48 research outputs found
Synergistic Effects of Andrographolide on DNA Damage Repair Mechanism and Apoptosis in Breast Cancer Cells
No full textBreast cancer is the second leading cause of cancer deaths in women. Several drugs including cisplatin and carboplatin have shown tremendous effectivity in reducing cancer; however development of drug resistance by breast cancer cells to overcome cytotoxic insults and recurrence of the disease is a major concern at the moment. Andrographolide is a diterpenoid with a potent anti-inflammatory and anti tumor activity and it’s usage in combination therapy would be ideal as it is proven for it’s apoptotic capability in varied number of cells. Antiproliferative and apoptotic activity of andrographolide in triple negative MDA-MB-231 cells was evaluated by clonogenic assay and flow cytometric analysis. Expression and phosphorylation of proteins were evaluated by immunoblotting. Our results revealed dose-dependent cytotoxic effects of andrographolide in MDA-MB-231 cells with and without carboplatin. It resulted in G2/M arrest of cells when treated alone, and further enhanced upon treatment in combination with carboplatin. Andrographolide alone and in combination with carboplatin enhanced apoptotic cells in early, mid and late stages and increased expression of DNA damage repair response proteins including FANCJ, FANCD2, RAD51, pRPA32 and p53. The present study strongly suggests that andrographolide inhibits breast cancer cell proliferation by apoptosis mediated through cell cycle arrest and up regulation of DNA damage repair response gene expression and shows synergistic effects upon usage in combination with carboplatin
Experience in Data Quality Assessment on Archived Historical Freeway Traffic Data
No full textabstract: Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This research is an exercise in measuring and reporting data quality. The assessment was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway sensors in the metropolitan region. Results of the assessment provide an example of describing the quality of the traffic data with each of six data quality measures suggested in the literature, which are accuracy, completeness, validity, timeliness, coverage and accessibility. An important contribution is made in the use of data quality visualization tools. These visualization tools are used in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. More significantly, they serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered valid. Recommendations from the experience gained in this assessment include that data quality visualization tools be developed and used in the processing and quality control of traffic data, and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed data.Dissertation/ThesisM.S. Civil and Environmental Engineering 201
On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices
No full textabstract: A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimation. The proposed method is applied to model commute mode choice among four alternatives (auto, transit, bicycle and walk) using travel behavior data from Argau, Switzerland. Comparisons between the multinomial logit model and the proposed semi-nonparametric model show that violations of the standard Gumbel distribution assumption lead to considerable inconsistency in parameter estimates and model inferences.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.018668
Stability and Agglomeration of Alumina Nanoparticles in Ethanol-Water Mixtures
No full textAbstractNanofluids have gained much attention in the last decade due to wide range of engineering applications. Agglomeration among nanoparticles in nanosuspensions accelerates settling of nanoparticles due to gravity and reduces overall thermal conductivity of nanofluid. Settling characteristics of alumina nanoparticles in six different concentrations in different proportions of ethanol and water mixtures are studied. Surfactant free nanofluids are prepared with Alumina nanoparticles of average diameter 40nm, 50nm and 100nm using two-step method. Settling behaviour of nanoparticles in nanosuspensions is observed under natural and sonicated conditions. Photographic technique is used to measure sediment height in a batch sedimentation apparatus. Heights of the sediments are observed for 24hours with and without sonication at room temperature. It is found that sonication has significant influence on the stability of nanofluids. Effect of ethanol concentration on the sediment ratio is studied with and without sonication. Aggregation among nanoparticles and interaction of particles with fluid mixtures are investigated by performing various analyses such as Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM) and particle size analyser
Enhancing the Cognition and Efficacy of Machine Learning Through Similarity
No full textSimilarity is a key element of machine learning and can make human learning much more effective as well. One of the goals of this paper is to expound on this aspect. We identify real-world concepts similar to hard-to-understand theories to enhance the learning experience and comprehension of a machine learning student. The second goal is to enhance the work in the current literature that uses similarity for transcoding. We uniquely try transcoding from Python to R and vice versa, something that was not attempted before, by identifying similarities in a latent embedding space. We list several real-world analogies to show similarities with and simplify the machine learning narrative. Next, we use Cross-Lingual Model Pretraining, Denoising Auto-encoding, and Back-translation to automatically identify similarities between the programming languages, Python and R and convert code in one to another. In the course of teaching machine learning to undergraduate, graduate, and general pool of students, the first author found that relating the concepts to real-world examples listed in this paper greatly enhanced student comprehension and made the topics much more approachable despite the math and the methods involved. When it comes to transcoding, in spite of the fact that Python and R are substantially different, we obtained reasonable success measured using various evaluation metrics and methods as described in the paper. Machines and human beings predominantly learn by way of similarity, a finding that can be explored further in both the machine and human learning domains
Relating Machine Learning to the Real-World: Analogies to Enhance Learning Comprehension
No full textMachine learning is an exciting field for many, but the rigor, math, and its rapid evolution are often found to be formidable, keeping them away from studying and pursuing a career in this area. Similarity has been substantially explored in machine learning algorithms such as in the K-nearest neighbors, Kernel methods, Support Vector Machines, but not so much in human learning, particularly when it comes to teaching machine learning. In the course of teaching the subject to undergraduate, graduate, and general pool of students, the author found that relating the concepts to real-world examples greatly enhances student comprehension and makes the topics much more approachable despite the math and the methods involved. This paper relates some of the concepts, artifacts, and algorithms in machine learning such as overfitting, regularization, and Generative Adversarial Networks to the real world using illustrative examples. Most of the analogies included in the paper were well appreciated by the students in the course of the author’s teaching and acknowledged as enhancing comprehension. It is hoped that the material presented in this paper will benefit larger audiences, drawing more learners to the field, resulting in enhanced contributions to the area. The paper concludes by suggesting deep learning for automatically generating similarities and analogies as a future direction
Empirical analysis and modeling of freeway merge ratios and lane flow distribution
No full textabstract: This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in the evolution and stability of congested traffic. At merges, drivers from conflicting traffic branches take turns to merge into a single stream at a rate referred to as the “merge ratio”. In this research, data from several freeway merges was used to evaluate existing macroscopic merge models and theoretical principles of merging behavior. Findings suggest that current merge ratio estimation methods can be insufficient to represent site-specific merge ratios, due to observed within-site variations and unaccounted effects of downstream merge geometry. To overcome these limitations, merge ratios were formulated based on their site-specific lane flow distribution (LFD), the proportion of flow in each freeway lane, for two types of merge geometries. Results demonstrate that the proposed methods are able to improve merge ratio estimates, reproduce within-site variations of merge ratio, and represent more effectively disproportionate redistribution of merging flow for merges where vehicles compete directly to merge due a downstream lane reduction.
Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.
Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201
Analysis of Freeway Bottlenecks
No full textabstract: Traffic congestion is a major externality in modern transportation systems with negative economic, environmental and social impacts. Freeway bottlenecks are one of the key elements besides the demand for travel by automobiles that determine the extent of congestion. The primary objective of this research is to provide a better understanding of factors for variations in bottleneck discharge rates. Specifically this research seeks to (i) develop a methodology comparable to the rigorous methods to identify bottlenecks and measure capacity drop and its temporal (day to day) variations in a region, (ii) understand the variations in discharge rate of a freeway weaving bottleneck with a HOV lane and (iii) understand the relationship between lane flow distribution and discharge rate on a weaving bottleneck resulted from a lane drop and a busy off-ramp. In this research, a methodology has been developed to de-noise raw data using Discrete Wavelet Transforms (DWT). The de-noised data is then used to precisely identify bottleneck activation and deactivation times, and measure pre-congestion and congestion flows using Continuous Wavelet Transforms (CWT). To this end a methodology which could be used efficiently to identify and analyze freeway bottlenecks in a region in a consistent, reproducible manner was developed. Using this methodology, 23 bottlenecks have been identified in the Phoenix metropolitan region, some of which result in long queues and large delays during rush-hour periods. A study of variations in discharge rate of a freeway weaving bottleneck with a HOV lane showed that the bottleneck discharge rate diminished by 3-25% upon queue formations, however, the discharge rate recovered shortly thereafter upon high-occupancy-vehicle (HOV) lane activation and HOV lane flow distribution (LFD) has a significant effect on the bottleneck discharge rate: the higher the HOV LFD, the lower the bottleneck discharge rate. The effect of lane flow distribution and its relationship with bottleneck discharge rate on a weaving bottleneck formed by a lane drop and a busy off-ramp was studied. The results showed that the bottleneck discharge rate and lane flow distribution are linearly related and higher utilization of the median lane results in higher bottleneck discharge rate.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201
Demographic Evolution Modeling System for Activity-Based Travel Behavior Analysis and Demand Forecasting
No full textabstract: The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.Dissertation/ThesisPh.D. Civil and Environmental Engineering 201
Transit-oriented Smart Growth Can Reduce Life-cycle Environmental Impacts and Household Costs in Los Angeles
No full textabstract: The environmental and economic assessment of neighborhood-scale transit-oriented urban form changes should include initial construction impacts through long-term use to fully understand the benefits and costs of smart growth policies. The long-term impacts of moving people closer to transit require the coupling of behavioral forecasting with environmental assessment. Using new light rail and bus rapid transit in Los Angeles, California as a case study, a life-cycle environmental and economic assessment is developed to assess the potential range of impacts resulting from mixed-use infill development. An integrated transportation and land use life-cycle assessment framework is developed to estimate energy consumption, air emissions, and economic (public, developer, and user) costs. Residential and commercial buildings, automobile travel, and transit operation changes are included and a 60-year forecast is developed that compares transit-oriented growth against growth in areas without close access to high-capacity transit service. The results show that commercial developments create the greatest potential for impact reductions followed by residential commute shifts to transit, both of which may be effected by access to high-capacity transit, reduced parking requirements, and developer incentives. Greenhouse gas emission reductions up to 470 Gg CO2-equivalents per year can be achieved with potential costs savings for TOD users. The potential for respiratory impacts (PM10-equivalents) and smog formation can be reduced by 28-35%. The shift from business-as-usual growth to transit-oriented development can decrease user costs by $3,100 per household per year over the building lifetime, despite higher rental costs within the mixed-use development.Dissertation/ThesisMasters Thesis Civil Engineering 201
