An Observational study to evaluate the effect of Vaitarana Basti in the management of Vatakaphaja Gridhrasi vis-à-vis Sciatica

Sciatica refers to the low back pain radiating to lower limb in a dermatomal distribution. Sciatica is a common cause of pain and disability; it is more persistent and severe than low back pain. Sciatica prevalence from different studies ranged from 1.2% to 43%. Physical activity at work and occupational workload, such as lifting, work related twisting of the trunk, occupational exposure to whole body vibration for example machine operators, motor vehicle drivers have also been suggested to be risk factors for Sciatica. Obesity has previously been linked to Sciatica. All these factors create pressure on spinal cord producing low back ache and radiating pain. Signs and Symptoms of Sciatica shows close resemblance with Gridhrasi. In Ayurveda, Acharya Vangasena has mentioned Vaitarana Basti in the management of Gridhrasi. Vaitarana Basti adopted here in this study has a direct reference for Gridhrasi in Vangasena samhita and Acharya Chakradutta while mentioning Vaitarana Basti has indicated it in Shula, Vatakaphaja vikaras. Considering the ingredients, it is a Shodhana, Teekshna type of Basti which is beneficial in Vatakaphaja Gridhrasi. A minimum of 20 subjects who fulfilled the diagnostic and inclusion criteria were subjected to the intervention. The overall results in the study revealed statistically highly significant result

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

Population structure and association studies for reproductive stage salinity tolerance in rice (Oryza sativa L.)

Salinity is a major abiotic stress responsible for yield loss in rice as it severely affects various yield contributing traits. Rice is categorised as salt sensitive crop and it is important to identify genomic regions associated to salinity tolerance. In the present study, association mapping was performed to investigate the functional relationship between microsatellite markers and salinity related traits in a set of 180 diverse rice accessions. Association analysis was carried out by employing mixed linear model (MLM) approach. Population structure analysis revealed four subgroups in entire study panel while the admixture level ranged from 0.7-57.2%. A total of 22 marker-trait associations were discovered and four marker-trait associations explained phenotypic variation (R2) greater than 10%. Furthermore, 7 markers were found close to the candidate genes loci. Several markers were significantly associated with more than one trait, suggesting pleiotropic effects. The phenotypic variation explained by associated markers ranged from 2.92 to 18.50%. Comparative genomic search revealed that associated markers were close to candidate genes which play significant role in signal transduction, metabolic pathways and transcription factor activity. The significant associations identified in the present study could be used to improve salt tolerance in rice with introgression of favourable alleles through marker assisted breeding

Influence of Water Absorption, Impact and Damping on CURAUA/Glass Fibers Polyester Composites

Curaua fiber is one of the strongest natural fibers rich in cellulosic content and is being currently considered as reinforcement for any polymer based matrix composites. The current work investigates the dynamic and mechanical properties of polymer matrix composites reinforced with continuous curaua fiber and E glass fiber. The fabricated polymer matrix composites by hand layup technique will be tested for water absorption, Izod, Charpy, damping test and SEM analysis

Pharmaceutico-Analytical study of Karaviradya taila

Introduction: Sneha kalpana (oleaginous preparations) is commonly prescribed Ayurvedic dosage form and it is the preparation of various kinds of medicated oils and ghee. Karaviradya taila is medicated oil preparation used externally in the form of abhyanga (massage) for lomashatana (depilation). Objectives: To prepare and carry out the physico-chemical analysis of karaviradya taila. Materials and Methods: Karaviradya taila was prepared by general method of taila kalpana i.e ¼:1:4 and analytical study like organoleptic characters and physico- chemical parameters were carried out based on the references available in the laboratory guide for the analysis of Ayurveda and Siddha formulation. Results and Discussion: The total oil obtained was 85% and the loss was 15%. Organoleptic characters of karaviradya taila showed translucent green viscous liquid with alkaline odor, Physico- chemical parameters like pH, specific gravity, viscosity, total suspended solids, and refractive index were tested. The increased Saponification value of karaviradya taila indicates the rate of absorption, low acid value of karaviradya taila indicates less chance of decomposition of taila.  Evaporation of moisture contents in karaviradya taila leads to the decrease in rancidity factors. Peroxide value and iodine value of karaviradya taila indicates the primary oxidation. Conclusion: Karaviradya taila is a sneha kalpana mainly indicated for loma shatana (depilation). Local applications is beneficial because they are quickly absorbable, protect the skin and promotes percutaneous absorption of incorporated drug. The results of pharmaceutical and analytical study of karaviradya taila can be considered as the preliminary standards for the preparation of karaviradya taila.   Keywords:  Lomashatana, Karaviradyataila, Bahirparimarjana chikitsa, Sneha kalpana, Depilation, Hair Removal

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR

Not Available

Not AvailableNot AvailableNot Availabl

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR

Exploring the diversity of virulence genes in the Magnaporthe population infecting millets and rice in India

Blast pathogen, Magnaporthe spp., that infects ancient millet crops such pearl millet, finger millet, foxtail millet, barnyard millet, and rice was isolated from different locations of blast hotspots in India using single spore isolation technique and 136 pure isolates were established. Numerous growth characteristics were captured via morphogenesis analysis. Among the 10 investigated virulent genes, we could amplify MPS1 (TTK Protein Kinase) and Mlc (Myosin Regulatory Light Chain edc4) in majority of tested isolates, regardless of the crop and region where they were collected, indicating that these may be crucial for their virulence. Additionally, among the four avirulence (Avr) genes studied, Avr-Pizt had the highest frequency of occurrence, followed by Avr-Pia. It is noteworthy to mention that Avr-Pik was present in the least number of isolates (9) and was completely absent from the blast isolates from finger millet, foxtail millet, and barnyard millet. A comparison at the molecular level between virulent and avirulent isolates indicated observably large variation both across (44%) and within (56%) them. The 136 Magnaporthe spp isolates were divided into four groups using molecular markers. Regardless of their geographic distribution, host plants, or tissues affected, the data indicate that the prevalence of numerous pathotypes and virulence factors at the field level, which may lead to a high degree of pathogenic variation. This research could be used for the strategic deployment of resistant genes to develop blast disease-resistant cultivars in rice, pearl millet, finger millet, foxtail millet, and barnyard millet