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Study of Radiation Damage in Metallic Systems using Molecular Dynamics Simulations
No full textRadiation damage in the metallic materials is a stochastic event both in temporal and spatial scales. The cladding and the structural components of the nuclear reactors are exposed to extreme conditions like high-energy particle collisions (kilo electron Volt to Mega electron Volt range), elevated temperature, thermal shocks, and chemically corrosive environments. Consequently, the structural components suffer transmutation in metallic microstructure, mechanical behavior, and thermodynamic characteristics respectively. The nuclear power plants, reliability, sustainability, and safety of nuclear reactor components are major concerning factors. The real-time evolution of the initial radiation-induced defect caused under the impact of highly-energetic neutron or ionic bombardment, which originates within few picoseconds in atomistic range, is difficult to record and investigate. However, literature suggests computational modelling via molecular dynamics (MD) simulations is reliable and precise technique to study the underlying dynamic radiation-induced defect evolution mechanisms. The thesis work is dedicated to understand and draw insights of the evolution of various defects evolved within the irradiated metallic systems, for example, vacancies, interstitials, dislocations segments, Frank loops, point-defects clusters etc. The original contribution of this thesis includes the study of radiation response of the irradiated metallic systems and its alloys at varying grain boundary orientations, grain architecture, primary knock-on atom (PKA) energy magnitude, PKA direction, PKA positions from interfaces within crystallographic lattice and temperature regime closer to nuclear plant operating temperatures respectively. The amalgamation of the grain boundary (GB) engineering concepts to the radiation cascade simulations have been implemented in this research work. Nanostructured materials strongly arrest the radiation-induced defects as the grain boundaries serve efficient defect sink sites. Firstly, this thesis work includes comparative study of radiation damage of single crystal (SC) Cu specimen and two nanocrystalline (NC) specimens: hexagonal columnar grain Cu (CG Cu) specimen with Ʃ3 and Ʃ9 GBs and Cu specimen randomly oriented grain boundary (RG Cu) were irradiated at 600 K at primary knock-on atom (PKA) energy magnitudes, EPKA = 10 keV, 20 keV, 30 keV respectively. By investigating the evolution of point defects, defect cluster distribution, and dislocation analysis it was observed that the irradiated nanostructured Cu specimens survived with comparatively lower defects at the end of cascade simulations. Also, sessile dislocations with networks (consisting dislocation locks and loops) were observed in the irradiated SC Cu specimens. Secondly, we employed radiation-based molecular dynamics (MD) numerical simulations in bi-crystal Nb specimen with Ʃ 13, Ʃ 29 and Ʃ 85 symmetric tilt grain boundaries (STGB) models respectively at varying magnitudes of primary-knock-on atom (PKA) energies, EPKA = 10 keV, 20 keV, and 30 keV attemperature regimes: 300 K, 600 K and 900 K, respectively. This study reveals that Nb-Ʃ 29 GB model with highest misorientation angle survived with the lowest number of residual defects. Also, the recombination rate of the irradiated Nb specimens increases with the increase in temperature and PKA energy magnitude due to enhanced atomic mobility of the dislodged atoms. Third research work in the thesis includes study of irradiated Nb Ʃ 5 STGB model at two high-angled grain boundaries (HAGB) with misorientation angle: 53.13 ° (Ʃ 5(2-10)/ (120)) and 36.86 ° (Ʃ 5(3-10)/ (130)) respectively. Also, both the irradiated bi-crystal Nb models were compared with bulk Nb specimen. It is reported in the thesis that the Nb system with greater misorientation angle i.e. Nb Ʃ 5 (ɵ = 53.13 ° ) survived with lower number point defects at the end of cascade simulations as well as the population small-sized interstitial clusters. Also, in comparison to both the Nb Ʃ 5 STGB models, higher numbers of peak damage and residual defects were recorded in irradiated SC Nb. Finally, radiation simulation studies were carried to study primary radiation damage of the FeNiCrCoCu high entropy alloys (HEAs) with crystalline-amorphous (SC/MG) interface at varying PKA energies of magnitude, EPKA = 10 keV, 20 keV, and 40 keV and varying PKA positions from the interface respectively. In this irradiated nanolaminate specimen we observed significant recovery of the Frenkel pairs by the SC/MG HEA interface. The varying interatomic distance between the imparted PKA and the crystalline amorphous interface also plays role in trapping the defects. The synopsis of the present thesis work elucidates the study and futuristic application of nanostructured and nanolaminated metallic systems as favourable radiation tolerant material in next-generation reactors
An Experimental and Numerical Study on the Free Vibration and Viscoelastic Properties Identification of Sandwich Structures
No full textSandwich structures, known for their high strength-to-weight ratio and excellent mechanical properties, have become integral in various engineering applications. In recent years, there has been a growing interest in utilizing viscoelastic materials as cores in sandwich structures due to their unique mechanical characteristics of viscous and elastic nature. Understanding the free vibration behaviour of sandwich structures with a core of viscoelastic material is of primary importance for several reasons. First and foremost, these structures are prone to dynamic loading conditions in real-world scenarios, such as those experienced during transportation, impact events, or operational conditions in aerospace and automotive applications. Free vibration analysis provides insight into the natural frequencies, mode shapes, damping characteristics and the crucial parameters those govern the dynamic response of these sandwich structures. In the pursuit of a thorough understanding, the present research investigates the critical area of free vibration behaviour of three-layer sandwich structures, with a specific focus on beams, plates and shells. The primary objectives are to combine experimental and numerical methodologies, including the application of artificial neural network (ANN) modelling, to analyze the dynamic characteristics of sandwich structures incorporating a viscoelastic material core. The present study considers a symmetric three-layered sandwich structure with face layers of aluminium and a core layer of natural rubber. The choice of materials in the proposed sandwich structure, specifically aluminium as the face layers and natural rubber as the core layer, has been made to create an elastic-viscoelastic-elastic configuration. In the experimental phase of this study, three-layer sandwich specimens with a viscoelastic core are fabricated, and their modal characteristics are investigated through vibration tests conducted with the impact hammer method. The numerical models are developed using the finite element method (FEM), adopting the first-order shear deformation theory (FSDT) to extract the natural frequencies and modal loss factors of these sandwich structures. The mechanical properties of the viscoelastic core, such as the storage modulus and loss modulus, play a crucial role in determining the dynamic behaviour of the sandwich structures. These properties are often frequency-dependent, necessitating a thorough understanding of the material's viscoelastic nature for accurate modelling and prediction of the structural response. A significant focus of the current research is on the identification of viscoelastic material properties of the core layer in the sandwich structures. This has been achieved through the implementation of inverse techniques. Considering the experimental vibration test results of the sandwich structures, optimization methods are employed to identify the constitutive material properties of the viscoelastic core, ensuring that the numerical models accurately reflect the experimental observations. Cut-outs are common in practical applications, and understanding their influence on structural dynamics is crucial for designing resilient and efficient sandwich structures. The study extends its investigation to explore the impact of cut-outs on the free vibration response of sandwich plates and shells. A parametric study is conducted to analyze the effects of shape, size, and position of the cut-out on the dynamic behaviour of sandwich structures. Furthermore, ANN models are developed to predict the free vibration and damping properties of sandwich plates and shells with cut outs. The developed ANN-based prediction models exhibit excellent agreement with the experimental and numerical results, showcasing their efficacy in capturing the complex relationships of the system parameters. Additionally, a graphical user interface (GUI) is designed using the developed ANN models to provide a user-friendly tool for predicting the modal characteristics of the sandwich structures with cut-outs
Sustainable Development of Pavement Quality Concrete Utilising Fly Ash and Copper Slag
No full textCement concrete pavements are widely used for the construction of durable pavements. The paving industry in general faces problems of increased cost and availability of ingredient materials from natural resources. In modern pavement construction projects, the key priorities in today's world are conservation of natural resources, sustainable development, and reduction of environmental issues. Construction materials have a significant impact on sustainability due to the large demand for raw materials. The use of waste and recycled materials reduces natural resource usage. Utilising industrial waste in construction sectors reduces waste disposal difficulties, pollution, and cost of materials. Due to sustainability concerns, the construction sector is urged to utilise waste materials. In this context, waste materials like, fly ash, blast furnace slag, steel slags etc. have been investigated for their effective utilisation in cement concrete pavement. However, existing study merely explain the effects of combinations of such waste materials on the performance of cement concrete pavement. Thus, the present study is motivated to investigate the combined utilisation of fly-ash (FA) produced from coal-based power plants and copper slag (CS) produced from copper industry in making pavement quality concrete (PQC) as one of the sustainable solutions for construction of cement concrete pavement. This study is divided into four parts. The first part of the study focuses on the influence of FA concentration (10%, 20% and 30%) as replacement of ordinary Portland Cement (OPC), and CS concentration (20%, 40% 60%, 80% and 100%) as replacement of river sand (RS), on physical, mechanical and microstructural properties of M40 and M50 grade PQC mixes. Forty-eight different PQC mixes of M40 and M50 grades were prepared. The combined effects of CS and FA on fresh and hardened concrete properties such as workability, density, water absorption, and volume of voids, cube compressive strength, split tensile strength, flexural strength, cylinder compressive strength, and ultrasonic pulse velocity (UPV), are experimentally investigated. All mixes containing up to 20% FA and up to 100% CS replacements showed increased strength as compared with that of the respective control mix. However, PQC mixes with 30% FA showed decrease in strength properties with respect to the control mix. The PQC mix containing 20% FA and 60% CS resulted in highest strength properties at 90 days’ curing period. The X-Ray Diffraction (XRD) and scanning electron microscope (SEM) studies were employed for the characterisation of selected PQC samples. Further, multiple linear regression equations were established to predict all strength parameters. The PQC mixes made with FA and CS provide superior strength, reduce waste disposal problems, and preserve natural resources for future generations, making such developed mixes sustainable. The second part of the study focuses on the influence of different FA and CS concentrations on durability and microstructural properties of M40 and M50 grade PQC mixes. Various durability tests of all such PQC mixes included resistance to acid attack, resistance to sulphate attack, resistance to chloride attack, drying shrinkage, accelerated carbonation resistance, chloride resistance, sorptivity, slake durability and abrasion resistance. It was observed that the combined use of FA and CS in PQC mixes helps in reducing mass and compressive strength loss as compared with that of control PQC mixes when exposed to 5% sulphuric acid and 5% magnesium sulphate solution. PQC mixes with up to 20% FA and 60% CS showed less chloride ingress depth as compared with that of control PQC mixes. Beyond the stated concentrations the chloride ingress depth goes on increasing. Incorporation of FA and CS together in PQC mixes helps in reducing the drying shrinkage up to 90 days of drying. The carbonation resistance of PQC increased for PQC mixes with combined use of FA and CS. PQC mix with FA and CS showed better resistance to chloride ion penetration. Sorptivity value of PQC decreased with use of 20% FA and 80% CS. Addition of FA and CS improved the slake durability and abrasion resistance of PQC mix. The third part of the study focuses on the flexural fatigue performance of M40 and M50 grade PQC mixes containing varying quantities of FA and CS. For this study the PQC samples (100 mm × 100 mm × 500 mm) were prepared for 90 days’ flexural strength and fatigue life. A servo-hydraulic flexural fatigue testing apparatus was used for determining the fatigue life of PQC samples under four-point bending. A constant frequency of 1 Hz and stress ratios of 0.7, 0.8 and 0.9 were used for fatigue testing. The fatigue characteristics of PQC samples were evaluated in terms of fatigue life distribution. Three methods were used to estimate the Weibull distribution’s parameters. It is observed that the two parameters- Weibull distribution was fitted for the fatigue life distribution of both M40 and M50 grade PQC mixes made with FA and CS. Different failure probabilities have also been considered to estimate the fatigue of PQC mixes. Fatigue characteristics of paving concrete are found to have improved with use of FA and CS as replacement of cement and fine aggregate. The fourth part of the study focuses on the influence of different FA and CS concentrations on economic and environmental implications of M40 and M50 grade PQC mixes for construction of cement concrete pavement considering a typical example problem. The pavement slab made with M40 and M50 grade PQC mixes with FA and CS required a lower design thickness than that with control PQC mixes. For a constant design thickness, adding only CS to PQC mixes raised costs by up to 3.3% over the cost of the control PQC mix. However, PQC mixtures containing both FA and CS reduced the cost by up to 11.5%. The cost of the PQC pavement slab was analysed using the designed thickness and 1 Km length of pavement. It is observed that the PQC mixes containing waste materials having 20% FA and 60% CS (4F2c6 and 5F2c6) can reduce the PQC pavement slab cost by 34.2% and 28.5%, respectively, when compared to the pavement with control PQC mix. By incorporating FA and CS into PQC, the environmental parameters such as global warming potential (GWP) and embodied energy (EE) were reduced by 27% and 25.8%, respectively. It is concluded that using FA (20%) and CS (60%) results in the development of both M40 and M50 grade PQC mixes with superior strength, durability and fatigue properties along with most savings in cost. The approach of utilisation FA and CS can also offer environmental advantages by reducing environmental impact and minimising waste disposal issues. Additionally, it provides economic benefits by reducing costs and societal benefits by preserving natural resources for future generations. Therefore, the PQC utilising FA and CS can be sustainable and suitable for concrete pavement applications
Development of Efficient Image Upscaling Techniques
No full textImage upscaling is a popular topic in recent years, and it is used to create a high resolution (aHR) image from low resolution (LR) image data. An efficient image upscaling approach must preserve the original LR image’s edge information, texture, geometrical regularities, and smoothness while producing its HR counterpart. The most typical use of image upscaling is to improve the visual effect of a digital image after resizing it for displaying and printing. Image upscaling uses a variety of polynomial interpolation algorithms due to their low computational complexity and applicability for a wide range of real-time applications. A polynomial interpolation approach uses the weighted average or convolution of surrounding pixels to obtain the interpolated value at a specific place. This might cause blurring effects in upscaled images due to high frequency deterioration. This issue can be addressed by utilizing edge-directed algorithms that maintain high frequency information in an upscaled image for improved visual quality. Although edge-directed interpolation strategies are effective at preserving fine details and edge information in an image during upscaling, they are computationally more complex than polynomial interpolation schemes due to the usage of adaptive and local-based techniques. Most transform-domain interpolation algorithms in the literature produce blurring effects in upscaled images, particularly at edges and high-variance regions. Learning-based picture interpolation algorithms can produce high-quality results with fine features, but they often require significant computing resources and training data. This dissertation suggests pre-processing approaches to reduce blurring effects in upscaled images, incorporating an improved discrete cosine transform that recovers lost information due to upscaling using a bilateral filter. The weighted missing details are then integrated with the LR image before interpolation, resulting in less blurring in the high-variance region. However, in the following method, a higher order Laplacian filter is used to sharpen the edge presence in each direction of the degraded image in order to predict small details before combining them into an LR image. This strategy reduces blurring caused by interpolation. However, with iterative optimization sharpening, missing details are sharpened repeatedly using an optimal filter before interpolation, resulting in a better recovered HR image with more detailed information. However, an effective method of image upscaling is suggested here that combines iterative-back projection to reduce blurring effects with a convolutional neural network to retrieve both shallow and deep data separately. Some post-processing solutions have also been proposed, including an improved transform-domain approach that employs discrete sine transform upscaling to improve the quality of the HR image via difference image after upscaling. Another cubic B-spline spatial-domain approach involves sharpening degraded high frequency data and combining it with an upscaled image to get the restored HR image. Then comes a new technique: adaptive edge sharpening-optimized directional anisotropic diffusion, in which the smooth and edge areas are treated individually after upscaling to eliminate blurring effects and improve fine details. Several hybrid techniques have been developed to reduce the blurring effect in interpolated images. Hybrid approaches are developed by merging pre- and post-processing algorithms. In optimal local adaptive edge preserving spline, the high frequency details of an LR image are increased to compensate for blurring in the equivalent upscaled image. Furthermore, edge expansion is used to anticipate high frequency features with local statistics, preserving the edge contents in the upscaled image. However, the edge-error (EE) method uses the LR image’s edge to guide interpolation, whereas the edge-residual (ER) approach uses both the LR edge and lost information, followed by sharpening with a higher order filter. The restored HR image is generated by combining the sharpened image and the interpolated image
Conformations and Binding of Chondroitin Sulfate, Heparan Sulfate, and Hyaluronic Acid with CXCL8 in Aqueous Medium from Molecular Dynamics Simulation
No full textChondroitin sulfate (CS), Heparan sulfate (HS), and Hyaluronic acid (HA) are an important class of polysaccharides, widely known as glycosaminoglycans (GAGs). CXCL8 belongs to the chemokine family, one of their target proteins. The bound form of these molecules participates in various biological processes where sulfation patterns, charge density, and solvent reorganization around them bring massive heterogeneity and distinct topologies in the GAG conformations. The prime objective of this thesis is to explore the effects of sulfation on the conformations and binding of CS/HS/HA with CXCL8, the role of solvent, and understand the molecular mechanisms of the recognition process from the molecular dynamics (MD) simulation approach. The thesis comprises seven chapters. Chapter 1 includes a concise overview discussing the present state of knowledge, recent advancements in the field, and the methodologies employed in this thesis. Chapter 2 investigated the conformational properties of the disaccharide building units of CS, HS, and HA with varying degrees of sulfation position (except HA) in an aqueous medium at ambient temperature. The study revealed that although the flexibility of the disaccharide building blocks of the three GAGs is relatively different from each other, the increase in the degree of sulfation by one unit has limited effects on some of the properties of the molecules. Therefore, considering the GAG chain length in general, an in-depth study of the increased chain length of these molecules is necessary. As a result, in Chapter 3, the conformations of hexameric HA, CS(di-sulfated), and HS(di-sulfated) were studied in free forms and when bound with CXCL8 monomer thoroughly in aqueous medium at ambient temperature. The relative binding free energy of the complexes was computed to understand the feasibility of the process. Further, the kinetics of hydrogen bonds (HBs) involving conserved water in mediating the interactions between CXCL8 and CS/HS/HA were explored by adopting the Luzar-Chandler model. After notifying the heterogeneous effects of sulfation on the hexameric molecules, the impact of the degree of sulfation at more diverse positions on octadecasaccharide CS and HS molecules was carried out in Chapter 4 and Chapter 5, respectively. The binding of these molecules with CXCL8 dimer was explored, and the binding motif of the protein was identified. In these chapters, emphasis was given to identifying the preferred conformations and stability of different CS and HS molecules by adopting a k-means algorithm, constructing various free energy landscapes, and computing conformational entropy from dihedral flexibility. In Chapter 6 of the thesis, an attempt was made to understand the comparative binding phenomenon of the hexameric CS (di-sulfated) and HS (di-sulfated) molecules to CXCL8 monomer and dimer. The HB property and solvent contribution were investigated thoroughly to unfold the differential recognition phenomenon of these GAGs towards the CXCL8 monomer and dimer. The last chapter, Chapter 7 of the thesis, summarizes the important findings of all chapters as conclusions
On the Development of Hybrid Optimization Techniques for Containerized Cloud
No full textContainer-as-a-Service (CaaS) in cloud has emerged as a prominent cloud computing paradigm, providing developers with a convenient platform for deploying and managing containerized applications. In CaaS environments, efficient resource management is crucial for optimizing performance, minimizing costs, and ensuring the timely execution of tasks. Makespan, the total duration required to complete a set of tasks or jobs, is a critical metric for evaluating resource utilization and workload efficiency. This thesis explores on the development of makespan-aware resource management strategies tailored explicitly for containerized cloud. It also talks about the architecture of CaaS model and the basic ideas behind containerization, the advantages of resource isolation, scalability, and portability in CaaS when deploying and managing applications using containers. The main objective of this thesis is to propose different hybrid optimization approaches for minimizing makespan in the containerized cloud while maintaining the required Quality of Service (QoS). Improvements in resource use at the server and virtual machine levels help to achieve the goal. First, a meta-heuristic approach for load balancing in CaaS cloud is proposed to distribute incoming workload across available resources in a balanced manner, minimizing makespan and optimizing resource utilization. Next, a Fractional Pelican Optimization based VM sizing is proposed, which make use of Deep-ConvLSTM to minimize makespan, task rejection rate and response time. Then, a Fractional Pelican Hawks Optimization (FPHO) based container consolidation is proposed to enhance the system performance where energy consumption, resource utilization, SLA violations, and makespan are considered as the performance metric. Simulations show that all three approaches improve the performance of the containerized cloud system. This thesis enhances the state of the art through the following key contributions: A detailed survey of resource management strategy in containerized cloud An approach for load balancing in containerized cloud. An efficient VM sizing technique for hosting containers in cloud. A framework for consolidation of containers in the containerized cloud
Exploring Efficacy of Machine Translation System for Indian Languages
No full textIn era of digital globalization, the necessity of efficient intercommunication among people from diverse language backgrounds is growing exponentially. Machine Translation System (MTS) can be utilized to facilitate seamless communication among diverse linguistic communities. In general, Machine Translation (MT) is the process of translating text from one language to another without the need for human intervention. In consideration with the Indian environment, the development of a quality MTS for the Indian Languages (ILs) is in huge demand and a challenging task, since many ILs are treated as low-resource languages. As a result, the performance of MTS built upon these Indian Languages is not upto the mark. However, the vast linguistic diversity and socioeconomic significance of ILs, in India and abroad, serve as the driving forces for a quick attention on this domain. The variability in morphology, syntax, and semantic expression among ILs poses significant obstacles in the process of developing an effective MTS. Addressing such complexities, this thesis presents five important developments aimed at improving the quality and efficiency of MT systems for ILs. The first contribution presents an effective Statistical Machine Translation (SMT) system designed especially to translate text from English into eleven (11) Indian languages and vice-versa. To enhance translation quality and clean up the data, many data preprocessing techniques are utilized. The effects of distance-based reordering and Morpho-syntactic Descriptor Bidirectional Finite-State Encoder (msd-bidirectional-fe) reordering on ILs are analyzed. However, fluency and context were problems for SMT, which prompted the development of NMT models for ILs. Neural Machine Translation (NMT) system that can translate between English and eleven (11) Indian languages in both directions is developed in the second contribution. The Backtranslation (BT) is utilized for data augmentation to enlarge the dataset. Therefore, the influence of data augmentation on NMT for ILs is investigated to evaluate its efficacy in enhancing translation robustness and quality. Despite this, NMT systems are limited in their ability to translate low-resource languages since learning meaningful cross-language mappings requires enormous amounts of data. Hence, the third contribution investigates methods for translating from English into eleven Indian languages and vice versa using Multilingual Neural Machine Translation (MNMT). The MNMT is a technique for MT that builds a single model for multiple languages. It is preferred over other approaches, since it decreases training time and improves translation in low-resource contexts. To enhance translation quality in linguistically diverse contexts, MNMT models are integrated with several techniques, including pivot-based machine translation (MT), backtranslation, and language-relatedness. The fourth contribution expands the capability of MNMT to include intra-Indian language pairs. The effect of the grouping of related languages, namely, East Indo-Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI) on the MNMT model are examined. The role of pivot-based MNMT models in enhancing translation quality is investigated. Owing to the presence of large good-quality corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are built and examined. Furthermore, the effect of transliteration on ILs is also analyzed. To explore transliteration, the best MNMT models from the previous approaches (in most of cases pivot model using related groups) are determined and built on corpus transliterated from the corresponding scripts to a modified Indian language Transliteration script (ITRANS). The final contribution assesses how SMT, NMT, and MNMT systems for Indian languages are affected by subword tokenization techniques like SentencePiece, WordPiece, and Byte-Pair Encoding (BPE). These methods are essential for improving translation performance and managing morphologically complex languages. All the approaches vis-a-vis their contributions are evaluated using standard evaluation metrics
Quantifying the Impacts of Mining Activities on Vegetation Ecology using Integrated Remote Sensing Approaches
No full textUnderstanding the impacts of mining activities on vegetation ecology is crucial, as vegetation plays a vital role in ecosystem stability, biodiversity conservation, and ecosystem services such as carbon sequestration and soil erosion prevention. Mining activities often result in significant vegetation cover loss, leading to ecosystem disruption, loss of biodiversity, among others. So, this thesis attempts to assess and quantify the direct and indirect impacts of mining activities on vegetation ecology using remote sensing approaches. Firstly, Chapter-4 aims to quantify the contributions of mining-induced FCL to carbon sequestration loss (CSL) and carbon dioxide (CO2) emissions from 2000 to 2019 using the proxy datasets. For FCL analysis, the global FCL data at 30 m spatial resolution, developed by Hansen et al. (2013), was employed in the Google Earth Engine (GEE) cloud platform. Furthermore, for CSL and CO₂ emissions assessment, Moderate Resolution Imaging Spectroradiometer (MODIS)-based Net Primary Productivity (NPP) data and Zhang and Liang (2020)-developed biomass datasets were used, respectively. The outcomes of the study exhibited approximately 16,785.90 km² FCL globally due to mining activities, resulting in an estimated CSL of ~ 36,363.17 Gg CO2/year and CO2 emissions of ~490,525.30 Gg CO2. Indonesia emerged as the largest contributor to mining-induced FCL, accounting for 3,622.78 km² of deforestation, or 21.58% of the global total. Brazil and Canada followed, with significant deforestation and CO2 emissions. Besides, the relative FCL was notably high in smaller countries like Suriname and Guyana, where mining activities constituted a significant proportion of total deforestation. Furthermore, Chapter-5 of the thesis employs medium-resolution (30 m) Landsat-series satellite datasets to derive vegetation greenness trends at a regional scale during 1988 – 2020 using the Google Earth Engine cloud platform. The Mann-Kendall test is used to characterize vegetation greenness trends in mining-dominated regions of Eastern India, specifically Jharkhand and Odisha states, during two distinct study periods: an earlier period from 1988 to 2004 and a later period from 2000 to 2020. The study outcomes revealed negative vegetation greenness trends covering approximately 2.97% and 3.91% of areas in Jharkhand state, and 5.68% and 3.20% of areas in Odisha state during 1988–2004 and 2000–2020, respectively. Anthropogenic activities, particularly opencast mining, have emerged as primary drivers of vegetation degradation, contributing to approximately 3–5.7% of vegetation destruction during the study epochs. The negative vegetation greenness trends are prominently observed in areas affected by mining, settlement encroachments, construction sites, roadways, and logging activities. Moreover, in mining-affected regions, climatic factors exhibit a lesser influence on vegetation greenness trends, accounting for ~ 27%, as opposed to forested areas, where they contribute to around 47% of the variation. Chapter-6 aimed to assess the extent of vegetation cover loss and associated vegetation primary productivity dynamics in the Rajmahal Hills of Jharkhand, India, caused by mining activity. Leveraging datasets on Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Vegetation Transpiration (VT), we analyzed the effects of mining activities on vegetation dynamics and productivity. The findings of the study revealed a substantial loss of vegetation cover, with ~ 340 km² of land converted to mining areas and a corresponding expansion of mining sites by ~ 54 km² between 1990 and 2020. Decadal analysis highlighted specific periods of intensified vegetation loss, with notable declines observed during 2000–2010, 2010–2020, and 2000–2020, totaling 3.06 km², 8.10 km², and 22.29 km², respectively. The conversion of vegetation to mining areas resulted in significant reductions in GPP, NPP, and VT, with daily losses ranging from 0.01 to 0.09 tonnes of carbon (tC) for GPP and 1.25 to 7.27 tC for NPP, along with corresponding VT losses of 5200 to 30,190 mm/day during the specified periods. Chapter-7 explores the potential of multi-sensors optical satellites (i.e., Landsat, Sentinel-2, and PlanetScope) and in-situ datasets for foliar dust approximation model development. Furthermore, this study investigates the impacts of foliar dust on the vegetation biochemical (e.g., chlorophyll content) and physiological processes (e.g., carbon sequestration, vegetation transpiration, etc.) using multi-source satellite/gridded datasets. The study highlighted the efficacy of near-infrared (NIR) and shortwave infrared1 (SWIR1) bands, along with specific radiometric indices such as the Global Environmental Monitoring Index (GEMI) and the Non-Linear Index (NLI), in precise foliar dust estimation. The comparative analysis of satellite sensors—Landsat-9, Landsat-8, Sentinel-2, and PlanetScope—reveals Landsat-9 as a robust performer. The study underscores the potential of satellite data and in-situ measurements for foliar dust estimation at the satellite footprint scale with considerable accuracy. Conversely, a negative correlation between foliar dust and various physiological parameters, including gross primary productivity (GPP), evapotranspiration (ET), water use efficiency (WUE), and a positive correlation with leaf temperature was observed. On average, the GPP loss, reductions in ET, and reduction in WUE per gram of foliar dust deposition were estimated as ~ 2 to 3 grams of carbon (gC), ~ 0.0005 to 0.0006 mm/m2/day, and ~ 0.0121 to 0.0207 gC/kg H2O, respectively. Besides, for every additional gram of foliar dust per square meter, leaf temperature was increased by ~ 0.0376 – 0.0454 K. Finally, Chapter-8 evaluates the impacts of aerosols on vegetation greenness. The COVID- 19 pandemic (SARS-COVID-19) had a devastating impact on human health, lives, and the global economy. However, the lockdowns imposed as a preventive measure led to reduced economic activities, which unexpectedly had positive effects on the environment. In Chapter-8, we used satellite data (such as NDVI for vegetation greenness, SIF for plant productivity, and AOD for air pollution) along with weather data (temperature, rainfall, and sunlight) to examine changes in vegetation during the lockdown period across India’s biogeographic regions. We compared these changes with average conditions from 2017 to 2019. The results showed that vegetation greenness and productivity increased significantly during the lockdown, by 37% and 55%, respectively. While weather factors like rainfall, temperature, and sunlight did play a role, they could not fully explain the improvement. This suggests that the reduced human activity during the lockdown was a key factor. Notably, areas around mining clusters saw the largest improvements in vegetation health—up to 78% in coal mining regions, 63% in iron ore mining regions, and 41% in stone mining areas. In summary, the COVID-19 lockdowns seemed to boost vegetation growth and health. Overall, this thesis highlights the profound and far-reaching effects of mining activities on vegetation ecology. The findings emphasize the urgent need for sustainable development in mining-affected regions, where the focus should be on conserving ecosystems, protecting biodiversity, and maintaining essential ecosystem services like carbon sequestration and water regulation. In this regard, remote sensing-based approaches, with their ability to monitor and assess environmental changes over large areas and timeframes, proven to be invaluable tools. They enable data-driven planning and decision-making, which are crucial for promoting sustainable mining practices. By integrating such innovative approaches into policymaking, we can ensure a balance between economic development and environmental conservation, fostering long-term sustainability and resilience in ecosystems impacted by mining
Entrepreneurial Marketing and its Impact on SMEs’ Performance: Role of Innovation, Organisational Agility and Business Ties
No full textRecently the effectiveness of entrepreneurial marketing (EM) for small and medium enterprises (SMEs) has been largely discussed. Researchers have suggested unlearning administrative marketing and moving towards EM considering its effectiveness in the current volatile environment. Despite such large applicability and acceptability of EM for achieving success and competitive advantage, its research and practice remain sparse in developing countries. According to the report of IBM and Oxford Economies “Entrepreneurial India” 9 out of 10 SMEs fails within initial five years. Since SMEs account for more than 90% of Indian firms and contribute 38% of GDP, reducing the failure rate has become essential. Previous studies have explored the issue through fields like finance, owners’ characteristics, and human resources; however very few studies have explored the problems through marketing lens. On the other hand, majority of studies on EM and SMEs in the developed countries have considered them as “uniform object of analysis” (considers service, manufacturing, construction and retail based SMEs as same). However, the research findings may not be relevant or directly applicable across various industries due to their distinctive operations and offerings. Considering the gaps, this study aims to investigate the effect of EM on manufacturing-based SMEs, as this sector has been reported to be the most affected sector after aviation and tourism. The study propose to explore the distinctive strategies and operational activities followed by manufacturing-based SMEs operating in developing economies like India and their impact on performance. The study aims to analyse the mediating effect of innovative performance (relevant to compete against large organisation) and organisational agility (important to survive in uncertain environment) in the relationship between EM and manufacturing-based SME. The research also examines the moderating role of business ties in such relationships (mediated moderation). The result indicates that EM significantly impacts both innovation and SME performance. Furthermore, the innovative performance partially mediates the link between EM and SME performance. Likewise, the findings demonstrate that EM is positively related to OA and the performance of manufacturing-based SME However, the study reveals that BT negatively moderates the direct and indirect relationship between EM and SME performance
Design and Development of Current-Sensorless Peak Current Mode Controlled Switched Converters
No full textThis thesis focuses on designing an observer-based controller with an accurate state space averaging (SSA) model for peak current mode (PCM) controlled non-isolated and isolated converters, including the duty ratio dynamics as a state variable. The previous methods of state estimation using SSA approach didn’t consider the dynamics of duty ratio. Hence, the accuracy of state estimation in peak current mode control (PCMC) was a matter of concern. Here, the investigations are done for ideal DC-DC converters, and then, the possible external disturbances and parameter uncertainties are considered to provide an accurate state and disturbance estimation. With the aid of well-established singular perturbation theories, the difficulties encountered in slow-fast switched converters (SCs) systems have been analysed and the design of an observer-based feedback controller has been performed. The various current mode control (CMC) techniques are reviewed and it is found that the PCMC is efficient and widely used techniques in power electronics systems due to their excellent transient responses and inherent current limiting capabilities. However, it has been found that the classical PCMC still have some inherent limitations and challenges, especially the subharmonic oscillations and noise sensitivity due to the use of current sensors. It is, therefore, necessary to address these issues and adopt an appropriate analysis technique to design a fast and robust PCMC under parameter uncertainty and input fluctuations. To deal with these problems, a well-established singular perturbation theory and the basic concepts of disturbance observer have been discussed. Then an observer-based PCMC technique for DC-DC buck converter in continuous conduction mode (CCM) is presented to eliminate the requirement of exact current sensing. This has been achieved by performing the successive time-scale decomposition of the composite slow-fast SCs system operating under the inner PCMC and then transforming it to a reduced slow subsystem model by neglecting the fast mode. Based on this reduced model, we design a full order state observer (FSO) and analyze the closed-loop system performances with the outer-loop integral controller. The state-space analysis technique with state augmentation is also used to obtain satisfactory steady-state performances and the natural mode of transient oscillations under different input fluctuations. We show that dynamic performances of the proposed control technique are not only in good agreement with the simulation results and confirm the theoretical analysis, but also exhibit very similar performances of classical PCM controllers having the full advantage of accurate current reference tracking without sensing the current at peaks. Thus, this new approach allows for simpler and more accurate state estimation. A significant contribution of this thesis is the proposed state estimation technique combined with an equivalent input disturbance (EID) estimator. This approach not only accurately estimates the system state and unknown bounded exogenous disturbances but also effectively eliminates the noise. It proves highly effective across a wide range of load, input, and parameter variations, enhancing system performance and robustness. Thus, a new robust controller is designed for multiscale PCMC to estimate states and reject the disturbances of uncertain SCs, e.g., buck and boost converters. This has been achieved by performing successive time-scale decomposition of PCMC and transforming them into the reduced-order subsystem model. Based on this reduced-order model, an FSO is designed and the closed-loop system with a PI controller and an EID estimator is analyzed to obtain desired transient and steady-state performances. Here, the Bellman-Gronwall lemma is employed to investigate the robustness analysis using the upper norm bounds of the uncertainties. The gains of the observer/controller are designed based on this stability condition. Experimental results showed that the developed method is quite effective and superior to the PI controller, and it can exhibit very similar performances to a classical PCMC without sensing the current at peaks. Unlike averaged current estimation techniques, this approach inherently acts as a current limiter to protect the SCs from overloads and reduces the impact of sensor noise when fast-scale duty-ratio dynamics are stable. Thus, this method allows us for robust and more accurate state estimation techniques than possible with previous methods and facilitates the use of advanced current-sensorless control concepts in many other uncertain SCs. The proposed technique is then implemented on an isolated flyback converter (FC). A similar observer-based feedback controller is designed based on the reduced system equation of a peak current mode controlled flyback converter (PCMC-FC) operating in CCM. Here, the reduced system dynamics have been achieved by performing time-scale decomposition of the slow-fast FC system, and an FSO is designed using the SSA technique. We show that dynamic performances of the proposed control technique are not only in good agreement with the simulation results and confirm the theoretical analysis but also exhibit very similar performances to classical pcm controllers having the full advantage of accurate current reference tracking without sensing the current at the peaks. In addition, the closed-loop system performances have been analysed for uncertain plant parameters under the variation of external inputs and reference voltage. The robustness analysis using Bellman-Gronwall’s lemma, and their experimental confirmations revealed that the proposed control approach can effectively handle significant input disturbances such as the wide load or input voltage variations with uncertain plant parameters. This new approach thus allows for both simpler and more accurate state estimation than that of the previous methods and allows the use of advanced control concepts in many other SCs and applications