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Go to Editorial ManagerBiodiesel produced from vegetable oils is a good alternative clean diesel. The present study was conducted because there are some variations or contradictions in literature on the use of CaO heterogeneous catalyst. In this study, biodiesel was produced from sunflower vegetable oil and methanol in presence of commercial calcium oxide catalyst in batch mechanical stirrer reactor. The effect of three operating conditions, methanol mole ratio (4-12), reaction time (0.5-2.5 h) and catalyst amount (2-10 %), on the yield of biodiesel was studied at constant reaction temperature of 60 oC. Response surface methodology (RSM) was used with central composite design (CCD) of experiments. Polynomial correlation was found for the dependent variable of the process (yield of biodiesel), satisfactorily predicted at 95% confidence level. The optimum yield biodiesel was about 98% and at operating condition of methanol ratio 10, reaction time 2 h and catalyst amount 8 %. The reaction time was found to be the most effective operating condition. Kinetics study of the process showed that first order reaction with triglyceride concentration and zero order with methanol concentration gave best fit with the experimental data, triglyceride with a reaction rate constant k= 1.53 h-1.
The standardization and preprocessing decisions determine the performance, reproducibility and generalizability of Artificial Intelligence (AI) models in cervical spine Magnetic Resonance Imaging (MRI). In this research, the author suggests a six-step standardized preprocessing pipeline that can help with the reliable analysis of heterogeneous MRI data using AI. It is obtained by the pipeline based on recurrent methodological patterns found in the literature, focusing on the operations reported to stabilize image intensity distribution and reduce unwanted sources of variability. A systematic literature review was screened on 2,882 records, and 43 articles were included per the inclusion criteria. Due to the large variability of imaging procedures, preprocessing plans, and reported outcomes metrics, no quantitative meta-analysis was performed and a qualitative synthesis was done. Basic methods such as segmentation, denoising, intensity normalization, augmentation, bias-field correction were consistently reported to improve AI performance. Studies evaluating tasks such as normalization and automated segmentation reported accuracies of 94% to 99.95%. Moreover, less-specific harmonization methods like Combatting Batch Effects (ComBat), z-score normalization and histogram matching, and Generative Adversarial Network (GAN)-based ones were also often attributed with lower scanner- and site-based variations. Regardless of these improvements, the AI workflow remains unstable because of the irregularity of predefined parameters, unequal standards of acquisition, and lack of methodological reporting. In efforts to deal with such challenges, the current work proposes a methodical six-stage preprocessing model that consists of quality control, noise removal, intensity normalization, anatomical segmentation, harmonization and validation. The suggested workflow model offers a practical and transparent basis of clinically translatable AI usage in cervical spine MRI based on conventional mathematical equations and clear documentation.
This study establishes a risk aware, multi objective methodology to select diesel plastic fuel α-terpineol blends that deliver robust engine performance across operating loads in a common rail direct injection diesel engine. Heterogeneous responses brake thermal efficiency, brake specific fuel consumption, oxides of nitrogen, carbon monoxide, hydrocarbons, smoke, and exhaust gas temperature are mapped to a unitless desirability scale and aggregated by load. Conditional Value at Risk across loads then prioritizes worst case behavior. By Conditional Value at Risk at level 0.20, the ranking is: diesel 0.242, diesel plastic fuel with 15% α-terpineol 0.197, 10% α-terpineol is 0.177, 5% α-terpineol is 0.157, diesel plastic fuel is 0.141, waste plastic fuel is 0.081. Relative to neat diesel plastic fuel, worst-case performance improves by 11% at 5% α-terpineol, 26% at 10% α-terpineol, and 40% at 15% α-terpineol, with the 100% load consistently governing the tail. Average brake specific fuel consumption decreases from 0.4553 to 0.4217 kg/kWh at 15% α-terpineol, average exhaust gas temperature falls from 321.4 to 293.0°C. Duty map mean oxides of nitrogen reduce from 512 to 474 ppm, hydrocarbons fall from 42.67 to 35.73 ppm. At full load, carbon monoxide declines from 0.52 to 0.46 %vol., and smoke opacity decreases from 52.28 to 49.71%. Within the tested window, 15% α-terpineol in diesel plastic fuel is the risk aware first choice, with 10% α-terpineol a near optimal alternative. The methodology remains stable under risk aversion and weighting changes at high-load calibration.