Experimental investigation into the effect of magnetic fuel reforming on diesel combustion and emissions running on wheat germ and pine oil

© 2019 Elsevier B.V. All rights reserved.The present study aims to explore the effect of fuel ionisation on engine performance, emission and combustion characteristics of a twin cylinder compression ignition (CI) engine running on biofuel. Wheat germ oil (WGO) and pine oil (PO) have been identified as diesel fuel surrogates with high and low viscosities, respectively. High viscosity biofuels result in incomplete combustion due to poor atomisation and evaporation which ultimately leads to insufficient air-fuel mixing to form a combustible mixture. Consequently, engines running on this type of fuel suffer from lower brake thermal efficiency (BTE) and higher soot emission. In contrast, low viscosity biofuels exhibit superior combustion characteristics however they have a low cetane number which causes longer ignition delay and therefore higher NO emission. To overcome the limitations of both fuels, a fuel ionisation filter (FIF) with a permanent magnet is installed upstream of the fuel pump which electrochemically ionises the fuel molecules and aids in quick dispersion of the ions. The engine used in this investigation is a twin cylinder tractor engine that runs at a constant speed of 1500 rpm. The engine was initially run on diesel to warm-up before switching to WGO and PO, this was mainly due to poor cold start performance characteristics of both fuels. At 100% load, BTE for WGO is reduced by 4% compared to diesel and improved by 7% with FIF. In contrast, BTE for PO is 4% higher compared to diesel, however, FIF has minimal effect on BTE when running on PO. Although, smoke, HC and CO emissions were higher for WGO compared to diesel, they were lower with FIF due to improved combustion. These emissions were consistently lower for PO due to superior combustion performance, mainly attributed to low viscosity of the fuel. However, NO emission for PO (1610 ppm) is higher compared to diesel (1580 ppm) at 100% load and reduced with FIF (1415 ppm). NO emission is reduced by approximately 12% for PO+FIF compared to PO. The results suggest that FIF has the potential to improve diesel combustion performance and reduce NO emission produced by CI engines running on high and low viscosity biofuels, respectively.Peer reviewe

A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP

Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances

A Deep Learning Multi-feature Based Fusion Model for Predicting The State of Health of Lithium-ion Batteries

Lithium-ion batteries have become the preferred energy storage method with applications ranging from consumer electronics to electric vehicles. Utilization of the battery will eventually lead to degradation and capacity fade. Accurately predicting the state of health (SOH) of the cells holds significant importance in terms of reliability and safety of the cell during its operation. The battery degradation mechanism is strongly non-linear and the physics-based model have their inherent disadvantages. The machine learning method has become popular for estimating SOH due to its superior non-linear mapping, adaptive, and self-learning capabilities, made possible by advances in deep learning technologies. In this study parallel hybrid neural network is formulated for predicting the state of health of lithium-ion cell. Firstly, the factors that have an effect on the cell state were analysed. These factors are cell voltage, charging & discharging time and incremental capacity curve. The features were then processed for use as input to the model. Spearman correlation coefficient analysis shows that all the factors had a positive correlation with SOH. While charging time has a negative correlation with the other features. Next the deep learning models namely convolution neural network (CNN), temporal convolution network (TCN), long-short-term memory (LSTM) and bi-directional LSTM were used to make fusion models. The number of layers in CNN and TCN were also varied. The hyperparameters used in the models were optimized using Bayesian optimization algorithm. The models were validated through comparative experiments on the University of Maryland battery degradation dataset. The prediction accuracy with CNN 3-layer LSTM was found to be the best for the training and the test dataset. The overall R2 value, root mean squared error (RMSE) and mean absolute percentage error (MAPE) with the model was found to be 0.999646, 0.003807 and 0.3, respectively. The impact of the features on the model was also analysed by removing one feature and retraining the model with the other features. The effect of discharging time and the peak of the discharge incremental capacity curve was maximum. The analysis also reveals that either charging voltage or discharging voltage can be used. Further, the proposed model was also compared with the other studies. The comparison shows that the R2, RMSE and MAPE values of the proposed model was better

Optimization techniques applied on image segmentation process by prediction of data using data mining techniques

The research work presents an enhanced method that combines rule-based color image segmentation with fuzzy density-based spatial clustering of applications with noise (FDBSCAN). This technique enhances super-pixel robustness and improves overall image quality, offering a more effective solution for image segmentation. The study is specifically applied to the challenging and novel task of predicting the age of tigers from camera trap images, a critical issue in the emerging field of wildlife research. The task is fraught with challenges, particularly due to variations in image scale and thickness. Proposed methods demonstrate that significant improvements over existing techniques through the broader set of parameters of min and max to achieve superior segmentation results. The proposed approach optimizes segmentation by integrating fuzzy clustering with rule-based techniques, leading to improved accuracy and efficiency in processing color images. This innovation could greatly benefit further research and applications in real-world scenarios. Additionally, the scale and thickness variations of the present barracuda panorama knowledge base offer many advantages over other enhancement strategies that have been proposed for the use of these techniques. The experiments show that the proposed algorithm can utilize a wider range of parameters to achieve better segmentation results

Phenotypic, genotypic characterization and antimicrobial resistance profiling of uropathogenic Escherichia coli in a tertiary care hospital, Puducherry, India

Background and Objectives: Uropathogenic Escherichia coli (E. coli) (UPEC) accounts for 70-95% of community-acquired urinary tract infections (UTIs) and a significant proportion of nosocomial UTIs. This study aimed to characterize the phenotypic and genotypic characteristics of E. coli isolates from symptomatic UTI patients and evaluate their antimicrobial susceptibility patterns. Materials and Methods: A hospital-based observational study was conducted at Aarupadai Veedu Medical College and Hospital, Puducherry, India, from August 2022 to April 2024. A total of 106 UPEC isolates were obtained from symptomatic UTI patients. Antimicrobial susceptibility testing (AST) was performed using the Kirby-Bauer method, and virulence genes (hlyA, fimH, papC) were detected using PCR. Results: The mean age of patients was 49.7 years, with a female predominance (69.8%). Diabetes mellitus was the most common comorbidity (29.2%). Fever (60.4%) and dysuria (38.7%) were the most common symptoms. AST showed high susceptibility (>90%) to amikacin, nitrofurantoin, meropenem, and piperacillin/tazobactam, while >60% resistance was observed to cefotaxime and ceftazidime. Phenotypically, 30.2% of the isolates produced mannose-resistant hemagglutinins, and 17.9% produced hemolysin. ESBL production was found in 46.3%. Biofilm production was moderate in 65.1%, weak in 30.2% and strong in 4.7% and significantly correlated with multidrug resistance (p<0.05). Genotypically, 80.2% had fimH, 51.9% had papC and 20.8% had hlyA. papC was associated with reduced cefotaxime susceptibility (p<0.05). Conclusion: The study highlights the significance of phenotypic and genotypic characterization in understanding UPEC virulence and resistance patterns, and emphasizes the need for targeted empiric therapy to improve UTI management

A novel EGFR inhibitor, HNPMI, regulates apoptosis and oncogenesis by modulating BCL-2/BAX and p53 in colon cancer

Background and Purpose: Colorectal cancer (CRC) is the second most lethal disease, with high mortality due to its heterogeneity and chemo-resistance. Here, we have focused on the epidermal growth factor receptor (EGFR) as an effective therapeutic target in CRC and studied the effects of polyphenols known to modulate several key signalling mechanisms including EGFR signalling, associated with anti-proliferative and anti-metastatic properties. Experimental Approach: Using ligand- and structure-based cheminformatics, we developed three potent, selective alkylaminophenols, 2-[(3,4-dihydroquinolin-1(2H)-yl)(p-tolyl)methyl]phenol (THTMP), 2-[(1,2,3,4-tetrahydroquinolin-1-yl)(4-methoxyphenyl)methyl]phenol (THMPP) and N-[2-hydroxy-5-nitrophenyl(4′-methylphenyl)methyl]indoline (HNPMI). These alkylaminophenols were assessed for EGFR interaction, EGFR-pathway modulation, cytotoxic and apoptosis induction, caspase activation and transcriptional and translational regulation. The lead compound HNPMI was evaluated in mice bearing xenografts of CRC cells. Key Results: Of the three alkylaminophenols tested, HNPMI exhibited the lowest IC50 in CRC cells and potential cytotoxic effects on other tumour cells. Modulation of EGFR pathway down-regulated protein levels of osteopontin, survivin and cathepsin S, leading to apoptosis. Cell cycle analysis revealed that HNPMI induced G0/G1 phase arrest in CRC cells. HNPMI altered the mRNA for and protein levels of several apoptosis-related proteins including caspase 3, BCL-2 and p53. HNPMI down-regulated the proteins crucial to oncogenesis in CRC cells. Assays in mice bearing CRC xenografts showed that HNPMI reduced the relative tumour volume. Conclusions and Implications: HNPMI is a promising EGFR inhibitor for clinical translation. HNPMI regulated apoptosis and oncogenesis by modulating BCL-2/BAX and p53 in CRC cell lines, showing potential as a therapeutic agent in the treatment of CRC.Peer reviewe

Twist1 Suppresses Senescence Programs and Thereby Accelerates and Maintains Mutant Kras-Induced Lung Tumorigenesis

KRAS mutant lung cancers are generally refractory to chemotherapy as well targeted agents. To date, the identification of drugs to therapeutically inhibit K-RAS have been unsuccessful, suggesting that other approaches are required. We demonstrate in both a novel transgenic mutant Kras lung cancer mouse model and in human lung tumors that the inhibition of Twist1 restores a senescence program inducing the loss of a neoplastic phenotype. The Twist1 gene encodes for a transcription factor that is essential during embryogenesis. Twist1 has been suggested to play an important role during tumor progression. However, there is no in vivo evidence that Twist1 plays a role in autochthonous tumorigenesis. Through two novel transgenic mouse models, we show that Twist1 cooperates with KrasG12D to markedly accelerate lung tumorigenesis by abrogating cellular senescence programs and promoting the progression from benign adenomas to adenocarcinomas. Moreover, the suppression of Twist1 to physiological levels is sufficient to cause Kras mutant lung tumors to undergo senescence and lose their neoplastic features. Finally, we analyzed more than 500 human tumors to demonstrate that TWIST1 is frequently overexpressed in primary human lung tumors. The suppression of TWIST1 in human lung cancer cells also induced cellular senescence. Hence, TWIST1 is a critical regulator of cellular senescence programs, and the suppression of TWIST1 in human tumors may be an effective example of pro-senescence therapy

The global, regional, and national burden of adult lip, oral, and pharyngeal cancer in 204 countries and territories:A systematic analysis for the Global Burden of Disease Study 2019

Importance Lip, oral, and pharyngeal cancers are important contributors to cancer burden worldwide, and a comprehensive evaluation of their burden globally, regionally, and nationally is crucial for effective policy planning.Objective To analyze the total and risk-attributable burden of lip and oral cavity cancer (LOC) and other pharyngeal cancer (OPC) for 204 countries and territories and by Socio-demographic Index (SDI) using 2019 Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study estimates.Evidence Review The incidence, mortality, and disability-adjusted life years (DALYs) due to LOC and OPC from 1990 to 2019 were estimated using GBD 2019 methods. The GBD 2019 comparative risk assessment framework was used to estimate the proportion of deaths and DALYs for LOC and OPC attributable to smoking, tobacco, and alcohol consumption in 2019.Findings In 2019, 370 000 (95% uncertainty interval [UI], 338 000-401 000) cases and 199 000 (95% UI, 181 000-217 000) deaths for LOC and 167 000 (95% UI, 153 000-180 000) cases and 114 000 (95% UI, 103 000-126 000) deaths for OPC were estimated to occur globally, contributing 5.5 million (95% UI, 5.0-6.0 million) and 3.2 million (95% UI, 2.9-3.6 million) DALYs, respectively. From 1990 to 2019, low-middle and low SDI regions consistently showed the highest age-standardized mortality rates due to LOC and OPC, while the high SDI strata exhibited age-standardized incidence rates decreasing for LOC and increasing for OPC. Globally in 2019, smoking had the greatest contribution to risk-attributable OPC deaths for both sexes (55.8% [95% UI, 49.2%-62.0%] of all OPC deaths in male individuals and 17.4% [95% UI, 13.8%-21.2%] of all OPC deaths in female individuals). Smoking and alcohol both contributed to substantial LOC deaths globally among male individuals (42.3% [95% UI, 35.2%-48.6%] and 40.2% [95% UI, 33.3%-46.8%] of all risk-attributable cancer deaths, respectively), while chewing tobacco contributed to the greatest attributable LOC deaths among female individuals (27.6% [95% UI, 21.5%-33.8%]), driven by high risk-attributable burden in South and Southeast Asia.Conclusions and Relevance In this systematic analysis, disparities in LOC and OPC burden existed across the SDI spectrum, and a considerable percentage of burden was attributable to tobacco and alcohol use. These estimates can contribute to an understanding of the distribution and disparities in LOC and OPC burden globally and support cancer control planning efforts