Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm

Water Resources Management - The present study investigates and evaluate the scope and potential of modern computing tools and techniques such as ensembled machine learning methods in estimating...     

Novel Short-chain Crosslinked Cationomeric Polyurethanes

Summary Novel PUs containing pyridinium moieties were synthesized by chain extending isocyanate endcapped prepolymers with N, N’-bis (2-hydroxyethyl) isonicotinamide. The pyridinium moieties in the PUs were chemically crosslinked using short-chain divalent quaternising agents. The polyurethane cationomers were characterized by spectral, thermal and mechanical analysis. Spectral results confirmed the quaternisation of tertiary nitrogen leading to crosslinking. Compared to conventional PUs, the crosslinked PU networks exhibited improved thermal stability. The damping value (i.e.) tan δ for cationomers were improved over a broad temperature range when compared to conventional PU.     

Constructing candidate word lists using acoustically similar wordgroups

A method for identifying a set of candidate words that matches well with a given utterance is discussed. The method uses precomputed groups of acoustically similar words. Given a vocabulary, for each word a short list of words that are acoustically similar to it is computed. These groups are constructed using training utterances recorded by several speakers and used in an isolated utterance recognition system to construct candidate word lists during recognition. Experiments show that the correct word appears on the short list of candidate words constructed during recognition using this scheme over 98% of the time     

A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors

The paper presents a neuro-fuzzy-based perspective to the automation of diagnosis and location of stator-winding interturn short circuits in CSI-fed brushless dc motors. Performance of the drive under normal and short-circuit conditions are obtained through classical lumped-parameter network models. Waveforms of the electromagnetic torque and summation of phase voltages are monitored to develop two independent diagnostic algorithms. Diagnostic indices derived from the characteristic waveforms using discrete Fourier transform (DFT) lead to identifying the number of shorted turns. Fault location is achieved through a different set of indices extracted by the short-time Fourier transform (STFT). Adaptive neuro-fuzzy inference systems (ANFIS) are trained based on simulation results to automate the diagnostic process. ANFIS testing along with the good agreement between simulated and measured waveforms show the effectiveness of the proposed techniques.     

Effect of temperature on recombinant protein production using the Bm5/Bm5.NPV expression system

A series of experiments have been conducted using a recombinant baculovirus/insect cell expression system (Bm5/Bm5.NPV.CAT) to establish the optimum temperature for both cell growth and virus infection. Bm5 cell growth was found to be limited at temperatures below 22°C and ceased completely at temperatures above 34°C. In the range between 24 and 28°C, final cell densities always reached 96% of the highest achievable viable cell density. The shortest population doubling time was obtained at 28°C. Overall, a consistent increase in metabolism with increasing temperatures was observed. During the infection/viral replication phase, an increase in the temperature from 25 to 31°C resulted in a faster decrease in viable cell density and an earlier production of chloramphenicol acetyltransferase (CAT). Furthermore, protein yield at temperatures above 28°C was significantly reduced. Overall, the best temperature for the infection phase for the Bm5/Bm5.NPV expression system was found to be 25°C when the cells are cultured in serum free media.     

SHARED: An information model for cooperative product development

One fundamental issue in developing collaborative engineering systems is the representation of product information which supports communication and coordination. This product information includes not only the geometric and physical properties of the product and its parts, but also information about functions, constraints and the design rationale. In this paper, we describe an information model, SHARED, which was developed for encoding product information in DICE, a distributed and integrated environment for computer-aided engineering. SHARED provides multiple levels of both functional and geometric abstractions, multiple views and techniques for maintaining consistency between the various abstractions and views. These elements are essential for a good representation model of product information. The use of the SHARED model is illustrated through an example, depicting the various representations of a product as it evolves through the design process. The SHARED model has been implemented over a distributed OODBMS as a toolkit/framework for developing environments which need to model, manipulate and communicate product information between distributed cooperating applications, while supporting coordination between them.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Neural Computing and Applications - “Brain–Computer Interface” (BCI)—a real-life support system provides a way for epileptic patients to improve their quality of life. In...