Search Results (5)

Methane (CH₄), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH₄ and H₂O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH₄-H₂O system and CH₄’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH₄ in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to before the update. However, the modified Henry’s law suffers from poor calculation accuracy under certain pressure conditions. To further improve the accuracy of solubility prediction, this work also trains a BP (Back Propagation) neural network model based on the database. In addition, MSE (Mean-Square Error) is used as the model evaluation index, and pressure, temperature, compression coefficient, salinity, and fugacity are preferred as input variables, which finally reduces the mean relative error of the model to 16.32%, and the calculation results are more accurate than the modified Henry’s law. In conclusion, this study provides a novel and more accurate method for predicting CH₄ solubility by comparing modified Henry’s law to neural network modeling. Full article

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas. Full article

The understanding of the structure of knowledge is an essential step of education. Although teachers offer the information foundation and relationship among knowledge points, there are still few methods to encourage students to explore the structure of knowledge by themselves outside of classes. This paper explores the gamification method and the knowledge structure of computer science. We assess the gamification method of “splitting and combining” (SC) to encourage students to finish the process of learning structured knowledge in the university. The results show that this method works well in promoting learning enjoyment and that splitting demonstrates better performance than combining. We can consider the SC method when recommending a gamification method to engage students in structural learning assistance in future smart university education. Full article

It is important to understand urban auto markets from a spatial perspective. Specifically, the question of how to simplify and visualize the relatedness of the complicated urban markets arises. Based on the concept of ‘product space’, this research explores the similarity between Chinese cities and identifies the city clusters using data of automobile sales in 2012. A city’s automobile market is shared by different manufacturers and the proximity between two cities is evaluated based on the similarity or relatedness in the structure of the two markets. The spatial structures of the ‘city clusters’ derived from the proximities of automobile markets among cities are mapped, examined, and interpreted. The analysis indicates that cities with higher proximity tend to be similar. According to the intercity proximity index, four geographical city-clusters are identified: the Southeast developed city-cluster, North China city-cluster, Northeast city-cluster, and West city-cluster. Cities in the same cluster tend to share many common characteristics while cities in different clusters exhibit obvious variances, especially in terms of economic status and dominant automakers. Full article

A novel optical biosensor based on simultaneous multiwave length detection surface plasmon resonance (SPR) has been developed for immunosensing. The sensor is designed on the basis of fixing angle of incidence and measuring the reflected intensities of light in the wavelength range of 400–800 nm. The SPR spectra are shown in terms of reflected light intensity verus wavelength of incident light. The intensity of the reflected light is the minimum at the resonant wavelength. The biorecognition surface, formed on a chemically modified gold layer, consists of avidin that is specifically bound with biotin. These sensing membranes were self-assembled on gold layer. The processes of sensing monolayer formation were studied in real time through observing the change of resonant wavelength. The modified surface was used as a model immunosensor and to detect successfully the human factor B (Bf). The Bf was determined in the concentration range of 0.5~100 μg/mL. Under optimum experimental conditions, the sensor has a good repeatability, reversibility and selectivity. Full article