Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
317
result(s) for
"Kumar, Sanjit"
Sort by:
Groundwater vulnerability assessment and mapping using DRASTIC model
This book shows the effectiveness of DRASTIC model in a geographical setting for validation of vulnerable zones and presents the optimization of parameters for the development of precise maps highlighting several zones with varied contamination. Impact of vadose zone has also been assessed by considering every sub-surface layer.
Book
Isolation and characterization of lumpy skin disease virus from cattle in India
Lumpy skin disease (LSD) has devastating economic impact. During the last decade, LSD had spread to climatically new and previously disease-free countries, which also includes its recent emergence in the Indian subcontinent (2019). This study deals with the LSD outbreak(s) from cattle in Ranchi (India). Virus was isolated from the scabs (skin lesions) in the primary goat kidney cells. Phylogenetic analysis based on nucleotide sequencing of LSD virus (LSDV) ORF011, ORF012 and ORF036 suggested that the isolated virus (LSDV/ Bos taurus -tc/India/2019/Ranchi) is closely related to Kenyan LSDV strains. Further, we adapted the isolated virus in Vero cells. Infection of the isolated LSDV to Vero cells did not produce cytopathic effect (CPE) until the 4 th blind passage, but upon adaptation, it produced high viral titres in the cultured cells. The kinetics of viral DNA synthesis and one-step growth curve analysis suggested that Vero cell-adapted LSDV initiates synthesizing its genome at ~24 hours post-infection (hpi) with a peak level at ~96 hpi whereas evidence of progeny virus particles was observed at 36–48 hours (h) with a peak titre at ~120 h. To the best of our knowledge, this study describes the first successful isolation of LSDV in India, besides providing insights into the life cycle Vero cell-adapted LSDV.
Journal Article
Computer vision syndrome, musculoskeletal, and stress-related problems among visual display terminal users in Nepal
The use of computers and other Visual Display Terminal (VDT) screens is increasing in Nepal. However, there is a paucity of evidence on the prevalence of Computer Vision Syndrome (CVS) and other occupational health concerns among employees working in front of VDT screens in the Nepalese population. This study aims to estimate the prevalence of CVS, musculoskeletal and work-related stress among VDT screen users in the office, as well as their understanding and usage of preventive measures. The study was a cross-sectional descriptive study among 319 VDT users in office settings in Kathmandu Metropolitan City, Nepal, using a semi-structured self-administered questionnaire. Multivariate logistic regression analysis was conducted to identify the associated factors at 95% CI. P-value <0.05 was considered as statistically significant. The prevalence of CVS was 89.4%. More than eight out of ten study participants reported at least one visual and musculoskeletal symptom. Work-related stress, which was moderate-difficult to handle, was present in 36.7% of the study population. The mean±SD computer usage per day was 7.9±1.9 hours. Tired eye (63.3%), feeling of dry eye (57.8%), headache (56.9%) were the common visual symptoms of CVS reported. Total computer use/day > = 8 hours OR 2.6, improper viewing distance OR 3.2, Not using an anti-glare screen OR 2.6, not using eye-drops, and not wearing protective goggles OR 3.1 were significantly associated with the presence of CVS. There was no statistically significant association between visual symptoms of CVS, musculoskeletal symptoms, and stress with gender. CVS was substantially related to not employing preventive measures, working longer hours, and having an incorrect viewing distance. With more hours per day spent in front of a VDT screen, work-related stress and musculoskeletal complaints were also found to be important correlates. Similarly, work-related stress was found more among those who had less than five years of job.
Journal Article
Transfer Learning for Improving Seismic Building Damage Assessment
The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.
Journal Article
Customers’ emotion regulation strategies in service failure encounters
Purpose
The purpose of this paper is twofold: first, to determine the role of emotions in customer evaluation of service failures; and second, to examine how customers’ emotion regulation impacts customer satisfaction and behavioural responses (e.g. repurchase intentions and negative word-of-mouth).
Design/methodology/approach
A scenario-based survey was used to elicit responses in a hospitality setting. Structural equation modelling and hierarchical regression analysis were used to test the proposed hypotheses.
Findings
Results show that both positive and negative emotions mediate the relationship between perceived injustice and customer satisfaction. The emotion regulation of customers through suppression and reappraisal influences the effects of satisfaction on both negative word-of-mouth and repurchase intentions.
Practical implications
This study advances service managers’ understanding of customer experience during service failure by demonstrating how emotion regulation influences customer response behaviours. With a better understanding of customers’ emotion regulation strategies, managers and frontline employees can more effectively develop and execute recovery strategies which adapt to customer emotions while eliciting more satisfying outcomes.
Originality/value
This research is one of the first to examine the moderating role of customers’ emotion regulation strategies in determining their behavioural responses. Conducted in the hospitality services context, this study provides support for relationships among perceived injustice, customer emotions, emotion regulation, customer satisfaction, negative word-of-mouth and repurchase intentions.
Journal Article
AI-driven predictions of geophysical river flows with vegetation
In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R
2
= 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.
Journal Article
Exploring the effects of different population projection datasets on global compound drought and heatwave exposure estimates under shared socioeconomic pathways
The simultaneous occurrence of both extreme droughts and heatwaves has become more frequent with global warming, resulting in increases in the frequency and potential impact of compound drought and heatwave (CDHW) globally. It is critical to evaluate the impacts of CDHW and assess global socio-economic risks to formulate appropriate risk mitigation strategies. Most studies have focused on projecting the likely variation in the multidimensional hazard of CDHW. However, the discrepancies among global population projection datasets based on shared socioeconomic pathways (SSPs) and their potential impacts on disaster risk assessments remain underexplored. In this study, multiple global high-resolution population projection datasets are used in combination with projected CDHW hazards via the multimodel ensemble from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate how different sources of population data could affect the assessment of CDHW-exposed populations under SSPs. The results show that at the global scale, the spatial pattern and temporal evolution of the CDHW-exposed population under climate change can be depicted consistently on the basis of different population data. However, at the subcontinental scale, substantial spatial heterogeneity exists in the projected exposure. For regions such as the Mediterranean, South Asia, and western Central Asia, the projections from different datasets are consistent with low uncertainty. In contrast, for regions including the northern hemisphere above 40°N, Oceania, eastern Central Asia, East Asia, the South American monsoon region, western Africa, Central Africa, etc., the uncertainty in the estimated exposed population is higher and is expected to increase from the 2020s to the end of the 21st century. Additional locational socioeconomic data should be collected in these areas to reduce uncertainty in future socioeconomic projections. The findings highlight the critical need to consider different elements-at-risk and choose fit-for-purpose datasets, providing essential guidance for disaster risk assessments that support climate adaptation strategies and sustainable development goals.
Journal Article
Site characterization through combined analysis of seismic and electrical resistivity data at a site of Dhanbad, Jharkhand, India
We present the seismic site characterization study using joint modelling of Horizontal-to-Vertical Spectral-Ratio (HVSR) and Rayleigh wave-phase velocity-dispersion curves obtained from Multi-channel Simulation with One Receiver (MSOR) in a part of Dhanbad, Jharkhand, India. The joint analysis of these two different but complementary datasets puts stronger constraints on the model parameter search space than one dataset and may help us in finding more unique shear-wave velocity model. The microtremor data from 12 observation points were utilized to iteratively search 1D shear-wave velocity profiles in a predefined model search space. These 1D shear-wave velocity models were interpolated to generate a 2D shear-wave velocity profile of the site using the cubic spline method. Our results show that the high peak amplitude value of HVSR is associated with low peak-period values of HVSR at a distance of ~ 60 m from the southern end of the profile; which may indicate the presence of the Basin Edge Effect. We identified four layers based on significant changes in the shear wave velocities to a depth of ~ 60 m. The major impedance contrasts are located at average depths of ~ 13 m, ~ 40 m and ~ 55 m, respectively. These layers from the surface may indicate the presence of soil, highly weathered rock mass, moderately weathered rock and bedrock, respectively. The depth of engineering solid bedrock (Vs > 600 m/s) is found at the depth of 55 m in the south which gradually decreases to a depth of 40 m in the northern end of the profile. The shear-wave velocity (Vs 30) for this area varies between 293 and 357 m/s; which can be classified as “D-type site”. For validation and comparison of our results, the Electrical Resistivity Tomography (ERT) data were also recorded along the same traverse using Wenner and Schlumberger configurations. Our results show a significant amount of correlation between the 2D shear-wave velocity and resistivity profiles obtained from joint analysis of tremor and ERT data.
Journal Article
Post-COVID syndrome: A prospective study in a tertiary hospital of Nepal
The post-coronavirus disease 2019 (COVID-19) syndrome is defined as the persistence of symptoms after viral clearance and the emergence of new symptoms after a few months following recovery from COVID-19. This study aimed to assess the prevalence of post-COVID-19 syndrome and the risk factors that contribute to its development.
This study was conducted prospectively in Tribhuvan University Teaching Hospital (TUTH), located in Maharajgunj, Kathmandu. The patients were followed up for three months.
The post-COVID status of 300 patients admitted to the COVID emergency of TUTH was studied. The mean age of the patients was 46.6±15.7 years, and the proportion of male (56%) was slightly higher than female (44%). Most of the patients (81.7%) had fever on their presentation to the emergency which was followed by fatigue (81.3%) and cough (78.3%). During the post-COVID phase, fatigue was the most common persistent symptom, with 34% experiencing fatigue after 60 days and 28.3% even after 90 days from the onset of symptoms. Univariate logistic regression showed sore throat (OR 4.6; 95% CI (2.8-7.6)), rhinitis (OR 3.6; 95% CI (2.1-5.9)), fatigue (OR 3.7; 95% CI (1.8-7.6)), diarrhea (OR 4.1; 95% CI (2.4-6.9)), anosmia (OR 6.7; 95% CI (3.9-11.3)), ageusia (OR 7.8; 95% CI (4.5-13.4)) and shortness of breath (OR 14.9; 95% CI (1.8-119.6)) at admission were all predictors of post-COVID syndrome after three months.
Even after recovering from COVID-19, people with COVID-19 may develop symptoms. As a result, COVID-19's long-term consequences should not be neglected, as they may lead to increased morbidity among patients, consumption of financial resources, and added burden on the health system.
Journal Article