assistants. Findings from this study support the inclusion of FV applications as a common practice during primary care well visits.
Demand forecasting can have a significant impact on reducing and controlling companies' costs, as well as increasing their productivity and competitiveness. But to achieve this, accuracy in demand forecasting is very important. On this point, in the present study, an attempt has been made to analyze the time series related to the demand for a type of women's luxury handbag based on a framework and using machine learning methods. For this purpose, five machine learning models including Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Perceptron Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN), Discrete Wavelet Transform - Neural Networks (DWTNN), and Group Model of Data Handling (GMDH) were used. The comparison of the models was also based on the accuracy of the forecasting according to the values of forecasting errors. The RMSE, MAE error measures as well as the R, correlation coefficient were used to assess the forecasting accuracy of the models. The RBFNN model had the best performance among the studied models with the minimum error values and the highest correlation value between the observed values and the outputs of the model. But in general, by comparing the error values with the data range, it is concluded that the models performed reasonably well.
The main purpose of this study is to identify the delay factors and evaluate the type ranking of delays in natural gas distribution projects. We have investigated 274 projects in Khuzestan gas company from 2015 to 2020. Projects investigated in this study included urban, rural, transmission lines, industrial pipelines, and construction. We identified 22 delay factors and categorized them into owner, contractor, and other related factors. This research shows that Most of the delay factors are related to owner causes. In addition to identifying the factors, this study also deals with the extent of their relationship. In addition to identifying the delay factors, this study also deals with the extent of their relationship. We showed that delay factors may not be independent and there is a significant association between them. These Relations clearly showed that reduction or elimination of many delay factors lead to eliminating many others. The findings of the research were validated and implemented by company experts.
The present study aims at presenting a model of cross-buying behavior of electronic service customers in Shahr Bank. This research is applied in terms of objective and survey-exploratory in terms of approach. The statistical population of this study consisted of a group of experts including senior managers of Shahr Bank, professors and marketing consultants familiar with the banking industry which in-depth interviews were conducted with them. The selection of experts and doing interviews with them continued until the theoretical saturation was reached and then stopped. Purposive sampling method was used in this study. Nine interviews were conducted in total. This research has been done in the period of October 2020 to May 2021. Due to using the data foundation theory in this research, the main data collection tool was unstructured in-depth interviews with experts. Finally, after three open, axial and selective kinds of coding, the conceptual model of the research was designed based on a paradigm model. In this research and according to the identified goals and categories, the category of motivating to use various electronic services of Shahr Bank was considered as the main and axial category. This means that the core of the conceptual model is the users' motivation and inner desire to use various electronic services of this bank.
One of the stages of crisis management is planning and initial preparation to deal with the crisis. During natural disasters, one of the main activities is the logistics of relief groups and the activities of relief teams to save the lives of the victims of the accident. A review of past events shows that the chances of rescuing the injured decrease and that a quick and correct decision is important in this situation. This paper presents a two-phase hybrid approach to decision-making and prioritization of affected regions to send relief teams. In this approach, multi-criteria decision-making methods in two phases are used to consider different indicators in achieving the optimal solution. In the first phase, with the help of the primary decision matrix, the AHP, TOPSIS and AHP-TOPSIS methods are used. And in the second phase, according to the results obtained from the first phase, the secondary decision matrix is created. With the CoCoSo method's help, one of the newest methods in this field, areas are prioritized for relief. In order to implement the proposed approach, the city of Amol has been studied.
