Investigating the Anticancer Potential of Shikonin by Targeting PLK1 and Enhancing Therapeutic Efficacy Through Micellar Encapsulation in Oral Squamous Cell Carcinoma

Natural bioactive alkaloid phytocompound shikonin is obtained from the Lithospermum erythrorhizon plant’s root and exhibits excellent pharmacological properties including anticancer activity. This study explores the mechanistic role of shikonin (Shk) in the proliferation and migration of oral squamous cell carcinoma (OSCC) cells. Shikonin suppresses the viability of SCC9 and H357 OSCC cells in a time and concentration-dependent manner. It promotes the generation of intracellular reactive oxygen species which then leads to the depletion of mitochondrial membrane potential (MMP). Further, this causes DNA damage and cell cycle arrest in the G2/M and S-G2/M phases in SCC9 and H357 cell lines respectively. Shk also induces apoptosis in OSCC cells via enhancing the expression of Bax and Caspase 3. It also suppresses colony formation and tumorigenicity in a dose-dependent manner. The molecular mechanism behind the anticancer activity of shikonin was analyzed using bioinformatics studies. It was found that out of all upregulated genes in oral cancer, polo-like kinase 1/PLK1 is the most significant one which is the key target of shikonin. The molecular docking and molecular dynamic simulation results showed that shikonin makes stable binding with PLK1 and inhibits its mode of action. It has been shown that after Shk treatment the mRNA expression level of PLK1 was decreased compared to the control. Knockdown of PLK1 reduces the proliferation and viability of OSCC cells. It also increases the apoptosis rate and DNA damage after siPLK1, similar to Shk treatment. Instead of having these excellent anticancer activities the clinical use of shikonin is still limited because of its poor bioavailability, solubility, and stability. To overcome this problem, polymeric micelles are used as a drug delivery vehicle. These micelles are smaller in size which helps micelles for easy penetration in cancer cells with increased permeability and retention effect. Here mPEG-SA micelles are used for shikonin encapsulation. The formation of blank and Shk-loaded micelles is characterized by 1HNMR, FTIR, CMC, drug-loading, and encapsulation efficiency, DLS, DSC, TEM, and drug release time. The shikonin-loaded micelles show better and prolonged toxicity compared to only shikonin treatment leading to cell death of OSCC cells. Hence it can be concluded that shikonin-loaded mPEG-SA micelles can be used as a therapeutic agent for effective delivery of shikonin in OSCC cells which can give prolonged and better anticancer effects

Multistep Improvement of Klebsiella sp. SWET4 Strain to Obtain Higher Ethanol Yield from Cellulosic Fruit Waste: Single Step for Waste to Energy Conversion

The pretreatment process involved in 2nd generation bioethanol production infers significant cost along with environmentally hazardous byproducts. Hence, its substitution with direct fermentation would significantly advance the process. Since raw substrate would be used during fermentation, its growth inhibitor content would be an important substrate selection criterion. This study revealed that the growth of Klebsiella sp. SWET4 was significantly reduced by phytate, phenolic acid, cyanide, and tannin at 3.09%, 0.22%, 0.38%, and 0.04% per μg/mL, respectively. Since banana peel contained the least amount of these growth inhibitors, it was predicted to be the best substrate for SWET4. Moreover, the potential of the banana peel as a probable substrate for ethanol production was evaluated with the help of logical prediction. The Whole Genome Sequencing of SWET4 (5665821 bases) revealed the presence of 5 major cellulose metabolizing (bcsZ, bglC, bglA, celA, chbA), besides 4 key xylan degrading (xynB, xynT, xylA, xylB) and 4 principal ethanol fermentation (nifJ, adhE, acs, adh1) genes. Expression study with qPCR confirmed the functionality of these genes. The lignolytic potential of SWET4 was evident in the kinetic study and the presence of yfeX/efeB, katG, katE, etc. genes was confirmed. SWET4adh1+adhE recombinant strain exhibited a remarkable 7.76-fold increase in ethanol productivity from the banana peel in facultative anaerobic conditions. qPCR analysis confirmed 106.15- and 22.78-fold higher expression of adh1 and adhE genes, respectively. Optimization using Artificial Neural Network modeling and Genetic Algorithm was found better than Response Surface Methodology (RSM) for predicting bioethanol production by SWET4adh1+adhE. After optimization, the enhanced biomass productivity of 2.33 g/L was achieved along with ethanol production of 24.47 g/L as confirmed by HPLC. The process demonstrated an ethanol yield of 0.44 g/g from carbohydrates surpassing many 2nd generation bioethanol processes. Further, a minimum selling price of $2/kg of distillate was found to make the process economically feasible which is significantly low. The breakeven point of the process was found to be 30% of its total capacity. The techno-economic analysis highlighted the feasibility, particularly emphasizing the economic advantages of eliminating pretreatment steps, highlighting the process’s innovation and viability in the field of 2nd generation bioethanol production

Chitosan-based Bioactive Nanofibrous Hemostatic Agent for Emergency Care

Uncontrollable bleeding in major arteries has been reported to cause preventable 50% of battlefield casualties and 31% of mortalities in civilians worldwide. In India, road traffic injuries are a major concern, which causes 40% of deaths due to hemorrhage, and there is a rise of 2.4% every year. Commercially available hemostatic agents require at least 1-2 minutes for blood clotting, and most are either difficult to apply, expensive, or produce exothermic reaction upon contact with blood to cause adverse reactions. The objective of this study was therefore to investigate a novel self- assembly-based facile method to fabricate chitosan-casein/gelatin nanofibers through polyelectrolyte complex (PEC) formation for rapid hemostasis. The efficacy of hemostasis was improved by optimizing the process parameters and incorporating biologically active nanoparticles to nanofibrous PECs. The also investigated the effect of nanoparticles (e.g., ZnO-NP, AgNP) on improving bioactivity of the chitosan-based nanofibrous PEC under in vitro and in vivo conditions. FTIR spectroscopy revealed that amide group (1630 cm-1) of chitosan and phosphate group (910 cm-1) of casein could form nanofibrous PEC with electrostatic interaction at pH 8.2±0.2. The chitosan and casein in the ratio of 30:70 (CC30), 50:50 (CC50), and 70:30 (CC70) nanofibrous PECs allowed platelet adhesion and rapidly absorbed blood fluid to form rapid blood clots within 9±3, 16±3, and 30±4 s, respectively, which were better than commercially available Celox™ (90±3s). Increasing the concentration of chitosan from 10% to 90% in the CC formulations increased the productivity (r=0.99) of PECs but led to increased blood clotting time (r=0.90) due to an increase in zeta potential (r=0.98), fiber diameter (r=0.93), and decreased surface porosity (r=- 0.99), absorption capacity (r=-0.99). The pH also influenced zeta potential of PEC, with an optimized pH of 8.0±0.1 yielding clear nanofibers. Sonication improved the segregation of nanofibers by promoting water removal. The optimized PECs containing chitosan and casein in ratio of 30:70 (CC30) at a pH of 8.0 and dehydration under sonication could clot the blood within 9±2s in vitro and 9±2s in rat femoral artery puncture model with no evidence of rebleeding. The CC-based nanofibrous PEC were highly hemocompatible, biocompatible, non-toxic, and non- immunogenic. The chitosan-casein PECs could also be developed as microporous hemostatic sponge (CC30G) with porosity of 73.00±4.74%, pore diameter of 42.66±5.33 μm, and rapid water absorption capacity (1165±55%). The CC30G sponge showed bacteriostatic action against both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli. Upon incorporation of AgNP, the sponge (CC30GS) acquired the bactericidal property. The sponge could also be combined with adhesive tape to develop as a hemostatic band-aid for bleeding in skin lacerations, cuts, and topical wounds. Bioactivity of CC-based PECs (CC30Z) could be improved by incorporation of ZnO-NPs without compromising their hemostatic efficiency or biocompatibility. ZnO-NP incorporated PECs (CC30Z) were bioactive in terms of their bactericidal effect against Gram-positive S. aureus and Gram-negative E. coli, promoting cellular metabolic enzyme activity (alkaline phosphatase, glutamate dehydrogenase, lactate dehydrogenase, and malate dehydrogenase) for skin regeneration, and enhanced platelet aggregation and activation for rapid hemostasis. Results further showed that replacing casein with gelatin in the chitosan-based PECs could also increase the compressive elastic modulus of PEC-induced blood clots from 21±2 kPa to 68.6 ± 6.4 kPa, which was five-fold better than a commercially available CeloxTM-induced blood clot. Ag-NP (CG30S) and ZnO-NP (CG30Z) incorporation in chitosan-gelatin PEC did not affect the fiber diameter, surface porosity, hemocompatibility, and biocompatibility of the chitosan-gelatin PECs nanofibers but had bactericidal effect on both Gram-positive S. aureus and Gram-negative E. coli. Both CG30S and CG30Z could clot the blood within 10 s under in vitro conditions and rat femoral artery puncture model in vivo. Further, CG30Z had excellent bioactivity in promoting cellular/tissue metabolic enzymes involved in skin regeneration and could enhance platelet aggregation as well as activation. Taken together, chitosan-casein/gelatin nanofibrous PEC could rapidly clot the blood with 10 s under in vitro conditions by promoting platelet activation and aggregation, rapid absorption of plasma, and activation of extrinsic coagulation pathway. It could also clot blood within 10 s in rat femoral artery puncture model and within 25s in rabbit ear artery model. The PEC was bioactive, bactericidal, hemocompatible, biocompatible, non-toxic, non-immunogenic and safe for use in animal models. The chitosan-based PECs could also be developed as hemostatic sponge for skin cuts and lacerations. Further pre-clinical trials on large animals should be conducted under GLP conditions before clinical trials in humans for a successful market-ready product

Hardware Design for Predicting Early Signs of Sudden Cardiac Arrests from ECG Signals

Electrocardiogram (ECG) is a non-invasive way to record the electrical activities of the heart and is recognized by its features, P-QRS-T. Any change in the amplitude or duration of these features indicates the presence of an abnormality, known as cardiac arrhythmias (CA). Among the most fatal CAs, sudden cardiac arrests (SCA) can cause death unexpectedly if left untreated, and hence, their early prediction is important to receive timely medical care. SCAs can be predicted by the detection of one of the most life threatening CAs, known as ventricular tachycardias (VT), which are characterized by the presence of three or more consecutively occurring premature ventricular contraction (PVC) beats in an ECG signal. PVCs are common among the general population, but they pose a threat to the human heart only if they occur frequently in a group of three or more with varying QRS morphologies. Based on the QRS morphology, PVC beats are classified as multifocal or unifocal, and based on the frequency of occurrences, PVCs are classified as ventricular bigeminies, ventricular trigeminies, non-sustained ventricular tachycardias (NSVT), and sustained ventricular tachycardias (SVT). However, according to medical experts, the highest risk of SCAs is related to frequent episodes of multifocal NSVTs and SVTs. On the other hand, ventricular bigeminy and trigeminy are not directly related to any fatal CAs, but they will require further medical aid if they occur frequently enough to prevent the heart from pumping the required amount of blood to all the organs of the body. In this work, a hardware-efficient FPGA-based design for predicting the early signs of SCAs is proposed by detecting PVC beats and classifying them into six major categories of ventricular arrhythmias (VAs), namely multifocal PVCs, unifocal PVCs, ventricular bigeminies, ventricular trigeminies, NSVTs, and SVTs. The SCA prediction system consists of the following stages: pre-processing, feature extraction, PVC detection, and VA classification. For pre-processing, a denoising technique using the modified lifting-based discrete wavelet transform (MLDWT) is used to combat all the ECG noises as well as enhance the QRS complexes in the ECG signal. For efficient detection of PVC beats, an accurate feature extraction stage that extracts R peaks, T peaks, and the Teager energy operator (TEO) is employed. With the extracted features, a characteristic matching algorithm is used for PVC detection, and an adaptive decision logic-based (ADL) classifier is utilized for VA classification, resulting in a detection accuracy rate of 98.2% when tested using the online ECG databases, viz., the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH supraventricular database (SVDB). The complete hardware design of the SCA prediction system, when implemented on the Nexys 4 DDR Artix-7 FPGA board, outputs the number of PVCs detected and VA classified, based on which an alert on the risk of SCAs is provided while utilizing 10.4% of the total available hardware resources on the FPGA board. For future integration of the SCA prediction system into wearable healthcare devices, an ASIC implementation of the PVC detection and VA classification is performed, resulting in a place and route area of 0.02 mm2 and a power utilization of 3.47 μW at an operating frequency of 100 KHz when implemented using SCL 180 nm CMOS technology

Application of Electrochemical Impedance Spectroscopy (EIS) to Study the Effect of Different Deposition Parameters During Electroplating

Copper (Cu) electrochemical deposition (ECD) on graphite was studied using Cu (noble metal) and Ni (active metal) to investigate nucleation and growth processes. Cu deposition was analyzed under varying potentials, ion concentrations, and temperatures in acidic and alkaline baths. In acidic conditions, different anodes (Pt and Cu) were used. The electrodeposition was investigated by various techniques i.e., cyclic voltammetry (CV), chronoamperometry (CA), electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD), atomic force microscopy (AFM) and scanning electron microscope (SEM). From cyclic voltammetry the redox potentials have been chosen. The selected potentials (-0.16 V, -0.26 V, -0.36 V, -0.46 V, -0.56 V and -0.66 V) are the input parameters for i-t curve and EIS. The Nyquist plots revealed that copper ion charge transfer happens at high frequencies and is represented by a single capacitive constant, while at low frequencies copper ion diffusion from the solution to the electrode surface is represented by a Warburg-type contribution. The corresponding Bode plots represent a decent ability between the experimental and fitting data. The effect of potential on double-layer capacitance, diffusion coefficient, and diffusion layer thickness along the interface of electrode and electrolyte has been discussed extensively. The morphologies of the copper particles depositing on the surface of electrode also studied and it shows that copper deposits during electrodeposition resulted in a transition from spherical to dendritic structure as a function of deposition potential. The current density-time (i-t) curve was recorded by the potentiostatic method for 300 sec various potentials, concentrations and temperatures. At low Potentials (-0.16, -0.26 and -0.36 V) Nyquist plots reveal that copper ion charge transfer occurs at high frequency, while at low frequency copper ion mass transfer occurs. At high Potentials (-0.46, -0.56 and -0.66 V) Nyquist plots reveal that copper ion charge transfer occurs at low and high frequency. A fitted equivalent circuit was utilised to determine EIS parameters. The Nyquist plots revealed that copper ion charge transfer happens at high frequencies and is represented by a single capacitive constant, while at low frequencies copper ion diffusion from the solution to the electrode surface is represented by a Warburg-type contribution. The corresponding Bode plots represent a decent ability between the experimental and fitting data. The effect of potential on double-layer capacitance, diffusion coefficient, and diffusion layer thickness along the interface of electrode and electrolyte has been discussed extensively. The morphologies of the copper particles depositing on the surface of electrode also studied and it shows that copper deposits during electrodeposition resulted in a transition from spherical to dendritic structure as a function of deposition potential. The effect of pH on copper electroplating from a cyanide-free alkali medium was studied using copper sulphate, glycine, and sodium hydroxide. Electrochemical impedance spectroscopy (EIS) was used to analyze phase transformations under varying ion concentrations (0.01, 0.05, and 0.1 M), temperatures (5–20 °C), and deposition potentials (-0.56 V, -0.66 V, -0.76 V) determined from cyclic voltammetry (CV). The initial CV and potentiostatic studies reveal that the system is mixed kinetics control one. EIS study further supplement to the observations and it was observed that at low ion concentration (0.01 M) mass transfer was dominated. To complement the EIS results, a thorough phase and morphological examination of the copper films was done by means of X-ray diffraction (XRD), FESEM and AFM. It was revealed that at low ion concentration and potential, the film was not uniform. The films were uniform and consisted of a thin oxide layer with increase of potential and concentration and decrease of temperature. Based on the findings of double layer capacitance and charge transfer resistance along with film resistance, a plausible deposition mechanism has been proposed. However, there can be further critical use of EIS to gain deeper insights into the electrochemical phenomena occurring at the electrode-electrolyte interface to manipulate the film properties as per the desired applications. Again, electrochemical impedance spectroscopy (EIS), chronoamperometry, and cyclic voltammetry are used to investigate Ni electroplating from three different types of electrolytic baths- sulphate, chloride, and watts at silent and ultrasonic conditions. The nucleation and growth mechanism were examined using the EIS method, and it was found that ultrasound had a discernible impact. After applying an applied potential of -1.4 V to deposit nickel, two unique depressed semicircles were observed in the impedance spectrum. These semicircles' frequency and capacitance varied according to the different electrolyte compositions, providing insight into the deposit morphology. According to X-ray diffraction studies, crystallite size decreased in sulphate to chloride bath. Atomic force microscopy and scanning electron microscopy were used to analyse the surface morphology of films produced in the presence of ultrasound and silent condition

Numerical Analysis of Time-fractional Parabolic Differential and Integro-differential Equations

Over the last few decades, the subject of fractional calculus emerged in various areas of science and engineering. In this regard, fractional partial differential equations are used to describe anomalous diffusion. The main feature of the fractional differential equations is their nonlocal and heredity property, which makes their solution challenging. Moreover, the smooth initial data in a differential equation involving fractional derivatives may not provide a smooth solution. Due to such uncertain behavior, obtaining analytic solutions of the fractional partial differential equations is intricate or impossible in many cases. Consequently, efficient numerical techniques play a major role in evaluating an approximate solution of fractional partial differential equations and analyzing its asymptotic behavior. The present thesis intends to develop and analyze some efficient and stable numerical schemes for solving a class of time-fractional partial differential and integro-differential equations of parabolic type in one and two dimensions. The thesis starts with a brief history of fractional calculus, followed by some preliminaries of time-fractional differential equations and the approximation techniques to obtain their solutions. This thesis can be divided into two parts. The first part is devoted to constructing some layer-adaptive numerical techniques for different classes (including linear, semilinear, time-delayed and interface models) of time-fractional partial differential models of parabolic type. In general, the typical solution to such types of problems undergoes a sharp change at t = 0, namely, the interior layer region of the domain due to the presence of the weak singularity. The traditional numerical methods on uniform mesh fail to grasp such abrupt changes inside the layer region, and they degrade the convergence rate. The layer-adaptive graded mesh with the user-chosen grading parameter is used in the temporal direction to achieve optimal accuracy. The widely-used L1 technique is employed to discretize the fractional order differential operator in the temporal direction for most of the model problems. The corresponding proposed schemes achieve a superlinear rate of convergence for suitable choices of the mesh grading parameter. In some cases, the newly-proposed L1-2 and L2-1σ technique is used in the temporal direction to construct a higher order (up to second order) scheme. The Newton linearization technique alongside the Daftardar-Gejji and Jafari method tackle the nonlinear part of the problems. The second part develops numerical techniques for the time-fractional partial Volterra integro-differential equations of parabolic type, along with their possible extensions to problems with semilinearity. The typical solution for these problems is assumed to be sufficiently smooth, subject to the prescribed initial data. For all the model problems, the fractional derivative is discretized using the L1-2 technique on a uniform mesh in the temporal direction. The composite trapezoidal rule approximates the integral part, whereas the composite product trapezoidal formula is employed to approximate the integral involving a weakly singular kernel. The classical central difference formula and the cubic B-spline collocation method are used on a uniform mesh in the spatial direction. The operator-splitting idea in time is discussed for the two-dimensional problem. To solve the resulting system of algebraic equations, the well established Thomas’s algorithm is used. On a suitable norm, the stability and convergence for all the proposed schemes are performed thoroughly under sufficient regularity assumptions on the initial data and true solution of the considered model problem. The efficiency and applicability of the proposed schemes are tested through numerical experiments. Computational results are presented through several plots and tables to support the theoretical findings. At the end of this thesis, a brief summary of the findings and the scope of further enhancement of the proposed study are provided. The novelty of these schemes is their simplicity and efficiency compared to the existing methods

Efficient Security Enhancement Techniques for Ultra-reliable Low Latency Communication in 5G and 6G Wireless Networks

The fifth generation (5G) wireless networks have revolutionized the current communication landscape by enabling many futuristic and smart applications. This brings 5G to introduce an innovative service like Ultra-reliable Low Latency communication (URLLC) to facilitate mission-critical 5G applications such as industrial automation, autonomous driving, smart healthcare, and smart grid operations. Most importantly, URLLC enables ultra-high reliability (i.e., up to 99.999%) and low latency (i.e., < 1ms) data transmission to achieve the desired Quality-of-Service (QoS) of these mission-critical applications. To ensure the stringent QoS requirement, URLLC uses short packet finite block length signals. However, the exponential rise in wireless data traffic generated from billions of smart Internet-of-Things (IoT) devices utilizing URLLC service is highly vulnerable to external eavesdropping and security threats. Moreover, the finite block length constraint and low latency criteria eliminate the possibility of utilizing complex cryptography-based security techniques for URLLC. In this regard, Physica layer security (PLS) has emerged as a potential technique for providing lightweight security enhancement for URLLC by exploiting the randomness of wireless channel characteristics. Therefore, this dissertation proposes the development of efficient security enhancement techniques utilizing PLS for URLLC mission-critical 5G applications. The first contribution of this dissertation is to ensure the security of URLLC signal transmission to the cell edge users in an IoT network. In this regard, the cooperative non-orthogonal multiple access (CNOMA) has emerged as a promising 5G technology that ensures the reliability of signal transmission by cooperatively transmitting the information of cell edge users through near users or relay networks in a multi-user scenario. However, the absence of a direct communication link between the base station (BS) and the cell edge URLLC user hugely degrades the reliability of data transmission and increases the chance of information leakage due to eavesdropping. Therefore, a coordinated direct and relayed transmission (CDRT) scheme is proposed for the CNOMA system to ensure the reliability and security of URLLC data transmission. A dedicated half duplex (HD) relay node is used to transmit an artificial noise (AN) signal along with the URLLC information intended for the cell edge user to confuse the eavesdroppers and decrease the information leakage. However, employing HD relay nodes in CNOMA may suffer from imperfect decoding and low throughput levels for legitimate users. Moreover, the large separation distance between the transmitter and the cell edge URLLC introduces challenges for ensuring the security and reliability of signal transmission. Therefore, the second contribution of the dissertation proposes an efficient PLS enhancement scheme for URLLC users at the cell edge by utilizing the CNOMA technique. An AN-assisted jamming, and full-duplex (FD) communication utilizing the near user to the BS as relay is proposed to improve the PLS of cell-edge URLLC users. The efficient resource optimization framework is proposed to improve the PLS performance for URLLC while managing the residual self-interference (RSI) at the FD relay and the intercept capabilities of the eavesdroppers. A large number of low-power IoT devices are deployed in an industrial IoT (IIoT) scenario where critical control information is exchanged among these devices in the form of short packets to facilitate URLLC. However, such confidential information transmission is vulnerable to information leakage and security threats due to the openness of wireless medium. Additionally, the presence of a large number of low-power devices in the system requires efficient utilization of system resources to achieve energy-efficient communication. Therefore, the third contribution of this dissertation is the development of an efficient PLS scheme for improving the secure energy efficiency of URLLC signal transmission in a multi-user and multi-eavesdropping scenario of the mission-critical IIoT application. The proposed PLS technique jointly optimizes the URLLC blocklength and pilot signal length to improve the secrecy throughput and optimizes the power allocation to ensure the secure energy efficiency of the system. In a large-scale IoT network ensuring the security of URLLC signal transmission is challenging because of centralized computing of confidential user information. To address this, the fourth direction of the dissertation is to propose efficient and secure decentralized computation of information using a Quantum-enhanced Federated Learning (QFL) framework to preserve the data privacy of edge URLLC users. The proposed QFL framework efficiently allocates the resources to ensure secure URLLC task offloading while managing the data heterogeneity in comparison to classical FL-based methods. Finally, the dissertation presents the concluding remarks on the research contributions and discusses the security challenges, enabling technologies, and future research directions for next-generation wireless service Hyper Reliable Low Latency Communication (HRLLC) in 6G

Data Driven Approaches using Statistical and Deep Learning Models for Time Series Analysis of Weather Data

In the realm of time series data prediction, especially in its application to meteorological data, time series analysis and forecasting are essential components of data science. It involves the development of models capable of accurately predicting future data points based on historical trends. In the context of weather prediction, the necessity for precise forecasting becomes apparent, as it plays a pivotal role in various real-world applications. In agriculture, precise predictions enable farmers to optimize planting and harvesting schedules, allocate resources efficiently, and enhance overall crop management. Energy utilities depend on weather forecasts to anticipate demand patterns, particularly in the realm of renewable energy sources. Transportation industries utilize forecasts for route planning, schedule optimization, and safety measures. Additionally, accurate weather predictions play a pivotal role in disaster preparedness, allowing governments and emergency services to proactively plan and respond to natural calamities, ultimately minimizing the potential impact on communities. The necessity of time series weather prediction extends beyond the immediate concerns of weather enthusiasts; it is an indispensable tool for informed decision-making across various sectors, contributing to the resilience and adaptability of societies in the face of dynamic environmental challenges. The fundamental requirements for effective time series prediction, encompassing data quality, feature engineering, and model selection. The significance of accurate weather predictions in mitigating risks, minimizing economic losses, and safeguarding lives underscores the direct impact of time series prediction on enhancing resilience in the face of dynamic environmental challenges. This thesis comprehensively explores the spectrum of time series forecasting methodologies, ranging from traditional univariate and multivariate statistical models to advanced techniques such as Vector Autoregressive - Gated Recurrent Unit (VAR-GRU), and Hybrid Deep Learning (DL) models such as channel attention based bidirectional GRU with Neural Basis Expansion Analysis for Time Series (ChAT-BiGRU-NBEATS) and self attention based bidirectional long-short-term memory with temporal convolution network (ABTCN) . The research delves into the theoretical underpinnings of univariate and multivariate statistical models, examining their strengths and limitations in capturing temporal dependencies. It subsequently transitions to the exploration of modern sequential modeling architectures, specifically VAR - GRU, which leverage the power of neural networks for enhanced predictive accuracy. The thesis further investigates the innovative realm of hybrid DL models, which amalgamate the strengths of diverse methodologies to achieve superior forecasting performance under missing and noisy data. The execution of each model has entails subjecting it to examination on benchmark data set. The methodological comparisons and empirical evaluations using performance evaluation parameter elucidate the efficacy of each approach, providing valuable insights for practitioners and researchers navigating the dynamic landscape of time series forecasting

Development of Process for Improving Functionality of Little Millet Flour using Cold Plasma

The abundant production of little millet in India and having rich nutritional profile, prompting a need for scientific interventions to optimize its utility compare to other primary cereal crops. This study investigates the effects of cold plasma on the functionality of little millet flour (LMF) for value addition. LMF was treated at applied voltages of 10 and 20 kV with treatment times of 10, 20, and 30 min. Functional characteristics such as oil absorption capacity, water absorption capacity, swelling capacity, and solubility index were enhanced significantly (p˂0.05) by plasma treatment from 1.10 to 1.35 g/g, 1.34 to 1.51 g/g, 2.92 to 4.23 g/g and 0.054 to 0.085 g/g respectively, while physical properties such as bulk density, dispersibility remained unchanged and not significant. Microstructural analysis showed starch granule breakdown, and X-ray diffraction indicated decreased crystallinity from 47.98 % to 43.97% due to starch depolymerization by reactive oxygen and nitrogen species. Rheological studies using varying voltages (10 – 20 kV) and durations (10, 20 & 30 min) demonstrated that plasma-treated LMF exhibited improved storage and loss moduli and pseudoplastic behavior, fitting the Herschel-Bulkley model with R2>0.99. Comparison studies such as functional rheological properties conducted between direct plasma and plasma activated water treatment. Enhanced functional characteristics, particularly in samples treated with multipin cold plasma at 15 kV for 30 min. Plasma treatment also enhanced total phenolic content and antioxidant activity significantly (p˂0.05) from 527.54 ± 8.94 to 575.82 ± 3.58 mg gallic acid equivalent /100 g, and 14.39±0.77 to 22.94± 1.84% respectively. On other hand, anti-nutritional factors like tannins and saponins (226.96±27.54 to 135.65 ± 2.90 mg tannic acid/100 g of d.m and 454.33±50.75 to 190.15±35.82 mg diosgenin/100 g of d.m) were reduced and significantly differed at p˂0.05. Besides moisture, ash and fat content of millet flour didn’t have significant difference for all treated voltage and times. However, protein, carbohydrate contents were increased with a rise in applied voltage and treatment time. The optimized conditions for all properties was obtained at 20 kV and 20 min. Accelerated storage studies were conducted at 40ºC & 90% RH, the treated flour had a shelf life of 2.52 months in high density polyethylene (HDPE) and 0.77 months in low density polyethylene (LDPE) packaging, while the untreated flour had a shelf life of 2.45 months in HDPE and 0.75 months in LDPE. Pasta was prepared by incorporating of 10% and 20% LMF with and without plasma treatment. Color parameters, such as the L-value and whiteness index, were improved in the treated pasta samples and close to control. Optimal cooking time was decreased in treated pasta than untreated pasta samples. Instrumental analysis such as infrared spectra, diffractograms, thermographs and micrographs were analyzed. These results highlight the potential of plasma treatment to enhance the functionality and rheological parameters of LMF, suggesting its application in diverse food products

Facile Low Temperature Synthesis of Bismuth Molybdate (Bi2MoO6) Based Heterostructure Materials for Photocatalytic Micropollutant Remediation and Reduction Reactions

In this thesis, various types of nano-heterostructure materials involving Bi2MoO6 as base semiconductor have been designed for water decontamination and activation of atmospheric molecules. Initially, Bi2MoO6 was synthesised by various low temperature methods. To improve the photoexcited charge carrier separation and photocatalytic activity, Bi2MoO6 was subsequently coupled with spinel metal ferrites and metal vanadates semiconductors to construct binary and ternary heterostructure materials. Further, to facilitate rapid formation of the composites, in many cases, one pot synthesis techniques were used while in some other cases, in situ synthesis method was successfully employed. In addition, defects such as doping and oxygen vacancy were introduced into the crystal structure of the composites to tailor the band positions and improve their spectral response. The prepared materials were characterized by XRD, Raman, FTIR, XPS, EPR, BET, FESEM, TEM, UV-vis DRS, PL, water contact angle and electrochemical techniques to investigate their structural, morphological, surface, photon absorption and charge separation properties. The photocatalytic activity of the materials were evaluated for decontamination of Bisphenol A, Cr(VI) and ciprofloxacin from aqueous sources and activation of atmospheric molecules like H2O, O2 and N2 to produce H2, H2O2 and NH3, respectively. In addition, numerous optimization experiments such as catalyst dosage, pollutant concentration, pH, h+ scavengers, water matrices and intervening anions were carried out. Degradation pathway of Bisphenol A and ciprofloxacin were analysed by identifying the reaction intermediates by HRMS analysis. The reactive radicals responsible for the photo-degradation reactions were identified by scavenger experiments. Further, various radical trapping studies were carried out to confirm the generation of reactive radicals such as •OH and •O2− in aqueous illuminated suspension of the photocatalysts. The band gap, band position, and type of semiconductivity of the individual semiconductors were estimated by Tauc plots, Mott-Schottky plots, and valence band spectra. Finally, by considering the band positions, XPS peak shifting, work function and radicals quenching studies, the mechanism of photoinduced charge carriers’ migration are rationally deduced for each heterostructure material to explain their photocatalytic activity. A mild reflux route was designed for facile synthesis of Bi-self doped Bi2MoO6 (BMO-A) with nanoplate morphology. Microstructural study revealed substitution of Bi5+ ions in the molybdate layer leading to partial reduction of Mo6+ to Mo5+ ions and creation of Mo vacancy. The defect engineered BMO-A exhibited improved optical and photoelectrochemical properties compared to its undoped analogue. The BMO-A material was subsequently used as host lattice for in situ construction of CaFe2O4/Bi2MoO6 0D-2D p-n heterojunctions. Well dispersed CaFe2O4 quantum dots over BMO-A nanoplates provided a strong interfacial contact conducive for fast charge mobilization. The CaFe2O4/Bi2MoO6 composites displayed improved photocatalytic performance for bisphenol A (BPA) degradation and Cr(VI) reduction with rates 5–9 times higher than pure components. The rapid production of •OH and •O2− radicals, construction of an interfacial p-n heterojunction with double charge migration mechanism accounted for the improved photocatalytic efficacy of the composite. A mild CTAB assisted one pot reflux synthesis route is designed for in situ integration of metal organic framework (MOF)-derived NiFe2O4 with tetragonal-BiVO4 and γ-Bi2MoO6 to prepare NiFe2O4/t- BiVO4/Bi2MoO6 ternary composites. Morphologically, fine dispersion of NiFe2O4 (NFO) quantum dots over Bi2MoO6 (BMO) and t-BiVO4 (BVO) nanoplates lead to microscopic heterojunction formation among BMO-BVO, BVO-NFO and BMO-NFO phases. The ternary composites displayed high surface area, strong optical absorption and superior charge mobility that accounted for its improved photocatalytic activity for ciprofloxacin (CIP) degradation (>99% in 90 min) and H2 evolution (1.11 mmolh-1g−1, photon conversion efficiency 18.5%). Kinetics study revealed 12–55 higher CIP degradation activity and 31–41 times higher H2 evolution rate in comparison to the pure semiconductors. A conjugated S-scheme charge transfer mechanism has been deduced from comprehensive band position analysis and radical trapping study to explain the enhanced photocatalytic activity. A series of Bi2MoO6/InVO4/CeVO4 ternary heterostructures were constructed by in situ deposition of Bi2MoO6 nanoplates over one pot synthesized InVO4/CeVO4 using a facile oil bath heating method. A distinct morphology consisting of Bi2MoO6 nanoplates, CeVO4 nanosheets and InVO4 nanorods was noted. The significant intergrowth among the constituent phases led to the construction of tight interfacial microscopic junctions. The ternary materials displayed intense absorption in UV–visible region, drastic decrease in charge recombination and higher excited state lifetime. Both InVO4 and CeVO4 individually as well as the ternary heterostructure contained surface oxygen vacancies that further promoted space charge separation. The optimised ternary photocatalyst displayed 2314 μmol/g/h H2 generation and 1700 μM/g/h H2O2 production which are 12–86 and 11–27 times higher than the pure materials, respectively. Band position assessment and radical trapping study suggested the occurrence of a dual S-scheme charge transfer mechanism that rationally accounted for the improved photocatalytic performance. A novel CeVO4/Bi/Bi2MoO6 ternary heterostructure was fabricated by in situ deposition of CeVO4 over one-pot-synthesized Bi/Bi2MoO6 binary composite. The effect of the salt precursor and reaction duration on the morphology and crystal structure of Bi/Bi2MoO6 was studied in detail. The initial formation of Bi2MoO6 nanoplates and their subsequent disintegration to nanorods upon prolonged reaction time was observed due to concurrent leaching and reduction of Bi3+ ions to plasmonic Bi0 metal. The CeVO4/Bi/Bi2MoO6 ternary heterostructure demonstrated a uniform deposition of CeVO4 nanoparticles (10–20 nm) over Bi2MoO6 nanorods that are embedded with ultrasmall Bi0 nanodots (2–5 nm). The ternary composites displayed improved optoelectronic features which have been ascribed to the creation of surface oxygen vacancies and plasmonic nature of Bi nanodots. The optimized ternary photocatalyst exhibited encouraging photocatalytic activity for H2O2 generation (953 μM/g/h) and NH4+ production (131 μmol/g/h) with reaction kinetics 7–20 and 4–5 times greater than those of pure semiconductors and CeVO4/Bi2MoO6 binary heterostructure. Based on experimental evidences, a switching of charge migration route from Type-I in CeVO4/Bi2MoO6 to Bi0-mediated all-solid-state Z-scheme for the CeVO4/Bi/Bi2MoO6 composite is proposed, which accounted for its improved photocatalytic activity