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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 -
Opportunities and challenges while conducting field trips to the museum: a narrative review
The museum visit field trip engages and motivates the children in various activities. Field trips to the museum provide the students with a constructivist and experiential learning environment as they construct knowledge through observing the artifacts. The present study describes the possible opportunities and challenges for school children while conducting field trips to the museum. The study employed a narrative review technique to address the research question raised. The study selected the literature reviews from 2012-2023, including studies on field trips to the museum for the academic engagement of school children. The data includes 50 peer-reviewed journal articles categorized into five categories: students overall development, experiential learning opportunities, the museum as a resource, the role of teachers, the school, and museum authority. Results revealed that the museum is a resource for learning and is perfect for improving students cognitive and affective development towards the various school subjects and helping them enhance their participatory learning opportunities. However, teachers knowledge, infrastructure, parental consent, and legitimization with the school authority are some challenges in conducting museum visit field trips. Future research may focus on conducting empirical studies, which include school-museum collaboration, to enhance the horizon of school and community knowledge. 2025, Institute of Advanced Engineering and Science. All rights reserved. -
Opportunities for women's rural entrepreneurship in deprived rural environments: Empowering the pathway for women entrepreneurs in rural environments
Empowering women entrepreneurs in rural areas is essential for encouraging gender equality. This study explores opportunities, challenges, and strategies to support rural women entrepreneurs, focusing on the role of digital transformation, social enterprises, and policy interventions. Women face issues such as gender barriers, and social constraints, yet they leverage agriculture, handicrafts, and service sectors to drive growth. Success stories show how women face financial inclusion and other challenges. Digital platforms help access the market, skill development, and financial transactions, bridge gaps and promote sustainability. Policies like Stree Shakti Yojana, Mahila Udyam Nidhi Scheme and other provide financial aid, train and foster entrepreneurs. Sustainable practices, including renewable energy and eco-friendly techniques, further enhance prospects. By integrating sustainable strategies, advancing digital literacy, and aligning with Sustainable Development Goals, rural women entrepreneurs can emerge as leaders, ensuring inclusive economic growth and lasting social impact. 2025, IGI Global Scientific Publishing. -
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. -
Opposite Color Multiscale Local Binary Pattern Features for the Prediction of Bread Edibility
Bread is one of the profoundly consumed staple bakery foods by many people in the world. Quality is a remarkable concern as it is a consumable food product. It depends on the raw ingredients and baking process involved during the preparation. After the purchase of the bread, the quality and in turn the shelf life period of the bread may likely to get affected by the storage method. Hence, the edibility of the bread needs to be estimated. Most of the studies do estimate this using sensory attribute measurements like strange odor, crust color, taste, aroma, hard texture and mold formation. On contrary, this study newly attempts to examine the edibility effortlessly through images. A new variant of texture based Local Binary Pattern features is proposed for the prediction of edibility through analysis of hard texture and mold formation. As there is no benchmark bread sample dataset available for the study, a new dataset of 18,513 images is created. It is observed from the experimentation that the proposed Opposite Color Multiscale Local Binary Pattern features provide good estimation on majority voting with reduced number of features through feature transformation and selection. The accuracy obtained is 0.8493 which is comparable with other common variants of local binary pattern features. Multiple classifiers are evaluated during experimental analysis and ensemble approach outperforms well. As this is a contemporary problem addressed in the domain based on images of bread being first of its kind, it is likely to open up new challenges to be undertaken. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
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 -
Optical and electrical properties of SnS semiconductor crystals grown by physical vapor deposition technique
Tin sulfide (SnS) is a material of interest for use as an absorber in low cost solar cells. Single crystals of SnS were grown by the physical vapor deposition technique. The grown crystals were characterized to evaluate the composition, structure, morphology, electrical and optical properties using appropriate techniques. The composition analysis indicated that the crystals were nearly stoichiometric with Sn-to-S atomic percent ratio of 1.02. Study of their morphology revealed the layered type growth mechanism with low surface roughness. The grown crystals had orthorhombic structure with (0 4 0) orientation. They exhibited an indirect optical band gap of 1.06 eV and direct band gap of 1.21 eV with high absorption coefficient (up to 103 cm-1) above the fundamental absorption edge. The grown crystals were of p-type with an electrical resistivity of 120 ? cm and carrier concentration 1.52015 cm-3. Analysis of optical absorption and diffuse reflectance spectra showed the presence of a wide absorption band in the wavelength range 3001200 nm, which closely matches with a significant part of solar radiation spectrum. The obtained results were discussed to assess the suitability of the SnS crystal for the fabrication of optoelectronic devices. 2011 Elsevier B.V. All rights reserved. -
Optical and Electrochromic Properties of CeO2/WO3 Hybrid Thin Films Prepared by Hydrothermal and Sputtering
Innovative chromogenic nanostructures like hybrids but also composite materials can be increased electrochromic efficiency because of their prospective application values in low-power displays, smart windows, electronic papers, and car anti-reflect mirrors. We used a hydrothermal approach to make Cerium oxide Nanorods have various ratios in this report. DC magnetron sputtering procedures cover the generated cerium oxide nanorods of various diameters with a tungsten oxide layer in one step. the surface plasmon effect varies depending on the size of Ce Nanorods, and this phenomenon impacts electrochromic results. the electrochromic performances of CeO2/WO3 nanorods on FTO-coated glass slides are examined using a 0.5 M solution of H2SO4 as the electrolyte in the visible range. These structures produce considerable optical modulation (47 %, 45 %, and 41 % at 700 nm) and coloration efficiency (11.6, 7.57, and 10.84 cm2C-1 at 700 nm). 2022 Seventh Sense Research Group -
Optical and Infrared Photometric Study of Pre-Main Sequence Stars in Young Open Cluster NGC 7419 Young Open Cluster NGC 7419
The embryonic stage of development of stars has been a field of astrophysics which still pose many unresolved queries. The problem is largely due to the solitary nature of embryonic stars. Even the largest optical telescopes were not able to reveal many details about such stars as they are formed deep inside molecular cloud thorough which optical wavelength cannot pass through. Forbidding distances to such stars is an added problem. However, recently, this field is slowly turning into observational science. Even though optical radiations fail to reach the destination, longer wavelength infrared (IR) radiations can penetrate through such dusty region. These infrared radiations have been known for decades. But it is only during very recent times that infrared telescopes that have very high sensitivity and resolution came in to scenario. So now the astronomers are able to observe these stellar embryos. Studies about this stage of star formation can help us to solve many questions like how the birth of a star takes place, how these stars accrete matter into it from surroundings, when will this accretion stops, why do some stars have planetary systems around them etc. Current study is an attempt to look for the presence of these embryonic stars in an open cluster using infrared data and to get more details on nature of those stellar embryos. We investigate the properties of young stars and their circumstellar disks in young open cluster NGC 7419. The pre-determined V magnitude and B magnitude of these stars are combined with infrared data from 2MASS J,H, K and 4 band WISE data. The color-color diagrams are made using these 9 bands to identify young stellar objects. The individual SED fitting is done for these identified stars and parameters like their age, mass, temperature, disk radius and disk mass are estimated. These are used to comment on the evolutionary stage of the pre-main sequence stars and their circumstellar disk. Dissertation has been divided into five chapters. Chapter 1 gives a general introduction to the work done. Literatures referred are explained in Chapter 2. Data used in the current study and methodology adopted is described in chapter 3. Current work done on the young stellar objects in cluster NGC 7419 and results obtained from the study are explained in Chapter 4 and Chapter 5 respectively. -
Optical and Infrared studies of herbig Ae Be stars
The work makes use of the unprecedented capability of the Gaia mission to study various properties of Herbig Ae/Be stars. We placed the Herbig Ae/Be stars in the Gaia color-magnitude diagram and accurately estimated their age and mass. The mass accretion rate is calculated from Hand#945; line newlineflux measurements of 106 HAeBe stars. The mass accretion rate is found to decay exponentially with the age of Herbig Ae/Be stars. Further, the immediate neighborhood of two Herbig Ae/Be stars, V1787 Ori and IL newlineCep, are studied using the astrometric and photometric data from the Gaia mission. We discovered a low mass binary companion to V1787 Ori using the analysis of distance and proper motion values from Gaia DR2. The newlinemass ratio of the coeval binary system is found to be 0.23. Such a skewed mass ratio system is rarely identified in Herbig Ae/Be binary systems. The method of identification and characterization of the V1787 Ori wide binary system opens up the possibility of identifying more such systems. The HBe newlinestar IL Cep tells a much more complex story. The star is identified with a cluster of low mass stars associated with it. We identified 79 co-moving stars that are coeval to IL Cep, within 2 pc radius, from the analysis of newlineGaia EDR3 astrometry. The triggered star formation process called the quotRocket effectquot caused by a massive star HD 216658 is identified to be the cause of the clustered star formation near IL Cep. The effect of this process is demonstrated for the first time using the proper motion data from Gaia. newlineThe immediate neighborhood of Herbig Ae/Be stars is identified as the formation region of long-chain carbon molecules such as Fullerenes and Polycyclic Aromatic Hydrocarbons.