Amrita Vishwa Vidyapeetham
Recent publications
In plants, the adventitious roots are mainly produced from the leaves, hypocotyls, stems, or shoots. They are highly useful for vegetative propagation (or clonal propagation). In this propagation technique, various types of stem cuttings are used especially in forestry and in horticulture practices. Adventitious root formation (ARF) in stem cuttings is controlled by several exogenous and endogenous factors. ARF in stem cuttings is quite often divided into physiological and metabolic markers. The main factors that control ARF are auxin, maturation, genotype, explant position, irradiance, temperature, water availability, season, mineral nutrition, rooting conditions, proliferation medium, and so on. The identification and use of correct combination and/or auxin treatment have improved the stem rooting potential, even in hard-to-root species. Auxin treatment to stem cuttings causes metabolic changes during the ARF, which consists of three successive but independent phases, namely induction, initiation, and expression. Further, using the cutting-edge tools of genome and proteome analysis, some notable suggestions have been proposed to understand molecular regulations, genes, and cellular processes involved in ARF. Considering these crucial points, it is essential to understand the underlying factors and also their interactions during ARF in stem cuttings. Therefore, in this chapter, the prime focus is on the developmental processes, and physiological and environmental control of ARF in shoot/stem cuttings.
Vehicular safety technologies play a vital role in preventing or minimizing the impact of vehicle collisions to reduce life-threatening injuries and keep down vehicle collision-related casualties. One such application is connected vehicles, powered by vehicle-to-infrastructure (V2I) technology to enhance safety on road. It enables all the vehicles on a road within its range to communicate their speed, position, and heading direction to roadside unit (RSU) through cooperative awareness messages (CAM). This process needs three major operations. The first one is receiving the data from the vehicles, the second one detects the collision, and the third one communicates it with the vehicle in case of an impending collision. In this study, we developed a sophisticated algorithm to detect collisions. On detection of the impending collision, RSU sends a warning message to the concerned vehicle. This alerts the driver to take control measures like brake and speed limiting. Here, we implemented the intersection and rear-end collision scenarios using simulation of urban mobility (SUMO) traffic simulator and developed vehicular network (VANET) on network simulator OMNET++ . Veins framework combines both traffic and network simulator. Now using this computerized testbed, we can simulate the collision scenarios on the connected network and evaluate the timeline and data delivery rate with which the latter received the signal in order to take control actions like brake or halt the vehicle.
This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to adopt rooftop solar power generation in their premises. The proposed policy framework optimizes Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Case studies with three residential prosumers of different demand and generation profiles are presented. The models for utility profit and prosumer savings are developed, and a multi-objective problem is formulated with FiT, generation capacity (as a function of demand) and ABR as decision variables. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoffs are selected based on sensitivity analysis of the impact on collective welfare. The suitability of prevailing tariff and FiT rates of two Indian utilities namely, MSEDCL and TATA POWER, Delhi are studied, and their impact on prosumer savings and utility profits is brought out. The workflow to fix tariff, FiT and local PV capacities in active residential distribution systems is devised, providing the policymakers an effective decision-making tool.
In response to the global challenge of climate change, 136 countries accounting for 90% of global GDP and 85% of the population have now set net-zero targets. A transition to net-zero will require the decarbonization of all sectors of the economy. Green-hydrogen produced from renewable energy sources poses little to no threat to the environment and increasing its production will support net-zero targets Our study examined the evolution of green-hydrogen research themes since the UN Sustainable Development Goals were adopted in 2015 by utilizing bibliographic couplings, keyword co-occurrence, and keyphrase analysis of 642 articles from 2016 to 2021 in the Scopus database. We studied bibliometrics indicators and temporal evolution of publications and citations, patterns of open access, the effect of author collaboration, influential publications, and top contributing countries. We also consider new indicators like publication views, keyphrases, topics with prominence and field weighted citation impact, and Altmetrics to understand the research direction further. We find four major thematic distributions of green-hydrogen research based on keyword co-occurrence networks: hydrogen storage, hydrogen production, electrolysis, and the hydrogen economy. We also find networks of four research clusters that provide new information on the journal’s contributions to green-hydrogen research. These are materials chemistry, hydrogen energy and cleaner production, applied energy, and fuel cells. Most green-hydrogen research aligns with Affordable and Clean Energy (SDG 7) and Climate Action (SDG 13). The outcomes of policy decisions in the United States, Europe, India, and China will profoundly impact green-hydrogen production and storage over the next five years. If these policies are implemented, these countries will account for two-thirds of this growth. Asia will account for the most significant part and become the second-largest producer globally.
Infectious diseases are considered as a significant threat to human health as well as environment. Due to the increasing microbial diseases, researchers are trying to find out new materials with superior antimicrobial properties. Insertion of various nanofillers into rubber matrices will result in the production of advanced rubber nanocomposites with improved mechanical, electrical, thermal and barrier properties. However, improvement in antimicrobial properties of rubber nanocomposites is still remaining as an unexplored area. But the existing studies show that, the addition of antimicrobial nanofillers can introduce antimicrobial properties into the final rubber nanocomposites and thus can be used in various biomedical applications. Thus the present review tries to bring together all the available antimicrobial works in the area of rubber nanocompositses.
In this research, the effect of external electromagnetic and ultrasonic fields was used individually and simultaneously, when the GTD-111 superalloy laser was coated with IN625 powder. The results showed that the application of external fields increases the area of the equiaxed region due to the decrease in a temperature gradient, the creation of heterogeneous nucleation sites, and the dynamic forces that cause the columnar grains to be crushed. In addition, the tendency to form pores is reduced when magnetic and ultrasonic forces are used simultaneously. The average micro-hardness and tensile strength of the cladding layer also increased by 61% and 36%, respectively, when in conditions without and using hybrid external fields.
In general, metallic substrates react with their environment and progressively degenerate into a stable state such as oxide or hydroxide. Nanocrystalline metallic coatings are extensively used to protect metallic substrates against corrosion in a variety of industrial and household applications. When compared to conventional polycrystalline metallic coatings, nanocrystalline metallic coatings can significantly improve the metallic substrate's corrosion resistance, surface hardness, wear, and scratch resistance. This review has elucidated the influence of corrosion, nanocoating materials, different nanocoating techniques, and challenges associated with nanocoating. The goal is to include existing research on the beneficial impacts of nanocoating material structure, grain size, coating technique, additive concentration, and pH value on corrosive behaviour.
Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.
In the past few years, phytochemicals from natural products have gotten the boundless praise in treating cancer. The promising role of cruciferous vegetables and active components contained in these vegetables, such as isothiocyanates, indole-3-carbinol, and isothiocyanates, has been widely researched in experimental in vitro and in vivo carcinogenesis models. The chemopreventive agents produced from the cruciferous vegetables were recurrently proven to affect carcinogenesis throughout the onset and developmental phases of cancer formation. Likewise, findings from clinical investigations and epidemiological research supported this statement. The anticancer activities of these functional foods bioactive compounds are closely related to their ability to upregulate p53 and its related target genes, e.g., p21. As the “guardian of the genome,” the p53 family (p53, p63, and p73) plays a pivotal role in preventing the cancer progression associated with DNA damage. This review discusses the functional foods bioactive compounds derived from several cruciferous vegetables and their use in altering the tumor-suppressive effect of p53 proteins. The association between the mutation of p53 and the incidence of gastrointestinal malignancies (gastric, small intestine, colon, liver, and pancreatic cancers) is also discussed. This review contains crucial information about the use of cruciferous vegetables in the treatment of gastrointestinal tract malignancies.
AZ91D magnesium alloy plates of thickness 4 mm were successfully welded by CO2 laser welding. The heat input and hence the microstructural evolution were characterized by varying the laser power and welding speed. Moreover, microhardness, tensile strength, and elongation of the weldment were characterized. Hybrid models integrating the quadratic function and radial basis function were developed to correlate the laser welding process parameters with the properties of the laser-welded specimens. A comprehensive analysis of the influence of laser welding process parameters on properties is presented. The laser welding process parameters for maximizing the tensile strength, elongation, and microhardness of the weldment were determined using multi-objective optimization. The optimum laser welding parameters for AZ91D alloy were as follows: laser power of 2 kW and welding speed of 4.5 m/min. Inclusive analysis of the microstructural evolution and fracture mechanism of the laser-welded specimens is presented.
Cu2Fe1-xBaxSnS4 (CFBTS) thin films have been fabricated by the low-cost successive ionic layer and adsorption reaction (SILAR) method. The bandgap energies of CFBTS thin films are observed to be tuned from ~ 1.67 to ~ 1.94 eV in a linear manner with increasing Ba content (0 ≤ × ≤ 1). The crystal structure of CFBTS thin films are affected by the change in the Fe/Ba ratio. The structural transition from stannite to trigonal is found with the increased barium content in CFBTS from X-ray diffraction and Raman spectroscopy analysis. Sulfurized CBTS thin film exhibited pure trigonal phase without impurity peaks. Partial replacement of Fe with Ba in CFBTS alters the electronic structure of bulk CFBTS thin films, which affects charge separation and causes a change in band alignment. Finally, photo electrochemical studies were carried out for the as synthesised and the sulfurized samples.
Fucoxanthin (FX) is a special carotenoid having an allenic bond in its structure. FX is extracted from a variety of algae and edible seaweeds. It has been proved to contain numerous health benefits and preventive effects against diseases like diabetes, obesity, liver cirrhosis, malignant cancer, etc. Thus, FX can be used as a potent source of both pharmacological and nutritional ingredient to prevent infectious diseases. In this review, we gathered the information regarding the current findings on antimicrobial, antioxidant, anti-inflammatory, skin protective, anti-obesity, antidiabetic, hepatoprotective, and other properties of FX including its bioavailability and stability characteristics. This review aims to assist further biochemical studies in order to develop further pharmaceutical assets and nutritional products in combination with FX and its various metabolites.
The COVID-19 pandemic has strained the healthcare system worldwide. Our study aimed to evaluate the impact of the COVID-19 pandemic on the diagnosis and surgical care of patients with breast cancer in Amrita Institute of Medical Sciences, Kochi. This is a single-centre retrospective observational cohort study conducted in a tertiary care institution intended to analyse the management of patients with breast cancer before and after the pandemic outbreak. The number of mammograms dropped from 3689 in the pre-pandemic phase to 1901 in the post-pandemic phase, whilst the number of core biopsies remained almost the same (391 before the pandemic and 367 after the pandemic). The number of new patients decreased by 57.7% (from 614 to 354). However, the number of breast cancer surgeries has remained almost the same (318 before the pandemic and 287 after the pandemic). The number of breast conservation surgeries dropped from 127 in 2019 to 93 in 2020 (p-value = 0.01). Conversely, 24 patients underwent neoadjuvant chemotherapy in 2019, and this number increased to 37 in 2020, representing a statistically significant increase (p = 0.04). Even during a pandemic, cancer care is possible with proper resource allocation and by adopting a multidisciplinary approach.
Norovirus (NoV) belongs to the Calciviridae family that causes diarrhoea, vomiting, and stomach pain in people who have acute gastroenteritis (AGE). Identifying multi-epitope dependent vaccines for single stranded positive sense viruses such as NoV has been a long due. Although efforts have been in place to look into the candidate epitopes, understanding molecular mimicry and finding new epitopes for inducing immune responses against the T/B-cells which play an important role for the cell-mediated and humoral immunity was not dealt with in great detail. The current study focuses on identifying new epitopes from various databases that were filtered for antigenicity, allergenicity, and toxicity. The adjuvant β-defensin along with different linkers were used for vaccine construction. Further, the binding relationship between the vaccine construct and toll-like immune receptor (TLR3) complex was determined using a molecular docking analysis, followed by molecular dynamics simulation of 100 ns. The vaccine candidate developed expresses good solubility with a score of 0.530, Z-score of -4.39 and molecular docking score of -140.4 ± 12.1. The MD trajectories reveal that there is a stability between TLR3 and the developed vaccine candidate with an average of 0.91 nm RMSD value and also the system highest occupancy H-bond formed between GLU127 of TLR3 and TYR10 of vaccine candidate (61.55%). Four more H-bonds exist with an occupancy of more than 32% between TLR3 and the vaccine candidates which makes it stable. Thus, the multi-epitope based vaccine developed in the present study forms the basis for further experimental investigations to develop a potentially good vaccine against NoV.Communicated by Ramaswamy H. Sarma.
In this study, the cottonseed as a potential renewable source for producing surrogate bio-fuel is elaborated. Indeed, this study included two stages, in which the extraction of the bio-oil from cottonseed through the intermediate pyrolysis process in a fixed-bed reactor was presented in the first stage. Furthermore, produced bio-oil has shown their physicochemical properties suitable for engine application. In the second stage, the analysis of performance (including brake thermal efficiency, brake-specific fuel consumption) and emission characteristics (including nitrogen oxides, carbon dioxide, unburnt hydrocarbon, carbon monoxide, and smoke opacity) of a water-cooled, 4-stroke, 1-cylinder diesel engine fueled with blends of bio-oil/diesel fuel in the case of changing compression ratio and engine load were carried out. More importantly, the response surface method technique was applied to optimize the operational parameters of the test engine. In conclusion, the validation experiments were performed to ensure the reliability of the obtained optimized results, indicating that the test engine operated with 5% bio-oil blended with 95% diesel fuel, a compression ratio of 18:1, and at 50% of engine load could offer the best performance and emission characteristics with a desirability factor of 0.617 and an error < 5% for the validated parameters.
With the increasing adoption of variable renewable sources (VRE) and the deployment of electric vehicles (EVs) in the market, the share of harmful carbon emissions is reducing continuously. Forming a microgrid (MG) is the best way to integrate renewable energy sources (RES) to make a low-carbon path for electrification. Second-life batteries (SLBs), derived from the first-life application of batteries used in EVs, are lower in price as compared to fresh EV batteries and have sufficient remaining capacity as energy storage for MG applications. Therefore, to show the techno-economic feasibility of SLBs, this paper shows a comparative study of MG consisting of photovoltaic (PV)/floating PV/wind/biogas with fresh Li-ion batteries (LIBs) and SLBs. This paper analyses 4 hybrid models with fresh LIBs in off-grid and grid-connected mode, and based on HOMER-Pro an optimal model is finalized for the Asian Institute of Technology (AIT), Pathumthani, Thailand. From, six different cases of fresh LIBs, the optimal case is also selected by considering the lowest net present cost (NPC) and cost of energy (COE). With the significant amount of reduction in NPC and COE, the grid-connected hybrid models are found to be more economically viable than the off-grid models. The grid-connected optimal hybrid model is further analyzed with SLBs where a range of cost multipliers (CMs) and energy throughputs are considered as the sensitivity parameters for SLBs. The results obtained from HOMER-Pro show that the grid-connected optimal model with SLBs reduces the NPC and COE by 36% and 35%, respectively, compared to the fresh LIBs. Hence, the optimal system shows the lowest NPC and COE as, 4,136,254$ and 0.0411$/kWh, respectively, with the SLBs.
Silicate ceramics are one of the established candidates employed in many industrial and medical applications. Not many of the existing reports mention and investigate the properties of the phases or individual components of these materials. In this exploratory study, one such sodium calcium silicate, combeite (Na2Ca2Si3O9) was synthesized by the solid-state route and studied for its properties. The preliminary investigation involved thermal treatments followed by characterization to identify the formation of the pure phase. The mechanical behaviour was evaluated by compression tests. Surface treatments were executed to enhance the capabilities to support cellular proliferation. The in-vitro acellular immersion test showed the formation of calcium phosphates. All the tested materials exhibited adequate cell viability properties for prospective applications in life sciences.
An acute seizure is a medical condition which, if untreated, can lead to brain damage and even death. Phenytoin sodium is a second-line drug of choice for seizure emergencies. Due to the administration issues as well as pharmacokinetic issues of the drug, it is now being replaced with newer anticonvulsant drugs despite short half life and sedative effects. This study aims to develop a polymeric nanomicelle of phenytoin sodium that decreases administration difficulties, enhances drug availability in the brain, and lowers the dose, all of which could be advantageous in the treatment of seizure emergencies. Phenytoin sodium–loaded polymeric nanomicelle was prepared by thin-film hydration method. A novel polymeric nanomicelle of size less than 20 nm loaded with phenytoin sodium was prepared with pluronic F127. Drug-loaded polymeric nanomicelle showed an immediate release profile with reduced PPB and improved lipid permeation when compared with the marketed iv injection of Phenytoin sodium. The in vivo acute toxicity study results revealed that the optimized formulation is safe for administration. The pharmacodynamic study by the MES model proved the enhanced brain availability as well as the reduced dose, thereby reducing peripheral toxicity. Results from the above studies confirm that the phenytoin sodium–loaded polymeric nanomicelle could potentially to be a better candidate for seizure emergencies, as it could reduce the administration issues, improve the brain availability of phenytoin, and lower the dose requirements. Graphical abstract
This study proposes an efficient framework employing Ensemble Empirical Mode Decomposition (EEMD) algorithm coupled with Time-Dependent Intrinsic Cross Correlation (TDICC) method to detect the teleconnection between large scale climatic oscillations and monthly rainfall of India. Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO) and the enhanced convective phases of Madden Julian Oscillation (MJO) have taken into consideration as climatic oscillations for the teleconnection study. EEMD method decomposed each signal to a set of zero mean oscillatory modes namely Intrinsic Mode Functions (IMFs)with definite periodicity. The time-dependent running correlation analysis of the IMFs of ENSO and IOD showed strong negative correlation with the modes of rainfall, at inter annual scales. All the IMFs of MJO indices 8–10 showed very strong positive correlation while MJO indices 2 and 3 showed very strong negative correlations with the corresponding IMFs of rainfall. TDICC analysis found the most influential antecedent information of climatic oscillation in the prediction of IMFs of rainfall at different time scales. On considering the different lags in TDICC analysis, the high frequency modes are associated with transition in correlation from positive to negative and vice versa while low frequency modes display a stable pattern in the teleconnections of rainfall with climatic oscillations. The TDIC-based identification of relevant modes and TDICC-based identification of significant lags will substantially alleviate the computational complexities and help in improved predictions of monthly rainfall over India.
Power supply regulation and load forecast are important factors in electric power distribution systems. The advent and ever-expanding adoption of renewables and distributed energy resources in the energy sector have introduced a lot of complexity into the day-to-day operations and maintenance of a wide-area power grid. Implementation of big data analysis and deep learning tools in power distribution systems has enabled predictive maintenance, grid health monitoring, demand forecasting, and reliability analysis, and also provided a host of other features for overall improvement of grid operations. A thorough analysis reflecting presents and future patterns can aid in critical decisions regarding generation capacity, transmission, and distribution systems for a successful load flow system planning. The focus of this paper is on ways to estimate load using the deep learning technique Long short-term memory (implemented by Python programming language).
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7,088 members
Jaya Kumar
  • Department of Electronics and Communication Engineering
Deepthy Menon
  • Amrita Center for Nanosciences & Molecular Medicine (ACNSMM)
Shyam Diwakar
  • Amrita Mind Brain Center
Dr. Krishnan Namboori
  • Computational Chemistry Research Group-AMMAS RESEARCH LAB- https://www.amrita.edu/center/amrita-molecular-modeling-and-synthesis-research-lab//http://ccg.amritafoss.org/
Dr Ilango Karuppasamy
  • Department of Electrical and Electronics Engineering
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Six campuses: Tamilnadu (Chennai, Coimbatore), Kerala (Amritapuri, Kochi), Karnataka (Bengaluru, Mysuru), Coimbatore, India
Head of institution
Dr. P Venkat Rangan, Vice Chancellor
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https://www.amrita.edu/
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