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The Need for Universal Design for Learning in Higher Education for the Specially-AbledAn Essay
Educators at any grade level or subject area can apply Universal Design for Learning (UDL), which is a set of principles for curriculum development that attempts to give all students an equal opportunity to learn. The provision of instructional alignment between objectives, instructional design, methods of delivery and assessment of learning outcomes, which could be individualized and which works for all is blueprinted in a UDL framework. The approaches and methods for instruction in UDL are adaptable and not the same for all the learners or it is not one size fits all approach according to the National Center for Universal Design for Learning (Harper, 2018). The guiding principles of UDL include acceptance and practice of various means of equivalent representation or acquiring information, various means of equivalent expression or demonstrating the learning and various means of equivalent engagement to enhance learning. Given the multiple potentials of specially-abled (SA) students, inclusive learning through UDL provides an environment of diversity and unison. The key attempt is to provide instructional delivery of the same topic to different learners with different learning abilities and approaches in the same course, resulting in comparable outcomes. This chapter highlights the various strategies of UDL that may be extended to assist SA students transition through the pandemic, some of which include customizing learning contents with assured accessibility, individualizing learning goals as per student potential, flexible/customized assessments, and qualitative grading. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Future Inclusive Education
The United Nations (UN) Sustainable Development Goals (SDGs) ensure inclusive and equitable quality education for promoting lifelong learning. Inclusive education fosters an environment for access to quality education by addressing diversity and barriers that can cause exclusion. COVID-19 has reimagined Higher Education with new challenges and opportunities for the present and future. Digital divide, gender inequality, addressing specially-abled students, and a non-inclusive learning environment are the major barriers to inclusive education. Inclusive education ensures that no one leaves behind, and higher education institutes can enhance their capacity building to promote inclusivity for the common good. Employability is one of the key concepts in higher education that builds the workforce and contributes to nation-building. With COVID-19, nature of work has seen radical changes; hence, graduate attributes have evolved with the 21st-century skills. The chapter emphasizes the role of inclusive education and reimagining higher education with suggestions to using existing strategies in life-long and futuristic inclusive learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Study on Spray Dried Yttria Stabilized Zirconia Dental Implants
Medical implants are devices, tissues or supports that are positioned in a suitable manner on any defective part of the human body to facilitate its smooth functioning again. Known as 'prosthetics', they may be used to offer support to a specific organ or tissues, distribute medication, or observe the body condition. While many of the implants are made from skin, bone or other tissues removed from the body itself, the artificial ones are made from engineering materials which could be any of the compatible metals, plastics, ceramics or even composites. The high end technologically advanced implant material is expected to withstand severe barriers and compatibility issues when in contact with the human body. One such application is dental implants, where, the materials must possess superior mechanical properties, exhibit good hydro-chemical and low thermal degradation characteristics. They are also required to possess characteristics such as low friction, strong wear resistance, good wettability and biocompatibility, when placed in the mouth. The only materials that come close to meeting the needs are ceramics, limited by the associated high fracture rate. Stabilized zirconia (stabilized with yttria, ceria etc.) has provided potential solution. Among the two stabilizers, ceria stabilized zirconia may be a better alternative to yttria stabilized zirconia. Other alternatives are alumina, apatites: but their use are constrained based upon technological and cost considerations. Implant product is a highly demanding technology. Spray drying is a suitable process methodology to obtain free flowing powders with uniform morphology and chemical composition, essential for an implant production. This paper presents (i) results from spray drying 8% Y2O3-stabilized ZrO2 and (ii) a review of published literature pertaining to dental implant materials, the various processing methodologies, with special reference to stabilized zirconia and spray drying. Published under licence by IOP Publishing Ltd. -
Counselling and psychological wellbeing of people living with HIV in Kerala
There is a dearth in the documentation of the benefits of HIV-counseiling in India. This article deals with how HIV-counselling facilitates the psychological wellbeing of Persons Living with HIV (PLHIV) in Kerala, India. About 269 PLHIV participated in the study. Meaning in Life Questionnaire, Illness Perception Questionnaire and Psychological Wellbeing Scale were used. It was noticed that counselling did not impact the scores on subscales such as Timeline, Emotional Representation and Consequences, while the scores on Self-Acceptance and Autonomy did not differ even with counselling. Findings call for a reconsideration of the way HIV-counselling is provided. -
Understanding stigma and burnout among HIV/ AIDS health care workers Implications for counselling
The article examines the association between burnout and stigma among Health Care Workers (HCWs) and highlights the need for counselling services in the care of the HCWs. Stereotypes of HIV/AIDS and burnout in HCWs caring for people living with HIV/AIDS (PLHIV) were assessed using self-report methods. Stereotypes about AIDS Scale (SAAS) and Maslach Burnout Inventory MBI were completed by 120 staff from 8 community care centres for PLHIV across south India. Results of SAAS showed that about 33 percent respondents manifested high level of stigma while 35 percent exhibited moderate levels. The results of MBI showed high level of burnout in about 31 percent and moderate in 35 percent respondents. -
From Text to Action: NLP Techniques for Washing Machine Manual Processing
This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals. 2024 Elsevier B.V.. All rights reserved. -
A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools
The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters. 2021 Inderscience Enterprises Ltd. -
Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE. -
Escape velocity backed avalanche predictor neural evidence from nifty /
International Journal of Recent Technology And Engineering, Vol.8, Issue 4, pp.486-490, ISSN No: 2277-3878. -
Multifractal analysis of volatility for detection of herding and bubble: evidence from CNX Nifty HFT /
Investment Management And Financial Innovations, Vol.16, Issue 3, pp.182-193 -
Power law in tails of bourse volatility – evidence from India /
Investment Management And Financial Innovations, Vol.16, Issue 1, pp.291-298 -
Medical Tourism in South India - A Relative Study of the Principal participants in hospital and hospitality industry in South India
International Journal of Management, IT and Engineering Vol.3, ISSUE 1, pp. 613-626 ISSN No. 2249-0558 -
A literature review on destination management organization /
Zenith International Journal of Multidisciplinary Research, Vol.4, Issue 12, pp.675-681, ISSN No: 2231-5780. -
Setting benchmarks through Destination Management Organizations (DMOs): A study on the tourism policy of Karnataka, India /
Asian Journal of Management Sciences, Vol.2, Issue 6, pp.33-39, ISSN No: 2348-0351. -
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Formation and photoluminescence of ZnS:Tb nanoparticles stabilized by polyethylene glycol
ZnS nanoparticles doped with 1 mol.% of Tb have been prepared at 70 C by simple chemical precipitation method using poly ethylene glycol (PEG) as capping agent. The synthesized nanoparticles have been analysed using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), photoluminescence (PL) and UV-Vis absorption spectroscopy. From X-ray diffraction analysis, it was found that nanostructured ZnS:Tb particles exhibited cubic structure with an average crystallite size of 2.75 nm. Room temperature photoluminescence (PL) spectrum of the doped sample exhibited broad emission in the visible region with multiple peaks at 395 and 412 nm due to 5D3?7F6and 7F5transitions and 492, 536, 600, 653 and 680 nm due to 5D4?7F67F57F4,7F1and 7F0transitions. 2020 Elsevier Ltd. All rights reserved. -
Highly luminescent ZnS:Mn quantum dots capped with aloe vera extract
This study demonstrates the optical properties of ZnS:Mn2+ qquantum dots synthesized by simple and eco-friendly chemical precipitation method using aloe vera (AV) extract as the stabilizing agent. The nanoparticles have been characterized by transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, diffuse reflectance spectroscopy (DRS), photoluminescence (PL) and time-resolved PL spectroscopy. Increase in band gap energy with decrease in particle size was observed from DRS studies due to quantum confinement effect. Dominant yellow emission was observed from characteristic 4T1?6A1 transitions of the Mn2+ions in the ZnS:Mn/AV nanoparticles. The results provide insight to the quantum confinement effect that occur and how it affect decay life time of the ZnS:Mn2+/AV nanoparticles. 2020 Elsevier Ltd -
Pure red luminescence and concentration-dependent tunable emission color from europium-doped zinc sulfide nanoparticles
Nano-sized Eu3+-doped ZnS particles were prepared by chemical precipitation method using polyethylene glycol as capping agent. The structural and morphological studies of ZnS:Eu3+ nanoparticles were carried out using X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). XRD results show that ZnS:Eu3+ nanoparticles have a cubic structure for all Eu3+ concentrations. Dependence of doping concentration on the photoluminescence (PL) of ZnS:Eu3+ nanophosphor was studied for excitations at 395nm and 465nm. At 395-nm excitation, emission colors of ZnS:Eu3+ nanophosphor lie in blue, green, yellow, and red regions of chromaticity diagram for different doping concentrations. But for all doping concentrations we got red emission when the excitation wavelength was 465nm and the color purity was 92% for 0.03M doped sample. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production
Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. 2023 American Institute of Physics Inc.. All rights reserved.