Doctoral Thesis

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Adaptive Synchronization based Control Strategies for Harmonic Mitigation in Photovoltaic-Integrated Single-Phase Systems under Weak Grid Conditions
(Department of Electrical Engineering, NITS, 2025) Hassan, Faridul
Environmental degradation and the rapid depletion of fossil fuel reserves have emerged as pressing global challenges. The combustion of fossil fuels remains a major contribu tor to greenhouse gas emissions, particularly carbon dioxide (CO2), which significantly accelerates climate change. In response, there is a worldwide shift toward the adop tion and advancement of sustainable green energy technologies. This transition has prompted a large-scale deployment of renewable energy (RE) sources particularly pho tovoltaic (PV) system and their increasing integration into the power grid. The modern power system has evolved into a more complex and dynamic grid to accommodate these changes. To improve energy reliability and sustainability, PV systems are often cou pled with energy storage units and connected to the utility network. However, if not appropriately managed, the integration of such systems can pose risks to grid stability and may even lead to operational failures. Moreover, this paradigm shift has resulted in the widespread utilization of power electronic converters. Furthermore, the inherent intermittency of PV sources, combined with the extensive use of nonlinear power elec tronic converters/devices and loads, has introduced several power quality issues. These include the presence of harmonics, interharmonics, DC offsets, grid voltage sags/swells, and frequency fluctuations, all of which can deteriorate voltage and current waveforms across the grid. In single-phase grid-connected photovoltaic (GCPV) systems, it is essential to compen sate for the harmonics while simultaneously injecting high-quality of PV power into the grid, thereby improving the overall power quality. Numerous control strategies have been explored in the literature to ensure reliable and efficient operation of single-phase grid-connected PV systems. Among these strategies, the synchronization unit plays a critical role in enabling stable grid interfacing for all grid-connected power converters. Among various synchronization techniques phase-locked loops (PLLs) and frequency locked loops (FLLs) being the most widely adopted. The implementation of these methods requires two orthogonal signals, an in-phase component and a quadrature phase component, both separated by 90◦. While in three-phase systems these signals can be easily generated using Clarke’s transformation, single-phase systems face chal lenges in generating the required quadrature component. Traditionally, transport delay methods have been used to obtain the quadrature signal; however, PLLs based on such techniques often perform poorly under distorted grid conditions, DC offsets, and vari ations in grid voltage parameters. Quadrature signal generation can also be achieved using derivative-based or integral based techniques, each offering distinct advantages and limitations. Derivative-based methods exhibit strong capability in rejecting DC-offsets and faster dynamic response. However, they tend to amplify higher-order harmonics when the input grid voltage is distorted, which can adversely affect system performance, therefore it required pre filtering stage. Conversely, integral based approaches offers effective filtering charac teristics. Yet, these methods are susceptible to issues such as accumulation and over lapping errors over time. The second-order generalized integrator (SOGI) has emerged as a widely accepted solution for QSG and also used for implementing synchroniza tion schemes such as PLLs and FLLs, and for control strategies in single-phase grid connected photovoltaic (GCPV) systems. In the present work, both derivative-based and generalized integrator-based quadrature signal generation (QSG) together with synchronization techniques are investigated and modified for improved performance. First, a comprehensive advancement in derivative based QSG and synchronization techniques for single-phase grid-connected systems is presented. An improved derivative-based PLL (IDPLL) is developed, incorporating two improved derivative-based QSG (IDQSG) blocks to eliminate gain tuning require ments and inter-loop dependencies, thereby simplifying the design and significantly enhancing dynamic performance. Furthermore, a frequency-adaptive enhanced deQSG (AEdeQSG) is proposed by introducing a tuneable feedback gain (γ) for bandwidth control and employing a mapping-based fractional-order method to achieve frequency adaptability. The proposed methods effectively address challenges such as amplitude amplification, harmonic distortion, and unequal quadrature signal amplitudes under varying grid frequency conditions. Following this, the generalized integrator-based QSG approach is further explored in this work. An improved SOGI-QSG, referred to as iSOGI-FLL, is proposed. This enhanced design integrates an Enhanced Dynamic and Harmonic/DC Rejection (ED HDCR) loop to improve the performance of the conventional SOGI under conditions involving low-order harmonics and DC offset in the grid voltage. The modification en sures better dynamic response and superior harmonic attenuation. A control strategy built upon the iSOGI-FLL is developed, aiming to deliver more accurate quadrature signals, current references, and synchronization signals under various non-ideal grid conditions, enabling seamless operation of grid-connected photovoltaic (GCPV) sys tems. Additionally, the modern complex grid exhibits weak grid characteristics—being heav ily distorted with lower-order harmonics, interharmonics/subharmonics, and DC offset, and is highly prone to disturbances. In such scenarios, the standard second-order gen eralized integrator (SOGI) fails to effectively attenuate these harmonics. Therefore, to address weak grid conditions and to extract more accurate fundamental components with improved dynamic performance, two solutions based on the generalized integrator complex filter (GICF), also known as the complex coefficient filter (CCF), have been proposed. First, an enhanced generalized integrator-based complex filter (eGICF)-FLL is introduced. This structure enables precise extraction of the fundamental voltage and current components, resulting in accurate synchronization and reference signal genera tion, particularly under weak grid conditions. Second, a higher-order complex filtering method, termed the enhanced second-order complex coefficient filter (eSO-CCF), is developed for fast and accurate extraction of fundamental components of quadrature signals in single-phase system applications. The eSO-CCF significantly improves fre quency selectivity, enhances the accuracy of fundamental component extraction, and exhibits robust DC offset rejection. In contrast to traditional second-order and fourth order GI filters, the eSO-CCF effectively isolates the fundamental frequency from low order harmonics, interharmonics, and DC components. To evaluate its performance, the proposed filter is rigorously benchmarked against widely used methods such as SOGI, FOGI, CCF, and SO-CCF in both simulation and experimental settings. A comparative analysis based on the frequency spectrum of the generated quadrature signal (QSG) is also presented. Building upon this foundation, a single-phase enhanced second-order complex co efficient filter-based frequency-locked loop (1Φ-eSO-CCF-FLL) is designed and im plemented. This advanced synchronization strategy is utilized for the control of a single-stage grid-connected PV system integrated with a battery energy storage sys tem (BESS). The proposed control scheme ensures accurate frequency tracking while significantly improving power quality, even under weak grid conditions characterized by severe distortion, including lower-order harmonics, interharmonics, and DC offsets. To assess the effectiveness of the proposed approach, encompassing fundamental com ponent extraction, quadrature signal generation, synchronization signals estimation and frequency-adaptive control : a 5 kW experimental hardware prototype of a single-phase grid-connected photovoltaic (PV) system integrated with a battery energy storage sys tem (BESS) was developed. At the core of the setup is a Semikron-based converter stack configured as a voltage source converter (VSC), which utilizes an IGBT switch (SKM100GB125DN) and SKYPER-32 Pro gate drivers. The AC side of the system in cludes essential interfacing and protection components such as fuses, a grid interfacing inductor, a ripple filter, and other auxiliary circuitry. Grid connection is established through an NHR 9410 grid simulator. On the DC side, a PV emulator (AMETEK TerraSAS) is interfaced via a DC-DC boost converter. The VSC performs multiple functions, as it converts the DC output from the PV emulator into AC for injection into the grid or for supplying local AC loads connected at the point of common coupling (PCC). A custom-fabricated rectifier is also included on the load side for testing pur poses. Additionally, it supports harmonic compensation and reactive power injection to improve power quality. For BESS integration, a dual active half-bridge (DAHB) converter is connected at the DC side of the VSC. A high-frequency transformer is coupled to the AC side of the DAHB through an external inductor, both of which are designed and built in-house. To mitigate high-frequency switching noise, a film capaci tor is placed at the DAHB output. An ITech battery emulator (IT6012B-500-80) is used to emulate battery behavior and support charging operations. The control strategy is implemented on a field-programmable gate array (FPGA)-based controller (WAVECT WCU300), built on the Xilinx Zynq SoC XC7Z020 platform. A HIOKI power quality analyzer is used to record power quality data. Resulting waveforms and performance behavior are recorded using a mixed-signal oscilloscope (MSO58, 350 MHz, 6.25 GS/s). Additionally, the entire system—including the PV interface, VSC control, and BESS integration—is modeled and simulated in the MATLAB/Simulink environment and val idated through hardware experimentation. The proposed approaches, such as IDPLL, AEdeQSG, iSOGI, eGICF, and eSO-CCF-based fundamental component extraction, synchronization and for grid-connected PV integrated with BESS—have all been vali dated on a developed hardware test setup.
An Empirical Study on Investigating Engagement and Integrity Monitoring in Online Education Systems Using Deep Learning Models
(Department of Computer Science & Engineering, NITS, 2025-12-03) Singh, Ruchi
Online learning has transformed education in ways that benefit both students and teachers. The biggest advantage of online learning is its flexibility. Students can study at their own pace. Unlike traditional classes that require everyone to be in the same place at the same time, online learning adapts to people’s real-world schedules. Online education is often much cheaper than traditional classroom learning. Though the rapid shift of online education has provided various advantages to the teacher and student, there are certain challenges related to student engagement, academic integrity, and be havioral monitoring during online classes/ examinations. State-of-the-art models are mostly complex, computationally intensive, and infeasible to implement in real-time. Moreover, there are very few approaches that provide a comprehensive analysis of student behavior throughout the examination process. Traditional proctoring systems, on the other hand, rely primarily on manual observation or rule-based mechanisms, which tend to be intrusive, inefficient, and constrained in terms of accuracy and scalability. To address these challenges, this thesis proposes an intelligent, comprehensive frame work that utilizes deep learning and multi-modal data analysis to ensure integrity while also assessing student behavior and engagement levels in real time. Firstly, a multi modal student engagement detection system is proposed that integrates a lightweight deep learning model with lip movement detection to recognize the facial emotion ex pressions and active speaker detection. The deep learning model developed in this process is designed with fewer parameters, making it computationally efficient while maintaining high accuracy. The effectiveness of the multi-modal system has been vali dated using benchmark datasets such as FER-2013, JAFFE, KDEF, and RAF-DB for facial emotion and online videos for active speaker detection. Performances have also demonstrated reliable performance, thereby supporting a more comprehensive assess ment of student engagement. To understand the students’ behavior during online education, a behavioral analy sis model is proposed that combines multiple behavioral cues. It incorporates facial expressions, head pose, object detection, and lip movement to detect patterns that signify focus, hesitation, distraction, or potential malpractice during online exams. In addition, Keyframe extraction is introduced to reduce the computational load and to enhance the performance of the deep learning model to be lightweight on facial emotion recognition. The pre-trained deep learning models are used to analyze other behavioral activities such as head pose, lip movement, and object detection. The effectiveness of the behavioral model has been evaluated using benchmark and online video recordings to enable real-time performance without compromising accuracy. A teacher’s dash board developed in this process aggregates and visualizes the analyzed data, offering actionable insights into student behavior, engagement levels, and integrity violations for the teacher’s reference. Finally, to maintain online exam integrity, an intelligent proctoring model is proposed, which provides a friendly environment for students and proctors. It combines academic misconduct, stress detection, and enabling an adaptive monitoring proctor interface in a single intelligent system to create a balance between integrity, fairness, and student well-being. The proposed model will perceive academic misconduct activity and other normal activities, such as writing the exam, being distracted by looking somewhere, etc. Specifically, it includes designing and implementing a deep learning model capable of real-time monitoring of student activity and facial expression. In addition, in this work, the analysis of facial expressions is used to propose an innovative approach to identifying student stress levels, and when they are highly stressed, the system will give motivational feedback to improve concentration and performance. The proposed model has been evaluated using real-time data from a student who was writing the online examination
An Empirical Study on Multi-class Classification of Hostile Posts in Hindi Language
(Department of Computer Science & Engineering, NITS, 2025-07-30) Rajak, Santosh
The exponential growth of social media usage has intensified the dissemination of hostile content online, posing significant risks to individuals and communities. While consider able research has focused on hostility detection in English, other linguistically rich but under-resourced languages such as Hindi remain severely neglected due to the lack of high-quality annotated datasets and specialized classification models. This thesis ad dresses these challenges by presenting a holistic framework for the detection of hostile content in Hindi, with contributions spanning dataset creation, model development, and architectural innovation. The first major contribution is the development of a comprehensive and fine-grained hostility detection dataset comprising approximately 8300 Hindi tweets manually anno tated into five distinct classes: Hate, Abusive, Defamation, Offensive, and Non-Hostile. To exploit this dataset effectively, we developed a suite of baseline and hybrid models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Multi-Layer Perceptrons (MLP), and Decision Trees. These were trained using multilingual embeddings such as GloVe, MuRIL, and XLM-RoBERTa, and fur ther enhanced with emoji-based semantic features to improve contextual interpretation. The hybrid models were fine-tuned to address challenges inherent in Hindi text, such as morphological complexity, and ambiguous phrasing. We leveraged MuRIL’s pooled output to improve Hindi text representation, benefiting from its multilingual understanding and proposed novel hybrid models by combining RNN and CNN architectures with traditional machine learning classifiers. As a methodological advancement, this thesis proposes the Scaled Multi-Head Atten tion–CNN Fusion Model (SMA-CFM), a novel architecture that combines Transformer based attention with convolutional layers to capture both long-range dependencies and local linguistic features from textual as well as emoji features. A trainable attention scaling mechanism is introduced within the multi-head attention block to dynami cally prioritize hostility-relevant tokens. To further enhance classification robustness, a multi-model ensemble strategy integrates feature representations from neural back bones using Random Forest and weighted voting mechanisms. This research makes significant contributions to multilingual NLP by offering a scal able, high-performance framework for hostile content detection in Hindi. It highlights the importance of combining dataset granularity, linguistic contextualization, and ar chitectural innovation to address challenges in low-resource language environments. The dataset, models, and methodologies presented herein lay a robust foundation for future advancements in socially responsible AI and content moderation technologies.
Assessing Attributes of Religious Tourism Experience and Behavioral Intentions
(Department of Management Studies, NITS, 2025-10-24) Das, Anirban
This study investigated the relationships between the experiential attributes and behavioral outcomes of tourist experiences in the context of religious tourism. While religious tourism has gained prominence as a growing segment of the tourism industry, driven by the rising popularity of religious places, there remains a gap in understanding what factors shape tourists' cognitive and emotional evaluations of experiences in religious places and their effects on future behavior. This research addresses that gap by developing a comprehensive conceptual model, which includes the relationships between key religious tourism attributes (spirituality, authenticity, interactions, service experience, and crowding) and examines how these factors impact tourist behavioral responses, through the mediating role of place identity and destination image. Data were collected from the tourists at the Kamakhya Temple in Guwahati, India— one of the most prominent Shakti Pithas in South Asia and a major pilgrimage destination. Using a structured questionnaire, 457 valid responses were gathered and analyzed through a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, complemented by a configurational analysis using fuzzy-set Qualitative Comparative Analysis (fsQCA). The integration of both variance-based and configurational approaches offers a richer understanding of both linear and non-linear pathways influencing behavioral intentions in religious tourism. This study initially developed a base model using structural model, incorporating five key experiential predictors—spirituality, authenticity, interactions, service experience, and crowding—to examine their direct influence on place identity, destination image, and behavioral intention. To evaluate the presence of non-linear effects in tourist experience behavior relationships, the base model was specified without quadratic terms to identify direct linear relationships. Building upon this, a quadratic model was then estimated to capture potential curvilinear effects, particularly for crowding, which prior literature suggests may have non-monotonic influences in dense religious settings. In the quadratic model, spirituality, authenticity, and interactions were found to have consistently positive and significant effects on both place identity and destination image. These attributes also positively influenced behavioral intentions, with the exception of interactions, which, while enhancing place identity and destination image, did not show a direct impact on future behavioral responses. This suggests that social engagement during religious tourism experiences enriches cognitive and emotional evaluations but may not alone drive revisit intentions. Service experience showed a limited influence, significantly contributing only to destination image. Its effects on place identity and behavioral intention were not significant, indicating that while service-related elements may shape how tourists perceive the destination, they are insufficient to create deeper emotional bonds or behavioral commitment on their own. A notable insight emerged with the construct of crowding, which exhibited an inverted U-shaped (curvilinear) relationship with all three outcome variables: place identity, destination image, and behavioral intentions. This result suggests that moderate levels of crowding may enhance the experience by reinforcing a sense of collective participation and ritual intensity, while excessive crowding begins to detract from satisfaction and emotional connection. The non-linear nature of this effect underscores the importance of managing visitor flow and density in religious destinations to maintain optimal experiential conditions. The mediation analysis revealed that place identity served as a significant mediating pathway for several experiential attributes. Specifically, the effects of spirituality, authenticity, and interactions on behavioral intention were significantly mediated through place identity. This confirms that these experiential factors enhance tourists’ emotional and symbolic attachment to the place, which in turn influences their intention to revisit or recommend the destination. Additionally, crowding exhibited an indirect negative effect on behavioral intention via place identity, reinforcing the inverted U-shaped understanding of crowding's role—where excessive density diminishes place-related emotional connections. In contrast, destination image did not mediate the relationship between any of the experiential attributes and behavioral intention in the quadratic model. This suggests that under conditions of complex experiential intensity—such as those shaped by varying levels of crowding—the cognitive evaluations captured by destination image are less influential in shaping future intentions than the affective and identity-based bonds formed through place identity. Overall, the findings emphasize that place identity is a more robust and consistent mediator than destination image in high-density religious tourism contexts, particularly when nonlinear experiential effects are considered. This underscores the importance of fostering emotional and symbolic place attachments in tourism strategy and design. To complement the structural model, fsQCA revealed six distinct configurations (C1–C6) that lead to high behavioral intentions, demonstrating the principle of equifinality—where different combinations of conditions produce the same outcome. These configurations illustrate that religious tourism experiences are conjunctural and interdependent. Configurations C1 to C3 reflected immersive and socially engaging experiences (Type I), with spirituality, interactions, and authenticity acting as core drivers in different combinations. C4 and C5 represented more selective or introspective pathways (Type II), where high behavioral intentions emerged even when certain experiential components like service or place identity were weak or absent. Configuration C6 suggested that a service aesthetic orientation (Type III), characterized by authenticity, service experience, and place identity, could also lead to strong intentions without necessarily involving spirituality or crowding. Crucially, the fsQCA results demonstrated that certain relationships not found to be statistically significant in the SEM model nonetheless contributed meaningfully within specific combinations. For example, crowding—although curvilinear in PLS-SEM—was either absent or peripheral in certain fsQCA solutions, suggesting that its impact is highly contingent on the presence of other experiential factors. This underscores the value of employing a configurational lens to reveal complex, context-dependent causal mechanisms that are not easily captured through linear models. The study contributes to theory by advancing a dual-method framework that integrates PLS-SEM and fsQCA, allowing for both generalizable inferences and context sensitive insights. It enriches the understanding of how tourist experiences in religious settings give rise to affective attachment (place identity), cognitive evaluations (destination image), and future behavioral intentions. The findings also provide managerial implications for religious tourism planners and destination marketers. For example, fostering authentic and spiritually resonant experiences, managing crowding to an optimal level, and supporting visitor interactions can enhance both individual satisfaction and destination loyalty. In conclusion, this research offers a nuanced understanding of the multidimensional and multi-pathway nature of tourist behavioral outcomes in religious tourism. By demonstrating both linear and non-linear effects, mediating mechanisms, and multiple sufficient configurations, the study underscores the importance of designing religious tourism experiences that are not only operationally efficient but also emotionally, socially, and spiritually meaningful.
Assessing latent initiator based multiple healing in epoxy matrix GFRP laminated composites
(Department of Mechanical Engineering, NITS, 2020-07) Prasad, Tankeshwar
The matrix of any structural materials typically consists of brittle polymer, which is susceptible to crack initiation and growth. To resist this flaw, the concept of self-healing is introduced. Present research work demonstrates a latent initiator based healing agent is used externally into the damage region to restore its original mechanical properties after healing followed by a strong covalent bond at the interface. Also, healing can be possible in the multi-step. In comparison to another healing approach, the healing efficiency of healed samples was found higher compared to the virgin sample. In order to achieve this objective, latent initiator based healing agent grafted silica nanoparticles were synthesized using the one-pot/sol-gel method (IFSs) and multi-step process/post-synthesis grafting method (IGSs). Further, the performance and healing ability IFSs and IGSs reinforced epoxy composites have been investigated. After detail investigations, the results concluded that IFSs/IGSs has no healing capability for the resistance of crack in the epoxy nanocomposites. Looking opportunity towards maximum healing efficiency with unaltered mechanical properties, imidazole as a suitable latent initiator based healing agent for extrinsic healing capabilities of IFSs/epoxy composites using have been investigated. After a successful demonstration towards enhancing the toughening and healing performance of nanocomposites, multiple healing capabilities of GFRPs laminated composites using the same healing agent have been investigated further. Initially, the effect of varying content of imidazole on tensile, flexural and hardness properties of epoxy nanocomposites was demonstrated. Composites (IENC-110) added with 10 wt. % of imidazole and 1 wt. % IFS showed maximum enhancement in tensile strength by 61 % (66 MPa) compare to NE (41 MPa) respectively. However, the tensile modulus is remained unchanged as compared to NE. For flexural properties, the significant enhancement in flexural strength and flexural modulus by ~96 % (102 MPa), and 15 % (2.83 GPa), respectively for IENC-15 composites was found. Similarly, the maximum enhancement in hardness for IENC-110 composites by 19 % (24.57 HV) was found as compared to that of NE (20.71 HV). Further, multiple fracture test of SENB was performed to evaluate fracture toughness and healing performance of epoxy nanocomposites. The results showed the maximum recovery in fracture toughness for IENC-110 composites by 67 % (1.17 MPa.m0.5) as compared to the virgin sample (0.70 MPa.m0.5) was achieved over 3rd number of healing cycles. However, the maximum healing efficiency for IENC-110 composites of 140 % due to critical peak loads while 167 % due to fracture toughness at the 3rd number of healing cycles was achieved. Finally, the appearances of hackel marks in the fracture surface under fractography analysis also justify the significant enhancement in surface roughness. Further, multiple healing capabilities of GFRPs laminated composites using the same healing agent have been investigated. The effect of varying content of imidazole on tensile, flexural and hardness properties of GFRPs composites was demonstrated. The maximum enhancement in tensile strength, tensile modulus, strain and modulus of the toughness of GFI-10 composites of ~34 % (~464 MPa), ~13 % (~6.80 GPa), ~13 % (~8.16 %) and ~64 % (~22 MJ.m-3) respectively were found. This is mainly ascribed to the formation of strong interface between the fiber and the matrix due to the addition of imidazole. Further, the optical micrograph of the tensile fracture surface of the virgin sample showed poor through-thickness delamination where the fibers are failed like a broom. In contrast, the addition of imidazole in epoxy/GFRPs composites restricts the through-thickness delamination and reduced the blooming effect with partial fiber breakages. For flexural properties, the maximum enhancement in flexural strength, flexural modulus, flexural strain and work of flexural of GFI-10 composites of ~34 % (~389 MPa), ~22 % (~19.70 GPa), ~100 % (~0.04 mm/mm) and ~72 % (~16.76 MJ.m-2) respectively were found. This attributed the formation of strong bonding between glass fiber and matrix due to sustaining the more flexural stress. Similarly, for hardness properties, the maximum enhancement in the hardness of composite (IEGF-110) added with 10 wt. % of imidazole and 1 wt. % of IFS of 21 % (74.44 HV) was found. This may be attributed to the high packing density of the final network. Further, multiple fracture test of DCB was performed to evaluate fracture energy and healing performance of GFRPs composites. The results demonstrate a maximum recovery in fracture energy for GFI-10 composites by 83 % (1.87 kJ/m2) as compare to the virgin (0.37 kJ/m2) over 12th healing cycles. It suggests that healing with excess (10 wt. %) imidazole with respect to epoxy within the delamination surface increases the number of bonds that bounds the delaminated surfaces strongly during the repetitive healing. However, the maximum healing efficiency for GFI-10 composites of 154 % due to critical peak loads while 182 % due to fracture toughness at 12th number of healing cycles was achieved. Finally, the optical micrograph of the mid fracture surface of DCB samples showed the relatively rough fractures surface because of fiber bridging and fiber pull-out for GFI-10. Thus, the capability of imidazole to restrict the damage in FRPs composites is evident from the fractography investigation. This work successfully demonstrates the capability of imidazole introduce healing capability in GFRPs.
ASSESSMENT OF TSUNAMI HAZARD AND TSUNAMI RESILIENT STRUCTURE PROPOSED FOR THE COASTAL REGION OF INDIA
(Department of Civil, NITS, 2026) Biswas, Soham
This study introduces a comprehensive, multi-scale framework for tsunami hazard assessment that combines global probabilistic forecasting, geospatial vulnerability mapping, and structural resilience analysis to guide risk-informed planning and mitigation. On a global level, a stochastic model estimates future tsunami occurrences across 14 high-risk tsunamigenic zones, using Conditional Probability (CP) and the Total Probability Theorem (TPT). This dual methodology considers both mixed tsunami conditions (any wave height) and high-impact events (wave height > 3 meters). Inter-arrival times are modelled with the best-fitting distributions, such as Gamma, Lognormal, Weibull, and Log-logistic. The tsunami-to earthquake ratio (r) is included in TPT calculations to account for spatial seismic-tsunamigenic variability. Results indicate that, under mixed conditions, the highest tsunami probabilities are in Zones 1 (Colombia, Ecuador, Brazil), 7 (Japan, China, North Korea), and 11 (Solomon Islands, Vanuatu, New Caledonia). For high-impact tsunamis, Zones 3 (Cuba, Jamaica, Haiti), 9 (Malaysia, Brunei, Australia), and again Zone 1 show the greatest risk. This probabilistic framework offers a robust basis for early warning systems and transnational preparedness. A comprehensive tsunami vulnerability assessment for India’s eastern coast was performed at the regional level, utilizing GIS-based spatial analysis combined with a multicriteria decision making framework. Six key parameters, elevation, slope, coastal proximity, land use/land cover (LULC), flow accumulation, and population density, were incorporated through the Analytic Hierarchy Process (AHP) to generate detailed vulnerability maps. These maps highlighted Nagapattinam (166.46 km²), Cuddalore (245.64 km²), Kanyakumari (37.65 km²), Karaikal (35.71 km²), and Chennai (35.63 km²) as highly susceptible coastal zones. To enhance these insights, connectivity overlays depicting major roads, railways, and high-elevation safe locations were added, offering essential guidance for emergency response and sustainable coastal development. On the structural front, the study assessed the tsunami performance of Open Ground Storey (OGS) G+2 reinforced concrete (RC) buildings, with a specific focus on retrofitted stepped column designs. Using Artificial Neural Networks (ANNs) and Monte Carlo Simulations (MCS), fragility curves were developed for three damage states: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP). Compared to traditional RC frames, buildings with stepped columns showed substantial performance improvements, with about a 50% reduction in inter-storey drift and lower exceedance probabilities of 27.05% (IO), 28.57% (LS), and 32.4% (CP). Stepped-column buildings withstood tsunami wave heights of 9.4 m (IO), 11.4 m (LS), and 12.9 m (CP), while regular RC buildings failed at lower wave heights of 7.2 m (IO), 9.3 m (LS), and 10.6 m (CP). The study also compared square and circular stepped column geometries, highlighting the crucial role of column shape in enhancing tsunami resistant performance. At a 50% exceedance probability, circular columns withstood greater wave heights of 10.3 m (IO), 12.1 m (LS), and 13.6 m (CP), outperforming square columns by 9.6%, 6.1%, and 5.5%, respectively. They also exhibited reduced inter-storey drift ratios and delayed hinge formation, indicating superior ductility and energy dissipation. Although circular columns involve approximately 9% higher initial construction costs, they provide long-term economic benefits, including 5–10% reductions in overall lifecycle losses. These findings confirm that circular stepped-column retrofits offer a cost-effective and structurally resilient solution for tsunami-prone infrastructure. Overall, this study integrates global tsunami forecasting, regional hazard mapping, and performance-based structural design into a unified framework, providing practical insights for infrastructure planning, disaster preparedness, and coastal policy-making, particularly in highly vulnerable areas such as coastal India.
BIDDING STRATEGY OF VIRTUAL POWER PLANT IN ELECTRICITY AND CARBON EMISSIONS TRADING MARKETS
(Department of Electrical Engineering, NITS, 2026) Singh, Kumari Nutan
The growing adoption of renewable energy and the shift toward decentralized power generation have driven the development of Virtual Power Plants (VPPs), which serve as an effective approach to combine various Distributed Energy Resources (DERs) into a single, adaptable, and market-oriented system. This thesis presents a comprehensive study aimed at enhancing the operational efficiency, economic viability, environmental sustainability, and cyber security of VPPs through five interrelated objectives. First, a forecasting model for renewable energy resources is designed to precisely estimate the generation from variable sources like solar and wind. Leveraging advanced machine learning methods, this model enhances the reliability and scheduling efficiency of VPPs, enabling more stable grid integration and improved participation in energy markets. Second, recognizing the volatile nature of carbon trading markets, a Carbon Price Forecasting Framework is proposed. This model incorporates market uncertainties and utilizes hybrid forecasting techniques, including deep learning, time series decomposition, and feature selection methods, to provide precise carbon price predictions. Accurate forecasting supports VPPs in making informed trading and operational decisions while complying with carbon regulations. Third, social welfare oriented bidding strategy is developed to guide VPPs in participating in both energy and carbon markets, aiming to balance profit maximization with broader social and environmental obligations. The forecasted results for solar PV and wind power from chapter 2 have been taken for participating in VPP trading. Various carbon emission reduction methods are presented namely: carbon tax, cap-and-trade scheme and uplift payment scheme. The carbon credit price is calculated using carbon tax and uplift payment scheme and then participated in EM. For a day ahead bidding strategy considers carbon trading constraints, emission penalties, and renewable integration while aiming to enhance overall welfare, particularly in systems with high renewable penetration. iii After considering bidding strategy, the thesis conducts a thorough analysis on Unit Commitment (UC) for VPP systems. Therefore, fourth objective this thesis, bi-level UC model for VPP has been presented. For UC model the VPP is again integrated with carbon trading market (CTM) and also with green certificate trading market (GCTM). The proposed bi-level unit commitment model aims to maximize the total profit of the overall VPP systems by minimizing the total cost. By integrating EM with CTM and GCTM, the model optimizes energy dispatch, minimizes emissions, and supports regulatory compliance. The analysis also evaluates the economic implications of carbon prices on dispatch decisions and generation cost. Since, the concepts of VPP is ICT based structure and therefore the maintaining its security is foremost challenge for a power engineer. Finally, fifth objective of the thesis explores the Cyber security aspects of VPPs, recognizing the critical importance of secure data communication and control in a digitally connected energy ecosystem. This also presents the possible cyber-attacks in in VPP systems and its impacts. Potential threats and vulnerabilities are identified, and a cyber-security framework is proposed, including anomaly detection, secure protocols, and resilience strategies to safeguard VPP operations against cyber-attacks. The proposed algorithm Autoencoder utilized for anomaly detection for 9-bus and IEEE-39 bus modified VPP systems created in MATLAB Simulink. The outcomes of this research offer meaningful insights and practical approaches to enhance the technical efficiency, economic viability, and environmental sustainability of VPPs. By addressing forecasting accuracy, carbon market integration, optimal bidding, emission reduction, and cyber security, the thesis contributes to the advancement of future-ready, low-carbon, and resilient power systems.
Commercialization of Horticulture and Its Impact on Farm Households’ Welfare: Evidence from Assam, India
(Department of Humanities and Social Science, NITS, 2026-02-20) Das, Hemanta
Agriculture remains a cornerstone of economic development, livelihood security, and poverty reduction for rural communities, especially in developing countries. Despite its crucial role, the economic condition of farmers in agrarian countries, including India, remains deplorable. Against this backdrop, agricultural commercialization, i.e., transitioning from subsistence farming to market-oriented production, has gained increasing attention as a strategy to enhance farmers' welfare and stimulate rural development. While many studies underscore the potential benefits of commercialization, empirical findings remain mixed, with some indicating improved welfare outcomes and others cautioning against adverse effects. In India, the commercialization of agriculture, particularly through crop diversification towards high-value horticultural crops, has been central to agricultural policy since 2016, especially after the Government’s announcement to double farmers' incomes by 2022. However, despite policy-level emphasis, little empirical evidence exists on how commercial farming influences farm household welfare in the Indian context. This study addresses this gap by evaluating the adoption, marketing, and welfare impacts of commercial horticulture farming in Assam, a state actively promoting horticultural commercialization. Specifically, the study examines the extent and determinants of commercial horticulture adoption, efficiency of marketing channels, determinants of marketing channel choice, and the welfare outcomes of commercialization in terms of farm productivity, income, and employment among farm households. The study utilized both secondary and primary data. Secondary data were sourced from official statistics of government to provide a macro-level overview of Assam’s agricultural performance, including trends in landholding, cropping patterns, and agro infrastructure development. To achieve the main objectives of the study, primary data were collected from 540 farm households across six districts, viz. Kamrup, Barpeta, Dima Hasao, Cachar, Golaghat, and Sonitpur, using a multi-stage sampling technique. vii From secondary data, it is found that Assam’s agriculture is undergoing structural changes, marked by a declining share in GSDP and a shift toward non-crop sectors, i.e., livestock and fisheries. A notable shift in cropping patterns is also seen, with farmers diversifying from traditional cereals to high-value crops such as maize, oilseeds, vegetables, and fruits. Horticulture is seen to contribute the most to agricultural gross value added, indicating its role in income growth and rural development. However, low fertilizer use, poor mechanization, limited irrigation, and weak storage and market infrastructure continue to hinder agricultural development in Assam. To assess commercialization, a composite Farm Commercialization Index (FCI) was constructed, and the Independent Double-Hurdle (IDH) model was used to identify the determinants of both adoption and intensity of commercial horticulture farming. Results indicate that although a significant proportion (69.81 percent) of farmers have adopted commercial horticulture, the intensity remains low (average commercialization at 40.01 percent). Key determinants influencing both adoption and intensity of commercialization include education, risk attitude, off-farm income, access to credit, membership in farmer groups, training, extension services, and market access. Younger farmers are more inclined towards adoption, while female participation remains limited. Interestingly, factors such as irrigation and farm size have differential impacts—while irrigation supports higher commercialization intensity, it does not influence adoption. Conversely, larger farm size encourages adoption but reduces intensity due to possible scale management challenges. Marketing efficiency and determinants of farmers’ channel choices were analyzed using the Acharya and Agarwal method and the Multinomial Logit (MNL) model. It is found that farmers used five main marketing channels, with the direct (Farmer–Consumer) channel emerging as the most efficient in terms of producer share and consumer affordability. However, indirect channels dominated due to challenges in direct marketing. Determinants influencing channel choice include socio-demographic characteristics (gender, age, education, family size), farm attributes (farm size, produce volume), and access to infrastructure (smartphones, vehicles, market yards). Farmers with better connectivity and smaller volumes preferred direct sales, while those with larger farm areas, higher off-farm income, and access to credit favored indirect channels. The study emphasizes that marketing efficiency declines due to intermediary layers, increased wastage, and higher transport costs, highlighting the need for infrastructural and institutional support for direct marketing. Subsequently, the impact of commercialization on farm households’ welfare examined using the Endogenous Switching Regression (ESR) model. This model controlled for selection bias and estimated welfare outcomes under both actual and counterfactual scenarios. The results demonstrate significant positive effects of commercial horticulture adoption on productivity, income, and employment. Specifically, adopters achieved a productivity level of ₹167.714 thousand/ha compared to a counterfactual of ₹59.491 thousand/ha, an increase of ₹108.223 thousand/ha. Similarly, income gains for adopters amounted to ₹42.626 thousand/ha. Non-adopters could potentially increase their productivity and income by ₹121.642 and ₹54.141 thousand/ha, respectively, if they chose to adopt commercial horticulture. Employment effects were also notable, with adopters generating 40.701 more labor days per hectare than if they had not adopted, and non-adopters having a potential gain of 20.359 labor days. These findings confirm the substantial welfare benefits of commercialization for farm households. The study’s policy implications are multi-faceted. First, it underscores the importance of targeting youth and women through training and financial support to increase their participation in commercial farming. Second, strengthening extension services is critical for disseminating knowledge and linking farmers to markets. Third, credit systems need to be tailored to ensure productive use in farm investments. Infrastructure development, such as irrigation systems, cold storage, local markets, and transportation networks, must be prioritized to reduce post-harvest losses and marketing inefficiencies. Additionally, digital tools and mobile platforms can transform farm marketing, offering price transparency, real time market information, and customer engagement. From a farmer’s perspective, the study highlights the importance of forming farmers groups, enhancing collective bargaining, and engaging in continuous learning to improve farming efficiency and market competitiveness. Efficient land and water use, along with diversification and precision farming, can further optimize farm productivity and sustainability. Farmers should leverage mobile technology and digital platforms for marketing and logistical planning, invest in transportable vehicles for improved market access, and engage in value addition to increase product shelf life and revenue.
Comprehensive Design of Nano scale Hetero Junction Dual Gate Vertical TFET: Analytical modelling, Sensing Functionality, and Circuit Level Integration
(Department of Electronic and Communication Engineering, NITS, 2025) Nasani, Karthik
The continuous scaling of CMOS technology has brought substantial improvements in performance and integration density. However, the corresponding rise in power consumption presents major challenges for modern electronic systems. Tunnel Field-Effect Transistors (TFETs) have emerged as strong contenders for low-power applications due to their band to-band tunneling (BTBT) mechanism. This thesis proposes and thoroughly investigates a novel device architecture, the Heterojunction Dual Gate Vertical TFET (HJ-DGVTFET), which is designed to overcome limitations in conventional TFETs including low ON-current (ION), high OFF-current (IOFF), large subthreshold swing (SS), and drain-induced barrier thinning (DIBT). The electrical characteristics of the HJ-DGVTFET are optimized using 2D TCAD simulations and validated through an analytical model based on the 2D Poisson equation. The device comprises an InP source and an In₀.₄₇Ga₀.₅₃As channel/drain region, forming a heterojunction that facilitates enhanced tunneling efficiency. Key design parameters such as indium mole fraction, oxide thickness, source doping, and channel length are varied and studied in detail. Results show that increasing indium content and source doping significantly enhance ION and the ION/ IOFF ratio, while optimizing oxide thickness and channel length further improves device performance. The optimized HJ-DGVTFET achieves a subthreshold swing of 36 mV/dec and a ION/ IOFF ratio of 109. Reliability analysis includes the impact of Gaussian and uniform traps, temperature variations, and lateral straggle on device behavior. Lateral straggle is shown to reduce effective channel length, improving tunneling and transconductance, while temperature primarily affects IOFF, leaving ION relatively stable due to its BTBT nature. Noise analysis is conducted for generation-recombination (GR), diffusion, and flicker noise, revealing that GR and flicker noise dominate at low to mid frequencies (0–1 GHz), while GR noise persists at higher frequencies (up to 10 GHz). This thesis also explores the sensor applications of the HJ-DGVTFET. A biosensor is designed and analyzed for detecting biomolecules such as gelatin, streptavidin, glucose oxidase, keratin, bacteriophage T7, and staphylococcal nuclease. The HJ-DGVTFET biosensor outperforms its single-metal-gate counterpart (SMG-HJ-DGVTFET), showing enhanced sensitivity due to reduced energy band gap and improved gate control. The device ix demonstrates excellent response to positive biomolecular charge and significant ON-current modulation. Additionally, the sensor is employed for CerbB-2 antigen detection, showing high sensitivity and design flexibility for array-based bio-screening. A gas sensor variant of the HJ-DGVTFET is developed, employing catalytic gate materials such as silver, cobalt, platinum, and palladium. Changes in work function due to gas adsorption modulate surface potential and electric field, enabling detection of gases like CO, NH₃, H₂, and O₂. Sensitivity is evaluated based on OFF-current variation, with platinum and palladium achieving the highest sensitivity for CO and ammonia detection, respectively. Furthermore, the thesis includes the development and simulation of a HJ-DGVTFET based digital inverter circuit. The inverter is analyzed under varying temperature conditions and interface trap densities, showing stable performance with minimal temperature dependence. Interface traps, however, introduce undershoot in transfer characteristics, highlighting the importance of trap minimization in digital applications. The results confirm the HJ-DGVTFET's suitability for low-power, high-performance logic circuits, particularly under environmental and process-induced variations. Overall, the HJ-DGVTFET presents itself as a robust and versatile solution for future nanoelectronic applications, combining high reliability, excellent low-power performance, and multi-functional capabilities across sensing and logic domains.
Comprehensive Seismic Hazard Assessment of Odisha State with Emphasis on Bhubaneswar City and Port Areas
(Department of Civil Engineering, NITS, 2025-11-10) Mishra, Satyaprakash
Design and Fabrication of a Gas Sensor for Food Freshness Detection
(Department of Electronics & Instrumentation Engineering, NITS, 2025) Biswas, Bedabrata
Food spoilage is a serious issue that affects both food safety and the economy, leading to health risks and financial losses. Various factors contribute to food deterioration such as natural chemical processes, biological activity, microbial growth, etc. Thus a food’s texture, taste, color, and smell can be changed. One of the clear indicators of spoilage is the release of specific gases. Various gases such as ammonia and formaldehyde are released which result from bacterial activity and chemical reactions. Detecting these gases can help determine whether food is still fresh. There are different methods to assess food spoilage, ranging from simple sensory eval uations to advanced electronic devices. Among these techniques, gas sensors, biosensors, spectroscopy, and electronic noses are popular and effective. Gas sensors provide real-time detection of spoilage gases, while biosensors, which use biological elements like enzymes, offer highly precise results. Spectroscopy techniques, such as infrared and ultraviolet light analysis, help identify chemical changes in food. Electronic noses combine multiple gas sensors and mimic the human sense of smell to improve detection accuracy. Though these methods are useful, some challenges remain. Continued research is necessary to enhance these technologies for limited accuracy, high costs, and lack of portability. Here, we focused on developing compact, highly sensitive gas sensors capable of quickly detecting spoilage-related gases. By improving sensor technology, this research contributes to better food safety measures. However, there are several challenges in designing better detection systems. Many sensors respond to multiple gases at once, making it difficult to pinpoint spoilage indicators. Some sensors take too long to deliver results, while others are too large or complex for practical use outside laboratory settings. Additionally, many existing sensors struggle to detect very low concentrations of spoilage gases. To address these challenges, this research explores different gas sensor technologies, including metal oxide, electrochemical, optical, and MEMS-based sensors. Their working principles, advantages, and limitations are analyzed in the context of food spoilage detection, highlighting gaps in current research. A key development in this study is a Metal-Insulator Semiconductor (MIS) capacitive sensor designed to detect formaldehyde in fish treated with formalin. Constructed using silver and reduced graphene oxide on a silicon wafer, this sensor achieves high sensitivity (142.1 pF/ppm) and provides results within 85 seconds, offering a reliable solution for real-time food safety monitoring. Another major advancement in this research is a resisto-capacitive sensor developed for ammonia detection, a key spoilage gas in fish. This sensor, featuring a gold electrode with a tin oxide layer, delivers quick response and recovery times of 69 and 135 seconds, respec tively. Experimental results confirm its accuracy in determining fish freshness under various storage conditions. Moreover, the study investigates microheater design and optimization, which play a crucial role in enhancing sensor performance. Different microheater structures, viz. tree-type, ladder-type, U-shaped, and spiral-type designs, are tested, with advanced simulations used to improve their efficiency and reliability. A gas sensor, for enhancing food spoilage detection has been successfully developed in this research to address key challenges in food safety monitoring. Formaldehyde is ef fectively detected by the MIS capacitive sensor. Ammonia is accurately identified using the resisto-capacitive sensor, reducing cross-selectivity issues in assessing fish freshness. Sensor performance is further improved through the integration of microheater technology. Future research will focus on increasing accuracy, minimizing sensor size, incorporating machine learning for smarter analysis, and adding wireless features for real-time food monitoring
Design and Synthesis of Engineered Biobased Catalysts for Biodiesel and its Fuel Additive Production: Study on Kinetics, Thermodynamics and Life Cycle Assessment
(Department of Chemistry, NITS, 2026-02-12) Ao, Supongsenla
This thesis focuses on the design, synthesis, and characterization of bio-based heterogeneous catalysts for applications in biofuels and their additives, with particular emphasis on materials derived from bio-waste. Utilizing these waste-derived catalysts not only promotes sustainability but also offers a low-cost and renewable alternative to conventional materials. To improve process performance and capture the interaction between key operational variables, response surface methodology is employed. Building on this optimization, the environmental viability of the overall approach is assessed using life cycle assessment, which provides quantitative insights into the ecological impact of the manufacturing processes and guides informed, sustainability-oriented decision-making This thesis has been organized into 8 chapters. Chapter 1 introduces the research problem and study area, beginning with a historical overview of biofuels and their additives, followed by a timeline of developments in heterogeneous catalysis. This background establishes the foundation for discussing the motivation and scope of the study. Chapter 2 provides a detailed systematic literature review on catalysis for trans/esterification and acetalization of glycerol for this study and formulates the objectives of this study. In Chapter 3, we present the synthesis of a novel nanocomposite, calcium oxide-magnetite (CaO-Fe₃O₄ NCs), derived from waste inorganic biomass (snail shell) using a wet impregnation method. This process involves sequential calcination, hydration, and dehydration steps to address the first research gap of this study minimizing catalyst active site leaching and enhancing recyclability. In Chapter 4, we divided the content into three sections: Chapter 4a, Chapter 4b, and Chapter 4c. In Chapter 4a, we produce a porous biochar catalyst through simultaneous activation and graphitization. This catalyst was then used to optimize the transesterification of JCO using the response surface methodology- central composite design method (RSM-CCD) method. A life cycle assessment was also performed to evaluate the environmental impact of biodiesel production from JCO. In Chapter 4b, a similar catalyst preparation approach was explored using lemon peel as the biomass raw material. This section specifically investigated the effect of porous activator ZnCl2 on the biochar. Likewise, Chapter 4c examined the impact of the porosity activator, but using paper waste as the biomass substrate. Chapter 5 explores the tendency of basic catalysts to induce saponification in environment that are high in free fatty acids, where a two-phase process involving both acid and basic catalysts is commonly needed. To address this challenge, bifunctional catalysts have been developed to facilitate single-step reactions, reducing the need for complex extraction and separation processes. Moving on from biodiesel production, in Chapter 6, the production of solketal, a potential-fuel additive to valorize the large waste stream generated from biodiesel industry was investigated by acetalization using a sulfonic acid-functionalized aromatic carbonaceous material (SAFACAM) as a catalyst under microwave irradiation. Here, catalyst recovery was probed over six cycles, with a modest loss in activity. Chapter 7, outlines the final objectives of our thesis, aiming to synthesize a low-cost biochar (lotus leaf) derived carbon nanodots catalyst through hydrothermal carbonization to form solketal. Employing the RSM-CCD methodology, the experimental matrix was executed, and subsequent data were scrutinized through multiple regressions to model a quadratic equation. Under specific reaction parameters, maximum solketal yield of 95.7 % was attained through the ultrasonication method. Furthermore, life cycle cost analysis (LCCA) for solketal production was studied here, which determined the overall cost of solketal production indicating high commercial applicability. Finally, Chapter 8 summarizes and concludes the findings of the study. In conclusion, this thesis demonstrates the synthetic value of several functionalized biomass-derived catalysts, addressing key limitations that exists in the current biofuel industry. The catalytic pathways and environmental impacts have been optimized for both efficiency and environmental sustainability. These catalysts hold significant potential for future research and could be explored for various organic synthesis processes in the near future.
Efficient Image Dehazing Algorithms with Conventional and Deep Learning Based Techniques Using Multi-Spectral Images
(Department of Electronics and Communication Engineering, NITS, 2026) Bhavani, Murapaka Dhana Lakshmi
Image haze is a common problem in photography and imaging that occurs due to particles like dust, smoke, and moisture in the atmosphere. These particles scatter light, which leads to a reduction in visibility and a degradation in image quality. This scattering effect reduces contrast and visual clarity. The impact of haze is particularly significant in real-time applications, such as autonomous driving, surveillance, and aerial photography, where the quality of visual data is crucial for performance and accuracy. In autonomous driving, for instance, clear images are essential for the vehicle’s vision systems to accurately detect and interpret the surrounding environment. Haze can obscure important details like road signs, pedestrians, and other vehicles, potentially leading to unsafe situations. Similarly, in surveillance, clear imagery is critical for the accurate identification of individuals and activities. Hazy images can hinder the effectiveness of these systems, compromising security and safety. To address these challenges, image dehazing techniques have been developed. These techniques aim to restore the clarity and contrast of hazy images, making them more useful for their intended applications. Image dehazing can be approached using conventional methods as well as deep learning methodologies. Conventional dehazing methods are often based on physical models of atmospheric scattering. However, conventional methods have limitations, particularly in handling complex and diverse haze conditions. This thesis mainly focus on the development of dehazing algorithms, both conventional and deep learning-based, has significantly advanced the field of image processing. These algorithms not only improve the visual quality of images but also enhance the performance of vision systems in various applications. By addressing issues related to low contrast and haze, these techniques contribute to safer autonomous driving, more effective surveillance, and higher-quality aerial photography. The first approach proposed in this thesis is a novel methodology for dehazing single images using multi-scale image fusion. The process involves extracting different scale images from a single hazy image, specifically a white-balanced image and a luminance-based image. These images are then fused using their corresponding weight maps. To avoid unwanted enhancements, pyramid decomposition is applied to the weight maps and input images, enhancing contrast and sharpening the image. This approach effectively removes haze, improving both color and texture information, and produces clear images from a single hazy input. A fast and efficient method is developed with an improved U-Net architecture for image dehazing, enhancing images captured under low illumination, fog, and snow conditions. The methodology involves replacing the pooling operations in the traditional U-Net with Discrete Wavelet Transform (DWT) for up and downsampling. Additionally, an attention module is introduced by fusing up and down samples to identify and recover missing low-level features in the up-samples. Another approach is introduced with a DeepNet convolutional network architecture for effective haze removal from RGB images by accurately estimating the transmission map, indicating light transmission at each pixel. The DeepNet model leverages deep learning to predict the initial transmission map, capturing essential features of haze-affected areas. This initial map is then refined using Near-Infrared (NIR) images, which provide additional, non-visible information. A multi-spectral transmission map fusion approach is employed to further enhance accuracy, combining transmission maps from both RGB and NIR images. The fusion process utilizes haze-aware weight generation, assigning appropriate weights to different regions based on their haze levels, thus leveraging the strengths of each spectral domain for a more detailed and accurate transmission map. Finally a novel image dehazing framework is presented using a Generative Adversarial Network (GAN) architecture, featuring a U-Net generator and dual discriminators. The methodology involves leveraging the GAN’s capabilities to transform hazy input images into clear, dehazed versions. The U-Net generator, renowned for its efficiency in image-to-image translation tasks, plays a central role in this process. Also, dual discriminators for two different spectral input (RGB and NIR) images. It enhances the dehazing by effectively learning and removing intricate haze patterns. The framework trains the GAN to generate high-quality, perceptually realistic images, resulting in significantly improved image quality. This advanced approach provides a robust and reliable solution for generating clear, haze-free images from hazy inputs, crucial for outdoor vision applications. The proposed image dehazing architectures were evaluated using various publicly available datasets, including HazySky, I-Haze, O-Haze, See-in-the-Dark (SID), Exclusively Dark Image Dataset (ExDARK), REalistic Single Image DEhazing (RESIDE), and RGB-NIR. Additionally, the performance of low-light image enhancement using an improved U-Net algorithm was analyzed with several real-time images captured on the campus of the National Institute of Technology Silchar. This study presents efficient and optimized approaches for image dehazing.
Enhancement of Operational Efficiency for PV System in the Application of Solar Energy
(Department of Electrical Engineering, NITS, 2022-11-04) Debnath, Dipanwita
In the modern decade, the intensive developments of technology and changing lifestyle of the current generation of people have made energy an essential and inseparable commodity, but at the same time growing shortage of finite natural resources and degradation of the environmental condition has focused attention on adding the renewable development principle with future technologies. Among all the possible green renewable resources, energy derived from solar power getting continuous significant importance due to countless clear reasons. It is fully clean and inexhaustible in nature and holds out the promise of providing the desired amount of electrical power in need. However solar energy has some drawbacks like lower operational efficiency and high investment cost which present room to expand the research problems. For a reliable solar unit, solar cell accurate modeling is necessary before actual implementation to improve the system performance. The modeling of PV cells involves the extraction of optimized parameters of a selected PV electrical diode model. Since the current vs. voltage (I-V) relationship of PV cell is nonlinear, accordingly model parameter estimation process is viewed as a complicated nonlinear optimization problem, and to denominate this problem numerous dynamic research is going on. So, this dissertation starts with the modeling of different photovoltaic solar cell models based on single diode model (SDM), double diode model (DDM), and a PV module to extract the most optimized parameters of selected models. Further, to maximize the energy of solar PV units, a maximum power point tracker with a potential tracking algorithm is needed for trapping the highest power continuously from a varying source. Under the situation of varying environmental conditions, the output power of the PV module fluctuates. The classical methods (like P&O and incremental conductance method) are unable to give an optimal response in that situation. Many MPPT techniques have been carried out by different researchers in the past to increase the operational efficiency of a PV system, but a lot of research probability still lies in developing an efficient MPPT algorithm. Adding more, to increase solar-based PV yields energy it is important to remember that the temperature of PV cells should not exit its expected module rated value. With increasing surface operating temperature, the operational efficiency of PV units decreases and for that reason to keep the surface temperature close to the surrounding ambient temperature a suitable smart cooling system is necessary. The parameter estimation represents a procedure where measured current-voltage data are used to calculate the parameters of a solar PV cell. Here a very modern and potential optimization technique, named sine-cosine algorithm (SCA) is utilized to accurately estimate the parameters of PV cells/module using experimentally measured I-V data for single and double diode models along with KC200GT PV module. Being SCA technique is a global optimization technique, it does not suffer from singularity problem and provide considerably the most optimized PV panel parameters with better system depiction. The comparison response of SCA outperforms the several published works in terms of root mean square of error (RMSE) and other statistical analysis means. After having success in estimating accurate parameters of solar cells, this thesis subsequently proceeds for developing a novel hybrid global MPPT algorithm to gain maximum power from PV module under the situation of partial shading conditions. Here, improved grey wolf optimization (IGWO) technique, a fusion of parallel grey wolf optimization (PGWO), and opposition-based learning is employed to design the MPPT control structure; for this study, the PV system along with the suggested MPPT algorithm with its components and subsystems is developed through simulation and experimental studies. Firstly, the potentiality of the recommended algorithm to handle the situations: (i) partial shading conditions (ii) gradual solar irradiance and level of temperature changes (iii) rapid irradiance and level of temperature changes have been tested through MATLAB/SIMULINK and subsequently, a prototype setup using ESP32 microcontroller is developed for its practical implementation under partial shading situation. The performance of IGWO has been benchmarked with two classical MPPT methods PSO and P&O. The simulated result of IGWO based MPPT shown here is more effective concerning tracking efficiency as compared to widely used PSO and P&O methods for gradual and rapid change in solar irradiance and temperature levels. The experimental outcomes also show an acceptable performance for the proposed technique. The proposed controller also offers stable sets of responses means there exists no oscillatory behavior around optimal value. The IGWO controller has also succeeded in tracking global maxima efficiently under partial shading. The third work recited in this thesis involves an experimental work equipped with Internet of Things (IoT) platform for enhancing the PV panel performance using the front panel water cooling method for the climate of Bhubaneswar, India. To achieve higher operational efficiency from PV panels, various cooling techniques have been proposed and implemented, but all those methods need continuous observation. A solar panel with no cooling arrangement is used as a base model and compares the test results for both with and without cooling conditions to get the consolidated outcomes. The main objective of using IoT is to give the cooling system a smart touch which ensured the underlying approach successfully records the real-time data for PV system. The result section of this part shows acceptable performance after considering the cooling circuit tooled in IoT system as compared to the non-cooled one.
Environmental Friendly Milling of Inconel 718: Dry, Air cooled and Bio-Based Minimum Quantity Lubrication Machining
(Department of Mechanical Engineering, NITS, 2026-01-07) Sharma, Deepak Kumar
Sustainable manufacturing has become increasingly important due to the environmental and health risks associated with mineral-based cutting fluids. As a safer alternative, bio-based lubricants especially those from vegetable oils are gaining attention because they are biodegradable, renewable, and naturally lubricating. This research focuses on the development, analysis, and use of nanoemulsions based on Putranjiva roxburghii oil for the eco-friendly and efficient milling of Inconel 718, a hard-to-machine nickel-based superalloy widely used in aerospace, marine, and high-temperature applications. The Putranjiva roxburghii seeds, available in certain regions, were processed using a traditional oil extraction method to obtain the base oil. Known for its good tribological performance, this non-edible oil was selected as a sustainable base fluid. To improve its heat, transfer and lubrication characteristics, two nanoparticles silicon dioxide (SiO2) and graphene were added in different weight percentages (0.25%, 0.5%, 0.75%, and 1%). Additionally, hybrid nanoemulsions were created using equal parts of SiO2 and graphene. Emulsions with varying water-to-oil ratios (from 100:5 to 100:30) were also prepared to find the best composition for Minimum Quantity Lubrication (MQL) use. A range of characterization techniques was applied to analyze the nanoparticles’ size, shape, and distribution, along with the thermal, rheological, and surface properties of the nanoemulsions. Results showed that adding nanoparticles significantly improved thermal conductivity, specific heat, and wettability compared to the base oil. The 0.75 wt.% graphene nanofluid showed the highest thermal conductivity, while the hybrid nanofluid (0.5 wt.% each of SiO2 and graphene) demonstrated the best wettability on Inconel 718. These findings suggest that no single nanofluid composition is perfect for every application, highlighting the need for multi-objective optimization based on machining requirements. The performance of these nanoemulsions was tested through face milling experiments on Inconel 718 under dry, air-cooled, and MQL conditions using cemented carbide cutting tools. Machining parameters like spindle speed, feed rate, and depth of cut were varied, and Box’s evolutionary optimization method was used to find the best settings. The nanoemulsions, delivered through an MQL system, were evaluated against performance indicators such as surface roughness, tool and workpiece temperatures, tool wear, cutting force, torque, and power consumption. The findings clearly showed that Putranjiva roxburghii oil-based nanoemulsions performed better under MQL conditions. The hybrid nanofluid in particular provided excellent cooling and lubrication of the combined benefits of wear resistance of SiO2 and high thermal conductivity and lubricity of graphene nanoparticles. This formulation achieved a minimum surface roughness of 0.29 µm 54.7% better than dry machining and 46.3% better than air cooling. The lowest recorded tool and workpiece temperatures were 41.1 °C and 31.46 °C, respectively, at 180 rpm spindle speed, 16 mm/min feed rate, and a 100:20 emulsion ratio. Microscopic inspection showed that tool wear was much lower under MQL, especially with the hybrid nanoemulsion, which showed minimal abrasive and crater wear. In contrast, dry machining resulted in noticeable abrasive wear and built up edge formation. MQL also led to more consistent cutting forces, reduced torque and power consumption, and longer tool life. However, improper dispersion or agglomeration of nanoparticles especially in hybrid mixtures could reduce surface quality and thermal stability. This emphasizes the need for stable and uniform nanoparticle dispersion for effective application. This study showed that nanoemulsions made from Putranjiva roxburghii oil, especially the hybrid type, are a promising and eco friendly alternative to traditional cutting fluids. Their better heat transfers and lubrication improved machining results. Future research can work on improving nanoparticle stability and studying the environmental impact and biodegradability of these fluids.
Ethnic Identity, Insurgency and Economic Development
(Department of Humanities and Social Science, NITS, 2026-02-18) Basumatary, Daoharu
In today’s world there is hardly any country that is ethnically homogenous. Polyethnic societies are often characterised by the competition for resources (including state privileges) across identities—be it ethnic, racial, religious or linguistic— which is often explained through a majority minority frame As economic condition of a social group is largely influenced by the development policy of the state, in most cases, particularly in electoral democracies, these policies are often biased towards the interest of the majority community leaving the minorities in the lurch. This phenomenon has been observed worldwide irrespective of the forms of government where the minority groups mostly find themselves excluded from the participation in mainstream political and economic opportunities which are embedded in the political processes. As a result, the minority groups in most of the countries are economically marginalised. In fact, this divide might further gets accentuated due to the discriminatory practice in the labour market in the lines of race and ethnicity and the practice of segregating the ethnic minorities in lower paid jobs and occupation. Thus, the uneven economic status among the of the minority ethnic groups in a polyethnic society is often an outcome of identity-based socio-politico-economic marginalization and discrimination. Therefore, governments across the globe are tasked with the responsibility of managing ethnic diversity. Broadly speaking the policy of integration or assimilationist is usually followed in managing the ethnic base of a country. Integrationist policy which allows co-existence of multiple cultural groups and accommodative of the claims and aspirations of the ethnic groups have resulted in peaceful existence between the ethnic groups. Contrary to integration is the policy of assimilation built on the notion of cultural homogenization in favour of the majority groups. This type of policy engineered by the majoritarian ruling elites is often faced with stiff resistance from the minorities leading to ethnic assertion, demand for separatism and sometimes call for secession. Inter-ethnic conflict arising out of ethnic assertion especially in developing and underdeveloped countries as they often lack the institutional capacity to manage the elements of conflicts inherent in ethnic diversity have often transcended into insurgent movements by the minority groups. Needless to say, that insurgency spews violence. This results in extreme difficulty for the government(s) in supplying public goods like education, health, housing, transport, electricity, drinking water, and sanitation particularly in rural areas. Moreover, the inducement to invest by the private investors also gets greatly reduced in an insurgent affected area. As a result, the avenues for employment and income generation get reduced, resulting in widespread unemployment that deteriorates the overall well-being and economic condition of the people living in an insurgency-affected area. In such a milieu, unemployed youths often become an easy prey to the ideological call of the insurgents and many joiners see it as the only alternative for meaningful engagement. Thus, a vicious cycle of insurgency and underdevelopment comes into play where one reinforces the other. Therefore, accommodating the claims and aspirations of the ethnic minorities relating to their identity, culture, politics and economy appears to be a way forward for conflict resolution that would allow development initiatives to take hold. In fact, the Bodo ethnic assertion that transcended into Bodo insurgency (1986 2021), can be attributed to their grievances relating to their identity, politics, economy, culture, and language. The Bodo insurgency was solved by federally accommodating the Bodos within the state of Assam. It was with the signing of the 3rd Bodo Accord in 2020, between the representatives of the Bodos on one hand and the Government of Assam and India on the other, under the provision of the 6th schedule of constitution of India, that resulted in the creation of the BTR (Bodoland Territorial Region) constituted by the five contiguous districts of Kokrajhar, Chirang, Baksa, Tamulpur and Udalguri that Bodo insurgency came to an end. The proposed study seeks to empirically investigate whether (a) there exist any difference in the economic status between the Bodos and the Non-Bodos, (b) the ethnic assertion of the Bodos was responsible for the Bodo insurgency, and (c) how this Bodo insurgency affected the economic development of the BTR. To do so three hypotheses were framed, which are 1. Major ethnic groups in the BTR like the Assamese, Bengali and Santhals, have higher levels of economic development in comparison to the Bodos. 2. Identity Crisis in a multi-ethnic society leads to ethnic insurgency. 3. Insurgency negatively affects economic development. For all the objectives the data collected from 408 respondents (determined by using Taro Yamane’s formula) in the BTR were estimated using partial least squares-structural equation modelling (PLS-SEM). Since for the first objective we found that in a polyethnic society the ethnic assertion of the underprivileged minority group(s) can be triggered by the existence of uneven economic development between of these groups. Hence economic development of a nation has to be inclusive that factor into the interests of the underprivileged groups as a way of conflict resolution Secondly, we determined that identity crisis of the Bodos acted as a major driver behind insurgency. This finding has a significant policy implication in polity management and governance particularly in a polyethnic social setting. Our finding calls for federalist accommodation of the ethnic groups as a way forward towards conflict resolution and peace building in a polyethnic social setting a la BTR. Lastly the effect of Bodo ethnic insurgency on the economic development was estimated. We found that insurgency negatively affected economic development. This calls for the development policy of a state to be accommodative of the grievances relating to the identity, culture, politics and economy of the ethnic minorities as a way forward for conflict resolution.
Integrating Data-Centric Insights With Bayesian Approach For Robust Geotechnical Predictions
(Department of Civil, NITS, 2026) Kumar, Dhawal
Uncertainty is an unavoidable aspect of geotechnical engineering, driven by the inherent variability of soils, the incompleteness of site investigations, and the practical limitations of time, cost, and accessibility. Deterministic methods remain the dominant practice, but they reduce uncertainty to fixed parameters and rely on conservative safety factors. While simple, such approaches often either overestimate risk, leading to uneconomical designs, or underestimate it. Probabilistic approaches provide a more rigorous way to manage uncertainty, and among these, Bayesian methods are particularly compelling. They allow engineers to incorporate prior knowledge, update predictions as new data becomes available, and represent both parameters and predictions as probability distributions. Despite these advantages, the adoption of Bayesian methods in practice has been limited by technical complexity, lack of software support, and unfamiliarity among practitioners. This thesis addresses this gap by demonstrating how Bayesian frameworks can be applied to key geotechnical problems, improving prediction, interpretability, and decision-making under uncertainty. The motivation for this research lies in the need to bridge the divide between probabilistic theory and engineering practice. Four geotechnical problems were chosen deliberately because they represent core aspects of design and analysis where uncertainty is unavoidable, existing deterministic or empirical models are inadequate, and data are available but often underutilised. The estimation of shear wave velocity (Vs) from SPT blow count (N) is fundamental for seismic site characterisation, yet regional variability leads to inconsistent predictions. Bearing capacity estimation from Plate Load Tests (PLTs) is central to foundation design but is hindered by limited data and model generalisability. Groutability prediction in permeation grouting is a safety-critical process where complex soil–grout interactions defy simple modelling. Soil liquefaction prediction is one of the most important challenges in earthquake engineering, where the consequences of misclassification are severe. Together, these problems span different domains of geotechnical engineering while sharing the unifying theme of decision-making under uncertainty, making them ideal for testing and demonstrating Bayesian approaches. Shear wave velocity (Vs) is a fundamental parameter for assessing soil stiffness and dynamic response, playing a central role in earthquake engineering, site classification, and liquefaction evaluation. Its empirical correlation with Standard Penetration Test (SPT) blow count (N) provides a practical means for estimating Vs from commonly available field data. However, this correlation is known to vary across regions due to differences in geological history, depositional environment, and testing practices. In this objective, a two-level Bayesian multilevel regression model is used to capture these regional differences systematically. By transforming both variables into their natural logarithms (ln Vs and ln N), the model assumes a common slope across regions and allows for region-specific intercepts. This hierarchical structure captures both within-region variability and between-region differences, enabling better predictions in data-scarce regions through partial pooling. The Bayesian framework also quantifies uncertainty in both parameters and predictions, facilitating the development of regional Vs maps for seismic hazard assessments. Compared to traditional single-level regressions, the multilevel model significantly improves predictive accuracy and interpretability by accounting for the hierarchical nature of the data. The Plate Load Test (PLT) is widely used to evaluate the bearing capacity of shallow foundations under both Ultimate Limit State (ULS) and Serviceability Limit State (SLS) conditions. However, the test is expensive, time-consuming, and often site-specific, limiting the availability of large-scale datasets. Existing machine learning (ML) models trained on PLT data often suffer from overfitting and limited generalizability. To address these challenges, the objective is to develop an ensemble learning framework using Adaptive Boosting (AdaBoost) and Gradient Boosting (GBoost) models optimised through Bayesian Optimisation (BO). A dataset of 576 PLT records from 58 sites was compiled, incorporating input features such as settlement (S), undrained cohesion (Cu), footing width (B), embedment depth (Df), groundwater depth (Dw), and unit weight (γt). The Bayesian optimised GBoost model achieved superior performance compared to both AdaBoost and conventional Artificial Neural Networks. Bayesian Optimisation enabled the efficient tuning of hyperparameters to minimise generalisation error and avoid overfitting. The proposed method provides a robust and interpretable approach for bearing capacity prediction and can be integrated into preliminary design stages, improving efficiency and reliability. Permeation grouting using cement-based suspensions is an essential ground improvement technique, especially for controlling groundwater inflow during tunnelling and underground construction. The effectiveness of grouting depends on several interacting factors, including soil gradation, grout composition, water-cement ratio, and injection pressure. Predicting whether a grouting attempt will succeed (groutability) is challenging due to these complex, nonlinear interactions and the inherent variability in soil conditions. Existing approaches, especially Discrete Bayesian Networks (DBNs), suffer from information loss due to the discretisation of continuous variables, limiting their predictive capabilities. This objective introduces a Hybrid Bayesian Network (HBN) that allows for the integration of both discrete and continuous variables, thereby preserving the richness of continuous soil parameters while maintaining the causal interpretability of BN structures. The HBN was constructed using the bnlearn package in R and benchmarked against DBNs and machine learning classifiers such as Support Vector Machines and Naïve Bayes. The HBN achieved high accuracy, recall, and AUC, while remaining robust to class imbalance and missing data. Its computational efficiency and interpretability make it a valuable tool for field practitioners dealing with groutability assessment and project risk management. Soil liquefaction is one of the most destructive phenomena that can occur during earthquakes. This objective explores the impact of variable transformation and model formulation on the performance of logistic regression for predicting earthquake-induced soil liquefaction. Using 399 field case histories, the variables qc1N and CSR7.5 were transformed into √qc1N and ln(CSR7.5) to better satisfy assumptions of linearity and normality. Probability contour plots for sand and sand mixtures reveal distinct patterns: a nonlinear interaction in sands. Bayesian logistic regression models were developed using both original and transformed variables and a benchmarked empirical method. Comparisons on an independent validation set of 101 cases show that the Bayesian model consistently offers higher accuracy and precision, particularly at lower probability thresholds. Unlike fixed-parameter models, the Bayesian framework captures uncertainty in model coefficients and produces probabilistic boundaries that better reflect the transitional nature of liquefaction behaviour. The model’s conservative classification at higher resistance levels makes it advantageous for seismic risk mitigation. Across these four studies, the thesis demonstrates the practical strengths of Bayesian frameworks in geotechnical engineering. They provide explicit quantification of uncertainty, enhance interpretability through distributions, feature importance, and causal structures, and adapt to new data through iterative updating. The consistent use of published datasets ensured reproducibility and transparency, showing that existing information can be leveraged effectively without requiring costly new testing. In conclusion, this thesis argues for a paradigm shift in geotechnical engineering from deterministic simplifications toward probabilistic, transparent, and data-conscious methods. By demonstrating how Bayesian approaches can be successfully applied to problems of central importance, it shows that uncertainty need not be treated as a limitation but as an opportunity to make predictions that are safer, more interpretable, and more adaptable. In doing so, it bridges the gap between statistical rigour and engineering practice, offering tools that are not only theoretically sound but also practical for real-world decision-making. Keywords: Bayesian inference, multilevel modelling, ensemble learning, Bayesian optimisation, hybrid Bayesian networks, liquefaction prediction, geotechnical uncertainty
Investigation of Mechanical and Tribological Properties of Mg-alloy based Hybrid Reinforced Composite Fabricated through Powder Metallurgy
(Department of Mechanical Engineering, NITS, 2025-11) Kumar, Sudhir
Advancements in material science have driven the rapid development of magnesium-based metal matrix composites (Mg-MMCs) to meet the growing demands of modern engineering applications. These composites offer an excellent strength-to-weight ratio, improved corrosion and wear resistance, and the ability to withstand elevated temperatures, making them ideal for high-performance applications. While pure magnesium has poor mechanical and physical properties, its limitations can be mitigated by incorporating reinforcements, such as ceramic particles in the form of fibers, whiskers, or particulates. These reinforcements greatly improve the strength, ductility, toughness, and abrasion resistance of the composites. The synergistic interaction between the magnesium matrix and reinforcing particles results in microstructural refinement and optimization of the composite’s mechanical behaviour. However, challenges such as inhomogeneous distribution, poor wettability, and weak interfacial bonding between the matrix and reinforcements often arise during liquid metallurgy-based fabrication techniques. Researchers have increasingly adopted the powder metallurgy technique to address these limitations. Overall, magnesium metal matrix composites are recognized as promising candidates for advanced structural and functional applications, providing a well-balanced combination of lightweight characteristics and high mechanical integrity. In this research, hybrid composites of magnesium alloy (AZ91) reinforced with titanium carbide (TiC) and reduced graphene oxide (rGO) were studied. The powder metallurgy method was used to synthesize magnesium alloy-based hybrid composites. The tribological behaviour of fabricated composite samples were examined using a pin-on-disc tribotester per the ASTM G99 standard. Using the Taguchi method, the design of the experiment (DOE) was constructed. And, the L9 orthogonal array opted for tribo-performance evaluation. Also, an ANOVA and regression analysis were carried out to ascertain the relative impact of the tribological factors on the responses. In the present investigation, three variable factors were considered, i.e., reinforcement percentage of TiC (0-10%) keeping constant 0.5 wt.% rGO, disc sliding velocity (0.5 to 1.5 m/s), and applied load varies between 10 to 30 N. It was found that the load parameter had a considerable impact on both the wear rate (WR) and the coefficient of friction (COF). Sliding velocity, on the other hand, had a minimal effect on WR. The concentration of reinforcement exhibits a moderate impact. Taguchi’s predictive results were also compared with experimental results. The optical microscope (OM) v examination of the microstructure of the fabricated hybrid Mg-MMC revealed significant grain refinement and a uniform distribution of reinforcement, thereby enhancing the microstructure. The worn surfaces were analyzed using scanning electron microscopy (SEM), and surface characterizations were used for wear mechanics analysis. In further research, the tribological and mechanical behavior of Mg-hybrid nanocomposites has been studied. Magnesium alloy (AZ91) based composite were fabricated by reinforcing nano-sized titanium carbide (nano-TiC), with weight percentages (wt.%) varying from 0.5% to 2% while maintaining a constant 0.5 wt.% of nano-reduced graphene oxide (rGO) throughout the experiments. Hybrid magnesium metal matrix nanocomposites (HMMMNCs) were prepared using a powder metallurgy procedure. The microhardness and compressive strength of the composite samples showed a significant improvement, with increases of approximately 45.9% and 44.3%, respectively, compared to the matrix material. This improvement in strength is attributed to the synergistic reinforcement effect of both nano-TiC and rGO. Additionally, at the optimal nano-TiC concentration of 1.5 wt.%, the wear rate and coefficient of friction (COF) of the HMMMNCs were decreased by ~40.7% and ~49.33%, respectively. This reduction is primarily due to the formation of a hardened ceramic phase in the contact region, which mitigates heat-induced softening. Additionally, various analytical tools, including XRD, SEM, and a surface roughness profilometer, were utilized to characterize the developed composite materials and analyze the worn surfaces. The analysis of the worn surfaces revealed that abrasive wear was the dominant wear mechanism at lower loads, whereas higher loads and sliding speeds led to adhesive wear, plastic deformation, and transfer film formation. Mg-MMCs have garnered significant interest across various industries, including automotive, aerospace, electronics, sports, marine, and equipment manufacturing. Further research is recommended to explore alternative processing methods as well as to examine the wear behaviour of HMMMCs under diverse environmental conditions. Additionally, the scope of analytical modeling and simulation may be considered in future work to predict and validate the tribological and mechanical performance of HMMMCs under different loading and environmental conditions.
Predictive Forward- Reverse-Hybrid Modeling of Microstrip Antenna Using Machine Learning Based Regression and Classification Techniques
(Department of Electronics and Communication Engineering, NITS, 2025) Kumar, Anjani
This Ph.D. thesis presents the design and development of a new class of printed microstrip antennas optimized for space and satellite communication applications. With the rapid advancement of intelligent computational techniques, particularly machine learning (ML), conventional approaches to antenna design, analysis, and optimization have been significantly improved. In this framework, the thesis proposes an integrated ML-based modeling strategy that combines forward, inverse, and hybrid methodologies utilizing both regression and classification algorithms. The research focuses on accurately predicting key electrical parameters—such as resonance, cut-off, and notched frequencies—and estimating their corresponding geometrical configurations for various antenna architectures, including single port, dual-port, and four-port models. These antenna designs are tailored to operate efficiently across the S-band to X-band frequency spectrum, meeting the high-performance requirements of contemporary satellite communication systems. The forward modelling phase, multiple supervised regression algorithms-such as Support Vector Regression (SVR), Gradient Boosted Decision Trees (GBDT), Ridge, Lasso, Elastic Net, and Deep neural networks (DNNs)-are implemented to predict frequency characteristics from antenna geometries. Conversely, in the reverse modelling approach, the algorithms predict optimal geometrical parameters from known electrical specifications. Hybrid models are further developed by integrating forward and reverse predictions to improve reliability and design efficiency. For antennas incorporating U-shaped slots and Electromagnetic Band Gap (EBG) structures, the ML models effectively learn the complex nonlinear relationships between inputs and outputs. Models are evaluated using data generated from antenna simulator in Ansys HFSS, and in selected cases, validated through fabricated prototypes. Deep learning architectures- artificial neural networks (ANN), convolution neural network (CNN), and recurrent neural network (RNN)-are employed to model more complex multi-output problems involving dual- and quad port antennas, achieving notched frequency responses up to 8.2 GHz. Additionally, the thesis explores classification tasks for recognizing antenna types such as circular, triangular, and rectangular patches using Random Forest and K-Nearest Neighbors (KNN) classifiers. The classifiers achieve accuracies exceeding 97.9%, confirming the robustness of the proposed models. The performance of all machine learning models is meticulously assessed using a comprehensive set of evaluation metrics. For regression tasks, metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), R² score, Mean Squared Logarithmic Error (MSLE), and Root Mean Squared Logarithmic Error (RMSLE) are employed. For classification models, evaluation is conducted using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s kappa, ensuring a rigorous and holistic performance analysis. The results indicate that ensemble methods and deep learning architectures significantly outperform conventional algorithms, confirming the effectiveness of machine learning in predictive antenna modeling. This study introduces a novel data-driven approach that accelerates the antenna design cycle and improves the functional performance of microstrip antennas, particularly for satellite communication applications spanning low, medium, and geostationary orbits.
REHABILITATION OF BEAM-COLUMN JOINT USING GEOPOLYMER ADOPTING REMOVAL AND REPLACEMENT TECHNIQUE
(Department of Civil Engineering, NITS, 2022) Choudhury, Arshad Hussain
The most critical component of reinforced concrete (RC) moment resisting frame is the beam-column joint. Beam-column joint plays an important role in load transfer mechanism between horizontal and vertical components of a RC framed structure. Integrity of beam column joint influences structural stability of RC frame during an earthquake. During lateral loading, beam-column joint goes through large inelastic displacement. Substandard beam-column joint which were designed, detailed and constructed prior to the development of any seismic code of practice lacks in adequate confinement reinforcement and sufficient anchorage length of the beam reinforcement. Such substandard beam-column joint when subjected to large lateral loads during an earthquake, may lead to formation of diagonal cracks and concrete crushing in joint core resulting in sudden brittle shear failure. With advent of seismic code of practice, beam-column joints are being constructed with special confining hoop reinforcement and adequate anchorage. Such seismically detailed beam column joints exhibit better stiffness, ductility and higher energy dissipation capacity. Recently some studies revealed that reinforced concrete (RC) structures designed as per seismic code of practice exhibited damage in joint region when maximum force acting on the joint exceeded the code expectation level. If the damaged joint is not repaired and strengthened, it may cause instability in structure. In order to mitigate structural instability, rehabilitation of damaged beam-column joint is carried out. Rehabilitation is a simple and effective process of repairing and strengthening the damaged structures to the service level so as to provide safety to the occupant. Rehabilitation by removal and replacement technique is generally used for severely damaged beam-column joint. In removal and replacement technique, the fragmented concrete is removed from the damaged joint region and replaced with suitable repair material. From reported literature it was observed that the repair materials that were used to rehabilitated beam-column joint adopting removal and replacement technique were mostly cement based (concrete, cement paste, cement mortar and commercial micro concrete). Due to utilisation of natural resources and huge environment cost associated with production of cement, scientist and researchers were looking for a sustainable binding material. Geopolymer has immerged out as an environmentally friendly cementitious material alternative to ordinary Portland cement (OPC). Use of industrial by-products like ground granulated blast furnace slag (GGBS), fly ash as source material contributed to lower CO2 emission and energy consumption during production stage makes geopolymer an eco friendly green material. In last decade, geopolymer due to better physical and mechanical properties, has shown immense potential as repair material alternative to cement-based materials. But the repair works reported in literatures was associated with structures subjected to monotonic loading only. Till date, no study was carried out to investigate suitability of geopolymer based material on rehabilitation of structures subjected to cyclic loading. In the present investigation, an attempt has been made to rehabilitate substandard as well as seismically detailed beam-column joints following removal and replacement technique using geopolymer mortar (GM). Two one-third scaled substandard beam-column joints having different percentages of beam and column longitudinal reinforcements designed as per pre 1993s design code (substandard) of Indian Standard were used as control specimens. Both control and rehabilitated specimens were tested under quasi-static cyclic loading. Test results indicated that geopolymer mortar were successful in restoring the cyclic performance of damaged substandard beam-column joint in terms of peak load, stiffness degradation, ductility, energy dissipation and damage index up to the level of control specimens but exhibited undesirable brittle joint failure, similar to that of substandard control specimens. For seismically detailed specimens, two one-third scaled beam-column joints of two different groups categorized on flexural strength ratio (1.2 and 1.4), designed as per seismic code of practice were considered in the study. To compare performance of geopolymer mortar with that of cement mortar, another set of seismically detailed beam-column joint (one from each group) were rehabilitated with cement mortar following same rehabilitation technique. Seismically detailed specimens rehabilitated with geopolymer mortar exhibited 20 – 21% higher initial stiffness, 19 – 22% higher displacement ductility and 23 – 24% higher cumulative energy dissipation compared to specimens rehabilitated with cement mortar. But irrespective of repair material, removal and replacement technique was only able to partially restore the cyclic performance of rehabilitated specimens. This was attributed to presence of yielded hoop reinforcement and cold joints in seismically detailed specimens. When removal and replacement technique was used to rehabilitate seismically detailed beam-column joint, only fragmented concrete was removed and yielded hoops from the damaged joint core are not removed and it remained inside the joint core after rehabilitation. Therefore, investigation was carried out to study the effect of yielding of hoops on cyclic performance of seismically detailed beam-column joint. For this purpose, four numbers of one-third scaled beam-column subassemblies with flexural strength ratio 1.4 were prepared. All four specimens were designed and detailed following relevant provisions of Indian Standard Code of Practice. Out of the four specimens, one was used as control specimen and the rest three were cast with different magnitude of pre-yielded hoop reinforcement in the joint core. It was observed that specimens with yielded hoop reinforcement showed lower peak load, lower ductility and lower energy dissipation along with higher stiffness degradation and damage index. On the other hand, due to removal and replacement, there exists cold joint between old concrete and new repair material. Moreover, yielded hoops remain inside the repaired joint core after rehabilitation adopting removal and replacement technique. Till date, no attempt has been made to investigate combined effect of cold joint and yielded hoop reinforcement on cyclic behaviour of seismically detailed beam-column joint. When tested under cyclic loading, specimens with both cold joint and yielded hoop reinforcement shown the highest reduction in cyclic performance in terms of initial stiffness, stiffness degradation, ductility and cumulative energy dissipation compared to individual effect of either cold joints or yielded hoops. Between individual parameters – cold joint and yielded hoop reinforcement, cold joint played a more dominant role. This was established by reduced cyclic performance of specimens with cold joint in terms of initial stiffness (15 -17% lower), displacement ductility (7 – 9% lower) and energy dissipation (18 – 19% lower) in comparison to specimens with yielded hoops. It was observed that though GM was able to enhance performance of rehabilitated specimens but was not able to alter mode of failure brittle joint failure to ductile beam failure in substandard specimens. Furthermore, when seismically detailed specimens were rehabilitated with geopolymer mortar, rehabilitated specimens displayed beam failure accompanied by joint failure as well as specimens exhibited lower performance compared to control under cyclic loading. Such failure is undesirable and unacceptable as per current seismic design philosophy. To mitigate underperformances in beam-column joints, steel fiber reinforced geopolymer mortar (SFRGM) was used for rehabilitation of both substandard and seismically detailed specimens adopting same repair technique. For substandard specimens, SFRGM not only increased performance of rehabilitated specimens substantially above the level of controls but also alter mode of failure to ductile beam failure. In case of seismically detailed beam-column joints, specimens rehabilitated with SFRGM exhibited beam failure along with 28 – 34% increased peak load, 20 – 26% increased initial stiffness, 109 – 113% increased ductility, 177 – 212% higher cumulative energy dissipation, substantial lower stiffness degradation and lower damage index.

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