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Nonlinear Dynamics and Energy Absorption Analyses of Auxetic Nanocomposite Structures
No full textThe present work deals with the modelling and analyses in order to study the nonlinear dynamic behaviours as well as energy absorption capacities of the different carbon nanotube (CNT) reinforced polymer composite (CNTRC) structures having negative Poisson’s ratio (NPR). This type of auxetic composite structures have many applications for energy absorption, impact resistance and fracture toughness in the various fields of engineering. Based on the classical lamination theory (CLT), the auxetic laminate has been designed by obtaining the special stacking sequences of layers in the thickness direction. The mathematical models have been formulated for determination of the effective out of plane negative Poisson’s ratios as well as material properties of different auxetic nanocomposite laminates of CNTRC-NPR, functional graded (FG) carbon nanotube reinforced composite having negative Poisson’s ratio (FG- CNTRC-NPR), hybrid nanocomposite having negative Poisson’s ratio (HBD-NC-NPR) and sandwich structures with various auxetic cores. A mathematical model is also developed for nonlinear dynamic analysis of the beams of such different auxetic nanocomposite laminates. The present formulations for nonlinear dynamic analysis are based on the Reddy’s third-order shear deformation theory and von-Karman nonlinear strain-displacement relations. The governing equations of motion of such auxetic structures are derived using the Hamilton’s principle. The obtained governing coupled nonlinear partial differential equations are converted to an ordinary nonlinear differential equation for the analysis of out-plane displacement using the Galerkin’s method. In the present study, three types of boundary conditions (such as clamped-clamped, simply supported and cantilever) of the beam are considered. The dynamic behaviours of the transverse displacements for the different auxetic nanocomposite structures under transverse excitation are analysed in terms of time series waveforms, phase portraits and Poincaré maps, and the routes of chaotic responses of the non-auxetic and auxetic structures have been found and reported based on the bifurcation diagrams. The jumps phenomena associated with the dynamics of the different non-auxetic and auxetic structures have also been presented and analysed. Based on the nonlinear dynamic analyses, the energy absorption (EA) capacities of the various non-auxetic and auxetic nanocomposite structures under shock loading have been determined and analysed. The effects of various important parameters (such as CNT/carbon fiber (CF) volume fractions, types of distributions of CNT volume fractions across the thickness direction of laminate, NPR, damping coefficient, amplitude of transverse excitation, and degree of nonlinearity) on the dynamics, energy absorption capacities and jumps phenomena have also been studied and reported
Minimization of Localization Error of Amorphous Algorithms for Wireless Sensor Networks
No full textThe exploration of wireless sensor network localization is a growing field of study. Achieving the precise location of sensor nodes is crucial for enhancing network longevity, expanding coverage, implementing geographical routing, and maintaining a congestion-free network. In this thesis, we introduced four range-free localization strategies aimed at minimizing the localization error associated with the conventional Amorphous algorithm. We conducted simulations and experiments to assess the effectiveness of these proposed schemes. A wireless sensor network (WSN) is made up of numerous tiny sensor nodes that are inexpensive, low-power, and need little computing. The purpose of these nodes is to collect essential environmental data in a uniform or random manner. To provide effective communication between known (beacon, anchor, reference) and unknown (dumb node), it is essential to determine the precise and accurate position of the sensor node. Rescue, traffic management and surveillance, underwater farming, target tracking, and many other uses for localization exist. In addition, people utilize it in their daily lives as a GPS to direct them while traveling. Therefore, the sensor’s precise location is required in order to use localization services accurately. An Ensemble approach consisting of a weighted Amorphous and DV-Hop algorithm is proposed in the first proposed work to reduce the localization error of traditional Amorphous algorithm. The distance between the unknown node and the beacon node is determined by two different distance measurements that consider hop value and hop size. To obtain the actual distance, probabilistic distance estimation is applied to the distances that were obtained. Lastly, the proposed Ensemble approach is compared with three different improved Amorphous algorithms and the conventional Amorphous algorithm. It is observed that the proposed approach provides higher accuracy in terms of MAE (Mean Absolute Error), MSE (Mean Square Error), and RMSE (Root Mean Square Error). Among all localization algorithms, Amorphous localization is highly suggested for usage in many application domains due to its simplicity, viability, low cost, and no additional hardware requirements. Position estimation of the dumb node in the Amorphous algorithm considers three different practical scenarios, such as the position of dumb nodes falling within the range of anchor nodes, in the opposite direction of the anchor node, and not within the range of the anchor node. However, the localization error for identifying the location of dumb sensor nodes by the Amorphous algorithm is high. To further address the limitations of the Amorphous algorithm, we have proposed a PSO-based Amorphous algorithm in our second work. The proposed work reduces the average hop size of anchor nodes and the localization error of the Amorphous algorithm. The simulation results demonstrate that, in comparison to other existing Amorphous algorithms, the proposed PSO-based Amorphous localization algorithm has a superior performance in terms of MAE, MSE, and RMSE. In the third work, the position error of the Amorphous algorithm is minimized by optimizing the hop size. For optimization of the hop size of the Amorphous algorithm, two different optimization algorithms, such as ALO and GWO, are considered. It is observed that the position errors of Amorphous-ALO and Amorphous-GWO are nearly the same. In order to determine the suitable optimization algorithm for Amorphous, the parameters such as minimum, average, and maximum execution times of Amorphous-ALO and Amorphous-GWO are considered for evaluation of the performance of the proposed algorithms. Whichever algorithm has lesser execution time is considered to be the suitable method for the localization of Amorphous. From the experimental outcome it is observed that the Amorphous-GWO takes less execution time than Amorphous-ALO; therefore, GWO is more suitable for optimization in Amorphous algorithm. In the fourth proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. To determine the coordinate of a single unknown node ten nearest beacon nodes are considered. The hop value and hop size of that unknown node are calculated from ten nearest beacon nodes and by using hop value and hop size, the weight is calculated. After estimation of weight the weighted sum is calculated. Using weighted sum, the coordinate of unknown node is determined. The simulation results demonstrate that, in comparison to other existing and proposed Amorphous algorithms, the proposed Weighted Centroid Amorphous localization algorithm has a superior performance in terms of MSE, and RMSE
Studies on the Synthesis of Rare Earth Elements-Based Nanomaterials for their Application to Remove Fluoride from Aqueous Medium
No full textWater is the lifeblood of our planet, sustaining all forms of life. However, the reckless use of freshwater by industries and individuals has resulted in a dire situation of water scarcity and contamination. Due to this overuse and the impact of climate change, groundwater is not being replenished at the same rate. The chemical composition of fresh water is now unfit for human consumption, as it is laden with a variety of harmful organic and inorganic contaminants. It is time to take action to safeguard the most precious resource - water. There are various inorganic contaminants, with fluoride being a major concern for many health institutions. Excessive fluoride beyond the permitted limit can lead to dental and skeletal fluorosis. Among the different techniques available for removing fluoride and other harmful ions, adsorption is preferred due to its ease of operation, cost-effectiveness, and efficiency. In this study, rare-earth-based nanomaterials (LCM, YCO, and CPP-2) had been synthesized to address the reported limitations. These nanomaterials based on cerium have effectively removed fluoride from aqueous medium, up to the permissible limit. Lanthanum cerate (LCM) was synthesized using the hydrothermal method, and it exhibited a ball-shaped morphology. It possesses a high specific surface area of 142 m2/g and efficiently removes fluoride, even in the presence of different coexisting ions. In another study, cerium-based yttrium cerate (YCO) was synthesized using Pechini’s method, showing a maximum Langmuir adsorption capacity of 324 mg/g. It can be reused up to five cycles, with a fluoride removal percentage of 72.9. Furthermore, a hybrid material, ceria polypyrrole (CPP-2), was synthesized. It is stable and easy to separate in aqueous medium, with a maximum Langmuir adsorption capacity of 351.8 mg/g. Chapter 1 delves into the concerning issue of fluoride-contaminated water and its profound impact on human life. The presence of excessive fluoride in groundwater is a widespread problem that affects numerous nations across the globe. This contamination stems from both natural geological factors and human activities. The detrimental effects of elevated fluoride levels in drinking water are far-reaching, leading to fluorosis, a condition that causes irreversible damage to bone and tooth tissues, as well as long-term harm to vital organs such as the kidney, liver, thyroid, and brain. Safeguarding potable water from fluoride contamination has long been a pressing priority. The process of adsorption has emerged as a cost-effective, efficient, and reusable method for defluoridation. However, it is crucial to further explore the commercial viability and reusability of adsorbents to minimize costs and waste. Additionally, the text explores various kinetic models and adsorption isotherm models to unveil the intricate mechanisms behind defluoridation. Chapter 2 discusses about the remarkable lanthanum cerate microspheres (LCM) and their extraordinary capacity to adsorb fluoride. Through meticulous batch studies following their synthesis using the hydrothermal approach, the remarkable efficacy of LCM was uncovered. The imaging techniques, including scanning electron microscopy (SEM) and transmission electron microscopy (TEM), provided captivating visuals of the ball-shaped microsphere morphology of LCM. The LCM adsorbent's optimal pH range of 3.5 - 4.5, coupled with its exceptional adsorption capacity across the pH spectrum of 3.0-7.0. The Langmuir adsorption isotherm model emerged as the superior descriptor of the adsorption isotherm, outshining the Freundlich isotherm model. At pH 4.0, the LCM adsorbent exhibited an unprecedented Langmuir adsorption capacity of 105 mg/g, surpassing all known adsorbents. The practical analytical approaches unveiled the adsorption mechanism, affirming the LCM adsorbent's prowess in eliminating fluoride. To top it off, the analysis in real groundwater solidified the adsorbent's exceptional efficacy. Through many practical analytical methods, we meticulously dissected the adsorption mechanism and conducted precise measurements, further validating the findings. Chapter 3 discusses about the formation and detailed analysis of a new adsorbing material, yttrium cerate (Y2Ce2O7), i.e., YCO, has been synthesized through the interaction of yttrium and cerium salts via Pechini’s method. Using batch experimental procedures, we assess the material's durability and selectivity as an intriguing adsorbent for defluoridation from both aqueous and genuine samples of water. SEM and TEM pictures, confirm the successful formation of porous YCO microspheres. The sample was found to be polycrystalline and the crystallite size was 13 ± 1 nm. The surface area of YCO was found out to be 54 m2/g exhibiting its porous nature. Fluoride was found to successfully bind to the YCO adsorbent which was further validated by FTIR, EDAX and XPS analysis. Defluoridation was barely impacted by the coexisting anions. An analysis of fluoride adsorption kinetics has revealed other rate- limiting steps other than the intraparticle diffusion model. At room temperature, the maximum Langmuir capacity of adsorption observed was 323.9 mg/g. For a practical outcome, the material's reusability was tested for up to five iterations in a row. In Chapter 4, the stability and effectiveness of a hybrid material in removing fluoride from drinking water is discussed. A new hybrid composite, CPP-2, was created using in situ oxidative polymerization and Pechini's method of CeO2 (CO). Various physicochemical techniques were used to analyze the properties of the adsorbent in its original state. The hybrid composite effectively removed F- ions from the effluent, with the adsorption isotherm best fitting the Langmuir model. The maximum adsorption capacity at room temperature was found to be 351.8 mg/g at a pH of 4 ± 0.2. Kinetic data indicated that the fluoride adsorption process followed a pseudo second-order kinetic model. To understand the defluoridation mechanism, FTIR and XPS spectra were examined, showing interactions between the polypyrrole moiety's nitrogen atoms and F- ions, exchange of hydroxyl groups with F-ions, and electrostatic attraction of protonated hydroxyls on the adsorbent surface. Thermodynamic data revealed an endothermic, spontaneous, and feasible adsorption process, with the capacity to endure five cycles of adsorption and desorption. Overall, the data demonstrate that CPP-2 has great potential for defluoridation from groundwater below WHO-specified pH thresholds. Chapter 5 discusses the conclusion and future scope of synthesized materials for the removal of fluoride. Excessive fluoride in drinking water can lead to various fluoride-related illnesses in the community. It is important to note that the World Health Organization recommends a maximum of 1.5 ppm of fluoride in drinking water. Globally, almost 80% of drinking water comes from groundwater sources. Researchers have developed various methods and materials to remove fluoride from water, with adsorption being the most practical, economical, and sustainable option for community use. Other methods include osmosis and ion exchange. While many researchers have synthesized adsorbents with significant defluoridation potential, practical success has been limited for various reasons. In this study, a simplified laboratory approach was used to synthesize and assess a new type of adsorbent, drawing from literature reviews and other accessible references. The focus was on synthesizing cerium-based metal oxide/hybrid materials with excellent adsorption capacity for defluoridation. The ideal characteristics of the material include high adsorption capacity, stability, affordability, and ease of use for fluoride removal. Consequently, Ce-based materials were synthesized, characterized, and tested to evaluate their effectiveness in removing fluoride
The Detailed Insight on a Novel rspA Gene of Salmonella Typhimurium and its Role in Modulating Bacterial Pathogenesis
No full textSalmonella infection is a major public health concern worldwide, which causes huge mortality and morbidity in developing and developed countries. Even though much has been known about Salmonella pathogenesis and whole genome sequencing is done, functional characterizations of certain genes are yet to be explored. The rspA (STM14_1818) is one such gene with putative dehydratase function, and its role in pathogenesis is unknown. The existing information showed that in the intracellular environment that is inside the macrophages, rspA gene expression of Salmonella enterica serovar Typhimurium (S. Typhimurium) (WT-STM) is significantly upregulated, which led us to investigate its role in Salmonella pathogenesis. In our study when we validated, we also found that the rspA gene expression was upregulated in the MGM-MES media (which mimics the intracellular environment with low Mg2+ and low pH) in a time- dependent manner. Thus, we generated the rspA knockout strain (ΔrspA-STM) and complement strain (ΔrspAc-STM) in S. Typhimurium 14028S. The mutant strain shows enhanced growth in intracellular mimicking media, which suggests that rspA might be regulating the intracellular growth of S. Typhimurium. The rspA gene has no role in motility and flagella number as these phenotypes are unaltered in mutant. The mutant strain is sensitive to higher ROS concentrations and is resistant to various antibiotics and complement system. Gene expression profile revealed that when bacteria grown in LB medium, Salmonella Pathogenecity Island (SPI)-1 genes responsible for Salmonella invasion, SPI-II genes, biofilm forming related genes and other stress related genes are downregulated in the mutant strain. The mutant strain differentially formed the biofilm at different temperatures by altering the expression of genes involved in the synthesis of cellulose and curli. At 20°C, the mutant strain formed less biofilm than the wild type strain, whereas at 25°C and 30°C, the mutant strain produced more biofilm than wild type strain. In the mutant strain, at 20°C master regulator csgD, cellulose synthesis genes (bcsA, bcZ, bcsB, and bcsC), curli synthesis genes (csgA and csgB), and adrA are downregulated but upregulated at 25°C except for bcsC. The cellulose and curli synthesized genes are downregulated in the mutant strain in in-vitro condition and when they reside inside the macrophage. Besides, the mutant strain is less invasive but hyperproliferative intracellularly. The macrophages generated less ROS and RNS when infected with rspA mutant than the wild type strain, resulting in better survival and intracellular hyperproliferation of the mutant. The mutant strain is less adhesive to Jasmin Pradhan (518LS1008) PhD Thesis, NIT Rourkela epithelial cells and macrophages than the wild type, resulting in less entry into the host cells. In in-vivo model of C. elegans, the mutant strain showed an increased bacterial burden than the wild type and is more infectious. The mutant caused faster death of the worms than the wild type and modulates the worm’s innate immunity, favouring its survival. In in-vivo murine model, the organ burden of the mutant strain is less than the WT-STM on early days of post-infection, whereas in the later days of post-infection, the organ burden is comparable between these two strains, which suggests that the mutant strain is able to persist better inside the murine model. Viability of the mutant infected Balb/C mice was significantly reduced than PBS control, but less severe than WT indicating that the rspA deletion mutant was still pathogenic in mice model. Overall, in this study, we concluded that the rspA gene differentially regulates the biofilm formation in a temperature dependent manner by modulating the genes involved in the synthesis of cellulose and curli and negatively regulates the Salmonella virulence for longer persistence inside the host system
Control of Adaptive Renewable Energy System with Distributed Energy Storage
No full textNowadays, energy management in a standalone system consists of distributed renewable energy sources, and distributed energy storage has been a significant challenge. An adaptable system architecture addresses this issue. The thesis proposes a solar photovoltaic system (SPVS) connected to distributed energy storage (battery) using two DC-DC converters. The proposed system would consist of a DC-DC boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). These converters coordinate the management of power sources, energy storage systems, and DC loads. The boost converter is connected to the SPVS to harvest maximum power from the PV panel using the maximum power point tracking (MPPT) algorithm. The MPPT perturbation period is largely affected by the actual insolation conditions at weather in the PV generation area. In practice, the traditional MPPT approaches often use a perturbation step size that is set according to the trade-off between efficiency and tracking speed requirements. Apart from this, during sudden changes in irradiance conditions, the conventional P&O algorithm gets confused when tracking the MPP and drifts away. This effect is observed to be severe for a rapid change in irradiance conditions. Thus, it is necessary to modify these algorithms to have an adaptive step size for tracking MPP at a faster rate without having any deviations. Therefore, to determine the optimum MPPT perturbation period and the magnitude of the perturbation voltage, it is necessary to analyze the performance of the MPPT controller for actual insolation conditions in a real weather environment. So, an MPPT algorithm with an improved variable step size (adaptive) is implemented in this research work. The proposed BSQZSDC can provide high gain at a moderate duty cycle. The low-voltage side of the BSQZSDC is connected to the distributed energy storage (battery), and the high-voltage side is connected to the DC-link side. The BSQZSDC operates as a boost converter when load demand exceeds the energy generated by the PV source and acts as a buck converter when the load is less than the generation. The proposed SPVS system with distributed energy storage has many operational modes. In each operating mode, regulating the voltage is the primary goal. Different controller techniques are used to regulate dc-link voltage. Firstly, the performance of the proposed system with distributed energy storage is examined using a dual loop proportional Integral controller (DLPIC). The linear control design techniques were used when the converter remained at an 16 operating point. The DLPIC is commonly designed by linearizing the model at a particular operating point. The transient behaviors of BSQZSDC with PI controllers are poor under large-signal perturbations. A nonlinear controller can be used to solve the above problem. Several nonlinear controller approaches were developed for DC-DC converters. Nonlinear controllers such as double integral sliding mode (DISMC) and Lyapunov function-based controllers have also been designed for the entire system. The DISMC has a quicker response for a wide range of operating circumstances with a significant reduction in steady state error (SSE). However, the transient response is significantly improved due to the Lyapunov-function-based controller’s global exponential stable feature. The performances of these controllers have been evaluated and compared in terms of transient and steady-state responses. Finally, flexible architecture develops coordination in proper energy management among PV generation, energy storage systems, and DC load. This flexible architecture presented in this thesis provides proper energy management in the stand-alone system. The major contributions of the proposed work are the development of an adaptive MPPT technique for effective operation in actual climate conditions, designing a bidirectional converter that provides wide gain at a moderate duty cycle, and designing linear and nonlinear controllers for energy management in the proposed system
Spectral analysis of Some Sequence-generated Graphs
No full textIn the study of graph theory, there are some classes of graphs that can be coded by a sequence, sometimes called the creation sequence. A graph G is called H-free if H is not an induced subgraph of G. Many significant classes of graphs can be described in terms of forbidden subgraphs. They include threshold graphs, chain graphs, cographs, bipartite graphs, perfect graphs, chordal graphs, split graphs, and trees, to name a few. Very interestingly, only a few of them can be generated by a sequence. Trees, threshold graphs, chain graphs, and certain cographs are examples of such graphs. The notion of this sequence is commonly known as binary sequence in the case of threshold graphs and chain graphs. Threshold graph has forbidden subgraphs P4, C4, and 2K2; chain graph has forbidden subgraphs C3, C5 and 2K2; whereas cograph forbids P4. In this thesis, we investigate various spectral properties of these graphs: threshold, chain, and certain cographs. The primary matrices of this study are the adjacency matrix A, the Laplacian matrix L, and the Seidel matrix S. In particular, we analyse the Seidel spectrum of threshold graphs, the spectrum and energy of the Seidel matrix for chain graphs, and the adjacency and Laplacian spectrum of certain cographs. Consider a connected threshold graph G. The threshold graph has several equivalent definitions. In this study, we consider the definition based on binary strings that will be relevant to this work. The underlying matrix is the Seidel matrix S. We compute the determinant and a recurrence formula for the characteristic polynomial of S. A formula for the multiplicity of the Seidel eigenvalues _1 and the characterization of threshold graphs with at most five distinct Seidel eigenvalues are derived. Also, a class of Seidel integral threshold graphs is obtained. For a connected chain graph G, we study its Seidel characteristic polynomial, Seidel energy, and Seidel cospectral property. We show that 1 is always an eigenvalue of S and all other eigenvalues of S can have a multiplicity at most two. We obtain the multiplicity of the Seidel eigenvalue 1, minimum number of distinct eigenvalues, the eigenvalue bounds, and the lower and upper bounds of the Seidel energy of a chain graph. It is also shown that the energy bounds obtained here work better than the bounds conjectured by Haemers. An important result is that two non-switching equivalent chain graphs may be Seidel cospectral, and we obtain a class of such graphs. Also, the class of Seidel integral chain graphs is shown here. In the context of cographs, we establish that it is possible to link a creation sequence for a particular kind of cograph (we call this sub-class of cograph a mathcalC-graph). Such cographs are constructed from a limited sequence of positive integers. Using that sequence, we can determine the multiplicity of the eigenvalues 0, 1, and inertia of the cograph under investigation. An extended eigenvalue-free interval from (1; 0) to _1 P 2 2 ;1) [ (1; 0) [ (0; 1+ P 2 2 _min _, (where _min _ 1 is the least of all natural numbers in the creation sequence) is obtained for C-graphs. Additionally, we derive an exact formula for the characteristic polynomial. Finally, we investigate the Laplacian eigenvalues and eigenspaces, various connectivity parameters with extremal property and clique number of C-graphs. Finally, we make an attempt to find a connection between algebraic connectivity and clique number
Terrestrial Carbon Cycle and its Feedback at the Regional Scale
No full textMapping ecosystem carbon across different scales and comparing estimates from various systems is essential, both in its own right and for understanding the increasing exchange of atmospheric carbon dioxide (CO2) between the atmosphere and biosphere. While the global terrestrial carbon sink helps mitigate the accumulation of atmospheric CO2, this process is contingent upon climate and ecosystem factors. The fundamental biophysical mechanisms governing ecosystem-carbon-climate interactions and their feedback mechanisms remain highly uncertain. Despite extensive efforts to track changes in ecosystem dynamics and attribute them to environmental factors using sophisticated data platforms, there is intense debate about whether studying regional carbon budgets can reconcile discrepancies in carbon flux calculations and improve global carbon budget estimates. Significant carbon variability is associated with large uncertainties stemming from Land Use Changes (LUC), resulting in a regional carbon source at seasonal to interannual scales, although without long-term positive or negative feedback. In the face of rapid LUC, continuous monitoring of carbon variability is crucial to understand India's role as a carbon sink in the global budget. Unlike other global regions, limited observational networks in India have hindered efforts to capture dynamics and fluctuations in the Indian carbon budget. However, recurrent Earth observation systems have facilitated monitoring carbon flux variability from diurnal to decadal timescales and from local to global spatial scales, aiding research in understanding ecosystem traits and subsequent carbon variability. Integrating predictive Earth System Models (ESMs) with diverse data streams reveals the sensitivity of carbon fluxes to various global environmental drivers across diverse climate and vegetation gradients. Focusing on understanding India's regional carbon dynamics in recent history, this dissertation employs in-situ, remote sensing, and process-based models to emphasise the interaction of regional carbon dynamics with multiple drivers. Flux variability, in both magnitude and pattern, differs across ecosystems but demonstrates a strong consistency among datasets. Integrating eddy covariance observations with remote sensing data emphasises the importance of synergistic use of multivariate datasets in understanding ecosystem productivity across temporal scales. India's diverse flora results in varying carbon uptake across biomes, with tropical ecosystems serving as dominant carbon storage hubs. However, the regional carbon cycle is reshaped by multiple environmental drivers, subsequently influencing climate patterns. Considering atmospheric aerosols as a hindrance, a remote sensing process-based model, the Carnegie Ames Stanford Approach (CASA), was employed to examine the potential effect of aerosol load on ecosystem productivity across diverse agroclimatic zones of India. Carbon flux sensitivity varies across ecosystems, with pronounced positive and negative feedback effects observed over forest and cropland ecosystems. To explore the complex dynamics of India's carbon uptake under various forcing scenarios over the past century, the Community Earth System Model (CESM) was utilised. This model traces how rising atmospheric CO2 concentrations and climate changes influence India's net land sink. Principal climate drivers were considered to identify their roles in potentially triggering long-term shifts in Indian ecosystem functionality as a carbon sink. Although the analysis indicates India's historical role as a carbon sink, asymmetries in decadal and seasonal trends from the integrated model ensemble suggest the potential for future terrestrial carbon loss, particularly in forest-based ecosystems. Explicit analysis by this research advances our understanding of India's carbon sink dynamics and underscores the sensitivity of carbon uptake to various environmental challenges. It emphasises the urgent need for adaptive strategies in the face of these challenges. Considering the ecosystem-specific sensitivities between carbon uptake and environmental drivers, future efforts incorporating divergent data platforms into process-based models within specific gradients will significantly enhance our understanding and prediction of future carbon uptake at regional and global scales
Investigating the Mechanistic Role of PPA2 in Mitochondrial Fission and its Modulation for Oral Cancer Therapeutics
No full textThe mitochondrial pyrophosphatase PPA2 is a mitochondrial matrix localized protein known for maintaining mitochondrial function. I found that PPA2 induced mitochondrial fission signaling through the MTP18-DRP1 axis. Interestingly, PPA2 overexpression upregulated mtDNA content and symmetric mitochondrial fission through MFF and DRP1, leading to mitochondrial proliferation. However, during mitochondrial stress following CCCP treatment, PPA2 induced asymmetric mitochondrial fission through FIS1 and DRP1 to segregate damaged mitochondrial parts. Furthermore, PPA2 interacted with MTP18 and induced mitophagy using the C terminal LC3 interacting region (LIR) of MTP18 to clear damaged mitochondria. Furthermore, I found that the expression of PPA2 was significantly increased in OSCC tissue compared to associated normal tissue. PPA2 knockdown inhibited oral cancer cell survival and promoted apoptosis during cisplatin treatment. Moreover, PPA2-mediated cell survival was compromised in mitochondrial fission-deficient conditions, suggesting PPA2 activated mitochondrial fission through the MTP18-DRP1 axis and protected against cisplatin-induced apoptosis. In this connection, therapeutic modulation of mitochondrial fission and mitophagy during cancer progression is an emerging approach to enhance anticancer therapy efficacy. The use of natural compounds as mitophagy modulators is highly encouraging due to their multi-target specificity and low side effects. Here, I explored the anticancer potential of Bacopa monnieri (BM) through the induction of mitochondria fission and mitophagy. I identified that the aqueous fraction of the ethanolic extract of BM (BM-AF) had a potent anticancer potential. BM-AF restricted oral cancer cell survival and promoted PARKIN-mediated mitophagy in oral cancer cells. The in vivo antitumor effect of BM-AF was further validated by the 4NQO- arecoline-induced oral cancer model in C57BL/6J mice. Further detailed mechanistic investigation revealed that Bacopaside-I (BS-I), a saponin from Bacopa monnieri, downregulated the arecoline-induced mitochondrial dysfunction and NLRP3 inflammasome activation in oral cancer cells. Moreover, BS-I induced mitochondrial fission by PPA2-mediated DRP1 activation and triggered PINK1-PARKIN-mediated mitophagy for elimination of the dysfunctional mitochondria to restrict oral cancer initiation and progression
Understanding The Role of Ac-93253 Iodide in Apoptosis as an Anti-Mycobacterial Response in Macrophages
No full textRecent studies suggest that host defense mechanisms like autophagy, inflammation, oxidative stress and apoptosis in macrophages play a significant role in host defense against intracellular pathogens like viruses, fungi, protozoan, and bacteria, including Mycobacterium tuberculosis (M. tb). It is still unclear if micromolecules inducing host defense mechanisms could be an attractive approach to combat the intracellular burden of M. tb. Hence, the present study has investigated the anti-mycobacterial effect of apoptosis mediated through phenotypic screening of micromolecules. Through MTT and trypan blue exclusion assay, 0.5 μM of Ac-93253 was found to be non-cytotoxic even after 72 h of treatment in phorbol 12-myristate 13-acetate (PMA) differentiated THP-1 (dTHP-1) cells. We have found that Ac-93253 treatment does not affect autophagy regulation, ROI, or RNI generation in uninfected and mycobacteria-infected dTHP-1 cells. At the same time point and same concentration, inflammation was also not affected upon Ac-93253 treatment. Significant regulation in the expression of various pro-apoptotic genes like Bcl-2, Bax, and Bad and the cleaved caspase 3 was observed upon treatment with a non-cytotoxic dose of Ac-93253. Ac-93253 treatment also leads to DNA fragmentation and increased phosphatidylserine accumulation in the plasma membrane's outer leaflet. Further, Ac-93253 also effectively reduced the growth of mycobacteria in infected macrophages, Z-VAD-FMK a broad-range apoptosis inhibitor, significantly brought back the mycobacterial growth in Ac-93253 treated macrophages. Ac-93253 treatment manipulates the mitochondrial membrane potential, CsA a mitochondrial membrane potential stabilizer, substantially inhibits the apoptosis and abrogates the anti-mycobacterial effect of Ac-93253. These findings suggest apoptosis may be the probable effector response through which Ac-93253 manifests its anti-mycobacterial property
Design of Novel Algorithms for Safety Message Dissemination in IEEE 802.11p-based Vehicular Ad-hoc NET Works (VANETs)
No full textVehicular Ad-hoc NETworks (VANETs) enable vehicles to exchange safety-based information via broadcasts to get updates on the vehicle’s speed, direction and road conditions. Highly dynamic topology, high mobility, and varying traffic density lead to network performance degradation in VANETs. MAC protocols are designed to provide reliable and rapid delivery of safety messages to safer and more efficient vehicles on the road. As vehicle density increases in the VANET environment, MAC protocols adapt to the changing data traffic patterns. The multi-channel access mechanism in MAC adapts to changing vehicular densities, thereby guaranteeing data transmission and increased throughput for various VANET applications. Providing efficient service with less delay and high throughput is a significant challenge while designing a MAC protocol for VANET. Therefore, the objective is to design novel algorithms for timely delivery of safety messages in VANET. The first approach is based on the optimization of CW with AIFS scheme to provide reliable and efficient data dissemination. Packet collision increases as vehicular density increases. Therefore, the collision probability is computed based on the number of contending vehicles, compared with a threshold value to adapt the CW for delay-tolerant channel access. Using the Poisson distribution, a numerical analysis-based result shows the collision probability, channel throughput, delay and busy probability of channel occupancy. The analytical study is then compared with the traditional 802.11p MAC protocol of VANET. The result of the analysis aid in selecting a CW value for different vehicular densities and analyzing the collision probability. The performance evaluation demonstrates that the optimal CW value reduces the packet collision rate by 50% and access delay by 56%, and maximizes the network throughput by 45%. This research work proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on Cooperative Adaptive Cruise Control (CACC), as the second contribution. Autonomous Vehicles (AVs) travelling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. For connected and automated vehicles, CACC systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to the CACC system, which is based on vehicle status data obtained through vehicular communications. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collision in uncertain situations. Different obstructing scenarios are identified during the travel, and solutions to these challenging situations are proposed. Various maneuvers are performed and analysed for the platoon’s steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions. The third introduces and harnesses the power of Machine Learning (ML) to learn the vehicular environment and dynamically adjust the CW parameter to maximise the throughput of a vehicular network. A Reinforcement Learning (RL) framework is formulated that compensates for actions that result in high utility by using local channel observations to overcome the absence of system knowledge. The proposed model implements a learning-based IEEE 802.11p protocol for the MAC channel control approach. The actor-critic model effectively learns the VANET environment to provide the best reward. The simulation result shows the proposed learning-based CW mechanism significantly improves the throughput requirements of the traditional IEEE 802.11p standard. The fourth approach is based on fair channel allocation to packets arriving at the MAC. The channel allocation problem is stated as a Multiple Knapsack Problem (MKP) which is proved to be NP-hard. The solution approach is based on a learning approach where the channel status is observed by the agent to take appropriate action. The available capacities of knapsacks, the total profits and weights of the selected items, and the normalized profits and weights of the unselected items are taken into consideration in our proposed deep reinforcement learning (DRL)-based approach to solve MKP. The knapsack is considered as the channel, whereas the items are treated as packets. This method then chooses the subsequent item to be mapped to the knapsack with the largest available capacity. An Asynchronous Advantage Actor-Critic (A3C) policy model is considered for the learning mechanism