INFLUENCE OF BUS BAY AND CURBSIDE BUS STOP IN AN URBAN ROAD

Purpose of Study: The purpose of this study is to investigate the efficiency of bus bay compare to the curbside bus stop in a midblock road segment of Dhaka city. Methodology: Vehicle composition and traffic volume were counted on-peak hours for the midblock of Azimpur road near the existing bus stop. Simulation models were developed in VISSIM, where Model 1 represented the existing road scenario with curbside bus stop, and Model 2 represented the same road segment with a bus bay. Main findings: The simulation result showed that Model 2 outperformed Model 1 due to the presence of bus bay. Comparing Model 1, travel time and delay reduced by varying 1.80% to 12.5% and 6.25% to 100% respectively in Model 2 during the simulation. Similarly, average speed increased by 1.39% and density decreased by 61.29% in model 2. Application of this study: Curbside bus stops result in abrupt halt, disrupt traffic flow, and queuing of the small-sized vehicle behind buses. These bus stops caused traffic congestion and delays in urban roads which can be alleviated by alternatives, such as, bus bay. The novelty of this study: The bus bay is a good alternative to the curbside bus stop, which can improve existing traffic conditions in urban roads

Short Duration Traffic Flow Prediction Using Kalman Filtering

The research examined predicting short-duration traffic flow counts with the Kalman filtering technique (KFT), a computational filtering method. Short-term traffic prediction is an important tool for operation in traffic management and transportation system. The short-term traffic flow value results can be used for travel time estimation by route guidance and advanced traveler information systems. Though the KFT has been tested for homogeneous traffic, its efficiency in heterogeneous traffic has yet to be investigated. The research was conducted on Mirpur Road in Dhaka, near the Sobhanbagh Mosque. The stream contains a heterogeneous mix of traffic, which implies uncertainty in prediction. The propositioned method is executed in Python using the pykalman library. The library is mostly used in advanced database modeling in the KFT framework, which addresses uncertainty. The data was derived from a three-hour traffic count of the vehicle. According to the Geometric Design Standards Manual published by Roads and Highways Division (RHD), Bangladesh in 2005, the heterogeneous traffic flow value was translated into an equivalent passenger car unit (PCU). The PCU obtained from five-minute aggregation was then utilized as the suggested model's dataset. The propositioned model has a mean absolute percent error (MAPE) of 14.62, indicating that the KFT model can forecast reasonably well. The root mean square percent error (RMSPE) shows an 18.73% accuracy which is less than 25%; hence the model is acceptable. The developed model has an R2 value of 0.879, indicating that it can explain 87.9 percent of the variability in the dataset. If the data were collected over a more extended period of time, the R2 value could be closer to 1.0.Comment: Shooting writing. Good researc

Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. Methods: The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. Findings: Global DALYs increased from 2·63 billion (95% UI 2·44–2·85) in 2010 to 2·88 billion (2·64–3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7–17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8–6·3) in 2020 and 7·2% (4·7–10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0–234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7–198·3]), neonatal disorders (186·3 million [162·3–214·9]), and stroke (160·4 million [148·0–171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3–51·7) and for diarrhoeal diseases decreased by 47·0% (39·9–52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54–1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5–9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0–19·8]), depressive disorders (16·4% [11·9–21·3]), and diabetes (14·0% [10·0–17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7–27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6–63·6) in 2010 to 62·2 years (59·4–64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6–2·9) between 2019 and 2021. Interpretation: Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. Funding: Bill & Melinda Gates Foundation

Investigating factors influencing pedestrian crosswalk usage behavior in Dhaka city using supervised machine learning techniques

Pedestrians are the most vulnerable road users and are over-represented in casualty statistics, particularly in low- and middle-income countries like Bangladesh. To ensure the safety of pedestrians, it is necessary to identify the factors underlying pedestrian behavior while crossing. Hence, this study aims to predict the pedestrian decision regarding crosswalks using supervised machine learning techniques namely, Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boost (XGBoost). A questionnaire survey was conducted in twelve important locations of Dhaka, Bangladesh using 8 attributes related to crosswalk behavior. Analysis suggests RF model is the most effective in terms of prediction performances, specifically having a 96.00% F1 score and 95.83% MCC value. It has been found that unsuitability of crosswalk location, absence of guard rails on median, and inadequate lightning at night near crosswalks are the most important features for preferring to use crosswalks. The findings of the study will help policymakers and transport planners to plan accordingly in order to develop safe crosswalks

Removal of Heavy Metals from Stormwater Using Porous Concrete Pavement

This study aimed to investigate the heavy metals, i.e. Cu, Pb, Ni, and Zn removal efficiency from stormwater runoff of a porous concrete pavement (PCP). A model of PCP was designed with the porosity and co-efficient of permeability of the pavement were 27.2% and 1.83 cm/sec, respectively. Artificial stormwater containing heavy metals are passed through the pavement at a constant rainfall rate to mimic the stormwater rainfall-runoff condition. The artificial stormwater infiltrated through the pavement were then collected at two different pavement layers at different time instances. From the experimental investigations, it is observed that Cu, Pb, Ni and Zn concentrations are significantly reduced in the treated stormwater. At the first collection point which is located below the sub-base layer and coarse sand layer of the pavement, the concentrations of Cu, Pb and Zn reduced 56%, 67% and 93% respectively compared to their initial concentration, Ni concentration reduced only 20%. At the second collection point which is located below the coarse and fine sand layers beneath the pavement, the concentrations of Cu, Pb, Zn, and Ni are reduced 92%, 89%, 100%, 100%, respectively

Machine Learning Based Approach for Automated Cervical Dysplasia Detection Using Multi-Resolution Transform Domain Features

Pattern detection and classification of cervical cell dysplasia can assist with diagnosis and treatment. This study aims to develop a computational model for real-world applications for cervical dysplasia that has the highest degree of accuracy and the lowest computation time. Initially, an ML framework is created, which has been trained and evaluated to classify dysplasia. Three different color models, three multi-resolution transform-based techniques for feature extraction (each with different filters), two feature representation schemes, and two well-known classification approaches are developed in conjunction to determine the optimal combination of “transform (filter) ⇒ color model ⇒ feature representation ⇒ classifier”. Extensive evaluations of two datasets, one is indigenous (own generated database) and the other is publicly available, demonstrated that the Non-subsampled Contourlet Transform (NSCT) feature-based classification performs well, it reveals that the combination “NSCT (pyrexc,pkva), YCbCr, MLP” gives most satisfactory framework with a classification accuracy of 98.02% (average) using the F1 feature set. Compared to two other approaches, our proposed model yields the most satisfying results, with an accuracy in the range of 98.00–99.50%

Potter′s sequence: A story of the rare, rarer and the rarest

Potter′s syndrome is a rare condition affecting one in 2000-5000. We present here two autopsy cases of Potter′s syndrome, with the rare finding of discoid adrenals and the even rarer finding of in situ neuroblastoma in one of the cases