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Energy Efficiency of Indian Manufacturing Industries: Evidence from Unit-level Analysis
No full textLike the dosage of glucose in blood cells, the energy intake of an industry must fall under an optimal limit; otherwise, it might harm the entire system. Often overseeing such idealism and driven by rapid industrialization, technological progress, and rising urbanization, emerging countries like India strive for ever-increasing energy demand to prove their stance on global production and meet their large consumer base. Such massive energy consumption exerts excessive strain on the availability of energy supplies and poses significant economic and environmental challenges. India ranks third in the world’s energy consumption and CO2 emission globally. Within India, manufacturing industries are the principal end-users, accounting for more than half of industrial energy and contributing as the highest carbon dioxide-emitting sector. Therefore, Indian production units must be energy efficient. Considering the industrial growth, energy consumption nexus, and trade-off, energy efficiency is the most effective reform. However, India has not yet achieved its energy efficiency capacity and lagged far behind. Several earlier empirical exercises adopted a piecemeal approach and, at best, contributed to increasing ambiguity in analyzing energy efficiency. Hardly any comprehensive effort exists that can combine a scientific measurement process of energy efficiency, exploration of its determinants, impacts, and spillover effects considering the latest parsimonious unit-level database. Therefore, in this present research, our objectives are fourfold. First, this study computes the energy efficiency scores of Indian manufacturing plants controlling their desirable and undesirable outputs. Second, the research investigates the role of different plant and industry-specific factors that determine the energy efficiency scores of Indian manufacturing industries. Third, the study examines the impact of energy efficiency on plants’ performance. Fourth, it investigates the spillover channels and their mediating effects on the plants. The present study is based on secondary data. The sources include unit-level panel data from the Annual Survey of Industries, firm-level data from the Centre for Monitoring the Indian Economy ProwessIQ, and the Input-Output Transaction Table (2015-16) of the Centre for Social and Economic Progress. The study covers twenty-one categories of two-digit classification types as mentioned in National Industries Classification-2008. However, the analysis is further divided into two sets of high and low-energy intensity groups, and finally, at the individual industry level. A total of 2251 plants from 21 manufacturing industries from 2001 to 2018 are considered, covering 40518 observations. However, data merging has further reduced the sample in spillover effect analysis. The study applies three variants of Data Envelopment Analysis: Directional Distance Function, Slack-Based Measure, and the Non-Separable Hybrid Model to compute the energy efficiency scores. This research applies the Global Malmquist Productivity Index to compute the manufacturing plants’ productivity level. The study uses fractional regression models to identify the determinants. The study further involves POLS, fixed and random effect, 3SLS, and SUR to investigate the impact of energy efficiency on manufacturing plant performance. The findings reveal that the Indian manufacturing industries have significant potential to improve their overall efficiency. The group-wise result highlights that the high energy-intensive group is less efficient than the aggregate manufacturing and low energy-intensive group. However, the result demonstrated that few industries have higher efficiency scores even after high energy consumption. The study further demonstrates that the plant-specific variables such as the size of labor, skilled labor, privately owned plants, urban located plants, resource allocation, technological progress, information and communication technology, higher capital intensity, import intensity, and the adaptation of ISO-certification have promoted energy efficiency. In contrast, size, age, and capital intensity have adversely affected the energy efficiency level in the Indian manufacturing sector. In addition, the result reveals that the industrial concentration has promoted energy efficiency, but the structure has negatively affected the energy efficiency. The study has mixed evidence in individual and group categories. The study also discovers that the effect of energy efficiency on gross value added and profit is positive, unlike the total factor productivity. Additionally, the study finds positive spillover effects both horizontally and vertically. Based on the empirical findings, the study provides comprehensive policy suggestions for the sustainable development of Indian manufacturing industries. At the end of the analysis, the study identifies its limitations and provides scope for future research
Graphitic Carbon Nitride (g-C3N4) Based Heterostructur Matters for Photocatalytic Environmental Applications under Natural Sunlight Illumination
No full textAn imminent threat of heightened environmental pollution is due to expanding and the rise of modern industries. Among the most pressing issues is water contamination from diverse pollutants, highlighting an urgent need for sustainable freshwater resources. Moreover, the ubiquitous presence of organic and inorganic pollutants in wastewater from various industries causes significant environmental damage. So, the shortage of fresh water and water pollution has become a global concern. To address these concerns, visible-light-driven photocatalysis is considered as a promising green technique capable of effectively degrading organic and inorganic contaminants into environmentally friendly products. The metal-free polymeric semiconductor g-C3N4 has been demonstrated to be one of the ideal compounds for environmental application because of its conjugated structure, non-toxicity, and cheap cost. Therefore, in this doctorate dissertation, an attempt has been made to investigate the development of distinct g-C3N4-based improved photocatalytic matters that may be employed for environmental pollution remediation. A thorough characterization was conducted to comprehend the fundamental, optical, morphological, and photoelectrochemical features of the synthesized heterojunction matters. The photocatalytic apply of the developed heterostructure matters were tested for photodegradation of organic (dyes) and inorganic (heavy metals) pollutants. In the first scenario, g-C3N4/metal-oxide heterojunction nanocomposites, i.e., heterojunction of graphitic carbon nitride with zirconium oxide (g-C3N4/ZrO2), were effectively developed via an ultra-sonication process and were discovered to have outstanding capabilities for rhodamine B dye degradation. In our second scenario, we attempted to build g-C3N4/metal sulfide heterojunction systems, namely hexagonal CuS coupled with g-C3N4 layer (g-C3N4/CuS) by in-situ hydrothermal approach, which were shown to have outstanding visible-light (sunlight) photocatalytic proficiency against various organic dye (mainly methyl orange and methylene blue) degradation. In final scenario, successfully produced a g-C3N4/metal oxy-halide system, i.e., a graphitic carbon nitride linked BiOBr nanoplate heterojunction (g-C3N4/BiOBr) with increased photocatalytic activity for dye degradation (rhodamine B and methylene blue) and Cr(VI) reduction. To comprehend the mechanism behind photocatalytic proficiency, thorough mechanistic studies as well as band structure studies of the all developed matters were carried out. Ultimately, the work reported in this thesis demonstrates the potential for effective photocatalysis in environmental applications involving visible-light (sunlight) active graphitic carbon nitride nanomaterials
Vulnerability Analysis of Power Grid Using Complex Network Theory
No full textThe topological analysis of complex networks provides an essential information about the structural and vulnerability assessment of real-world power grids (PGs). However, there are many ways to represent a complex network as a graph. The electrical complex PG (CPG) can be analyzed as a simple graph by ignoring the electrical dynamics and considering it as an unweighted and undirected network. These methods do not include the weight of lines, which is a disadvantage of such an analysis. For a PG network, weights (impedance) relate to electrical concepts, such as maximum power through the line. Therefore, a weighted network assessment can be efficiently used to improve the PG network representation. Here, we explore the structural analysis of various synthetic and real-world PGs such as the IEEE 39, 118, 300 bus networks, and the Indian PG based on both topological and electrical connectivity. The topological analysis is carried out by taking into account these networks as unweighted and weighted networks. Our work is here divided into three categories. Firstly, we analyze the structural details of the PG networks. Secondly, through the centrality matrices adapted from complex network theory, we identify the most critical or vulnerable transmission lines and buses/nodes. We estimate the impact on the performance of the electrical PG by modeling cascade events from the removal of these lines and nodes. Based on these removal strategies, the AC optimal power flow problem is solved and then compared for both weighted and unweighted networks. It is shown that a weighted network can provide a better insight for the vulnerability assessment in comparison to an unweighted network. We also utilize these two graph models, e.g., unweighted and weighted graphs, to compute the centrality metrics for targeted/intentional node attacks. Based on these centrality measures (CMs), we develop various node-attack strategies and empirically examine the effects of targeted attacks on the structural and operational performance of the CPGs. These measures help us describe the transfer capability and performance under normal operation and evaluate the vulnerability of the power system under cascading failures. Through a series of simulations on IEEE 118, 300 bus, and Indian PG networks, we demonstrate that key nodes with high electrical centrality can be effectively identified. The resulting cascading failures can also lead to a significant reduction in the size of the giant component and capacity index, thereby confirming the accuracy and effectiveness of the proposed analysis. Moreover, the CM measure is one of the most fundamental metrics for evaluating the efficiency and vulnerability analysis of CPGs. Despite an abundance of different CMs for individual nodes, there are only a few metrics available to measure the centrality of individual lines without geodesic shortest path-length. Here, we also propose the current-flow line centrality to identify the ranking of lines, where each set of lines is associated with a different level of importance. We find the CMs using effective resistance and apply it not only to identify the important lines in a commonly used IEEE 118 bus network but also to analyze the efficiency and vulnerability of the CPGs
Optimal Load Frequency and Voltage Control in Interconnected Power Systems: A Model Predictive Control Approach
No full textModern electric power systems are exceptionally complex human engineered system, constantly evolving in scale and complexity. This growth stems from the integration of renewable energy sources, sophisticated power electronics, and other cutting-edge electrical equipment. However, ensuring the smooth flow of electricity to consumers remains paramount. To achieve this, the power-system system must maintain a state of continuous stability. This dissertation explores into the intricate dynamics of power system stability, focusing primarily on two crucial aspects: frequency stability and voltage stability. It explores the complexities of frequency and voltage stability, their impact on power systems, and the measures taken to preserve them. The work introduces innovative control methods employed to keep the power grid steady, guaranteeing a continuous supply of dependable electricity to users. A disturbance in a power system causes the frequency to deviate from its nominal value. The load and generation of the system are strategically adjusted to restore the synchronous frequency. This work introduces novel shrinking-horizon Model Predictive Control (MPC) technique, which employs a centralized controller for managing the load-frequency of a single-area power system and distributed controllers for multi-area systems. The controller optimally changes generation settings and sheds non-critical loads to make the frequency and tie-line power deviation zero. In contrast to existing approaches that use an approximate first-order transfer function model, this work presents a structure-preserving linear state-space model for power systems. This model takes into account frequency and voltage dependencies of both load and generation, allowing for more accurate representation of power system behavior. During rescheduling, the controller minimizes additional cost associated with changes while satisfying various operational and physical constraints. The increasing penetration of Renewable Energy Sources (RESs) in a power system makes the conventional LFC more challenging, since the power output from RESs is unpredictable or stochastic. In this thesis, a novel MPC based Stochastic Load Frequency Control (SLFC) technique is proposed, that enables high amounts of RESs to be integrated while maintaining reliable and stable operation. A structure-preserving linear state-space model for power systems is derived which more precisely represents a practical power system behavior. The proposed controller calculates optimal generation settings by minimizing an optimization problem subject to a set of constraints. To model the stochastic nature of RES power outputs, frequency deviation is added as a chance constraint in the optimization problem, transforming it to a chance constrained optimization problem. This ensures that the probability of frequency deviation at the end of prediction horizon, lying outside a specific range, is always less than some predefined confidence level. Voltage instability in power systems arises due to the shortage of reactive power and may cause abnormally low bus voltages leading to a partial or complete blackout. In order to maintain the system voltages within a safe limit, voltage control techniques such as shunt capacitor banks, Static VAR Compensators (SVCs), load shedding, transformer tap-changer blocking, etc., are employed. In this dissertation, a novel receding-horizon MPC based voltage controller is proposed, which by optimally controlling generator reactive power and SVC output, maintains the voltage stability of a power system. For this, a sensitivity-based analysis is performed to design a state-space model of the power system. The frequency and voltage dependency of load and generation are considered in the system equations. The voltage control is done step-wise, and the optimal control action in each step is calculated by minimizing a cost function subject to a set of relevant constraints. Different Voltage Stability Indices (VSIs) are used as a measure of voltage stability and also used in the constraints for the optimization problem. The performance of the proposed controller is evaluated on the standard IEEE 9, 39 and 118 bus test systems to prove the efficacy of the proposed control techniques, under different operating conditions and in the presence of different contingencies
Study of UV-bright Stars in Galactic Globular Clusters Using Ultraviolet Imaging Telescope (UVIT) Observations
No full textThe hot stellar systems of a globular cluster (GC), such as horizontal branch (HB) stars, hot post-HB stars evolving towards asymptotic giant branch (AGB) phase, AGB-manqué stars, post-AGB stars, post-(early) AGB stars, white dwarfs (WDs), blue straggler (BS) stars, and blue-hook (BHk) stars, among others, dominate the ultraviolet (UV) emission of the cluster. UV imaging of GCs enable us to determine the evolutionary status of these hot sources and to study the dynamics of the GCs based on the quantity and nature of such hot stellar systems present in the cluster. We present the study of five Galactic GCs, NGC 4147, NGC 7492, NGC 4590, NGC 5272, and NGC 6205 using UV imaging observations with Ultraviolet Imaging Telescope (UVIT) onboard Indian space observatory satellite AstroSat. The UVIT observations were performed in several far-UV (FUV: 1300−1800 Å) and near-UV (NUV: 2000−3000 Å) filters. Apart from the UV observations, several publicly available archival catalogs of GCs such as Hubble Space Telescope (HST) GC survey, Gaia catalog of GCs, Ground-based photometry of GCs, Pan-STARRS catalog, 2MASS catalog, etc., were also used to get the optical and infra-red counterparts of the UVIT observed cluster member stars. The UV and UV-optical color-magnitude diagrams (CMDs) were constructed using UVIT FUV and NUV filters along with optical Gaia G, HST F606W, and V filters to identify the stellar evolutionary phases of the hot UV-bright sources present in the cluster. We identified a total of 1027 HB stars, 34 post HB stars (a mixture of AGB-manqué, post (early) AGB, and hot post-HB stars evolving towards AGB phase), 55 BS stars, and 48 WDs in the five GCs. The morphology of the observed HB stars in the five GCs was studied. The HB stars observed in far UV filters were found to be spread from blue-HB to extreme-HB regions whereas HB stars observed in near-UV filters were found in red-HB, blue-HB, and extreme-HB regions. The UVIT observed HB stars in NGC 4147 were found in the blue-HB region and belong to the second-generation population. There are two discrete groups among the HB stars of NGC 4147 visible in the BaF2−V versus BaF2 CMD. The empirical gap between the two groups was found to be 500 K with Teff range of 8,000 − 9,500 K for the first subgroup and 10,000 − 11,300 K for the second subgroup. The He abundances of HB stars in three GCs NGC 7492, NGC 5272, and NGC 6205 were estimated by matching the observed and theoretical HB stars, generated using BaSTI-IAC stellar models. The He abundances were found to be in the range of 0.247−0.350, 0.252−0.265, and 0.247−0.310 for NGC 7492, NGC 5272, and NGC 6205, respectively. There are 3 and 31 post-HB stars detected in NGC 5272 and NGC 6205, respectively. The UV-optical CMDs were able to detect only those post-HB stars which have evolved from HB mass (MHB) ≤ 0.55 M⊙. The UV-bright WD population of NGC 5272 and NGC 6205 were studied in this thesis and found that most of the hot WD candidates in NGC 6205 are extremely low mass (ELM) WDs which are evolved from the binary stellar systems in the cluster whereas the hot WD candidates of NGC 5272 are mostly lying on single stellar evolution WDs or high mass WDs cooling sequences. The cluster dynamics of GC NGC 4590 were studied using the normalized radial distribution of the observed BS and HB stars and found that the cluster belongs to Family II and is one of the youngest clusters among dynamically intermediate-age Galactic GCs. The dynamical age of the cluster is calculated using A + parameter and found to be 0.423 ± 0.096 Gyr
Robust Numerical Techniques Based on Layer Resolving Meshes for Time-delayed Singularly Perturbed Parabolic Problems
No full textThis thesis enunciates some robust numerical schemes for singularly perturbed parabolic problems with a large time lag/delay in both one and two dimensions. In every problem, a tiny “singular perturbation parameter” (0 < ε ≪ 1) is multiplied by the highest-order spatial derivative term. This small parameter plays a crucial role in determining the nature of solutions to these problems. As ε approaches zero, the solution undergoes a sharp change at some regions namely, the boundary layer and/or interior layer region of the domain. The conventional numerical methods (finite difference methods on uniform mesh) fail to tackle this abrupt change inside the layer regions. Further, the time lag makes finding solutions to the considered problems quite challenging and time-consuming. So, the primary concern is to develop some computationally efficient “parameter-uniform schemes” that provide approximate solutions converging to the exact solution, being independent with respect to both parameters. The “parameter-uniform schemes” are developed via some efficient fitted mesh methods for one and two-dimensional time-delayed singularly perturbed parabolic problems with one or two perturbation parameters along with their possible extensions to problems with semilinearity. The thesis starts with some preliminaries of time-delayed singularly perturbed problems and the numerical strategies to be used for their solutions. All the proposed schemes are applied to two different non-uniform meshes. The well-known Shishkin mesh is used that provide a leading-order approximation with the drawback of a logarithmic term in the convergence. This effect is further rectified by the use of Bakhvalov-Shishkin mesh. The widely-used upwind scheme is tested on some semilinear problems as well as problems with interior layers and problems of higher dimensions with multiple perturbation parameters. In some cases, the Richardson extrapolation is used to elevate the accuracy of the upwind scheme. A second-order accurate hybrid scheme and its modified versions are studied thoroughly to be used for problems with small spatial shifts, problems with interior layers, and problems of higher dimensions. Further, a weighted-variable-based monotone hybrid scheme is also discussed that overrides the need for two separate spatial schemes. For time discretization, along with the conventional implicit Euler scheme, two other efficient approaches are discussed. In the case of second-order accurate schemes, the Crank-Nicolson scheme is used in time to match up the accuracy in the spatial domain. Again, a generalized scheme namely the θ-scheme is applied that can provide first-order as well as second-order accuracy by varying θ. The idea of splitting in time is discussed for the problems of higher dimensions. Finally, the highly efficient Thomas’ algorithm is used to solve the resulting system of linear difference equations. The convergence analysis for all the schemes is thoroughly discussed using the truncation error and barrier function approach. The efficacy of the schemes is proved through numerical experiments. Numerical outputs are presented in the form of various plots and tables to support the theoretical claims. These simulations prove proposed schemes to be leading-order accurate and beneficial over many existing schemes in the literature. A summary of all the major contributions and possible ideas for future enhancement are discussed at the end of this thesis
Effect of Rare-earth Oxides on the Formation, Densification and Property Development of Magnesium Aluminate Spinel Prepared from Different Oxide Reactants in a Single Stage Firing Process
No full textMagnesium aluminate (MgAl2O4) spinel has a high melting point (2135 oC), high strength at room and elevated temperatures, low thermal expansion coefficient, high thermal shock resistance, and high chemical resistance; these properties are very useful during service conditions. MgAl2O4 is an important refractory material for cement rotary kilns, steel teeming ladle linings, regenerator checker-work bricks for glass tank furnaces, etc. Despite these advantages, spinel is difficult to sinter since its formation is accompanied with a volume expansion of ~5-8% due to the density differences between the reactant oxides, MgO and Al2O3. Single-stage firing may densify the material poorly, limiting its applicability in refractory applications. Two-stage sintering improves spinel product densification and characteristics, but it is too expensive and time-consuming for large-scale production. Among the various methods for spinel synthesis, solid-state reaction (SSR) route is regarded as the most cost-effective and easy method for bulk manufacturing from an industrial standpoint. Additives are foreign substances that are deliberately added to a system to aid in processing and improve its resultant properties. The effect of different additives, predominantly compounds of alkali/alkaline earth or transition metals, on the formation and densification of spinel has already been explored extensively. Rare-earth oxides (REOs) are characterised by useful combination of properties such as high melting point, high thermal stability, chemical inertness, etc. and therefore have a great potential to serve as key additives in the development of spinel refractories. The number of studies on the synthesis of magnesium aluminate spinel via a single-stage firing method and use of REOs as additives on reaction sintering of spinel, and their effects on mechanical and thermal shock behaviour are limited, leaving room for further research. Also, only a handful of studies have adhered to the continuous incremental addition of REOs. Intermediate milling prior to sintering stage improves spinel properties, however it may add to synthesis costs and hinder commercialization. Use of commercial grade raw materials for producing in-situ spinel (preformed spinel are otherwise expensive) and use of a single-stage solid state reaction sintering process can offset the cost of REO-doped spinel. Additionally, if the amount of REO addition can be maintained low relative to bulk spinel, the overall cost of such materials can be reduced. Therefore, the current work was performed to discern the role of incremental addition of each of the rare-earth oxides (Y2O3, Sm2O3 and Dy2O3), separately, between 1 to 4 wt. %, on the reaction sintering of various magnesium aluminate spinel batches using different commercial-grade oxides and employing a single-stage solid-oxide reaction sintering route without an intermediate milling step. Initially, a stoichiometric batch of spinel was prepared, and then the effect of each of the REOs on the densification, phase formation, microstructure, and mechanical properties such as flexural strength and strength retained after thermal shock of the spinel product were also investigated. The study showed that 2 wt. % Y2O3 spinel batches showed the highest densification. Y2O3-containing batches showed garnet (YAG, Y3Al5O12) phase at all sintering temperatures. Y2O3 doping densified spinel due to YAG's same cubic crystal structure isotropy with that of formed spinel. Microstructural study showed that 2 wt. % Y2O3 containing batches sintered at 1650 oC have a controlled grain structure. They also revealed better strength and thermal shock behavior than undoped spinel batches. In a similar fashion, for the samarium oxide and dysprosium oxide-based spinel batches, 1 wt. % was obtained as the optimized concentration level. Phases samarium aluminate (SmAlO3) and dysprosium aluminum garnet (DAG, Dy3Al5O12) were found in the Sm2O3 and Dy2O3 containing spinel batches, respectively, at all sintering temperatures which helped in the process of densification, and led to improved mechanical properties. Overall, among all the rare-earth oxides used in the present study, 1 wt. % Dy2O3 showed maximum improvement in property development
Design and Development of Atrial Lead System with Integrated Artificial Intelligence Models for Enhanced Diagnosis of Atrial Arrhythmias
No full textThe electrocardiogram or ECG remains the most widely used and cost-effective tool for diagnosing cardiac arrhythmias. The morphological features of the P-wave on an ECG offer valuable insights into abnormalities in interatrial and atrioventricular (AV) conduction. Analyzing changes in P wave morphology is essential for identifying and characterizing atrial arrhythmias. Despite the extensive use of ECG, discriminating between different types of atrial arrhythmias can be time-consuming and prone to false positives, primarily due to the small size of P-waves and their vulnerability to interference. Traditional 12-lead ECG systems often struggle to detect these subtle P-wave abnormalities, leading to diagnostic redundancies and increased risk of misdiagnosis. Atrial arrhythmias, such as Atrial Fibrillation (AF), Atrial Flutter (AFL), and Atrial Tachycardia (AT), are common among hospitalized individuals with cardiac abnormalities and can significantly impact morbidity and mortality rates. Therefore, improving the quality of ECG signals is essential for minimizing false positives and enhancing diagnostic accuracy in the identification of atrial arrhythmias. The research reported in this thesis was performed to enhance the detection of atrial arrhythmias by improving the signal strength of atrial activity (P/f/F -waves) through optimal lead modification and the implementation of automated algorithms. Machine Learning (ML) and Deep Learning (DL) based algorithms driven by Artificial Intelligence (AI) were used to optimize lead selection and accurately classify arrhythmias, thereby facilitating improved detection and diagnosis of atrial arrhythmias. In the initial stage of the study, a novel Atrial Lead System (ALS) was introduced to enhance the strength of P-wave signals. Advanced Gradient Boosting (GB) and DL algorithms were further employed to improve the detection of atrial activity by ranking optimal bipolar leads. Various ML models, including GB and DL algorithms, were used to evaluate and rank optimal bipolar leads based on P-wave parameters, indices, and AV ratios. AL-I and AL-II were found to have significantly higher median amplitudes, RMS values, and area under the curve for recorded P-waves compared to other leads. The proposed models identified P-lead, AL-II, and AL-I as the top three leads. The selection of optimal leads is critical for improving the detection of P-wave changes. Our study proposed an automated lead selection technique using the CatBoost ML model to enhance the detection of P-wave changes among optimal bipolar leads under various heart rates (HR). P-wave features and AV ratios were extracted for statistical analysis and ML classification. CatBoost outperformed other ML models in Standard Limb Lead-II (SLL-II), achieving the highest accuracy and sensitivity. It demonstrated superior performance for AL-I and AL-II compared to other optimal leads, identifying them as the top two best-performing optimal leads for enhanced detection of P-wave alterations. This study also explored ALS to improve signal strength, noise resistance and reliability for smart and mobile health (mHealth) applications. ECG data from individuals with normal Sinus Rhythm (SR) and various atrial arrhythmias, including AF, AFL, and Left Atrial Enlargement (LAE), were recorded using ALS leads (AL-I and AL-II). Statistical analysis, correlation analysis, and reliability analysis using Bland-Altman plots confirmed significant differences in amplitude, duration, area, and A/V ratio between SR and AF populations when comparing SLL- II, AL-I, and AL-II. The ALS leads demonstrated a substantial improvement in signal quality, Signal-to-Noise Ratio (SNR) and reliability when compared to SLL-II for both SR and AF conditions. Finally, an automatic classification technique using a 1D-CNN and BiLSTM model ensemble was proposed for differentiating atrial arrhythmias from normal SR. Dataset preparation included data from Lead-II obtained from Chapman University and Shaoxing People’s Hospital (CUSPH), which underwent preprocessing, segmentation, and augmentation to ensure balanced classes. The proposed model achieved the highest accuracy of 94% across cross-validation and testing datasets when classifying atrial arrhythmias, showcasing its effectiveness in detecting various atrial arrhythmias. These findings highlight the effectiveness of ALS in enhancing P-wave signal strength and AV ratios, indicating its potential as a valuable tool for enhancing clinical screening and diagnosis of atrial arrhythmias. Additionally, the results from this research may induce diagnostic specialists to incorporate the optimized lead configuration of ALS into long-term, ambulatory, telemetric, and mHealth devices to improve monitoring and ensure accurate diagnosis of various atrial arrhythmias. This approach is reliable for precisely diagnosing atrial arrhythmias in real-time clinical settings
Designing of a Novel Electro-galvanizing System for Improved Anti-corrosion and Mechanical Properties
No full textThe objective of electro-galvanizing was to replace hot dip galvanizing (HDG). In the current investigation, Zn, Zn-Al composite and Zn-Ni alloy electrodeposition was done in acidic sulphate bath at the pH of 3.5 onto mild steel both in direct current (DC) as well as pulsed current mode to explore whether the two techniques would lead to a difference in corrosion behavior of the plated films. A systematic cyclic voltammetry (CV) study was done to get the range of current density (CD) for deposition i.e. -50, -150, -180, -250 mA/cm2. Pulse deposition was done after the DC deposition at the average current density of -180 mA/cm2 at different duty cycles, frequencies and peak current density (PCD). After deposition, coating thickness, adherence and structure was analysed thoroughly. The thickness of all the coatings was found to be in the range of 10 μm to 42 μm. After the analysis of coating, the corrosion behaviour was obtained by potentiodynamic polarization followed by Tafel extrapolation and electrochemical impedance spectroscopy (EIS) with 10 mV AC potential in between 105 to 0.01 Hz in 3.5 wt% NaCl. For pure Zn deposits, best corrosion result was found to be 0.35 mm/a and 0.102 mm/a for the films deposited at -180 mA/cm2 CD and a combination of duty cycle 25 % i.e. PCD of -720 mA/cm2 and frequency of 75 Hz respectively. Best adhesive property was also obtained at -180 mA/cm2 current density whereas for pulse deposition, it shows good properties at -720 mA/cm2 PCD at frequency of 25 Hz with 25 % duty cycle. For Zn-Al deposition, Al nano-powder (< 80 nm) was added to synthesize the composite coatings. Best adherence of coating was also obtained at -180 mA/cm2 current density for DC deposition and for pulse deposition, it was found for -720 mA/cm2 PCD at the frequency of 200 Hz with 25 % duty cycle. The best corrosion outcome was obtained at a current density of -180 mA/cm2 with 0.37 mm/a corrosion rate. Whereas less corrosion rate (0.13 mm/a) was obtained at high peak current density i.e. -720 mA/cm2 at 200 Hz frequency with 25 % duty cycle. done by different technique. A coarse grain structure was acquired and the coating’s corrosion resistance was somewhat improved as compared to Zn coatings. After obtaining the effect on Zn and Zn-Al composite coating, Zn-Ni coating was attempted to check whether the corrosion resistance can further be improved. From the PDP, EIS and adherence analysis, the best-performing film was found to be deposited at - 180 mA/cm2. For PCD, the optimized parameter was obtained at low frequency (25Hz) for high (-720 mA/cm2) and medium (-360 mA/cm2) PCDs whereas at low PCD (-240 mA/cm2), it was obtained for medium frequency (75 Hz). Furthermore, a Hull cell deposition was used to investigate and evaluate the impact of current pulsing and industrialization of the process based on the optimal parameter achieved. The good corrosion properties were obtained for Zn, Zn-Al and Zn-Ni coating at -180 mA/cm2 current density compared to hot dip galvanized sheet. The salt spray test is also a typical process for industrial validation which was used for Zn-Al composite coating. It has been found that after 96 hours of salt spraying at a rate of 0.8 cm3/s, the deposits remained unaltered
Understanding the Diversity, Carbon Metabolism, Bioprospecting Potential and Impact of Environmental Alterations on Heterotrophic Bacteria from Bhitarkanika Mangrove Ecosystem
No full textThe thesis illustrates the bacterial diversity of the Bhitarkanika mangrove ecosystem, Odisha, India with potential cellulose degradation and antibiotic production by mangrove bacteria. Additionally, the thesis also focusses on the impacts of anthropogenic and environmental stressors on cellulose degradation and bioactive secondary metabolite production of the bacteria. Sampling was carried out from five study sites of Bhitarkanika mangrove ecosystem, i.e. Dangmal, Gupti, Habalikhati, Ekakula and Kalibhanjadiha. Bacterial diversity was elucidated utilizing both culture-dependent and culture- independent approaches. Overall, culture-dependent total heterotrophic bacteria varied from 2.16 ± 0.5 to 168.25 ± 1.73 × 103 cfu/g in sediment and 1.53 ± 0.915 to 38.33 ± 0.108 × 103 cfu/ml in water. Key regulatory factors influencing aquatic bacterial abundance included nitrate phosphate, conductivity, and dissolved oxygen in different seasons. Culture-independent study depicted that operational taxonomic unit (OTU) varied from 5077 to 21,207 in sediment and 14,213 to 17,501 in water. The dominance of Bacilli, Gammaproteobacteria, and Betaproteobacteria was observed from culture-based approach while the dominance of Gammaproteobacteria, Alphaproteobacteria and Bacilli was recorded from culture-independent study. α and ß diversity analysis revealed that in sediment, Dangmal site hold the most diverse bacterial community whereas in water all the sampling sites showed distinctly different community composition compared to the other. The study also aims to explore the cellulose degradation process of Bacillus haynesii DS7010 isolated from Bhitarkanika mangrove ecosystem. Optimization revealed highest cellulose degradation by the bacterium at 48 h of incubation and 41°C incubation temperature, with 1% substrate concentration. PCR amplification and homology modelling confirmed the presence of both exoglucanase (glycoside hydrolase 48 or GH48) and endoglucanase (glycoside hydrolase 5 or GH5) within the bacterial genome. Native PAGE, along with a subsequent zymogram assay, unveiled the presence of eight isoforms of cellulase ranged from 78kDa to 245kDa within the bacterium. Anthropogenic and environmental stressors such as pH, salinity, and lead (Pb) affected the bacterial metabolism. Most detrimental effect was observed under Pb followed by pH stress which was determined by reduced bacterial growth, increased intracellular ROS production along with reduced enzyme activity and downregulation of cellulase producing genes (celA and celB). Salinity augmented bacterial growth and cellulose metabolism up to 3% concentration of NaCl. Microcosm study revealed 4.05% reduction in total carbon (C%) content in natural condition while 0.97% decrease in C% under combined stress condition. The study further emphasizes on bioactive compound producing bacteria B. velezensis ES8024 and characterization of bioactive compound effective against potential fish pathogen Aeromonas hydrophila ATCC 35654. Optimization revealed maximum production of bioactive compound at 48 h of incubation at 37°C. Characterization using TLC and ATR-FTIR indicated the lipopeptidic nature of the compound, belonging to the surfactin family lipopeptide. The 1H NMR spectrum highlighted characteristic features of a lipopeptide compound, with a long aliphatic chain of the lipid moiety and aliphatic and amide bonds of the peptide moiety. HRMS data confirmed four surfactin isomers within the compound, with specific peak signals denoting C13 (1030.83 m/z) and C14 (1044.85 m/z) isomers and two C15 (1058.87 m/z and 1059.87 m/z) isomers. Two surfactin synthase genes (srfAA and srfAB) were identified in the bacterial genome. External modifiers were found to impact both bacterial growth and secondary metabolite production, with Pb at 1600 ppm concentration having the most adverse effect. Structural analysis using ATR- FTIR and 1H NMR revealed modifications in both protein and lipid moieties under stress conditions. Functional analysis through well diffusion assay demonstrated variable inhibition zones under different stress conditions. The expression of srfAA and srfAB showed 11.44 fold and 17.41 fold downregulations respectively in response to 1600 ppm Pb concentration. Thus, the overall outcome suggests that Bhitarkanika mangrove ecosystem harbors an overwhelming bacterial diversity. Bacillus is a potential genus within this mangrove ecosystem possessing cellulose degradation and antibacterial compound production capabilities. Both B. haynesii DS7010 and B. velezensis ES8024 are able to sustain under stressors such as pH, salinity and Pb. However, stressed condition can drastically affect both the primary and secondary metabolisms in bacteria