Showing papers by "Indian Institute of Technology Guwahati published in 2017"

A review of multi criteria decision making (MCDM) towards sustainable renewable energy development

Abstract: In the current era of sustainable development, energy planning has become complex due to the involvement of multiple benchmarks like technical, social, economic and environmental. This in turn puts major constraints for decision makers to optimize energy alternatives independently and discretely especially in case of rural communities. In addition, topographical limitations concerning renewable energy systems which are mostly distributed in nature, the energy planning becomes more complicated. In such cases, decision analysis plays a vital role for designing such systems by considering various criteria and objectives even at disintegrated levels of electrification. Multiple criteria decision making (MCDM) is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. This tool is becoming popular in the field of energy planning due to the flexibility it provides to the decision makers to take decisions while considering all the criteria and objectives simultaneously. This article develops an insight into various MCDM techniques, progress made by considering renewable energy applications over MCDM methods and future prospects in this area. An extensive review in the sphere of sustainable energy has been performed by utilizing MCDM technique.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

Abstract: Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

Curcumin, the golden nutraceutical: multitargeting for multiple chronic diseases

Abstract: Curcumin, a yellow pigment in the Indian spice Turmeric (Curcuma longa), which is chemically known as diferuloylmethane, was first isolated exactly two centuries ago in 1815 by two German Scientists, Vogel and Pelletier. However, according to the pubmed database, the first study on its biological activity as an antibacterial agent was published in 1949 in Nature and the first clinical trial was reported in The Lancet in 1937. Although the current database indicates almost 9000 publications on curcumin, until 1990 there were less than 100 papers published on this nutraceutical. At the molecular level, this multitargeted agent has been shown to exhibit anti-inflammatory activity through the suppression of numerous cell signalling pathways including NF-κB, STAT3, Nrf2, ROS and COX-2. Numerous studies have indicated that curcumin is a highly potent antimicrobial agent and has been shown to be active against various chronic diseases including various types of cancers, diabetes, obesity, cardiovascular, pulmonary, neurological and autoimmune diseases. Furthermore, this compound has also been shown to be synergistic with other nutraceuticals such as resveratrol, piperine, catechins, quercetin and genistein. To date, over 100 different clinical trials have been completed with curcumin, which clearly show its safety, tolerability and its effectiveness against various chronic diseases in humans. However, more clinical trials in different populations are necessary to prove its potential against different chronic diseases in humans. This review's primary focus is on lessons learnt about curcumin from clinical trials. Linked Articles This article is part of a themed section on Principles of Pharmacological Research of Nutraceuticals. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.11/issuetoc

A White Paper on keV sterile neutrino Dark Matter

Abstract: We present a comprehensive review of keV-scale sterile neutrino Dark Matter, collecting views and insights from all disciplines involved—cosmology, astrophysics, nuclear, and particle physics—in each case viewed from both theoretical and experimental/observational perspectives. After reviewing the role of active neutrinos in particle physics, astrophysics, and cosmology, we focus on sterile neutrinos in the context of the Dark Matter puzzle. Here, we first review the physics motivation for sterile neutrino Dark Matter, based on challenges and tensions in purely cold Dark Matter scenarios. We then round out the discussion by critically summarizing all known constraints on sterile neutrino Dark Matter arising from astrophysical observations, laboratory experiments, and theoretical considerations. In this context, we provide a balanced discourse on the possibly positive signal from X-ray observations. Another focus of the paper concerns the construction of particle physics models, aiming to explain how sterile neutrinos of keV-scale masses could arise in concrete settings beyond the Standard Model of elementary particle physics. The paper ends with an extensive review of current and future astrophysical and laboratory searches, highlighting new ideas and their experimental challenges, as well as future perspectives for the discovery of sterile neutrinos.

Measurement of the τ Lepton Polarization and R (D * ) in the Decay B ̄ →D * τ - ν ̄ τ

Abstract: We report the first measurement of the tau lepton polarization P-tau(D*) in the decay (B) over bar -> D* tau(-) (v) over bar (tau) as well as a newmeasurement of the ratio of the branching fractions R(D*) = B((B) over bar -> D* tau(-) (v) over bar (tau)) / B((B) over bar -> D* l(-) (v) over bar (l)), where l(-) denotes an electron or a muon, and the tau is reconstructed in the modes tau(-) -> pi(-) v(tau) and tau(-) -> rho(-) v(tau). We use the full data sample of 772 x 10(6) B (B) over bar pairs recorded with the Belle detector at the (KEKB) over bar electron-positron collider. Our results, P-tau(D*) = -0.38 +/- 0.51 (stat)(-0.16)(+0.21) (syst) and R(D*) = 0.270 +/- 0.035 (stat)(- 0.025)(+0.028) (syst), are consistent with the theoretical predictions of the standard model.

Lepton-Flavor-Dependent Angular Analysis of B→K^{*}ℓ^{+}ℓ^{-}

Abstract: We present a measurement of angular observables and a test of lepton flavor universality in the B -> K(+)l(+)l(-) decay, where l is either e or mu. The analysis is performed on a data sample corresponding to an integrated luminosity of 711 fb(-1) containing 772 x 10(6) B (B) over bar pairs, collected at the Upsilon(4S) resonance with the Belle detector at the asymmetric-energy e(+)e(-) collider KEKB. The result is consistent with standard model (SM) expectations, where the largest discrepancy from a SM prediction is observed in the muon modes with a local significance of 2.6 sigma.

Search for invisible decays of a dark photon produced in e+e- collisions at BaBar

Abstract: We search for single-photon events in 53 fb^{-1} of e^{+}e^{-} collision data collected with the BABAR detector at the PEP-II B-Factory. We look for events with a single high-energy photon and a large missing momentum and energy, consistent with production of a spin-1 particle A^{'} through the process e^{+}e^{-}→γA^{'}; A^{'}→invisible. Such particles, referred to as "dark photons," are motivated by theories applying a U(1) gauge symmetry to dark matter. We find no evidence for such processes and set 90% confidence level upper limits on the coupling strength of A^{'} to e^{+}e^{-} in the mass range m_{A^{'}}≤8 GeV. In particular, our limits exclude the values of the A^{'} coupling suggested by the dark-photon interpretation of the muon (g-2)_{μ} anomaly, as well as a broad range of parameters for the dark-sector models.

Preferred Interpersonal Distances: A Global Comparison

Abstract: Human spatial behavior has been the focus of hundreds of previous research studies. However, the conclusions and generalizability of previous studies on interpersonal distance preferences were limited by some important methodological and sampling issues. The objective of the present study was to compare preferred interpersonal distances across the world and to overcome the problems observed in previous studies. We present an extensive analysis of interpersonal distances over a large data set (N = 8,943 participants from 42 countries). We attempted to relate the preferred social, personal, and intimate distances observed in each country to a set of individual characteristics of the participants, and some attributes of their cultures. Our study indicates that individual characteristics (age and gender) influence interpersonal space preferences and that some variation in results can be explained by temperature in a given region. We also present objective values of preferred interpersonal distances in different regions, which might be used as a reference data point in future studies.

Language-Directed Hardware Design for Network Performance Monitoring

Abstract: Network performance monitoring today is restricted by existing switch support for measurement, forcing operators to rely heavily on endpoints with poor visibility into the network core. Switch vendors have added progressively more monitoring features to switches, but the current trajectory of adding specific features is unsustainable given the ever-changing demands of network operators. Instead, we ask what switch hardware primitives are required to support an expressive language of network performance questions. We believe that the resulting switch hardware design could address a wide variety of current and future performance monitoring needs.We present a performance query language, Marple, modeled on familiar functional constructs like map, filter, groupby, and zip. Marple is backed by a new programmable key-value store primitive on switch hardware. The key-value store performs flexible aggregations at line rate (e.g., a moving average of queueing latencies per flow), and scales to millions of keys. We present a Marple compiler that targets a P4-programmable software switch and a simulator for high-speed programmable switches. Marple can express switch queries that could previously run only on end hosts, while Marple queries only occupy a modest fraction of a switch's hardware resources.

Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset.

Abstract: Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.

Dehydrogenation of Alkanes and Aliphatic Groups by Pincer-Ligated Metal Complexes

Abstract: The alkyl group is the most common component of organic molecules and the most difficult to selectively functionalize. The development of catalysts for dehydrogenation of alkyl groups to give the corresponding olefins could open almost unlimited avenues to functionalization. Homogeneous systems, or more generally systems based on molecular (including solid-supported) catalysts, probably offer the greatest potential for regio- and chemoselective dehydrogenation of alkyl groups and alkanes. The greatest progress to date in this area has been achieved with pincer-ligated transition-metal-based catalysts; this and related chemistry are the subject of this review. Chemists are still far from achieving the most obvious and perhaps most attractive goal in this area, the dehydrogenation of simple alkanes to yield alkenes (specifically monoenes) with high yield and selectivity. Greater progress has been made with tandem catalysis and related approaches in which the initial dehydrogenated product undergoes a desira...

Curcumin mediates anticancer effects by modulating multiple cell signaling pathways.

Abstract: Curcumin, a component of a spice native to India, was first isolated in 1815 by Vogel and Pelletier from the rhizomes of Curcuma longa (turmeric) and, subsequently, the chemical structure of curcumin as diferuloylmethane was reported by Milobedzka et al. [(1910) 43., 2163-2170]. Since then, this polyphenol has been shown to exhibit antioxidant, anti-inflammatory, anticancer, antiviral, antibacterial, and antifungal activities. The current review primarily focuses on the anticancer potential of curcumin through the modulation of multiple cell signaling pathways. Curcumin modulates diverse transcription factors, inflammatory cytokines, enzymes, kinases, growth factors, receptors, and various other proteins with an affinity ranging from the pM to the mM range. Furthermore, curcumin effectively regulates tumor cell growth via modulation of numerous cell signaling pathways and potentiates the effect of chemotherapeutic agents and radiation against cancer. Curcumin can interact with most of the targets that are modulated by FDA-approved drugs for cancer therapy. The focus of this review is to discuss the molecular basis for the anticancer activities of curcumin based on preclinical and clinical findings.

Extraction of $|V_{cb}|$ from $B\to D^{(*)}\ell\nu_\ell$ and the Standard Model predictions of $R(D^{(*)})$

Abstract: We extract |V cb | from the available data in the decay B → D(∗)lν l . B → D∗lν l , the unfolded binned differential decay rates of four kinematic variables including the q2 bins have been used. In the CLN and BGL parameterizations of the form factors, the combined fit to all the available data along with their correlations yields |V cb | = (39.77 ± 0.89) × 10−3 and (40.90 ± 0.94) × 10−3 respectively. In these fits, we have used the inputs from lattice and light cone sum rule (LCSR) along with the data. Using our fit results and the HQET relations (with the known corrections included) amongst the form factors, and parameterizing the unknown higher order corrections (in the ratios of HQET form factors) with a conservative estimate of the normalizing parameters, we obtain R(D∗) = 0.259 ± 0.006 (CLN) and R(D∗) = 0.257 ± 0.005 (BGL).

Constraints on Oscillation Parameters from νe Appearance and νμ Disappearance in NOvA

Abstract: Results are reported from an improved measurement of ν_{μ}→ν_{e} transitions by the NOvA experiment. Using an exposure equivalent to 6.05×10^{20} protons on target, 33 ν_{e} candidates are observed with a background of 8.2±0.8 (syst.). Combined with the latest NOvA ν_{μ} disappearance data and external constraints from reactor experiments on sin^{2}2θ_{13}, the hypothesis of inverted mass hierarchy with θ_{23} in the lower octant is disfavored at greater than 93% C.L. for all values of δ_{CP}.

Acceleration-Deceleration Behaviour of Various Vehicle Types

Abstract: Acceleration/deceleration (A/D) behaviour of vehicles is important for various applications like length of yellow light at inter- section, determination of sight distances at intersection, determination of length of A/D lanes, ramp design, traffic simulation modelling, vehicular emission modelling, instantaneous fuel consumption rate modelling, etc. Literature reports A/D studies for cars in lane disciplined homogeneous traffic. However, Indian traffic stream is weak lane disciplined and heterogenous, containing various vehicle types like truck, motorized three and two wheeler and diesel and petrol driven cars. Also, the reported studies are based on out of date data, collected using traditional and less accurate methods. Hence, this work aims to study the A/D behaviour of various vehicle types using modern instruments like Global Positioning System (GPS) in controlled manner including maximum A/D envelop. It is observed that acceleration rates of vehicles observed in this study, differed from acceleration rates reported in literature. On finding existing acceleration models insufficient to explain the acceleration behaviour of vehicles observed in this study, new models have been proposed and validated using statistical tools. Acceleration behaviour of cars varied with the change in gears, though the pattern remained similar in all gears.

Hydrogen Evolution Reaction Activity of Graphene–MoS2 van der Waals Heterostructures

Abstract: Determining a suitable noble-metal-free catalyst for hydrogen evolution reaction (HER) by photoelectrocatalytic (PEC) water splitting is an enduring challenge. Here, the molecular origin of number of layers and stacking sequence-dependent PEC HER performance of MoS2/graphene (MoS2/GR) van der Waals (vdW) vertical heterostructures is studied. Density functional theory (DFT) based calculations show that the presence of MoS2 induces p-type doping in GR, which facilitates hydrogen adsorption in the GR side compared to the MoS2 side with ΔGH closer to 0 eV in the MoS2/GR bilayer vertical stacks. The activity maximizes in graphene with monolayer MoS2 and reduces further for bilayer and multilayers of MoS2. The PEC HER performance is studied in various electrodes, namely, single-layer graphene, single- and few-layered MoS2, and their two different types of vertical heterojunctions having different stacking sequences. The graphene on top of MoS2 sequence showed the highest photoresponse with large reaction curren...

New quaternary half-metallic ferromagnets with large Curie temperatures

Abstract: New magnetic materials with high Curie temperatures for spintronic applications are perpetually sought for. In this paper, we present an ab initio study of the structural, electronic and magnetic p ...

Measurement of the Neutrino Mixing Angle θ23 in NOvA

Abstract: Click on the DOI link to access the article (may not be free). WSU authors: Meyer, H.; Muether, M.; Solomey, N. The complete list includes: Adamson, P.; Aliaga, L.; Ambrose, D.; Anfimov, N.; Antoshkin, A.; Arrieta-Diaz, E.; Augsten, K.; Aurisano, A.; Backhouse, C.; Baird, M.; Bambah, B. A.; Bays, K.; Behera, B.; Bending, S.; Bernstein, R.; Bhatnagar, V.; Bhuyan, B.; Bian, J.; Blackburn, T.; Bolshakova, A.; Bromberg, C.; Brown, J.; Brunetti, G.; Buchanan, N.; Butkevich, A.; Bychkov, V.; Campbell, M.; Catano-Mur, E.; Childress, S.; Choudhary, B. C.; Chowdhury, B.; Coan, T. E.; Coelho, J. A. B.; Colo, M.; Cooper, J.; Corwin, L.; Cremonesi, L.; Cronin-Hennessy, D.; Davies, G. S.; Davies, J. P.; Derwent, P. F.; Desai, S.; Dharmapalan, R.; Ding, P.; Djurcic, Z.; Dukes, E. C.; Duyang, H.; Edayath, S.; Ehrlich, R.; Feldman, G. J.; Frank, M. J.; Gabrielyan, M.; Gallagher, H. R.; Germani, S.; Ghosh, T.; Giri, A.; Gomes, R. A.; Goodman, M. C.; Grichine, V.; Group, R.; Grover, D.; Guo, B.; Habig, A.; Hartnell, J.; Hatcher, R.; Hatzikoutelis, A.; Heller, K.; Himmel, A.; Holin, A.; Hylen, J.; Jediny, F.; Judah, M.; Kafka, G. K.; Kalra, D.; Kasahara, S. M. S.; Kasetti, S.; Keloth, R.; Kolupaeva, L.; Kotelnikov, S.; Kourbanis, I.; Kreymer, A.; Kumar, A.; Kurbanov, S.; Lang, K.; Lee, W. M.; Lin, S.; Liu, J.; Lokajicek, M.; Lozier, J.; Luchuk, S.; Maan, K.; Magill, S.; Mann, W. A.; Marshak, M. L.; Matera, K.; Matveev, V.; M\'endez, D. P.; Messier, M. D.; Meyer, H.; Miao, T.; Miller, W. H.; Mishra, S. R.; Mohanta, R.; Moren, A.;Mualem, L.; Muether, M.; Mufson, S.; Murphy, R.; Musser, J.; Nelson, J. K.; Nichol, R.; Niner, E.; Norman, A.; Nosek, T.; Oksuzian, Y.; Olshevskiy, A.; Olson, T.; Paley, J.; Pandey, P.; Patterson, R. B.; Pawloski, G.; Pershey, D.; Petrova, O.; Petti, R.; Phan-Budd, S.; Plunkett, R. K.; Poling, R.; Potukuchi, B.; Principato, C.; Psihas, F.; Radovic, A.; Rameika, R. A.; Rebel, B.; Reed, B.; Rocco, D.; Rojas, P.; Ryabov, V.; Sachdev, K.; Sail, P.; Samoylov, O.; Sanchez, M. C.; Schroeter, R.; Sepulveda-Quiroz, J.; Shanahan, P.; Sheshukov, A.; Singh, J.; Singh, J.; Singh, P.; Singh, V.; Smolik, J.; Solomey, N.; Song, E.; Sousa, A.; Soustruznik, K.; Strait, M.; Suter, L.; Talaga, R. L.; Tamsett, M. C.; Tas, P.; Thayyullathil, R. B.; Thomas, J.; Tian, X.; Tognini, S. C.; Tripathi, J.; Tsaris, A.; Urheim, J.; Vahle, P.; Vasel, J.; Vinton, L.; Vold, A.; Vrba, T.; Wang, B.; Wetstein, M.; Whittington, D.; Wojcicki, S. G.; Wolcott, J.; Yadav, N.; Yang, S.; Zalesak, J.; Zamorano, B.; Zwaska, R.

A Novel Highly Bioavailable Curcumin Formulation Improves Symptoms and Diagnostic Indicators in Rheumatoid Arthritis Patients: A Randomized, Double-Blind, Placebo-Controlled, Two-Dose, Three-Arm, and Parallel-Group Study.

Abstract: Rheumatoid arthritis (RA) is an autoimmune, chronic systemic inflammatory disorder. The long-term use of currently available drugs for the treatment of RA has many potential side effects. Natural phytonutrients may serve as alternative strategies for the safe and effective treatment of RA, and curcuminoids have been used in Ayurvedic medicine for the treatment of inflammatory conditions for centuries. In this study, a novel, highly bioavailable form of curcumin in a completely natural turmeric matrix was evaluated for its ability to improve the clinical symptoms of RA. A randomized, double-blind, placebo-controlled, three-arm, parallel-group study was conducted to evaluate the comparative efficacy of two different doses of curcumin with that of a placebo in active RA patients. Twelve patients in each group received placebo, 250 or 500 mg of the curcumin product twice daily for 90 days. The responses of the patients were assessed using the American College of Rheumatology (ACR) response, visual analog scale (VAS), C-reactive protein (CRP), Disease Activity Score 28 (DAS28), erythrocyte sedimentation rate (ESR), and rheumatoid factor (RF) values. RA patients who received the curcumin product at both low and high doses reported statistically significant changes in their clinical symptoms at the end of the study. These observations were confirmed by significant changes in ESR, CPR, and RF values in patients receiving the study product compared to baseline and placebo. The results indicate that this novel curcumin in a turmeric matrix acts as an analgesic and anti-inflammatory agent for the management of RA at a dose as low as 250 mg twice daily as evidenced by significant improvement in the ESR, CRP, VAS, RF, DAS28, and ACR responses compared to placebo. Both doses of the study product were well tolerated and without side effects.

Near UV-Visible electronic absorption originating from charged amino acids in a monomeric protein

Abstract: Electronic absorption spectra of proteins are primarily characterized over the ultraviolet region (185–320 nm) of the electromagnetic spectrum. While recent studies on peptide aggregates have revealed absorption beyond 350 nm, monomeric proteins lacking aromatic amino acids, disulphide bonds, and active site prosthetic groups are expected to remain optically silent beyond 250 nm. Here, in a joint theoretical and experimental investigation, we report the distinctive UV-Vis absorption spectrum between 250 nm [e = 7338 M−1 cm−1] and 800 nm [e = 501 M−1 cm−1] in a synthetic 67 residue protein (α3C), in monomeric form, devoid of aromatic amino acids. Systematic control studies with high concentration non-aromatic amino acid solutions revealed significant absorption beyond 250 nm for charged amino acids which constitute over 50% of the sequence composition in α3C. Classical atomistic molecular dynamics (MD) simulations of α3C reveal dynamic interactions between multiple charged sidechains of Lys and Glu residues present in α3C. Time-dependent density functional theory calculations on charged amino acid residues sampled from the MD trajectories of α3C reveal that the distinctive absorption features of α3C may arise from two different types of charge transfer (CT) transitions involving spatially proximal Lys/Glu amino acids. Specifically, we show that the charged amino (NH3+)/carboxylate (COO−) groups of Lys/Glu sidechains act as electronic charge acceptors/donors for photoinduced electron transfer either from/to the polypeptide backbone or to each other. Further, the sensitivity of the CT spectra to close/far/intermediate range of encounters between sidechains of Lys/Glu owing to the three dimensional protein fold can create the long tail in the α3C absorption profile between 300 and 800 nm. Finally, we experimentally demonstrate the sensitivity of α3C absorption spectrum to temperature and pH-induced changes in protein structure. Taken together, our investigation significantly expands the pool of spectroscopically active biomolecular chromophores and adds an optical 250–800 nm spectral window, which we term ProCharTS (Protein Charge Transfer Spectra), for label free probes of biomolecular structure and dynamics.