Srinivas Vadrevu, Fatih Gelgi, Saravanakumar Nagarajan and Hasan Davulcu
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Datta Bharadwaz Yellapragada, Govinda Rao Budda and Kavya Vadavelli
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…
Abstract
Purpose
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.
Design/methodology/approach
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 (NOx) 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.
Findings
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, NOx 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.
Originality/value
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 NOx except for HC emission which is 12.8%.
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Jhanvi Dass, Rajiv Yeravdekar and Ankit Singh
This study aims to assess the collective impact of social media engagement and anxiety due to COVID-19 on telemedicine adoption intentions with other constructs of the Technology…
Abstract
Purpose
This study aims to assess the collective impact of social media engagement and anxiety due to COVID-19 on telemedicine adoption intentions with other constructs of the Technology Acceptance Model (TAM) concerning anxiety linked to COVID-19 and the influence of privacy concerns on TAM constructs. These constructs encompass the perception of ease of use, perceived usefulness, one’s attitude toward telemedicine and the intention to utilize telemedicine.
Design/methodology/approach
A total of 178 comprehensive responses were gathered over a six-month period from residents in Mumbai, India, to examine the proposed model. The data was analyzed using software tools, including SPSS version 23 and IBM AMOS 21, to compute factor loadings, assess model fit, estimate path relationships and conduct hypothesis testing.
Findings
Privacy concerns with telemedicine usage had a significant negative impact on behavioral engagement (B = −0.20, SE = 0.08, p < 0.05) and positively impacted affective engagement (B = 0.25, SE = 0.06, p < 0.01). Similarly, anxiety due to COVID-19 had a negative impact on the perceived usefulness of telemedicine (B = −0.10, SE = 0.03, p < 0.05).
Research limitations/implications
This research addresses a void in the existing literature by merging the TAM and the Social Media engagement theory. This study reaffirms the impact of past and relevant experiences, privacy concerns and COVID-19-induced anxiety on various components of TAM, thus expanding and enriching the TAM model.
Practical implications
Healthcare administrators should implement strategies to alleviate privacy-related apprehensions associated with telemedicine platforms. Additionally, they should promote existing users to create and disseminate positive content to mitigate COVID-19-induced anxiety and foster meaningful engagement, thereby enhancing the willingness to adopt telemedicine.
Social implications
Providers and promoters of telemedicine platforms and services may lean toward employing digital marketing campaigns that rely on emotional persuasion, including tapping into the fear factor to boost subscription and service sales. Such practices raise ethical questions, underscoring the need for well-defined advertising standards to govern the marketing of these products.
Originality/value
This article is among the relatively rare studies that document the favorable influence of emotional engagement on the intention to utilize telemedicine, underscoring the significant role of emotions in shaping telemedicine adoption Intentions.
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Saikrishnan G., Jayakumari L.S. and Vijay R.
The purpose of this paper is to deal with the tribological study on the brake pads developed using various purity-based graphitized graphite.
Abstract
Purpose
The purpose of this paper is to deal with the tribological study on the brake pads developed using various purity-based graphitized graphite.
Design/methodology/approach
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.
Findings
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.
Originality/value
This paper discusses graphitized graphite of different purity influences brake pad's tribological performance by modifying tribo-films and reducing friction undulations.
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Rishabh Rathore, J. J. Thakkar and J. K. Jha
This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.
Abstract
Purpose
This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.
Design/methodology/approach
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.
Findings
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.
Practical implications
The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.
Originality/value
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.