Military Institute of Science and Technology

Due to the rising rates of injury, morbidity, and fatalities in motorcycle crashes, drivers in Bangladesh are now very concerned about their safety. On the other hand, empirical data regarding the severity of motorcycle crashes is lacking. This study aims to investigate the relationship between the motor cycle crash risk and unsafe riding behavior in Dhaka city. A structural equation model (SEM) has been developed with 318 normally distributed data to identify the motor cycle crash risk and unsafe riding behavior. The developed SE model is further validated by evaluating the responses by means of relative importance index (RII) approach. From the study, “Behavioral impact” latent variable has the most influence on motor cycle crashes. Under the “Behavioral impact” latent variable, “Aggressive riding”, “Violating traffic rules”, and “Breaking traffic rules” have the most significance impact on motor cycle crash risk and unsafe riding behavior. These findings can help the policy makers and stakeholders to minimize the motor cycle crashes and ensure roadway safety in Dhaka city.

The geographical location of the Sundarbans, lying along the Bay of Bengal, makes it vulnerable to various natural features such as climate change, sea-level rise, reduction of fluvial input, increase of salinity, etc. Temperature around this region has been observed to be rising leading to an increase in the surface water temperature in the Bay of Bengal along the mangrove forest. As a result, the local sea-level also has been rising which has become a growing threat for this region and is expected to lead to permanent land losses either by inundation or by erosion. In this study, the extent of impacts due to temperature variations along the coastline of Sundarbans was assessed by establishing a correlation between temperature, local sea-level rise, and coastal land changes. Landsat images, spanning from 1989 to 2018 were analyzed to observe the net morphological changes along the coastline. Long-term temperature and tidal water level variations were used to check their trends and finally establish a correlation among them for better understanding.

Debugging consumes a substantial portion of the software development lifecycle, yet the effectiveness of Large Language Models(LLMs) in this task is not well understood. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debugging on competitive programming problems and introduce DePro, a test-case driven approach that assists programmers by correcting existing code rather than generating new solutions. DePro combines brute-force reference generation, stress testing, and iterative LLM-guided refinement to identify and resolve errors efficiently.Experiments on 13 faulty user submissions from Codeforces demonstrate that DePro consistently produces correct solutions, reducing debugging attempts by up to 64

Single image deraining seeks to restore a clear scene from a single image affected by rain, a task that presents inherent challenges due to the spatially varying, anisotropic, and multiscale characteristics of rain streaks. The current convolutional neural network (CNN)-based approaches are efficient in preserving local texture but poor in long-range dependencies, whereas the Transformer-based approaches are more efficient to model global contextual reasoning at a high cost in computing time. Furthermore, most of the frameworks fail to consider explicit rain-layer models that can be of great structural help when restoring. This paper presents HybridDerainNet, which is a rain-layer guided two-stream CNN-Transformer network that consumes an estimated rain layer as auxiliary input to aid in rain-background separation. The suggested design combines fine-grained spatial preservation using residual convolutional blocks with a small self-attention block to preserve spatial fine details and to effectively combine global information in a simple U-shaped encoder-decoder structure. This design allows local–global representation learning to be balanced and it is computationally efficient. Extensive experiments on Rain1400 bench-mark showed that HybridDerainNet results in 32.78 dB PSNR and 0.93 SSIM in only 0.75 M parameters and 12.8G FLOPs, making a good trade-off between the quality of restoration and model complexity. These findings indicate that explicit rain-aware modeling and hybrid feature extraction is effective in the practical single image deraining.

The pharmaceutical manufacturing faces critical challenges due to the global threat of counterfeit drugs. This paper proposes a new approach of protected QR codes to secure unique product information for safeguarding the pharmaceutical supply chain. The proposed solution integrates secure QR code generation and encrypted data transmission to establish a comprehensive anti-counterfeit ecosystem. The protected QR codes encapsulate product information that cannot be identified using traditional QR code scanners which protect the information against replication and tampering. The system is developed with scalability in mind, which can be easily implemented without introducing any additional modification in the traditional supply chain.