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The purpose of this research is to automatically separate and extract meta‐data and instance information from various link pages in the web, by utilizing presentation and linkage regularities on the web.

Research objectives have been achieved through an information extraction system called semantic partitioner that automatically organizes the content in each web page into a hierarchical structure, and an algorithm that interprets and translates these hierarchical structures into logical statements by distinguishing and representing the meta‐data and their individual data instances.

Experimental results for the university domain with 12 computer science department web sites, comprising 361 individual faculty and course home pages indicate that the performance of the meta‐data and instance extraction averages 85, 88 percent F‐measure, respectively. Our METEOR system achieves this performance without any domain specific engineering requirement.

Important contributions of the METEOR system presented in this paper are: it performs extraction without the assumption that the object instance pages are template‐driven; it is domain independent and does not require any previously engineered domain ontology; and by interpreting the link pages, it can extract both meta‐data, such as concept and attribute names and their relationships, as well as their instances with high accuracy.

The present work aims at improving the performance of the engine using optimized fuel injection strategies and operating parameters for plastic oil ethanol blends. To optimize and predict the engine injection and operational parameters, response surface methodology (RSM) and artificial neural networks (ANN) are used respectively.

The engine operating parameters such as load, compression ratio, injection timing and the injection pressure are taken as inputs whereas brake thermal efficiency (BTHE), brake-specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NO_x) and smoke emissions are treated as outputs. The experiments are designed according to the design of experiments, and optimization is carried out to find the optimum operational and injection parameters for plastic oil ethanol blends in the engine.

Optimum operational parameters of the engine when fuelled with plastic oil and ethanol blends are obtained at 8 kg of load, injection pressure of 257 bar, injection timing of 17° before top dead center and blend of 15%. The engine performance parameters obtained at optimum engine running conditions are BTHE 32.5%, BSFC 0.24 kg/kW.h, CO 0.057%, HC 10 ppm, NO_x 324.13 ppm and smoke 79.1%. The values predicted from ANN are found to be more close to experimental values when compared with the values of RSM.

In the present work, a comparative analysis is carried out on the prediction capabilities of ANN and RSM for variable compression ratio engine fuelled with ethanol blends of plastic oil. The error of prediction for ANN is less than 5% for all the responses such as BTHE, BSFC, CO and NO_x except for HC emission which is 12.8%.

The purpose of this paper is to deal with the tribological study on the brake pads developed using various purity-based graphitized graphite.

This paper deals with developing copper-free brake pads by using graphite as a key lubricant produced using a graphitization process with purity percentages (85, 90 and 95%). The brake pads were developed using traditional manufacturing processes and evaluated for their physical, chemical, thermal and mechanical properties as per industrial standards. Fade and recovery characteristics were analyzed using a full-scale inertia brake dynamometer as per JASO-C-406. The scanning electron microscope was used to analyze the worn surfaces of the brake pads.

The testing findings reveal that the brake pads with 95% graphitized graphite showed better shear strength with good adhesion levels and lesser density, hardness, acetone extract value, loss on ignition and higher porosity. Effectiveness studies of brake pads with graphite (95% graphitized) showed better results at higher pressure speed conditions than others because of better plateau formation and adequate lubrication.

This paper discusses graphitized graphite of different purity influences brake pad's tribological performance by modifying tribo-films and reducing friction undulations.

This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.

This paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.

The findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.

The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.

Specifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.