Ranjan Deka, A.K. Pachauri and Bharat Bhushan
The purpose of this paper is to strive to develop a rock fall velocity model in C++ language and to give spatial attributes to the model using Geographic Information System (GIS…
Abstract
Purpose
The purpose of this paper is to strive to develop a rock fall velocity model in C++ language and to give spatial attributes to the model using Geographic Information System (GIS) capabilities. Interaction between the parameters involved in the model is evaluated through GIS embedded techniques.
Design/methodology/approach
The mathematical model developed in C++ is based on the physical law of gravitation pull, adjudging the potential fall between two points at different elevation. Further, parameters influencing the velocity gradient – namely local relief, coefficient of land use friction, slope amount and slope length – are incorporated in the model. GIS is used extensively to generate the data required for the model. GIS capabilities are also explored for visualisation and interpretation of the model output. Section profiles and a co‐relation coefficient further strengthen the velocity map.
Findings
The rock fall velocity map generated using GIS shows variations in the velocity gradient at selected sections. It is concluded from analysis that friction values play a pivotal role in drastically changing the velocity gradient.
Research limitations/implications
The model presented is restricted to rock fall velocity evaluation for a rectangular matrix of input data and spatial extent, rather than for specific locations. Incorporating parameters to delineate source areas and runout zones would produce a more realistic scenario. Trials along this line are in progress and are expected to be executed successfully very shortly.
Practical implications
The paper presents a versatile model with easily extractable parameters to compute rock fall velocity at a regional scale, conditioned for rugged terrain. The model has specific implications in infrastructure development and planning management for rocky terrain. Moreover, the model's output can be implemented effectively in preliminary investigations of the protection of forest development and erecting defensive measures in rock fall‐prone areas.
Originality/value
Not many models are available for rock fall velocity estimation on a regional scale. The model developed through this research work provides a platform for a regional‐scale study using parameters that can be easily derived from DEM and a land use map. It is reiterated that the model output is helpful for land planners and managers engaged in mountain development. The model is an effective tool in the strategic development of hazard management plans in slide‐prone areas.
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Shrawan Kumar Trivedi, Pradipta Patra, Amrinder Singh, Pijush Deka and Praveen Ranjan Srivastava
The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most…
Abstract
Purpose
The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.
Design/methodology/approach
The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.
Findings
Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.
Originality/value
While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
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Right to Information (RTI) is a formidable tool in the hands of responsible citizens to fight corruption and ensure transparency and accountability within a participatory…
Abstract
Right to Information (RTI) is a formidable tool in the hands of responsible citizens to fight corruption and ensure transparency and accountability within a participatory democracy. The RTI Act was promulgated in India in October 2005, and has fundamentally changed the power equation between the government and citizens. T.his chapter examines the contribution of the Act, in particular playing a significant role by providing information necessary to combat corruption in India. It is also noted, however, that RTI is not an unmixed-blessing as it is seen how costly it has been for zealous investigative journalists.
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Suvarna Hiremath, Ansumalini Panda, Prashantha C. and Srinivas Subbarao Pasumarti
Food and grocery, which accounts for around 60% of the overall retail market in India, is the most promising area for launching a retail firm. The objective of this research paper…
Abstract
Purpose
Food and grocery, which accounts for around 60% of the overall retail market in India, is the most promising area for launching a retail firm. The objective of this research paper is to conduct a thorough investigation of the impact of customers’ geographic, demographic and psychographic characteristics on the selection of retail store format choice behavior in the quickly growing Indian food and grocery retail industry, also to analyze the mediating role of store image on the store choice behavior.
Design/methodology/approach
A descriptive research design is used to collect data using the survey method and a structured questionnaire. The data collected from more than 400 food and grocery retail customers from neighborhood Kirana stores, supermarkets and hypermarkets in Karnataka, India, would be analyzed using both descriptive (mean and standard deviation) and Structural equation modeling (SEM) techniques. SEM techniques are used for validation of the model with independent constructs namely Demographics factors, Socio-Economic factors, Geographic factors, Lifestyle and Shopping Motives, a Mediating variable Store Image, and a dependent variable Store choice behavior. Partial least squares structural equation modeling (PLS-SEM) is used to examine the suggested theoretical framework.
Findings
The model is tested to reveal the impact of shoppers’ age, gender, occupation, education, monthly household income, family size, and distance traveled to the store, which all play a role in their retail format choice. Also, the socio economic and life style factors of shoppers influence their purchasing decisions as well; store image partially mediates between customer characteristics and store choice behavior.
Implications
The study has practical implications for food and grocery retailer in understanding customer behavior in the context of changing customer demographic and psychographic features in the Indian retailing sector. The findings aid retail merchants, allowing them to develop more successful retail marketing strategies and gain a competitive advantage.
Originality
This study could serve as a springboard for future research in this field. Retail marketers will benefit from the findings in terms of format creation and reorientation of marketing strategies in the shortest time.
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Shelza Dua, Sanjay Kumar, Ritu Garg and Lillie Dewan
Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete…
Abstract
Purpose
Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.
Design/methodology/approach
The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one.
Findings
These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India.
Originality/value
The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.