Bijay Lal Pradhan
@bijaylalpradhan.com.np
Associate Professor
Tribhuvan University
EDUCATION
PhD in Quality Management, M. Sc. (statistics), FDPM IIM Ahemadabad
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Statistics, Probability and Uncertainty, Management Science and Operations Research, Environmental Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Bineeth Kuriakose, Ida Marie Ness, Maja Å skov Tengstedt, Jannicke Merete Svendsen, Terese Bjørseth, Bijay Lal Pradhan, and Raju Shrestha
Elsevier BV
Rajeshwori Malla-Pradhan, Bijay Lal Pradhan, Khamphe Phoungthong, and Tista Prasai Joshi
College of Graduate Studies, Walailak University
Microplastic particles have been detected in water sources such as in oceans, lakes and rivers worldwide, which have received much attention in recent years. This review includes a summary of the analytical techniques such as sampling, processing and identification used by researchers to evaluate microplastic in lake water from 35 studies. Microplastic abundance and its morphological characteristics were also compared. Bulk sampling and volume-reduced sampling were almost equally implemented during sample collection. During sample processing, digestion were followed by 85.71 % of the researcher’s using hydrogen peroxide (H2O2) to degrade the organic matter present in the sample. Though density separation is the most common approach to extract microplastic from sediment sample, this was not the case in lake water as only 31.43 % used this method. Fibers were the most dominant shape and the maximum abundance of microplastics were found to be in < 0.5 and < 2 mm2 size class. Most studies reported microplastic to be of polyethylene (PE) and polypropylene (PP). HIGHLIGHTS Digestion is an important step during sample processing In lake water density separation is less frequently used method Fibers dominated in the size class < 0.5 and < 2 mm Microplastic in lake water is commonly expressed in volume unit GRAPHICAL ABSTRACT
Rajeshwori Malla-Pradhan, Khamphe Phoungthong, Thitipone Suwunwong, Tista Prasai Joshi, and Bijay Lal Pradhan
Springer Science and Business Media LLC
Rajeshwori Malla-Pradhan, Bijay Lal Pradhan, Khamphe Phoungthong, and Tista Prasai Joshi
Springer Science and Business Media LLC
Rajeshwori Malla-Pradhan, Thitipone Suwunwong, Khamphe Phoungthong, Tista Prasai Joshi, and Bijay Lal Pradhan
Springer Science and Business Media LLC
Rajeshwori Malla-Pradhan, Bijay Lal Pradhan, Tista Prasai Joshi, and Khamphe Phoungthong
Informa UK Limited
Prabjot Kaur, Bijay Lal Pradhan, and Anjali Priya
Hindawi Limited
TODIM-based multicriteria decision-making approach for assessing inventory policy in a Pythagorean fuzzy environment is proposed in this study. In supply chain, inventory appears in several forms. For effective supply chain, management businesses must choose an effective inventory policy. It is difficult to rank inventory policies. Inventory policy selection and evaluation include a procedure that can be thought about, checked after some time, and ideally improved through certain measures. Determination of the right inventory control policy is a challenge in the dynamic business environment as it enables organizations to gain an upper hand in terms of cost, quality, and service, which in turn provides a vital step in fulfilling the requirements of the customer. The multicriteria question has qualitative and quantitative factors that are vague and contradictory. Such variables are not necessarily clear and often suffer vagueness owing to the inconsistent existence of the gathered data. In the Pythagorean fuzzy setting, they are therefore considered to eliminate this vagueness by assigning membership and nonmembership roles to these factors. Finally, in this article, we performed a sensitivity analysis to demonstrate the stability of our model and to illustrate the utility of implementing such algorithms.
Ramswarth Sah, Mamata Bhattarai, Bijay Lal Pradhan, Shanta Lall Shrestha, Benu Lohani, and Ramesh Bhatta
Nepal Health Research Council
Background: Knowledge of normal renal volume is a vital parameter for clinical assessment of renal diseases because renal size is altered by various medical conditions. Variations in renal dimension in different populations and it’s relation to individual’s body parameters are evident. Different studies have recommended the need for measurement of renal dimension for specific population. This study assesses normal range of renal volume in the study population and measures their correlation with individual’s body parameters.Methods: This descriptive study was done in 261 adults. After renal length measurement on reformatted coronal images, renal width and renal thickness on axil images, renal volume was calculated by ellipsoidal formula. Descriptive statistics and parametric tests were used to evaluate the association between renal volume and different parameters.Results: This study showed a significant difference in mean renal volume between male (right and left mean renal volume 120.52 ± 26.84 cm3 and 121.00 ± 27.23 cm3 respectively) and female (right and left mean renal volume 110.11 ± 21.79 cm3 and 111.15 ± 22.34 cm3 respectively) on each side. Similarly, a significant positive correlation was found between renal volume and body height, body weight and BMI of participant for both kidneys however a significant negative correlation was observed between renal volume and age 40 years and above for both kidneys. Conclusions: This study provides morphometric data regarding normal kidneys and concludes that male renal volume is more than female and renal volume is correlated to individual’s body parameters.Keywords: Morphometric; nomogram; renal volume
Prabjot Kaur, Vaishnudebi Dutta, Bijay Lal Pradhan, Subhomoy Haldar, and Supriya Chauhan
Hindawi Limited
Several firms have become increasingly concerned with sustainability in recent decades and are thus implementing environmental and social changes in their businesses and supply networks. This article aims to assess suppliers based on green design, corporate social responsibility, energy consumption, and other sustainability factors that might aid the growth of a company. Characteristics used in this study will help to accomplish economic, environmental, and social responsibility for organizations to reduce global warming and natural resource depletion. We have used the data given in the article of Zolfani et al. by implementing Pythagorean fuzzy TODIM (an acronym in Portuguese for iterative multicriteria decision making) to calculate the rank of suppliers based on the Triple Bottom Line (TBL) sustainability framework. Both TODIM and PF-TODIM are simple to compute, stable, consistent, and accurate, but we have proved by calculations why Pythagorean fuzzy TODIM should be chosen over TODIM in such situations, where decision makers do not have access to a reliable data source. Finally, we performed a sensitivity analysis on both TODIM and PF-TODIM, and the results bolstered the utility of the model.
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CONSULTANCY
Statistical Data Analysis