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The Effect of Plant Growth Regulators on Secondary Metabolite Production in Coriandrum sativum L.
No full textThe present study was undertaken to investigate the effect of PGRs (BAP and IBA) and elicitor
(MeJA) on the in vitro production of secondary metabolites in C. sativum L. (Royal Bliss
variety). Seeds were first cultured on basal MS medium for germination. Subsequently, the in
vitro plantlets were transferred to MS medium containing different concentrations of BAP (0.0,
1.0, 2.5, 5.0, 10.0 µM) and IBA (0.0, 1.0, 2.5, 5.0, 10.0 µM). Morphological observations
showed that the highest shoot multiplication and shoot length occurred on MS medium
supplemented with 5.0 μM BAP, while 5.0 μM IBA resulted in the maximum number of roots
per explant. Combining these regulators with MeJA revealed differential effects. Although 5.0
μM IBA + 50 μM MeJA inhibited root and shoot formation, it significantly enhanced the
production of secondary metabolites, making it the most effective elicitor combination in this
study.
After sufficient growth, plantlets were harvested, and untargeted metabolite profiling was
performed using Gas Chromatography-Mass Spectrometry (GC-MS). GC-MS analysis
identified numerous bioactive compounds, and MetaboAnalyst 6.0 with KEGG pathway
enrichment analysis was used to determine the affected biosynthetic pathways.
Results showed that fatty acid biosynthesis was markedly enhanced under MS + IBA treatment,
indicating a strong influence on lipid metabolism. Cutin, suberine, and wax biosynthesis
pathways were significantly enriched in MS + BAP and IBA + MeJA treatments, suggesting
stress-induced cuticle thickening and volatile compound release. Moreover, phenylacetic acid
levels were particularly elevated in MS + 5.0 μM BAP-treated plantlets, pointing to possible
modulation of auxin-related pathways. 5.0 μM IBA + 50.0 μM MeJA treatment consistently
resulted in the highest diversity and quantity of secondary metabolites, including benzylic
alcohols, retinoids, and other aromatic compounds.
In conclusion, this study demonstrates that the application of PGRs, particularly in combination
with MeJA, can significantly enhance secondary metabolite biosynthesis in coriander. The
findings provide new insights into the interaction between PGR treatments and metabolic
regulation, offering promising strategies for enhancing secondary metabolite composition in
medicinal plants
Development of Computer-Aided Diagnosis Algorithm(S) for Medical Image Analysis
No full textHealthcare serves as a fundamental pillar of human well-being, encompassing the prevention, diagnosis, and treatment of diseases. However, various challenges, including excessive costs, limited resources, and inadequate infrastructure, hinder its accessibility and overall efficiency. The integration of artificial intelligence (AI) into medical sciences has the potential to transform healthcare by enhancing precision, efficiency, and personalization. This thesis examines AI-driven diagnostic systems to address critical gaps in the early detection of breast cancers, colorectal cancer and segmentation of burn tissues, introducing innovative methodologies to automate and improve diagnostic workflows.
This thesis is structured into five chapters. The introductory chapter establishes the theoretical foundations, identifies existing research gaps, and defines the overarching objective: the development of robust diagnostic frameworks capable of accurately and effectively categorizing medical images as either healthy or diseased one.
Second chapter is related to the diagnosis of breast cancer as breast cancer incidence in India as well as world is rising, necessitating early and accurate detection to improve survival rates. This study proposes a novel ensemble classification framework for breast cancer detection using optical coherence tomography images. Given the variability in datasets and performance metrics, a single classifier may be insufficient. Thus, we integrate the technique for order of preference by similarity to ideal solution for multi-criteria decision making with the crow search algorithm to optimize classifier selection and weight assignment. Additionally, SHapley Additive exPlanations values are employed to enhance model interpretability by visualizing feature attributions. This framework aims to reduce reliance on skilled pathologists, minimize interobserver variability, and accelerate breast tissue assessments.
Third chapter belongs to accurate assessment of burn tissue. Accurate assessment of burn injuries is critical for effective treatment and can significantly impact patient outcomes. However, the complexity of burn injuries makes timely and precise diagnosis challenging, particularly in differentiating burn depth. In severe cases, segmentation of burn tissue is essential, yet manual segmentation of three-dimensional (3D) optical coherence tomography datasets is highly time-consuming. Traditional deep learning methods often process each scan in isolation, overlooking valuable inter-slice or longitudinal information that could enhance segmentation accuracy. To address this limitation, we propose a Bi-directional Long Short-Term Memory UNET (Bi-LSTM-UNET) for OCT segmentation, leveraging inter-slice dependencies while maintaining computational efficiency. Experimental results demonstrate the model’s effectiveness, achieving high recall, precision, accuracy, Dice Coefficient, and Intersection over Union (IoU). The developed model has significant learning potential and can assist surgeons by providing a rapid second opinion on burn tissue assessment. Furthermore, its application reduces the workload on medical professionals and can be extended to other medical imaging domains.
Fourth chapter is focused on diagnosis of colorectal cancer. Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and its early and accurate diagnosis is critical for improving patient outcomes. Traditional diagnostic methods, such as histopathological analysis, rely on manual examination, which is time-consuming, subjective, and prone to interobserver variability. Artificial intelligence (AI) has emerged as a powerful tool to enhance diagnostic accuracy, efficiency, and consistency in CRC assessment. This research presents an advanced AI-driven histopathological image classification system that integrates spectral and spatial features to improve colorectal cancer diagnosis. Convolutional neural networks (CNNs) extract spatial features, while a gray-level co-occurrence matrix (GLCM) captures texture information. A multi-resolution wavelet transform is employed for spectral feature extraction. Classification is performed using a random forest for spectral-spatial features and a support vector machine (SVM) for spatial features, with classifier weights optimized through the equilibrium optimization technique.
The concluding chapter provides a comprehensive summary of the key findings presented in this thesis and outlines potential future directions for advancing artificial intelligence (AI) in medical imaging. While AI has significantly contributed to improving diagnostic accuracy, efficient segmentation and automation in healthcare, several critical challenges still remains. These include the need for large, diverse, and high-quality datasets to enhance model generalizability, as well as the necessity of improving model interpretability and explainability to foster trust among medical professionals and regulatory bodies.
Future research will focus on addressing these limitations by developing more robust and ethically responsible AI models, incorporating advanced techniques for explainable AI (XAI), and ensuring equitable AI deployment across diverse populations. Additionally, interdisciplinary collaboration between AI researchers, clinicians, and policymakers will be essential to facilitate the seamless integration of AI-driven diagnostic systems into clinical practice. By overcoming these challenges, AI can be more effectively leveraged to revolutionize medical imaging, ultimately leading to improved patient outcomes and more accessible healthcare solutions
Nomophobia: A Modern Challenge to Sports Performance, Athlete Anxiety & Self-esteem
No full textThe purpose of this study was to assess the impact of nomophobia (no mobile phobia)
on sports performance. Participants (N=87) were athletes who played sports
professionally. Sports anxiety and self-esteem were taken as mediators to gain further
insight into the topic. Nomophobia Questionnaire (NMP-Q), Rosenberg self-esteem
Scale (RSE), Sports Performance Perceptions Scale (SPPS), and Sports Anxiety
Scale-2 (SAS-2) were the tools used to gather the data. Data was collected through
Google forms. SPSS 25 was used to analyse the compiled the data.
Results revealed that there is a significant negative correlation between nomophobia
and sports performance and the impact of nomophobia on sports performance can be
explained by a full mediation through sports anxiety and self-esteem
Multi-Scenario SoC Timing Closure and Eco Generation in XEON Server Chips at Advanced Tech Nodes
No full textIn today’s modern VLSI chips, as the process nodes continue to shrink, the impact of process variations
has increased significantly. This has given rise to research into extending traditional static timing
analysis so that it can be performed statistically. Industry standards now work on the much more
advanced Parametric On-Chip Variation (POCV) approach leaving behind the traditional on-chip
variation (OCV) approaches. The performance of an integrated circuit (IC) largely depends upon its
clock frequency and is mostly evaluated considering the worst-case delays. This pessimism has led
industry experts to optimize the design further. The main contributor to the delay of the circuit is the
interconnect delays. Hence, the number of scenarios required to analyse and fix the design is enormous
and increasing, at deep sub-micron levels. Engineering Change Orders (ECOs) at the deep sub-micron
level are used to make small, incremental changes without having to go through the entire design
process again. ECOs are essential for handling late-stage design modifications and are critical for saving
time, reducing costs, and improving the overall efficiency of the design cycle.
This work can be briefly divided into three parts (all performed at the full chip level):
i. Performing the timing closure (bifurcated as die wise)
ii. Implementing Engineering Change Orders (ECOs) to fix the hold violations
iii. Performing the quality checks, before final signof
Methodology and Validation of Power Management Flows
No full textThe quick rise of mobile devices, high-performance computers, and data centres has made it necessary
to use advanced power management methods to cut down on energy use without hurting performance.
Dynamic Voltage and Frequency Scaling (DVFS) is one of the best ways to control how much power
processors use. It changes the voltage and frequency based on how much work they have to do. This
report talks about the ideas, uses, and power controller architectures that make DVFS work, focusing
on its importance in today's power management strategies.
The report also talks about different power management states, such as C-states, G-states, and P-states,
and how they work together to make the system use less power when it's idle and when it's working.
The study talks about two types of power management: idle power management, which tries to use less
power when the system is not doing much, and active power management, which tries to use power
more efficiently when the system is working hard. The method includes looking at current power
management models and getting a full picture of the technologies involved. The report also talks about
the problems and limits of DVFS, such as latency, lower performance when scaling, and the difficulties
of implementing it in multi-core and heterogeneous systems. The report gives a complete picture of
how dynamic voltage and frequency adjustments can save a lot of energy and improve performance by
looking at real-world uses like mobile devices, embedded systems, and data centres. The conclusion
talks about possible future directions for research on power management, especially in relation to more
advanced processors and systems that care about energy
Thermo-chemotherapeutic effects of magnetic nanoparticles on cancer cells
No full textMagnetic nanoparticle-based hyperthermia has garnered significant attention as a promising approach in cancer treatment. This study presents the synthesis and comprehensive evaluation of polyethylene glycol (PEG)-coated Fe₃O₄ nanoparticles, designed specifically for magnetic hyperthermia applications. The Fe₃O₄ nanoparticles were synthesized using the chemical coprecipitation method, followed by surface modification with PEG coating to enhance their biocompatibility and stability in biological systems. To characterize the synthesized nanoparticles, various advanced techniques were employed. X-ray powder diffraction (XRD) confirmed the crystalline structure of the Fe₃O₄ nanoparticles, revealing an inverse-spinel configuration with a crystallite size of 9.1 nm. Fourier-transform infrared spectroscopy (FTIR) further confirmed the successful coating of the nanoparticles with PEG, as evidenced by characteristic absorption bands. The magnetic properties were analysed by vibrating sample magnetometer (VSM), which indicated that both the uncoated Fe₃O₄ nanoparticles and the PEG-coated variants exhibited superparamagnetic behaviour, a key attribute for magnetic hyperthermia. The physical size of the uncoated Fe₃O₄ nanoparticles was determined using high-resolution transmission electron microscopy which showed an average size of 9.5 ± 0.12 nm. Upon PEG coating, dynamic light scattering (DLS) measurements revealed that the hydrodynamic size of the PEG-coated nanoparticles increased to 118 ± 0.25 nm from 87 ± 0.31 nm (bare Fe₃O₄), reflecting the successful addition of the PEG layer and its impact on particle size in solution.
The magnetic hyperthermia efficiency of PEG-coated Fe₃O₄ nanoparticles was systematically evaluated by varying several key parameters: magnetic field frequency (ranging from 162 to 935.6 kHz), field strength (from 5 to 12 mT), and nanoparticle concentration (between 1 to 100 mg/mL). To assess their performance, the temperature rise in an aqueous dispersion of the nanoparticles as monitored over a 20 minutes time period. This temperature increase reflects the nanoparticle’s ability to generate heat when subjected to an alternating magnetic field, a critical factor for effective hyperthermia treatment in cancer therapy. The specific loss power (SLP), a measure of the nanoparticle’s heat-generating capacity, was calculated by using the corrected slope method. The analysis revealed a clear relationship between the SLP values and the tested parameters. Specifically, the SLP of the PEG-coated Fe₃O₄ nanoparticles increased linearly with both the frequency and strength of the magnetic field, meaning that higher frequencies and stronger fields led to greater heat generation. Conversely, the SLP values vii decreased exponentially as the concentration of nanoparticles increased, indicating that more diluted nanoparticle dispersions were more efficient at converting electromagnetic energy into heat.
The conditions for magnetic hyperthermia efficiency were identified at a magnetic field frequency of 580.8 kHz, a field strength of 10 mT, and a nanoparticle concentration of 25 mg/mL. These findings demonstrate that the synthesized PEG-coated Fe₃O₄ nanoparticles exhibit strong potential as candidates for magnetic hyperthermia-based cancer treatment. Their ability to efficiently convert electromagnetic energy into heat under optimal conditions makes them promising for use in targeted, localized hyperthermia therapy, where precise heat delivery is essential for effective tumour cell destruction without harming surrounding healthy tissue.
Magnetic nanoparticle-based hyperthermia, combined with chemotherapy, represents a promising and innovative strategy for cancer treatment. In this study, a targeted drug delivery system was developed, consisting of a doxorubicin (DOX)-loaded magnetic core, coated with polyethylene glycol (PEG), and functionalized with the targeting ligand [D-Trp6] luteinizing hormone-releasing hormone (LHRH) or Triptorelin. This system was designed to enhance the effectiveness of cancer therapy by selectively delivering DOX to cancer cells while simultaneously employing magnetic hyperthermia to further induce cell death. Fourier transform infrared (FTIR) spectroscopy confirmed the successful conjugation of the LHRH ligand to the PEG-coated magnetite (Fe₃O₄) nanoparticles, as evidenced by characteristic peaks in the spectrum. The drug loading efficiency of the LHRH-targeted PEG-coated Fe₃O₄ nanoparticles was analysed using UV–vis spectroscopy, revealing a 66 % loading efficiency for DOX, indicating that a substantial amount of the drug was successfully incorporated into the nanoparticles.
To assess the therapeutic potential of these synthesized nanoparticles, their effects were tested on human lung cancer cells (A549) and human breast cancer cells (MCF-7) using an MTT cell viability assay. The LHRH-targeted PEG-coated Fe₃O₄ nanoparticles, loaded with DOX, were evaluated for cytotoxicity under three different treatment conditions: thermotherapy (magnetic hyperthermia), chemotherapy (DOX alone), and a combination of thermotherapy and chemotherapy (thermo-chemotherapy). In the A549 lung cancer cells, thermo-chemotherapy where both DOX and magnetic hyperthermia were applied resulted in an 88 % reduction in cell viability at the highest DOX concentration tested (10 µg/mL). This was a notable improvement over chemotherapy alone, which reduced cell viability of 62 %, and thermotherapy alone, viii which caused a 47 % reduction. A similar trend was observed in MCF-7 breast cancer cells, where thermo-chemotherapy led to a 91 % reduction in cell viability at highest DOX concentration (8 µg/mL), surpassing chemotherapy (57%) and thermotherapy (45 %) when used as standalone treatments.
In addition to assessing cell viability, the study also explored the immune response generated by the treatment, specifically focusing on the production of interferon-gamma (IFN-γ), a cytokine known for its role in immune-mediated cancer cell inhibition. IFN-γ production was measured in A549 lung cancer cells using both targeted and non-targeted drug-loaded nanoparticle conjugates, with and without the application of an alternating magnetic field. The findings demonstrated that the use of targeted, DOX-loaded magnetic nanoparticles led to a significant increase in IFN-γ production, regardless of magnetic field application, compared to non-targeted nanoparticles. This suggests that the targeted delivery of DOX, coupled with the magnetic hyperthermia, not only enhances direct cell killing but may also stimulate a more robust immune response against the cancer cells.
Overall, this study highlights the potential of targeted magnetic hyperthermia approach in combination with chemotherapy to significantly enhance the effectiveness of cancer treatment. The dual action of the therapy inducing direct cancer cell death through hyperthermia while delivering cytotoxic drugs like DOX along with the stimulation of an immune response through increased IFN-γ production, provides a compelling case for the use of this strategy in treating cancers such as lung and breast cancer. By leveraging the advantages of magnetic fields to focus treatment on tumour cells and increase drug efficacy, this combined thermo chemotherapy approach could offer a promising and more effective method for combating cancer
Design and Development of Band-Notched UWB Microstrip Patch Antenna using Nature Inspired Optimization Algorithms
No full textWireless communication has experienced remarkable and rapid growth in recent times, driven by advancements in wireless technology. This progress has led to continuous reductions in the size of modern wireless devices. These devices now provide a wide range of applications within wireless personal area networks (WPAN). WPAN applications include home entertainment purposes such as digital video discs (DVDs) and LCD High-Definition TVs (HDTVs), academic purposes like wireless USB and wireless printer/scanners, as well as multimedia applications like video conferencing and online gaming. WPAN technology provides the convenience of interconnecting various devices in both home and office settings without the need for physical wires. Achieving fast data rates is a critical priority for such devices, and this can be accomplished by expanding the bandwidth. Ultra-wideband (UWB) technology presents the most promising solution to achieve large bandwidth while consuming minimal power, primarily due to its extensive frequency spectrum ranging from 3.1 GHz to 10.6 GHz. Therefore, UWB antennas play an important role in establishing a wireless link for the WPAN appliances by providing a large signal bandwidth. However, designing a UWB antenna is a challenging task as it must meet the requirements for providing wideband performance in accordance with the Federal Communications Commission (FCC) norms.
Some specific UWB systems suffer from narrow-band signal interference of other unlicensed ISM band wireless communication devices, such as WI-MAX, ARN, WLAN, and ITU-8 X-band of satellite downlink frequency channel. In order to avoid this interference, UWB antennas require in-built filter properties by incorporating different types of slots, strips, or slits into the radiating patch. To achieve this objective, the research proposes three different designs of UWB antennas with dual band-notch (DBN), triple band-notch (TBN), and quadruple band-notched (QBN) characteristics. These designs exhibit sharp band-rejection features. The antennas are constructed using a direct geometrical method to determine the dimensions of the band-notch elements, which serve as notch resonators. However, this process presents challenges and is time-consuming as it involves a trial-and-error approach to adjust the dimensions of these elements. To address this challenge effectively, global optimization techniques are required. Nature-inspired algorithms are one of the most important types of global optimization methods, which have been used to solve problems of various real-world applications. These sophisticated algorithms draw inspiration from natural processes observed in various species and have proven efficient in determining the optimal solutions for a wide range of engineering problems, including electromagnetic antenna design. These algorithms provide more effective solutions compared to the traditional hit-and-trial methods, leading to improved antenna design and performance. Therefore, the research proposes three novel optimization techniques based on the recently introduced naked mole-rat (NMR) algorithm. The first technique, called Levy mutated naked mole-rat algorithm (LNMRA), aims to optimize the structure of a dual band-notch UWB (DBN-UWB) antenna. The second technique uses a hybrid differential evolution and naked mole-rat algorithm (HDN) to optimize a triple band-notch UWB (TBN-UWB) antenna. For the third technique, a hybrid binary version of grey wolf optimizer and naked mole-rat (HbGNMR) is proposed to design a planar fragment-type binary UWB (P-UWBFT) antenna. All of these techniques are employed for single-objective optimization purposes.
Furthermore, single-objective optimization techniques may not be the most suitable approach to achieve optimal antenna designs, especially for complex design structures. Since the antenna design is inherently a multi-objective problem, it involves several contrary objectives that need to be addressed simultaneously. These objectives include balancing size reduction with gain maximization, size reduction with operating frequency minimization, maximizing both gain and beamwidth, and minimizing signal reflection (|S11|) by focusing on gain maximization. Thus, the research work proposes two multi-objective optimization algorithms. First is the multi-objective naked mole-rat (MONMR) algorithm to address the design challenges of basic UWB (B-UWB) and TBN-UWB antennas. Secondly, a multi-objective multi-hybrid naked mole-rat (moIGDN) algorithm to optimize the B-UWB and DBN-UWB printed monopole antennas.
This thesis focuses on developing compact-sized UWB antennas with band-notched characteristics suitable for WPAN applications. The design process utilizes an analytical model to achieve the desired frequency response without relying on specific mathematical values. These antennas are designed and analyzed using an electromagnetic (EM) simulator. For prototype fabrication, an FR-4 substrate with a thickness of 1.6 mm is used, and the antennas are fabricated through a wet etching and photo-lithography process. The fabricated antennae are then measured on RF experimental testbed using a vector network analyzer (VNA) model no. E5063A. Various parameters of the proposed antennas, such as reflection coefficient (ᴦ), voltage standing wave ratio (VSWR), and input impedance (Z), are carefully observed. Additionally, radiation measurement analysis are performed, including 2D and 3D radiation patterns, antenna gain, and current distribution, achieved by placing the antenna in an anechoic chamber. The measured results show good agreement with the simulated results, with only minor variations observed. The designed antennas have been demonstrated to be good candidates for UWB applications
Development of Polarization/Pattern Reconfigurable Antennas for Wireless Applications
No full textThe rapid advancement of wireless communication technologies, especially with the rise of 5G and the Internet of Things (IoT), necessitates antenna designs that are adaptable, efficient, and able to accommodate a variety of operational requirements. Reconfigurable antennas, which provide capabilities like polarization agility, beam pattern switching, and frequency tunability, have become essential components in meeting these requirements. This thesis examines the development of reconfigurable antennas designed specifically for sub-6 GHz frequency bands, addressing the demand for compact, adaptable, and high-performance solutions in contemporary wireless systems.
Conventional antenna designs encounter challenges in fulfilling the diverse operational requirements of contemporary wireless systems, particularly in maintaining compactness with multi-functionality. Conventional fixed antennas often lack the necessary adaptability to deal with dynamic environments, leading to constraints on data rates, signal quality, and interference mitigation. Furthermore, the integration of multiple-input multiple-output (MIMO) configurations with reconfigurability introduces challenges, such as increased complexity, higher losses, and reduced isolation, which adversely affect the performance in densely packed frequency bands like sub-6 GHz. Moreover, the existing reconfigurable antenna designs often experience increased complexity, high power consumption, and reduced performance, which hinder their practical implementation in compact wireless systems.
This thesis offers a thorough approach to tackle these challenges through the design and optimization of diverse reconfigurable antenna configurations for the sub-6 GHz band. Polarization reconfigurable antennas are designed to enhance signal robustness, whereas pattern reconfigurable antennas focus on improving directional coverage and managing interference. Hybrid reconfigurable antennas integrate various functionalities to enhance adaptability. Furthermore, polarization reconfigurable MIMO antennas and pattern reconfigurable MIMO antennas are introduced to provide improved data rates and spatial diversity while maintaining compact designs and effective isolation. The thesis follows a progressive workflow, beginning with the development of a polarization reconfigurable antenna that includes a slotted ground plane design, demonstrating LP and LHCP and an AMC-integrated antenna, demonstrating reliable LP, LHCP, and RHCP switching for sub-6 GHz 5G applications. Building on this, a compact pattern reconfigurable antenna is introduced, utilizing semi-circular and elliptical patch structures with PIN diodes to enable directional control without compromising size or performance. The research is further extended to hybrid reconfigurable antennas, combining frequency and pattern reconfigurability, and achieving dual-band operation with optimized gain and efficiency. Finally, the work culminates in advanced MIMO antenna designs, supporting polarization and pattern reconfigurability, high port isolation, low ECC (<0.5), and other diversity metrics such as DG, MEG, and TARC. Each chapter is interlinked, illustrating a comprehensive workflow from single-element reconfigurable designs to MIMO systems, validated through both simulation and experimental results, and ultimately targeting practical deployment in 5G wireless networks. The designs utilize efficient switching mechanisms, advanced materials, and optimization techniques to fulfil performance requirements while maintaining compactness and minimizing complexity. Several reconfigurable modes of the proposed designs are simulated, fabricated, and tested. CST-MWS (Computer Simulation Technology Microwave Studio) software is used for all of the proposed work's simulations, while VNA and anechoic chambers are taken into consideration for measurement. The effectiveness of the suggested antennas in providing improved performance is demonstrated by key outcomes from both simulated and measured data, such as strong impedance matching across the intended frequency ranges and stable radiation patterns across different operating conditions. By providing feasible solutions to the changing needs of contemporary wireless systems, this research advances the expanding field of reconfigurable antenna design and eventually supports reliable and efficient communication