A longitudinal mobile phone data that include both location and communication logs is analyzed to infer social influence in terms of ego-network effect in the commute mode choice. The results show that person’s strong ties are more important to determine if driving is the person’s transport mode choice, whereas weak ties are more important to determine if public transit is the person’s choice. It is also evident from the results that social ties that are geographically closer are more influential for the commute mode choice than the ones who are farther away. For public transit, access distance is also one of the influential factors. The portion of transit users decreases as the access distance becomes larger. Moreover, social network is shown to influence the commute mode choice, as the likelihood of choosing a particular mode choice rises with the portion of social ties choosing that specific mode.

With growing concern of data privacy violations, privacy preservation processes become more intense. The k-anonymity method, a widely applied technique, transforms the data such that the publishing datasets must have at least k tuples to have the same link-able attribute, quasi-identifiers, values. From the observations, we found that, in a certain domain, all quasi-identifiers of the datasets, can have the same data type. This type of attribute is considered as an Identical Generalization Hierarchy (IGH) data. An IGH data has a particular set of characteristics that could utilize for enhancing the efficiency of heuristic privacy preservation algorithms. In this paper, we propose a data privacy preservation heuristic algorithm on IGH data. The algorithm is developed from the observations on the anonymous property of the problem structure that can eliminate the privacy constraints consideration. The experiment results are presented that the proposed algorithm could effectively preserve data privacy and also reduce the number of visited nodes for ensuring the privacy protection, which is the most time-consuming process, compared to the most efficient existing algorithm by at most 21%.

Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.