Impedimetric Chemosensing of Volatile Organic Compounds Released from Li-Ion Batteries.

Detection of toxic and flammable gases and volatile organic compounds (VOCs) released from Li-ion batteries during thermal runaway can generate an early warning. A submicron (∼0.15 µm)-thick poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) sensor film is coated on a platinum electrode through a facile aqueous dispersion. The resulting sensor reliably detected different volatile organic compounds (VOCs) released during the early stages of thermal runaway of lithium-ion batteries (LIBs) even at low concentrations. The single-electrode sensor utilizes impedance spectroscopy to measure ethyl methyl carbonate and methyl formate concentrations at 5, 15, and 30 ppm independently and in various combinations using ethanol as a reference. In contrast to DC resistance measurement, which provides a single parameter, impedance spectroscopy provides a wealth of information, including impedance and phase angle at multiple frequencies as well as fitted charge transfer resistance and constant-phase elements. Different analytes influence the measurement of different parameters to varying degrees, enabling distinction using a single sensing material. The response time for ethyl methyl carbonate was measured to be 6 s. Three principal components (PCs) preserve more than 95% of information and efficiently enable discrimination of different classes of analytes. Application of low-power PEDOT:PSS-based gas sensors will facilitate cost-effective early detection of VOCs and provide early warning to battery management systems (BMS), potentially mitigating catastrophic thermal runaway events.

Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG.

BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. METHODS: This work addresses the classification problem for two groups; Group 1: "inter-ictal vs. ictal" for which case 1(C-E), and case 2(D-E) are included and Group 2; "activity from controlled vs. inter-ictal activity" considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. RESULTS: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. CONCLUSION: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

A Weighted Ensemble Model for Prediction of Infectious Diseases.

BACKGROUND: The ensemble building is a common method to improve the performance of the model in case of regression as well as classification. OBJECTIVE: In this paper we propose a weighted average ensemble model to predict the number of incidence for infectious diseases like typhoid and compare it with applied models for prediction. METHODS: The Monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. The data was processed by three regressions such as support vector regression, neural network and linear regression. RESULTS: To evaluate the prediction error and compare it with different models, different performance measures have been used such as MSE, RMSE and MAE and it was found that proposed ensemble method performed better in terms of forecast measures. CONCLUSION: Our main aim in this paper is to minimize the prediction error; the resulting proposed weighted average ensemble model has shown a significant result in terms of prediction errors.

Consensus-Based Odor Source Localization by Multiagent Systems.

This paper presents an investigation of the task of localizing an unknown source of an odor by heterogeneous multiagent systems. A hierarchical cooperative control strategy has been proposed as a potential candidate to solve the problem. The agents are driven into consensus as soon as the information about the location of source is acquired. The controller has been designed in a hierarchical manner of group decision making, agent path planning, and robust control. In group decision making, the particle swarm optimization algorithm has been used along with the information of the movement of odor molecules to predict the odor source location. Next, a trajectory has been mapped using this predicted location of source, and the information is passed to the control layer. A variable structure control has been used in the control layer due to its inherent robustness and disturbance rejection capabilities. Cases of movement of agents toward the source under consensus and parallel formation have been discussed. The efficacy of the proposed scheme has been confirmed by simulations.

Bacteriorhodopsin-ZnO hybrid as a potential sensing element for low-temperature detection of ethanol vapour.

Zinc oxide (ZnO) and bacteriorhodopsin (bR) hybrid nanostructures were fabricated by immobilizing bR on ZnO thin films and ZnO nanorods. The morphological and spectroscopic analysis of the hybrid structures confirmed the ZnO thin film/nanorod growth and functional properties of bR. The photoactivity results of the bR protein further corroborated the sustainability of its charge transport property and biological activity. When exposed to ethanol vapour (reducing gas) at low temperature (70 °C), the fabricated sensing elements showed a significant increase in resistivity, as opposed to the conventional n-type behaviour of bare ZnO nanostructures. This work opens up avenues towards the fabrication of low temperature, photoactivated, nanomaterial-biomolecule hybrid gas sensors.

Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem.

Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

Human opinion dynamics: an inspiration to solve complex optimization problems.

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.