DEVELOPMENT OF UV SPECTROPHOTOMETRIC METHOD FOR THE DETERMINATION OF BENIDIPINE HYDROCHLORIDE BY USING QUALITY BY DESIGN (QbD) APPROACH

Objective: To develop a simple, rapid, accurate, robust and inexpensive spectrophotometric method for the estimation of benidipine hydrochloride by using quality by design (QbD)†approach.Methods: A UV spectrophotometric method was developed on Shimadzu UV-1800 double beam spectrophotometer using methanol as solvent and wavelength of 236 nm was selected as absorbance maxima (ðœ†max). Effect of input variables on spectrum characteristics were studied for the selection of critical parameters and proposed method was validated for various parameters like system suitability, linearity, precision, accuracy, detection limits and quantification limits as per the International Conference on Harmonization guidelines ICH Q2(R1).Results: Linearity of the method was found to be excellent over the concentration range 3 to 18 µg/ml with high correlation coefficient value of 0.9999. Limits of detection and quantification were found to be 0.20 µg/ml and 0.60 µg/ml respectively. The mean recovery was found to be 100.35 % with low percentage relative standard deviation (% RSD) value. The precision study also has shown low % RSD value (<1). No interfering peaks were observed during specificity studies.Conclusion: Obtained result indicated that the developed spectrophotometric method is robust and efficient for the determination of benidipine hydrochloride

Numerical benchmark campaign of cost action tu1404 – microstructural modelling

This paper presents the results of the numerical benchmark campaign on modelling of hydration and microstructure development of cementitious materials. This numerical benchmark was performed in the scope of COST Action TU1404 “Towards the next generation of standards for service life of cement-based materials and structures”. Seven modelling groups took part in the campaign applying different models for prediction of mechanical properties (elastic moduli or compressive strength) in cement pastes and mortars. The simulations were based on published experimental data. The experimental data (both input and results used for validation) were open to the participants. The purpose of the benchmark campaign was to identify the needs of different models in terms of input experimental data, verify predictive potential of the models and finally to provide reference cases for new models in the future. The results of the benchmark show that a relatively high scatter in the predictions can arise between different models, in particular at early ages (e.g. elastic Young’s modulus predicted at 1 d in the range 6-20 GPa), while it reduces at later age, providing relatively good agreement with experimental data. Even though the input data was based on a single experimental dataset, the large differences between the results of the different models were found to be caused by distinct assumed properties for the individual phases at the microstructural level, mainly because of the scatter in the nanoindentation-derived properties of the C-S-H phase.</jats:p

MAPPING OF STRIP FOREST IN ADAMPUR RANGE (HARYANA) A GEO-INFORMATICS APPROACH

Haryana state is an intensively cultivated state, and deficient in natural forests. One of the mandate of Haryana Forest Department (HFD) is to afforest for maintenance of environmental stability and restoration of ecological balance affected by serious depletion of forests, woodlands and water, and to increase tree cover in the state. National Forest Policy (1988) has set a goal to bring one third of Country’s area under forest and tree cover. Stock and dynamics of Trees Outside Forests (TOF) along with natural forests need to be understood holistically to appreciate the ecosystem services e.g., timber and non-wood products as tangible benefits along with services like carbon, water and weather moderation. The present study has attempted to demonstrate the utility of High Resolution Worldview-II (WV) satellite data (ortho rectified) that offeres immense scope to analyze the strip forests in Hisar district (Haryana, India). The study area Adampur Range (Hisar District) lies between the north latitudes 29&deg;0′52.229″ to 29&deg;25′6.746″ and east longitudes 75&deg;14′0.266″ to 75&deg;45′11.093″ with a total geographical area of about 1092.04&thinsp;sq.&thinsp;km. The adopted methodology involves onscreen digitization of the strip forest areas in the Adampur range (Hisar Distirct). The ToF formation identification and delineation includes the forest land besides roads, river, streams, canals, distributaries and railway lines etc. The shape files were converted into .kml files and overlaid on the Google Earth data for validation. An attempt has been made to compare the area difference between the Haryana Forest Department (HFD) notification details with that of the digitized strip forest lands. It was observed that the surveyed forest area is found to be 1717.37&thinsp;ha. against the notified forest area of 1714.45&thinsp;ha. showing a difference of 2.92&thinsp;ha. approximately in the studied beat boundaries

Revolutionizing physics: a comprehensive survey of machine learning applications

In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review

Downy Mildew of Pearl Millet and its Management

Pearl millet (Pennisetum glaucum [L.] R. Br.) is the third most important rainfed cereal crop of India grown over 9 million hectares with an annual production of 9.5 million tonnes (Yadav and Rai, 2013). Globally, it is the sixth most important cereal grain cultivated on more than 30 million hectares which accounts for approximately 50% of world’s millet production. Though the most of the crop area is in Asia (>10 m ha) and Africa (about 18 m ha), pearl millet cultivation is being expanded in some of the non-traditional areas, with Brazil having the largest area (about 2 m ha). It is also being experimented as a grain and forage crop in the USA, Canada, Mexico, the West Asia and North Africa (WANA), and Central Asia. Pearl millet, being a C4 plant, has a very high photosynthetic efficiency and dry matter production capacity. It is usually grown under the most adverse agro-climatic conditions where other crops like sorghum and maize fail to produce economic yields. Besides, pearl millet has a remarkable ability to respond to favourable environments because of its short developmental stages and capacity for high growth rate, thus making it an excellent crop for short growing seasons under improved crop management..