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Manufacturing energy consumption data contains important process signatures required for operational visibility and diagnostics. These signatures may be of different temporal scales, ranging from monthly to sub-second resolutions. We introduce a hierarchical machine learning approach to identify automotive process signatures from paint shop electricity consumption data at varying temporal scales (weekly and daily). A Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and Principal Component Analysis (PCA) combined with Logistic Regression (LR) are used for the analysis. We validate the utility of the developed algorithms with subject matter experts for (i) better operational visibility, and (ii) identifying energy saving opportunities., Comment: 5 pages, 4 figures

We investigate the effect on neutrino oscillations generated by beyond-the-standard-model interactions between neutrinos and matter. Specifically, we focus on scalar-mediated non-standard interactions (NSI) whose impact fundamentally differs from that of vector-mediated NSI. Scalar NSI contribute as corrections to the neutrino mass matrix rather than the matter potential and thereby predict distinct phenomenology from the vector-mediated ones. Similar to vector-type NSI, the presence of scalar-mediated neutrino NSI can influence measurements of oscillation parameters in long-baseline neutrino oscillation experiments, with a notable impact on CP measurement in the case of DUNE. Our study focuses on the effect of scalar NSI on neutrino oscillations, using DUNE as an example. We introduce a model-independent parameterization procedure that enables the examination of the impact of all non-zero scalar NSI parameters simultaneously. Subsequently, we convert DUNE's sensitivity to the NSI parameters into projected sensitivity concerning the parameters of a light scalar model. We compare these results with existing non-oscillation probes. Our findings reveal that the region of the light scalar parameter space sensitive to DUNE is predominantly excluded by non-oscillation probes, except for scenarios with very light mediator mass., Comment: 21 pages, 8 figures, 3 tables

Compared to other neutrino sources, the huge anti-neutrino fluxes at nuclear reactor based experiments empower us to derive stronger bounds on non-standard interactions of neutrinos with electrons mediated by light scalar/vector mediators. At neutrino energy around $200$~keV reactor anti-neutrino flux is at least an order of magnitude larger compared to the solar flux. The atomic and crystal form factors of the detector materials related to the details of the atomic structure becomes relevant at this energy scale as the momentum transfers would be small. Non-standard neutrino-electron interaction mediated by light scalar/vector mediator arises naturally in many low-scale models. We also propose one such new model with a light scalar mediator. Here, we investigate the parameter space of such low-scale models in reactor based neutrino experiments with low threshold Ge and Si detectors, and find the prospect of probing/ruling out the relevant parameter space by finding the projected sensitivity at $90 \%$ confidence level by performing a $\chi^2$-analysis. We find that a detector capable of discriminating between electron recoil and nuclear recoil signal down to a very low threshold such as $5$~eV placed in reactor based experiment would be able to probe a larger region in parameter space compared to the previously explored region. A Ge (Si) detector with $10$~kg-yr exposure and 1 MW reactor anti-neutrino flux would be able to probe the scalar and vector mediators with masses below 1 keV for coupling products $\sqrt{g_\nu g_e}$ $\sim$ $1 \times 10^{-6}~(9.5 \times 10^{-7})$ and $1\times 10^{-7} ~(8\times 10^{-8})$, respectively., Comment: Manuscript is revised, results are unchanged, updated figures, references added

Lately, personalized marketing has become important for retail/e-retail firms due to significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers, and hence, rolling out optimal offers has become imperative to maintain balance between number of transactions and profit. In this paper, we propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting. To optimize offer, we first build a generalized non-linear model using Temporal Convolutional Network to predict the item purchase probability at consumer level for the given time period. Secondly, we establish the functional relationship between historical offer values and purchase probabilities obtained from the model, which is then used to estimate offer-elasticity of purchase probability at consumer item granularity. Finally, using estimated elasticities, we optimize offer values using constraint based optimization technique. This paper describes our detailed methodology and presents the results of modelling and optimization across categories., Comment: 8 pages of main content. arXiv admin note: text overlap with arXiv:2010.06952

Predicting future consumer behaviour is one of the most challenging problems for large scale retail firms. Accurate prediction of consumer purchase pattern enables better inventory planning and efficient personalized marketing strategies. Optimal inventory planning helps minimise instances of Out-of-stock/ Excess Inventory and, smart Personalized marketing strategy ensures smooth and delightful shopping experience. Consumer purchase prediction problem has generally been addressed by ML researchers in conventional manners, either through recommender systems or traditional ML approaches. Such modelling approaches do not generalise well in predicting consumer purchase pattern. In this paper, we present our study of consumer purchase behaviour, wherein, we establish a data-driven framework to predict whether a consumer is going to purchase an item within a certain time frame using e-commerce retail data. To model this relationship, we create a sequential time-series data for all relevant consumer-item combinations. We then build generalized non-linear models by generating features at the intersection of consumer, item, and time. We demonstrate robust performance by experimenting with different neural network architectures, ML models, and their combinations. We present the results of 60 modelling experiments with varying Hyperparameters along with Stacked Generalization ensemble and F1-Maximization framework. We then present the benefits that neural network architectures like Multi Layer Perceptron, Long Short Term Memory (LSTM), Temporal Convolutional Networks (TCN) and TCN-LSTM bring over ML models like Xgboost and RandomForest., Comment: 7 pages of main content

Sub-100 nm polymeric spherical plano-convex nano-lens arrays are fabricated using short electron beam exposures to selectively modify the ultrathin (< 30 nm) polymer films, followed by their intensified self-organized dewetting under an aqueous-organic mixture. A short exposure to e-beam locally modifies the polymer chains to effectively change the viscosity of the film in small domains, thus bringing in the dynamical dewetting contrast in the film that produces aligned and ordered dewetted nanostructures. Both negative and positive e-beam tone polymers are thus used to produce array of nano-lenses. The intensified self-organized dewetting under a water-organic solvent mixture overcomes the limitations on surface tension and dewetting force and thus facilitates the formation of sub-100 nm diameter polymer nanolenses of tunable curvature. By varying the extent of e-beam exposure, various configurations from isolated to connected nano-lens arrays can be fabricated., Comment: 16 pages, 8 figures

Dewetting of ultrathin (< 100 nm) polymer films, by heating above the glass transition, produces droplets of sizes of the order of microns and mean separations between droplets of the order of tens of microns. These relatively large length scales are because of the weak destabilizing van der Waals forces and the high surface energy penalty required for deformations on small scales. We show a simple, one-step versatile method to fabricate sub-micron (>~100 nm) droplets and their ordered arrays by room temperature dewetting of ultrathin polystyrene (PS) films by minimizing these limitations. This is achieved by controlled room temperature dewetting under an optimal mixture of water, acetone and methyl-ethyl ketone (MEK). Diffusion of organic solvents in the film greatly reduces its glass transition temperature and the interfacial tension, but enhances the destabilizing field by introduction of electrostatic force. The latter is reflected in a change in the exponent, n of the instability length scale, {\lambda} ~h^n, where h is the film thickness and n = 1.51 \pm 0.06 in the case of water-solvent mix, as opposed to its value of 2.19 \pm 0.07 for dewetting in air. The net outcome is more than one order of magnitude reduction in the droplet size as well as their mean separation and also a much faster dynamics of dewetting. We also demonstrate the use of this technique for controlled dewetting on topographically patterned substrates with submicrometer features where dewetting in air is either arrested, incomplete or unable to produce ordered patterns.

We report an ultrafast, parallel and beyond-the-master micro-patterning technique for ultrathin (30 nm-400 nm) non-absorbing polymer films by diffraction of a laser light through a two dimensional periodic aperture. The redistribution of laser energy absorbed by the substrate causes self-organization of polymer thin-film in the form of wrinkle like surface relief structures caused by localized melting and freezing of the thin-film. Unlike the conventional laser ablation and laser writing processes, low laser fluence is employed to only passively swell the polymer as a pre-ablative process without the loss of material, and without absorption/reaction with the incident radiation. Self-organization in the thin polymer film aided by the diffraction pattern produces micro-structures made up of thin raised lines. These regular microstructures have far more complex morphologies than the mask geometry and very narrow line widths that can be an order of magnitude smaller than the openings in the mask. The microstructure morphology is easily modulated by changing the film thickness, aperture size and geometry and by changing the diffraction pattern, e.g., by changing the aperture-substrate distance., Comment: 23 pages

Field-induced self-organized patterning in ultrathin (< 100 nm) polymer films produces resolutions of the order of 10 {\mu}m or more because of the high energy penalty for the surface deformations on small scales. We propose here a very simple but versatile method to fabricate sub-micron (~100 nm) ordered and tunable polymeric structures by self-organized room temperature dewetting of ultrathin polystyrene films by minimizing the surface tension limitation. We illustrate this technique by fabricating sub-micron lens arrays of tunable curvature. This is achieved by switching to controlled room temperature dewetting under an optimal mix of water, acetone and methyl-ethyl ketone (MEK). Organic solvents used decrease the glass transition temperature, greatly decrease the interfacial tension, intensify the field and increase the contact angle/aspect ratio of the resulting tunable nano-structures, without a concurrent solubilization of PS owing to water being the majority phase in the outside mixture., Comment: 13 pages and 5 figures

A simple and novel method to fabricate and miniaturize surface and sub-surface micro-structures and micro-patterns in glassy carbon is proposed and demonstrated. An aqueous resorcinol-formaldehyde (RF) sol is employed for micro-molding of the master-pattern to be replicated, followed by controlled drying and pyrolysis of the gel to reproduce an isotropically shrunk replica in carbon. The miniaturized version of the master-pattern thus replicated in carbon is about one order of magnitude smaller than original master by repeating three times the above cycle of molding and drying. The micro-fabrication method proposed will greatly enhance the toolbox for a facile fabrication of a variety of Carbon-MEMS and C-microfluidic devices., Comment: 16 pages, 5 figures

The synthesis of xAgI(1-x)[Ag2O.B2O3] amorphous superionic conductors is done via mechanochemical synthesis route (for x = 0.5 and 0.7). Ionic conductivities of 3 mOhm-1cm-1 for x = 0.5 sample and 5mOhm-1cm-1 for x = 0.7 sample at room temperature, are observed, which are higher than those of the melt quenched glassy samples. Impedance plots (Nyquist plots) are found to be depressed semicircles with a tail at low frequency end. The corresponding equivalent circuit is postulated and each circuit element is related to a physical process. The ac conductivity is analyzed in view of the universal dynamic response (Bode Plots). A unique feature of the mechnochemically synthesized glasses is that their dc conductivity vs. 1/T behavior exhibit two distinct regions with different activation energies. The activation energy for dc conductivity (Edc) and that for ac conductivity (Eac) and the frequency exponent n are found to satisfy the UDR-relation, Eac= (1-n)Edc in the lower temperature regime. In the high temperature region, however, this correlation could not be established due to lack of sac data over the available frequency domain., Comment: Presented in the "6th National Conference on Solid State Ionics" held at Jadavpur University, Kolkata, India, during October 5-7, 2004. Contains 4 pages and 6 figures