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Load Frequency Control of Two Area Interconnected Shipboard Microgrid Power System Using PID and Fuzzy Based Controllers
No full textThe increasing environmental issues and declining availability of fossil fuels have positioned
renewable energy as a highly feasible replacement for traditional energy sources. A power
system (PS) that integrates distributed generation units, particularly renewable energy
sources and storage devices, is referred to as a microgrid (MG). Within marine power
applications, MGs enhance electrical efficiency, improve supply reliability, and ensure better
power quality. Incorporating Renewable Energy Sources (RESs) into shipboard power
systems not only strengthens system stability and operational efficiency but also reduces
generation costs and supports environmental sustainability. RESs, however, have
the problem of grid security and maintenance. As RES prominence grows, industrial
concerns regarding frequency quality have increased. Because RES are inherently
intermittent, they can cause a constant supply-demand mismatch, causing the system
frequency to fluctuate about the required nominal value with unacceptable quality of
response. This unpredictability of the RESs has an impact on the frequency regulation and
system generation demand balance. Only by utilizing energy storage systems (ESSs) like
batteries, flywheels, or supercapacitors (SC) can this system instability be avoided. The MPS
will become more reliable and power quality will be maintained with the aid of these energy
storage devices with RESs.
This study integrates Renewable Energy Sources (RES) with the Shipboard Microgrid
(SMG) of a two-area power system to address frequency regulation challenges. Proposed
model considers an interconnected system that incorporates photovoltaic (PV), wind, and
fuel cell (FC) generation, supported by supercapacitors and battery storage. For control, both
a PID controller and a Fuzzy Logic Controller are employed to mitigate frequency deviations
and regulate tie-line power exchange. Without controllers, the frequency error fails to return
to zero; therefore, a PID controller with automatic tuning is applied to reduce the Area
Control Error (ACE) and improve system dynamics. Further enhancement is achieved
through the application of an FLC, which refines the transient response. The developed
MATLAB/Simulink test model successfully demonstrates that frequency deviations remain
within acceptable limits, while overshoot is significantly reduced
Degradation of Low-Density Polyethylene (LDPE) by bacterial isolates
No full textLow-density polyethylene (LDPE), poses a significant environmental challenge due to its
resistance to natural degradation. This study aimed to isolate and characterize bacterial strains
capable of degrading plastic polymers, specifically PEG-4000 and LDPE. Four bacterial strains
DGK4, DGK5, DGK7, and DGK8 isolated previously from waste disposal site near M Hostel,
Thapar University, Patiala were examined for degradation of PEG and LDPE. Initial screening
using zone of clearance assays on Bushnell Haas agar supplemented with yeast extract and 5%
PEG-4000 indicated positive degradation activity by all four isolates, suggesting their ability to
utilize synthetic polymers as carbon sources. Further the effect of PEG on bacterial isolates was
studied in Bushnell Haas medium (with and without yeast extract) supplemented with varying
concentrations of PEG (1%, 2%, 4%, and 8%). Among the isolates, DGK7 and DGK4 consistently
demonstrated the highest degradation potential. DGK7 reduced PEG concentration from
1.96 ± 0.03 to 0.95 ± 0.02 at 2% PEG in enriched medium within 72 hours. DGK4 also exhibited
strong degradation, with PEG reduction from 1.98 ± 0.03 to 1.29 ± 0.01 under similar conditions
in Bushnell Haas supplemented with yeast extract and 5% polyethylene glycol (PEG). Gravimetric
analysis of LDPE films further validated these results. After 30 days in Bushnell Haas medium
supplemented with yeast extract and under UV pre-treatment, DGK4 and DGK7 caused weight
loss of 10.21 ± 0.59% and 9.67 ± 0.70%, respectively, compared to non-UV treatments which
resulted in 7.52 ± 0.75% and 6.05 ± 0.68%. After 60 days, DGK7 showed the highest degradation
by 31.0 ± 0.89% (UV) and 20.89 ± 0.78% (non-UV), while DGK4 reached 25.6 ± 0.91% (UV)
compared to 13.64 ± 0.97% (non-UV). DGK5 and DGK8 showed moderate or limited
enhancement under UV conditions. Scanning electron microscopic analysis of LDPE films
degraded by DGK7 revealed significant structural damage, such as pits, cracks, and surface
erosion, confirming enzymatic action and microbial colonization. These findings suggest that the
combination of UV treatment and microbial activity enhances LDPE degradation. Phylogenetic
analysis of the two potential bacterial isolates was also carried out, and the result indicates that the
16 S rRNA sequence of isolate DGK7 had similarity with Paenibacillus sp. and isolate DGK4 had
similarity with Klebsiella sp
An Investigation into the Flowability and Conveyability of Fly Ash
No full textThis thesis presents the results of an ongoing investigation into the pneumatic fly ash conveying
systems in thermal power plants that often cannot transport ash as per their expected duty due to either
variability of ash characteristics and/or inadequate system sizing, resulting in generation loss and
reduced ash utilization. Based on a comprehensive test program, including the pneumatic conveying
(in a pilot plant) and flow property testing of 23 ash samples obtained from five different power
stations, predictions for conveyability and flowability have been made using bulk property
characterization. Of all the different particle and bulk parameters investigated, the angle of repose is
the significant bulk parameter linking conveyability and flowability. A newly developed design tool
based on the angle of repose is expected to assist designers and operational engineers predict the flow
condition and appropriate sizing of equipment/system with suitable operating parameters.
Accurately predicting the flow mode is essential for the design of reliable pneumatic conveying
systems. The existing popular powder classification diagrams use particle or loose poured bulk density
and average particle diameter. An evaluation of powder characterization and conveying data of 59
powders reveals that all the existing classification diagrams have overlapping zones between fluidized
dense- and dilute-phase. Such uncertainty significantly limits the use of existing classification
diagrams. A novel classification diagram has been developed using the powder characterization and
conveying data of 59 powders for fluidized dense to dilute-phase regime using a modified particle
Froude number term (based on loose poured bulk density) and particle size distribution. The novelty
of this classification diagram is that it uses particle size distribution (instead of average particle size)
and quantitatively marks the uncertain zone in the classification diagram, ensuring design reliability.
Accurate blockage conditions or the minimum transport boundary prediction is essential for the
reliable design and operation of a pneumatic powder conveying system. Many existing empirical
models for minimum transport boundary do not consider essential powder properties and operating
conditions, such as loose poured bulk density, particle size, and air density. Based on the conveying
results of 13 different powders, this paper has developed a new empirical model for the minimum
transport boundary. The model includes a Froude number based on particle size and bulk density and
a dimensionless gas density term, which makes the model inherently adaptable to variations in powder
properties and operating conditions. Results of validation show that the new model provides a significantly improved prediction of minimum Froude Number (in the range of 7 to 13% relative error
only) compared to the existing models, which provided relative errors in the range of 19 to 67%.
A new approach for estimating the force of adhesion has been developed by using the angle of repose
and flow function test data of 23 fly ash samples and modifying an existing approach. Adhesion force
has been used to determine the Bond number, which has been used subsequently to predict powder
flowability by considering particle size distribution. The predicted values using the developed model
for ash flowability have been validated against 10 other fly ash data, which provided a correlation
coefficient value of 91% (indicating a good fit). The new adhesion model resulted in a correlation
coefficient value of 95% when the predicted values (using this model) were compared with the
experimental data of other researchers, thus indicating a good fit.NTP
UPIO Delayline Delay Extraction Using ELDO
No full textThis report focuses on the extraction of delay in Universal Programmable Input/output (UPIO)
Delayline circuits using Eldo, a high-precision SPICE simulator. The accurate determination of delay
characteristics in UPIO Delaylines is essential for the performance and reliability of high-speed digital
circuits.
The study begins with an overview of the importance of delay lines in digital circuit design and the
specific applications of UPIO Delaylines. It then outlines the theoretical foundations of delay extraction,
emphasizing the critical parameters and metrics that influence delay performance.
The methodology section details the simulation setup, including the configuration of the Eldo simulator,
the design of test benches, and the simulation conditions. It provides a step-by-step guide for setting up
the Eldo environment, running simulations, and extracting delay data to ensure precision and
reproducibility.
Simulation results are presented and analyzed, illustrating the delay characteristics of the UPIO Delayline
under various operating conditions. The report compares the simulated results with theoretical
predictions and discusses any observed discrepancies.
In this report, we present a novel method for extracting resistance values in electronic circuits using a
custom routing approach. The accurate extraction of resistance values is crucial for ensuring the
performance and reliability of electronic designs, particularly in high-speed and high-frequency
applications where precise impedance control is necessary.
Our method involves the development of a custom routing algorithm that not only optimizes the physical
layout of the circuit but also accurately calculates the resistance values of the interconnects. The
algorithm takes into account various factors such as wire length, cross-sectional area, and material
properties to compute the resistance. Additionally, it integrates seamlessly with existing electronic design
automation (EDA) tools, providing a comprehensive solution for designers
Human Silhouette Detection in Images using Machine Learning
Full text linkDetecting human outlines, or silhouettes, has emerged as a crucial task in the field of machine learning, with important applications in areas such as surveillance, human–computer interaction, healthcare monitoring, and autonomous navigation. Accurate silhouette detection is essential not only for ensuring safety but also for improving accessibility and user experience in systems designed to assist individuals in real-world environments. Unlike traditional computer vision techniques that rely on hand-crafted rules, modern machine learning models—particularly convolutional neural networks (CNNs)—are capable of learning visual patterns from data, making them more effective in handling complex and cluttered scenes. This research introduces a practical machine learning framework for human silhouette detection, focusing on identifying individuals in natural environments where backgrounds may include occlusions from trees, rocks, and other visual distractions. A custom dataset was created for this purpose, containing annotated images that reflect diverse backgrounds, human postures, and varying levels of visibility. This dataset supports realistic model training and evaluation by simulating challenging real-world scenarios. The study employs and compares two deep learning architectures: YOLOv8n (You Only Look Once) and DETR (Detection Transformer). YOLOv8n is a lightweight, real-time object detection model optimized for high-speed performance, making it suitable for deployment in resource-constrained systems such as drones or embedded devices. In contrast, DETR applies transformer-based attention mechanisms to capture global context within an image, offering improved detection performance in scenes with overlapping or partially occluded human figures. By evaluating both models on the custom dataset, the research highlights their relative strengths in terms of accuracy, speed, and suitability for different deployment scenarios. Overall, the work outlines a structured approach to designing and evaluating human silhouette detection systems using state-of-the-art models and a dataset tailored to real-world conditions. The findings contribute to a better understanding of the trade-offs involved in deploying deep learning-based detection systems and provide a foundation for further development of reliable and adaptable computer vision solutions
Development of Hydrogel Encapsulated with plant- derived exosome- like- nanoparticles for Wound Healing for Type-2 Diabetes
No full textThe prevalence of type 2 diabetes mellitus (T2DM) is a growing global health concern, often
accompanied by complications such as impaired wound healing due to vascular damage and
chronic inflammation. This thesis presents the development and evaluation of innovative
hydrogel patches encapsulated with hybrid exosomes derived from ginger and garlic, aimed at
accelerating wound healing in T2DM patients. Ginger and garlic are renowned for their potent
anti-inflammatory and antioxidant properties, which are harnessed in this study to address the
delayed wound healing associated with diabetes.
The hydrogel patches were synthesized using a biocompatible polymer matrix, ensuring
optimal delivery and sustained release of the encapsulated exosomes at the wound site. The
hybrid exosomes were characterized for their size, morphology, and bioactive compound
content, confirming their potential to modulate inflammatory responses and oxidative stress. In
vitro assays demonstrated the ability of the exosome-loaded hydrogels to enhance cellular
proliferation and migration, critical processes in wound repair. Furthermore, in vivo studies
using a diabetic wound model revealed significant improvements in wound closure rates and
histological markers of healing, compared to conventional treatments.
This research underscores the therapeutic potential of ginger and garlic hybrid exosomes in
promoting efficient wound healing in diabetic patients, offering a promising avenue for the
development of advanced wound care solutions. The findings contribute to the growing body of
knowledge on the application of natural bioactive compounds in regenerative medicine, with
implications for improving the quality of life for individuals suffering from diabetes-related
complications. Future work will focus on optimizing the formulation and exploring the
mechanistic pathways involved in the observed healing effects, paving the way for clinical
translation of this novel therapeutic approach
Analytical and Experimental Study of Light Weighted 3D printed Bolt using Stereolithography Process
No full textThe growing demand for lightweight, high-performance components in fields such as aerospace,
automotive, and biomedical engineering has led to increased research into innovative design and
manufacturing methods. Among these, additive manufacturing (AM), particularly Stereolithography
(SLA), has emerged as a promising technology due to its high precision, smooth surface finish, and
ability to fabricate complex geometries with ease. This thesis presents an analytical and experimental
investigation into the design, fabrication, and performance evaluation of light-weighted 3D printed
bolts using the SLA process.
The study primarily focuses on two aspects: weight optimization through structural modification of
standard bolt geometry, and the mechanical performance of the printed components under
compressive loading. Bolts are critical fastening components, traditionally manufactured using
subtractive processes and metallic materials. However, in lightweight applications where mechanical
loads are moderate and weight is a limiting factor, polymer-based, additively manufactured bolts
present a novel alternative. This research aims to explore this potential by integrating design
optimization and stereolithography-based fabrication, followed by experimental validation. Print
settings such as layer thickness, exposure time, and orientation were carefully selected based on prior
Taguchi and response surface methodology (RSM)-based optimization studies to ensure dimensional
accuracy and strength.
This thesis also discusses the limitations of SLA-printed polymer bolts, including their brittleness,
limited thermal resistance, and unsuitability for high-load or fatigue-prone environments. However, it
also highlights the potential advantages, such as weight savings, design flexibility, and the ability to
create functionally integrated parts with embedded features. The present study demonstrates that
SLA-based 3D printing is a viable method for producing lightweight bolts for specific engineering
applications where moderate strength, low weight, and custom geometry are required. The outcomes
of this research can guide future work in multi-material printing, composite resin development, and
topology-optimized functional parts for structural and semi-structural applications
Optimization and Enhancement of Catechin Production by Endophytic Fungi Isolated from Camellia sinensis
No full textCatechins are powerful polyphenolic chemicals exhibiting notable antioxidant, anti-inflammatory, and anticancer characteristics, typically derived from tea plants (Camellia
sinensis). The investigation of endophytic fungi as alternate sources for catechin synthesis
offers a viable biotechnological strategy to address the limitations of plant-based extraction
methods. This study examined the optimization and augmentation of catechin production by
endophytic fungi derived from C. sinensis to establish an effective microbial production system
for these key medicinal chemicals.
The study concentrated on a particular fungal strain coded as PP5B, extracted from tea plant
tissues and subjected to thorough morphological and molecular analysis. The fungal isolate had
unique morphological characteristics, including a dense, cottony to woolly mycelial texture,
homogeneous white pigmentation, and septate hyphae with prominent cross-walls. The CTAB
method was employed for genomic DNA extraction, followed by amplification of the internal
transcribed spacer (ITS) by PCR region for molecular identification, with sequencing results
awaited for conclusive taxonomic categorization.
A systematic optimization strategy was utilized, incorporating both the one-variable-at-a-time
technique and RSM, to identify optimal culture conditions for maximal catechin synthesis. The
study assessed many aspects, including radial growth characteristics across distinct media
types, growth variability throughout diverse incubation durations, pH optimization, and the
interacting effects of agitation speed, incubation duration, and pH. The assessment indicated
that the isolate attained a maximum radial growth of 8 cm on day 5, with optimal growth
occurring at pH 4.5.
The RSM study utilized a Central Composite Design with 17 experimental runs to optimize
three essential parameters: agitation speed (0-120 rpm), incubation duration (5-15 days), and
pH (4.5-7.5). UV-Visible Spectrophotometry and High-Performance Liquid Chromatography
(HPLC) were employed for the accurate quantification of catechin production. The
mathematical model created via RSM exhibited statistical significance with an F-value of 30.90
(p < 0.0001) and a coefficient of determination (R²) of 0.8114, signifying strong model fit. The
model equation indicated that agitation speed, incubation duration, and pH were significant
variables, in addition to specific interaction terms (AC and BC) and the quadratic term C².
This study effectively illustrates the possibility of endophytic fungi from C. sinensis as a viable
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and sustainable source for catechin synthesis. The improved fermentation parameters establish
a basis for scaling up the production process, presenting benefits such as regulated production
conditions, less reliance on seasonal fluctuations, and opportunities for genetic improvement.
This study greatly advances microbial biotechnology and natural product synthesis, offering
useful insights into optimizing bioactive compound biosynthesis by endophytic fungi. Future
study directions may encompass scale-up investigations, genetic modification for improved
production, and thorough analysis of the catechin profile generated by the fungal system
Spatio-Temporal Assessment of Atmospheric Pollution (Aerosols, Black Carbon) and Snow Cover Dynamics in Himachal Pradesh Using Sentinel Data
No full textThe mountainous Indian state of Himachal Pradesh, located in the western Himalayas, is
becoming more and more vulnerable to climate change, particularly with regard to its
Cryospheric systems and snow cover. In this area, snow and ice are essential for maintaining
perennial rivers as well as controlling biological balance and producing electricity.
Anthropogenic activities have increased atmospheric aerosols and black carbon levels in
recent years, which are known to speed up snowmelt by changing surface albedo and regional
radiative forcing. Using three primary indicators—the Aerosol Index (AI), Black Carbon
(BC), and Normalized Difference Snow Index (NDSI)—this thesis examines the
spatiotemporal variability of snow cover and air pollution throughout Himachal Pradesh in
2020, 2022, and 2024. Sentinel-5P data was used to estimate AI and BC, while Sentinel-2
imagery was used to compute NDSI, which provides high spatial resolution information on
the extent of seasonal snow cover. Google Earth Engine (GEE) was used to process and
analyse all datasets, and maps, statistical charts, and seasonal composite pictures were used to
display the findings. The choice of 2020 as a baseline year, when emissions were much lower
because of the worldwide COVID-19 lockdown, is a novel feature of this study. This offers a
natural control setting for analysing the effects of pollution and environmental recovery in
later years. Four major seasons were examined: Winter (October–February), Spring
(February–April), Summer (May – June), and Monsoon (July–September). The findings
show that, especially in the spring and summer, there is a constant negative correlation
between NDSI values and atmospheric pollution levels (AI and BC). While 2022 and 2024
showed decreased NDSI and increased black carbon deposition, indicating rapid melting,
2020 had the maximum snow cover. This study demonstrates that air pollutants are a major
contributor to the Himalayan cryosphere's deterioration. Recommendations for community-
level monitoring, renewable energy transitions, and pollution management have also been
proposed. The combined use of Sentinel-2 and Sentinel-5P demonstrates the effectiveness of
multi-sensor satellite measurements in directing policy and climate adaptation in high-altitude
areas
Defining and Measuring Excellence Orientation as a Cultural Dimension
No full textThis study introduces excellence orientation as a new cultural dimension and develops a scale to measure it. The development of the scale followed an iterative process that included an extensive literature review, expert validation, and face validation to ensure the content’s relevance and accuracy. The scale was designed to capture two aspects of excellence orientation: practices (what people actually do) and perceptions (how people perceive their society's focus on excellence). The data collected were analysed for Cronbach’s alpha reliability, inter-item correlations, and exploratory factor analysis (EFA) to test the scale’s construct validity. The results of the EFA indicated that both the practices and perceptions subscales loaded onto a single factor, confirming the unidimensional nature of the scale. To further validate the scale, a confirmatory factor analysis (CFA) was conducted with a new sample, and the fit indices confirmed that the model was a good fit to the data, establishing its validity and reliability.
As a result of this process, a 5-item scale for measuring excellence orientation was developed, consisting of separate items for practices and perceptions, both rated on a 5-point Likert scale. The study’s findings confirm that the scale is a valid and reliable tool for assessing excellence orientation in different cultural contexts.
This study contributes to the literature by introducing excellence orientation as a distinct cultural dimension, expanding our understanding of how cultural values can influence societal progress and behaviour. The scale developed in this study offers a valuable tool for researchers and practitioners to assess and understand cultural dynamics, particularly in organizational and societal settings