A High Performance Lattice Boltzmann Solver with Applications to Multiphase Flow in Porous Media

Multiphase flow is significant to many industrial processes such as the geologic storage of CO2 and oil recovery. Microscale simulation of flow in complex geological formations such as saline aquifers or oilfields is a complex and challenging task. The main goal of our study is to overcome high computational demand of multiphase flow simulations by using high performance computing. To model multiphase flow in porous media, we used a multiphase flow lattice Boltzmann (LB) method, which is recognized as an alternative to the classical computational fluid dynamics (CFD) methods. The developed LB model used an extended Color-Gradient approach with improved numerical stability, and it can be used to compute multiphase flow simulations with low capillary number and high viscosity ratios. To optimize computational efficiency, we apply the LB model to a parallel scheme written in C++ using the Message Passing Interface (MPI). Highly parallel runs of these simulations were performed using the HPC system at the Texas Advanced Computing Center at the University of Texas at Austin. We herein introduce the capability of our tool for multiphase flow simulation in porous media and present its application to CO2 sequestration in geological formations. The model has been applied to the simulation of CO2 and brine in sandstone rocks, by employing three-dimensional micro-CT images of rock samples. Injection of supercritical CO2 into the brine-saturated rock samples is simulated and complex displacement patterns under various reservoir conditions are identified.Texas Advanced Computing Center (TACC

Holistic Integration of Product Attributes with Consumer Behavioral Aspects for the Use of Wearable Technology

The use of wearable technology is rapidly increasing to meet diverse consumer needs and desires. To design, develop, and produce a successful product embedding wearable technology, it is crucial to know consumers’ preferences, expectations, and needs, which enables industry professionals to predict consumers’ attitudes towards the wearables and their purchase intentions. The purpose of this study is to propose a holistic framework, embedding various concepts (e.g., intrinsic and extrinsic attributes) that should be considered when conducting study on consumers’ purchase intention towards the use of wearables. Various models related to the consumer behavior and product design and development have been examined and used to develop this proposed framework, which can assist product designers, developers, manufacturers, and merchandisers to identify the essential product attributes, consumers’ needs and expectations of wearables and let them have a more precise and successful product design and development based on the target consumers’ needs

Psycho-Demographic Determinants of Young Consumers’ Intention towards Purchasing Counterfeit Apparel in a U.S. Counterfeit Capital

The purpose of this study was to examine key psycho-demographic determinants that influenced consumers’ attitude and purchase intention of counterfeit apparel. Using a survey method, a convenience and purposeful sample of 118 young consumers in Los Angeles County was used for this study. From the series of multiple regressions, we found that none of the six personality traits (integrity, materialism, novelty seeking, personal gratification, status consumption, and value consciousness) played a significant role of conforming the participants’ attitudes towards counterfeit apparel. However, the two personality traits (value consciousness and integrity) along with the two demographic variables (gender and income) played a significant role for their intention of purchasing counterfeit apparel, which are the interesting findings of this study. Limitations and implications were also presented for future studies

A combined optimization–simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated to potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers’ physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated to potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50\%, 66\%, and 80\% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30\% by applying our approach, while keeping the transmission risk at the lowest level

Analysis of human resource management in an electronic financial system

In recent decades, studies conducted by researchers suggest that information and communication technologies have had a profound impact on human resource management. Experts believe that electronic Human resource management can potentially reduce administrative costs, increase productivity, shorten response times, improve customer service and develop decision-making process and therefore helps human resources management to be more strategic, flexible, and also more affordable in terms of cost. Today, the necessity of using Automated financial system, due to its potentiality of quick timely and accurate access to information, is clearly evident in most companies and organizations. So that, considering information needs and presence of the computer requires the use of Automated Systems. This study examines the impact of e-HR management systems of accounting, auditing and financial systems as well as other related issues in this context. In the present study the concept of Electronic Human Resource Management, analysis of the impacts of this technology on financial systems, the process of tax systems improvement, update information and financial statements is analysed and solutions and suggestions provided to improve these relationships

Analysis of human resource management in an electronic financial system

In recent decades, studies conducted by researchers suggest that information and communication technologies have had a profound impact on human resource management. Experts believe that electronic Human resource management can potentially reduce administrative costs, increase productivity, shorten response times, improve customer service and develop decision-making process and therefore helps human resources management to be more strategic, flexible, and also more affordable in terms of cost. Today, the necessity of using Automated financial system, due to its potentiality of quick timely and accurate access to information, is clearly evident in most companies and organizations. So that, considering information needs and presence of the computer requires the use of Automated Systems. This study examines the impact of e-HR management systems of accounting, auditing and financial systems as well as other related issues in this context. In the present study the concept of Electronic Human Resource Management, analysis of the impacts of this technology on financial systems, the process of tax systems improvement, update information and financial statements is analysed and solutions and suggestions provided to improve these relationships

DeepSense: A Physics-Guided Deep Learning Paradigm for Anomaly Detection in Soil Gas Data at Geologic CO2 Storage Sites

Full text available at: https://pubs.acs.org/doi/10.1021/acs.est.1c04048Driven by the collection of enormous amounts of streaming data from sensors, and with the emergence of the internet of things, the need for developing robust detection techniques to identify data anomalies has increased recently. The algorithms for anomaly detection are required to be selected based on the type of data. In this study, we propose a predictive anomaly detection technique, DeepSense, which is applied to soil gas concentration data acquired from sensors being used for environmental characterization at a prospective CO2 storage site in Queensland, Australia. DeepSense takes advantage of deep-learning algorithms as its predictor module and uses a process-based soil gas method as the basis of its anomaly detector module. The proposed predictor framework leverages the power of convolutional neural network algorithms for feature extraction and simultaneously captures the long-term temporal dependency through long short-term memory algorithms. The proposed process-based anomaly detection method is a cost-effective alternative to the conventional concentration-based soil gas methodologies which rely on long-term baseline surveys for defining the threshold level. The results indicate that the proposed framework performs well in diagnosing anomalous data in soil gas concentration data streams. The robustness and efficacy of the DeepSense were verified against data sets acquired from different monitoring stations of the storage site.Bureau of Economic Geolog

Correlation study of rs833061, rs2010963 polymorphisms in VEGF-A gene in Iranian colorectal cancer patients

Background and aims: Colorectal cancer (CRC) is the third most common cancer worldwide and is caused by the interaction of genetic and environmental factors. Angiogenesis is the formation of new blood vessels in the body that plays a critical role in tumor growth and metastasis. In this study, it was aimed to examine 2 Gene Polymorphism of the Vascular Endothelial Growth Factor (VEGF) and susceptibility to colorectal cancer in Iranian patients. Methods: In this case-control study, 280 patients with colon adenocarcinoma selected of the pathology centers in Tehran (Sina and Taleghani) hospital as cases and 372 healthy subjects as controls were selected from the same centers. Control subjects were matched according to sex and age. Patients with positive family history of cancer were excluded. Data collected included age, sex, tumor location, stage of disease and cancer tissue. In this study, it was used Real time PCR techniques to genotype rs833061 and rs2010963 polymorphisms. Results: Colorectal cancer in men was more than women (62.7%). Age of most people was under 60 years. Most tumors were located in the colon (69. From the point of tumor tissue differentiation, most of them were placed in the moderate level (54.8%) and pathology stage most of them were in stage III (72.5%). In the current study, between the two groups in terms of susceptibility to colorectal cancer, it was a significant relationship based on onset age T/C genotype of rs833061 polymorphism with age<60 (P=0.008). Conclusion: The results of this study show that people who have been named genotype are more sensitive than others to the risk of colorectal cancer, and also due to the increasing prevalence of colorectal cancer diseases in the worldwide and possibility of prevention, it can be used this genotype as molecular markers for early detection of the disease

Convection-diffusion-reaction of CO2-enriched brine in porous media: A pore-scale study

Bureau of Economic Geolog