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Production of Lactic Acid from Lignocellulosic Hydrolysates by Thermotolerant Bacteria
No full textLactic acid (LA), is an industrially important organic acid with extensive applications
in pharmaceuticals and food industries as well as biodegradable plastics. It has garnered
significant attention for sustainable production from renewable lignocellulosic biomass
resources. This study was aimed at lactic acid production by isolating thermotolerant
and inhibitor-tolerant bacterial strains, optimizing lignocellulosic biomass utilization
and employing co-cultivation strategies for enhancing lactic acid yields.
Among 45 bacterial isolates obtained from compost, soil, and fermented food two
strains Bacillus licheniformis DGB and Bacillus sonorensis DGS15 were identified as
high lactic acid producers which were thermotolerant and inhibitor-tolerant. They
demonstrated robust growth in Bushnell Haas medium and efficiently utilized glucose
and xylose at elevated temperatures (45°C–50°C), while tolerating inhibitory
compounds (furfural and hydroxymethyl furfural) commonly derived from
lignocellulosic biomass pretreatment. Initial fermentation trials yielded 13.2 g/L and
12.8 g/L of lactic acid for DGB and DGS15, respectively. Morphological, biochemical
and molecular analyses confirmed their identity, and their thermotolerance and
inhibitor resistance ensured suitability for industrial applications. Furthermore, both
strains exhibited efficient carbon catabolite repression (CCR) bypass features, enabling
simultaneous glucose and xylose metabolism.
Rice straw and wheat straw were selected as lignocellulosic feedstocks due to their
abundance and high cellulose content. Pretreatment processes involving acid hydrolysis
and enzymatic saccharification were optimized using Response Surface Methodology
(RSM) to maximize sugar release. Pretreated rice straw yielded 50.8 g/L of total
reducing sugars (TRS) from 100 g of biomass, while wheat straw yielded 48.6 g/L.
Structural analysis using FTIR confirmed effective delignification, with the
disappearance of lignin peaks at 1670 cm⁻¹, and SEM analysis revealed significant
disruption of biomass structure. The crystallinity index (CrI) of pretreated rice straw
increased by 15.5% compared to untreated material, highlighting enhanced
accessibility for enzymatic digestion. In fermentation trials, DGB and DGS15 achieved
lactic acid yields of 46.5 g/L and 44.7 g/L from rice straw and wheat straw hydrolysates,
with yield efficiencies of 96.8% and 94.1%, respectively. Among the two biomass
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sources, rice straw emerged as the most suitable substrate due to its higher sugar release
and conversion efficiency.
To enhance productivity, a co-cultivation strategy was employed, leveraging the
complementary metabolic pathways of DGB and DGS15. The co-culture system
effectively utilized glucose and xylose, achieving 70% sugar consumption within 15
hours and complete substrate utilization within 48 hours. Optimized fermentation
conditions (10% (w/v) mixed hydrolysate substrate (rice straw and wheat straw), 1:1
inoculum ratio, 50°C, pH 6.0) resulted in a maximum concentration of 64.3 g/L lactic
acid, with a yield of 0.98 g/g and productivity of 1.036 g/L/h. Compared to monoculture
fermentation, co-cultivation increased yields by 28% and reduced fermentation time by
12%. The Separate Hydrolysis and Co-Fermentation (SHCF) process addressed key
challenges in lignocellulosic biorefining, demonstrating scalability and process
efficiency. Co-cultivation proved to be the most suitable approach for lactic acid
production, offering higher yields and better process efficiency compared to
monoculture systems.
Present study successfully demonstrated a comprehensive sustainable approach to
lactic acid production by integrating thermotolerant and inhibitor-tolerant microbial
strains, optimized lignocellulosic biomass utilization, and co-cultivation strategies.
Among the strains, Bacillus licheniformis DGB was identified as the best performer
due to its higher lactic acid yield and adaptability. Rice straw was identified as the most
suitable biomass source for lactic acid production and co-cultivation emerged as the
most efficient production strategy, offering enhanced yields, reduced fermentation
times and robust process stability. Future studies could focus on refining biomass
pretreatment, exploring genetic engineering of strains and incorporating integrated
biorefinery models to enhance sustainability and economic feasibility
Epigenetic modifications for developing a commercial isolate from endophytic Fusarium sp. for Epigallocatechin gallate (EGCG) production
No full textParkinson’s Disease (PD) is a progressive neurodegenerative condition, characterized by motor
dysfunctions. The clustering of certain genes, alpha-synuclein’s misfolding and aggregation
creates fibril and oligomeric formations, are reported to be the key contributor to the
development of PD. The existing therapies for PD mainly focus on managing symptoms and
dopamine replacement rather than halting or reversing the progression of PD. Research
indicates the therapeutic potential of certain plant derived polyphenolic compounds,
specifically Epigallocatechin gallate (EGCG), that have the ability to prevent α-synuclein
aggregation, effectively halting the onset of disease at its earliest stages. These polyphenolic
compounds are capable of inhibiting α-synuclein aggregation and reducing oxidative stress,
positioning them as promising candidates for neuroprotective drug development, however its
limited natural occurrence restricts its utilization in therapeutic approaches.
This study focuses on improving the production of EGCG in endophytic fungi, through
the application of epigenetic modifications using 5-Azacytidine and Butyric acid. The crude
extracts were subjected to various concentrations of modulators.
The findings indicated maximum yield of tannic acid at 10M AZA and 200M butyric
acid. EGCG showed an increase in yield at the same concentrations, as determined using
spectrophotometric analysis. Treated extracts also exhibited enhanced antioxidant potential
and increased total phenolic content, depicting enhanced antioxidant potential
The results confirm the potential of epigenetic modulators as non-recombinant method
for enhancing secondary metabolites production, making scalable and sustainable production
of EGCG possible for therapeutic applications, specifically in neurodegenerative disorders such
as PD.
Keywords: Parkinson’s Disease, -Synuclein, Epigallocatechin gallate, Polyphenolic
compounds, Endophytic Fungi, Epigenetic modification
Automatic Stenosis Detection using Deep Learning Techniques
No full textConditions like angina, heart attack, heart failure, and arrhythmias can be life-threatening,
often linked to or caused by stenosis. Manual diagnosis is time-consuming, but automation
can improve speed and accuracy. Hence, this work presents a custom Attention
U-Net architecture for stenosis detection from X-ray angiography images. The U-Net is
widely used for medical image segmentation but struggles with small or thin structures
like blood vessels. To achieve clear segmentation results and precise boundaries, an
attention mechanism is employed in this work to minimize the impact of background
information and highlight important features. Although the attention block increases the
model performance, it also increases the load on the system, which in turn increases the
model training time. To address this, the proposed work introduces residual block to reduce
complexity and enhance feature propagation further stabilizing deeper training and
allowing the network to benefit from fine-grained focus and strong gradient flow. The
model pipeline incorporates deep supervision in the middle and interpretation layers,
directly training them with auxiliary loss functions, which makes the model more robust.
These coordinated components results in improved training efficiency and performance
as compared to the isolation in the state-of-the-art. The model is evaluated using F1-
score, accuracy, and IoU, achieving results of 93.2%, 99.83%, and 88.7%, respectively.
The proposed approach shows a significant improvements in F1-score and IoU (47.2%
and 89.4%, respectively) compared to the baseline U-Net model, with accuracy remains
comparable
Influence of Affective Priming Modality on Dual Mechanism of Control
No full textThe study explored how prime types, emotional valence and proportion congruency influenceDual Mechanism of Control through a spatial stroop task. The study used a within subject design, the task used 2 primes (image & words), and 3 emotional valence (happy, neutral andsad), 5proportion congruency (High LWPC, Low LWPC, Equal LWPC , High ISPC and LowISPC)and congruence (congruent & incongruent) as within subject factors. 80 participants volunteeredfor this study and they were asked to perform a spatial stroop task preceded by exposuretoemotionally valenced primes. The results found showed significant effects of prime, emotion, proportion congruence and congruence. Robust stroop effect was found as participants respondedquickly to congruent trials in comparison to incongruent trials through all conditions. Emotional
valence modulated interference with neutral primes leading to faster reaction time as compared to happy primes and sad primes overall leading to delayed reaction time during incongruent trials. Effect of proportion congruency also modulated its effect in incongruent trials significantly during High ISPC, Equal LWPC and Low LWPC conditions. The finding support the DMC framework, showing how emotions and proportion effect proactive and reactive control
Teachers’ Emotion Regulation and its Impact on Classroom Environment and Students’ Academics
No full textTeaching is an emotionally demanding profession that requires continuous emotional regulation
to maintain flexibility, job satisfaction and effective class management. Despite the important role
of emotional labor in teaching, these demands often get non -recognition, reducing high level
stress, burnout and job performance between teachers. Social expectations faster the pressure to
work as role models and educational achievements on teachers, which contribute to emotional
exhaustion. Emotional mobility between teachers and students is central for educational
experience, as the emotional states of teachers directly affect the climate, student motivation and
learning results. This study examines the emotional challenges faced by teachers, which
emphasize the importance of auxiliary work environment - described by collegium relationships,
administrative support and adequate resources in reducing these challenges. Emotional welfare of
teachers, workplace support, and teacher-student relationships, by highlighting mutual activity,
research teachers promote flexibility, promote the environment of positive classes and increase
the need for targeted intervention and policy initiatives that increase educational results.
Addressing the emotional realities of teaching is necessary to maintain teacher health, improve
job satisfaction and nurture overall student development
Design of peptide vaccine candidate for Epizootic Hemorrhagic Disease Virus using an Immunoinformatics based approach
No full textEpizootic Hemorrhagic Disease Virus (EHDV) has recently emerged as a significant pathogen
affecting domestic cattle, leading to severe clinical disease and economic losses worldwide.
Traditionally associated with wildlife, EHDV outbreaks in cattle are increasing, with EHDV-2
identified as the most virulent serotype responsible for high mortality rates. The absence of a
commercially available vaccine for cattle or deer, coupled with the limitations and safety
concerns of experimental vaccines, underscores the need for a targeted and effective vaccine
strategy. In this study, a multi-epitope peptide vaccine was designed with the employment of
various immunoinformatics tools. Conserved regions of the EHDV-2 VP2 protein were first
selected and then screened to identify antigenic peptides capable of eliciting T-cell and B-cell
responses. Selected epitopes showed strong binding affinity to both BoLA class I and II alleles,
indicating potential activation of CD8⁺ and CD4⁺ T-cell-mediated immunity in cattle. Three
peptides containing multiple epitopes were assembled into two constructs: one consisting solely
of the peptides, and another incorporating β-defensin-2, a bovine TLR4 agonist, as an adjuvant
to enhance innate immune activation. Structural modeling and docking confirmed proper folding
and receptor engagement for both constructs, with the adjuvant-containing construct exhibiting
stronger and more stable binding to the TLR4/MD-2 complex. Molecular dynamics simulations
further demonstrated that the adjuvanted construct maintained lower RMSD, reduced residue
flexibility, and greater compactness, supporting its structural stability. Collectively, these
findings suggest that the designed multi-epitope vaccine construct, particularly with the βdefensin-2 adjuvant, holds significant promise for stimulating both adaptive and innate
immunity in cattle against EHDV-2. Experimental validation will be essential to confirm its
immunogenicity and protective efficacy.
Keywords: Epizootic Hemorrhagic Disease Virus (EHDV), immunoinformatics, epitope
mapping, multi-epitope peptide vaccine, immune receptor interactio
ਡਾ. ਸੁਨੀਲ ਕੁਮਾਰ ਸਿੰਗਲਾ ਦਾ 28ਵੀਂ ਪੰਜਾਬ ਸਾਇੰਸ ਕਾਂਗਰਸ 'ਚ ਇੰਜੀਨੀਅਰ ਗੁਰਚਰਨ ਸਿੰਘ ਓਰੇਸ਼ਨ ਪੁਰਸਕਾਰ ਨਾਲ ਸਨਮਾਨ
No full textDesign and Analysis of Three-Phase Bidirectional Active Front End Converter for V2G Application
No full textPhD ThesisThe rapid growth of electric vehicles (EVs) and their increasing integration with power systems
highlight the significant potential for EVs to support the grid through Vehicle-to-Grid (V2G)
technology. By enabling bidirectional power flow, EVs can function as distributed energy
resources, balancing grid demands, stabilizing voltage levels, and facilitating the integration
of renewable energy. However, the development of high-power bidirectional chargers presents
challenges such as maintaining power quality, ensuring dynamic control, and providing rapid
response capabilities.
This research addresses these challenges through a comprehensive framework involving the
design of a 15 kW Active Front-End (AFE) converter, adaptive control strategies, and an ad-
vanced non-linear stability analysis for closed-loop system dynamics. These components col-
lectively enhance grid interaction, improve system reliability, and enable efficient power flow
between the grid and EV batteries. The proposed solutions aim to advance V2G technology for
high-power applications, contributing to sustainable and resilient energy systems.
The first approach focuses on the design of a three-phase bidirectional grid-connected AFE
converter optimized for high power quality and stable operation under varying grid conditions.
The use of an adaptive synchronous reference frame (SRF) model-based controller ensures
seamless transitions between constant current (CC) and constant voltage (CV) charging modes.
Additionally, the system integrates a bidirectional DC/DC converter, minimizing total harmonic
distortion (THD) and reducing DC voltage ripple. Experimental results validate the system’s
robust performance, demonstrating compliance with industry standards.
In the second work, an adaptive control mechanism is introduced for a three-phase bidi-
rectional battery charging system that incorporates an interleaved buck-boost converter with an
optimized voltage-oriented control (OVOC) model-based approach. This system employs feed-
forward decoupled current control and pulse width modulation to maintain unity power factor
(UPF) and minimize voltage ripple. Validated through a 12.5 kW experimental setup, the con-
trol scheme achieves low THD and reliable performance under dynamic conditions, making it
suitable for real-world V2G applications where reliability is paramount.
The third study presents an advanced Continuous Control Set Model Predictive Control
(CCS-MPC) strategy tailored for the three-phase bidirectional AFE converter with an inte-
grated interleaved buck-boost DC/DC converter. This control framework enhances operational
quality by maintaining fixed switching frequencies and minimizing THD, achieving energy ef-
ficiency in both Grid-to-Vehicle (G2V) and V2G operations. It is particularly effective under
unstable grid conditions, as the CCS-MPC ensures fast transitions and accurate state estima-
tion, enabling robust system stability and adaptability. Experimental validation on a 12.5 kW
hardware prototype demonstrates THD levels below 2%, stable DC-link voltage (DLV), and
unity power factor, underscoring its viability for high-performance EV charging stations. The
condensed approach reduces system complexity while maintaining its ability to respond to
fluctuating grid scenarios and external disturbances.
Finally, a Lyapunov function-based advanced non-linear stability control strategy is also
developed to address robustness in closed-loop dynamics, ensuring stability under parame-
ter variations and external disturbances such as voltage sags and reactive power fluctuations.
Simulation studies confirm the effectiveness of this method in maintaining power quality and
stability, reinforcing the potential of V2G systems as reliable grid components.
Together, these advancements represent a scalable, high-performance solution for next-
generation bidirectional V2G chargers. By combining adaptive control, optimized converter
designs, and robust stability measures, this research supports sustainable energy goals and en-
hances grid resilience through efficient EV-grid interactions
Improved Extreme Learning Machine (ELM) based approach(es) for data analysis in Smart City Applications
No full textIn today’s era of rapid urbanization and advancements in Information and Communication Technology (ICT), Smart Cities have paved the way for efficient management of resources. In these cities, sensors are strategically placed at various locations to collect real-time data, enabling enhanced monitoring, optimization of resources, and improved decision-making for urban management. These sensors, integrated into the urban infrastructure, facilitate continuous data collection that supports smarter city planning, predictive analytics, and the development of responsive, sustainable environments. The data generated by various sensors needs to be analyzed to provide useful information to the users, but it is a challenging task. This can be achieved using various Machine Learning (ML) and Deep Learning (DL) approaches such as support vector machine (SVM), Convolution neural network (CNN) etc. In recent years, Extreme Learning Machine (ELM) has gained importance in the research domain due to its significant features of no backpropagation, no hyperparameter tuning, no human intervention, and simple architecture. In this research work, three ELM-based hybrid approaches have been proposed for accident severity classification, parking space detection, and bike-sharing demand prediction in Smart Cities. In addition to this, a comprehensive survey work on ELM and different variants of ELM is conducted to understand the various applications of ELM.
First approach, ELM-based SVM (E-SVM), utilizes feature mapping of ELM and performs classification using SVM, which contributes towards the decision boundary by maximizing the distance between the hyperplanes. The proposed framework for accident severity classification in Smart Cities outperforms other traditional ML algorithms for a majority of the datasets used in the experimental analysis in terms of accuracy, precision, and F1 score.
Second approach, CNN-based ELM (CNN-ELM), uses the best properties of CNN and ELM to identify vacant and occupied parking spaces in Smart Cities. The first step involves feature extraction using CNN, and the second step includes classification using ELM. The proposed approach has been evaluated on publicly available PkLot dataset. It has been experimentally proved that CNN-ELM not only reduces the computational time but also improves the classification accuracy as compared to traditional CNN models.
Third approach, Grey wolf optimization-based Incremental ELM (GWO-IELM), has been proposed for predicting the count of shared bikes in Smart Cities. The significant features from the dataset are selected using GWO in the first phase, and then the regression is performed using I-ELM in the second phase. The effectiveness of the proposed approach has been validated on a publicly available London bike-sharing dataset. The experimental results verify the superior performance of the proposed approach as compared to conventional ML approaches in terms of coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and root mean square log error (RMSLE), respectively.Department of Science and Technology (DST), Government of India, under the Innovation in Science Pursuit
for Inspired Research (INSPIRE) Fellowshi
Design and Development of Metamaterial Based Antennas for Hyperthermia Application
No full textHyperthermia treatment for cancer involves raising the temperature of cancerous tissues or tumors in the body. The elevated temperature is typically maintained within a specific range (usually between 410C - 450C) for one hour. This approach utilizes various heating techniques, such as electromagnetic (EM) heating, ultrasound, hyperthermia perfusion, and conductive heating, tailored to the patient's condition, tumor location, and size. Among these, microwave (MW) hyperthermia stands out as a promising non-invasive technique due to its ability to create targeted hot spots in tumors while minimizing damage to healthy tissues. The effectiveness of hyperthermia treatment depends on several factors, including the type and size of the applicator, operating frequency, and the specific absorption rate (SAR). Two critical parameters, penetration depth (PD) and effective field size (EFS), describe the SAR and significantly influence the design of hyperthermia applicators. Applicators are generally categorized into planar and non-planar designs, which operate across specific industrial, scientific, and medical (ISM) frequency bands such as 434 MHz, 915 MHz, and 2450 MHz. The choice of frequency is crucial, as lower frequencies allow deeper tissue penetration but require larger applicators, whereas higher frequencies provide shallower penetration and are more suitable for superficial tumors.
To address these challenges, advancements in metamaterial based antennas offer a promising direction. Metamaterials, engineered to manipulate EM waves, enhance PD and EFS, improving hyperthermia efficacy. This work focuses on the development of compact planar applicators compatible with different tissue types, featuring precise focusing abilities. These applicators are designed, optimized, and analyzed using CST Microwave Studio software, based on the finite integration technique. To prevent burns in the skin's upper layers, the applicators are positioned at a specific distance from the body and integrated with a water bolus. Testing is conducted on heterogeneous human phantom models and Gustav voxel models to validate the performance of the applicators.
The study involves designing various applicators to improve the efficacy of hyperthermia treatments. The first applicator, a compact Archimedean double spiral antenna (DSA) operating at 2.45 GHz, targets superficial tumors. This antenna achieves a bandwidth of 120 MHz, which increases to 180 MHz when integrated with an artificial magnetic conductor (AMC) acting as a reflector. The AMC also enhances the antenna’s gain from 1.17 dB to 4.28 dB, focusing energy toward the tumor. Simulations with a heterogeneous phantom reveal significant improvements in PD and EFS, and thermal analysis indicates that the applicator can maintain tumor temperatures between 41°C and 45°C at 2.5 W of power. To further enhance PD and EFS, a frequency selective surface (FSS) lens is integrated with the DSA. This lens focuses energy more effectively toward deeper tumors, converting the bidirectional radiation pattern into a unidirectional one. Simulations show a peak tumor temperature of 43°C at 2.5 W of power, demonstrating its suitability for treating larger tumor areas. For even deeper tumors, a compact focused metamaterial based applicator is developed. This design combines the DSA with an AMC and an FSS lens. The AMC directs radiation forward, while the FSS lens enhances energy focusing. This applicator achieves uniform heating patterns and a high PD, maintaining a temperature of 44°C at 2.5 W input power for tumors located 16 mm beneath the skin. To optimize PD further, a novel spiral shaped frequency-selective surface (SFSS) lens is introduced. This spiral lens, integrated with the DSA, ensures uniform heating while protecting superficial tissue layers from hot spots. The design is tested on heterogeneous phantom models and voxel models, both with and without a water bolus. The results show improved PD and EFS compared to previous designs. Thermal analysis confirms a peak tumor temperature of 44.7°C at 1.9 W of power, validating its effectiveness for deep seated tumors.
For practical applications, the applicators are enclosed in a protective Teflon layer and integrated with a PVC water bolus layer to ensure safety and environmental durability. The designs are experimentally validated through SAR measurements in tissue mimicking phantoms, showing consistent results with simulated data. Prototypes are fabricated and tested for different configurations, confirming their efficacy in generating controlled heating for hyperthermia.
This work demonstrates significant advancements in hyperthermia treatment through the development of compact, efficient, and targeted applicators. The integration of metamaterial structures enhances PD and EFS, addressing the limitations of conventional applicators. The findings highlight the potential of these designs to treat a wide range of tumor types effectively while ensuring patient safety. Future research could focus on further miniaturization, real-time monitoring, and adaptive mechanisms to enhance the precision and versatility of hyperthermia applicators