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One-Pot Synthesis of Silver Nanoparticles Derived from Aqueous Leaf Extract of Ageratum conyzoides and Their Biological Efficacy
The main objective of the present research work is to assess the biological properties of the aqueous plant extract (ACAE) synthesised silver nanoparticles from the herbal plant Ageratum conyzoides, and their biological applications. The silver nanoparticle syntheses from Ageratum conyzoides (Ac-AgNPs) were optimised with different parameters, such as pH (2, 4, 6, 8 and 10) and varied silver nitrate concentration (1 mM and 5 mM). Based on the UVvis spectroscopy analysis of the synthesised silver nanoparticles, the concentration of 5 mM with the pH at 8 was recorded as the peak reduction at 400 nm; and these conditions were optimized were used for further studies. The results of the FE-SEM analysis recorded the size ranges (~3090 nm), and irregular spherical and triangular shapes of the AC-AgNPs were captured. The characterization reports of the HR-TEM investigation of AC-AgNPs were also in line with the FE-SEM studies. The antibacterial efficacies of AC-AgNPs have revealed the maximum zone of inhibition against S. typhi to be within 20 mm. The in vitro antiplasmodial activity of AC-AgNPs is shown to have an effective antiplasmodial property (IC50:17.65 ?g/mL), whereas AgNO3 has shown a minimum level of IC50: value 68.03 ?g/mL, and the Ac-AE showed >100 ?g/mL at 24 h of parasitaemia suppression. The ?-amylase inhibitory properties of AC-AgNPs have revealed a maximum inhibition similar to the control Acarbose (IC50: 10.87 ?g/mL). The antioxidant activity of the AC-AgNPs have revealed a better property (87.86% 0.56, 85.95% 1.02 and 90.11 0.29%) when compared with the Ac-AE and standard in all the three different tests, such as DPPH, FRAP and H2O2 scavenging assay, respectively. The current research work might be a baseline for the future drug expansion process in the area of nano-drug design, and its applications also has a lot of economic viability and is a safer method in synthesising or producing silver nanoparticles. 2023 by the authors. -
One-Pot Synthesis of Silver Nanoparticles from Garcinia gummi-gutta: Characterisation, Antimicrobial, Antioxidant, Anti-Cancerous and Photocatalytic Applications
Background: Methods like the bio-synthesis of silver nanoparticles (Ag NPs) using plant extracts have become promising due to their eco-friendly approach. The study aimed to examine the utilization of Garcinia gummi-gutta fruit phytochemicals as agents in the biosynthesis of Ag NPs, evaluation of the antimicrobial, antioxidant, and anti-cancerous properties, as well as the photocatalytic ability of bio-synthesized Ag NPs against Crystal Violet (CV), a triphenylmethane dye. Methods: The characterization of the physical properties of the Ag NPs synthesized via the green route was done using UVVis spectrophotometry (UVVis), X-ray Diffraction (XRD), Fourier Transform Infrared Spectrophotometry (FTIR), Scanning Electron Microscopy (SEM), Zeta potential analysis, and Transmission Electron Microscopy (TEM). The dye degradation efficiency of CV was determined using synthesized Ag NPs under UV light by analyzing the absorption maximum at 579 nm. The antimicrobial efficacy of Ag NPs against E. coli, S. aureus, Candida tropicalis, and Candida albicans was examined using the broth dilution method. The antioxidant and anti-cancer properties of the synthesized Ag NPs were assessed using the DPPH and MTT assays. Results: The UV analysis revealed that the peak of synthesized Ag NPs was 442 nm. Data from FTIR, XRD, Zeta potential, SEM, and TEM analysis confirmed the formation of nanoparticles. The SEM and TEM analysis identified the presence of spherical nanoparticles with an average size of 29.12 nm and 24.18 nm, respectively. Maximum dye degradation efficiency of CV was observed at 90.08% after 320 min without any silver leaching, confirming the photocatalytic activity of Ag NPs. The bio-efficiency of the treatment was assessed using the Allium cepa root growth inhibition test, toxicity analysis on Vigna radiata, and Brine shrimp lethality assay. Conclusions: The findings revealed the environmentally friendly nature of green Ag NPs over physical/chemically synthesized Ag NPs. The synthesized Ag NPs can effectively be used in biomedical and photocatalytic applications. Copyright: 2023 The Author(s). -
One-step synthesized Pt-dispersed N, P co-doped graphene as an efficient oxygen reduction reaction ORR electrocatalyst
A simple yet effective strategy to simultaneously deposit Platinum and co-doped (Nitrogen & Phosphorous) graphite oxide (rGO) using microwave irradiation has been carried out, labeled as Pt/NP-rGO. The successful deposition of Pt was confirmed using X-ray diffractogram (XRD), transmission electron microscope (TEM), and selected area electron diffraction (SAED) pattern. The Pt particles with average size of 3.5nm were deposited during microwave irradiation. Further, co-doping was confirmed using X-ray photoelectron spectroscopy (XPS). The synthesized Pt/NP-rGO was analyzed in situ as an oxygen reduction reaction electrocatalyst in acidic medium. Our results indicate that Pt/NP-rGO follows a four e? ORR process attributed to increased adhesion between the Pt and N, P co-doped graphene oxide due to e? transfer from graphene to N and P atoms, thus indicating its potential in energy-related applications as an effective ORR electrocatalyst. The Author(s), under exclusive licence to The Materials Research Society 2024. -
Online cooperative learning: exploring perspectives of pre-service teachers after the pandemic
Mainly, research has explored pre-service teachers perspectives toward cooperative learning within face-to-face teaching. However, in a post-pandemic scenario, previous research has yet to effectively explore pre-service teachers (PSTs) perspectives toward online cooperative learning (OCL) in teacher education programs. So, recognizing the gap in the literature, this paper aims to explore the perspectives of PSTs towards OCL. The researchers employed a qualitative research design for the present study. The researchers conducted semi-structured interviews with 10 PSTs who underwent OCL during the pandemic. These PSTs may possess digital proficiency, virtual collaboration abilities, flexibility in evolving educational environments, and an enhanced understanding of online cooperative learning methodologies within modern education. Researchers employed a thematic analysis to analyze the qualitative data obtained. The various themes that emerged from the study are perceived benefits of OCL, challenges to OCL, technological proficiency, learning strategies and support, and building a supportive online learning community. Future researchers may contribute to advancing effective online learning practices by gaining a deeper understanding of pre-service teachers perspectives towards OCL through research on a larger scale, including various teacher education programs in various countries. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Online Education and English Language Learning Among Tribal Students of Kerala
Kerala, a South Indian state has tribal population in all her districts. About 1.5% of the total population of the state constitute tribal population. They depend upon natural environment and resources for their survival. Children from the same community usually depend on government funded schools for their education. Education for this deprived section during COVID 19 Pandemic was a massive exclusion and an uphill task. Digital divide and medium of communication (Standard Malayalam) were some of the critical concerns to knowledge acquisition among tribal children. This paper primarily focuses on the challenges of online education among tribal students with a clear emphasis on the English language acquisition. This study was conducted in four most tribal populated districts of the State, namely, Wayanad, Malappuram, Palakkad, and Idukki. This is a qualitative explorative study that explores the experiences of the tribal students' English language learning challenges from the teachers' perspective in these districts. The Electrochemical Society -
Online fake review identification based on decision rules
Posting of online reviews play a dominant role in sharing the customers opinion in social Medias. But the challenge is how to trust these reviews. Many researchers carried their work on sentimental analysis, predictions or forecasting but very few focused on fake reviews analysis. Fake reviews also change the mood of the people on their buying pattern. In the online shopping at a greater extent. In this paper, several conditions are applied on the reviews to identify fake reviews using support vector machines. Experimental results are validated using various accuracy measures and compared to state of the art methods to demonstrate the efficacy of the proposed method. 2019, World Academy of Research in Science and Engineering. All rights reserved. -
Online Health Information Behavior: A study based on PLS-SEM
In this digital era, internet provides a speedy, economical and convenient platform for seeking information on health. Moreover, the presence of audio visual resources for health and option to get expert opinion directly makes online health information seeking behaviour more adaptable among the health consumers. The major purpose of this study is to investigate the relationship between online health information seeking behaviour and the consequences of post-search. For doing the analysis, Smart PLS2 is used to execute structural equation modelling technique to understand the relationship between variables under study. The results of the study recommend that one's intention to search health information online is a significant predictor of post-search behavior in terms of altering health condition, visiting physician or sharing the same information with others. The present study gives a strong indication to the health care practitioners to understand the mechanism of desires and intentions of a healthcare consumer towards online health information seeking behavior. 2021 IEEE. -
Online Higher Education : A Mixed Method Study of Delhi NCR, India
The education system in the new era displays extensive adaptability, as proven by presentday realities. In ancient India, the Gurukul system prevailed during the Vedic period, where Gurus guided students in an ashram or hermitage. This is quite different from the contemporary education system we have today. The modern education system involves structured classrooms, where teachers guide students in an institutional setting. The COVID-19 pandemic has brought about significant changes in the contemporary education system worldwide. This and the emergence of Digital India and the internet led to a structural change in the modern education system, from physical classrooms to online and remote classrooms. That means a paradigm shift happened in the educational system where Gurus are replaced by e-gadgets, books are being replaced by eBooks and traditional classrooms are being replaced by smart classrooms and online classes. The shift to remote learning has brought forth several challenges to the educational system which includes lack of meaningful teacher-student interaction, lack of motivation and engagement and its impact on mental health. This opens the scope for understanding the pros and cons of different modes of educational practices followed in the Indian online higher education system. Therefore, newlinethe present research captured experiences of teachers and students from Delhi NCR region newlineabout online higher education and the learning environment to understand the effectiveness of online learning. This research also focuses on social and psychological behavior along with the perceptions of teachers and students about online learning. The study explored the newlinechallenges and problems faced by them in online learning. A mixed method approach was newlineused for understanding the changing structure of the digital classroom. Along with the newlinedigital participatory approach, structured interviewing was used to have a better and in-depth understanding of online learning. -
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Online Voting System Using Blockchain
One of the major areas in technical development is blockchain and bitcoin. These technologies have enabled many simulations in in-hostile applications that have major issues with security and integrity of data. To provide more relevance to the available cyberphysical systems in the dimension of security, the blockchain technology offers a major help. If the present scenario is considered, we have multiple day-to-day applications that have been simulated and require more security enhancement. For example, the E-voting systems are a trend and their security features have to be upgraded to authenticate both systems and processes. The present research paper focuses on the same application and aims to provide security upgradation by proposing a working model of e-voting systems. 2020, Springer Nature Singapore Pte Ltd. -
Open global shadow graph and its zero forcing number
Zero forcing number of a graph is the minimum cardinality of the zero forcing set. A zero forcing set is a set of black vertices of minimum cardinality that can colour the entire graph black using the colour change rule: each vertex of G is coloured either white or black, and vertex v is a black vertex and can force a white neighbour only if it has one white neighbour. In this paper we identify a class of graph where the zero forcing number is equal to the minimum rank of the graph and call it as a new class of graph that is open global shadow graph. Some of the basic properties of open global shadow graph are studied. The zero forcing number of open global shadow graph of a graph with upper and lower bound is obtained. Hence giving the upper and lower bound for the minimum rank of the graph. 2023, Proyecciones. All Rights Reserved. -
OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures. 2023, The Author(s). -
Operational excellence in relation to high performance engagement and quality of care among executives in the healthcare sector in kerala
Background Operational Excellence is a philosophy of leadership, teamwork and problem solving, to focus on the needs of the consumer, to empower employees, for ptimizing existing activities, continuous improvement and excellence. It is a competitive advantage which translates increased flexibility to improved consumer responsiveness, and lean management. Quality of care is about patient safety, institutional culture, attitude, clinical performance, clinical freedom with management as facilitators, efficient delivery of quality, high standard services, effective patient outcome, integration of legislation with regards to communities, health service providers, local health authorities and the government (WHO, 2013). The outcome of quality of care is health consumer (patient) satisfaction. High newlineperformance Engagement reflects how employees are engaged in their work, with commitment and passion, rather than mere compliance to impact performance. Health care is a balancing act between business excellence newlineand quality outcomes in practice. It is from the premise of high performance engagement and quality of care provided to health consumers with patient centered focus, the pedestal of success in operational excellence is achieved. Purpose This study focuses on establishing Operational Excellence in relation to High Performance Engagement and Quality of Care among executives in the health care sector. Method A descriptive study was carried out using quantitative method with a sample of 410 health care executives from NABH accredited and nonaccredited hospitals and qualitative analysis among patients in Kerala. Results newlineThe results indicate a positive correlation of operational excellence with high performance engagement and quality of care. The independent variables, high performance engagement and quality of care are significant predictors of operational excellence. -
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Operational pattern forecast improvement with outlier detection in metro rail transport system
Transportation is an unavoidable part of every humans life. The mobility system handles the transport of humans from different places using various transport modes. According to a station in a populated area, the main problem is the presence of traffic in peak hours and wasting their valuable time on the road. The only medium which runs above the traffic is metro rails/subways. For these reasons, metro rails become a point of interest for each researchers prophecy and provide valuable recommendations for the smooth functioning of services. Even though, in many cases, the metro systems are affected by abnormal passenger flow. So, this study handles abnormal passenger flow detection and station clustering for the behavior study of a passenger flow system. The research compares outlier detection and anomaly identification for the behavioral analysis of the metro rail passenger flow. The study use data from Kochi Metro Rail Limited for the period 2017 to 2019. Outlier removal has used in passenger flow data before building a forecasting system. In pattern recognition algorithm those components which lie outside the patterns can be considered abnormal (anomaly).The outliers are the component falling apart from the region of interest. The effect of removing the outlier from the time-series pattern is studied against the outlier included pattern to show the improvement. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Operational plan on teaching-learning at departmental level /
Journal Of Education & Social Policy, Vol.1, Issue 2, pp.77-79, ISSN No: 2375-0790 (Online) 2375-0782 (Print). -
Opinion mining on newspaper headlines using SVM and NLP
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Opportunistic mycoses in COVID-19 patients/survivors: Epidemic inside a pandemic
Being considered minor vexations, fungal infections hinder the life of about 15% of the world population superficially, with rare threats to life in case of invasive sepsis. A significant rise in the intrusive mycoses due to machiavellian fungal species is observed over the years due to increased pathology and fatality in people battling life-threatening diseases. Individuals undergoing therapy with immune suppressive drugs plus recovering from viral infections have shown to develop fungal sepsis as secondary infections while recovering or after. Currently, the whole world is fighting against the fright of Coronavirus disease (COVID-19), and corticosteroids being the primitive therapeutic to combat the COVID-19 inflammation, leads to an immune-compromised state, thereby allowing the not so harmful fungi to violate the immune barrier and flourish in the host. A wide range of fungal co-infection is observed in the survivors and patients of COVID-19. Fungal species of Candida, Aspergillus and Mucorales, are burdening the lives of COVID-19 patients/survivors in the form of Yellow/Green, White and Black fungus. This is the first article of its kind to assemble note on fungal infections seen in the current human health scenario till date and provides a strong message to the clinicians, researchers and physicians around the world non-pathological fungus should not be dismissed as contaminants, they can quell immunocompromised hosts. 2021 -
Opportunity Recognition, Career Decision-Making, Self-Efficacy and Social Entrepreneurial Intention among Higher Education Students
Building on the entrepreneurship cognition literature, the present research aims to develop a model to examine the direct and indirect effects of opportunity recognition, career decisionmaking and self-efficacy on social entrepreneurial intention. The research adopted a crosssectional design. The research was divided into three distinct studies, each conducted with a specific objective. The data collected for three studies included higher education students newlineacross India. Studies 1 and 2 aimed to develop and validate two scales, namely social entrepreneurial opportunity recognition and social entrepreneurial career decision-making following steps in tool construction. The sample size was 600 for study 1 and 845 for study 2. The social entrepreneurial opportunity recognition scale had 24-items that measures opportunity recognition with six motivating factors as the lower order constructs which are life experiences, social awareness, social inclination, community development, institutional voids, and natural option for a meaningful career. The social entrepreneurial career decision scale had 20 items focusing on the appraisal components in pre-entry social entrepreneurial career decision-making and has four factors, which are relevance, coping potential, knowledge and resources, and normative significance. Study 3 examined the direct and indirect effects of opportunity recognition, career decision-making and self-efficacy on social newlineentrepreneurial intention using a sample of 605 students. The findings show that opportunity recognition influences social entrepreneurial intention and is partially mediated by career decision-making. Furthermore, self-efficacy moderates the mediating role of career decisionmaking between opportunity recognition and intention. This research facilitates a profound understanding of social entrepreneurial cognition and pre-entry decision-making. -
Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication
In recent times, fiber optic communication networks have become commonly applied for commercial as well as military applications. Fiber optic networks have gained popularity owing to the high data rate. At the same time, the generation of huge quantity of data at a faster rate poses a major challenge in the storing and transmission process. To resolve this issue, data compression approaches have been presented to reduce the quantity of transmitted data and thereby minimizes bandwidth utilization and memory. Vector quantization (VQ) is a commonly employed image compression technique and Linde Buzo Gray (LBG) is used to construct an optimum codebook to compress images. With this motivation, this paper presents a new oppositional glowworm swarm optimization based LBG (OGSO-LBG) technique for image compression in fiber optic communication. The OGSO algorithm involves the integration of oppositional based learning (OBL) concept into the GSO algorithm to boost its convergence rate. The OGSO-LBG algorithm produces the codebook at a faster rate with minimal computation complexity. In order to highlight the enhanced compression performance of the OGSO-LBG technique, a series of experiments were carried out and the results are examined under different dimensions. 2021 IEEE