Rajshahi University of Engineering and Technology

• BTE improvement of 2.65% was observed for PPO 60 D 40 A 150 at 21 N load w.r.t. diesel. • BSFC reduction of 19.25% was noticed for PPO 30 D 70 T 150 at 21 N load w.r.t. diesel. • A significant reduction of CO (46.27%) and NO (71.30%) emissions was found for PPO 15 D 85 T 150 and PPO 60 D 40 T 150 , respectively. • The multiple regression model was statistically significant and capable of properly predicting engine performances and emission characteristics. • Overall, the ANOVA results confirmed the developed of regression model that provides a reliable prediction. The exploration of alternative fuels for internal combustion engines has gained significant attention due to the need to improve engine performance while reducing harmful emissions. In this context, this study investigates the performance and emission characteristics of a Petter diesel engine fueled with blends of pyrolytic plastic oil (PPO), diesel, and metal oxide nanoparticles, namely aluminum oxide (Al 2 O 3 ) and titanium dioxide (TiO 2 ). Fuel blends were prepared using a magnetic stirrer and homogenizer with PPO–diesel volumetric ratios ranging from 0% to 100% in 15% increments. Nanoparticles were added separately at concentrations of 75 ppm and 150 ppm. Experimental results show that the PPO 60 D 40 A 150 blend achieved the highest brake thermal efficiency (BTE) of 23.904% at a load of 40 N, representing an improvement of up to 2.65% compared with neat diesel at 21 N load. The PPO 30 D 70 T 150 blend exhibited the maximum reduction in brake-specific fuel consumption (BSFC) of 19.25% at 21 N load. Furthermore, PPO 15 D 85 T 150 achieved the highest reduction in carbon monoxide (CO) emissions of 46.27% at 30 N load, while PPO 40 D 60 T 150 demonstrated the maximum nitrogen oxide (NO x ) reduction of 71.30% at 40 N load. Al 2 O 3 nanoparticles at 150 ppm primarily improved engine efficiency by increasing BTE, whereas TiO 2 nanoparticles at the same concentration were more effective in reducing BSFC, fuel consumption rate, and exhaust emissions. These results suggest that nanoparticle-enhanced PPO–diesel blends are promising alternative fuels for diesel engines in heavy-duty applications, offering improved performance and lower environmental impact.

A connecting rod or connective rod is a very important part of internal combustion engines that connect the piston to crankshaft, which enables conversion of the reciprocating motion to another rotary motion. Connecting rods have traditionally been constructed out of forged steel because of its fatigue strength, although its density adds weight and decreases efficiency to engines. As the requirements for lightweight and fuel-efficient engines increase, so too do the negative influences of the mass of forged steel connecting rods leading to easier induction of inertial forces and loss of performance. The purpose of the research is to design and analyze connecting rod materials alternative to reduce weight, increase stiffness and fatigue life compared to conventional forged steel connecting rods. A connecting-rod was modeled parametrically in SolidWorks and assessed using SolidWorks Workbench finite element analysis (FEA). The paper compared Forged Steel to Titanium Alloy, Beryllium Alloy-25, Magnesium Alloy and Aluminum 360 such in stress strain, deformation, safety factor and fatigue life. Out of the tested material, Aluminum 360 had the lowest deformation (1.950e-05 mm), least stress (2.992e+04 N/m 2), greatest margin of safety and substantial weight reduction compared to forged steel. The results encourage the use of Aluminum 360 in place of forged steel used in two-wheeler engines since it provides better performance, efficiency, and a longer life.

The impact of climate change may pose a threat to the magnitude and occurrence of river flow. Efficient watershed management requires a rational approach to predicting future climate change impacts. The Nith River in Ontario, Canada has experienced frequent flooding as a result of extreme storm events every year or sometimes multiple times per year. The purpose of this study is to provide guidance on building watershed resiliency through flooding condition assessments and the use of climate scenarios for the watershed. Three future climate scenarios are assessed, to identify the most suitable one. The North American Regional Climate Change Assessment Program (NARCCAP) provided a scenario using Canadian Regional Climate Model and Canadian Global Climate Model version 3 (CRCM-CGCM3); another scenario involved Regional Climate Model version 3 and CGCM3 (RCM3-CGCM3), while the Ministry of Natural Resources (MNR) provided an additional scenario. This statistical analysis represents the best match between the observed and projected data of MNR. According to future climatic data, precipitation-runoff was calculated by a hydrological model which is known as the Soil and Water Assessment Tool (SWAT). Calibration and validation were performed to optimize the model's results. Bias factors were calculated from the observed period data and used to adjust future runoff estimates. This research showed significant changes in storm magnitude and frequency due to climate change, providing insights for watershed management and development. However, the results may vary, if different climate scenarios and precipitation-runoff models are used.

Rapid urbanization has increased municipal solid waste, making accurate garbage image classification essential for sustainable waste management. This paper proposes a high-accuracy garbage classification framework based on Efficient-NetB3 enhanced with a Convolutional Block Attention Module (CBAM) and a multi-scale feature fusion strategy. The model is evaluated on a 10-class garbage image dataset containing 19,762 images with a 60/20/20 train–validation–test split. Experimental results show that the proposed approach achieves 96% overall accuracy and a macro F1-score of 0.96, with consistently strong performance across challenging categories such as metal, plastic, and trash. Compared with standard CNN baselines, the proposed method provides improved accuracy while maintaining a reasonable model size. These results demonstrate the effectiveness of attention and feature fusion for robust garbage classification and highlight the proposed model as a strong baseline for future research and practical applications.

This study introduces a high-performance dual-channel surface plasmon resonance (SPR) sensor based on a photonic crystal fiber (PCF) platform, designed to detect analytes with refractive indices (RIs) both lower and higher than that of silica. The proposed configuration integrates two distinct analyte channels to achieve broad detection capability and enhanced structural adaptability. The first channel, positioned externally to the PCF, employs a gold layer to induce SPR excitation, whereas the second channel is embedded within the fiber at the centre. A gold film deposited along the inner wall of a neighboring air hole of the second channel works as a plasmonic medium. This indirect plasmonic configuration effectively eliminates direct contact between the metallic surface and liquid analytes, thereby mitigating oxidation and significantly improving the sensor’s operational stability. The optical performances of the sensor were numerically analyzed and optimized using the finite element method (FEM). Simulation outcomes reveal superior sensing performance, with the external channel exhibiting a maximum wavelength sensitivity (WS) of 16,000 nm/RIU and an amplitude sensitivity (AS) of 5030 RIU −1 , while the internal channel achieves 15,000 nm/RIU and 724 RIU −1 , corresponding to resolutions of 6.25×10 −6 RIU and 6.67×10 −6 RIU, respectively. The proposed sensor demonstrates wide detection ranges of 1.31–1.42 for the external channel and 1.44– 1.52 for the internal channel, enabling precise and reliable identification of analytes across diverse refractive indices. Owing to its wide sensing range, high sensitivity, and oxidation-resistant architecture, this dual-channel SPR sensor presents a promising platform for advanced applications in biochemical diagnostics, food quality control, environmental monitoring, and chemical analysis.

Abstract Norovirus is recognized as a pathogen with pandemic potential, exhibiting a higher fatality rate in low-income countries, particularly affecting young children. Currently, vaccine or specific antiviral treatment for norovirus is lacking. For evaluating antiviral compounds, this study was conducted by targeting viral protein 1 (VP1) and RNA-dependent RNA polymerase (RdRP) proteins through in silico approach, which included modeling protein, assessment stability, molecular docking, molecular simulation, non-covalent interaction (NCI) analysis, and pharmacokinetic profiling. Higher binding affinity revealed by molecular docking (-7.8 kcal/mol to -9.4 kcal/mol) for the ligands Zingiberol, Cardeonolide, Boeravinone B, Beta-Elemene, and Fisetin with VP1 and RdRp. The molecular dynamic simulation and subsequent analyses demonstrated significantly expected stable docked complexes of the protein-ligand in comparison to the ribavirin antiviral drug. Furthermore, these ligands exhibited acceptable drug-likeness and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) profiles. This study initially suggests that these compounds have potentiality as antiviral candidates, warranting wet lab experiment to be considered for norovirus infection.