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While rapid increase in demand for foods but limited availability of croplands has forced to adopt input-intensive farming practices to increase yield, there are serious long-term ecological implications including degradation of biodiversity. It is increasingly recognised that ensuring agricultural sustainability under the changing climatic conditions requires a change in the production system along with necessary policies and institutional arrangements. In this context, this chapter examines if climate-smart agriculture (CSA) can facilitate adaptation and mitigation practices by improving resource utilisation efficiency in India. Such an attempt has special significance as the existing studies have very limited discussions on three main aspects, viz., resource productivity, adaptation practices and mitigation strategies in a comprehensive manner. Based on insights from the existing studies, this chapter points out that CSA can potentially make significant contribution to enhancing resource productivity, adaptation practices, mitigation strategies and food security, especially among the land-constrained farmers who are highly prone to environmental shocks. In this connection, staggered trench irrigation structure has facilitated rainwater harvesting, local irrigation and livelihood generation in West Bengal. However, it is necessary to revisit the existing approaches to promotion of CSA and dissemination of information on the design of local adaptation strategies. This chapter also proposes a change in the food system from climate-sensitive to CSA through integration of technologies, institutions and policies.

The climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.

The investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.

The yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.

This study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

The purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.

In this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.

In this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.

For the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.

The purpose of this paper is to characterise the failure model of a system that is covered by a two‐dimensional warranty, one dimension depicting time and the other usage. Specifically, the authors study the effect of use‐rate on system life when each constituent component life is described by an accelerated‐failure‐time (AFT) type model.

The paper evaluates the effect of use‐rate on the expected failure time of a system having different internal configurations involving components. Firstly, the coherent structure as well as modules with redundant structures (standby and load‐sharing) are analysed, whereby component failures are assumed to be conditionally independent. Study systems of some basic configurations with a general dependence structure among component failure instances are also studied.

The results strongly indicate that, irrespective of the internal component‐configuration of the system, the models should posses the property that the expected failure time given the use‐rate is a non‐increasing function of use‐rate. The study also reveals that dependence among components arises quite naturally due to variability in use‐rate, even when all other factors of the usage environment are held constant.

The findings of this paper are the outcome of a theoretical investigation. Although they are supported by some real‐life cases as cited from the literature, it is important that they are validated through more examples.

The results would find a direct application in the analysis of two‐dimensional warranty policies as well as the reliability assessment of consumer durables.

The proposed approach to the characterisation of a bivariate failure model (indexed by time and usage) is quite realistic. This investigation may lead to the development of a more suitable model.

This research aims to explore the impact of augmented reality (AR), the digital technology that superimposes virtual elements in a real environment, on consumers in the context of experiential marketing. Specifically, this study proposes a research model based on the stimulus-organism-response model, which considers AR media characteristics as external stimuli, consumers’ value perceptions as the organisms, and purchase intentions as the responses. The research model was tested with 248 consumers using structural equation modelling. The results show that informativeness, ease of use, and telepresence have positive effects on consumers’ utilitarian value perception and that telepresence and interactivity have positive effects on hedonic value perception. Overall, this study contributes to the growing body of knowledge on AR and provides actionable insights for managers implementing digital transformation strategies and AR applications in marketing practices.

The purpose of this paper is to assess the present level of knowledge about disaster preparedness and mitigation among undergraduate medical students. Rarely a week goes by when a major disaster is not reported in the media – a disaster that results in death and destruction. There is a general reluctance among the people to accept that tragedy can appear any time in the form of a disaster. Unfortunately, disasters are seen more in context of emergency responses than pre‐planning or preparedness measures. Continuous preparedness saves lives, lessens personal suffering and loss and reduces the destruction of property and economic losses. Emergency medical assistance is the most important and immediate post‐disaster need, second only to search and rescue operations. Hence, knowledge about disaster preparedness and mitigation is essential for medical students.

A total of 375 undergraduate medical students who volunteered for participation were included in the study. A pre‐tested and pre‐designed, structured questionnaire was administered for assessing the current level of knowledge, attitude and practice about disaster preparedness and mitigation. The percentage marks were analyzed and compared for statistically significant difference.

The mean score was 8.77 percent, which was slightly higher in females and was maximum in age group 26‐30 years. There was little variation according to the year (professional) of the MBBS course.

The paper shows that undergraduate medical students have little knowledge about disasters and disaster preparedness.

The quality of production is an essential factor for the performance measure of a system; a casting process is the same section. It is a type of metal-forming practice in which the required shape of metal is acquired by pouring molten metal into the mold cavity and allowing it to solidify. Casting is done to provide strength and rigidity to the parts of a system for bearing mechanical impacts. The purpose of this paper is to investigate the various aspects which affect the casting process in the foundry industry, in order to optimize the quality of casting, with the assumption that sufficient repair facility is always available.

The considered casting system can have many defects such as the mold shift defect, blowhole defect, defect of shrinkage and porosity, defect of inclusion, defect of cold shut and much more. The studied system can be in three states during the process, namely, good state, failed state and degraded state. The system can repair after minor failures as well as a major failure. The average failure rates of various defects of the system considered as constant and repairs follow the general time distribution. The system is analyzed with the help of the supplementary variable technique and the Laplace transformation for evaluating its various performance measures in order to improve its performance/production.

This work provides a strong understanding of the casting industry, that which failure affects the production of casting and how much. For better understanding, the results have been demonstrated with the help of graphs.

In the present paper, a mathematical model based on the casting process in manufacturing industry has been developed.

This paper aims to review the industrial and biomedical applications of state-of-the-art fused deposition modelling (FDM)-assisted investment casting (FDMAIC). Brief literature survey of methodologies, ideas, techniques and approaches used by various researchers is highlighted and use of hybrid feedstock filament-based pattern to produce metal matrix composite is duly discussed.

Pattern replica required for investment casting (IC) of biomedical implant, machine parts, dentistry and other industrial components can be directly produced by using FDM process is presented. Relevant studies and examples explaining the suitability of FDMAIC for various applications are also presented.

Researches to optimize the conventional IC with FDM solutions and develop new hybrid feedstock filament of FDM done by researchers worldwide are also discussed. The review highlights the benefit of FDMAIC to surgeons, engineers and manufacturing organizations.

The research related to this survey is limited to the suitability and applicability of FDMAIC.

This review presents the information regarding potential IC application, which facilitates the society, engineers and manufacturing organizations by providing variety of components for assisting FDM. The information reported in this paper will serve doctors, researchers, organizations and academicians to explore the new options in the field of FDMAIC.

The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management.

In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management.

The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network.

This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain.

The process by which the system failure allowance is allocated in some logical manner among its subsystems is termed Reliability Allocation. Many methods are available for such an allocation for series system but no method exists in case the system is non‐series‐parallel. In this article, the optimum allocation of reliability among its subsystems for general non‐series‐parallel systems has been discussed by extending the Minimum Effort Method which in its present form is applicable for series systems only. A number of effort functions are listed with a view to finding one which is suitable for application in this method and the same has been used for further calculations.