Equity divestment and profit rate performance for sustainable development: a study of central public sector enterprises in India

Disinvestment can be described as a way of action whereby the Government withdraws its equity capital either in part or in full in public sector enterprises. From 1991-92, the Government of India undertake the process of divesting its equity shares in CPSEs. The present study has inspected the impact of equity divestment on profit rate performance in terms of ROCE of the CPSEs in India during the period 1998-99 to 2017-18. The study concluded that CPSEs have a proficient negative impact of equity divestment on their overall profit rate performance during the study period. However, the CPSEs have been able to sustain satisfactory average ROCE in the competitive market economy. Key Words: Equity Divestment, Profit Rate, CPSEs, ROCE

Role of Magnesium Sulfate in Prolonging the Analgesic Effect of Spinal Bupivacaine for Cesarean Section in Severe Preeclamptics

Background: Magnesium sulfate, N‑methyl‑d‑aspartate receptor antagonist, has both analgesic and sedative properties. Aim: The aim was to evaluate the analgesic efficacy of perioperative intravenous (i.v) magnesium sulfate in severe preeclamptic patients scheduled for cesarean section under spinal anesthesia. Subjects and Methods: A double blind prospective randomized controlled study was designed conducted on 80 patients randomly allocated into two equal groups (n = 40) to receive either bupivacaine heavy intrathecally – Group B (control group) or bupivacaine heavy intrathecally along with i.v magnesium sulfate – Group BM (study group). Magnesium sulfate 40 mg/kg diluted in 100 ml of normal saline was administered over 15 min about 30 min prior to surgery followed by continuous infusion at the rate of 10 mg/kg/h for the next 24 h while the other group received similar volume of normal saline in the same manner. Intraoperatively, patients were monitored for hemodynamic perturbations, respiratory rate, urine output, Apgar score, uterine tonicity, and any other adverse effects. Postoperatively, duration of analgesia, number of rescue analgesics, signs of any magnesium toxicity, and incidence of postpartum eclampsia in the first 24 h were recorded. Data were analyzed using SPSS version 16. Results: At different time intervals, patients in Group BM had less pain than Group B when compared on visual analog scale. Patients in Group BM were significantly more sedated as compared to Group B patients. None of the patients demonstrated bradycardia, hypotensive episodes, hypoxia, or hypoventilation in the postoperative period in the recovery room. There was no significant respiratory depression, Apgar score was comparable, and uterine tonicity was adequate in both the groups. Postoperatively, time required for first analgesic dose was significantly more in Group BM 270 (35.1) min than Group B 223 (31.4) min. There was a significant decrease in total rescue analgesic requirement in Group BM 2.5 (0.4) compared to Group B 3.6 (0.4). Incidence of postpartum eclampsia in study group (one patient) was less than the control group (four patients). Conclusion: Preoperative i.v magnesium sulfate, in severe preeclampsia not only reduces the probability of developing peripartum eclampsia, but also significantly prolongs the duration of analgesia and reduces postoperative analgesic consumption without any significant side effects.Keywords: Intravenous magnesium sulphate, preecclempsia, spinal anaesthesi

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Simulation-based analysis of micro-robots swimming at the center and near the wall of circular mini-channels

Swimming micro robots have great potential in biomedical applications such as targeted drug delivery, medical diagnosis, and destroying blood clots in arteries. Inspired by swimming micro organisms, micro robots can move in biofluids with helical tails attached to their bodies. In order to design and navigate micro robots, hydrodynamic characteristics of the flow field must be understood well. This work presents computational fluid dynamics (CFD) modeling and analysis of the flow due to the motion of micro robots that consist of magnetic heads and helical tails inside fluid-filled channels akin to bodily conduits; special emphasis is on the effects of the radial position of the robot. Time-averaged velocities, forces, torques, and efficiency of the micro robots placed in the channels are analyzed as functions of rotation frequency, helical pitch (wavelength) and helical radius (amplitude) of the tail. Results indicate that robots move faster and more efficiently near the wall than at the center of the channel. Forces acting on micro robots are asymmetrical due to the chirality of the robot’s tail and its motion. Moreover, robots placed near the wall have a different flow pattern around the head when compared to in-center and unbounded swimmers. According to simulation results, time-averaged for-ward velocity of the robot agrees well with the experimental values measured previously for a robot with almost the same dimensions

Instability of black hole formation under small pressure perturbations

We investigate here the spectrum of gravitational collapse endstates when arbitrarily small perfect fluid pressures are introduced in the classic black hole formation scenario as described by Oppenheimer, Snyder and Datt (OSD) [1]. This extends a previous result on tangential pressures [2] to the more physically realistic scenario of perfect fluid collapse. The existence of classes of pressure perturbations is shown explicitly, which has the property that injecting any smallest pressure changes the final fate of the dynamical collapse from a black hole to a naked singularity. It is therefore seen that any smallest neighborhood of the OSD model, in the space of initial data, contains collapse evolutions that go to a naked singularity outcome. This gives an intriguing insight on the nature of naked singularity formation in gravitational collapse.Comment: 7 pages, 1 figure, several modifications to match published version on GR

Involvement of mTOR in CXCL12 Mediated T Cell Signaling and Migration

CXCL12 is a pleiotropic chemokine involved in multiple different processes such as immune regulation, inflammatory responses, and cancer development. CXCL12 is also a potent chemokine involved in chemoattraction of T cells to the site of infection or inflammation. Mammalian target of rapamycin (mTOR) is a serine-threonine kinase that modulates different cellular processes, such as metabolism, nutrient sensing, protein translation, and cell growth. The role of mTOR in CXCL12-mediated resting T cell migration has yet to be elucidated.Rapamycin, an inhibitor of mTOR, significantly inhibits CXCL12 mediated migration of both primary human resting T cells and human T cell leukemia cell line CEM. p70(S6K1), an effector molecule of mTOR signaling pathway, was knocked down by shRNA in CEM cells using a lentiviral gene transfer system. Using p70(S6K1) knock down cells, we demonstrate the role of mTOR signaling in T cell migration both in vitro and in vivo.Our data demonstrate a new role for mTOR in CXCL12-induced T cell migration, and enrich the current knowledge regarding the clinical use of rapamycin

Correlation Functions of Large N Chern-Simons-Matter Theories and Bosonization in Three Dimensions

We consider the conformal field theory of N complex massless scalars in 2+1 dimensions, coupled to a U(N) Chern-Simons theory at level k. This theory has a 't Hooft large N limit, keeping fixed \lambda = N/k. We compute some correlation functions in this theory exactly as a function of \lambda, in the large N (planar) limit. We show that the results match with the general predictions of Maldacena and Zhiboedov for the correlators of theories that have high-spin symmetries in the large N limit. It has been suggested in the past that this theory is dual (in the large N limit) to the Legendre transform of the theory of fermions coupled to a Chern-Simons gauge field, and our results allow us to find the precise mapping between the two theories. We find that in the large N limit the theory of N scalars coupled to a U(N)_k Chern-Simons theory is equivalent to the Legendre transform of the theory of k fermions coupled to a U(k)_N Chern-Simons theory, thus providing a bosonization of the latter theory. We conjecture that perhaps this duality is valid also for finite values of N and k, where on the fermionic side we should now have (for N_f flavors) a U(k)_{N-N_f/2} theory. Similar results hold for real scalars (fermions) coupled to the O(N)_k Chern-Simons theory.Comment: 49 pages, 16 figures. v2: added reference

Development of Morphologically engineered Flower-like Hafnium-Doped ZnO with Experimental and DFT Validation for Low-Temperature and Ultrasensitive Detection of NOX Gas

This is the final version. Available on open access from the American Chemical Society via the DOI in this recordSubstitutional doping and different nanostructures of ZnO have rendered it an effective sensor for the detection of volatile organic compounds in real-time atmosphere. However, the low selectivity of ZnO sensors limits their applications. Herein, hafnium (Hf)-doped ZnO (Hf-ZnO) nanostructures are developed by the hydrothermal method for high selectivity of hazardous NOX gas in the atmosphere, substantially portraying the role of doping concentration on the enhancement of structural, optical, and sensing behavior. ZnO microspheres with 5% Hf doping showed excellent sensing and detected 22 parts per billion (ppb) NOX gas in the atmosphere, within 24 s, which is much faster than ZnO (90 s), and rendered superior sensing ability (S = 67) at a low temperature (100 °C) compared to ZnO (S = 40). The sensor revealed exceptional stability under humid air (S = 55 at 70% RH), suggesting a potential of 5% Hf-ZnO as a new stable sensing material. Density functional theory (DFT) and other characterization analyses revealed that the high sensing activity of 5% Hf-ZnO is attributed to the accessibility of more adsorption sites arising due to charge distortion, increased oxygen vacancies concentration, Lewis acid base, porous morphology, small particle size (5 nm), and strong bond interaction amidst NO2 molecule with ZnO-Hf-Ovacancy sites, resulting from the substitution of the host cation (Zn2+) with doping cation (Hf4+).Korea government (Ministry of Education)Engineering and Physical Sciences Research Council (EPSRC

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

<p>Abstract</p> <p>Background</p> <p>Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.</p> <p>Results</p> <p>In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.</p> <p>Conclusion</p> <p>Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.</p

Listening In on the Past: What Can Otolith δ18O Values Really Tell Us about the Environmental History of Fishes?

Oxygen isotope ratios from fish otoliths are used to discriminate marine stocks and reconstruct past climate, assuming that variations in otolith δ18O values closely reflect differences in temperature history of fish when accounting for salinity induced variability in water δ18O. To investigate this, we exploited the environmental and migratory data gathered from a decade using archival tags to study the behaviour of adult plaice (Pleuronectes platessa L.) in the North Sea. Based on the tag-derived monthly distributions of the fish and corresponding temperature and salinity estimates modelled across three consecutive years, we first predicted annual otolith δ18O values for three geographically discrete offshore sub-stocks, using three alternative plausible scenarios for otolith growth. Comparison of predicted vs. measured annual δ18O values demonstrated >96% correct prediction of sub-stock membership, irrespective of the otolith growth scenario. Pronounced inter-stock differences in δ18O values, notably in summer, provide a robust marker for reconstructing broad-scale plaice distribution in the North Sea. However, although largely congruent, measured and predicted annual δ18O values of did not fully match. Small, but consistent, offsets were also observed between individual high-resolution otolith δ18O values measured during tag recording time and corresponding δ18O predictions using concomitant tag-recorded temperatures and location-specific salinity estimates. The nature of the shifts differed among sub-stocks, suggesting specific vital effects linked to variation in physiological response to temperature. Therefore, although otolith δ18O in free-ranging fish largely reflects environmental temperature and salinity, we counsel prudence when interpreting otolith δ18O data for stock discrimination or temperature reconstruction until the mechanisms underpinning otolith δ18O signature acquisition, and associated variation, are clarified