ALBERT

Description: Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

Description: Thermoacoustic refrigerators have huge potential to replace conventional refrigeration systems as an alternative clean refrigeration technology. These devices utilize conversion of acoustic power and heat energy to generate the desired cooling. The stack plays a pivotal role in the performance of Standing Wave Thermoacoustic Refrigerators (SWTARs), as the heat transfer takes place across it. Performance of stacks can be significantly improved by making an arrangement of different materials inside the stack, resulting in anisotropic thermal properties along the length. In the present numerical study, the effect of multi-layered stack on the refrigeration performance of a SWTAR has been evaluated in terms of temperature drop across the stack, acoustic power consumed and device Coefficient of Performance (COP). Two different aspects of multi-layered stack, namely, different material combinations and different lengths of stacked layers, have been investigated. The combinations of four stack materials and length ratios have been investigated. The numerical results showed that multi-layered stacks produce lower refrigeration temperatures, consume less energy and have higher COP value than their homogeneous counterparts. Among all the material combinations of multi-layered stack investigated, stacks composed of a material layer with low thermal conductivity at the ends, i.e., RVC, produced the best performance with an increase of 26.14% in temperature drop value, reduction in the acoustic power consumption by 4.55% and COP enhancement of 5.12%. The results also showed that, for a constant overall length, an increase in length of side stacked material layer results in an increase in values of both temperature drop and COP.

Description: The release of large quantities of CO2 into the atmosphere is one of the major causes of global warming. The most viable method to control the level of CO2 in the atmosphere is to capture and permanently sequestrate the excess amount of CO2 in subsurface geological reservoirs. The injection of CO2 gives rise to pore pressure buildup. It is crucial to monitor the rising pore pressure in order to prevent the potential failure of the reservoir and the subsequent leakage of the stored CO2 into the overburden layers, and then back to the atmosphere. In this paper, the Minjur sandstone reservoir in eastern Saudi Arabia was considered for establishing a coupled geomechanical model and performing the corresponding stability analysis. During the geomechanical modeling process, the fault passing through the Minjur and Marrat layers was also considered. The injection-induced pore-pressure and ground uplift profiles were calculated for the case of absence of a fault across the reservoir, as well as the case with a fault. The stability analysis was performed using the Mohr–Coulomb failure criterion. In the current study, the excessive increase in pore pressure, in the absence of geological faults, moved the reservoir closer to the failure envelope, but in the presence of geological faults, the reservoir reached to the failure envelope and the faults were activated. The developed geomechanical model provided estimates for the safe injection parameters of CO2 based on the magnitudes of the reservoir pore pressure and stresses in the reservoir.

Description: Business incubators create value by combining the entrepreneurial spirit of start-ups with the resources that are typically available to new businesses. It is widely recognized that knowledge-based entrepreneurial companies are the main creators of economic growth, and such enterprises require special business development services. Therefore, the study aims to examine the role of business incubators in providing greater services (networking services, capital support, and training programs) in entrepreneurship development. Secondly, it also examines the mediating and moderating role of business start-up and government regulations for entrepreneurship. Using a quantitative methodology, we examine 567 samples through structural equation modeling. We find that the business incubators are playing an effective mediating role in providing networking services, capital support, and training programs to individuals and entrepreneurs, which are significant for entrepreneurship development, whereas business start-up positively mediates the relationship between networking services, capital support, training programs, and entrepreneurship development. Government regulations for entrepreneurship have a direct effect on entrepreneurship development. More importantly, government regulations for entrepreneurship have a positive moderating effect between business start-up and entrepreneurship development. Our study identifies the critical resources needed to improve the quality of business incubators and to ensure the availability of such resources to improve entrepreneurship development.

Description: Well logging is a significant procedure that assists geophysicists and geologists with making predictions regarding boreholes and efficiently utilizing and optimizing the drilling process. The current study area is positioned in the Punjab Territory of Pakistan, and the geographic coordinates are 30020′10 N and 70043′30 E. The objective of the current research work was to interpret the subsurface structure and reservoir characteristics of the Kabirwala area Tola (01) well, which is located in the Punjab platform, Central Indus Basin, utilizing 2D seismic and well log data. Formation evaluation for hydrocarbon potential using the reservoir properties is performed in this study. For the marked zone of interest, the study also focuses on evaluating the average water saturation, average total porosity, average effective porosity, and net pay thickness. The results of the study show a spotted horizon stone with respect to time and depth as follows: Dunghan formation, 0.9 s and 1080.46 m; Cretaceous Samana Suk formation, 0.96 s and 1174.05 m; Datta formation, 1.08 s and 1400 m; and Warcha formation, 1.24 s and 1810 m. Based on the interpretation of well logs, the purpose of petrophysical analysis was to identify hydrocarbon-bearing zones in the study area. Gamma ray, spontaneous potential, resistivity, neutron, and density log data were utilized. The high zone present in the east–west part of the contour maps may be a possible location of hydrocarbon entrapment, which is further confirmed by the presence of the Tola-01 well.

Description: The Rub’ Al-Khali basin in Saudi Arabia remains unexplored and lacks data availability due to its remoteness and the challenging nature of its terrain. Thus far, there are neither digital geologic models nor synthetic seismic data from this specific area accessible for testing research techniques and analysis. In this study, we build a 2D viscoelastic model of the eastern part of the Rub’ Al-Khali basin and generate a corresponding dual-component seismic data set. We compile high-resolution depth models of compressional- and shear-wave velocities, density, as well as compressional- and shear-wave quality factors from published data. The compiled models span Neoproterozoic basement up to Quaternary sand dunes. We then use the finite-difference technique to model the propagation of seismic waves in the compiled viscoelastic medium of eastern Rub’ Al-Khali desert. In particular, we generate vertical and horizontal components of the shot gathers with accuracy to the fourth and second orders in space and time, respectively. The viscoelastic models and synthetic seismic datasets are made available in an open-source site for prospective re-searchers who desire to use them for their research. Users of these datasets are urged to make their findings also accessible to the geoscience community as a way of keeping track of developments related to the Rub’ Al-Khali desert.

Description: Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.