DAM BREACH ANALYSIS AND PARAMETER SENSITIVITY ANALYSIS ALONG A RIVER REACH USING HECRAS

A dam break is a low-probability, high risk catastrophe event that is extremely destructive and has a substantial negative socio-economic impact on downstream and nearby areas. Simulating dam breach and analyzing flood propagation downstream from those events is vital for identifying and minimizing the risks associated downstream of dam location. This study is intended to anlayse the effect of overtopping failure of dam for two scenario  (a) base-case scenario (scenario with average value of dam breach parameters from their range) and (b) worst case scenario (the breach with largest geometry, shortest formation time and highest peak outflow magnitude). Further, a hydrodyanmic modelling is perfomed to  investigate  the sensitivity analysis (local and global) of five dam breach parameters (dam breach elevation, dam breach width, breach formation time, weir coefficient, trigger failure elevation) on breach outflow in a proposed hydropower project located in Nepal. Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS), Hydraulic Engineering Center River Analysis System (HEC-RAS) and OriginPro 2022b are utilized to analyse the effect of  dam breach and parameter sensitivity. Generation of outflow hydrograph shows that worst case scenario has devasting effect downstream with innudation of 1047 of househols and 50.83 kilometers of roads. The breach velocty was recorded as 15.16 m/s and 20.85 m/s for base and worst case respectively. The minimum depth and maximum depth of flooding downstream from dam location was found to be 24.51 m and 73.6 m for base case and 47.43 m and 106.75 m for worst case. Due to backwater effect at Bheri river, peak flow at 14 km downstream from dam reduces significantly to 124852.57 m3/s and 244204.41 m3/s for base and worst case respectively. From local sensitivty analysis it has been found that, dam breach elevation is more sensitive and triggering failure elevation is less sensitive for peak outflow hydrograph. Whereas, dam breach width seems more sensitive and TFE seems least sensitive for peak outflow using Monte Carlo Simulation for gloal sensitivity

Habitat suitability modeling of Asian Elephant Elephas maximus (Mammalia: Proboscidea: Elephantidae) in Parsa National Park, Nepal and its buffer zone

Asian Elephants Elephas maximus in Nepal are known to have habitats and movement corridors in Parsa National Park (PNP) and its buffer zone (BZ), located east of Chitwan National Park. A study was conducted in this area to assess the suitability of PNP and BZ as elephant use areas, and to determine factors relevant to the presence of elephants in PNP. Field measurements were carried out in 67 plots for vegetation analysis. Boosted Regression Tree (BRT) analysis was used to examine the relationship of habitat suitability and variables including topography (slope, aspect, altitude), climate (precipitation, temperature), habitat preference, ground cover and crown cover. The results indicate that elephant habitat suitability is mainly determined by the dominant plant species, temperature, altitude, habitat preference and precipitation. Slope, ground cover, crown cover and substrate have lesser effects. Elephants were recorded up to 400m in the northeast and southeast aspects of the study area. Most suitable habitats were low slope forest dominated by Acacia catechu and Myrsine semicerate that received 300mm annual precipitation. The model emphasizes environmental suitability, and contributes to knowledge for conservation of elephants in PNP and BZ by delineating sites that require specific planning and management.Peer reviewe

Experience in laparoscopic cholecystectomy in Nobel Medical College, Nepal

Objective: A laparoscopic cholecystectomy (LC) is the treatment of choice for gallbladder diseases. The aim of this study is to analyze laparoscopic cholecystectomies performed by a single surgeon over 8 years at Nobel Medical College Teaching Hospital and Research Centre Pvt. Ltd. of Biratnagar, Nepal (NMCTH). Methods: We performed a retrospective analysis of 7557 patients that underwent a LC by a single surgeon, from October 2010 to July 2018. We divided it into 3 groups that include data of every three years. Results: The hospital stay, operation time and conversion rates were decreased by years of experience. Female patients outnumbered male (M:F=1:3.7) patients in this study. The hospital stay (3 days), operation time (21 min), conversion (0.3%) and complications (1.9%) rates decreased from Group 1 to Group 3 (p<0.05). Conclusion: As surgical experience increased with a rising number of cases, the conversion rate, complications, hospital stay and mean operation time decreased. Key words: cholecystectomy, laparoscopy, elective, conversion, complications, mortality, gallstone, cholecystiti

Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans

Burden of tuberculosis and hepatitis co-infection among people living with HIV in Nepal: a systematic review and meta-analysis

People living with HIV (PLHIV) are prone to tuberculosis (TB) and hepatitis co-infections, which cause substantial burden on morbidity and mortality. However, data on the burden of HIV co-infection from a specific low- and middle-income country are limited. To address this gap in evidence, a meta-analysis of published literature and country surveillance report was conducted to estimate the burden of TB, hepatitis B (HBV) and hepatitis C (HCV) co-infection among PLHIV in Nepal. Twenty-three studies, including 5900 PLHIV, were included in the meta-analysis. The pooled prevalence of HIV-TB, HIV-HBV and HIV-HCV co-infection was 19% (95% CI, 10-28%), 3% (2-5%) and 19% (4-33%), respectively. Low CD4 cell count (pooled odds ratio [OR] 4.38, 95% CI 1.11-17.25), smoking (3.07, 1.48-6.37) and alcohol drinking (3.12, 1.52-6.43) were significantly correlated with HIV-TB co-infection. The odds of HCV co-infection was greater in PLHIV, who were male (5.39, 1.54-18.89) and drug users (166.26, 15.94-1734.44). PLHIV who were on antiretroviral therapy had a reduced risk of HCV co-infection (0.49, 0.36-0.66) than the general PLHIV population. The burden of TB and hepatitis co-infection among PLHIV in Nepal was high. Regular screening of PLHIV for co-infections and prompt initiation of treatment are essential to reduce the transmission of infection and improve quality of life

Fear, risk perception, and engagement in preventive behaviors for COVID-19 during nationwide lockdown in Nepal

The world has faced huge negative effects from the COVID-19 pandemic between early 2020 and late 2021. Each country has implemented a range of preventive measures to minimize the risk during the COVID-19 pandemic. This study assessed the COVID-19-related fear, risk perception, and preventative behavior during the nationwide lockdown due to COVID-19 in Nepal. In a cross-sectional study, conducted in mid-2021 during the nationwide lockdown in Nepal, a total of 1484 individuals completed measures on fear of COVID-19, COVID-19 risk perception, and preventive behavior. A multiple linear regression analysis was used to identify factors associated with COVID-19 fear. The results revealed significant differences in the fear of COVID-19 in association with the perceived risk of COVID-19 and preventive behaviors. Age, risk perception, preventive behavior, and poor health status were significantly positively related to fear of COVID-19. Perceived risk and preventive behaviors uniquely predicted fear of COVID-19 over and above the effects of socio-demographic variables. Being female and unmarried were the significant factors associated with fear of COVID-19 among study respondents. Higher risk perception, poor health status, and being female were strong factors of increased fear of COVID-19. Targeted interventions are essential to integrate community-level mental health care for COVID-19 resilience

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

STABLE PRICES IN B2B RELATIONSHIPS: HOW RESELLERS REACT TO LONGER-TERM PRICING POLICY FROM DISTRIBUTORS?

This dissertation examines whether and when longer-term pricing policies in B2B relationships can result in higher profits for selling firms (distributors) who set prices for the products they sell to buying firms (resellers). Further, this dissertation aims to identify buying firm-specific characteristics that might affect the attractiveness of longer-term pricing policies and seeks to find the conditions under which longer-term pricing policies can be made even more effective and profitable to both buying and selling firms. Drawing upon the “cost of price adjustment” (COPA) literature (Levy et al., 1997), this dissertation argues that firms incur huge costs just to adjust their prices which could affect their net margins. It is then argued that longer-term pricing policies offer resellers more value/utility/benefits through reduced costs which are ultimately reflected in the distributors’ profitability. However, since resellers differ by various characteristics (size, the extent of competition they are facing, etc.), not all resellers may find the longer-term policy equally attractive. This dissertation identifies some of these reseller-specific characteristics due to which some resellers might find the longer-term pricing policies more attractive than others do and provide suggestions that are more relevant to the selling firms. Utilizing a quasi-experiment from a sample that consists of all purchases from 2015 through 2017 by 8,987 business customers of the inside sales force of a global U.S.-based price-setting industrial distributor, this research shows that longer-term pricing policies are more profitable than short-term policies due to the reduced cost of price adjustments. Second, this research identifies certain reseller characteristics that might influence how resellers incur/perceive the costs of price adjustments and consequently affect the profitability of the distributor. Finally, this research identifies and recommends the distributors the ways to make the longer-term pricing policy more effective and profitable

Development of Climate Data Bias Corrector (CDBC) Tool and Its Application over the Agro-Ecological Zones of India

The use of global and regional climate models has been increasing in the past few decades, in order to analyze the future of natural resources and the socio-economic aspects of climate change. However, these climate model outputs can be quite biased, which makes it challenging to use them directly for analysis purpose. Therefore, a tool named Climate Data Bias Corrector was developed to correct the bias in climatic projections of historical and future periods for three primary climatic variables—rainfall, temperature (maximum and minimum), and solar radiation. It uses the quantile mapping approach, known for its efficiency and low computational cost for bias correction. Its Graphical User Interface (GUI) was made to be feasible to take input and give output in commonly used file formats—comma and tab delimited file formats. It also generates month-wise cumulative density function (CDF) plot of a random station/grid to allow the user to investigate the effectiveness of correction statistically. The tool was verified with a case study on several agro-ecological zones of India and found to be efficient