Forced Symmetry Breaking from SO(3) to SO(2) for Rotating Waves on the Sphere

We consider a small SO(2)-equivariant perturbation of a reaction-diffusion system on the sphere, which is equivariant with respect to the group SO(3) of all rigid rotations. We consider a normally hyperbolic SO(3)-group orbit of a rotating wave on the sphere that persists to a normally hyperbolic SO(2)-invariant manifold $M(\epsilon)$. We investigate the effects of this forced symmetry breaking by studying the perturbed dynamics induced on $M(\epsilon)$ by the above reaction-diffusion system. We prove that depending on the frequency vectors of the rotating waves that form the relative equilibrium SO(3)u_{0}, these rotating waves will give SO(2)-orbits of rotating waves or SO(2)-orbits of modulated rotating waves (if some transversality conditions hold). The orbital stability of these solutions is established as well. Our main tools are the orbit space reduction, Poincare map and implicit function theorem

Study of Biaxial Fatigue Behavior of Fiber Reinforced Polymers Under Tensile and Shear Loadings

Fiber reinforced polymers are used in many structural applications in the aerospace and automotive industries because of their high strength to weight and high modulus to weight ratios. In many of these applications, they are used as thin laminated panels comprising of multiple layers of continuous fibers embedded in a polymer matrix. In general, these laminates behave as an orthotropic material and their properties are direction-dependent. While their uniaxial static and fatigue characteristics have been studied extensively, their biaxial static and fatigue characteristics are not well established. One reason for this is the difficulty of conducting biaxial tests, especially under cyclic loading conditions. The objectives of the current research are two folds: (1) develop a biaxial test method that can be applied to a range of normal and shear loadings, and (2) study the biaxial fatigue behavior of a fiber reinforced polymer laminate using the new test method. The test method developed in this research is based on a butterfly-shaped Arcan specimen. The versatility of the Arcan specimen is that it can be utilized for testing materials under uniaxial normal loading, shear loading or a combination of in-plane normal and shear loadings. The laminate considered in this study was a [0/90/04/0]S Eglass/epoxy. Finite element analysis of a butterfly-shaped Arcan specimen was conducted first to establish its optimum geometry and delineate the importance of the stiffness of the test fixture on the stresses in the significant section of the specimen. An Arcan loading fixture was designed with the capability of loading of flat laminate specimens under various combinations of in-plane tensile and shear stresses. Quasi-static and fatigue tests were conducted with four different specimen configurations containing either 0, 30, 45 or 90o fiber orientations in the outer layers. The quasi-static strength followed a quadratic failure envelope on a normal stress-shear stress plane. Biaxial fatigue tests were conducted under combined tensile and shear stresses to determine the effect of biaxiality on the fatigue performance of the laminate. Development of fatigue damage under biaxial loading was also studied. A new fatigue life prediction model was proposed that can be used to account for the effect of biaxiality on the fatigue life of fiber reinforced polymer laminates.Ph.D.Automotive Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136076/1/Mandapati Final Approved Dissertation.pdfDescription of Mandapati Final Approved Dissertation.pdf : Dissertatio

Synthesis and Coordination Chemistry of Phenanthridine-Containing Monoanionic N^N-^N Pincer Motifs

This thesis involves a series of projects that explore the synthesis of phenanthridine-containing monoanionic N^N-^N ‘pincer’ motifs as ancillary ligands which can stabilize group 10 transition metals (Ni, Pd and Pt). Phenanthridines are 14π electron annulated tricyclic aromatic N-heterocycles with extended π-conjugation. Phenanthridines have been reported in the context of chemical synthesis, material synthesis, and catalysis, and show interesting photophysical and luminescence properties which find applications in bio-imaging techniques as fluorescent markers. Compared to its congener quinoline, however, phenanthridines have been rather underexplored in coordination chemistry. This thesis demonstrates how functionalized phenanthridines can be easily accessed in one-pot syntheses via palladium catalyzed Suzuki C-C coupling followed by condensation at high temperatures. With a route to functionalized phenanthridines with electron donating groups (Me, tBu) and electron withdrawing groups (CF3) in hand, they were incorporated into ‘pincer’ ligand frameworks using Buchwald-Hartwig C-N coupling, to isolate a series of phenanthridinyl/quinolinyl containing symmetric and asymmetric monoanionic N^N-^N proligands (L1-L16). My subsequent work then focused on examining the effects of systematic benzannulation in pincer-type ligands, through studies of the electronic, material and catalytic properties compared to quinoline in the presence of transition metals. To study these properties, a series of square-planar metal complexes of Group 10 metals (Ni, Pd and Pt) were synthesized containing both phenanthridines and quinolines. Platinum(II) complexes of phenanthridinyl/quinolinyl containing symmetric and asymmetric monoanionic N^N-^N ligands are photo-emissive in nature. These emissive complexes with electron donating groups (Me, tBu) and electron withdrawing groups (CF3) provided a platform to study the effect of site selective benzannulation and ring substituent effects on highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO) and their impact on absorption and emission properties. The results from these studies will be discussed in the following chapters. Although these complexes exhibit interesting photophysical properties, the complexes are relatively less soluble when compared to metal complexes of bis(quinolinyl)amine, in common organic solvents due to strong π-π stacking interactions, which hindered the opportunity to explore reactivity of the complexes. To overcome the issue of solubility, a new ligand design approach was made to synthesize proligands with solubilizing NMe2 groups that help break the planarity of their coordination complexes. Divalent nickel and palladium chloride complexes were synthesized with these proligands, the complexes were soluble in common organic solvents. Moreover, phenanthridine-containing nickel(II) chloride complexes of these more soluble ligands were found to be active catalyst for alkylation of azoles.  February 202

Hydrothermal Synthesis and Photocatalytic Efficiency of Turmeric Leaves Biochar/TiO2 Composite for Photooxidation of Congo Red Dye

The hydrothermal synthesis of an eco-friendly photocatalyst using turmeric leaves biochar (TLB) and titanium dioxide nanoparticle composite has been reported for the degradation of Congo Red (CR) dye. The synthesized composite was characterized using various analytical techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) analysis, Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS), confirming its structural and morphological attributes that ensured the uniform dispersion and intimate contact between TLB and TiO2 particles. The degradation performance of the TLB/TiO2 composite was examined by using UV light irradiation as a catalyst. The results demonstrated that the TLB/TiO2 composite exhibited superior photocatalytic activity compared to pristine TiO2, attributed to the enhanced light absorption, improved charge separation, and increased surface area provided by the biochar. The degradation kinetics followed a pseudo-first-order model, with a significant reduction in the dye concentration within a short time frame. The eco-friendly nature, cost-effectiveness, and high photocatalytic efficiency of the TLB/TiO2 composite highlight its potential application in wastewater treatment, thus providing a novel approach to valorize agricultural waste into functional materials for sustainable environmental technologies

Remote sensing leaf area index (LAI) data assimilation with crop model for yield predictions in rice

Crop yield estimation has gained prominent importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. Precise and timely crop yield estimates at regional, national and international levels is essential for making policy to overcome food security worldwide and helping farmers for crop insurance through insurance premium pricing by the companies. Rice is considered the major staple food which is having highest area and production in India. Telangana contributes to 4.49 % of rice area (1.9 million ha) and 5.54 % of production (6.25 million tons) with a productivity of 3176 kg ha-1. Several studies revealed that remote sensing technology had resulted in higher accuracy in crop growth monitoring with added advantage of high revisit frequency and precision. On the other hand, crop simulation models were also been recognized to assess the effects of different scenarios like climate change, drought, stress etc., on crop yield under varied climatic conditions. LAI is main criterion for evaluating the grain yield as it shows good correlation with the grain yield. There are lack of studies on comparing the ceptometer LAI to any crop model simulated LAI and also yields estimation at local level though they were done at a broad level like state or district. Hence this research was focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Optimization of crop cutting experiments were performed based on the criterion encompassing a wide range of potential combinations, further four villages each in Kharif and Rabi were selected for study and 15 fields were selected in each village for study. Ground data visits were planned according to the satellite passing dates and during the visits LAI readings in each field were collected using the LP-80 ceptometer. Supervised classification was performed using the ERDAS imagine. It has been noted that most of the area in the district was occupied by rice in both the seasons. Accuracy showed that overall accuracy of 94.23% and 88.5% was recorded, while kappa coefficient of 0.89 and 0.85 was resulted in kharif and rabi season respectively. On an average, kharif and rabi rice grain yields were 5324 kg ha-1 and 6436 kg ha-1 respectively in selected villages. The average simulated rice grain yield in kharif and rabi were 5339 kg ha-1 and 6858 kg ha-1 respectively with DSSAT model which considered sentinel-2 satellite for estimation of LAI. The R2 values of above 0.72 in kharif and above 0.85 in rabi, D index of 0.70 in both the seasons in all the villages showed the model is accurate for predicting yields. In both the seasons, correlation of above 0.8 was observed between observed rice grain yield with the quantity of nitrogen applied, whereas above 0.77 was noted between ceptometer measured and model simulated LAI. However LAI showed a good R2 of above 0.75 with the grain yield. Due to its strong correlation with LAI of above 0.80, the Normalized Difference Vegetation Index (NDVI) was selected as the critical element for integration with the model. Hence, it can be noted that NDVI is one among the important parameter which can be used to integrate with LAI for grain yield estimation. By utilizing the linear equation generated between the NDVI and model LAI a spatial LAI map was generated for the Karimnagar district. Further the linear equation developed between the model LAI and model grain yield, spatial yield map was generated. From the spatial yield map, it can be concluded that most of the areas fall under the rice grain yield range of 5700 to 6000 kg ha-1 in kharif, while in rabi in the range of 6500 to 7000 kg ha-1. These spatial mean yields for kharif and rabi were 5300 kg ha-1 and 6458 kg ha-1 which were then compared with the Telangana government statistics and it has been noted that a deviation of less than 10 %. Therefore, this study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers

The Role and Mechanism of Action of BRK in Tamoxifen-resistant Breast Cancer

The anti-Estrogen Receptor (ER) therapy Tamoxifen has historically been used as a first -line treatment against ER-positive breast cancer. However, 30% of Tamoxifen-treated tumours develop resistance against the drug (TamR). Breast Tumour Kinase (BRK), a tyrosine kinase, presents itself as a possible target to combat TamR resistance as it drives tumourigenesis in breast cancer cells. Previous research has shown that BRK knockdown re-sensitizes TamR cells to the drug, though the mechanisms behind BRK’s functioning in TamR have yet to be elucidated. To address this, I used a global phosphoproteomics approach to compare MCF7 cell lines, that differed in their sensitivity to Tamoxifen, and TamR T47D cells, that differed in BRK expression, and found a total of 1048 differentially expressed phosphopeptides. Pathway analysis revealed overrepresentation of the IGFR and insulin receptor signaling in both MCF7 and T47D TamR cells as well as when BRK was knocked down in T47D TamR cells. Specifically, BRK knockdown resulted in the inhibition of Insulin Receptor Substrate-1 (IRS1) through the hyperphosphorylation of the S1101 site and the hypophosphorylation of the Y896. Subsequent RT-PCR and ChIP-qPCR analyses revealed that both BRK knockdown and inhibition reduced downstream changes in cyclin D1 gene expression mediated by IRS1. To further identify BRK-specific targets, phosphotyrosine-enriched phosphoproteomics analysis was also conducted, comparing T47D Parental, T47D TamR and T47D TamR BRK knockdown cells. Out of 6492 phosphosites identified, 118 high -confidence phosphotyrosine sites were analyzed for significant changes in phosphorylation levels to identify differentially regulated pathways in TamR versus Parental cells and changes in these pathways when BRK is knocked down in TamR. Total proteomics analysis was then used to calculate the phosphorylation levels of these peptides relative to their total levels. Through this, I identified potential BRK-specific targets involved in TamR such as CDK1, GSK3-beta and catenin delta-1. Of these targets, I was able to validate that both the knockdown and inhibition of BRK in TamR cells resulted in the hypophosphorylation of both CDK1 and catenin delta-1 at the Y15 and Y904 phosphosites respectively. Overall, these findings indicate that BRK helps regulate TamR through its interaction with signaling intermediaries in the IGFR/insulin receptor signaling pathway

Field-level rice yield estimations under different farm practices using the crop simulation model for better yield

Crop yield estimation is essential for decision-making systems and insurance policy makers. Numerous methodologies for yield estimation have been developed, encompassing crop models, remote sensing techniques, and empirical equations. Each approach holds unique limitations and advantages. The primary aim of this study was to assess the accuracy of the DSSAT (Decision Support System for Agro Technology Transfer) model in predicting rice yields and LAI (Leaf Area Index) across various management methods. Additionally, the study sought to identify the optimal management practice for attaining higher yields. Crop models facilitate the expeditious evaluation of management strategies aimed at improving crop yield and analyzing the balance between production, resource efficiency, and environmental impacts. The study region selected for analysis is Karimnagar district of Telangana state. DSSAT has been chosen as the preferred tool due to its high efficiency in evaluating crop yield. The model's simulated yield was compared to the observed yield obtained from crop-cutting experiments. The results indicate a correlation of 0.81 and 0.85 between observed and simulated yields, as well as between model LAI and yield. An observation was made regarding a discrepancy between predicted and actual yields, which can be attributed to biotic stress. However, it should be noted that the current model does not account for this factor. The observed average yield was 5200 kg ha-1, whereas the projected yield was 5400 kg ha-1. The findings indicate that the model's performance is influenced by both the timing of sowing and the amount of nitrogen applied. The findings indicate that the DSSAT model has demonstrated a high level of accuracy in predicting both yields and leaf area index (LAI) across various management strategies. This study showcases the potential use of crop simulation models as a technology-driven tool to identify the most effective management strategies for rice production

Communication Strategies for Building Climate-Smart Farming Communities

Farming communities across the globe, especially in the drylands of Asia and Africa, are already facing the effects of climate change. With droughts, unseasonal rains and unpredictable dry spells becoming more frequent, reaching farmers with timely climate information and cropping advice is crucial as are coping strategies to face future climate shocks. There is also an urgent need to create, among farming communities, an awareness on reducing agricultural greenhouse gas emissions (GHGs) which contribute to a third of all human-generated GHGs. For this, holistic communication strategies that use best available technologies and target not just farmers but link all the stakeholders along the agricultural value chain are needed. Working for over 40 years in the semi-arid tropics with varied partners, ICRISAT has developed resilient dryland crops and a pool of climate-smart technologies besides researching on biofuels as alternatives for fossil fuels. These technologies are being implemented in locations across sub-Saharan Africa and Southeast Asia. This article records the various approaches used, lessons learnt and successes achieved in building climate smart villages/communes that restore/nurture the environment, use scientific innovations and climate information for cropping decisions, diversify livelihoods, link to markets, influence policy makers and ultimately make agriculture profitable

Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques

Lung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings