http://nopr.niscpr.res.in/handle/123456789/61855 2026-10-10T12:24:32Z http://nopr.niscpr.res.in/handle/123456789/61891 Title: Development and Evaluation of Garlic Harvester for Raised Beds Authors: Jat, Dilip; S, Syed Imran; Singh, Krishna Pratap Abstract: Harvesting is considered one of the most time-consuming and laborious agricultural operation in garlic cultivation. This work can be easily done by mechanical means, thereby saving time, labor and money. Working of a mechanical garlic harvester in vertisol soil is difficult under flat conditions but it can be made easier by making raised beds. There is a need for a machine that can perform the intended harvesting task with its uniquely designed blade that penetrates vertisol soil easily and is able to pull out the garlic without damaging the bulb. Therefore, a garlic harvester has been developed and its operating parameters were optimized for harvesting the garlic crop sown in raised beds. An experiment was carried out in the field to investigate the effect of forward speed, conveying speed, and dropping height of a garlic harvester on digging efficiency, bulb damage, and fuel consumption using Response Surface Methodology (RSM). A forward speed of 1.53 km/h, dropping height of 0.47 m and conveying speed of 0.65 m/s were found to be optimal for operation of the machine on raised bed. The RSM successfully optimized the operational parameters of machine and predicted the performance parameters with less error. The optimum operating parameters improved the performance of machine in field by lowering bulb breakage, reducing fuel consumption and increasing garlic digging efficiency. The effective field capacity of harvester was 0.21 ha/h at field efficiency of 72.1%. The savings in cost and labor requirement were found to be 63.4 and 96.2% respectively as compared to manual method of garlic digging. The developed machine with triangular point blade and optimized operating parameters makes it better for working in black cotton soil. It will help farmers, garlic growers and agricultural machinery manufacturers to increase mechanization in garlic cultivation. Page(s): 493-503 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61890 Title: Identification of Herbal Molecules for the Treatment of Alzheimer's Disease Through a Combination of Molecular Docking and In-Vitro Analysis Authors: Nagu, Priyanka; Pathan, Amjad Khan A; Mehta, Vineet Abstract: Currently, there is a lack of therapeutic interventions that can modify the development and progression of Alzheimer's Disease (AD). The thorough pathology of AD remains unclear, creating ample opportunities for research aimed at developing innovative therapeutic approaches for managing the disease. The present research involved a literature survey to identify 100 herbal molecules that could potentially be beneficial in inhibiting Acetylcholinesterase (AChE), Butyrylcholinesterase (BChE), β-Secretase, and mitigating oxidative and inflammatory stress, as well as neurodegeneration. The herbal molecules were screened against AChE, BChE, and β-Secretase using AutoDock Tools-1.5.6 docking software with Protein Data Bank (PDB) ID 1B41, 1P0I, and 1FKN, respectively. After assessing the docking parameters, it was determined that quercetin, rutin, vitisinol-C, dihydrotanshinone-I, and β-carotene exhibited the strongest potential against their respective protein receptors. Additionally, our in-vitro AChE and BChE assay results showed that quercetin and rutin have the ability to modulate cholinergic pathways associated with AD, thus providing potential therapeutic benefits. Our in-vitro studies on neurodegeneration revealed that quercetin and rutin exhibit a neuroprotective effect against neurodegeneration induced by HgCl2, which suggests that they may have a potential role in protecting against neurodegeneration in AD. Nonetheless, additional preclinical investigations are essential to validate the potential effects of these molecules on AD pathogenesis. Page(s): 504-514 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61889 Title: Controlled Crystallization of Acetazolamide from Aqueous Polymeric Solutions for Enhancing Dissolution Rate: Application of Statistical Moment Theory and Molecular Docking Authors: Sahoo, Rudra Narayan; Dash, Rasmita; Nandi, Souvik; Bose, Anindya; Si, Sudam Chandra; Mallick, Subrata Abstract: Presence of additives in crystallization process in a controlled manner can lead to different crystal morphologies which could have a favourable impact on drug dissolution rate. Four different hydrophilic polymers (methylcellulose, hydroxypropyl methylcellulose, polyvinyl alcohol, and carboxymethyl cellulose) were used for the controlled crystallization of acetazolamide (ACZ) by solvent evaporation technique. Crystal imperfections of ACZ occurred in the lattice of growing crystal when crystallized from aqueous polymeric solution and evaluated using both the traditional Full Width at Half Maximum (FWHM) () and statistical mean value of the XRD peak width (). Crystal imperfection has brought about significant improvement in the dissolution of newly produced acetazolamide crystals. ACZ crystal produced in presence of Hydroxypropyl methylcellulose (AHPMC) showed crystal imperfection to the maximum extent and also the greatest dissolution of the drug was noticed from AHPMC compared to other crystals. Statistical mean value of the peak width of XRD data as the error-free technique has been utilized successfully for estimating crystallite properties of acetazolamide crystallized from ethanol as solvent and aqueous polymeric solution as anti-solvent. Crystallite properties using traditional Full Width Half Maxima method and the error-free Statistical Moment Analysis were compared. This controlled crystallization technique could be utilized in the design and development of formulation for improved solubility and bioavailability of the drug. Page(s): 515-521 2023-05-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61888 Title: Enhancing Network Forensic and Deep Learning Mechanism for Internet of Things Networks Authors: Avanija, J; Kumar, K E Naresh; Kumari, Ch Usha; Jyothi, G Naga; Raju, K Srujan; Madhavi, K Reddy Abstract: The integration of intelligence into everyday products has been possible due to the ongoing shrinking of hardware and a rise in power efficiency. The Internet of Things (IoT) area arose from the tendency to add computational capabilities to so-called non-intelligent daily items. IoT systems are attractive targets for cyber-attacks because they have many applications. Adversaries use a variety of Advanced Persistent Threat (APT) strategies and trace the source of cyber-attack events to safeguard IoT networks. The Particle Deep Framework (PDF), which is proposed in this study, is a novel Network Forensics (NF) that encompasses the digital investigative phases for spotting & tracing attack activity in IoT networks. The suggested framework contains three novel functionalities for dealing with encrypted networks, such as collecting network data flows & confirming their integrity, using a PSO algorithm, "Bot-IoT "& "UNSW NB15" datasets. The suggested PDF is related to several deep-learning methods. Experimental outcomes show that the proposed framework is very good at discovering & tracing cyber-attack occurrences when compared to existing approaches. The proposed design is implemented using neural network technology. The proposed design has 10% accuracy when compared with the existing structure. This paper is expected to offer a quick reference for researchers interested in understanding the use of network forensics and IOT. Page(s): 522-528 2023-05-01T00:00:00Z