7940 research outputs found
Sort by
Numerical and Analytical Study of the Impact of Droplets on Substrates of Various Topologies
No full textDroplet impact, a common occurrence in everyday life such as rain hitting surfaces or ink spreading, has captured considerable interest across various scientific and technological domains like medicine, aerospace, and materials science. When a liquid droplet meets a solid surface, its behavior is shaped by a complex interplay of physical forces, including interfacial tension, gravity, and viscous effects, influencing its motion and spreading. This research primarily focuses on numerically and analytically studying the impact of droplets on substrates with various topologies. The present computational study utilizes finite volume-based axisymmetric simulations, employing the volume of fluid (VOF) method to anticipate intricate hydrodynamic phenomena. To conduct these simulations, the conservation equations for mass, momentum, and volume fraction are solved using the ANSYS Fluent 18.1 solver. Initially, the droplet surface undergoes continuous deformation upon impacting the thin cylindrical target, progressing through several critical stages: free fall, impact, cap formation, encapsulation, uncovering, and detachment. The computational study considers a range of cylinder-to-droplet diameter ratios ( ⁄ ) from 0.13 to 0.4 to observe various deformation patterns of the droplet. The influence of parameters such as contact angle (), ( ⁄ ), Weber number (), Ohnesorge number (ℎ), and Bond number (), on the maximum deformation factor is analyzed based on numerical results. The findings indicate that the maximum deformation factor increases with rising and decreasing contact angle. An analytical model is developed to explain the maximum deformation factor, showing excellent agreement with numerical findings. Additionally, a correlation is established to predict maximal deformation factors in terms of , ⁄ , , and ℎ, demonstrating strong accuracy within ±1% of the computational data. Following that, numerical studies have investigated the impingement and spreading dynamics of a water droplet around a small right-angled circular cone suspended in the air. An increase in the Weber number () leads to a shorter interaction duration between the droplet and the substrate, particularly for specific values of , ℎ, and ( ⁄ ). Additionally, the interaction time significantly decreases with an increase in the Ohnesorge number (ℎ), while , , and ⁄ kept constant. The droplet dynamics of each stage were clearly observed using pressure contour and velocity vector. Moreover, to characterize the morphological and hydrodynamic behavior of water droplets impingement onto the hemispherical substrate, it has been studied computationally. The effects of various parameters are hemisphere-to-droplet diameter ratio (ℎ ⁄ ), contact angle (), Bond number (Bo), Ohnesorge number (ℎ), and release height (ℎ ⁄ ) on deformation factor () of the droplet is delineated thoroughly. The droplet fails to detach from the target at higher Oh and greater ℎ ⁄ . Based on this, a scatter regime plot has been represented to distinguish between two different hydrodynamic behavior of droplets. Furthermore, the impingement mechanism of a liquid droplet on a solid torus surface is explored through numerical simulations and analytical methods. Key findings reveal that the central film ruptures early when the ratio of torus diameter to droplet diameter ( ⁄ ) is lower, attributed to the development of a relatively thin film. Simultaneously, tiny droplets pinch off at ⁄ = 0.83, while larger detached drops are observed at lower ⁄ = 0.16 due to increased drainage through the hole. Moreover, the first pinch-off occurs more rapidly with the continuous increase of the Weber number () for a specific ⁄ and value, along with a scattered regime map aiding in distinguishing droplet configurations during impinge. A numerical investigation into the binary head- on collision of vertically aligned drops of equal size on a cylindrical substrate is presented. Various dimensionless parameters, including Weber number (), contact angle (), Ohnesorge number (), Bond number (), and diameter ratio ( ⁄ ), are employed to characterize the coalescence and subsequent impingement on the cylindrical substrate. A higher value of ,is attained at greater Weber numbers (), holding , ⁄ , and ℎ constant. When the diameter ratio ( ⁄ ) is lower, and is higher, the ring drop separates from the merged parent drop. Additionally, a theoretical model has been formulated to ascertain the maximum deformation factor. Again, the mechanism of collision and drainage of liquid mass around the spherical substrate suspended within the hollow cylinder using Gerris open-source code by employing VOF methodology. The pattern of the interfacial morphology of droplet collision and drainage mechanism is presented using numerical contours. It has been observed that quantify the drainage of liquid volume passes through the passage, which is denoted as (∗ = ⁄ ) is increasing pattern of ⁄ with continuous progress of time stamp for all cases of ⁄ for a fixed value of . Finally, numerical simulations have demonstrated the impingement mechanism of a hollow droplet on a solid cylindrical surface. The key findings show that the maximum deformation on the cylindrical target increases with a higher Weber number (We), a larger target-to-droplet diameter ratio ( ⁄ ), and a decreasing contact angle. Additionally, as the Ohnesorge number (Oh) decreases, both the spreading diameter and the height of the counter-jet formed after the hollow droplet impact increase
Oceanic and Atmospheric Characteristics Associated with Distinct Intensification Scenarios of North Indian Ocean Cyclonic Disturbances
No full textObservations and numerical modeling have greatly advanced our understanding of tropical cyclones (or TCs) through both research and operational forecasting. Despite the strides made, challenges persist in achieving precise predictions. The integration of satellite and radar data into Numerical Weather Prediction (NWP) models has proven instrumental in enhancing TC forecasts. However, there is a noticeable gap in the research on cases such as Highly Intensified Cyclonic Storms (HICS), Concurrent Cyclonic Disturbance (CCD) pairs and an entire TC season specific to the North Indian Ocean (NIO). While climate phenomena such as El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Convectively Coupled Equatorial Waves (CCEWs) influence the genesis and intensification of TCs, focused investigations of the NIO TCs lag behind those in other basins. The HICS frequency over the NIO basin exhibits an upward trend in both seasons, accompanied by variability in reaching the LMI (Life-time Maximum Intensity) stage. Conversely, there is a positive correlation between accumulated cyclone energy (ACE) and HICS frequency. Examination of HICS occurrences in the Arabian Sea (AS) and the Bay of Bengal (BOB) during climatological analysis reveals distinct patterns, with mid tropospheric relative humidity (MRH) consistently playing a dominant role, predominantly in the AS. Insights into HICS formation are further provided by anomalies in Genesis potential Index (GPI) and Genesis Potential parameter (GPP), highlighting the significant contributions of MRH and vorticity in the central BOB. The formation of HICS is also influenced by ocean-atmosphere heat exchange and various CCEWs. Spatial analysis indicates that Equatorial Rossby (ER) waves coincide with higher relative vorticity, while Kelvin (KV) waves yield mixed results. Madden Julian Oscillation (MJO) supports HICS genesis over the AS but not in the BOB. MT waves create favorable conditions for HICS genesis in the NIO basin, although MRH values do not consistently support this over the BOB. The variability in filtered vorticity can be considered as a precursor for HICS genesis over the AS, with MRH serving as a secondary precursor, though inconclusive over the BOB. In the case of SuCS, it is observed that the genesis is influenced by weak to moderate vertical wind shear (VWS) in conjunction with low-level relative vorticity and MRH, whereas of the CCEWS, MJO and ER waves predominantly govern the cyclogenesis. Sea Surface Temperature (SST) and Tropical Cyclone Heat Potential (TCHP) also significantly impact the genesis process. It is noted that SST, VWS, vorticity, and MRH play roles in the Rapid Intensification (RI) process for SuCS category TCs, with SST emerging as the primary factor, followed by MRH and vorticity. The SuCS type of TC is furthermore characterized by a slower translational speed, allowing considerable interaction between storms and the underlying ocean, contributing to increased intensity. This underscores the significance of translational speed in the intensification of cyclones like SuCS, ultimately reaching their peak intensity. It is observed that various atmospheric and oceanic factors play a crucial role in influencing the behavior of HICS in the NIO basin. The utilization of NWP models further enhances our comprehension of the dynamic and thermodynamic parameters impacting HICS. When evaluating the predictive performance of the atmospheric component of the model for prediction across scales (MPAS-A) for HICS in the NIO, it becomes evident that the model, initiated with ERA-5 data as the initial condition, consistently outperforms FNL consideration in predicting HICS tracks. This superiority is attributed to the higher resolution of the ERA-5 dataset. However, a detailed comparison of HICS track simulations using GDAS and ERA-5 data reveals intriguing dynamics. While GDAS-simulated tracks closely align with observations and exhibit superiority over ERA-5 beyond 72 hours, quantified track errors expose GDAS's advantage in later forecast periods. Examination of the along-track and cross-track errors indicates a rightward bias and slower movement in both types of simulations. The TC intensity root mean square suggests that the simulation considering GDAS, tends to underestimate stronger TCs and overestimate weaker ones. Indian meteorological department (IMD) error assessments reveal ERA-5's superior intensity forecasting from 42 to 72 hours, with subsequent decline. The model successfully replicates cyclonic wind patterns, vertically integrated moisture transport, and moisture conveyor belts crucial for TC development. Analyzing potential vorticity (PV) at the 320°K isentropic surface underscores the model's ability to capture PV structures, highlighting the significance of moisture transfer from the ocean for intensification. Notably, GPP analysis emphasizes the model's improved predictability with ERA-5 as the initial condition compared to GDAS, especially in forecasting key parameters influencing TC genesis. Delving into the dynamic and thermodynamic aspects of HICS cases with ERA-5 as the initial condition, the analysis identifies moisture concentration in the eyewall, intense mid to upper-level warming, and upward drafts as critical factors influencing HICS intensification. Although the model effectively captures these signatures, challenges are observed in representing distinct characteristics of radial wind, temperature, tangential wind, and diabatic heating. The maximum relative vorticity tendency typically occurs within the 600 to 700-hPa range before peak intensity, highlighting the significant contribution of upper-level positive advection to higher positive vorticity, favoring intensification. However, no substantial convergence offset is noted in any of the cases. The NIO basin further experienced an exceptionally active and record-breaking tropical TC season in 2019, characterized by a significantly higher ACE. The ACE for that season was around 4.5 times the climatological average and nearly double the previous record established in 2007. Notable features of the season included unusual positive anomaly values of potential intensity (PI) and elevated SST across AS, creating favorable environment for increased TC frequency and higher ACE values. Negative anomaly values of VWS were observed in both BOB and AS during specific periods, particularly from April to June and October to December, facilitating the genesis and intensification of TCs. MRH anomalies varied across the NIO throughout different months of the TC season. Unfavorable MRH anomalies were observed over the NIO from April to June, while higher and positive anomaly values were noted over the AS from October to December. Positive anomalies of PI, a crucial factor influencing TC activity through local thermodynamic processes, were limited to the southern NIO from April to June. In October to December, positive PI anomalies were confined to the central part of the NIO. The IOD also exhibited an unprecedented positive value in 2019, contributing to El Niño-like rising and sinking motion across the tropics and elevated SST over the western Indian Ocean. This aided the development of HICS type TCs over the AS. Also, the MJO actively influenced TC genesis and intensification across the NIO in 2019. The thermodynamic conditions over the AS were notably more favorable from October to December due to higher and more prolonged SST, resulting in a unique thermodynamic environment that supported the genesis of highly intense cyclones and led to significantly higher ACE values in the latter half of 2019. CCD pairs in the NIO basin generally develop on one side of the equator, but in both the sub basins. It has been observed that higher convection along with lower-level westerlies can function as an early indicator for the onset of CCD pairs. Among CCEWs, the MJO emerges as a prominent factor influencing the genesis of CCD pairs, while El Niño and a positive IOD plays a secondary role. The formation of CCD pairs is more likely within low-level cyclonic anomalies characterized by strong convective conditions associated with various CCEWs. TD and MRG waves display stronger connections with amplified vorticity anomalies and drier conditions with reasonable thermal conditions, indicating them as crucial precursors for such events in the NIO basin
Optimization Techniques and Machine Learning Strategies for Small and Dense Energy Efficient LoRa Networks
No full textLow-Power Wide-Area Network (LPWAN) technologies, particularly LoRa and Lo- RaWAN, are pivotal in the rapidly expanding Internet of Things (IoT) landscape, en- abling wide-area communication with minimal power consumption. However, these technologies face significant challenges related to energy efficiency, scalability, and secu- rity, especially in dense deployments specially in smart cities and other IoT applications. This research addresses these challenges through a comprehensive investigation into op- timization strategies, machine learning applications, and security measures designed to enhance the performance of LoRa networks. Initially, the study introduces the importance of LoRa (Long Range) and LoRaWAN (Long Range Wide Area Network) in wireless communication, emphasizing the necessity for energy-efficient strategies in wireless sensor networks (WSNs). Common optimiza-tion techniques and machine learning models for energy prediction and detection are discussed, setting the stage for subsequent research aimed at improving LoRaWAN de- ployments in IoT environments. Recognizing the limitations of existing energy-efficient methods, such as Adaptive Data Rate (ADR) and its adaptations, especially for mobile devices within WSNs, the research identifies the impracticality of traditional energy optimization techniques re-quiring frequent updates on channel conditions due to LoRa’s duty cycle constraints. To address this, two novel algorithms are introduced, designed to dynamically opti-mize transmission power and Spreading Factor (SF) based on node distance, thereby minimizing energy consumption. These algorithms offer significant advancements in energy-efficient communication for WSNs, particularly beneficial in scenarios involving mobile devices. The theoretical descriptions are strengthened by the hardware imple- mentation of the algorithms. This practical validation not only confirms the conceptual foundations but also showcases the algorithm’s real-world feasibility and performance. However, the algorithms are developed for dynamic nodes and not generalized for a large scale deployment. The next step is to focus on generalizing the algorithm on a network management level where mathematical optimization models are aimed at opti-mizing communication efficiency by balancing energy consumption and delivery ratios. An integer linear programming model was introduced to offer a structured method for network configuration. By optimizing SF and transmission power settings, the algo- Abstract rithm enhances packet delivery ratios and energy efficiency, ensuring the sustainability and performance of IoT networks in dense deployment scenarios. This two-step ap-proach effectively addresses the objectives of minimizing time on air and transmission power while maintaining robust communication. The integration of machine learning (ML) models are explored, proposing an ML-based system for predicting energy consumption in LPWAN-based WSNs. By training ML models with historical data on transmission parameters, environmental conditions, and energy consumption patterns, the system can have optimal settings for transmission parameters tailored to current environmental factors. Real-time monitoring facilitated by ML models allows dynamic adjustments of parameters in response to changing net- work conditions, ensuring optimal network performance. This system also helps identify excessive energy consumption and greedy behavior, enabling proactive management of network resources. An extensive evaluation of twelve machine learning regression models was conducted to pinpoint the most effective model for predicting energy consumption. Performance metrics were utilized throughout the evaluation process to assess the ac- curacy and reliability of each model. Finally, a detection algorithm and a classification model are employed to distinguish between power greedy and standard nodes in the network. By utilizing data from the network, detection and classification models were devised to differentiate between stan-dard and greedy nodes based on their transmission parameters and energy consumption patterns. They aim to develop a method that can effectively identify nodes consuming excessive energy or operating sub-optimally, enabling better management and optimiza-tion of the network. In conclusion, the key findings and contributions of the research are summarized, emphasizing the significance of the developed strategies in addressing en- ergy constraints and enhancing the sustainability of IoT applications. Potential avenues for future research are outlined, including further refinement of optimization techniques, integration of emerging technologies such as artificial intelligence and edge computing, and expanding the applicability of these solutions to other IoT domains
Employer Branding: A Mechanism for Talent Acquisition and Retention in Indian Information Technology Organisations
No full textThis study delves into how employer branding (EB) impacts talent acquisition and retention strategies within India's burgeoning IT sector, where competition for skilled professionals is intense amid rapid industry growth and global demands. Recognizing human capital as a critical asset for sustained competitive advantage, organisations in this sector face the challenge of attracting and retaining top talent amidst global technological advancements and demographic shifts. Employer branding emerges as a crucial tool in this context, aiming to enhance organizational attractiveness and foster employee engagement and retention. The study employs fuzzy analytical hierarchy process (FAHP) to prioritize key antecedents of EB, revealing that factors like work-life balance, competitive compensation, and career advancement opportunities significantly influence both current employees and prospective job applicants. Using structural equation modeling (SEM) with SPSS and AMOS, the research establishes causal relationships among EB initiatives, employee engagement, and retention outcomes. It finds that effective EB not only enhances employee satisfaction and commitment but also strengthens employer attractiveness in the eyes of potential candidates. Social media presence is identified as a critical moderator, amplifying the impact of EB efforts on employer attractiveness and talent acquisition. Practically, these insights provide valuable guidance for HR managers and policymakers tasked with developing EB strategies tailored to the specific needs and expectations of the Indian IT workforce. By understanding and leveraging the drivers of EB effectiveness, organisations can better align their HR practices with strategic goals, thereby enhancing their ability to attract, engage, and retain skilled talent essential for sustained growth and competitiveness in the global marketplace
Development of Hierarchical Control Framework For Stand-Alone Microgrid With Networking Capability To Ensure Long Term Sustainability
No full textIntegration of Renewable Energy Source (RES) provides several benefits such as onsite generation, zero carbon emission, and improved local supply reliability through the formation of microgrids. The stand-alone operation of microgrids faces several abnormalities with the variation of generation and demand. Further, stand-alone microgrid also faces many challenges in maintaining the voltage and frequency because of the intermittent nature of RES and uneven/sudden load switching. The changing load dynamics may pose a severe threat to the microgrid stability due to its low inertia. Thus, a proper control mechanism is mandatory. During off-peak hours or sufficient generation conditions, the generation end (inverter control) control can maintain voltage and frequency to its nominal value adhering to IEEE 1547.1. However, during peak hour or deficit generation conditions, generation end control is unable to maintain the system’s nominal values. Thus, in such a scenario microgrid may collapse due to the large voltage and frequency deviations. Further, a high RoCoF caused due to load switching may degrade the power quality and inverter operation. Therefore, voltage control combined with an inertia-based frequency control mechanism is essential to enhance power flow continuity and alleviate brownouts caused by significant load switching. Furthermore, the heterogeneous switching of loads among the phases also decreases the power quality and increases the Voltage Unbalance Factor (%VUF) and circulation current in a stand-alone microgrid. The dissertation proposes Positive Negative and Zero sequence (PNZS) compensation control on the generation side to mitigate the abnormalities caused due to uneven switching of loads. Additionally, virtual inertia-based frequency control is implemented to address frequency transients caused by sudden load switching. A thorough small signal stability analysis is conducted to demonstrate the effectiveness of the proposed controller under various system dynamics. The sequence component-based inverter control strategy can mitigate abnormalities during sufficient generation conditions and fails during deficit generation. Thus, load-end control through load management is employed to maintain power quality during peak hours/ deficit generation. Further, manual load management is challenging and unreliable. Thus, intelligent load aggregation and management are carried out. However, the resilience of the stand-alone microgrid is degraded during adverse conditions while inverter control and load end control fails to mitigate the abnormalities. In such a scenario, the demand for critical loads can be met through power sharing from possible neighboring microgrids. To realize power sharing between microgrids, networking is done with Interlinking Converter. Thus, the dissertation presents a priority-based Interlinking Converter Control (ILC) strategy to enable power-sharing in network microgrids (NM). Further, a network reconfiguration algorithm is required to restore the supply after a High Impact Low Probability (HILP) event. Thus, a hierarchical control mechanism is mandatory to improve stability and enhance microgrid resilience during adverse conditions (HILP events), peak hours, and off-peak hours. The proposed hierarchical control mechanism is validated under various system dynamics using MATLAB/Simulink and the Digsilent PowerFactory environment. Additionally, the OPAL-RT platform is utilized to assess the real-time performance of the proposed strategy
Multi-metal Resistance Mechanisms in Biofilm Forming Bacteria and Applications of Biofilm Associated Extracellular Polymeric Substances in Multi-metal Bioremediation
No full textThe thesis elucidates the multi-metal resistance, biofilm-forming ability, and enhanced heavy metal removal efficiency of bacteria isolated from metal contaminated sites. Soil, water, and sediment samples were collected from the Sukinda chromite mine and Paradip Port, Odisha, India. A total of 93 bacterial strains were isolated from chromium (Cr), lead (Pb), and cadmium (Cd) supplemented medium, and 58 isolates were found to show resistance to ˃100 mg/L of all the metal ions. The biofilm screening of 58 isolates exhibited strong biofilm formation in 17 strains, moderate biofilm formation in 15 strains, weak biofilm formation in 21 strains, and the remaining isolates showed no biofilm formation. Out of the 17 strong biofilm formers, 8 strains exhibited tolerance to high concentrations of Cr, Pb, and Cd, i.e., ˃500 mg/L. The potent multi-metal resistant biofilm-forming bacterial strains were identified as Pseudomonas aeruginosa OMCS-1, Staphylococcus sp. OMCS-4, Bacillus cereus OMCS-20, Exiguobacterium indicum OMCW-10, Staphylococcus hominis BASS-10, Bacillus cereus BASW-3, Enterobacter cloacae BASW-16 and Pseudomonas chengduensis PPSS-4. These strains showed viable growth in the presence of Cr, Pb, and Cd and efficiently formed moderate to strong biofilm in different concentrations of multi-metal ions. In addition, these strains exhibited tolerance to various other heavy metals, including Ni, Zn, Cu, Mn, and Hg. Scanning electron microscopy (SEM) unveiled closely aggregated bacterial cells embedded within the EPS matrix. Confocal laser scanning microscopy (CLSM) exhibited different biofilm components, providing a three-dimensional structure to the biofilm. The Cr, Pb, and Cd removal efficiency of bacterial strains in biofilm mode was significantly greater (p<0.0001; two-way ANOVA) compared to their planktonic counterparts. The biofilm culture of P. aeruginosa OMCS-1 exhibited greater removal of heavy metals, followed by P. chengduensis PPSS-4. The biomass of P. aeruginosa OMCS-1 showed higher removal of Cr, Pb, and Cd compared to P. chengduensis PPSS-4 at 37°C and pH 6 within 4 h of contact time. The bacterium P. aeruginosa OMCS-1 possesses multiple metal resistance genes, including chrA and chrR for Cr resistance, cadA and cadR for Cd resistance, and metallothionein (mt) for Pb and other metal resistance. The relative expression of these genes was significantly higher (p<0.05; one-way ANOVA) in biofilm mode and under different heavy metal concentrations. The adsorption behavior and interaction mechanisms of extracellular polymeric substances (EPS) of P. aeruginosa OMCS-1 towards Cr, Pb, and Cd were investigated. EPS-covered (EPS-C) cells exhibited significantly higher (p<0.0001; two-way ANOVA) removal of Cr (85.58±0.39%), Pb (81.98±1.02%), and Cd (73.88±1%) than the EPS-removed (EPS-R) cells and followed predominant monolayer adsorption and chemisorption mechanism. Thermodynamics of binding interactions between EPS-heavy metals were spontaneous (ΔG < 0). EPS-Cr(VI) and EPS-Pb(II) binding were exothermic (ΔH < 0), while EPS-Cd(II) binding was endothermic (ΔH ˃ 0) process. The enhanced rigidity of metal treated EPS along with the accumulation of Cr, Pb, and Cd, suggested the biosorption of metal ions onto EPS. The significant increase (p<0.001; one-way ANOVA) in the zeta potential of EPS after interaction with Cr, Pb, and Cd inferred the involvement of electrostatic interactions in metal binding. The unchanged crystallinity (CIXRD = 0.13) and no additional crystalline peaks in the metal treated EPS specified that complexation was the prevalent mechanism in metal sequestration. The hydroxyl, amide, carboxyl, and phosphate groups in EPS predominantly contributed to metal binding. The binding of metal ions altered the degree of stretching in the peptide chain, resulting in deviations in the secondary structure of EPS protein. A strong static quenching mechanism (Kq ˃ 2.0×1010 L M-1 s-1) was evidenced between the tryptophan protein-like substances in EPS and Cr, Pb, and Cd, with binding constants of 3.38 M-1, 3.0 M-1, and 2.81 M-1, respectively. Cr 2p, Pb 4f, and Cd 3d peaks in Cr, Pb, and Cd loaded EPS confirmed the sequestration of metal ions by EPS. In addition, EPS sequestered heavy metals via the complexation with C-O, C-OH, C=O/O-C-O, and NH/NH2 groups and ion exchange by the –COOH group. Further, a multifaceted experimental design, including factorial design, Face centered composite design (FCCD), and mixture design, was implemented to explore the competitive interaction and adsorption behavior of Cr, Pb, and Cd by the immobilized EPS based biosorbent of P. aeruginosa OMCS-1, in single as well as ternary metal solution. The prepared biosorbent preferentially adsorbed Cr (47.6 mg/g), Pb (46.38 mg/g), and Cd (42.02 mg/g) in the single metal system, and Pb (43.32 mg/g), Cr (40.03 mg/g) and Cd (35.9 mg/g) in the ternary metal system. The uptake behavior of all the metal ions was successfully represented by the Freundlich isotherm model (R2 ˃ 0.988), confirming the multilayer adsorption of tested heavy metal. The rate of adsorption of metal ions followed the second-order kinetics (R2 ˃ 0.997), validating chemisorption as the predominant mechanism in the adsorption of tested metal ions. The declined porosity and enhanced rigidity of metal treated EPS Ca-alginate beads, along with the accumulation of Cr, Pb, and Cd, suggested the adsorption of metal ions onto the immobilized biosorbent. The hydroxyl, amine, carboxyl, and phosphate functional groups of the formulated biosorbent contributed to the Cr, Pb, and Cd sequestration. The desorption study exhibited the reusability potential of immobilized EPS biosorbent after four cycles of adsorption-desorption reaction with significant decline (p<0.0001; one-way ANOVA) in the adsorption efficiency. However, the biosorbent efficiently adsorbed 61.52±0.13% of Cr, 70.27±0.12% of Pb, and 42.64±0.04% of Cd in the single metal system after the 4th adsorption cycle with regeneration efficiency of 72.2±0.45%, 78.65±0.6%, and 66.96±0.02%, respectively. Similarly, in the ternary metal system, the adsorption-desorption efficiency retained by the biosorbent was 35.41±0.2% and 51.44±0.98% for Cr, 51.58±0.15% and 63.98±0.24% for Pb, and 30.68±0.13% and 60.39±0.46% for Cd, respectively. Hence, the present study suggests that multi-metal resistant biofilm-forming bacterium P. aeruginosa OMCS-1 and secreted polymer (EPS) can be competently applied to remove heavy metals from multi-metal contaminated wastewater
Health Disparity in India: An Enquiry into Convergence and Divergence
No full textPopulation health is vital to a nation’s overall well-being and development. Reducing health inequalities and increasing interstate convergence in health indicators are necessary for sustainable human development. Evaluation of the convergence patterns can aid the government in monitoring the health progress across the Indian states. Since 1990s, India has witnessed remarkable economic growth, resulting in significant improvements in population health. However, alongside these advancements in the health system, the country also faces challenges in the form of widening health disparities among its states. In this context, the study has examined four primary objectives: first, to investigate the convergence hypothesis in the health status of individuals among Indian states from 1990 to 2020; second, to examine whether health inequalities have widened or converged during this period and explore the impact of economic development on health outcomes; third, to assess the potential regional convergence in healthcare spending among Indian states and also examine the relationship between healthcare spending and fiscal space; lastly, to explore the potential convergence of human development indices across the Indian states. The data used in the thesis are extracted from various rounds of the Global Data Lab (GDL) and the Reserve Bank of India (RBI). The Gini and Theil index are used to measure the absolute and relative health inequality across Indian states. The study tests the convergence hypothesis using the standard parametric (Catching plots, Absolute and conditional beta-convergence, sigma-convergence, and log t-test), and non-parametric model (kernel density estimators) to detect the presence of convergence, divergence, and club convergence among Indian states. Furthermore, the study employs the System-Generalized Method of Moments (S-GMM) model, cross-sectional dependence test, second Generation Cointegration test, fully modified ordinary least square (FMOLS), and Dumitrescu-Hurlin Panel Granger Causality test to examine the above-mentioned objectives. The study findings indicate both convergence and divergence in health outcomes, healthcare expenditure, and human development index. The log t-test results support club convergence, heterogeneity, and divergence in the overall health indicator analysis. The result also highlights inter-state inequality across the health indicators in India. Improvements in economic development, including increased healthcare expenditure, education, and per capita income, have a substantial positive impact on health outcomes. Conversely, income inequality has a detrimental effect on health outcomes, reducing overall well-being. Health improvement and income equality policies are crucial to reduce health disparities and promote economic growth. The findings further emphasize the importance of fiscal space and economic growth in healthcare expenditure. A bidirectional causality between healthcare spending, fiscal space, and economic growth. Tax revenue, non-tax revenue, fiscal transfers, and per capita income positively impact per capita health expenditure, while borrowing negatively affects it in the long run. Enhancing fiscal capacity is essential to ensure balanced healthcare expenditure among the states. This thesis provides a methodological toolkit for empirical analyses of health transition and convergence, specifically emphasizing inequalities in population and health indicators. It sheds new light on the analysis of health inequalities by linking it to health progress and convergence while promoting economic growth
Magnetoelectricity in LaYFe₂O₆ and its Derivatives for Energy Harvesting
No full textAmid the rapid development of technology, magnetoelectric (ME) multiferroic materials provide a fertile playground to explore fascinating electric and magnetic properties. In this backdrop, the development and establishment of magnetoelectricity in a new material or insight may prove beneficial in the evolution of this field. As a variant of perovskite structure, double perovskite oxides have been coveted much research attention in recent years because of various intriguing properties. In this thesis, double perovskite LaYFe2O6 and its derivatives are studied for magnetoelectric applications. Here, an ordered double perovskite LaYFe2O6 is prepared by the sol-gel auto combustion method and its structural, magnetic, and magnetoelectric properties have been studied depending upon three different sintering temperatures (800, 1000, and 1200 °C). The phase purity of all the samples is checked by the X-ray diffraction (XRD) technique followed by the Rietveld refinement method. The diffraction studies establish orthorhombic symmetry having the space groups of P21nm (~ 90%) and Pbnm (~ 10%). The effect of sintering temperature is discernible in the surface morphology, which is inspected by a Field Emission Scanning Electron Microscope (FESEM). The grain size increases from ~ 50 nm to ~ 150 nm by the influence of sintering temperature. Magnetization study reveals antiferromagnetic (AFM) ordering of the spins at the room temperature (RT) having the transition temperature ~ 700 K. Double perovskite formation and AFM ordering is also asserted by the neutron diffraction (ND) measurement. With the increasing temperature, spin canting appears and become maximum near the magnetic transition, which is suggested by the isothermal magnetization study. The enhanced crystallinity with the increasing sintering temperature (displayed in XRD profile and FESEM depiction) is also substantiated by the magnetization measurement. The highest converse magnetoelectric coefficient ~ 2.26(6) mOe ∙ cm V-1 and direct magnetoelectric coefficient ~ 0.45(3) mV cm-1 Oe-1 is recorded at RT. Nonetheless, this material exhibits linear magnetoelectric effect even for temperature as high as 400 K. To enhance the ME coefficient further, magnetic ‘Sm’ is substituted in place of non-magnetic ‘La’ (La1 xSmxYFe2O6; 0 ≤ x ≤ 1). To inspect the role of Sm in this compound, the structural, electric, magnetic, and ME properties have been investigated. Here, XRD study confirms the successful formation of double perovskite structure (P21nm phase only). The surface morphology of x = 0.75 sample asserts good crystallinity along with the sharp edges of the grains, indication of improved structural order. It is observed that, AFM ordering (~ 700 K) exist in pristine sample become enriched by the Sm substitution, which is also substantiated by the depletion of short-range ordering in the form of non-Griffith’s phase. The spin-reorientation transition observed in low temperature region (in lower Sm content sample) moved towards the RT region, which is very effective for application purposes. The x = 0.75 sample displayed 1st-order direct ME coupling coefficient ~ 0.59(4) mV cm-1 Oe-1 (~ 31 % higher than x = 0) at RT. The induced magnetoelectricity in this composition is found to be mediated by spin-lattice coupling. Thereafter, polymer composite of LaYFe2O6 and poly(vinylidene fluoride) hexafluoropropylene [LaYFe2O6/P(VDF-HFP)] is synthesized in the form of thick film by the solution casting method. The prepared films are then characterized via XRD, FESEM, Fourier Transform Infrared Spectroscopy (FTIR) techniques. Depending upon the wt% of LaYFe2O6 nanoparticles (NPs), the excellent phase to phase connectivity and enhanced beta phase fraction in the composite film is ascertained. The strong interaction between polymer matrix and magnetic nanoparticles is also substantiated by the enhanced ferroelectric response (electric-field dependent polarization measurement). Interestingly, 10 wt% NPs based nanocomposite manifests the maximum ME voltage coefficient of ~ 2.92(5) mV cm-1 Oe-1 at RT (~ 1 order higher than the pristine LaYFe2O6). Now, to meet the need for Internet of Things (IoT), miniaturization, and power efficiency, a flexible, lightweight, cost-effective, and portable ME nanogenerator (MENG) is made by this composite film. This MENG can be deployed as an energy harvester to harvest wasted magnetic energy into electric energy with an efficiency of 1.5 % and can charge the capacitor followed by the glowing of a light-emitting diode (LED)
Patient-Specific ECG Beat Classification using Deep Learning Techniques
No full textThe electrocardiogram (ECG) is a non-invasive medical tool used to capture the electrical activity of the heart. It records the electrical impulses generated by the heart muscle and displays them as a waveform on a screen or on paper. Manually, analyzing ambulatory ECG records can be a challenging task due to their duration and a high number of ECG beats. Therefore, an automated diagnosis tool is required for the automatic classification of ECG beats. This motivates to develop a better ECG beat classification system with efficient feature extraction methods for identifying different classes. In this regard, at first deep two-dimensional residual network (2D-ResNet) is developed, and S-transform (ST) based time-frequency ECG beat images are utilized as input to the network. In this context, ST-based ECG depiction in the time-frequency domain offers a response with consistent amplitude across frequencies and adaptable resolution. The resulting ST visuals serve as input for the suggested 2D-ResNet, which classifies five distinct ECG beat types in a manner tailored to each patient, following the guidelines set by the Association for the Advancement of Medical Instrumentation (AAMI). The first five minutes of ECG data from the test subjects are also included in the training data during the training phase of the proposed methods. Therefore, the proposed techniques can be treated as patient-specific in this work. The proposed 2D-ResNet, which does not utilize handcrafted features, leverages the benefits of ST and the 2D-ResNet model to detect the ECG beats automatically. It can capture both the signal’s time and frequency information and reveal patterns that are not apparent in the raw signal. Next, a combination of convolution neural network (CNN) and Bidirectional long short-term memory (Bi-LSTM) architecture is developed to utilize input data effectively during the training phase. Bi-LSTMs, in the proposed network architecture, traverse the input data in both forward and backward directions. Hence, the proposed model benefits from additional training information, contributing to improved performance and robustness. The Bi-LSTMs are better compared to the standard unidirectional LSTMs due to their fixed sequence-to-sequence prediction and increased training capacity. In this proposed model, the CNN component is usually employed as an initial feature extractor. It applies convolutional operations to capture local patterns in the input data. The Bi-LSTM layers in the model are responsible for capturing temporal dependencies and modeling long-range context information in both forward and backward directions. The major Abstract limitations of the above-discussed techniques are: first, the performance of the existing algorithms degrades comprehensively in the presence of similar morphological patterns with minor variations from different classes. Second, the generalization capability of the existing techniques with different datasets and higher-end graphical processing unit (GPU) requirements. To overcome the above-mentioned issues, a novel multi-stream deep learning algorithm with the random forest is proposed, which effectively extracts the features from similar morphological patterns with minor variations. Three different individual streams are utilized with CNN, residual, and bidirectional gated recurrent units (Bi-GRU) to extract more distributed representative, hierarchical and condensed, and long-term dependency features, respectively. These extracted features are utilized to form deep features with the help of concatenation and fusion techniques. The resulting features are able to capture both the morphology and temporal dynamics of the ECG signal. These features are more effective in identifying different types of arrhythmias, predicting future cardiac events, and filtering out noise and artifacts. The unique nature of the features obtained by combining CNN, residual blocks, and Bi-GRU enables a more comprehensive and accurate analysis of the ECG signal, which is particularly important for diagnosing and monitoring cardiac abnormalities. Finally, the extracted deep feature set is utilized to train and test the random forest algorithm. All the above discussed techniques performed well when the ECG signal quality is good and at an acceptable level, but detecting the acceptability of the ECG signal is a challenging and crucial task. To handle this issue, an automatic signal quality assessment (SQA) technique is also developed using the proposed deformable CNN architecture to verify the signal acceptability for further ECG beat classification. The performance of the proposed methods for ECG beat classification is tested with acceptable unacceptable ECG segments. The proposed algorithms are validated on three publicly available ECG arrhythmia datasets such as MIT-BIH arrhythmia, INCART, and MIT-BIH supraventricular. Furthermore, the qualitative and quantitative analysis of the proposed techniques outperforms the state-of-the-art methods on three different publicly available datasets in the literature
Enhancement of Fluorescence Emission Intensity in the Derivatives of Tri(biphenyl-4-yl)amine by Breaking of Aggregates via Solid-State Grinding with Various Metal Salts: Chemosensing and Light-Emitting Diode Applications
No full textIn the present era, solid-state fluorescent materials are in huge demand due to their vast applications in LEDs, sensors, recognition, and imaging. However, molecular aggregations in the solid state limit their usage in practical applications. Typically, J-aggregates exhibit enhanced emission, while H-aggregates result in quenching the fluorescence emission in the solid state. Several techniques like making crystalline polymorphs, applying external stimuli in the solid state, cocrystallizing the fluorophores with other molecules, acid-protonated molecular crystals, supramolecular engineering, crystal engineering, and incorporating metal ions during solid-state grinding have been developed to manipulate the π-π and other supramolecular interactions in solid-state aggregates. Due to extended conjugations, triphenylamine-based small organic molecules and covalent organic cages show immense potential applications in developing optoelectronic devices and sensors. In the present thesis work, numerous tri(biphenyl-4-yl)amine (TBA) derivatives were synthesized and characterized using spectroscopic techniques. The synthesized tri(biphenyl-4-yl)amine derivatives showed J-aggregates and exhibited lower fluorescence emission intensity in the solid state compared to the solution form. These aggregations were disrupted through simple solid-state grinding combined with different inorganic metal salts, resulting in enhanced fluorescence emission. The DFT computational modeling revealed that dipole-ion interactions were the driving force for breaking the aggregations in TBA derivatives. Herein, the surfaces of the micro/nanometer-sized salt crystal particles serve as templates for the molecules of TBA derivatives, or in the broad sense, the inorganic metal salt crystal surfaces act as a “solid-state solvent.” The sulfonyl hydrazide derivative of the TBA (TBA-THZ) exhibited negligible emission in the solid state due to the aggregation- caused quenching phenomenon while grinding the TBA-THZ solid powder with the alkali and alkaline earth metal salts showed enhanced fluorescence emission through dipole-ion (S=O···Mn+) interactions. The fluorescent active salt ground matrices exhibited excellent acidochromism properties in the solid state. The TBA-based imine-linked covalent organic cage (COC1) molecular materials exhibited green emission in the solid state. The photophysical characteristics and crystal structure analysis revealed that the COC1 cage molecules form J-aggregates in the crystal lattice, which was disrupted by grinding the COC1 crystal with different inorganic metal salts, resulting in enhanced fluorescence emission through dipole-ion (C–H∙∙∙X−, X = oxygen or halogen from anion) interactions. The 2-hydrazinopyridine derivative of the TBA (TBA-2HP) also displayed green fluorescence emission in the solid state, and the photophysical properties, as well as DFT computational modeling, elucidated that the TBA-2HP molecules form J-aggregates in the solid state. Upon grinding the TBA-2HP solid powder with the different inorganic metal salts, it showed enhanced fluorescence emission through both dipole-ion (C–H∙∙∙X−, X = oxygen or halogen from anion) interactions and coordination bond formation (N→Zn2+). Interestingly, TBA-2HP, upon grinding with different salts of zinc, exhibited enhanced fluorescence emission intensity as well as a change in the maximum emission wavelength. Moreover, it was observed that the emission colour was tuned by varying the ratio of the TBA-2HP with the zinc acetate. Furthermore, the electroluminescence properties of the green emissive TBA derivatives were investigated. Green light emission occurred upon coating the green-emissive TBA derivatives (COC1 or TBA-2HP) doped PMMA polymer film over the surface of a near-ultraviolet LED. Upon coating, a thin layer of COC1 or TBA-2HP, along with a red phosphor-doped PMMA polymer on the surface of a blue LED, resulted in white light emission. Further investigation revealed that the COC1 cage material exhibited excellent acidochromic properties in the solid and solution states. Among different nitroaromatic compounds, picric acid is one of the most widely used chemicals in chemical laboratories and dye, pharmaceuticals, fireworks, and matchbox industries. Because of improper waste management, PA easily contaminates groundwater and soil, which causes severe health problems for human beings. Therefore, developing cost- effective sensory materials for detecting high-energy nitro-explosive materials has become a massive global demand due to the unremitting rise in health and environmental concerns. In the present thesis report, the TBA-based amine-linked fluorescent covalent organic cage (COC2) and TBA-2HP materials were utilized for highly sensitive and selective detection of picric acid at the nano to micromolar level. The COC2 cage material was also investigated for the acidochromism studies