Dental decay is the most common chronic disease in children. Fluoridevarnish (FV) is a preventive oral health service with proven effectiveness atreducing dental caries in dental and primary care settings. The objective ofthis study was to determine how long it takes to apply FV treatments duringprimary care well visits to address one of the most common barriers as reportedby pediatricians – lack of time. FV treatment videos were collected atsix clinics in Georgia with rigorous time studies conducted on each video todetermine the Standard Time for the FV treatment process as well as the FVApplication Component of the process and reasons for delays. Median StandardTimes varied by clinic, ranging from 67.7 seconds to 166.9 seconds with anoverall median of 109.7 seconds. This results in per FV application labor costsof approximately $2.38 for pediatricians, $1.16 for registered nurses, and $0.53for medical assistants. Findings from this study support the inclusion of FVapplications as a common practice during primary care well visits.

Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms.

In this article, a mathematical model of the inverse problem is considered. Based on this model a formulation of inverse problem for heat equation is proposed. Shifted Chebyshev Tau (SCT) method is suggested to solve the inverse problem. The aim of this determined effort is to identify unknown function and unknown control parameter of the mathematical model. In order to achieve highly accurate solution to this problem, the operational matrix of shifted Chebyshev polynomials is investigated in conjunction with tau scheme. To demonstrate the validity and applicability of the developed scheme, numerical example is presented.

The sentiment analysis is a subtask of text classification that is known as a domain dependent problem. In order to obtain an accurate classifier for a particular domain, a large labelled dataset is needed. To tackle the challenge of data scarcity in some domains, in the area of multi-domain problems, the classifier is trained on a set of labelled data from some domains and then it is applied to the target domains. In addition, another important issue in classification-based approaches in order to reach the better performance is that the nature of train and test data should be similar. So, a model trained by data from a specific domain, leads to poor results when it comes to another domains. This paper proposes three Weighted(deep)Neural Networks Ensemble approaches for multi-domain sentiment classification to address the mentioned issues, by training individual deep learning models (including CNN, LSTM and Bi-GRUCapsule) on specific domains. Using a weighted score of DBD and the initial polarity of the sample test data on each domain, a new aggregated score of final polarity is obtained. The DRANZIERA protocol is used for evaluation of the proposed models. The results have shown more than 0.03 improvements in average accuracy in comparison to the other state-of-the-art approaches.

Supply chain risk management involves identifying, ranking, and adopting appropriate strategies to control and deal with risks that could disrupt chain performance. These risks can be caused by different issues and descriptions and surveys about these risks are associated with uncertainty, ambiguity, qualitativeness and incomplete and sometimes contradictory information. Therefore, their ranking needs the techniques that can model the mentioned issues. neutrosophic logic makes it possible to model propositions with uncertainty, incomplete information, ambiguity, qualitativeness, and even inconsistency. Accordingly, the approach of the present study is to use a combined method of neutrosophic hierarchical analysis and TOPSIS for ranking the risk. Core of this paper is proposed a hybrid decision making method for identification and ranking of supply chain management by a Neutrosophic analytical hierarchy process and TOPSIS approach. The case study is Mobarakeh Steel Company of Isfahan and three criteria including resilience, agility and robustness are considered as major strategies to deal with risk and seventeen risk-related issues are ranked as options. The results show that government constraints, economic and environmental risks, inventory shortages, technology risk, forecast risk and financial (cash) problems are the most important risks threatening the supply chain. Therefore, we believe that the proposed framework provides managers with valuable knowledge for decision making.

The aim of analyzing passengers' behavioral patterns is providing support for transportation management. In other words, to improve services like scheduling, evacuation policies, and marketing, it is essential to understand spatial and temporal patterns of passengers' trips. Smart Card Automated Fare Collection System (SCAFCS) makes it possible to utilize data mining tools for the purpose of passengers' behavioral pattern analysis. The specific goal of this research is to obtain functional information for passenger's behavioral pattern analysis in city express bus which is called BRT, and classification of passengers to improve performance of bus fast transportation system. Additionally, it is attempted to predict usage and traffic status in a line through predicting passenger's behavior in a bus line. In this paper, smart card data is applied to provide combinational algorithms for clustering and analysis based on data mining. To this end, we have used a combination of data mining methods and particle swarm optimisation algorithm and leveraged multivariate time series prediction to estimate behavioral patterns. Results show that price and compression ratio features are the most influencing features in the separability of transportation smart card data. According to obtained Pareto front, four features include a card identification number, bus identification number, bus line number, and charge times are influencing clustering criteria.

Human life today is intertwined with abundant trade and economic exchanges, and life would not be possible without trade and commerce. One of the main pillars of financial exchanges are banks and financial and credit institutions, which, as the vital arteries of the economy, are responsible for transferring funds and keeping the economy alive. In the world of economic competition between organizations, profitability and proper performance for stakeholders are the basic principles of the organization's survival. To increase profitability, banks must take measures that, in addition to reducing costs, increase the level of service and customer satisfaction. The best way to do this is to use new technologies and orient the bank's policies to provide services in person and independent of time and place. The use of new technologies in the banking system sometimes leads to customers' distrust and distrust of the bank. Therefore, solutions to detect fraud in banking transactions should be provided. This article aims to discover a model for face-to-face transactions and to establish a system to block fraudulently issued transactions. Therefore, a big data clustering method is designed to timely identify bribery in banking transactions. The results show that using the big data clustering method in the fastest time can detect and stop possible fraud in customers' banking transactions.

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.

One of the most important decisions taken in a supply chain is the issue of aggregate production planning where a program-within a medium time-range-- is determined for optimum manufacturing of all products using shared equipment and resources. This research presents a multi-objective model that helps the decision makers to make such decisions. The proposed model comprises four main objectives, the first one of which considers minimizing costs (including costs of manufacturing product, supplying, maintenance, inventory stock shortage, and expenditures related to man power). The second objective is defined as maximizing customers’ satisfaction. Minimizing suppliers’ satisfaction makes up the third objective and maximizing the quality of the manufactured products constitutes the fourth objective. In this model, the demand parameter is investigated under uncertain conditions; hence, other parameters influenced by this parameter are also presented under uncertain conditions occurring within three differing scenarios. This model is solved through LP- metric and the LINGO v14.0.1.55 software. At first the model is solved by means of numerical example; then it is solved by the actual data that are related to a military industry. Finally, process, variables like inventory level, overtime work hours etc, are valued with the help of closed-loop supply chain of the proposed model.

Pathology of a competency-based human resource management system results in the managers developing appropriate schedules for employee growth and excellence to gain a competitive advantage. The present study is meant to be an applied study of a descriptive exploratory nature. Information and data gathering involves library and field research making use of interviews and questionnaires. The statistical population of this study is comprised of experts including 10 human resource managers and chief officers, selected using a targeted method. The components were determined and analyzed using the experts’ opinions and the Delphi method; furthermore, by implementing the importance-performance analysis, low-performance but high-importance components were categorized in 6 groups of “Human Resource Supply System”, “Human Resource Training and Development System”, “Human Resource Retention System”, “Human Resource Labor Relations System”, “Human Resource Performance Management System”, “Human Resource Reward System”. Moreover, to rectify the problems, the Fuzzy DEMATEL method was implemented with the results showing the “Human Resource Supply System” problem to be the most effective, eventually leading to the problem of “Human Resource Performance Management System”, being the most affected. Managers can reach a desirable human resource management system by identifying individuals of professional competence to compete in a dynamic economy.