Energy Planning in the Big Data Era: A Theme Study of the Residential Sector

This paper re-conceptualizes the planning process in the big data era based on the improvements that non-linear modeling approaches provide over the mainstream linear approaches. First, it demonstrates challenges of conventional linear methodologies in modeling complexities of residential energy use, addressing the “variety” from the three Vs of big data. Suggesting a non-linear modeling schema to analyze household energy use, the paper develops its discussion around the repercussions of the use of non-linear modeling in energy policy and planning. Planners / policy-makers are not often equipped with the tools needed to translate complex scientific outcomes into policies. To fill this gap, this work proposes modifications in the traditional planning process in order to be able to benefit from the abundance of data and the advances in analytical methodologies. The conclusion section introduces three short-term repercussions of this work for energy policy (and planning, in general) in the big data era: tool development, data infrastructures, and planning education

Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care

Fluid administration, also called fluid resuscitation, is a medical treatment to restore the lost blood volume and optimize cardiac functions in critical care scenarios such as burn, hemorrhage, and septic shock. Automated fluid administration systems (AFAS), a potential means to improve the treatment, employ computational control algorithms to automatically adjust optimal fluid infusion dosages by targeting physiological variables (e.g., blood volume or blood pressure). Most of the existing AFAS control algorithms are model-based approaches, and their performance is highly dependent on the model accuracy, making them less desirable in real-world care of critically ill patients due to complexity and variability of modeling patients physiological states. This work presents a novel model-free reinforcement learning (RL) approach for the control of fluid infusion dosages in AFAS systems. The proposed RL agent learns to adjust the blood volume to a desired value by choosing the optimal infusion dosages using a Q-learning algorithm. The RL agent learns the optimal actions by interacting with the environment (without having the knowledge of system dynamics). The proposed methodology (i) overcomes the need for a precise mathematical model in AFAS systems and (ii) provides a robust performance in rejecting clinical noises and reaching desired hemodynamic states, as will be shown by simulation results

A Comparison of Large Deflection Analysis of Bending Plates by Dynamic Relaxation

In this paper, various dynamic relaxation methods are investigated for geometric nonlinear analysis of bending plates. Sixteen wellknown algorithms are employed. Dynamic relaxation fictitious parameters are the mass matrix, the damping matrix and the time step. The difference between the mentioned tactics is how to implement these parameters. To compare the efficiency of these strategies, several bending plates’ problems with large deflections are solved. Based on the number of iterations and analysis time, the scores of the different schemes are calculated. These scores determine the ranking of each technique. The numerical results indicate the appropriate efficiency of Underwood and Rezaiee-Pajand & Alamatian processes for the nonlinear analysis of bending plates

Multiple Sclerosis Gene Therapy Using Recombinant Viral Vectors: Overexpression of IL-4, IL-10 and Leukemia Inhibitory Factor in Wharton's Jelly Stem Cells in The EAE Mice Model

Objective: Immunotherapy and gene therapy play important roles in modern medicine. The aim of this study is to evaluate the overexpression of interleukin-4 (IL-4), IL-10 and leukemia inhibitory factor (LIF) in Wharton's jelly stem cells (WJSCs) in the experimental autoimmune encephalomyelitis (EAE) mice model. Materials and Methods: In this experimental study, a DNA construction containing IL-4, IL-10 and LIF was assembled to make a polycistronic vector (as the transfer vector). Transfer and control vectors were co-transfected into Human Embryonic Kidney 293 (HEK-293T) cells with helper plasmids which produced recombinant lentiviral viruses (rLV). WJSCs were transduced with rLV to make recombinant WJSC (rWJSC). In vitro protein and mRNA overexpression of IL-4, LIF, and IL-10 were evaluated using quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA) and western blot (WB) analysis. EAE was induced in mice by MOG-CFA and pertussis toxin. EAE mice were injected twice with 2x10(5) rWJSCs. The in vivo level of IL-4, LIF, IL-10 cytokines and IL-17 were measured by ELISA. Brain tissues were analyzed histologically for evaluation of EAE lesions. Results: Isolated WJSCs were performed to characterize by in vitro differentiation and surface markers were analyzed by flow cytometry method. Cloning of a single lentiviral vector with five genes was done successfully. Transfection of transfer and control vectors were processed based on CaPO4 method with > 90% efficiency. Recombinant viruses were produced and results of titration showed 2-3x10(7) infection-unit/ml. WJSCs were transduced using recombinant viruses. IL-4, IL-10 and LIF overexpression were confirmed by ELISA, WB and qPCR. The EAE mice treated with rWJSC showed reduction of Il-17, and brain lesions as well as brain cellular infiltration, in vivo. Weights and physical activity were improved in gene-treated group. Conclusion: These results showed that gene therapy using anti-inflammatory cytokines can be a promising approach against multiple sclerosis (MS). In addition, considering the immunomodulatory potential of WJSCs, an approach using a combination of WJSCs and gene therapy will enhance the treatment efficacy

Multiple Sclerosis Gene Therapy with Recombinant Viral Vectors: Overexpression of IL-4, Leukemia Inhibitory Factor, and IL-10 in Wharton's Jelly Stem Cells Used in EAE Mice Model.

OBJECTIVES: Immunotherapy and gene therapy play important roles in modern medicine. The aim of this study is to evaluate the overexpression of interleukin-4 (IL-4), IL-10 and leukemia inhibitory factor (LIF) in Wharton's jelly stem cells (WJSCs) in the experimental autoimmune encephalomyelitis (EAE) mice model. MATERIALS AND METHODS: In this experimental study, a DNA construction containing IL- 4, IL-10 and LIF was assembled to make a polycistronic vector (as the transfer vector). Transfer and control vectors were co-transfected into Human Embryonic Kidney 293 (HEK-293T) cells with helper plasmids which produced recombinant lentiviral viruses (rLV). WJSCs were transduced with rLV to make recombinant WJSC (rWJSC). In vitro protein and mRNA overexpression of IL-4, LIF, and IL-10 were evaluated using quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA) and western blot (WB) analysis. EAE was induced in mice by MOG-CFA and pertussis toxin. EAE mice were injected twice with 2×105 rWJSCs. The in vivo level of IL-4, LIF, IL-10 cytokines and IL-17 were measured by ELISA. Brain tissues were analyzed histologically for evaluation of EAE lesions. RESULTS: Isolated WJSCs were performed to characterize by in vitro differentiation and surface markers were analyzed by flow cytometry method. Cloning of a single lentiviral vector with five genes was done successfully. Transfection of transfer and control vectors were processed based on CaPO4 method with >90% efficiency. Recombinant viruses were produced and results of titration showed 2-3×107 infection-unit/ml. WJSCs were transduced using recombinant viruses. IL-4, IL-10 and LIF overexpression were confirmed by ELISA, WB and qPCR. The EAE mice treated with rWJSC showed reduction of Il-17, and brain lesions as well as brain cellular infiltration, in vivo. Weights and physical activity were improved in gene-treated group. CONCLUSIONS: These results showed that gene therapy using anti-inflammatory cytokines can be a promising approach against multiple sclerosis (MS). In addition, considering the immunomodulatory potential of WJSCs, an approach using a combination of WJSCs and gene therapy will enhance the treatment efficacy

The Impacts of Household Behaviors and Housing Choice on Residential Energy Consumption

Thesis (Ph.D.)--University of Washington, 2014Despite efforts made in the past decade to curb excessive energy consumption and the corresponding greenhouse gas (GHG) emissions, both energy consumption and GHG emissions are expected to increase in coming years. Not only does such increasing trends epitomize the escalating, enduring human contribution to global warming, it verifies that our current policies are not working, at least not as well as expected or hoped. Globally, approximately a quarter of our total energy consumption is in the home, almost as much as in any other sector. Yet an understanding of the processes, determinants, and consequences of household energy consumption remains elusive. Conventional research on residential energy consumption has often applied linear methodologies and overwhelmingly focused on physical attributes of the housing stocks and systems. This approach, therefore, has failed: 1) to provide a coherent perspective of energy consumption processes, and 2) to account for the role of household behaviors. Accordingly, conventional energy policy has been left without the essential understanding of the phenomenon that would allow it to take effective action. To rectify issues with conventional research and policy, this research applies a non-linear and interdisciplinary approach to household energy consumption as an outcome of housing consumption and choice behaviors. Using data from the latest Residential Energy Consumption Survey, I use a set of Structural Equation Models to estimate the direct, indirect, and total effects of household and housing characteristics on energy use. Outcomes demonstrate that household characteristics have an indirect effect on energy consumption by influencing housing unit attributes, the housing choice effect on energy consumption. That is, a household's choice of housing unit has a permanent effect on the household's energy consumption, as an outcome, up until they relocate. Results of this study show that, accounting for the housing choice effects, the overall effect of household characteristics on energy consumption is almost twice as important as anticipated by conventional research. This study's findings highlight the role of housing choice and consumption behaviors in shaping residential energy consumption patterns. Energy consumption is expected to increase due to inevitable sociodemographic and economic changes. In addition to investing in improved building efficiencies and technologies, smart energy policies aimed at reducing energy consumption should promote more sustainable housing consumption behaviors and provide better housing choices

Replication Data for: kluster: An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning

182 simulated datasets (first set contains small datasets and second set contains large datasets) with different cluster compositions – i.e., different number clusters and separation values – generated using clusterGeneration package in R. Each set of simulation datasets consists of 91 datasets in comma separated values (csv) format (total of 182 csv files) with 3-15 clusters and 0.1 to 0.7 separation values. Separation values can range between (−0.999, 0.999), where a higher separation value indicates cluster structure with more separable clusters. Size of the dataset, number of clusters, and separation value of the clusters in the dataset is printed in file name. size_X_n_Y_sepval_Z.csv: Size of the dataset = X number of clusters in the dataset = Y separation value of the clusters in the dataset =