Inferring transportation modes from GPS trajectories using a convolutional neural network

Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C: Emerging Technologie

Fungal contamination of indoor public swimming pools and their dominant physical and chemical properties

Introduction: Considering to the existence of both parasitic and fungal pathogens in the indoor public swimming pools and non-utilization of suitable filtration and disinfection systems in these places, this research aimed to determine the relationship between the indoor public swimming pools and possible pollution with parasitic and fungal agents, as well as physical and chemical characteristics of these pools and compare the results with national standards. Methods: In this study, 11 active indoor swimming pools of Zahedan city were sampled, using plastic pumps techniques, at the middle of winter to the late summer season. A total of 88 water samples (eight water samples from each pool) were examined to determine the residual chlorine, contamination with parasitic and fungal agents, using culture media and slide culture techniques. Results were analyzed with SPSS software (V16) and, Microsoft Excel (V2010). Results: The findings revealed parasitic fungal contamination with Cladosporium, Penicillium, Aspergillus flavus and Aspergillus fumigatus, etc. and the physicochemical factors comply with the minimum standards had which indicates the need for continuous monitoring and control of water filtration and disinfection of water is swimming. Conclusion: The results show reasonable derangement of physicochemical and microbial factors of the evaluated pools. Efforts shall be made by the concerned authorities to provide health education to users, quality water at the pools and to maintain the safety and quality of the water through proper and adequate chlorination

A Comparative In Vivo Study of Tissue Reactions to Four Suturing Materials

INTRODUCTION: The purpose of this study was to compare the histopathologic reaction of four suturing materials: silk, polyvinylidene fluoride (PVDF), polyglycolic acid, and catgut in the oral mucosa of albino rabbits. MATERIALS AND METHODS: The twenty-one male mature albino rabbits which were used in this study were randomly divided into three groups of seven each. Silk, PVDF, polyglycolic acid and catgut suture materials were tested in the oral mucosa of these animals. The animals were sacrificed 2, 4, and 7 days after suturing. Two pathologists evaluated the samples by determining the presence and level of inflammation, granulation tissue, and fibrosis formation. Data were statistically analyzed by Kruskal Wallis and Mann-Whitney U tests. RESULTS: Histological features of the samples showed that PVDF and plain catgut suture materials produced more fibrous tissue (favorable response) on the fourth day in comparison with silk suture (P=0.02). Also, in the 7-day samples PVDF sutures produced the mildest inflammation when compared with the silk sutures (P=0.015). CONCLUSION: According to the results of this study, it can be convey that PVDF suture materials created mild tissue reactions and can be a reasonable candidate for suturing oral tissues

A Comparative In Vivo Study of Tissue Reactions to Four Suturing Materials

INTRODUCTION: The purpose of this study was to compare the histopathologic reaction of four suturing materials: silk, polyvinylidene fluoride (PVDF), polyglycolic acid, and catgut in the oral mucosa of albino rabbits. MATERIALS AND METHODS: The twenty-one male mature albino rabbits which were used in this study were randomly divided into three groups of seven each. Silk, PVDF, polyglycolic acid and catgut suture materials were tested in the oral mucosa of these animals. The animals were sacrificed 2, 4, and 7 days after suturing. Two pathologists evaluated the samples by determining the presence and level of inflammation, granulation tissue, and fibrosis formation. Data were statistically analyzed by Kruskal Wallis and Mann-Whitney U tests. RESULTS: Histological features of the samples showed that PVDF and plain catgut suture materials produced more fibrous tissue (favorable response) on the fourth day in comparison with silk suture (P=0.02). Also, in the 7-day samples PVDF sutures produced the mildest inflammation when compared with the silk sutures (P=0.015). CONCLUSION: According to the results of this study, it can be convey that PVDF suture materials created mild tissue reactions and can be a reasonable candidate for suturing oral tissues

Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials

One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes

Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials

One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes