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Lithium photodisintegration with linearly polarized photons at astrophysical energies
We present here a model-independent theoretical discussion of differential cross-sections in photodisintegration of lithium with unpolarized and linearly polarized photons. In recent years, experimental measurements are being carried out on the photodisintegration of lithium in the reaction channel 7Li(?, n)6Li to study the angular dependence of cross-section. In this regard, we have studied the spin structure of amplitudes in 7Li(?, n)6Li by expressing the differential cross-section in terms of Legendre polynomials. 2023 Oxford University Press. All rights reserved. -
Lived Experiences, Challenges, and Coping Mechanisms of Undergraduate Students on Cybersecurity in Digital Environments
Higher education students use digital devices for learning, entertainment, and financial transactions. The present study examines undergraduate (UG) students challenges faced in cyber security space amid digital environments. The study employed a qualitative research design with a narrative inquiry method to capture the lived experiences, challenges, and coping mechanism of UG students through semi structured interviews. The researchers analyzed the qualitative data of ten UG students using the inductive thematic analysis method. UG students shared overall that they face varied experiences, multiple challenges, and cope with cyber security issues in multiple ways. Findings from the study led to recommendations for stakeholders, which includes designing higher education classes in a more secure way. This may offer students orientation on cybersecurity knowledge which keeps them safe and helps them understand the digital environment overall. 2024 Taylor & Francis Group, LLC. -
Living with Coronavirus outbreak in India
The present paper focuses on living with coronavirus outbreak in India. This piece emphasizes on various policies adopted by the government of India to face the coronavirus crisis. It brings into perspective what financial strides the economy is going through, the mental health of the citizens, and the current situation of health care in the country. The current commentary reflects the learnings from COVID-19, the role of defined governmental policies, and support in surviving such an unforeseen situation. 2020 American Psychological Association. -
Load shedding using GA and ACO in smart gird environment
Increasing pressure on the utilities to accommodate energy efficiency, load management and progress in advanced technology has led to transformations for existing grid into a smarter grid. Creating awareness among the end-users to participate in load management programs instead of capacity addition is the best solution for maintaining the stability in the grid. Load shedding is a strategy under load management in which load connected to the smart grid is individually controlled via two- way communication. In this paper, a Smart Load shedding approach is developed based on load prioritization. The required amount of load to be shed under lack of sufficient generation level is optimized by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. The proposed approach is implemented using a real time feeder data from the substation, India. The results reflect the effectiveness of proposed algorithms taken into practical applications. -
Local community involvement in wildlife resorts: Issues and Challenges
The Global Code of Ethics for Tourism Article 5 states that tourism should be a beneficial activity for host countries and communities (UNWTO). The code also emphasises on equitable distribution (between host countries and communities) of the economic and sociocultural benefits generated by tourism activities. The tourism resorts and accommodation sector have to involve local communities in socio-economic activities and priority should be given to local manpower. A wildlife resort has vast opportunities to involve local communities in their day to day operation by purchasing local products, promoting local festivals, providing employment opportunities to locals, and involving local communities in decision-making. Wildlife resorts can also promote local culture, create environment awareness among local people, provide educational support to the local children, and support development of infrastructure and medical facilities for the locals. Though local communities can be involved in various activities of wildlife resorts, it is essential to address the issues and challenges that hinder wildlife resorts from doing so. This paper attempts to determine the issues and challenges faced by wildlife resorts in involving local communities in their day to day operations and suggests ways and means to overcome those challenges. The scope of the study covered selected wildlife resorts in Karnataka. The targeted respondents of the research survey were resort managers and data were collected using open-ended questions to understand real-time issues and challenges involving local communities in resort activities. The data were then analysed using thematic text analysis. The findings from the study will help explore means of providing a better framework which will help wildlife resorts overcome issues and challenges involving local communities. The Author(s) 2017. -
Local post-hoc interpretable machine learning model for prediction of dementia in young adults
Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to mitigate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided diagnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Locating Indian universities in knowledge societies: A critique
Knowledge societies characterize a defining feature of the present era. Veering away from their initial connotation of scientific temper and reasoning, today, they assume a new meaning in which the basis of economy, polity, and social action is knowledge. In the post-capitalist, post-industrial societies, knowledge has become the foundation of industrial productivity and social wellbeing. The crux of knowledge production has been shifting from the traditional disciplinary contexts promoted by academic interests in the universities to its applications for better productivity and wellbeing. Nevertheless, productivity and usefulness are accorded an epistemological appeal in defining what counts as knowledge. In this context, the present paper discusses the changes in knowledge production and dissemination processes in knowledge societies and their implications for universities in India. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
Logistic growth and SIR modelling of coronavirus disease (COVID-19) outbreak in India: Models based on real-time data
The logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework are utilized for the mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India. Karnataka, Kerala and Maharashtra, three states of India, are selected based on the pattern of the disease spread and the prominence in being affected in India. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. The models classify the pandemic into five stages based on the nature of the infection growth rate. According to the estimates of the models it can be concluded that Kerala is in a stable situation whereas the pandemic is still growing in Karnataka and Maharashtra. The infection rate of Karnataka and Kerala are lesser than 5% and reveal a downward trend. On the other hand, the infection rate and the high predicted number of infectives in Maharashtra calls for more preventive measures to be imposed in Maharashtra to control the disease spread. The results of this analysis provide valuable information regarding the disease spread in India. 2020, International Information and Engineering Technology Association. -
Long memory investigation during demonetization in India
Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as 'Long Memory', LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both 'Noah Effect' and 'Joseph Effect', proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot's specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly. Bikramaditya Ghosh, Saleema J. S., Aniruddha Oak, Manu K. S., Sangeetha R., 2020. -
Long run relationship between macroeconomic indicators and Indian sectoral indices
Investors and fund managers continuously strive to find new ways to diversify their portfolio and minimise risk exposure. The study aims to find out whether the macroeconomic indicators exert the same influence on stock prices across the entire stock market or varies across different sectors. The impact of macroeconomic indicators would not be the same on all the sectors. This paper provides empirical evidence of macroeconomic indicators such as crude oil prices, interest rates, foreign currency rates, money supply and inflation rates having a varied impact on Nifty50 index and each of the select sectoral stock indices namely, Nifty Bank, Nifty IT and Nifty financial services. The sample period runs from Jan 2009 to Jan 2019. The study employs the Error Correction Mechanism to study whether the macroeconomic indicators have the same impact across sectoral stock indices in the long run. The findings show that variations in macroeconomic variables do not trigger the same response from all the sectoral stock indices. While most of the variables chosen have a significant influence on Nifty50 index and NiftyIT; Nifty financial services and Nifty Bank remain unaffected by changes in few major macroeconomic variables or show opposite reaction than the other sectors. The findings of the study have significant implications for long term investors and investment managers for building a diversified portfolio and thereby protecting themselves from financial losses during adverse market conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Long Term X-Ray Spectral Variations of the Seyfert-1 Galaxy Mrk 279
We present the results from a long term X-ray analysis of Mrk 279 during the period 2018-2020. We use data from multiple missions - AstroSat, NuSTAR and XMM-Newton, for the purpose. The X-ray spectrum can be modeled as a double Comptonization along with the presence of neutral Fe K? line emission, at all epochs. We determined the sources X-ray flux and luminosity at these different epochs. We find significant variations in the sources flux state. We also investigate the variations in the sources spectral components during the observation period. We find that the photon index and hence the spectral shape follow the variations only over longer time periods. We probe the correlations between fluxes of different bands and their photon indices, and found no significant correlations between the parameters. 2024. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Long-term Optical and ?-Ray Variability of the Blazar PKS 1222+216
The ?-ray emission from flat-spectrum radio quasars (FSRQs) is thought to be dominated by the inverse Compton scattering of the external sources of photon fields, e.g., accretion disk, broad-line region (BLR), and torus. FSRQs show strong optical emission lines and hence can be a useful probe of the variability in BLR output, which is the reprocessed disk emission. We study the connection between the optical continuum, H? line, and ?-ray emissions from the FSRQ PKS 1222+216, using long-term (?2011-2018) optical spectroscopic data from Steward Observatory and ?-ray observations from Fermi Large Area Telescope (LAT). We measured the continuum (F C,opt) and H? (F H? ) fluxes by performing a systematic analysis of the 6029-6452 optical spectra. We observed stronger variability in F C,opt than F H? , an inverse correlation between the H? equivalent width and F C,opt, and a redder-when-brighter trend. Using discrete cross-correlation analysis, we found a positive correlation (DCF ? 0.5) between the F ??ray>100 MeV and F C,opt (6024-6092 light curves with a time lag consistent with zero at the 2? level. We found no correlation between the F ??ray>100 MeV and F H? light curves, probably dismissing the disk contribution to the optical and ?-ray variability. The observed strong variability in the Fermi-LAT flux and F ??ray>100 MeV ? F C,opt correlation could be due to the changes in the particle acceleration at various epochs. We derived the optical-to-?-ray spectral energy distributions during the ?-ray flaring and quiescent epochs that show a dominant disk component with no variability. Our study suggests that the ?-ray emission zone is likely located at the edge of the BLR or in the radiation field of the torus. 2022. The Author(s). Published by the American Astronomical Society. -
Long-term optical and infrared variability characteristics of Fermi blazars
We present long-term optical and near-infrared flux variability analysis of 37 blazars detected in the ?-ray band by the Fermi Gamma-Ray Space Telescope. Among them, 30 are flat spectrum radio quasars (FSRQs) and 7 are BL Lac objects (BL Lacs). The photometric data in the optical (BVR) and infrared (JK) bands were from the Small and Moderate Aperture Research Telescope System acquired between 2008-2018. From cross-correlation analysis of the light curves at different wavelengths, we did not find significant time delays between variations at different wavelengths, except for three sources, namely PKS 1144-379, PKS B1424-418, and 3C 273. For the blazars with both B- and J-band data, we found that in a majority of FSRQs and BL Lacs, the amplitude of variability (?m) in the J band is larger than that in B band, consistent with the dominance of the non-thermal jet over the thermal accretion disc component. Considering FSRQs and BL Lacs as a sample, there are indications of ?m to increase gradually towards longer wavelengths in both, however, found to be statistically significant only between B and J bands in FSRQs. In the B-J v/s J-colour magnitude diagram, we noticed complicated spectral variability patterns. Most of the objects showed a redder when brighter (RWB) behaviour. Few objects showed a bluer when brighter (BWB) trend, while in some objects both BWB and RWB behaviours were noticed. These results on flux and colour characteristics indicate that the jet emission of FSRQs and BL Lacs is indistinguishable. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Looking at psychological well-being through the lens of identity among adolescent girls: An exploration
Purpose: This research endeavours to delve into the intricate dimensions of adolescent girls' psychological well-being and identity, aiming to shed light on their interplay and identify key predictors of psychological well-being. The study, conducted with a sample of adolescent girls, seeks to enrich our understanding of the multifaceted nature of their developmental experiences. Psychological well-being is attained by achieving a state of balance affected by both challenging and rewarding life events and a stable sense of identity. Approach: The present research is an ex-post facto research falling in the area of quantitative research design. Data has been collected on 348 adolescents, purposely recruited from different schools of Delhi NCR. The age range of the respondents was 15 to 17 years. Findings: The results reveal that psychological well-being is being predicted by identity processes among adolescent females. The different dimensions of identity processes are found to be explaining almost 19% variance in the regression model. Commitment has been found to have a ? value of 0.197 (t= 3.511; p<.01), in-depth exploration has a ?= 0.161 (t= 2.867; p<.01), and reconsideration of commitment has a ?= 0.314 (t= 6.294; p<.01). Value: By addressing the objectives of this research, valuable insights may be received by educators, mental health professionals, and policymakers to better support and enhance the well-being of adolescent girls through having a stable sense of identity. 2024 RESTORATIVE JUSTICE FOR ALL. -
Lora-WAN Powered by Renewable Energy, and Its Operation with Siri / Google Assistant
LoRa WAN is a newly emerged game changing communication technology for sending small data packets of size 50 bytes or less, wirelessly over an area of up to 10 Km without the need of an internet connection. LoRa WAN has its own frequency band and the band is different for every country. This technology is now starring to boost WSN technology better than ever before. This paper aims to, power up a LoRa Enabled Device or a LoRa Gateway by using a reliable dual mode non-conventional energy resource for storage and utilization, find peak performances altering the data rate that can be achieved in a LoRa WAN Communication (using Indoor RAK Gateway), make use data compression techniques, data packet encoding/decoding, Coding Apple Shortcuts, setting up Siri and Google Assistant for voice control and future scope. 2020, Asian Research Association. All rights reserved. -
Low cost calibrated mechanical noisemaker for hearing screening of neonates in resource constrained settings
Background & objectives: There is a need to develop an affordable and reliable tool for hearing screening of neonates in resource constrained, medically underserved areas of developing nations. This study valuates a strategy of health worker based screening of neonates using a low cost mechanical calibrated noisemaker followed up with parental monitoring of age appropriate auditory milestones for detecting severe-profound hearing impairment in infants by 6 months of age. Methods: A trained health worker under the supervision of a qualified audiologist screened 425 neonates of whom 20 had confirmed severe-profound hearing impairment. Mechanical calibrated noisemakers of 50, 60, 70 and 80 dB (A) were used to elicit the behavioural responses. The parents of screened neonates were instructed to monitor the normal language and auditory milestones till 6 months of age. This strategy was validated against the reference standard consisting of a battery of tests - namely, auditory brain stem response (ABR), otoacoustic emissions (OAE) and behavioural assessment at 2 years of age. Bayesian prevalence weighted measures of screening were calculated. Results: The sensitivity and specificity was high with least false positive referrals for 70 and 80 dB (A) noisemakers. All the noisemakers had 100 per cent negative predictive value. 70 and 80 dB (A) noisemakers had high positive likelihood ratios of 19 and 34, respectively. The probability differences for pre- and post- test positive was 43 and 58 for 70 and 80 dB (A) noisemakers, respectively. Interpretation & conclusions: In a controlled setting, health workers with primary education can be trained to use a mechanical calibrated noisemaker made of locally available material to reliably screen for severe-profound hearing loss in neonates. The monitoring of auditory responses could be done by informed parents. Multi-centre field trials of this strategy need to be carried out to examine the feasibility of community health care workers using it in resource constrained settings of developing nations to implement an effective national neonatal hearing screening programme. -
Low temperature performance evaluation of asphalt binders and mastics based on relaxation characteristics
Low temperature cracking is one of the main distresses of asphalt pavement in cold regions. Stress relaxation characteristics is critical for cracking resistance of asphalt materials, especially at low temperatures, but there are few studies on the relaxation characteristic of asphalt mastics. To evaluate the effects of relaxation characteristics of asphalt binders and mastics on its low temperature performance, beam bending relaxation test was carried out through dynamic thermomechanical analyzer at low temperatures. Relaxation rate and relaxation time were proposed to illustrate the relaxation characteristics of asphalt binders and mastics. Then, the low-temperature performance of asphalt binders and mastics was evaluated by bending beam rheometer (BBR), glass transition temperature (Tg), and single edge notch beam bending test. Finally, the correlation of relaxation characteristics with low-temperature properties was analyzed based on Pearsons correlation coefficient and Spearman rank correlation coefficient. The results show that the elasticity of asphalt mastics increases with incorporation of mineral fillers and thus the viscous deformation potential is reduced, which affects the stress relaxation capability. The low-temperature cracking performance of asphalt mastics is indeed compromised as compared with asphalt binders, and the asphalt mastics prepared with fly ash performs the worst since it presents a stronger hardening effect. Fracture energy is determined not to be suitable for evaluating the low-temperature performance of asphalt mastics since its results contradict the BBR and Tg tests. The maximum displacement at fracture can better characterize the brittleness of asphalt materials at low temperatures. The relaxation characteristic index has the strongest correlation with Tg of asphalt binders and mastics, followed by maximum displacement at fracture and comprehensive compliance parameter (Jc). The correlation coefficients are almost larger than 0.5, suggesting that relaxation time and relaxation rate can characterize the low-temperature properties of asphalt binders and mastics. 2022, RILEM. -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors