Nirma University Journals
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Implementation of Nursing Assistant Robot
Full text linkCyber physical systems are need of modern era to cater the need of humanity in a better way. Unpredicted pandemic situation demands the deployment of cyber physical systems in the healthcare industry to prevent the spread of infectious disease in the medical staff while serving the infectious patients. CORONA has exhibited a need for autonomous assistance to carry out nursing tasks. For nursing staff, it is almost impossible to maintain the norms of social distancing as their job involves the personal care of the patients. Monitoring the patient\u27s body temperature and oxygen level, Supply of food and drug at regular intervals are the main duties a nursing staff needs to perform for a corona-infected person. Such tasks can be performed using a cyber physical system, which enables the nursing staff to perform their duties while maintaining the social distancing norms. This paper discusses a cyber physical system that is remotely controlled and capable of gathering important health-related data of patients like body temperature and oxygen level. The system also enables audio and video communication between the nursing staff and a patient without employing over a Internet. A state-of-the-art Raspberry Pi board is used with an ESP32 camera module to set up the wireless audio and video link for communication. The system is implemented and tested in the controlled environment of the laboratory
Digital Humanities in Action: Bibliometric Analysis of Peer-Reviewed Research on Critical Thinking
Full text linkBibliometrics is a subfield within the digital humanities that focuses on the quantitative analysis of scholarly publications, particularly their citations and references. It plays a crucial role in understanding the structure and impact of academic research, as well as in shaping the field of digital humanities itself. This research paper examines the concept of critical thinking and underscores the pivotal role of bibliometric analysis in comprehending the landscape of research work in this critical domain. Critical thinking is a cognitive skill which is fundamental to the development of informed and responsible citizens, effective problem-solvers, and successful professionals. Its significance spans various disciplines and sectors, making it a cornerstone of academic and practical discourse. However, the extant body of research on critical thinking is vast and diverse, encompassing a plethora of publications in numerous academic journals and repositories. In this context, bibliometric analysis emerges as a valuable tool for assessing the quality, impact, and evolution of research in the field of critical thinking. It also serves as a foundational framework for future investigations and discussions, intending to enhance the cultivation and application of critical thinking skills in both academic and real-world contexts. This research work uses bibliometric analysis, data analysis and data visualization to map scholarship of Critical Thinking.
THE ROLE OF HERBAL COSMETICS FOR THE MITIGATION OF THE PSORIASIS
Full text linkPsoriasis is a chronic inflammatory skin disease with a strong genetic predisposition and autoimmune pathogenicity. The potential molecular targets for psoriasis are JAK, STAT3, Interleukin 8. By inhibiting theses targets it marks in alteration in immune response and suppresses the abnormal activation of inflammatory cascade like psoriasis. The lack of possible cure and certain adverse reactions to several synthetic treatments has led toextensive research for anti-psoriatic activity in herbal based formulation. The recent synthetic treatments available for treating psoriasis include phototherapy, oral medications like methotrexate, cyclosporine, and azathioprine but due to severe side effects of phototherapy which include pain, uneven pigmentation and scarring and certain side effects of oral medications like methotrexate increased the risk of liver fibrosis, cyclosporine can lead to hypertriglyceridemia. Due to these severe side effects which can lead to discomfort in the body. Therefore, the herbal preparations which are naturally available can avoid this problem and can be used to treat psoriasis. This review aimed for the exploration of the herbal cream formulation containing pure herbs, viz. oil extracts and methanolic extracts, extract of leaves of basil, thyme tulsi, turmeric, neem, beeswax, olive oil, rose oil assessed the antipsoriatic activity of various cream formulation
DEVELOPMENT AND VALIDATION OF STABILITY INDICATING RP-HPLC METHOD FOR ESTIMATION OF VERICIGUAT IN MARKETED FORMULATION
Full text linkFor the estimation of Vericiguat in the API and Marketed formulation, Following ICH criteria, an accurate, precise, specific, and robust stability indicating RP-HPLC technique was designed and validated. The development of method utilized by Zorbax eclipse plus C18 (250 mm × 4.6mm × 5μm) column with mobile phase of 10mM Potassium dihydrogen phosphate: Methanol (60:40 v/v) in isocratic mode. The flow rate kept at 1.0 mL/min at 256 nm wavelength. Vericiguat has retention time 6.9 min. Developed method to be validated according to ICH guideline in term of linearity, Accuracy, Precision, Robustness, and Forced degradation studies was performed. Linearity was achieved with a correlation coefficient was 0.9995 between the concentration 50 μg/mL to 150 μg/mL. Percentage recovery was discovered within the limits of 98% to 102%. %RSD was found to be less than 2% which is the specified in range. Studies of forced degradation reveal that Acid and Alkali have the highest rates of degradation. Impurity peak eluted at 3.1 and 3.0 mins in Acid and Alkali degradation respectively. Vericiguat did not degrade in Thermal and Photolytic degradation
An Efficient Approach for Synchronous Generator-Based Diesel-PV Hybrid Micro-Grid with Power Quality Controller
Full text linkIncorporating renewable energy sources, such as photovoltaic (PV) systems, into microgrids offers a promising path for sustainable and robust power generation. However, the intermittent nature of renewable energy sources requires robust solutions to ensure continuous power supply and high-quality electricity. This abstract presents an innovative and effective method for a hybrid microgrid combining a synchronous generator, diesel, and PV sources, enhanced by a sophisticated power quality controller. The proposed hybrid micro-grid system is designed to address the challenges associated with intermittent and variability of PV generation, ensuring uninterrupted power supply to meet the demands of both urban and remote areas. This approach leverages the synchronous generator\u27s reliable power output, complemented by the clean and renewable energy generated by PV systems. Through advanced control algorithms and innovative technology, this system optimally manages the power flow. A power quality controller is incorporated into the hybrid microgrid, which improves the stability and reliability of the electricity supply. This controller actively monitors and regulates voltage and frequency levels, mitigating fluctuations and disturbances caused by sudden load changes or intermittent renewable energy generation. The combination of a synchronous generator, PV systems, and a power quality controller not only enhances the energy efficiency of the microgrid but also contributes to reducing environmental impact. The objective of this research is to offer a sustainable and cost-effective solution for meeting energy demands while upholding high power quality standards
Machine Learning Algorithms for Fault- Prediction
Full text linkWith the advancement in VLSI technology, the number of transistors on a device increases along with the reduction in the size of transistor. The likelihood of a manufacturing failure rises as feature sizes continue to contract. The overall testing cost and testing efforts are increases exponentially with each new technology node. Therefore, it is necessary to explore the techniques which guarantee the circuit functioning with less efforts and cost. As with each new technology node, not only the possible number of faults in circuit increases but also new types of faults are being introduced. In this scenario, this paper aims to explore the various existing Machine Learning (ML) methods for the prediction of number of faults in circuit. This paper also aims to categorize the fault prediction and prediction of test vector set. The paper includes the comparison analysis of different ML algorithms in fault prediction. With the use of ML algorithm, the Automatic Test Pattern generator (ATPG) shortens the time needed to generate test set required for manufacturing testing.
 
IDENTIFICATION OF NOVEL SARS-COV-2 ENTRY INHIBITORS VIA STRUCTURE BASED HIERARCHICAL VIRTUAL
Full text linkA novel coronavirus (2019-nCov) is a pneumatic infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has declared pandemic by the World Health Organization (WHO). However, there is no efficient drug therapy available to combat with this deadliest disease. By considering the public-health emergency, most of the SARS CoV inhibitors and antiviral drugs are utilized for the treatment of COVID-19 infection. Herein, we presented integrated drug design strategies using E-pharmacophore modeling, molecular docking and molecular dynamic simulation studies based on the recently published SARS-CoV-2 RBD protein structure. Structure-based pharmacophore model (ADHRR) and high-throughput virtual screening (HTVS) were used to screen ZINC and ChEMBL molecular databases for identifying novel SARS-CoV-2 entry inhibitors. The retrieved potential hits were taken for the comparative molecular docking against SARSCoV-2 and SARS-CoV enzymes to understand the different binding interactions. Further, the stability of receptor-ligand complex and specific amino acid interactions was evaluated by performing molecular dynamic simulation of 30 ns in solvated model system. This study identified ZINC00662497, ZINC00669387, ZINC08426008, ZINC0660155 and ZINC0844005 as promising leads for inhibiting the entry of 2019-nCoV virus
APPLICATION OF 3D BIOPRINTER GENERATED ORGANS TO OVERCOME SHORTAGE OF ORGAN DONATION
No full textReliability of human organs for transplantation remains severely compromised, even with the remarkable advancements in transplant technologies. Of the 154,324 individuals in need of organs in 2009, just 18% were able to receive them; there were 8,863 deaths on the waiting list, or an average of 25 deaths every day. About 120,000 people in America were on the waiting list for organ transplants at the start of 2014. A fresh approach to the production of 3D organs is presented by 3D bioprinting, which appears to be a viable remedy to this grave situation. Using this innovative technique, three-dimensional structures can be produced which can mimic tissues by layering cells onto a biocompatible substrate using a combination of tissue engineering and 3D printing. Through the utilization of modern computer systems, powerful computer programming, and CAD file instructions, 3D bioprinting presents a promising solution to reduce the gap between organ supply and transplantation requests. In addressing the important gap in organ availability for transplantation, it stands as a ray of hope. In this review the current state of scientific research on 3D bioprinting, tissue engineering, and epithelialized organs will be examined. Additionally, it will also examine the practical applications for these exquisitely crafted, three-dimensional printed organs
THE ROLE OF MACHINE LEARNING IN BIOLOGICAL SCIENCES
Full text linkIn the biological sciences, machine learning (ML) has become an essential technology that is revolutionizing research methods and speeding up discoveries in a variety of fields. A thorough overview of the various uses of ML in biological sciences is discussed in this article, including drug development, protein sciences, vaccines, biosystems, and computational biology. ML models facilitate the rapid discovery of innovative drug candidates with decreased side effects and increased efficacy, hence speeding up the drug development pipeline by utilizing large-scale biological data. ML techniques are improving the prediction of protein interactions, structures, and functions in the field of protein sciences. The design of vaccines, epitope prediction, and antigen selection are all greatly aided by ML techniques. ML models evaluate genetic and proteomic data based on individual immune responses, facilitating the generation of personalized immunizations that are optimal for immunogenicity and vaccine efficacy. Furthermore, by replicating cellular processes, modeling intricate biological networks, and forecasting gene regulatory mechanisms, ML techniques are revolutionizing the study of biosystems. In computational biology, ML is utilized for phenotypic prediction, gene expression profiling, and sequence analysis. ML models facilitate the development of precision medicine techniques, the characterization of medication response patterns, and the identification of disease biomarkers by combining multi-omics data. To fully explore the potential of ML for tackling significant issues in healthcare, computer scientists, biologists, and bioinformaticians must collaborate across disciplinary boundaries. This review emphasizes the revolutionary impact of ML on biological sciences
CASE STUDIES ON APPLICATIONS OF COMPUTATIONAL TECHNIQUES IN DRUG DESIGN
Full text linkComputer-aided drug design (CADD) along with Artificial Intelligence (AI) based machine learning technologies have a powerful impact in the field of drug discovery as it can handle vast biological data which in turn reduces the cost and time of drug discovery and development process. Identifying hits through virtual screening and its further optimization for the development of lead molecule through ligand- or structure-based drug designing have played a vital role. Docking and molecular dynamics studies highlighted the binding of ligands with targeted proteins and their binding affinity. ADMET prediction studies prevent the failure of many drugs in clinical trials and thus prevent loss of time and money. The availability of open-source big data has facilitated the screening of vast libraries intending to come up with novel potent and target-specific drugs. Present review has given an overview of the technology used in drug discovery by highlighting some of the case studies