Wan-Huan Zhou, Ankit Garg and Akhil Garg
Water balance is measured by transpiration, which has a significant impact on the performance of geotechnical infrastructure (vegetated slopes), ecological infrastructure…
Abstract
Purpose
Water balance is measured by transpiration, which has a significant impact on the performance of geotechnical infrastructure (vegetated slopes), ecological infrastructure (wetlands), urban infrastructure (green roof, biofiltration units) and agricultural infrastructure. Past studies have formulated models using analytical modeling to evaluate the transpiration index based on energy balance and suction. In circumstance of impartial and uncertain information about the root and shoot properties and its effect on the transpiration index, the present work aims to introduce the new optimization algorithm of genetic programming (GP) to quantify and optimize the transpiration index of plant.
Design/methodology/approach
The GP framework, having objective function of structural risk minimization, is used for formulating the transpiration index model. The statistical metrics with 2D and 3D analyses of the models are conducted to determine its accuracy and understand the transpiration process.
Findings
The model analysis reveals that the proposed model extrapolates the transpiration index values accurately based on five inputs. 2D and 3D relationships between the transpiration index and the five inputs suggest that the total root area has the highest impact on the transpiration index followed by shoot length and root biomass. There is not much impact of the shoot mass and stem basal diameter on the transpiration index. It was also found that the transpiration index increases with an increase in total root area and root biomass.
Originality/value
This work is a first-of-its-kind study involving the extensive computation analysis for quantifying and optimizing the transpiration index of the soil for the complex civil systems.
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Harsha Vardhan, Sanandam Bordoloi, Akhil Garg, Ankit Garg and Sreedeep S.
The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on…
Abstract
Purpose
The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).
Design/methodology/approach
The present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.
Findings
The performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.
Research limitations/implications
The explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.
Originality/value
This work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.
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Akhil Garg, Venkatesh Vijayaraghavan, Kang Tai, Pravin M Singru, Liang Gao and K S Sangwan
The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that…
Abstract
Purpose
The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues.
Design/methodology/approach
Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis (SA) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships.
Findings
The results indicate that the proposed Ensemble-MGGP model outperforms the standardized MGGP model. SA and parametric analysis reveals relationships and insights into vibratory finishing process.
Originality/value
Literature emphasises on characterization of vibratory finishing process using the experimental-based-studies. In addition, the issue of difficulty in model selection in genetic programming is addressed. This work proposes a new ensemble evolutionary approach to counter these issues.
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Ankit Garg, Akhil Garg, Wan-Huan Zhou, Kang Tai and M C Deo
For measuring the effect of crop root content on soil water retention curves (SWRC), a simulation approach (multi-gene genetic programming (MGGP)), which develops the model…
Abstract
Purpose
For measuring the effect of crop root content on soil water retention curves (SWRC), a simulation approach (multi-gene genetic programming (MGGP)), which develops the model structure and its coefficients automatically can be applied. However, it does not perform well due to two vital issues related to its generalization: inappropriate formulation procedure of the multi-gene model and the difficulty in model selection. The purpose of this paper is to propose a heuristic-based-MGGP (N-MGGP) to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content).
Design/methodology/approach
A new simulation approach (heuristic-based-MGGP (N-MGGP)), was proposed to formulate the functional relationship between the water content and two input parameters (soil suction and volumetric crop root content). The proposed approach makes use of a statistical approach of stepwise regression and classification methods (Bayes naïve and artificial neural network (ANN)) to tackle the two issues. Simulated data obtained from the models was evaluated against the experimental data.
Findings
The performance of proposed approach was found to better than that of standardized MGGP. Sensitivity and parametric analysis conducted validates the robustness of model by unveiling dominant input parameters and hidden non-linear relationships.
Originality/value
To the best of authors’ knowledge, an empirical model is developed that measures the effect of crop root content on the SWRCs. The authors also proposed a new genetic programming approach in simulating the crop root content dependent SWRCs.
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Akhil Garg and Kang Tai
Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to…
Abstract
Purpose
Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.
Design/methodology/approach
For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.
Findings
The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.
Originality/value
This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.
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N.S.B Akhil, Vimal Kumar, Rohit Raj, Tanmoy De and Phanitha Kalyani Gangaraju
Even the greatest developed countries have capitulated to the destructions imposed on the global supply systems, as the COVID-19 pandemic has revealed. The purpose of this study…
Abstract
Purpose
Even the greatest developed countries have capitulated to the destructions imposed on the global supply systems, as the COVID-19 pandemic has revealed. The purpose of this study is to explore human resource sourcing strategies for managing supply chain performance during the COVID-19 outbreak. There are six human resource sourcing strategies such as outsourcing, near sourcing, integration, the requirement of suppliers, joint ventures and virtual enterprise that are considered to measure supply chain performance.
Design/methodology/approach
Based on collecting data from the potential respondents of Indian manufacturing companies, the elevation of human resource sourcing strategies to supply chain performance is measured considering the multiple regression analysis techniques.
Findings
The results of the study revealed that four of the six hypotheses have a significant and positive relationship with supply chain performance during the COVID-19 outbreak while two hypotheses are partially supported that lent good support to this study.
Research limitations/implications
In this critical situation, this study will enable managers and practitioners to support the business in giving customers the best services on time.
Originality/value
The novelty of this study is to identify the key human resource sourcing strategies by using multiple regression analysis methods, considering the case of Indian manufacturing companies to measure their supply chain performance during the COVID-19 outbreak era.
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Akhil Khajuria, Modassir Akhtar, Manish Kumar Pandey, Mayur Pratap Singh, Ankush Raina, Raman Bedi and Balbir Singh
AA2014 is a copper-based alloy and is typically used for production of complex machined components, given its better machinability. The purpose of this paper was to study the…
Abstract
Purpose
AA2014 is a copper-based alloy and is typically used for production of complex machined components, given its better machinability. The purpose of this paper was to study the effects of variation in weight percentage of ceramic Al2O3 particulates during electrical discharge machining (EDM) of stir cast AA2014 composites. Scanning electron microscopy (SEM) examination was carried out to study characteristics of EDMed surface of Al2O3/AA2014 composites.
Design/methodology/approach
The effect of machining parameters on performance measures during sinker EDM of stir cast Al2O3/AA2014 composites was examined by “one factor at a time” (OFAT) method. The stir cast samples were obtained by using three levels of weight percentage of Al2O3 particulates, i.e. 0 Wt.%, 10 Wt.% and 20 Wt.% with density 1.87 g/cc, 2.35 g/cc and 2.98 g/cc respectively. Machining parameters varied were peak current (1-30 amp), discharge voltage (30-100 V), pulse on time (15-300 µs) and pulse off time (15-450 µs) to study their influence on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).
Findings
MRR and SR decreased with an increase in weight percentage of ceramic Al2O3 particulates at the expense of TWR. This was attributed to increased microhardness for reinforced stir cast composites. However, microhardness of EDMed samples at fixed values of machining parameters, i.e. 9 amp current, 60 V voltage, 90 µs pulse off time and 90 µs pulse on time reduced by 58.34, 52.25 and 46.85 per cent for stir cast AA2014, 10 Wt.% Al2O3/AA2014 and 20 Wt.% Al2O3/AA2014, respectively. SEM and quantitative energy dispersive spectroscopy (EDS) analysis revealed ceramic Al2O3 particulate thermal spalling in 20 Wt.% Al2O3/AA2014 composite. This was because of increased particulate weight percentage leading to steep temperature gradients in between layers of base material and heat affected zone.
Originality/value
This work was an essential step to assess the machinability for material design of Al2O3 reinforced aluminium metal matrix composites (AMMCs). Experimental investigation on sinker EDM of high weight fraction of particulates in AA2014, i.e. 10 Wt.% Al2O3 and 20 Wt.% Al2O3, has not been reported in archival literature. The AMMCs were EDMed at variable peak currents, voltages, pulse on and pulse off times. The effects of process parameters on MRR, TWR and SR were analysed with comparisons made to show the effect of Al2O3 particulate contents.
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Parlad Kumar Garg, Rupinder Singh and IPS Ahuja
The purpose of this paper is to optimize the process parameters to obtain the best dimensional accuracy, surface finish and hardness of the castings produced by using fused…
Abstract
Purpose
The purpose of this paper is to optimize the process parameters to obtain the best dimensional accuracy, surface finish and hardness of the castings produced by using fused deposition modeling (FDM)-based patterns in investment casting (IC).
Design/methodology/approach
In this paper, hip implants have been prepared by using plastic patterns in IC process. Taguchi design of experiments has been used to study the effect of six different input process parameters on the dimensional deviation, surface roughness and hardness of the implants. Analysis of variance has been used to find the effect of each input factor on the output. Multi-objective optimization has been done to find the combined best values of output.
Findings
The results proved that the FDM patterns can be used successfully in IC. A wax coating on the FDM patterns improves the surface finish and dimensional accuracy. The improved dimensional accuracy, surface finish and hardness have been achieved simultaneously through multi-objective optimization.
Research limitations/implications
A thin layer of wax is used on the plastic patterns. The effect of thickness of the layer has not been considered. Further research is needed to study the effect of the thickness of the wax layer.
Practical implications
The results obtained by the study would be helpful in making decisions regarding machining and/or coating on the parts produced by this process.
Originality/value
In this paper, multi-objective optimization of dimensional accuracy, surface roughness and hardness of hybrid investment cast components has been performed.
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Akhil Kumar and R. Dhanalakshmi
The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7…
Abstract
Purpose
The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.
Design/methodology/approach
The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).
Findings
The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.
Originality/value
This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.
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Strategic management and management of innovation and technology.
Abstract
Subject area
Strategic management and management of innovation and technology.
Study level/applicability
The course can be used for undergraduate and postgraduate students. The case would be relevant in the strategic management course to understand the concept of technology strategy and the various evaluation parameters guiding firms in their technology decisions. A refresher of the concept of value chain analysis can also be done through the case. It can also be used to teach innovation and technology management to understand the innovation process and the importance of various organizational factors for taking technology decisions.
Case overview
The case tries to bring together different aspects of technological innovation and technology strategy at North Delhi Power Ltd, Delhi which has taken various initiatives to turnaround the dilapidated power distribution industry in India. It details the various technological initiatives taken by the company to revamp the power distribution situation of the country. Discussion in the case also revolves around the technology decisions (technology strategy) taken by the company to drive the technological initiatives. The organizational culture supporting technology decisions and the technological initiatives are also woven into the case.
Expected learning outcomes
After a discussion on the case students will be in a better position to appreciate various decisions which firms take with respect to technology. They will get an understanding of what is technological innovation and about the technological innovation process. The importance of organizational factors to supplement technology decisions and innovation will be brought out in the case.
Supplementary materials
Teaching note.