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Soil pH is one of the essential vital features that increases or decreases the nutrient availability in soil. The lower pH lessens the secondary macronutrient availability while higher pH limits the available micronutrient in soil. Furthermore, the pesticides efficiency, use of organic and inorganic fertilizers sources to soil also required proper pH for maximum utilization by plants. Therefore, soil pH is termed as "principal soil indicator" that affect the biogeochemical cycles and has broader effects on the soil microbial community. Mineral approaches used to alter the soil pH had demonstrated drawbacks that it is too difficult to change. That’s why alteration in rhizospheric pH can be a practical approach. Hence, a microbial-breeding technique such as genome replication of microbes may be a suitable approach to alter rhizospheric pH. It might be possible that microbes genetic product releases too much acidic or basic compounds that increase or decrease the pH of rhizosphere. Greater exploitation of microbes in this respect would be essential to pursue, as they have the ability to resolve several stresses in a more sustainable manner. In order to breed the microbes selectively for optimal nutritional interactions with plants, the genetic components of different traits must first be practiced.

Corona virusdisease 2019 (COVID-19) is threatening the worldwide population by causing symptoms like fever, cough,shortness of breathand tirednessetc.A huge population of world is suffering from this disease and no specific vaccine for this pandemic disease has been developed. According to the guidelines of Ministry of AYUSH, this disease can be prevented by taking immunity boosters, as immune system plays a vital role in defence against any disease. In Unani system of medicines, many drugs of plant origin are mentioned in classical literature for strengthening and increasing the immunity of humans. Darchini is one of the potent immune boosters and it is believed that consumption of Darchini decoction with honey strengthens the immune system.The Ministry of AYUSH, Govt. of India in its Advisory has considered Darchini as one of the important ingredient of AYUSH Joshanda/ Kwath/ Kudineer.Since, the drug Darchini is being given to COVID-19 patients as prophylaxis regime, it has become necessary to authenticate and develop its pharmacopoeial standards so that quality raw material can be provided to needy mass. The present study is aimed to develop identity, purity and strength of drug using pharmacognostical, physico-chemical and quality control methods. &nbsp

Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good., {"references":["The Essential Guide to Brain Tumors, National Brain Tumor Society, http://www.braintumor.org","A. Drevelegas (ed.), \"Imaging of Brain Tumors with Histological Correlations,\" Springer-Verlag Berlin Heidelberg 2011.","El-Sayed A. El-Dahshan et al. \"Computer-Aided Diagnosis of Human Brain Tumor through MRI: A Survey and a New Algorithm,\" Expert Systems with Applications, 41 (2014), 5526–5545.","Jainy Sachdeva et al. \"A Novel Content-Based Active Contour Model for Brain Tumor Segmentation,\" Magnetic Resonance Imaging, 30 (2012), 694–715.","Stephen M. Smith, \"Fast Robust Automated Brain Extraction,\" Human Brain Mapping, 17:143–155(2002).","K. Somasundaram, T. Kalaiselvi, \"Automatic Brain Extraction Methods for T1 Magnetic Resonance Images Using Region Labeling and Morphological Operations,\" Computers in Biology and Medicine, 41 (2011), 716–725.","Jiang et al. \"Brain Extraction from Cerebral MRI Volume using a Hybrid Level Set Based Active Contour Neighborhood Model,\" BioMedical Engineering OnLine 2013, 12:31, http://www.biomedical-engineering-online.com/content/12/1/31","Gordillo N et al. \"State of the Art Survey on MRI Brain Tumor Segmentation,\" Magnetic Resonance Imaging, 31 (2013), 1426–1438.","Nobuyuki Otsu, \"A Threshold Selection Method from Gray-Level Histograms,\" IEEE Transactions on Systems, Man, and Cybernetics, VOL. 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Moallem et al. \"Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization,\" http://www.researchgate.net/publication/233814424\n[20]\tOsher, S., and Sethian J.A., \"Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations,\" Journal of Computational Physics, 79, pp.12-49, (1988).\n[21]\tVicent Caselles et al. \"Geodesic Active Contours,\" International Journal of Computer Vision, 22(1), 61–79 (1997).\n[22]\tO. Michailovich, Y. Rathi, A. Tannenbaum, \"Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient flow,\" IEEE Trans. Image Process. 15 (11) (2007) 2787–2801.\n[23]\tHongzhe Yang et al. \"Brain Tumor Segmentation Using Geodesic Region-based Level Set without Re-initialization,\" International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7, No.1 (2014), pp.213-224.\n[24]\tTony F. Chan, Luminita A. Vese, \"Active Contours Without Edges,\" IEEE Transactions on Image Processing, Vol. 10, No. 2, February 2001.\n[25]\tXiaomin Xie et al. \"A Robust Level Set Method Based on Local Statistical Information for Noisy Image Segmentation,\" Optik 125 (2014) 2199–2204, http://dx.doi.org/10.1016/j.ijleo.2013.10.026\n[26]\tKiran Thapaliya et al. \"Level Set Method with Automatic Selective Local Statistics for Brain Tumor Segmentation in MR Images,\" Computerized Medical Imaging and Graphics, 37 (2013) 522– 537.\n[27]\tSandeep Chaplot et al. \"Classification of Magnetic Resonance Brain Images Using Wavelets as Input to Support Vector Machine and Neural Network,\" Biomedical Signal Processing and Control, 1 (2006) 86–92.\n[28]\tDipali M. 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Sankhaholi (Evolvulus alsinoides Linn.) of family Convolvulaceae which is available in Indian herb and spices market with the name of Shankhpushpi, widely used in the traditional system of medicine including Unani medicine since ages. It is a perennial herb with a small woody branched rootstock which contains alkaloids: shankhapushpine and evolvine. Fresh plant of sankhaholi contains volatile oil. It also contains a yellow neutral fat, an organic acid, and saline substances. Therapeutic uses of Sankhaholi, mentioned in the Unani text are elexiteric (Mufarreh), cardiac tonic (Muqawwi-e Qalb), brain tonic (Muqawwi-e Dimag), blood purifier (Musaffi-e-Khoon), general tonic (Muqawwi-e-aam), diuretic (Mudirr-e-Baul), anti-inflammatory (Muhallil-e-waram), hypoglycemic (Dafa-e-Ziabitus). It has been used in various ailments such as headache, asthma, hyperlipidemia, etc. In this article, we have provided standardized value of a specimen of sankhaholi, assessed on physico-chemical and analytical parameters viz (i) Macroscopic and microscopic features (ii) extractive values (iii), moisture contents (iv), Ash values (v), loss of weight on drying, (vi), pH of 1 % and 10 % solution (vii) TLC and (viii) HPTLC.

This thesis addresses radiation pattern synthesis problems for small linear pe-riodic phased arrays (with array elements less then 10). Due to the small arraysize conventional pattern synthesis techniques fail to produce the required re-sults. In the case of practical small arrays, mutual coupling and element patternasymmetric effect degrade the array radiation performance. The main perfor-mance metrics considered in this thesis include sidelobe level (SLL), gain, halfpower beamwidth (HPBW) and mainbeam scan direction. The conventionalpattern synthesis approaches result in sub optimal gain, SLL and HPBW dueto the limited number of elements and the mutual coupling involved. In caseof difference pattern synthesis these factors resulted in lower difference patternslope, degraded SLL and difference peak asymmetry. The sum and differencepatterns are used in monopulse arrays and a simplified feed that could produceboth patterns with acceptable radiation properties is of interest and has beenexamined (chapter 5).A conventional technique is applied to small arrays to synthesise a sectorbeam and there is limited control over the radiation pattern. It is shown thatthe mutual coupling has significant effect on the array radiation pattern andmitigation is necessary for optimum performance (chapter 6). Furthermore,wideband phased arrays may have a natural limitation of the HPBW in lowgain applications and minimisation of the variation becomes important. Alsothe SLL variations for wideband antenna arrays in the presence of mutualcoupling considerably degrade the radiation pattern. The mutual coupling de-grades significantly the radiation pattern performance in case of small scanningwideband arrays (chapter 7).It is the primary goal of this thesis to develop an optimisation scheme thatis applied in the above scenarios (chapters 3 & 4). The only degree of freedom assumed is the array excitation. Optimised amplitude and phase for each element in the array are determined by the proposed scheme, concurrently. The deterministic optimisation techniques reported in the literature for the pattern synthesis may involve complicated problem modelling. The heuristic opti-misation techniques generally are computationally expensive. The proposedIntelligent z-space Boundary Condition-Particle Swarm Optimiser (IzBC-PSO)is based on a heuristic algorithm. This scheme can be applied to a wider rangeof problems without significant modifications and requires fewer computationscompared to the competing techniques.In order to verify the performance of IzBC-PSO antenna array measure-ments were performed in the receiving mode only using the online and offlinedigital beamforming setups described in chapter 8. The measurement resultsshow that the proposed scheme may be successfully applied with both onlineand offline digital beamformers for a practical small array (chapter 8).