DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF IMPURITIES FROM OLMESARTAN MEDOXIMIL AND HYDROCHLOROTHIAZIDE TABLET

Objective: To develop and validate stability indicating RP-HPLC gradient method for simultaneous estimation of impurities and degradation products from Olmesartan Medoximil and Hydrochlorothiazide tablet.Methods: The chromatographic separation was achieved by using Inertsil ODS (250 mm x 4.6 mm, 5μ) column. The mobile phase-A consists of 0.01M potassium dihydrogen phosphatebuffer pH 3.2 adjusted using orthophosphoric acids and acetonitrile as mobile phase-B. The flow rate was 1 ml/min, and chromatograms extracted at wavelength 225 nm.Results: The method was found linear from LOQ to 0.4% level with respect to target concentrations of Olmesartan Medoximil (1.6 mg/ml) and Hydrochlorothiazide (0.5 m/ml) for all impurities, with correlation coefficient found greater than 0.99. The method found robust in all deliberate variations of method parameters as a resolution between adjacent peaks found greater than 2.0. The % RSD results for precision and intermediate precision found less than 5.0%.Conclusion: The proposed analytical method was found to be robust, stability indicating and can be used for estimation of impurities and degradation products of Olmesartan Medoximil and Hydrochlorothiazide from tablet dosage form.Keywords: Stability indicating, RP-HPLC, ICH, Olmesartan Medoximil and Hydrochlorothiazid

Bio medical Waste Management : A Case Study of Pune City

There are number of hospitals in all over the India which emits the Bio medical waste in large quantities in the form of contaminated and non-contaminated waste which are hazardous to health. Proper handling, treatment and disposal of Bio medical waste play a vital role in hospital infection controlled program. Unfortunately lack of adequate training, improper management, illiteracy about handling and awareness and no execution of Bio medical handling rules leads to staid health and environmental apprehension. Careless handling and disposal of these infectious wastes may lead to serious threat to life human as well as animals. This study explains the existing information about bio medical waste management, segregation, transportation, storage, treatment and disposal. Also this study explains lacunas of existing management system of bio medical waste, the recommendations and suggestions of bio medical waste management

Experimental evidence for fast cluster formation of chain oxygen vacancies in YBa2Cu3O7-d being at the origin of the fishtail anomaly

We report on three different and complementary measurements, namely magnetisation measurements, positron annihilation spectroscopy and NMR measurements, which give evidence that the formation of oxygen vacancy clusters is on the origin of the fishtail anomaly in YBa2Cu3O7-d. While in the case of YBa2Cu3O7.0 the anomaly is intrinsically absent, it can be suppressed in the optimally doped state where vacancies are present. We therefore conclude that the single vacancies or point defects can not be responsible for this anomaly but that clusters of oxygen vacancies are on its origin.Comment: 10 pages, 4 figures, submitted to PR

Direct observation of the oxygen isotope effect on the in-plane magnetic field penetration depth in optimally doped YBa2_2Cu3_3O7−δ_{7-\delta}

We report the first direct observation of the oxygen-isotope ($^{16}$O/$^{18}$O) effect on the in-plane penetration depth $\lambda_{ab}$ in a nearly optimally doped YBa$_2$Cu$_3$O$_{7-\delta}$ film using the novel low-energy muon-spin rotation technique. Spin polarized low energy muons are implanted in the film at a known depth $z$ beneath the surface and precess in the local magnetic field $B(z)$. This feature allows us to measure directly the profile $B(z)$ of the magnetic field inside the superconducting film in the Meissner state and to make a model independent determination of $\lambda_{ab}$. A substantial isotope shift $\Delta\lambda_{ab}/\lambda_{ab}=2.8(7)$% at 4 K is observed, implying that the in-plane effective supercarrier mass $m_{ab}^\ast$ is oxygen-isotope dependent with $\Delta m_{ab}^\ast/m_{ab}^\ast = 5.5(1.4)$.Comment: 4 pages, 2 figure

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction

Separation of quadrupolar and magnetic contributions to spin-lattice relaxation in the case of a single isotope

We present a NMR pulse double-irradiation method which allows one to separate magnetic from quadrupolar contributions in the spin-lattice relaxation. The pulse sequence fully saturates one transition while another is observed. In the presence of a Delta m = 2 quadrupolar contribution, the intensity of the observed line is altered compared to a standard spin-echo experiment. We calculated analytically this intensity change for spins I=1, 3/2, 5/2, thus providing a quantitative analysis of the experimental results. Since the pulse sequence we used takes care of the absorbed radio-frequency power, no problems due to heating arise. The method is especially suited when only one NMR sensitive isotope is available. Different cross-checks were performed to prove the reliability of the obtained results. The applicability of this method is demonstrated by a study of the plane oxygen 17O (I = 5/2) in the high-temperature superconductor YBa_2Cu_4O_8: the 17O spin-lattice relaxation rate consists of magnetic as well as quadrupolar contributions.Comment: 7 pages, 6 figure

Mapping and classification of ports and marinas for the definition of long-term development strategy

Mapping and classification of ports may be of great help to define effective development strategies based on the concept of “intelligent, green and integrated port”, within the frame of sustainable development. To this end, classification tools and knowledge of the initial situation are crucial points needed, just as an example, to boost the maritime and short-sea connectivity by promoting the creation of regional touristic port network, capable of implementing a smart, green, and integrated transport system. This work deals with the mapping and classification of ports and marinas. A possible methodology to define a priority matrix intervention rank is proposed and applied to all the harbors in the Puglia region, as a case study. The collected open data aim to describe several aspects: the services, the urban planning whereby the port is thought, the facilities and structures, the connection with multi-modal local transport. The mapping activity has been performed within the frame of the AI-SMART project funded by the European Regional Development Fund that aims to implement and develop a common port network in the Adriatic-Ionian area. The case study served to highlight the feasibility and applicability of the proposed method to a real case

Local Industrialization Based Lucrative Farming Using Machine Learning Technique

In recent times, agriculture have gained lot of attention of researchers. More precisely, crop prediction is trending topic for research as it leads agri-business to success or failure. Crop prediction totally rest on climatic and chemical changes. In the past which crop to promote was elected by rancher. All the decisions related to its cultivation, fertilizing, harvesting and farm maintenance was taken by rancher himself with his experience. But as we can see because of constant fluctuations in atmospheric conditions coming to any conclusion have become very tough. Picking correct crop to grow at right times under right circumstances can help rancher to make more business. To achieve what we cannot do manually we have started building machine learning models for it nowadays. To predict the crop deciding which parameters to consider and whose impact will be more on final decision is also equally important. For this we use feature selection models. This will alter the underdone data into more precise one. Though there have been various techniques to resolve this problem better performance is still desirable. In this research we have provided more precise & optimum solution for crop prediction keeping Satara, Sangli, Kolhapur region of Maharashtra. Along with crop & composts to increase harvest we are offering industrialization around so rancher can trade the yield & earn more profit. The proposed solution is using machine learning algorithms like KNN, Random Forest, Naïve Bayes where Random Forest outperforms others so we are using it to build our final framework to predict crop