Purpose Although depression has adverse effects on all aspects of university students' quality of life, fewer studies have been conducted in Bangladesh; which was investigated herein. Design and Methods A cross‐sectional study was carried out among 1844 students enrolled at the University of Dhaka, Bangladesh. Hierarchical regression analyses were performed to investigate the explanatory power of the variables predicting depression in this population. Findings Depression prevalence was 28.7%; and female gender, first‐year student status, substance use, past‐year physical and psychological illness, stressful life events, family psychiatric history, and personal suicidal behaviors were the main risk factors. The final model considering all the studied variables explained 23.5% of the variance in depression. Practical Implications Effective psychological help services, awareness and intervention programs, and so on, should be implemented to reduce students' psychological burdens.
Wrong-way driving (WWD) has been problematic on United States highways for decades despite its rare occurrence. Since WWD crashes are rare, recent researchers have studied WWD non-crash events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. This paper demonstrates the regional nature of the WWD problem and proposes regional transportation systems management and operations (TSM&O) solutions to combat this problem. Specifically, it was found that 11% of all WWD multi-data events (e.g., multiple 911 calls for the same WWD event) involved travel from one county to another. Additionally, 30% of all WWD single-data and multi-data events occurred at or near interchanges between two limited access highways in counties with multiple operating agencies. This indicates that a significant proportion of WWD events could potentially travel from one limited access facility to another. Moreover, 28% of WWD events occurred on limited access facilities shared by multiple operating agencies. To emphasize the regional nature of WWD, this paper determined the vulnerable demographic groups in different regions of Florida by developing WWD crash and citation prediction models. The models’ findings indicate that certain demographic groups (e.g., elderly drivers) increase WWD risk. The models’ results can be used to improve driver education and increase law enforcement presence in high risk WWD locations. Regional TSM&O solutions, such as coordination and communication among agencies and traffic management centers (TMCs), law enforcement co-location with TMCs, and strengthening statewide TSM&O programs to manage WWD events are also proposed.
In this study, we developed a compartmental SIRD model to analyze and forecast the transmission dynamics of the COVID-19 pandemic in Bangladesh during the third wave caused by the Indian delta variant. With the help of the nonlinear system of differential equations, this model can analyze the trends and provide reliable predictions regarding how the epidemic would evolve. The basic reproduction number regarding the pandemic has been determined analytically. The parameters used in this model have been estimated by fitting our model to the reported data for the months of May, June, and July 2021 and the goodness of fit of the parameter’s value has been found by the respective regression coefficients. Further, we conducted a sensitivity analysis of the basic reproduction number and observed that decreasing the transmission rate is the most significant factor in disease prevention. Our proposed model’s appropriateness for the available COVID-19 data in Bangladesh has been demonstrated through numerical simulations. According to the numerical simulation, it is evident that a rise in the transmission rate leads to a significant increase in the infected number of the population. Numerical simulations have also been performed by using our proposed model to forecast the future transmission dynamics for COVID-19 over a longer period of time. Knowledge of these forecasts may help the government in adopting appropriate measures to prepare for unforeseen situations that may arise in Bangladesh as well as to minimize detrimental impacts during the outbreak.
Wrong-way driving (WWD) can result in severe crashes. By responding quickly to WWD dispatch calls, law enforcement officers (LEOs) could stop the wrong-way vehicle before a crash occurs. This paper analyzed law enforcement (LE) response times to WWD dispatch calls in Florida between January 2003 and April 2018 to determine significant effects. The average LE response time was much lower for 2013 onward than before 2013. Average response time was lower during nighttime and in urban areas and was higher for county roads and toll roads. Two ordinal logit models were also developed. These models found that dispatch calls closer to regional traffic management centers or rest areas, in urban areas, or on state roads or local roads typically had lower response times than calls not in these locations. In addition, WWD dispatch calls on toll roads had lower response times than calls on non-toll limited access facilities. Intelligent transportation system (ITS) WWD countermeasures with flashing signs, detection devices, cameras, and direct communication with traffic management centers also help LEOs respond quickly to detected WWD events and more accurately identify the vehicle’s location. As of June 2018, these technologies located at 70 toll road exit ramps in Florida have prompted 307 wrong-way drivers to turn around, possibly preventing nine crashes and saving LEOs over 116 h. The results of this research can help identify locations where increasing LEO presence or installing ITS WWD countermeasure technologies could help reduce WWD response time and WWD crashes, potentially saving lives.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.