Environment-induced corrections to the spin Hamiltonian as dynamic frequency shifts in nuclear magnetic resonance

We derive an expression for the correction to the spin-system Hamiltonian that arises due to the system-bath interaction, starting both from the standard master equation for the spin density matrix and a perturbative diagonalization of the system-bath Hamiltonian to the second order in the interaction. We show that the dynamic frequency shifts observed in the evolution of the nuclear spin coherences are a result of these Hamiltonian corrections. We present a systematic decomposition of the relaxation superoperator into Hermitian and anti-Hermitian parts as opposed to the usual practice of partitioning it into real and imaginary parts. We point out that the relaxation-induced corrections to the coherent motion arise exclusively from the anti-Hermitian part and the dissipative effects, from the Hermitian part, both, in general, being complex. However, the secular terms of this correction are found to depend only on the imaginary and the real parts, respectively

A comprehensive study of ondansetron hydrochloride drug as a green corrosion inhibitor for mild steel in 1M HCl medium

AbstractThe inhibiting action against the corrosion of mild steel by ondansetron hydrochloride (ODSH) drug was studied, using various studies such as weight loss, electrochemical impedance spectroscopy, potentiodynamic polarization measurement, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), FT-IR spectroscopy and reactivity of molecule is studied using quantum chemical calculation. The result shows that ondansetron hydrochloride (ODSH) gives better inhibition on mild steel. The value of activation energy (Ea) and various thermodynamic parameters such as adsorption equilibrium constant (Kads), free energy of adsorption (ΔG0ads), adsorption enthalpy (ΔHads) and adsorption entropy (ΔSads) was calculated and discussed. The adsorption of ODSH on mild steel surface obeys the Langmuir adsorption isotherm. Potentiodynamic polarization measurement reveals that ODSH acts as a mixed-type inhibitor. Electrochemical impedance spectroscopy (EIS) spectra exhibit one capacitive loop indicating that, the corrosion reaction is controlled by charge transfer process. SEM, EDX, AFM, FT-IR conforms to the protective film formation. Quantum chemical calculation was calculated and discussed, and it supports the results

Local module identification in dynamic networks with correlated noise: the full input case

The identification of local modules in dynamic networks with known topology has recently been addressed by formulating conditions for arriving at consistent estimates of the module dynamics, typically under the assumption of having disturbances that are uncorrelated over the different nodes. The conditions typically reflect the selection of a set of node signals that are taken as predictor inputs in a MISO identification setup. In this paper an extension is made to arrive at an identification setup for the situation that process noises on the different node signals can be correlated with each other. In this situation the local module may need to be embedded in a MIMO identification setup for arriving at a consistent estimate with maximum likelihood properties. This requires the proper treatment of confounding variables. The result is an algorithm that, based on the given network topology and disturbance correlation structure, selects an appropriate set of node signals as predictor inputs and outputs in a MISO or MIMO identification setup. As a first step in the analysis, we restrict attention to the (slightly conservative) situation where the selected output node signals are predicted based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision and Control, Nice, 201

Diurnal variation of phytoplankton community in the coastal waters of South Andaman Island with special emphasis on bloom forming species

1383-1397Species composition, abundance and distribution of phytoplankton dynamics were studied from the coastal waters of South Andaman Islands during December 2015 to February 2016. Physico-chemical parameters and phytoplankton species composition were observed during the study. Environmental parameters such as surface water temperature - 33ºC (r=0.96; pppp-1) were recorded. A total of 82 species under 50 genera of phytoplanktons were recorded belonging to Bacillariophyceae, Dinophyceae, Cyanophyceae and Silicoflagellate groups. Diatoms were represented by 68 species belonging to 40 genera, Dinoflagellates were represented by 12 species belonging to 8 genera, and 1 species belonging to a genera Cyanophyceae and Silicoflagellate were observed. Among the diatoms, Coscinodiscus centralis, Rhizosolenia alata, R. imbricata, Bacteriastrum furcatum, Leptocylindrus danicus, Odentella sinensis, Pleurosigma sp., Skeletonema costatum and Thalassionema nitzschioides and among the dinoflagellates, Ceratium furca, Prorocentrum micans, P. divergens and Pyrophacus sp. were the most prevalent diatoms and dinoflagellates encountered in the samples. The population density fluctuated between in the range of 53-63034 cells.mL-1. The highest population density was recorded in January at (St.1) Chatham bay due to the blooming of diatom R. imbricata (63000 cells.mL-1), were observed. Moreover, increase in water temperature and salinity was also found to be an influencing co-factor that had contributed to the algal bloom

Forecasting Using Vector Autoregressive Models (VAR) Applying Vector Autoregressive Model For Smart Irrigation

Forecasting data can give a better understanding, control and manage unexpected results better. Vector Autoregressive Models (VAR Models) have for long been used to find trends in a set of non-discreet values. In this paper, we focus on building VAR models, to determine the best fit using various tests and the results obtained when we applied VAR models to estimate the future values and trends in soil moisture. Along with the application of VAR models to predict the soil moisture, we have also additionally applied it to the temperature and mean sea level pressure forecasting, results of which are presented

Home Automation Using Smart AI Assistant

Artificial intelligence is taking over all the platforms and applications and making them more and more user friendly and intelligent and gives the application a human like behaviour and thinking. AI is being used everywhere now-a-days because applications that use AI are able to learn and improve and require less maintenance. This paper “Home automation using smart AI assistant” is a voice command-based AI assistant coded using python and Natural Language Processing NLP that can respond to user queries and responds to voice-based commands of the user. Using this AI Assistant users can send emails to anyone just by speaking the receiver’s name, subject of email and the email body. The users can send WhatsApp messages to any of their contacts just speak the message and to whom you want to send it, users can do google searches and open google tabs by voice search, users can do Wikipedia searches and can also open any YouTube video they want to watch. With this AI Assistant we can also get weather updates and news updates, just speak what news updates you want and the Assistant will read it for you.  We can also check the computer performance, add remainders and to-dos. We can open our documents, play our favourite music or watch our favourite movie and do many more thigs just by a simple voice command. This assistant is also used for voice-controlled home automation. Using this Assistant, we can control our home electrical appliances with simple voice commands like turn on the room lights, fans, AC etc

A Brief Survey on Cluster based Energy Efficient Routing Protocols in IoT based Wireless Sensor Networks

The wireless sensor network (WSN) consists of a large number of randomly distributed nodes capable of detecting environmental data, converting it into a suitable format, and transmitting it to the base station. The most essential issue in WSNs is energy consumption, which is mostly dependent on the energy-efficient clustering and data transfer phases. We compared a variety of algorithms for clustering that balance the number of clusters. The cluster head selection protocol is arbitrary and incorporates energy-conscious considerations. In this survey, we compared different types of energy-efficient clustering-based protocols to determine which one is effective for lowering energy consumption, latency and extending the lifetime of wireless sensor networks (WSN) under various scenarios