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.
The objectives of this work are to investigate the status of implementation of quality initiatives by manufacturing firms in Madhya Pradesh, India, and to compare large and small-medium scale industries. Very few researchers have attempted to compare large scale and small-medium scale firms in order to know the extent of implementation of quality initiatives for a state like Madhya Pradesh. In this study, the survey questionnaire method was used for the collection of data. Nine quality initiatives were selected for the study. The obtained data are grouped into two groups: 1) large scale firms, and 2) small-medium scale firms. Descriptive and inferential statistics are used for analysis and the results are presented. Hypothesis testing was used to investigate for any significant difference between the two groups of firms in implementing each quality initiative. Results from inferential statistics reveal that there is a significant difference in the implementation of quality initiatives in large and small-medium scale industries. The findings of the present work will guide firms to identify areas where improvement is required at each quality initiative level. The study will help small-medium scale firms in the Madhya Pradesh state of India to conduct training programs in the areas of relevant quality initiatives for improving their quality of products.
Exponential Weighted Moving Average (EWMA) control charts have been widely used in Statistical Process Control (SPC) to detect small and persistent process shifts. In theory, EWMA control limits monotonically increase over time to account for the continual growth of the EWMA statistic’s variance. However, these control limits are often assumed constant and are set to their respective asymptotic limits to simplify the process of applying and analyzing EWMA control charts. One-sided EWMA charts are often implemented when it is only desirable to detect shifts in a specific direction. When using one-sided EWMA charts, reflecting boundaries (resets) can be used to prevent the statistic from drifting too far from the chart’s control limit, which can delay shift detection. There have been several research efforts into designing and studying the performance of one-sided EWMA charts with reflecting boundaries. However, these efforts have maintained the constant control limit assumption. When implementing a reflecting boundary, the EWMA statistic’s variance is constantly being reset to zero, which may significantly affect the constant control limit assumption’s validity. The focus of this paper is to understand behavior of the one-sided EWMA control charts with constant and time-varying limit assumption through simulation studies.
The personality in the present world plays a critical role in social interactions, the use of modern technologies, and individuals' success. Therefore, in the last two decades, the study of Automatic Personality Perception (APP) and Automatic Personality Recognition (APR) has become more prevalent than speech processing. These studies have shown that personality traits affect acoustic features. However, the intrinsic imbalanced distribution of personality classes across the dataset is an issue mentioned in most previous studies and the classification results suffer from it. In this paper, an innovative supervised k-fold Cross-Validation (CV) method was proposed to cope with the problem of affecting the imbalanced distribution of data across different classes. The classification outcomes showed better performance in comparison with three traditional data balancing methods. Moreover, the obtained results of the proposed evaluation method indicated that the proposed method acts as a k-fold CV method if the data distribution is balanced; otherwise, it will improve the classification results.
Bangladesh is blessed with various agro-based natural resources like Date sap, extracted from date trees. As this date sap is found in rural areas in large quantities annually but a very small fraction is converted into some value-added delicious foods at a domestic level while a large portion is left underutilized due to negligence, improper collection, and preservation system from the industry level. The processed delicious foods have conspicuous demand in the national market due to their nutritious value and the growth of the national economy. Despite its economic importance, very little researches have been conducted in this field for its industrial processing. So, this research implies to improve this straggled sector providing much attention for collecting raw sap from source and processing into value-added products from industrial level cost-effectively. The key objectives of this paper are to determine optimal facility location for processing date sap and set vehicle routes that can pick up date sap from source to processing plant simultaneously curtailing operational transportation costs. Initially, a Mixed Integer Linear Programming (MILP) model is introduced to determine optimal facility location. Besides, the Large Neighborhood Search (LNS) algorithm has been used to find the optimal set of vehicle routes. This paper outlines a summary of final results that Jessore (A south-western city in Bangladesh) is an optimal plant location and 10 vehicles are necessary for covering 15 areas which ultimately optimize the total supply time, respecting constraints concerning routing, timing, capacity, and supply as well transportation costs.
The Single-Minute Exchange of Dies (SMED) methodology proved to be an effective approach for reducing setup times on a bottleneck 66” Koppers rotary die-cutter machine center in a corrugated box plant. Root cause analysis techniques, such as a check sheet and control charts, aided the analysis. The supervisor and work crew, consisting of both the operator and setup person, played an integral role in reducing setup times by consenting to being videotaped during a typical setup and then breaking down the video to distinguish internal versus external steps. By converting many internal steps to external steps, average setup times were reduced from 55 minutes to 32 minutes, a 42% improvement.
In this paper, we proposed a numerical approach to solve a distributed order time fractional COVID 19 virus model. The fractional derivatives are shown in the Caputo-Prabhakar contains generalized Mittag-Leffler Kernel. The coronavirus 19 disease model has 8 Inger diets leading to system of 8 nonlinear ordinary differential equations in this sense, we used the midpoint quadrature method and finite different scheme for solving this problem, our approximation method reduce the distributed order time fractional COVID 19 virus equations to a system of algebraic equations. Finally, to confirm the efficiency and accuracy of this method, we presented some numerical experiments for several values of distributed order. Also, all parameters introduced in the given model are positive parameters.
The aim of this paper is to propose a mathematical model for two dental centers in a competitive market of dental tourism. Dental tourists are looking for cheaper treatment with proper quality, and dental centers are looking to maximize their profits by providing services to tourists. Government also monitors dental centers by setting tariffs (subsidies or taxes). This problem is modeled and solved in the form of Stackelberg (or Leader-Follower) game. The government as the leader determines the amount of tariffs and then the dental centers as the followers simultaneously determine the price and quality level of their services. To solve the game, first the equilibrium values related to the price and quality level of the services of the dental centers have been calculated by Nash equilibrium. Then, according to the equilibrium values obtained for dental centers, the optimal amount of tariffs are calculated. Finally, to clarify the proposed model a numerical example is provided and sensitivity analysis is performed on some parameters. In this paper, for the first time a mathematical model is developed for pricing and determining the quality of services in a competitive market of dental tourism. The obtained results indicate that increasing the amount of subsidy will lead to a decrease in the prices of service provided by the dental centers. Moreover, by increasing the amount of subsidies allocated to the dental centers, the government can expand the dental tourism industry.
Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose of this paper is to model agent-based the impulse buying behavior of consumers (customers), with regards to the factors of discount and swarm in the purchase. In terms of executive purpose and with agent-based modeling approach, the present paper examines the existing reality of consumer impulse buying behavior. This paper develops consumption models, examines and analyzes consumer behavior under the NetLogo software environment. In comparing the optimal points of discounts and sales volume in both discount and swarm-discount functions that lead the stores to maximize profits and sales volume simultaneously, it can be debated that with running this model (swarm-discount) stores would be gaining more sales by less discounts. Results could describe customer behavior by implementing discount and swarm factors. Understanding the Customer behavior prepared the comparing possibility of customer behavior in store in each introduced mathematical model. The contributions could be considered in two points of view. On the applicable view, this research can provide the managers and decision makers with significant information, includes possibility of forecasting sales volume and incomes of any policies in stores, so the comparing of policies and strategies analysis would be possible. This method is rather less expensive, because of virtual environment nature. Users of this model can study other sections by changing the research assumptions.
One of the critical concerns in the Audit Court is to study budgetary deviations of the executive organizations. Audit Court seeks methods to evaluations the executive organizations based on their budget deviations. The aim of this study is to rank executive agencies aimed at the improvement of their performance. We use a ranking method based on data envelopment analysis that can simultaneously use multi-indexes for ranking and we use budget split indexes of the Audit Court for ranking of executive organizations. The results enable managers to identify the best and worst executive agencies based on the considered indexes of the budget split of the Audit Court. The objectives of this paper are to investigate which executive organizations have more budget deviations. Any organization that had a lower rank shows that it has based on the indexes under evaluation more deviation. To study the performance process of each of the executive agencies, we collected data for two years and analyzed the performance of the executive agencies during these two years.
Considering the position of free zones in the country's commercial development, the present study identifies and ranks the factors affecting the attraction of domestic and foreign investments in the development of Iran's free trade zones and is customized in Amirabad port free zone. The present study is in the group of descriptive-survey research in terms of applied purpose and data collection method, which used statistical tests and Multi-Criteria Decision Making (MCDM) approach. In this regard, the statistical population of the study includes all actual and potential investors and managers of economic units located in the Amirabad Behshahr Free Zone, of which 385 people were analyzed using a questionnaire. The results obtained in the present study at the second level show that the first and the most important criteria is the management issues, followed by strategic planning, infrastructure, economic policies, laws and regulations, and finally location. Next, the most important factor is related to facilities and infrastructure, followed by the existence of natural resources and public budget allocation.
The present study investigated the role of strategic cost management as a moderating variable on the relationship between supply chain practices, Top Management Support (TMS), and financial performance improvement. Financial performance improvement was considered as a dependent variable, while supply chain practices, strategic cost management, and TMS were taken as the main independent variables. Besides, the financial structure and the firm size were considered as control variables. The research sample included 165 companies that were selected using random sampling from among companies listed on the Tehran Stock Exchange. The data were collected using the Senior Management Survey (SMS) to measure supply chain practices, TMS, and strategic cost management. The results of the structural equation modeling after discriminant tests showed that strategic cost management had a positive and significant effect on the relationship between different subscales of supply chain practices and different measures of financial performance improvement. The results also showed that the relationship between strategic cost management, senior management support, supply chain activities and improving financial performance is non-linear and so that Supply Chain Integration (SCI) is more effective at low and medium levels and at higher levels. The above has a lesser or even negative effect.
In this study, the effects of corrosion rate on post welded annealed heat-treated medium carbon steel in seawater was investigated. The medium carbon steel samples were butt-welded by using the Shielded Metal Arc Welding (SMAW) technique and, afterwards, heat treated by annealing at different annealing temperature was carried out. The microstructure of the unwelded and post welded heated samples was characterised by means of optical microscopy. The as received (control), unwelded and post welded annealed medium carbon steel samples were immersed in sea water for a duration of one hundred (100) days, and this was to stimulate the effect on equipment in offshore and food processing applications. Post welded heat treatment on the microstructure, weight loss and corrosion rate were evaluated. The results obtained showed an initial increase in both the weight loss and corrosion rate of samples up to 40 days and started decreasing afterwards. It was equally observed that the post welded annealed samples showed more corrosion activities than the un-welded annealed samples. Above and beyond, corrosion activity was more prominent in samples with the highest annealing temperature. More so, the unwelded annealed medium carbon steel showed a dispersion of coalescence cementite and ferrite grain while the post welded annealed medium carbon steel samples showed a martensite (light area marked by arrows) distributed in the ferrite (dark area) matrix.
This study aimed to identify factors affecting the green management process readiness of banks and determining the interactions and priority of these factors. To this end, factors affecting the green management process readiness in the banking industry were extracted by using an in-depth study of the extant research and qualitative content analysis. Also, fuzzy DEMATEL method was used to explain and assess the interrelationships between the identified factors. The research sample included 14 experts in the process management field in the industry and academicians with knowledge of the concepts of emerging technologies and more than 10 years of experience at the level of managerial activities in the banking industry. Application of DEMATEL revealed that “green awareness”, “green attitude”, “green governance”, and “green technology” are the influential factors, while “green operation”, “green infrastructure”, “green lifecycle”, “green strategy”, and “green policies” are permeable factors. It was also found that “green awareness” has the greatest impact on other factors and "green operations "is the most permeable factor. The obtained results might help to raise the awareness of individuals including managers, policymakers of the organizations toward establishing and fostering a green attitude to adopting green operations.
Job satisfaction of the employees is a concerning issue, accelerates the productivity of any organization. Higher job satisfaction among the employees means the higher chance of profitability of the employers. An effective understanding of factors associated with job satisfaction of the employees is precious to push organizational development. This study based on the data collected from three aluminum industries inRajshahi cityofBangladesh during January 21, 2016 to March 20, 2016 to identify the determinants of job satisfaction among the employees. In this study, age, sex, education, work experience, satisfaction with salary, workplace environment and workplace management system are revealed as the determinants of job satisfaction. For instance, higher aged respondents in more likely to satisfy with job than lower aged (Odds Ratio (OR): 1.30 [0.48-3.57]). Female are more likely (OR: 1.90 [1.03-3.51]) to satisfy with their job than the male. Higher educated employees are less likely (OR: 0.97 [0.56-1.70]) to satisfy with their job than the lower educated employees. Respondents with 15 & above years of experience are more likely (OR: 1.05 [0.33-2.15]) to satisfy with their job than those have less than 5 years of experience. Satisfaction with salary (OR: 1.97 [1.14-3.41]), workplace environment (OR: 1.62 [0.59-6.51]), management system (OR: 1.24 [1.25-3.98]) are significantly associated with job satisfaction of the employees. As the first study, it provides the determinants of job satisfaction among the employees in aluminum industries in Rajshahi city of Bangladesh. Relevant authorities are suggested to consider the study’s findings and recommendations to create new policies regarding job satisfaction.
In recent years, the use of intelligent methods for automatic detection of sleep stages in medical applications to increase diagnostic accuracy and reduce the workload of physicians in analyzing sleep data by visual inspection is one of the important issues. The most important step for the automatic classification of sleep stages is the extraction of useful features. In this paper, an EEG-based algorithm for automatic detection of sleep stages is presented using features extracted from the recurrence plot and artificial neural network. Due to the non-stationary of the EEG signal, the recurrence plot was used in this paper for nonlinear analysis and extraction of signal features. Various extracted features have different numerical ranges. Normalization was performed to prevent the undesirable effects of large values of data. As all normalized features could not correctly classify different stages of sleep, effective features were selected. The results of this paper show the selected features and the Multi-Layer Perceptron (MLP) neural network able to achieve the values of 98.54 ± 1.88%, 99.03 ± 1.43%, and 98.32 ± 2.11%, respectively, for specificity, sensitivity, and accuracy between the two types of sleep, i.e., Non-Rapid Eye Movement (Non-REM) and Rapid Eye Movement (REM). Also, the results show that the selection of Pz-Oz channel compared to Fpz-Cz channel leads us to a higher percentage for the separation of stages I-IV, awake, while the separation of REM stage using Fpz-Cz channel is better. The results show that the proposed method has a higher success rate in classifying sleep stages than previous studies. The proposed method could well identify and distinguish all stages of sleep at an acceptable level. In addition to saving time, automatic analysis of sleep stages can help better and more accurate diagnosis and reduce physicians' workload in analyzing sleep data through visual inspection.
