Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems

In the current water resources scientific literature, a wide variety of engineering design problems are solved in a simulation-optimization framework. These problems can have single or multiple objective functions and their decision variables can have discrete or continuous values. The majority of current literature in the field of water resources systems optimization report using heuristic global optimization algorithms, including evolutionary algorithms, with great success. These algorithms have multiple parameters that control their behavior both in terms of computational efficiency and the ability to find near globally optimal solutions. Values of these parameters are generally obtained by trial and error and are case study dependent. On the other hand, water resources simulation-optimization problems often have computationally intensive simulation models that can require seconds to hours for a single simulation. Furthermore, analysts may have limited computational budget to solve these problems, as such, the analyst may not be able to spend some of the computational budget to fine-tune the algorithm settings and parameter values. So, in general, algorithm parsimony in the number of parameters is an important factor in the applicability and performance of optimization algorithms for solving computationally intensive problems. A major contribution of this thesis is the development of a highly efficient, single objective, parsimonious optimization algorithm for solving problems with discrete decision variables. The algorithm is called Hybrid Discrete Dynamically Dimensioned Search, HD-DDS, and is designed based on Dynamically Dimensioned Search (DDS) that was developed by Tolson and Shoemaker (2007) for solving single objective hydrologic model calibration problems with continuous decision variables. The motivation for developing HD-DDS comes from the parsimony and high performance of original version of DDS. Similar to DDS, HD-DDS has a single parameter with a robust default value. HD-DDS is successfully applied to several benchmark water distribution system design problems where decision variables are pipe sizes among the available pipe size options. Results show that HD-DDS exhibits superior performance in specific comparisons to state-of-the-art optimization algorithms. The parsimony and efficiency of the original and discrete versions of DDS and their successful application to single objective water resources optimization problems with discrete and continuous decision variables motivated the development of a multi-objective optimization algorithm based on DDS. This algorithm is called Pareto Archived Dynamically Dimensioned Search (PA-DDS). The algorithm parsimony is a major factor in the design of PA-DDS. PA-DDS has a single parameter from its search engine DDS. In each iteration, PA-DDS selects one archived non-dominated solution and perturbs it to search for new solutions. The solution perturbation scheme of PA-DDS is similar to the original and discrete versions of DDS depending on whether the decision variable is discrete or continuous. So, PA-DDS can handle both types of decision variables. PA-DDS is applied to several benchmark mathematical problems, water distribution system design problems, and water resources model calibration problems with great success. It is shown that hypervolume contribution, HVC1, as defined in Knowles et al. (2003) is the superior selection metric for PA-DDS when solving multi-objective optimization problems with Pareto fronts that have a general (unknown) shape. However, one of the main contributions of this thesis is the development of a selection metric specifically designed for solving multi-objective optimization problems with a known or expected convex Pareto front such as water resources model calibration problems. The selection metric is called convex hull contribution (CHC) and makes the optimization algorithm sample solely from a subset of archived solutions that form the convex approximation of the Pareto front. Although CHC is generally applicable to any stochastic search optimization algorithm, it is applied to PA-DDS for solving six water resources calibration case studies with two or three objective functions. These case studies are solved by PA-DDS with CHC and HVC1 selections using 1,000 solution evaluations and by PA-DDS with CHC selection and two popular multi-objective optimization algorithms, AMALGAM and ε-NSGAII, using 10,000 solution evaluations. Results are compared based on the best case and worst case performances (out of multiple optimization trials) from each algorithm to measure the expected performance range for each algorithm. Comparing the best case performance of these algorithms shows that, PA-DDS with CHC selection using 1,000 solution evaluations perform very well in five out of six case studies. Comparing the worst case performance of the algorithms shows that with 1,000 solution evaluations, PA-DDS with CHC selection perform well in four out of six case studies. Furthermore, PA-DDS with CHC selection using 10,000 solution evaluations perform comparable to AMALGAM and ε-NSGAII. Therefore, it is concluded that PA-DDS with CHC selection is a powerful optimization algorithm for finding high quality solutions of multi-objective water resources model calibration problems with convex Pareto front especially when the computational budget is limited

APPLICATION OF MULTI-LAYER SELF-ORGANIZING MAPS (MLSOM) ON ANALYZING FACEBOOK ACTIVITIES

Facebook is the largest and the most popular online social network that records the large amount of users’ behavior expressed in various activities such as Facebook Likes, status updates, posts, comments, photos, tags and shares. One of the major attractions of such a big data offered by Facebook relates to the predictability of individuals’ psychological traits from their digital footprints which helps researchers and service providers to improve personalized products and services. The goal of this research project is to investigate the predictability of Facebook users’ personality traits measured by BIG5 test as a function of their digital records of behavior such as Facebook Likes. This research is based on a dataset of 92,255 users who provided their Facebook Likes and the results of their BIG5 personality test. For preprocessing the Likes data including 600 attributes, the proposed model uses the R Package “fscaret” to automatically determine the importance level of the attributes as a function of the set of learning algorithms applied to this problem. Two supervised versions of the Multi-Layer Self-Organizing-Map (MLSOM) algorithm is used to visualize the data and predict the users’ personality profiles as a function of Facebook profiles. The model predicts Facebook users\u27 BIG5 personality traits with mean squared error of at most 0.053 for neuroticism and correlation of at most 0.3 for openness

TGF-β/Smad signaling pathway as a candidate for EMAST phenotype in colorectal cancer patients

Objective: Elevated microsatellite alteration at selected tetranucleotide repeats (EMAST) is a prognostic biomarker in colorectal cancer (CRC). EMAST phenotype appears to be linked to deficiency in DNA mismatch repair (MMR) proteins including MSH3. The TGF-β signaling pathway has a pivotal role in tumorigenesis of CRC. Since the biological causes of EMAST phenomenon has remained a matter of debate, this study aimed to investigate the association between Smad-dependent canonical signaling TGF-β pathway and EMAST phenotype in colorectal cancer patients. Patients and Methods: EMAST status was analyzed in normal and paraffin-embedded tumor tissues of 122 CRC patients using QIAxcel capillary PCR and electrophoresis. Immunohistochemical method was used to determine the expression of canonical TGFβ-signaling pathway and MSH3 proteins. Eventually, the relationship between canonical TGF-β signaling pathway activation and EMAST phenotype and, therefore, MSH3 expression, was evaluated. Results: 40.2% of CRC tumors had EMAST+ phenotype. The canonical TGF-β signaling pathway was activated in 27.9% of patients. Furthermore, 43.4% of patients indicated low expression of MSH3. 64.7% of tumors characterized with activated canonical TGF-β signaling pathway were EMAST+. Finally, a significant relationship between TGF-ß signaling pathway activation and MSH3 expression was observed. Conclusions: In current study, the activation of canonical TGF-β signaling pathway in CRC tumors mediated by Smad proteins was significantly associated with EMAST phenotype and MSH3 expression

Downregulation of human leukocyte antigen Class I expression: An independent prognostic factor in colorectal cancer

Aim: In the present study, we evaluated the clinical prognostic value of human leukocyte antigen (HLA (Class I tumor cell expression in a series of colorectal cancer (CRC) patients and also explored the association of this expression profile with molecular features such as mutation status of KRAS and BRAF, microsatellite stability status, and clinicopathological characteristics of the patients. Patients and Methods: Formalin-fixed paraffin-embedded tumor tissue of 258 CRC patient's sections were immunohistochemically stained and subsequently quantified for HLA Class I expression by the tumor cells. Determination of microsatellite instability (MSI) tumor status was ascertained using mononucleotide repeat microsatellite targets. KRAS and BRAF mutations were screened by polymerase chain reaction (PCR)-sequencing and cast-PCR, respectively. Results: HLA Class I expression was normal in 91 cases (35.3%), downregulated in 119 (46.1%), and loss of expression in 48 (18.6%) cases. Forty (15.5%) tumors were MSI-H (MSH), 49 were MSI-L (19%), and 169 were microsatellite stable (MSS) (65.5%). Thirty-six (14%) and 15 (5.8%) of the patients exhibited mutation in the KRAS and BRAF, respectively. It was found that patients with downregulated expression of HLA Class I were associated with Stage II tumors (P < 0.001) and a MSS tumor status (P < 0.001), while patients with loss of expression were associated with MSH status (P < 0.001). Univariate and multivariate analyses revealed that HLA Class I downregulated expression was an independent prognostic parameter for shorter overall patient survival time (hazard ratio: 1.8, P = 0.003). Conclusions:HLA Class I expression is an independent and sensitive clinical prognostic marker that might be used in CRC patients.Surgical oncolog