Uncovering Randomness and Success in Society

An understanding of how individuals shape and impact the evolution of society is vastly limited due to the unavailability of large-scale reliable datasets that can simultaneously capture information regarding individual movements and social interactions. We believe that the popular Indian film industry, 'Bollywood', can provide a social network apt for such a study. Bollywood provides massive amounts of real, unbiased data that spans more than 100 years, and hence this network has been used as a model for the present paper. The nodes which maintain a moderate degree or widely cooperate with the other nodes of the network tend to be more fit (measured as the success of the node in the industry) in comparison to the other nodes. The analysis carried forth in the current work, using a conjoined framework of complex network theory and random matrix theory, aims to quantify the elements that determine the fitness of an individual node and the factors that contribute to the robustness of a network. The authors of this paper believe that the method of study used in the current paper can be extended to study various other industries and organizations.Comment: 39 pages, 12 figures, 14 table

A Machine Learning Framework for Energy Consumption Prediction

Energy needs to be used very efficiently in today\u27s world. With fast paced improvements in the industrial sector, demand is increasing, and energy efficiency programs become vital to reduce the energy wastage while also meeting the demand. The analysis of several scenarios used by policy makers suggest that for the global temperature to raise by less than 2° C by the end of this century, it is necessary to reduce industrial energy consumption increase by at least a half. To be on track with these scenarios and to achieve the desirable targets, it is important that we incorporate a dependable forecasting tool that can be used to predict the energy consumption based on several expected parameters. In this thesis, a survey is conducted on energy consumption forecasting algorithms to compare the advantages and disadvantages of each, explaining for what applications they would be the best fit. Also discussed in this thesis is a machine learning supported regression model that has a higher accuracy when compared to conventional regression models. The Industrial Assessment Center database contains data from all assessments conducted on manufacturing facilities that include plant area, production hours, number of employees, annual sales and the region the facility is from. These variables, along with average annual temperature are the independent variables and represent the various factors affecting energy consumption. The dependent variable is annual energy consumption. The suggested model incorporates random forest feature selection to identify the most important variables in the dataset. The dataset is first divided into 3 groups based on the value of the most important variable, production hours. Each of these groups is further divided into three groups based on the value of the second most important variable, plant area. The algorithm then fits linear, polynomial and support vector regression models to each of these 9 groups for training. While testing, the model uses the respective regression plane based on the testing data\u27s value of the most important two variables. This approach to regression gave 23% lesser percentage deviation than conventional regression modeling. Polynomial regression works better for the entire dataset whereas linear regression performs equally good in the subsets, suggesting that the linearity of data increases as the dataset is divided into homogenous subsets. Production hours and plant area have the highest impact on energy consumption. To reduce energy consumption, these two factors must be analyzed. This model can be used by various Industrial Assessment centers to find out future expected energy consumption of clients to give a more accurate figure of the payback periods of various recommendations

3D printed inserts for reproducible high throughput screening of cell migration

Cell migration is a fundamental and complex phenomenon that occurs in normal physiology and in diseases like cancer. Hence, understanding cell migration is very important in the fields of developmental biology and biomedical sciences. Cell migration occurs in 3 dimensions (3D) and involves an interplay of migrating cell(s), neighboring cells, extracellular matrix, and signaling molecules. To understand this phenomenon, most of the currently available techniques still rely on 2-dimensional (2D) cell migration assay, also known as the scratch assay or the wound healing assay. These methods suffer from limited reproducibility in creating a cell-free region (a scratch or a wound). Mechanical/heat related stress to cells is another issue which hampers the applicability of these methods. To tackle these problems, we developed an alternative method based on 3D printed biocompatible cell inserts, for quantifying cell migration in 24-well plates. The inserts were successfully validated via a high throughput assay for following migration of lung cancer cell line (A549 cell line) in the presence of standard cell migration promoters and inhibitors. We also developed an accompanying image analysis pipeline which demonstrated that our method outperforms the state-of-the-art methodologies for assessing the cell migration in terms of reproducibility and simplicity

Counting triangles

In this article, I study the problem of counting the number of triangles formed in a triangle if n segments are drawn from one vertex to its opposite side, and h segments are drawn from another vertex to its opposite side. This kind of counting problem is often seen in puzzle collections; e.g.: “Count the number of triangles visible in Figure 1”. Making a manual count for such a problem is tedious; also, it is easy to make an error in the count. We need a more analytic and systematic procedure

Linear-model-based estimation in wall turbulence: Improved stochastic forcing and eddy viscosity terms

We use Navier–Stokes-based linear models for wall-bounded turbulent flows to estimate large-scale fluctuations at different wall-normal locations from their measurements at a single wall-normal location. In these models, we replace the nonlinear term by a combination of a stochastic forcing term and an eddy dissipation term. The stochastic forcing term plays a role in energy production by the large scales, and the eddy dissipation term plays a role in energy dissipation by the small scales. Based on the results in channel flow, we find that the models can estimate large-scale fluctuations with reasonable accuracy only when the stochastic forcing and eddy dissipation terms vary with wall distance and with the length scale of the fluctuations to be estimated. The dependence on the wall distance ensures that energy production and energy dissipation are not concentrated close to the wall but are evenly distributed across the near-wall and logarithmic regions. The dependence on the length scale of the fluctuations ensures that lower wavelength fluctuations are not excessively damped by the eddy dissipation term and hence that the dominant scales shift towards lower wavelengths towards the wall. This highlights that, on the one hand, energy extraction in wall turbulence is predominantly linear and thus physics-based linear models give reasonably accurate results. On the other hand, the absence of linearly unstable modes in wall turbulence means that the nonlinear term still plays an essential role in energy extraction and thus the modelled terms should include the observed wall distance and length scale dependencies of the nonlinear term.This work was supported by the National Natural Science Foundation of China (grant nos. 91752201, 12002147 and 12050410247), the Shenzhen Science and Technology Innovation Committee (KQTD20180411143441009), the Department of Science and Technology of Guangdong Province (grant nos. 2019B21203001 and 2020B1212030001) and the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou (GML2019ZD0103). We acknowledge support from the Centers for Mechanical Engineering Research and Education at MIT and SUSTech, as well as from the Center for Computational Science and Engineering at SUSTech. AM and SJI are grateful for the financial support of the Australian Research Council

One-step transformation of 2-oxa-3-azabicyclo[2.2.1]hept-5-ene and methyl 2,3-diazabicyclo[2.2.1]heptane-2-carboxylate to ion uptake systems

Linker generated duplexes of the title compounds- prepared from cyclopentadiene- with possibility for positioning of four oxygen in the cavity, are shown to be excellent ion uptake systems. Mass spectrometric doping studies with lithium, sodium, potassium and silver ions, show a clear preference for lithium complexation. The lithium salts of the best examples have been prepared and characterized

Determination of Copper, Total Chromium and Silver in Impregnated Carbon

Carbon samples were impregnated with ammonical solutions of silver salt alone and in combination with Cu and Cr salts. The impregnated samples were characterised for Cu, total Cr and Ag. Copper was extracted as CuCl/sub 2/ using concentrated HCl and Cr with NaOH. Silver was extracted from impregnated carbon using HNO3 and sodium thiosulphate (Na/sub 2/S/sub 2/O/sub 3/-5H/sub 2/0) and ashed impregnated carbons using aqua regia. The extracted metals in their solutions were quantitatively determined by titrimetric method and atomic absorption spectroscopy. The results were within acceptable limits of error. Sodium thiosulphate is recommended for extraction of Ag, as it accomplishes complete leaching of Ag faster than the other extracting agents

Ytterbium triflate (and trimethylsilyl triflate) catalyzed isomerization of glycidic esters to α-hydroxy-β,γ-unsaturated esters and their conversion into cyclopentanoids using Johnson-Claisen rearrangement

A variety of glycidic esters undergo smooth isomerization to the corresponding α-hydroxy-β, γ-unsaturated esters upon reaction with Yb(OTf)3 or TMSOTf. These α-hydroxy-β, γ-unsaturated esters undergo Johnson-Claisen rearrangement to appropriately substituted diesters, some of which are converted into cyclopentanoids