Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon

Indian monsoon is an important climatic phenomenon and a global climatic marker. Both statistical and numerical prediction schemes for Indian monsoon have been widely studied in literature. Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. Meteorologists revise their models on different years based on prevailing global climatic incidents like El-Niño. These indices often have degree of severity associated with them. In this paper, we cluster the monsoon years based on their fuzzy degree of associativity to these climatic event patterns. Next, we develop individual prediction models for the year clusters. A weighted ensemble of these individual models is used to obtain the final forecast. The proposed method performs competitively with existing forecast models

Dual Stimuli-Responsive Self-Assembly Behavior of a Tailor-Made ABC-Type Amphiphilic Tri-Block Copolymer

This investigation describes the synthesis of a dual stimuli-responsive, amphiphilic ABC tri-block copolymer (BCP) based on the functional monomers via RAFT polymerization. In this case, ABC-type BCP was prepared based on N-isopropylacrylamide, n-butyl acrylate, and 4-vinylpyridine in DMF solvent using cyanomethyl dodecyl trithiocarbonate as the RAFT agent and azobisisobutyronitrile as a thermal initiator in a subsequent macro-RAFT approach, respectively. The BCPs were characterized by SEC, 1H-NMR, FTIR spectroscopy, and DSC analyses. Temperature and pH-dependent properties of the smart BCP micelles in aqueous medium were investigated using dynamic light scattering. Transmission electron microscopic images were taken at cryogenic and dry conditions to study the morphology of molecular assemblies of block copolymers in an aqueous medium. The phase and topographical images were captured by atomic force microscopy to understand the assembly of block copolymers in solvents of different polarities. The morphology of BCP micelles was transformed from flower-like to spherical in the presence of solvents with different polarities (H2O or CHCl3). © 2020 The Authors. Journal of Polymer Science published by Wiley Periodicals, Inc

Synthesis and characterization of a new water-soluble non-cytotoxic mito-tracker capped silicon quantum dot

19-25Allyl triphenylphosphonium bromide based mito-tracker capped silicon quantum dot (Mito-SiQDs) has been synthesized through an inverse micelle process. It was then fully characterized by transmission electron microscopy, energy-dispersive X-ray spectroscopy, dynamic light scattering techniques and X-ray photoelectron spectroscopic method. Energy dispersive X-ray spectroscopy analyses of the quantum dots confirm the presence of carbon, silicon, phosphorous and bromine atoms in Mito-SiQDs. Morphological study by transmission electron microscopy experiment showed the formation of the particles of size 11-12 nm of quantum dot dimension. The high negative zeta potential value of –23.7 mV calculated from dynamic light scattering study indicates the high stability of the circumvent agglomeration of Mito-SiQDs. The mito-tracker capped silicon quantum dot has blue emission at 400 nm wavelength upon excitation at 327 nm. Mito-SiQDs has not shown any significant cytotoxic effect with 10 to 50 μL/mL concentration on HeLa cell line for at least up to 12 h of its treatment. The Mito-SiQDs would be useful a possible fluorescent marker to visualize mitochondrial subcellular compartment in living cell through fluorescence imaging study

Thermoresponsive zwitterionic poly(phosphobetaine) microgels: Effect of macro-RAFT chain length and cross-linker molecular weight on their antifouling properties

Adsorption of proteins on biological surfaces is a detrimental phenomenon that reduces the work-life of the implants in various biomedical applications. Here, we synthesized a new class of thermoresponsive zwitterionic poly(phosphobetaine) (PMPC) microgel with excellent surface antifouling property by macro-RAFT mediated thiol-epoxy click reaction. End-group modified zwitterionic PMPC homopolymers with well-defined molecular weight and narrow dispersity were grafted onto poly(N-vinylcaprolactam-co-glycidyl methacrylate) (PVG) copolymer backbone followed by addition of a cross-linker, leading to microgel formation. While no upper critical solution temperature (UCST) was found in poly(N-vinylcaprolactam-co-glycidyl methacrylate-g-2-methacryloyloxyethyl phosphorylcholine) (PVGP) graft copolymers, the corresponding microgels exhibited both UCST and lower critical solution temperature (LCST) transitions, related to the swelling and collapse of PMPC and poly (N-vinylcaprolactam) (PVCL) components respectively. An increase in the molecular chain length of the PMPC increased the shifting of UCST and LCST of the microgels to higher temperatures, due to the ability of zwitterionic groups to coordinate a large number of water molecules. The effect of the variation in the molecular weights of amphiphilic poly(ethylene glycol) diamine (PEG-NH2) cross-linker was also reflected in both temperature and salt responsiveness of the microgels. The efficacy of the microgels as potential antifouling materials was further studied by fluorescence microscopy and XPS analysis on microgel coatings treated with FITC-BSA solution and pure BSA solution respectively. Lower protein adsorption was observed for microgels grafted with higher molecular chain length of PMPC, whereas, the microgels synthesized using higher molecular weight PEG-NH2 diamine cross-linker displayed greater protein adsorption on their surfaces

Finite Element Modeling and Structural Behavior of Concrete Tunnel Linings

251 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982.An improved understanding of the structural behavior of concrete tunnel linings is obtained through a series of numerical analyses of the ground-lining interaction problem with emphasis on nonlinear lining behavior. A rational analysis approach is recommended for linings in rock and soil that will include the nonlinear effects and interaction in a realistic manner. For numerical analysis, a nonlinear finite element computer program was developed to perform parametric studies to investigate a wide range of the key variables and determine how they affect the design.Different analysis methods, closed form or numerical analysis, were compared to come up with a rational analysis approach. The performance of different finite element models and their applicability to specific situations have been investigated with consideration to the various interaction components and the way the ground loads reach the final lining. In the finite elements models the lining is represented by beam elements that can account for the nonlinear behavior of the concrete and the reinforcing steel, if present and can include the nonlinearity due to geometry change if deformations are large. The medium can be represented by radial and tangential springs with linear or nonlinear properties, or it can be represented by two-dimensional elastic isoparametric continuum elements. Also in the latter representation an interface element developed for this study can be included between the lining and the medium that has failure conditions defined by the cohesion and angle of internal friction, and elastoplastic stress-strain properties. In the recommended analysis for linings in rock, the beam-spring is suggested for use with loosening rock loads. For linings in soft ground, the excavation loading and a linear analysis is suggested for which available closed form solution could be used or a beam-continuum model is also appropriate. For some creep sensitive soils with low cohesion, fissures or other discontinuities, if the designer feels that a loosening load might occur, the lining should be checked for such loading using a beam-spring model.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Dynamic algorithm for graph clustering using minimum cut tree

We present an efficient dynamic algorithm for clustering undirected graphs, whose edge property is changing continuously. The algorithm maintains clusters of high quality in presence of insertion and deletion (update) of edges. The algorithm is motivated by the minimum-cut tree based partitioning algorithm of [3] and [4]. It takes O(k 3) time for each update processing, where k is the maximum size of any cluster. This is the worst case time complexity, and in general update time taken is much less. The clusters satisfy the bicriteria for quality guarantee proposed in [3].

Identification of Indian monsoon predictors using climate network and density-based spatial clustering

The Indian summer monsoon is a complex climatic phenomenon with a large variability over the years. The climatic predictors affecting the phenomenon evolve with time, and consequently new predictors have gained importance. Several statistical approaches are being explored in the literature to identify the potential predictors influencing the Indian summer monsoon. A complex network paradigm involving climatic variables at the grids over the globe has been proposed for predictor identification and monsoon prediction. The approach initiates with the identification of communities in the climate network considering mutual similarity and the influence of climate variables of grids on the Indian summer monsoon. Spatial clustering is performed over the communities to identify the geographical regions of significance. The climatic predictors extracted from variables of these regions are evaluated in terms of their correlation with the monsoon as well as their forecasting skills in predicting the summer monsoon of the country. The newly identified predictors forecast monsoon with an error of 4.2%, which is significant for the prediction of the complex phenomenon of monsoon

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models