Flux and Speed estimation of decoupled induction motor

This paper presents the rotor flux and speed estimation of induction motor using a novel technique. The induction motor model in rotor reference frame is considered. Controllers used for sensor less control of the drive. The estimation technique works well and the sensor less speed control scheme can achieve fast transient response as good as that of the induction motor with sensors and at the same time maintain a wide speed control range

Correlation of Hyperglycaemia with Inflammatory Marker and Biomarker of Thromboembolism in Patients with Covid 19

Introduction: COVID-19 constitutes a significant global public health challenge. New mutants are on the rise and sufficient information is not available for the management of infection by the same. Studies suggest that the combination of elevated glucose possessed high risk for mortality from COVID-19.   Objective: 1. To Study the correlation between hyperglycaemia and inflammatory markers in Covid-19. To Study the correlation between hyperglycaemia and biomarker of thromboembolism in Covid-19   Methodology: Between August and September 2021, a study was carried out on 40 adult patients who tested positive for COVID-19 using RT-PCR. Among the participants, 20 had diabetes, and the other 20 did not have diabetes. The glucose levels were assessed by Hexokinase methods. Severity of Covid 19 was assessed by Neutrophil Lymphocyte ratio (NLR) detected by Flowcytometry and D-dimer measured by Turbidimetric Immunoassay. The study commenced after obtaining approval from the Institutional Ethical Committee, and informed consent was obtained from every patient before the study initiation.   Results: The median age of patients included in this study is 43. Analysis of COVID-positive patients revealed that diabetic individuals had higher mean WBC and Neutrophil counts, while their mean Lymphocyte count was lower compared to non-diabetic patients. Consequently, the Neutrophil Lymphocyte Ratio (NLR) was significantly higher in COVID-positive patients with diabetes (Mean NLR 6.8380 versus 3.3050). Moreover, D-dimer levels were notably higher in COVID-positive diabetic patients in comparison to COVID-positive non-diabetic patients (P = 0.048). Additionally, a positive correlation was observed through Pearson correlation between random blood sugar levels and inflammatory markers like NLR (P = 0.015, r = 0.194) and D- dimer (P = 0.048, r = 0.474).   Conclusion: A positive correlation was observed between hyperglycaemia and elevated inflammation levels, as well as a hypercoagulable state associated with more severe illness. Consequently, hyperglycaemia poses as a risk factor for the increased severity of COVID-19. Monitoring plasma glucose levels upon hospitalization could potentially aid in identifying a subset of patients predisposed to a worse clinical course

GREEN SYNTHESIS AND CHARACTERIZATION OF SILVER NANOPARTICLES FROM WITHANIA SOMNIFERA (L.) DUNAL

The metal nanoparticle synthesis is highly explored the field of nanotechnology. The biological methods seem to be more effective because of slowreduction rate and polydispersity of the final products. The main aim of this study is too the rapid and simplistic synthesis of silver nanoparticlesby Withania somnifera Linn. at room temperature. The exposure of reaction mixtures containing silver nitrate and dried leaf powder of W. somniferaresulted in reduction of metal ions within 5 minutes. The extracellular synthesized silver nanoparticles were characterized by ultraviolet-visible,infrared (IR) spectroscopy, X-ray diffraction studies, zeta potential, Fourier transform IR, and scanning electron microscopy. The antibacterial andantifungal studies showed significant activity as compared to their respective standards. From the results, W. somnifera sliver nanoparticle has attainedthe maximum antimicrobial against clinical pathogens and also seen very good stability of nanoparticle throughput processing. As we concluded, thistype of naturally synthesized sliver nanoparticle could be a better green revolution in medicinal chemistry.Keywords: Antimicrobial activity, Silver nanoparticles, Withania somnifera

Motivational factors towards fast-food joint selection in under-developed country setting: A Partial Least Square and Structural Equation Modeling (PLS-SEM) approach

The abrupt rise in the fast food business the world over calls for research attention to the phenomenon, especially, in underdeveloped and developing economies. Research is scanty regarding the phenomenon; especially what motivates patrons to select fast food joints in under developed economies such as Ghana. The study sought to ascertain the motivational factors that actuate (or stimulate) consumers' intent to select a fast-food joint in an under-developed country setting, particularly, in Ghana, a sub-Sahara African region. Additionally, the partial goal of this survey is to examine the mediating role of convenience (CONV), and taste and preference (TASPRE) given the indirect effect of traditional advertising communication medium (ACM); Radio/Tv and word-of-mouth. Using a quantitative research approach, a structured survey questionnaire was used to intercept buyers of fast-food at vantage points in the Cape Coast metropolis in the Central region of Ghana. A non-randomized sampling technique, precisely, the convenience sampling, was adopted to consider popular fast-food joints that aided the researchers to intercept customers/buyers for the study. Results from the application of partial least square and structural equation modelling (PL-SEM) of 305 valid responses revealed that the mediation (indirect) analysis supported all the mediate-hypotheses. The research implications and future study directions are discussed in the concluding part of the paper.Internal Grant Agency of FaME through Tomas Bata University in Zlin, Czech Republic [IGA/FaME/2019/008

Nitric oxide sensing in plants is mediated by proteolytic control of group VII ERF transcription factors

Nitric oxide (NO) is an important signaling compound in prokaryotes and eukaryotes. In plants, NO regulates critical developmental transitions and stress responses. Here, we identify a mechanism for NO sensing that coordinates responses throughout development based on targeted degradation of plant-specific transcriptional regulators, the group VII ethylene response factors (ERFs). We show that the N-end rule pathway of targeted proteolysis targets these proteins for destruction in the presence of NO, and we establish them as critical regulators of diverse NO-regulated processes, including seed germination, stomatal closure, and hypocotyl elongation. Furthermore, we define the molecular mechanism for NO control of germination and crosstalk with abscisic acid (ABA) signaling through ERF-regulated expression of ABSCISIC ACID INSENSITIVE5 (ABI5). Our work demonstrates how NO sensing is integrated across multiple physiological processes by direct modulation of transcription factor stability and identifies group VII ERFs as central hubs for the perception of gaseous signals in plants

Low-Power AI Models for Personalized Healthcare and Bioinformatics Applications

The increasing demand for personalized healthcare and bioinformatics applications necessitates efficient AI-driven solutions capable of operating on resource-constrained edge devices. Traditional deep learning models are often computationally intensive, making them unsuitable for real-time analysis in IoT-based healthcare systems. This research proposes the development of ultra-lightweight, energy-efficient AI models optimized for low-power wearable devices, biosensors, and mobile health (mHealth) applications. By leveraging model compression techniques such as quantization, pruning, and knowledge distillation, the study aims to reduce computational complexity while maintaining high accuracy in disease prediction, genomic analysis, and real-time patient monitoring. Additionally, a cross-layer optimization strategy will be explored to enhance the energy efficiency of AI-driven wireless transmission in body area networks (BANs). The proposed framework will be validated using real-world biomedical datasets, ensuring robust performance across varied physiological conditions. This research contributes to the advancement of low-power AI for next-generation bioinformatics, enabling scalable, real-time, and energy-efficient personalized healthcare solutions

Long-Range, Low-Power IoT for Adaptive Biomedical Monitoring: AI-Driven Analytics for Remote Patient Care

The integration of Long-Range, Low-Power Internet of Things (IoT) technologies into biomedical monitoring has revolutionized remote healthcare by enabling real-time data collection with minimal energy consumption. This paper proposes an AI-driven, adaptive biomedical monitoring system leveraging LoRaWAN and AI-based analytics to enhance patient surveillance in remote and resource-constrained areas. The proposed system integrates wearable biosensors, ultra-low-power edge computing, and cloud-based AI algorithms to analyze vital parameters such as heart rate, oxygen levels, and blood glucose in real time. An adaptive cognitive sensor node is implemented to dynamically adjust sensing frequency based on patient conditions, thereby optimizing energy efficiency while maintaining high diagnostic accuracy. Advanced compressed sensing and predictive analytics minimize data transmission, reducing power consumption and extending device lifespan. The system is designed to work seamlessly with Unmanned Aerial Vehicles (UAVs) and LPWAN networks to facilitate data collection in unconnected remote regions. By combining AI-driven anomaly detection with blockchain-based data security, the proposed framework ensures reliable, privacy-preserving, and intelligent remote healthcare monitoring. Experimental evaluations demonstrate a significant reduction in energy consumption compared to traditional monitoring systems while improving diagnostic efficiency. This research paves the way for scalable, cost-effective, and robust IoMT (Internet of Medical Things) solutions for global healthcare accessibility

Green AI-Driven Low-Power Biochemical Data Processing for Sustainable Healthcare and Drug Discovery

The rapid advancements in artificial intelligence (AI) have revolutionized healthcare and drug discovery by enabling faster and more accurate biochemical data analysis. However, traditional AI models often demand substantial computational resources, leading to high energy consumption. This paper explores the application of Green AI principles to develop energy-efficient AI models tailored for biochemical data processing in healthcare and pharmaceutical research. By leveraging ultra-lightweight deep learning architectures, optimized neural networks, and cross-layer optimization techniques, we propose a novel approach to reduce the carbon footprint of AI-driven medical diagnostics, clinical laboratory automation, and drug development. Our study evaluates the effectiveness of these techniques in accelerating drug discovery while maintaining high accuracy in biochemical analysis. We also discuss the integration of Green Chemistry principles in AI-powered pharmaceutical research to enhance sustainability. The findings underscore the potential of low-power AI solutions in making healthcare and drug discovery more efficient, cost-effective, and environmentally sustainable

Exploration of the effect of botanicals on controlling tea mosquito bug (Helopeltis antonii Signoret) in the cashew ecosystem

The tea mosquito bug (TMB) Helopeltis antonii Signoret poses a significant threat to cashew plantations, causing substantial damage to the trees and affecting crop productivity. Botanicals have been examined for their effectiveness against tea mosquito bugs (TMB) in cashew plantations that impose damage on cashew trees. A field experiment was conducted at the Regional Research Station, Vridhachalam, Tamil Nadu, to evaluate the effectiveness of various botanical pesticides against TMB. The study included seven treatments using different botanicals and one untreated control. Applications were made at critical growth stages, namely flushing, flowering and nut formation, at fortnightly intervals, ensuring the pest population remained below the economic threshold level (ETL). Five spray rounds were administered, with a maximum of 10 L of spray suspension applied per tree for each treatment. The results demonstrated a significant reduction in TMB incidence in plots treated with botanical pesticides. Fifteen days after the third, fourth and fifth sprays, TMB incidence was completely absent in treated plots, whereas the untreated control recorded a damage score of 3.25. Furthermore, a marked decline in fresh TMB infestations was observed within seven days following each spray application. Among the treatments, a mixture of leaf extracts from adathoda (Adathoda vasica), datura (Datura metel), vitex (Vitex negundo), calotropis (Calotropis gigantea) and neem (Azadirachta indica) showed the highest efficacy, reducing TMB incidence to damage scales of 0.660 and 0.550. Similarly, Pongamia oil (5 % concentration) exhibited substantial effectiveness, reducing TMB incidence to scales of 0.845 and 0.645. These findings highlight the potential of botanical pesticides as eco–friendly and effective alternatives for managing TMB in cashew plantations

Molybdenum status and critical limit in the soil for green gram (Vigna radiata) growing in Madurai and Sivagangai districts of Tamil Nadu, India

A survey was undertaken during 2008 to determine molybdenum (Mo) status of soils and to establish critical limits in soils of Madurai and Sivagangai districts of Tamil Nadu. A total of 202 surface soil samples were collected from 16 soil series of the study areas based on their percent coverage. The samples were analyzed for extractable or available Mo. Extractable Mo varied from 0.028 to 0.661 mg kg−1 and 0.035 to 0.961 mg kg−1 at Madurai and Sivagangai districts, respectively. Based on the results of a pot culture experiment, the critical limit of available Mo was determined to be 0.043 mg kg−1 for green gram [Vigna radiata (L.) Wilczek] (Var; CO 6) in both the districts. Based on this critical limit, we classified the soils into three categories: (1) low: 0.082 mg kg−1. Green gram responded highly to Mo application in soils below the critical limit whereas soils with Mo greater than 0.082 mg kg−1 did not respond. Among rates of Mo application, 0.075 mg kg−1 showed better yield than others. Overall, 3–41% and 7–46% of total area in Madurai and Sivagangai districts were in the low to medium Mo status, respectively. The application of 0.075 mg of Mo kg−1 or 0.4 kg ha−1 as sodium molybdate was sufficient to optimize green gram yield in the major soil series of the districts. These results will be useful in decision-making to apply Mo for improving green gram yields in the two districts studied