Zahid Akhtar Khan

This study presents a comprehensive experimental investigation into the performance and emission characteristics of a single-cylinder diesel engine fuelled with a novel MXene (Ti₃C₂) nanoparticle–biodiesel blended fuel. The primary objective was to explore the influence of four critical input parameters i.e. engine load (LD), compression ratio (CR), nanoparticle concentration (NC), and biodiesel blend ratio (BD) on key engine responses. The experimental matrix was developed using Taguchi’s L16 orthogonal array, encompassing sixteen unique test conditions to efficiently explore the input space while minimizing the number of trials. Five output responses i.e. brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and emissions of carbon monoxide (CO), unburnt hydrocarbons (HC), and nitrogen oxides (NOₓ) were selected for analysis. To address the multi-objective nature of performance and emission trade-offs, a hybrid optimization framework was adopted. The CRITIC (Criteria Importance Through Inter Criteria Correlation) method was utilized to assign objective weights to each output based on both their variability and interdependencies. These weights were integrated into the Weighted Aggregated Sum Product Assessment (WASPAS) method to compute a unified performance index for each experimental run, enabling simultaneous multi-response optimization. Further statistical analyses, including Signal-to-Noise (S/N) ratio and Analysis of Means (ANOM), were employed to identify the optimal combination of input parameters. The optimal configuration i.e. engine load at 100%, compression ratio at 17:1, nanoparticle concentration at 75 ppm, and a B40 biodiesel blend yielded the most favorable trade-off between performance enhancement and emission reduction. Results demonstrated that all four input parameters significantly influenced the output responses, with the MXene-enhanced biodiesel blend exhibiting superior performance compared to neat diesel. Specifically, the BTE increased notably, while BSFC and emissions of CO, HC, and NOₓ were substantially reduced. These findings suggest that the incorporation of MXene nanoparticles into biodiesel blends can offer a promising pathway towards cleaner combustion.

In the present study machining performance of a novel ABS P400 based EDM electrode is experimentally investigated. Three EDM parameters that is, current ( I), pulse on time ( T_on) and pulse off time ( T_off), each at three levels, are considered, and mild steel is machined using these electrodes according to the response surface methodology (RSM) based face-centred central composite design (FCCCD). Machining performances such as material removal rate ( MRR), tool wear rate ( TWR) and surface roughness ( SR) are measured. Experimental results are analysed using analysis of variance (ANOVA), response graphs and 3D surface plots. Current is found to be the dominating parameter for MRR, TWR and SR. The optimal combination of EDM parameter for multi-performance is determined using teaching-learning based optimization (TLBO) algorithm. The effectiveness of the new electrodes is established by comparing their machining performances with the already developed electrode at the same machining parameter setting. MRR resulting from the new electrode and also its TWR are found to be in good agreement with those of already produced electrodes. However, SR of the machined surface using new electrodes is found to be relatively higher.

: COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.

The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.

Laser powder bed fusion (LPBF) is an additive manufacturing technology which uses a heat source (laser) to sinter or fuse atomized powder particles together. A new layer of powder is spread over the previous layer using a roller, and then the laser power fuses them. This mechanism is repeated until the part model is completed. To reduce the time, effort, and cost, the present study incorporated the design of an experimental approach conjoined with finite element analysis (FEA) to simulate the LPBF process. A three-dimensional (3D) bi-material model was subjected to FEA with variations in temporal and spatial material characteristics. A Gaussian moving heat source model for the multi-scanning of a single layer was developed to understand the effect of process parameters, namely laser power, scan speed, and scan pattern on melt pool dimensions. Although, similar simulation models have been reported in the literature, the majority of these did not consider parametric variations. A few studies adopted multiple parameters which varied simultaneously, but the major limitation of these studies was that most of them did not consider multiple characteristics under a constrained environment. In the present research, the multi-parameter multi-level simulation study was performed to understand the process mechanism with fewer simulations. Results showed that the studied dimensions were sensitive to parameter setting, and that temperature variation within the melt pool was dependant on the material phase in the vicinity of the melt pool. This research proposed that melt pool dimensions must be accurately controlled for optimum process performance to achieve proper overlap between the adjacent scan lines and sufficient depth to complete bonding with the bottom layer. Since the involved criteria were of a conflicting nature, the problem of determining a single factor setting to obtain the desired results was solved using grey relational analysis (GRA). It was found that, among all the considered process parameters, scan velocity was the most significant one. This research recommended a maximum scan velocity i.e., v = 1.5 m/s, with a minimum laser power i.e., P = 80 W. In addition, it was also suggested that low energy density be used to melt the powder layer properly.

In this study, compacted hematite (Fe₂O₃) preforms were made and sintered at various temperatures, such as 1250 °C and 1300 °C, using both conventional and microwave sintering methods. The density, porosity, microhardness, cold crushing strength, microphotographs, and X-ray diffraction (XRD) analysis of the sintered preforms were used to evaluate the performance of the two sintering methods. It was found that microwave sintered preforms possessed lesser porosity and higher density than conventionally sintered preforms owing to uniform heating of the powdered ore in microwave sintering method. Furthermore, it was also observed that microwave sintered preforms exhibited relatively higher cold crushing strength and hardness than conventionally sintered preforms. Thus, the overall results revealed that microwave sintering yielded better properties considered in the present study.