Ashim Roy

Here, we have investigated some physicochemical parameters to understand the molecular interactions by means of density (ρ) measurement, measurement of viscosity (η), refractive index(nD) measurement, and conductance and surface tension measurements between two significant aqueous ionic liquid solutions: benzyl trimethyl ammonium chloride (BTMAC) and benzyl triethyl ammonium chloride (BTEAC) in an aqueous l-methionine (amino acid) solution. The apparent molar volume (Φv), coefficient of viscosity (B), and molar refraction (RM) have been used to analyze the molecular interaction behavior associated in the solution at various concentrations and various temperatures. With the help of some important equations such as the Masson equation, the Jones-Doles equation, and the Lorentz-Lorenz equation, very significant parameters, namely, limiting apparent molar volumes (Φv0), coefficient of viscosity (B), and limiting molar refraction (RM0), respectively, are obtained. These parameters along with specific conductance (κ) and surface tension (σ) are very much helpful to reveal the solute-solvent interactions by varying the concentration of solute molecules and temperature in the solution. Analyses of Δμ10#, Δμ20#, TΔS20#, ΔH20#, and thermodynamic data provide us valuable information about the interactions. We note that l-Met in 0.005 molality BTEAC ionic liquid at 308.15 K shows maximum solute-solvent interaction, while l-Met in 0.001 molality BTMAC aqueous solution of ionic liquid at 298.15 K shows the minimum one. Spectroscopic techniques such as Fourier transform infrared (FTIR), 1H-NMR, and UV-vis also provide supportive information about the interactions between the ionic liquid and l-methionine in aqueous medium. Furthermore, adsorption energy, reduced density gradient (RDG), and molecular electrostatic potential (MESP) maps obtained by the application of density functional theory (DFT) have been used to determine the type of interactions, which are concordant with the experimental observations.

The importance of non-Watson−Crick base pairs in the three-dimensional structure of RNA is now well established. The structure and stability of these noncanonical base pairs are, however, poorly understood. We have attempted to understand structural features of 33 frequently occurring base pairs using density functional theory. These are of three types, namely (i) those stabilized by two or more polar hydrogen bonds between the bases, (ii) those having one polar and another C−H···O/N type interactions, and (iii) those having one H-bond between the bases and another involving one of the sugars linked to the bases. We found that the base pairs having two polar H-bonds are very stable as compared to those having one C−H···O/N interaction. Our quantitatively analysis of structures of these optimized base pairs indicates that they possess a different amount of nonplanarity with large propeller or buckle values as also observed in the crystal structures. We further found that geometry optimization does not modify the hydrogen-bonding pattern, as values of shear and open angle of the base pairs remain conserved. The structures of initial crystal geometry and final optimized geometry of some base pairs having only one polar H-bond and a C−H···O/N interaction, however, are significantly different, indicating the weak nature of the nonpolar interaction. The base pair flexibility, as measured from normal-mode analysis, in terms of the intrinsic standard deviations of the base pair structural parameters are in conformity with those calculated from RNA crystal structures. We also noticed that deformation of a base pair along the stretch direction is impossible for all of the base pairs, and movements of the base pairs along shear and open are also quite restricted. The base pair opening mode through alteration of propeller or buckle is considerably less restricted for most of the base pairs.

The High Luminosity phase of the Large Hadron Collider will deliver 10 times more integrated luminosity than the existing collider, posing significant challenges for radiation tolerance and event pileup on detectors, especially for forward calorimetry. As part of its upgrade program, the Compact Muon Solenoid collaboration is designing a high-granularity calorimeter (HGCAL) to replace the existing endcap calorimeters. It will feature unprecedented transverse and longitudinal readout and triggering segmentation for both electromagnetic and hadronic sections. The electromagnetic section and a large fraction of the hadronic section will be based on hexagonal silicon sensors of 0.5–1 cm2 cell size, with the remainder of the hadronic section being based on highly-segmented scintillators with silicon photomultiplier readout. The intrinsic high-precision timing capabilities of the silicon sensors will add an extra dimension to event reconstruction, especially in terms of pileup rejection. First hexagonal silicon modules, using the existing Skiroc2 front-end ASIC developed for CALICE, have been tested in beams at Fermilab and CERN in 2016. We present results from these tests, in terms of system stability, calibration with minimum-ionizing particles and resolution (energy, position and timing) for electrons, and the comparisons of these quantities with GEANT4-based simulation.

Abstract Due to the extent of unionization in India’s coal and other carbon-rich sectors, trade unions can resist the tide of privatization and play an active role in formulating a just transition that integrates worker and social concerns into climate responses. An Indian just transition will be located around the need to peak coal usage soon and transition to renewables, with the additional complication of protecting livelihoods, as India’s coal-rich states are also its poorest. This chapter puts forth that democratic, public, and cooperative management of energy systems can prioritize social alongside climate concerns, as part of a wider industrial strategy to retrain workers and decarbonize industry. Climate change will also impact working conditions and workers’ health, with the burden likely to fall on households. Access to social services in workplaces, streets, and homes becomes necessary to alleviate the impacts of climate change on the most vulnerable.

Deep learning models are being utilised increasingly within finance. Given the models are opaque in nature and are now being deployed for internal and consumer facing decisions, there are increasing concerns around the trustworthiness of their results. We test the stability of predictions and explanations of different deep learning models, which differ between each other only via subtle changes to model settings, with each model trained over the same data. Our results show that the models produce similar predictions but different explanations, even when the differences in model architecture are due to arbitrary factors like random seeds. We compare this behaviour with traditional, interpretable, ‘glass-box models’, which show similar accuracies while maintaining stable explanations and predictions. Finally, we show a methodology based on network analysis to compare deep learning models. Our analysis has implications for the adoption and risk management of future deep learning models by regulated institutions.

Orthodontic treatment has demonstrated efficacy in enhancing dental health and rectifying tooth misalignments. Nevertheless, patients experience substantial discomfort and distress. Advancements in orthodontic technology and treatment procedures have led to a decrease in orthodontic discomfort. Orthodontic discomfort refers to the inflammation that occurs due to the obstruction of blood vessels by orthodontic force. This leads to inflammatory responses, which encompass alterations in blood vessels, recruitment of inflammatory and immune cells, and heightened sensitivity of nerves along with the release of chemicals that promote inflammation. The body's inherent analgesic systems ultimately regulate the inflammatory response, thereby diminishing pain. Orthodontic pain signals are transmitted by three-order neurons, beginning with the trigeminal neuron located in the trigeminal ganglia. The signals subsequently arrive at the trigeminal nucleus caudalis located in the medulla oblongata, as well as the ventroposterior nucleus in the thalamus, where the sensation of pain is perceived. The processing of orthodontic pain involves the interplay of emotion, cognition, and memory in many parts of the brain. The structures encompassed in this list are the insular cortex, amygdala, hippocampus, locus coeruleus, and hypothalamus. The inherent analgesic neuronal pathway of the periaqueductal gray and dorsal raphe regions alleviates orthodontic discomfort. Various techniques are employed to manage orthodontic discomfort. These therapies encompass pharmacological, mechanical, behavioral, and low-level laser treatments. Nonsteroidal anti-inflammatory medicines (NSAIDs) alleviate pain, but their impact on tooth movement remains uncertain. Additional research is required to establish the effectiveness of alternative modalities. Gene therapy provides a new, practical, and hopeful approach to treating orthodontic pain. This article explores new advancements and techniques that have enhanced the level of comfort experienced by orthodontic patients.

Advances in storage, networks, and hardware technology have resulted in an explosion of data and given rise to multiple sources of overlapping data. This, combined with general apathy towards privacy issues while designing systems and processes, leads to frequent breaches in personal identity and data security. What makes this worse is that many of these breaches are committed by the legitimate users of the data. Major countries like the U.S., Japan, Canada, Australia and EU have come up with strict data distribution laws which demand their organizations to implement proper data security measures that respect personal privacy and prohibit dissemination of raw data outside the country. Since companies are not able to provide real data, they often resort to completely random data. It is obvious that such a data would offer complete privacy, but would have very low utility. This has serious implications for an IT services company like Tata Consultancy Services Ltd. (TCS), since application development and testing environments rely on realistic test data to verify that the applications provide the functionality and reliability they were designed to deliver. It is always desirable that the test data is similar to, if not the same as, the production data. Hence, deploying proven tools that make de-identifying production data easy, meaningful and cost-effective is essential. Data masking methods came into existence to permit the legitimate use of data and avoid misuse. In this paper, we consider various such techniques to come up with a comprehensive solution for data privacy requirements. We present a detailed methodology and solutions for enterprise-wide masking. We also present the data masking product MASKETEERTM, developed at TCS, which implements these techniques for providing maximum privacy for data while maintaining good utility.

A simulation study of energy resolution, position resolution, and π0-γ separation using multivariate methods of a sampling calorimeter is presented. As a realistic example, the geometry of the calorimeter is taken from the design geometry of the Shashlik calorimeter which was considered as a candidate for CMS endcap for the phase II of LHC running. The methods proposed in this paper can be easily adapted to various geometrical layouts of a sampling calorimeter. Energy resolution is studied for different layouts and different absorber-scintillator combinations of the Shashlik detector. It is shown that a boosted decision tree using fine grained information of the calorimeter can perform three times better than a cut-based method for separation of π0 from γ over a large energy range of 20 GeV–200 GeV.

The Forward Electromagnetic Calorimeter of the CMS detector is going to be upgraded in the high luminosity running as the energy of the present Electromagnetic Calorimeter (PbWO4) will degrade in the high luminosity (luminosity 1034cm-2s-1) running due to extensive radiation (hadron flux 1013neutrons cm,-2). Shashlik Electromagnetic Calorimeter which consists of alternate layers of 1.5 mm LYSO(Ce) crystal plates and 2.5 mm Tungsten absorbers, was a proposal for high luminosity running. One of the performance points for any electromagnetic calorimeter is the ability to separate π0 s from true photons, since final states with photons are a clean and one of the most important final states in proton-proton collisions at the LHC. The objective of this project is to study the possibility of π0 and γ separation in the Shashlik detector using Multivariate Analysis (MVA) technique.

Centrality metrics for simple graphs are well-defined. For each centrality measure, multiple main-memory algorithms exist for their computation. With main memory algorithms, the size of the graph that can be analyzed is bounded by the available memory. For the analysis of complex data, it has been shown that multilayer networks (or MLNs) are better suited as it has a number of advantages for modeling and provides semantic clarity. Briefly, MLNs are layers where each layer is a simple graph and further nodes from two different layers may also be connected. MLNs with different node sets and interlayer edges are termed heterogeneous MLNs (or HeMLNs) and are the focus of this paper. Hence, there is a need for algorithms to compute centrality metrics directly on the MLN representation.Currently, centrality metrics are not defined for Heterogeneous Multilayer Networks (HeMLNs), which are widely used for modeling complex data sets. Typically, HeMLNs are converted into a simple graph using aggregation and projection alternatives for computing centrality metrics using the traditional main-memory algorithms. However, this approach has been shown to lose information and structure (and hence semantics) and makes interpreting the results difficult.In this paper, we present a definition of stress (betweenness) centrality for HeMLNs and propose a heuristic-based algorithm to improve the accuracy of computed metrics with respect to ground truth. We provide intuition behind the heuristic proposed and provide extensive experimental results on different types of graphs with diverse characteristics to support our heuristics. Large synthetic data sets are used to control graph characteristics to validate the hypothesis and accuracy consistency. For computation, we use the decoupling approach, proposed specifically for MLNs, which has been shown to be significantly more efficient than the computation of ground truth. We validate that as well with our algorithm.

Centrality metrics for simple graphs/networks are well-defined and each has numerous main-memory algorithms. MultiLayer Networks (MLNs) are becoming popular for modeling complex data sets with multiple types of entities and relationships. When data sets are modeled using MLNs, it is imperative that algorithms for each metric (e.g., degree, betweenness, closeness, etc.) are developed including definitions. As there are no definitions and algorithms for computing centrality measures directly on MLNs, existing strategies are used that reduce (aggregate or collapse) MLN layers to simple graphs using Boolean AND or OR operation on layer edges. This approach negates the benefits of MLN modeling as these result in loss of structure and semantics and further are not, typically, efficient. In this paper, we address the degree centrality metric for homogeneous MLNs (HoMLNs) starting with its definition. We then develop heuristics-based algorithms for computing degree centrality on MLNs directly (i.e., without reducing them to simple graphs) using the decoupling-based approach which has been shown to be efficient as well as structure and semantics preserving. We compare the accuracy and precision of our algorithms with Boolean OR-aggregated graphs of Homogeneous MLNs as ground truth. The decoupling approach is used because it can take advantage of parallelism and is more efficient than aggregation- or projection-based approaches. We also highlight through several observations and lemmas the information needs from each layer for the decoupling-based approach. Extensive experimental analysis is performed on large synthetic, real-world-like, and actual real-world data sets of varying sizes and graph characteristics to validate the accuracy, precision, and efficiency of our algorithms.

Abstract This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model. Two different closed-form solutions of the advection-diffusion partial differential equation (A-D PDE), known as the Gaussian plume model and Gaussian puff model, are typically used to model the atmospheric dispersion of gas emission. The Gaussian puff model is a more complex physics-based model that requires more computational effort to generate the high-fidelity solutions, as compared to the simpler Gaussian plume model that has several assumptions and approximations. An encoder-decoder architecture of Long Short-Term Memory (LSTM) network is trained to predict the solutions of the more complex Gaussian puff model using the solutions of the simpler Gaussian plume model for various leak rate, wind speed, and wind direction. The LSTM model, with 3 LSTM layers with 16 neurons each, efficiently simulated the concentrations of the entire set of 2014 samples in a mere 1.34 minutes. This presents a significant contrast to the traditional software's time-consuming simulation process, which took 14 hours to achieve similar concentration outcomes in this study. The implementation of LSTM network has achieved a computational speed up of 625.15 times.

Agriculture accounts for a fifth of India's GDP, however the research in this field does not reflect this significant contribution. Farming practices remain archaic, with little emphasis on data-driven approaches to maximising yield and profits. Predicting crop yield is crucial for maximising profits in agronomy, with suitable fertiliser selection vital for maintaining soil health. This paper presents an extensive three-tiered architecture for comprehensive crop and fertiliser prediction using historical data with features such as soil pH, moisture, and temperature. The first tier predicts crops based on the area under cultivation and geographical region, with an accuracy of 99.54% using the random forest classifier. The yield for the given crop is predicted using linear regression with an accuracy of 89.57%. The second tier predicts the cost of cultivation, and the third predicts an appropriate fertiliser based on soil nutrients and environmental factors using Naïve Bayes with 100% accuracy.

A search is conducted for dark matterDark matter pair-production and for graviton production predicted by the ADD large extra dimensionsExtra dimension model in a final state with a photon and missing transverse energy in pp collisions at $$\sqrt{s}=13~\mathrm {TeV}$$ . Data taken by the CMS experimentCMS experiment at the CERN LHC in 2016 corresponding to an integrated luminosity of $$12.9~\mathrm {fb}^{-1}$$ is analyzed. We find no deviation from the Standard Model prediction for this final state, and achieve an extension of the current limits on parameter space.

An investigation on the diverse molecular interactions between implausible food chemicals (potassium oxalate, sodium oxalate and lithium Oxalate) and 1-butyl-1-methyl-pyrrolidinium chloride in aqueous solutions has been presented. The experiments have been discovered thoroughly by different types of physicochemical methodologies like density, refractive index, viscosity, and electrical conductivity at three different temperatures 298.15 K, 308.15 K and 318.15 K. The genesis of diverse interactions of the ternary mixtures were exposed by measurement of the apparent molar volume (ϕv), limiting apparent molar volume (ϕv0), viscosity B coefficients, molar refraction (RM), limiting molar refraction (RM0). The results have been revealed the predominant solute–solvent interaction over the solute–solute as well as solvent–solvent interactions. The ionic liquid strongly interacts with potassium oxalate than sodium oxalate, which in turn is greater than lithium oxalate at a higher range of temperature. Moreover Density functional theory calculations were performed to evaluate parameters like adsorption energies, molecular electrostatic potential maps and mode of binding which corroborate the experimental observations.

Diesel soot particulate matter is considered to be the most harmful pollutant as because the air borne particulate matters are known to constitute a major human risk. Recent epidemiological studies reported that particles with diameters of less than 2.5 micron are most fetal for living beings due to inhaling of the same. The reduction of particulate emissions from diesel engine is one of the most challenging problems in modern society. Diesel particulate emission can easily enter human respiratory system and are capable of causing cancer because of their association with absorbed and condensed potential occupational carcinogenic compounds such as poly-nuclear aromatic hydrocarbon, Nitro-PAH and sulphates. The main particulate fraction of diesel exhaust consists of fine particles. Diesel particulate matters are inhalable and may easily penetrate deep into the lungs because of their small sizes. The rough surfaces of these particles make it easy for them to bind with other toxins in the environment, thus increasing the hazards of particle inhalation. Several solutions have been proposed to date like ceramic filtration, wire mesh filtration, direct contact type filtration systems etc., which suffer from high engineering complexity, high costs as well as increased backpressure. Most of the proposed solutions deteriorate diesel engine combustion performance and simultaneously increases fuel consumptions. This paper presents the electrostatic attraction of ultra fine diesel soot particulate matter for exhaust gas treatment and the theoretical modeling of effect of collection efficiency of ultra fine particulate matter emitted from diesel engine exhaust gas through an electro-cyclone and other operating parameters i.e. applied voltage, flow rate, the particle parameters like particle size, shape and dielectric properties, exhaust gas density, viscosity on the performance of electro cyclone separator as a Diesel Soot Particulate Emission Arrester. Improved modified design of a non-contact type filtration system of electro-cyclone separator presents lower back pressure drop with enhanced particulate collection efficiency for reducing ultra fine diesel engine exhaust soot particulate matter emissions. Particulate diameter, exhaust gas density, viscosity presents similar matching trends with the published studies. Graphical trend of applied voltage vs particulate diameter is well justified by the theoretical analysis of the model.

Grid computing is a computation methodology using group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources. Integrating a set of clusters of workstations into one large computing environment can improve the availability of computing power. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. A secure scheduling model is presented, that performs job grouping activity at runtime. In a Grid environment, security is necessary because grid is a dynamic environment and participates are independent bodies with different policies, objectives and requirements. Authentication should be verified for Grid resource owners as well as resource requesters before they are allowed to join in scheduling activities. In order to achieve secure resource and job scheduling including minimum processing time and maximum resource utilization, A Secure Resource by using RSA algorithm on Networking and Job Scheduling model with Job Grouping strategy(JGS) in Grid Computing has been proposed. The result shows significant improvement in the processing time of jobs and resource utilization as compared to dynamic job grouping (DJG) based scheduling on smart grids (SG).

Corrected by: A Short and Simple Synthesis of 1-Deoxynojirimycin Derivatives from d-GlucoseSynthesis 2006; 2006(14): 2446-2446DOI: 10.1055/s-2006-942457

Author(s): Collaboration, STAR; Adamczyk, L; Adkins, JK; Agakishiev, G; Aggarwal, MM; Ahammed, Z; Alekseev, I; Aparin, A; Arkhipkin, D; Aschenauer, EC; Ashraf, MU; Attri, A; Averichev, GS; Bai, X; Bairathi, V; Bellwied, R; Bhasin, A; Bhati, AK; Bhattarai, P; Bielcik, J; Bielcikova, J; Bland, LC; Bordyuzhin, IG; Bouchet, J; Brandenburg, JD; Brandin, AV; Bunzarov, I; Butterworth, J; Caines, H; Sanchez, M Calderon de la Barca; Campbell, JM; Cebra, D; Chakaberia, I; Chaloupka, P; Chang, Z; Chatterjee, A; Chattopadhyay, S; Chen, X; Chen, JH; Cheng, J; Cherney, M; Christie, W; Contin, G; Crawford, HJ; Das, S; Silva, LC De; Debbe, RR; Dedovich, TG; Deng, J; Derevschikov, AA; Ruzza, B di; Didenko, L; Dilks, C; Dong, X; Drachenberg, JL; Draper, JE; Du, CM; Dunkelberger, LE; Dunlop, JC; Efimov, LG; Engelage, J; Eppley, G; Esha, R; Evdokimov, O; Eyser, O; Fatemi, R; Fazio, S; Federic, P; Fedorisin, J; Feng, Z; Filip, P; Fisyak, Y; Flores, CE; Fulek, L; Gagliardi, CA; Garand, D; Geurts, F; Gibson, A; Girard, M; Greiner, L; Grosnick, D; Gunarathne, DS; Guo, Y; Gupta, A; Gupta, S | Abstract: We report the measurement of the leptonic ($e^{+}e^{-}$) decay channel of the $\phi$(1020) meson in Au+Au collisions at $\sqrt{s_{_{NN}}}$ = 200 GeV by the STAR experiment. The transverse momentum ($p_{\rm T}$) spectrum is measured for 0.1 $\le p_{\rm T} \le 2.5$ GeV/$c$ at mid-rapidity ($|y|\le1$). We obtain the $p_{\rm T}$-integrated $\phi$(1020) mass $M_{\phi}=1017.7\pm0.8 (\rm {stat.}) \pm0.9 (\rm {sys.})$ MeV/$c^{2}$ and width $\Gamma_{\phi} = 8.0\pm 2.5(\rm {stat.}) \pm 2.3(\rm {sys.}) \textrm{MeV/}c^{2}$, which are within 1.5\,$\sigma$ and 1.1\,$\sigma$ of the vacuum values, respectively. No significant difference is observed in the measured $p_{\rm T}$ spectrum, $dN/dy$, or $\left$ of the $\phi$(1020) meson between the $e^{+}e^{-}$ and hadronic ($K^{+}K^{-}$) decay channels as measured by the same experiment. The experimental results are compared to a theoretical model including medium-modified $\phi$(1020).

This article presents a new class of the kink soliton, anti-kink soliton solution for the Zakharov-Kuznetsov-Burgers (ZKB) equation. To establish the existence of such type of model in a real physical situation, an unmagnetized viscous plasma containing cold ions and the electrons obeying Cairns-Tallis distribution is considered, and employing reductive perturbation method (RPM) classical ZKB equation is derived. The Generalised Kudryashov method (GKM) is employed to explore the solution of the aforesaid equation and the symbolic software package Maple is adopted in carrying out the complicated algebraic computation. Finally, the physical significance of different parameters on wave propagation is demonstrated through numerical understanding.

Correction to: A Short and Simple Synthesis of 1-Deoxynojirimycin Derivatives from d-GlucoseSynthesis 2006; 2006(06): 1035-1039DOI: 10.1055/s-2006-926369

A new telecommunications service for location determination via satellite is being proposed for the continental USA and Europe, which provides users with the capability to find the location of, and communicate from, a moving vehicle to a central hub and vice versa. This communications system is expected to operate in an extremely noisy channel in the presence of fading. In order to achieve high levels of data integrity, it is essential to employ forward error correcting (FEC) encoding and decoding techniques in such mobile satellite systems. A constraint length k = 7 FEC decoder has been implemented in a single chip for such systems. The single chip implementation of the maximum likelihood decoder helps to minimize the cost, size, and power consumption, and improves the bit error rate (BER) performance of the mobile earth terminal (MET).