The present paper proposes a new application of the prediction of human Behavior using TOPSIS as an appropriate tool for data optimization. Our hypothesis was that the analysis of the candidates with this method was influenced by the change of their Behavior. We found that the Behavior change could occur in more than one time span when the Behavior of two candidates changed simultaneously. One of the advantages of this study is that the pattern of the Behavior change with time is predicted with this method. Another advantage is that the modifications in the TOPSIS algorithm have made the predictions independent from the need of changing the fuzzy membership degrees of the candidates. This is the first time that these modifications in this technique with a new application including the numerical analysis of cognitive date are reported. Our results can be used in cognitive science, experimental psychology, cognitive informatics and artificial intelligence.

The vehicle load repetition on the pavements leads to further deflections due to the material softening and the reduction of pavement stiffness. In this research, the stiffness reduction studied using a back analysis of surface bowls under wheel load. In this back analysis the visco-elastoplastic model and the finite element method used. There seems to be no evidence of evaluating the stiffness reduction of pavements using visco-elastoplastic modeling of asphalt concrete Behavior. The results indicated that this modeling approach provides a more accurate predication of performance and useful cycle life of asphalt mixtures when compared to the visco-elastic models.

Environment and its conservation is one of the present issues in risky modern life. Although, the present world has been improving during last years, it hasn’t been able to solve environmental problems and it has caused its deterioration. Littering in jungle, seashore, public places and passages is one of the significant environmental issues in society of Iran. The Purpose of the present study is investigation of the reasons of littering formation by the approach of Causal Layered Analyses (CLA) and planning scenario. The present study analyzed the layers forming the present social issue and searched the origin of it. In order to accomplish this study, 15 experts in environment and society were interviewed by Qualitative and exploring method and applying deep interview technique. Then, after exploiting the contents and content analysis of interviews, their opinions presented in 4 forms as litany, systematic, discourse analysis and metaphor. By applying the present discourses, the gap between state-people and people-people were recognized as important variables and were applied in presenting scenario. Finally 4 scenarios presented in a diagram which can help strategists and officials of the society.

ABSTRACT Despite a wide range of components and criteria affecting travel Behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel Behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan in Tehran, Iran, as the old, conventional, and new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the factors affecting travel Behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered that factors such as car ownership, dependence and pro-liking for private cars, density and access to educational centers and parks, access to medical and service centers, and variety and density of retail stores had been introduced as the factors affecting travel Behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, and pro-liking for other than a private car, having a license, number of children under five years old, and age have influenced travel Behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding factors affecting travel Behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including land use, along with demographic-economic characteristics, travel Behavior, and attitudes of people, has been raised by urban planners. Studies seek to find factors affecting travel Behavior, especially land use characteristics. Despite presenting a wide range of components and criteria affecting travel Behavior, the results of the studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel Behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel Behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical and non-physical characteristics affect travel Behavior due to distinctions between different development patterns?   Methodology The present research method is analytical and experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, and walking as the travel Behavior. According to the research's purpose, indicators and criteria affecting travel Behavior were extracted after reviewing the theoretical and experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan as the old, conventional, and new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of land use, travel habits, and access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the factors affecting travel Behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered which factors affecting travel Behavior were due to the differences in study areas and which factors affect travel Behavior regardless of development patterns.   Results and discussion This research aims to identify the factors affecting travel Behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, and pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of factors affecting travel Behavior, such as perceptually environmental characteristics of the neighborhood, dependence and pro-liking for personal cars, variety and density of retail stores, density and access to educational units and parks, access to medical and service centers, and car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical and non-physical factors (the same indicators in the same study areas) with travel Behavior, the factors affecting travel Behavior owing to different development patterns were identified. Factors such as car ownership, dependence and pro-liking for private cars, density and access to educational units and parks, access to medical and service centers, and variety and density of retail stores have been introduced as the factors affecting travel Behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, and pro-liking for other than a private car, having a certificate, number of children under five years old, and age have influenced on travel Behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the factors affecting travel Behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the factors affecting travel Behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel Behavior and finding the main components affecting it, considering the extent of variables and data, can pave the way for professionals in transportation planning and urban planning, in addition to providing detailed methods and criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Background: Road traffic injuries (RTIs) are one of the most critical factors that endanger human health. More specifically, head and neck injuries are the main causes of deaths and disabilities among motorcyclists. This study aimed to investigate the predictive factors of helmet use Behavior among motorcyclists based on the theory of planned Behavior (TPB). Study Design: This study followed the cross-sectional design. Methods: This study was conducted on randomly selected 730 motorcyclist employees in Qom, Iran, in 2021. The data collection tool was a self-administered researcher-made questionnaire, including items on demographic characteristics, history of RTIs, and constructs of TPB. Data were analyzed using descriptive summary statistics, analysis of variance, independent samples t test, Pearson correlation coefficient, and structural equation modeling (SEM). Results: In this study, only 9. 8% of the participants reported that they always used a helmet while riding a motorcycle. About 60% reported a history of a motorcycle crash, and 11. 5% had a history of head injuries. The direct effect of attitude, subjective norms, and perceived Behavioral control on the intention to use a helmet were statistically significant, explaining 59% of the variation in Behavioral intention (intention to use a helmet) (R2 = 0. 59). Moreover, perceived Behavioral control and Behavioral intention had significant effects on helmet use Behavior (R2 = 0. 26). Conclusion: The prevalence of helmet use among the studied population was very low. Moreover, TPB was useful in identifying the determinants of Behavior and especially Behavioral intention of helmet use among motorcyclists.

There is an urgent need for precise and trustworthy models to forecast device Behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device Behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device Behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device Behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.