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Phase Evolution and Emission Behaviour of Dual Precipitants Derived Y-Al-B-O-Based Luminescent Materials
No full textLuminescent materials based on oxides and borates, such as yttrium aluminium garnet (YAG) and yttrium borate (YBO3), are indispensable for advanced applications due to their robust chemical stability and high luminescence efficiency. Ce-doped YAG emits yellow light, essential for enhancing white LED technology. YBO3 is renowned for its UV absorption capability and thermal and chemical stability, making it highly suitable for demanding environments. Cerium-doped YBO3 phosphors emit blue light and offer high tunability for applications spanning LEDs, flat displays, and optoelectronic devices. The synergistic combination of these materials optimizes color emission properties, significantly enhancing their versatility across a wide range of luminescent applications. This study focuses on advancing Ce-based Y-Al-B-O-based luminescent materials through innovative synthesis methods, particularly using dual precipitating reagents, i.e., boron-containing sodium borohydride (SB) and hydroxide-containing ammonia solution. These reagents enable precise control over phase formation, which is crucial for developing advanced luminescent materials tailored for applications in lighting and displays. Several characterizations were carried out using these calcined powders, including XRD, FTIR, Raman, FESEM, EDX mapping, photoluminescence spectra, CIE color coordinates, CCT, lifetime, electroluminescence, and quantum yield. Based on this concept, this research is focused on developing phase evolution and emission behavior of dual precipitants-derived Y-Al-B-O-based luminescent materials by varying boron-containing precipitants, cerium content, and calcination temperatures. Depending on calcination temperatures, distinct phases were produced, and the variations of the emission intensity of blue and yellow-green allowed the production of tunable color- emitting materials, including white emission, which is essential for achieving high-quality lighting and display functionalities. Borate, mixed borate-oxide, and oxide-based phases have been derived via different synthesis methods, such as gelation and precipitation. For the gelation-precipitation method, white emission was achieved at 1500°C 6h, which was reduced to 1h holding time for the precipitation method by reducing the boron-containing precipitant, i.e., SB. Further, optimizing the SB volume and varying the cerium concentration is crucial to studying their effects on emission behavior. With optimized SB volume to 10ml, samples calcined at 1000°C and 1200°C produced primary YAG with secondary YBO3 and Al2O3, which produced tunable emissions, including near white at reduced calcination temperature. Moreover, increasing cerium concentration from 2-8 mol% shifted emission peaks from blue to yellow-green. The optimal cerium concentration was 6 mol%, producing near-white emissions suitable for various luminescent applications. Additionally, with the optimized cerium concentration, further synthesis was conducted with reduced SB volumes from 8ml-2ml, examining the effects on phase evolution and emission behavior. Lower SB amounts resulted in primary YAG with secondary YBO3 at 1000°C and 1200°C. The emission spectra of the calcined samples with variable lowered SB volumes showed tuning of emission colors, covering blue, yellow-green, and near white. The samples with 6 mol% Ce-based YAG powder prepared with 6 ml SB calcined at 1000°C successfully produced white light when encapsulated over a blue LED. Dual precipitants by varying SB amounts followed by ammonia solution may be a unique approach for developing Y-Al-B-O-based luminescent materials for various lighting applications, including white light generation
Development of Efficient Machine Learning and Deep Learning -based Techniques for Detection of Breast Cancer with Small Datasets
No full textBreast cancer is a serious health issue among women across the globe, affecting millions of lives every year. Breast cancer is broadly defined as the irregular and unusual growth of breast tissues that results in the formation of a tumor-like mass. The growth rate of the tumor determines whether it is benign or malignant. Mammogram and ultrasound are very frequently used popular imaging modalities for breast cancer screening, since these two are cost effective, less harmful, and easily available. Early and accurate detection of this disease plays a vital role in providing proper treatment plan to save valuable lives, which highlight the need for researchers to develop even more accurate techniques for timely detection of breast cancer. In response to this requirement, technological developments and the incorporation of cutting-edge methodologies such as machine learning and deep learning have led to substantial innovation in breast cancer detection. Hence, Computer-Aided Diagnosis (CAD)-based automatic breast cancer diagnosis has emerged as a critical research field in medical image analysis. As a result, this dissertation focuses on developing of efficient and high-performing frameworks for identifying breast cancer utilizing mammography and ultrasound images of the breast. Currently, breast cancer diagnosis faces challenges in achieving high accuracy and efficiency, particularly when dealing with different datasets and limited data. Conventional approaches may struggle to extract specific information from medical imaging data, limiting the early detection and classification performance of breast cancer. Additionally, computational complexity remains an issue. In this scenario, there is a huge need for developing and optimizing machine and deep learning-based approaches for breast cancer detection that may address these problems while also improving accuracy, flexibility to varied datasets, and computational efficiency. Hence, in this dissertation, focus is given on developing efficient high performance frameworks for detection of breast cancer using mammogram and ultrasound breast images. For this purpose, eight different breast cancer detection schemes including one machine learning-based scheme, five deep learning-based schemes, and two sparse learning along with transfer learning schemes are proposed. In Chapter 3, a machine learning-based hybrid framework for automatic breast cancer detection is presented i.e. capable of recognizing abnormalities and malignancy in both mammographic and ultrasonic datasets. The method combines a variety of cutting-edge technologies to improve the accuracy and efficiency of breast cancer detection. To improve image visibility, preprocessing begins with the Laplacian of a Gaussian-based modified high boosting filter (LoGMHBF). Furthermore, for feature extraction both Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT) features are used to extract texture and frequency information from pre-processed images. Principal Component Analysis (PCA) is then used to reduce dimensionality while preserving important variance in high-dimensional feature spaces, resulting in improved classification performance and computing efficiency. The classification stage employs a hybrid model that combines Support Vector Machines (SVM) and Random Forest (RF), which is optimized using a probability-based weight factor and threshold value. This complete approach guarantees both computational efficiency and diagnostic accuracy in breast cancer diagnosis. In contrast to deep learning, which uses end-to-end networks, its overall system performance is significantly dependent on the outcome of each stage. The next chapter, Chapter 4 presents five effective deep learning-based strategies for early and accurate detection of breast cancer. Deep learning, especially transfer learning networks are now widely recognized as a feasible technique for addressing the difficulty of restricted datasets in the medical industry, hence lowering training errors. The first technique in this chapter presents an EfficientNetB0-based breast cancer detection technique demonstrating good performance even with limited datasets. Its adaptive scaling of depth, width, and resolution achieves efficient detection by carefully balancing classification accuracy and computational cost. Furthermore, use of LoGMHBF in pre-processing improves the performance. The second strategy proposes a ShuffleNet-Random Forest-based hybrid frame work to improve detection performance. This system combines ShuffleNet, a deep lightweight CNN to extract significant deep features, with a Random Forest classifier to take advantage of the strengths of both methods. Channel shuffling and group convolution help in faster processing while maintaining discriminative features. In addition, RF is applied instead of softmax layer to boost the classification performance further. The third strategy provides an efficient deep ensemble breast cancer detection system, which ensembles three popular transfer learning networks: AlexNet, ResNet, and MobileNetV2. This ensemble approach attempts to obtain better performance in breast cancer diagnosis by utilizing the distinctive features of each design. The benefits of Rectified Linear Unit (ReLU) activation functions and overlap pooling are added by using AlexNet, which improves the network’s ability to recognize complex spatial patterns and nonlinear correlations in the breast image. To further solve the vanishing gradient issue, ResNet incorporates skip connections that enhances the model’s ability to extract hierarchical features and facilitates the training of deeper networks. Incorporation of residual learning results in a faster system. Furthermore, the addition of MobileNetV2 optimizes computing performance without compromising accuracy by introducing depth-wise separable convolutions and an inverted residual bottleneck structure. This deep ensemble technique achieves amazing results by using the strengths of these three networks. Though deep ensemble classifier gives good result, to boost computational efficiency further, the fourth strategy introduces a deep hybrid framework that combines ShuffleNet and ResNet18. The use of probability-based weight factor (w2) and threshold value ( ) improves performance greatly. Experimentally selected optimum threshold value ( ) makes the system faster and more accurate as the second classifier is functional only when the weight factor w2 > ; a threshold value. The fifth strategy introduces a modified Relation and Margin Network (MReMarNet) to identify breast cancer more efficiently. This model focuses on increasing intraclass compactness and interclass separability, which is very useful for small sample datasets. The relation unit loss and cross-entropy loss by a relation unit (RU) and a fully connected (FC) unit helps in feature learning and decision boundary-based classification, respectively. The coupled benefits make the system more efficient. Though all five schemes introduced in Chapter 4 result in notable performances even with small data, to lessen the computational time, two effective sparse-based deep learning schemes are presented in Chapter 5. The first method provides three alternate deep layer cascade breast cancer detection models (ADLCBCDMs): ADLCBCDM-1, ADLCBCDM-2, and ADLCBCDM-3 are proposed. In ADLCBCDM-1, projection to discriminative subclasses (PDS) is used as pre-processing, where as in ADLCBCDM-2, Laplacian of Gaussian-based modified high boosting filter (LoGMHBF) alongwith PDS is applied prior to alternate deep layer cascade model (ADLCM) to boost system performance. ADLCBCDM-3, an efficient hybrid deep layer cascade representation-based breast cancer detection method is proposed by effectively hybridizing ADLCBCDM-2 and ADLCBCDM-1. By using a class discriminant softmax vector representation at the interface, it cascades both sparse and collaborative representations and so combines the benefits of both. Furthermore, it enhances hierarchical learning capability by expanding traditional shallow-sparse representation to an efficient multi-layer learning approach. The second technique proposes an efficient Convolutional Neural Network with Structured Analysis Dictionary Learning (SADL)-based Feature Selection for Breast Cancer Detection. An effective deep learning network is used to extract critical deep features in all three parallel paths from the original image, resultant image after using Canny edge detector, and resultant images after applying LoGMHBF, respectively. Furthermore, to get more important class-discriminatory features, Structured-Analysis-Dictionary-Learning (SADL) is used, resulting in an improved classification performance. Finally, SVM is used to increase the classification performance by efficiently optimizing hyperplane placements. Thus, the combined benefits of transfer learning-based feature extraction, sparse learning-based feature selection, and machine learning-based classification improve the overall performance. Finally, the experimental findings indicate that the proposed MReMarNet-based scheme yields best classification performance among all the proposed schemes across all three datasets: mini-DDSM, BUSI, and BUS2 due to good intraclass compactness in the relation unit, and good interclass separability in Fully-connected unit, which leads to an improvement in feature discrimination capability
Linear and Non-Linear Mathematical Models with Imprecise Parameters having Uncertain Memberships
No full textLinear and non-linear mathematical equations are essential tools for understanding and describing a wide range of phenomena across scienti_c and engineering disciplines. These equations can be algebraic systems, di_erential equations, integral equations, or integro-di_erential equations, etc. Typically, parameters associated with these governing equations are regarded as deterministic entities. However, these parameters may contain imprecision or uncertainty due to observational or experimental errors or a lack of information, which challenges the idealised assumption of determinism. Uncertainty in parameters may be e_ciently managed by utilising probability theory, interval analysis, and fuzzy set theory. Unfortunately, probabilistic methods may not be able to deliver reliable results at the required precision without su_cient data. It may be due to the probability density functions involved in it. On the other hand, interval and fuzzy theory approaches emerge as valuable tools in such situations. In this work, we have focused on the fuzzy sets to handle the parameters of the problems undertaken. Fuzzy sets have assigned membership grades. Further, uncertainty in the membership grade of the fuzzy parameters may also be possible. In these scenarios, interval type-2, type-2 and interval type-3 fuzzy sets may be bene_cial. Accordingly, this thesis is dedicated to investigate di_erent linear and non-linear mathematical models under higher orders of fuzzy uncertainty, and we have used the type-2 and type-3 fuzzy sets to target impreciseness. Particularly, we focused on analysing mathematical equations, such as systems of linear equations, eigenvalue problems, di_erential equations (both integer and fractional order), and integral equations in type-2 or type-3 fuzzy environments. In this regard, new analytical and numerical methods are developed to solve the type-2 and type-3 fuzzy mathematical equations. Various analytical methods are proposed to address linear systems of equations, linear and non-linear eigenvalue problems, di_erential equations, and Fredholm integral equations under type-2 and type-3 fuzzy environments. Additionally, numerical and semi-analytical methods, viz., the Homotopy Perturbation Method (HPM), Adomian Decomposition Method (ADM), Elzaki Transformed HPM (ETHPM), Fractional Reduced Di_erential Transform Method (FRDTM), Legendre Wavelet Method (LWM), and Generalised Modi_ed Euler Method (GMEM), are also developed in the type-2 fuzzy environment to solv the fractional order ordinary and partial di_erential equations. Numerical examples and application problems are solved to demonstrate the e_ciencies and capabilities of the developed methods. In this regard, various sample problems are solved _rst. Further application problems governed by a linear system of equations, such as beams, trusses, and rectangular sheets, are considered. Structural problems, viz., spring-mass systems, are studied under linear and non-linear eigenvalue problems. Application problems related to di_erential equations, viz., electric circuits and spring-mass systems, are also investigated. Fractional order di_erential models, viz., prey-predator model, HIV transmission model, SEIR model of measles, smoking giving up model, heat equation, modi_ed Camassa-Holm equation (MCHE) and modi_ed Degasperis-Procesi equation (MDPE) are analysed in this investigation. In special cases, comparisons are made with existing solutions to show the e_cacy and reliability of the present methods
Development of Hydrodynamic Cavitation Assisted Functionalized Egg White Protein Hydrolysate Powder
No full textProteins are key ingredients in food processing that govern both nutritional and functional behavior of food systems. They are macromolecules composed of amino acids linked together by peptide bonds. Being a macromolecule has its disadvantages in the human diet, such as longer digestibility and absorbability. Moreover, majority of food allergens reported are proteins. Significant efforts have been made to address this issue, and one of the major solutions explored was to hydrolyze the proteins. Protein hydrolysates are end products of hydrolysis of proteins obtained by enzymes, and acids or alkali that break down macromolecules into simpler structures. Recent research has largely focused on the modification of food proteins, to provide complete nutritional support to individuals who are incapable of digesting food. In this study, we have explored a novel approach to produce stable and functionalized protein hydrolysate powder from hen egg white. Egg white protein is well-known for its high-quality protein content; however, egg proteins have relatively low absorption rate in the human body (~3 g/h). Thus, producing hydrolysates can broaden their application in both textural and nutraceutical aspects. Moreover, hydrolysate powders are hygroscopic in nature when compared to native proteins which makes them unstable. Thus, the aim of this research is to develop a stable and commercially viable instant protein supplement. To intensify the hydrolysis process, a pre-treatment like hydrodynamic cavitation was employed to level up the production of amino acids and peptides. When compared to ultrasound the hydrodynamic cavitation as a pretreatment was beneficial in improving the functional, structural, and rheological properties of egg white proteins. Thus, an orifice plate hydrodynamic cavitator of 2 mm was utilized for pretreatment of egg white protein hydrolysate (EWPH) production. The obtained EWPH were evaluated for various physicochemical (degree of hydrolysis), functional (emulsifying, foaming), structural and nutritional properties (antioxidant activity and in-vitro digestibility). The egg white solutions (5% solid content) were pretreated for 10, 15 and 20 min with HC and later hydrolyzed using papain enzyme for 90 min. The structure analysis revealed that HC unfolded the protein structure confirmed through formation of β-sheet (from 15 to 46%) and loss of α-helix (34 to 14%) content with increasing treatment time. Through the exposure of hydrophobic bonds, the degree of hydrolysis and surface hydrophobicity increased which eventually improved the nutritional and functional properties of EWPH. The HC-15 min treated samples had the highest zeta potential (-25.4 mV) with lowest average particle size (346.5 nm) and denaturation temperature (70.67 °C) and with further increase in treatment time the stability of hydrolysates decreased. The study concluded that HC treatment can effectively improve the functional and nutritional properties of EWPH and a treatment time of 15 min is recommended for obtaining EWPH with improved properties. The stability of egg white protein hydrolysates was tested using a desugarization process. This process involves the removal of glucose, which can react with cephaeline, a phospholipid, and potentially affect the stability of the resulting protein hydrolysate powder. The desugared samples, especially those that underwent yeast fermentation, exhibited higher foaming and emulsifying abilities compared to the non-desugared samples. However, the desugarization process did not have a significant impact on the stability of the egg white protein hydrolysate powder. Thus, the protein hydrolysate powder was further studied by sorption isotherm analysis to understand the stability during storage and transport. EWPH obtained from two different drying methods namely freeze and vacuum drying, were assessed for their stability at elevated temperatures of 25, 35, 40, 45, and 55 °C. The sorption isotherms of EWPH were found to be a typical Type III sigmoid curve, representing a hygroscopic material. The EWPH samples had a steep increase in monolayer moisture content above 40% relative humidity. With respect to temperature, the samples had varied differences, were the vacuum dried samples when compared to the freeze dried samples had better stability. The HC treated samples did not have much variation with temperature. The DSC and XRD results confirmed that HC aided in improving the stability of freeze dried egg white protein hydrolysate powder. The study revealed that the HC treated freeze dried samples had the highest stability and functionality. An optimum storage temperature of 25 °C and relative humidity of 40% is suitable for maintaining the stability of EWPH powder. Overall, from the outcomes it can be suggested that the hydrodynamic cavitation technique provided a novel approach for developing stable egg white protein hydrolysate powder for producing nutrient-rich instant protein powder enriched with functional bioactive compounds and the process could be scaled up for large scale processing of protein powders
Machine Intelligence Modeling for Dynamical Problems
No full textArtificial intelligence (AI) has been a subject of intense media hype in the 21st century. In the last decade, AI has taken the world by storm, showing its potential to revolutionize public services, industries, legal, finance, and defence sectors by surpassing human levels of accuracy for a variety of applications. Major companies and startups around the globe are developing intelligent products and services with the help of AI techniques. Applications of AI can be observed in Tesla, Apple’s Siri, Amazon’s Alexa, and the newly introduced ChatGPT and Google Gemini. Some of the AI techniques include Artificial Neural Networks (ANNs), Experts Systems, Robotics, Natural Language Processing, and Fuzzy logic have shown exponential growth. However, the most successful AI technique, which has been influencing science and technology over the decades is ANNs. Furthermore, ANN has increasingly been used in highly sensitive areas such as healthcare, weather forecasting, biomedical informatics and criminal justice for high-stakes prediction that have a significant impact on human lives. As regards, machine intelligence (MI) techniques for handling multimodal dynamic systems have achieved a flurry of research and experienced rapid evolution. In the real world scenario, the dynamical systems are governed by different types of differential, integral and algebraic equations. Various numerical techniques such as the Euler, Runge-Kutta, finite element, finite volume, homotopy perturbation, homotopy transformation, and Adomian decomposition methods have effectively been employed to obtain the solutions of these dynamical problems. Despite the well-documented success of these aforementioned traditional methods, it is still evident that they are insufficient for addressing a variety of real-world non-linear problems. Additionally, each of these traditional methods possesses its own intrinsic value, limitations and applicability. Furthermore, they are problem-specific, some of them are not mesh-free and require repetitions of simulations. In contrast, neural network based approaches provide alternative and mesh free solutions for differential equations. It often characterized as a black box and predicts closed form solution in the given domain. In view of the above, the primary objective of this investigation has been to propose computationally efficient unsupervised MI techniques, specifically ANN models, for solving challenging real world dynamical problems. As such, Scalable Symplectic Artificial Neural Networks and a nature inspired machine learning algorithm known as Curriculum Learning have been employed to solve various oscillator and astrophysical problems. In order to investigate time series problems, Wavelet Neural Networks with L-BFGS optimization algorithm have been utilized. Additionally, to capture the physics inherent in dynamical problems and corresponding synthetic/generated (if available) data, a SciML algorithm, viz. modified Physics-informed Neural Networks has been proposed Lastly, dynamical problems of fractional order are also addressed by proposing unsupervised deep neural network
“Tribromides in Oxidative Dearomatisation: A Toolbox to Solve Molecular Complexities”
No full textThe immanent reactivity and functionality of aromatic compounds furnish numerous possibilities for the synthesis of three dimensional complex organic structures via dearomatization reactions. Under oxidizing conditions, the dearomatization of ortho- and para-substituted phenols delivers cyclohexadienones, and these products are found to be omnipresent in the chemical synthesis of natural products and pharmaceutically active compounds. With the continuation to the recent trends, the proposed research work was designed to synthesize biologically active core molecules employing oxidative dearomatisation and photo-catalysis. Many groups have been putting serious efforts on dearomatisation from years albeit, spiro-oxacycle synthesis has been a less explored direction. The thesis chapters highlight the transformation of planar arenol derivatives into multifunctionalised spiro molecules employing Tribromides (TBs) and photo-catalysis. Heterocyclic motifs have been important point of attraction due to their demanded biological activity and pharmaceutical importance. The thesis presently divided into the following four chapters. Chapter 1.1 A series of ammonium tribromides were screened for exploring the role of ammonium counterpart attached to tribromides on generation of stereoselective spiro-furans via oxidative dearomatisation of naphthols. The proposition enlightens a suitable combination of the ammonium tribromide and sol vent employed, deliver the best achieved diastereoselectivities. This in turn, has also envisioned the mechanistic aspects related to this category of reactions. The mentioned dearomative spirofurano naphthalones has also been successfully achieved on a large-scale. Chapter 1.2 It is well known that phenols are strongly prone to aromatic substitution at the ortho-and para- positions. Although, selectivity at those positions was difficult due to very small difference in electron density. Here, we overcome this problem with recall of umpolung chemistry and found a tribromide mediated chemical engineering process for the controlled generation of ortho- and para- quinomethide product via oxidative dearomatization reactions. Chapter 2 In chapter 2 of the thesis, we have discussed a catalytic strategy towards the tribromide catalyzed dearomative spiro-cyclization reaction is described using TBAB and V2O5/H2O2 or H2MoO4/HClO4/H2O2 or Oxone as an oxidising agent. This methodology leads to the exclusive formation of spirofurano dienone product without subsequent rearomatisation or halogenated side product. The oxidation of naphthols as well as phenols, which are hardly reactive, are readily proceed under this mild condition generating only inorganic wastes from the used oxidant. This is the first report on tribromide-catalyzed dearomative transformation, resulting high functional group tolerance and broad substrate scope with excellent yield (up to 98%) and good diastereoselectivity (de up to 88%). The reactive spiro furano dienone core was utilised further for the synthesis of Vitamin E core system and other synthetic functionalisation. Several control experiments, Raman analysis, UV Visible studies and IR-Spectroscopy are performed to figure out the reaction pathway, which revealed that in situ generated tribromide is the active species. Chapter 3 Achiral tribromides are well-known compounds and widely used as an alternative source of molecular bromine. Herein, we present the first synthesis of a new set of air- and moisture-stable chiral tribromides. Categorically, first we introduced proline-based chiral tribromides, followed by Cinchonine-based tribromides and Maruoka-based tribromides. NMR, IR and UV spectroscopy of chiral tribromides revealed the presence of tribromide. This strategy enables the synthesis of a variety of air-, moisture-, and benchtop- stable quaternary tribromide derivatives. Further studies showed that these reagents are optically pure and quite efficient for pursuing asymmetric transformation via oxidative dearomatization reactions. Chapter 4.1 In the past few decades, annulated arene heterocyles via radical initiated or cation initiated cascade reactions experienced a period of rapid development, and numerous biologically important building blocks or natural products have been synthesised. Till now, a significant number of transformations accomplished with metal-catalysis, non metal, electrocatalysis and so on. Among these methodologies, C-C, C-Si, C-S, C-P bond-forming cascade reactions are common. We found few reports on C-halogen bond-forming cascade reactions using oxidant, although it has great potential of post functionalization. In Figure 5, tribromide-catalyzed N-heterocyclic ynone dearomatization reactions are shown. Chapter 4.2 In this part of the chapter, we are presenting first visible light-catalyzed C halogen bond-forming dearomative ipso-cyclisation of N arylpropiolamide
Development of Group Recommendation in Collaborative Framework
No full textRecommender systems (RS) provide personalized suggestions to users regarding products and services. These suggestions are generated for individual users only. However, group activities are gaining popularity in several applications, such as movie recommendations, travel and e-tourists, etc. Therefore, there is a need for a group recommendation system that helps people provide better recommendations for group members instead of a single user. This thesis addresses cold start, data sparsity issues and group member satisfaction. An effective group recommendation approach named GR Slope One is introduced to incorporate dynamic changes in the user-item rating information. The proposed method works in two ways: group aggregate prediction and group aggregate model. Firstly, groups are formed using kmeans clustering, and later, the group aggregate prediction is performed based on the group’s individual prediction information. Secondly, the group aggregate model is determined by aggregating individual user preferences. A novel modelling technique (Max-after-threshold) is introduced. It adopts an aggregate prediction feature to provide better recommendations to the group. The proposed approach is compared with popular existing methods such as Matrix factorization (MF), BaseGRA, and Improved GRA. The deep Collaborative Filtering Approach suffers from the cold-start issue in the presence of a group recommender system. To overcome the issue, metadata information is considered while predicting the rating information. The approach presents a rating prediction for groups that leverage multi-layer perceptrons, general matrix factorization using metadata, and neural collaborative filtering techniques. The proposed approach is discussed in two steps. The first step is to learn from group-item interaction, perform one hot encoding for the group and item, and then utilize this information to perform dot product by applying the GMF layer. In the second step, learn group-item interactions using group and item metadata. This information is concatenated using the MLP layer. Finally, the GMF and MLP layers are combined to get the final prediction ratings. Recently, the attention mechanism approach has drawn attention to the group recommendations system. Certain unsolved problems, such as the weights of group members, are crucial during the recommendation process. Existing works consider all the members in the group to be given equal priority. Moreover, preference aggregation is not considered. Therefore, group recommendation using the attention mechanism can be exploited to address the issue of preference aggregation, which uses a neural attention network and a neural collaborative filtering framework. The attention component is used to capture the effect of every member within the group. In addition, a neural collaborative filtering framework is utilized to learn the group-item interactions in the data. It strengthens the performance of the group recommendations and their user recommendations. Group recommendation using the Attention mechanism addressed the preference aggregation(GRAM). It has certain limitations, such as restrictions on group sizes, ignoring group modelling strategies, and group satisfaction metrics. Therefore, it is essential t consider all the mentioned limitations to achieve better performance. Major restrictions enforced on the literature studies are: (1) small number of users, (2) a large number of groups, (3) median number of group participants and (4) various centrality techniques. To address these limitations, we propose a preference network-based approach. It performs prediction based on weighting individual users in linear preference. The weight computing is based on the node centrality score. Multiple centrality techniques are analyzed for score calculation. This work also introduced two new modeling strategies AV GMP and MPAV G. This study uses the group satisfaction metric (GSM) to evaluate member satisfaction and satisfaction error for a group (SEG) to improve member satisfaction and recommendations for groups. The proposed method outperforms baseline aggregation techniques. The experiments were conducted on standard datasets and validate the effectiveness of the proposed approaches
Numerical Methods Based on Polynomials and Orthogonal Basis Functions for Solving Stochastic Differential and Integral Equations Arising in Mathematical Modelling
No full textA stochastic process is a mathematical object that is intended to model the evolution in time of a random phenomenon. The best-known stochastic process to which stochastic calculus is applied is the Wiener process (named in honor of Norbert Wiener), which is used for modelling Brownian motion as described by Louis Bachelier in 1900 and by Albert Einstein in 1905 and other physical diffusion processes in the space of particles subject to random forces. Since the 1970s, the Wiener process has been widely applied in financial mathematics and economics to model the evolution of stock prices and bond interest rates. The main objective of this dissertation is to provide various spectral methods, such as the operational matrix method, collocation method, Galerkin method, etc., to solve stochastic equations such as stochastic differential equations, stochastic integral equations, and stochastic integro-differential equations of both integer and fractional order arise virtually in every field of scientific endeavor. Therefore, in the present dissertation shifted Jacobi operational matrix method, the shifted Jacobi Galerkin method is applied to solve both linear and nonlinear stochastic Itô-Volterra integral equations numerically. The computational methods based on the Lucas polynomial, Genocchi polynomial, Lerch polynomial, and Pell polynomial are implemented to solve the multi-dimensional and fractional stochastic Itô-Volterra integral equations. The fractional Brownian motion is a generalization of Brownian motion and was first introduced by A. N. Kolmogorov in 1940 when it was called Wiener Helix. The stochastic equations with fractional Brownian motion are very useful in modelling many problems in biology, physics, mathematical finance, etc. So, in this work, the operational matrix method based on shifted Jacobi polynomial, barycentric rational interpolation collocation method, quintic B-spline collocation method, shifted Chebyshev spectral Galerkin method, and collocation method based on Chebyshev cardinal function to solve stochastic differential equations driven by fractional Brownian motion
Studies on Phenol Biodegradation with Simultaneous Lipid Production by Rhodosporidium Toruloides 9564T for Potential Biodiesel Feedstock
No full textWaste management has become a significant concern in recent decades as a result of urbanization and the exponential growth of the global population. Industries such as paper and pulp, petroleum refining, coal processing, pharmaceuticals, and dyes discharge a wide range of hazardous organic and inorganic compounds. Organic compounds, including heavy metals and aromatic compounds, have detrimental impacts on the environment. Prominent environmental pollutants include heavy metals (Cr, Cd, Co, Pb, Ar, Hg, Zn, and Fe), the most prevalent aromatic compounds (phenol and its derivatives, catechol, 4 nitrophenol, and 4-chlorophenol). These substances are known to be genotoxic, carcinogenic, and mutagenic. All of these contaminants are discharged into the effluent during the pulping stage of paper manufacturing, resulting in its dark colour. These pollutants possess a potent odor, are toxic and carcinogenic to aquatic and terrestrial organisms, and are classified as aqueous pollutants due to their solubility in water. The EPA designates these pollutants as priority pollutants for removal from the environment based on their toxicity. Consequently, in order to safeguard the environment and organisms, it is imperative that these contaminants be eliminated from the effluents originating from the paper and pulp sectors. Oleaginous yeast is more valuable for the remediation of these contaminants in wastewater because it produces valuable products after utilizing the wastewater. Biofuel is one of the most frequently produced byproducts of oleaginous yeast. To thoroughly treat the organic debris in the wastewater effluent, the research investigation has been organized around five distinct objectives. The initial objective was to investigate phenol degradation and lipid production by using Rhodosporidium toruloides 9564T, an oleaginous yeast. It was found that R. toruloides completely degraded 0.75 g/L phenol with a lipid accumulation of 26.3% by following the Ortho-cleavage pathway. After completing the first objective, the second objective involved the degradation of phenol derivatives and their impact on cell morphology. The results obtained for this objective confirm that R. toruloides possesses the ability to fully degrade catechol (upto 1 g/L), 4-CP (upto 0.5 g/L), and 4-NP (upto 0.1 g/L). The maximal lipid yield achieved during this investigation was 36% (catechol). The impact of heavy metals on phenol degradation and lipid production was investigated in objective 3. The heavy metals Zn, and Fe have been observed to improve phenol degradation by reducing the degradation period. Conversely, Cr, Cd, and Co have been found to have an adverse impact on phenol degradation and lipid production. The SEM and confocal image confirmed the change in cell morphology, size, and accumulation of lipids within the cells confirms the toxicity level of the metals during phenol degradation. The maximum lipid content produced in the case of Zn and Fe containing phenol MSM media i.e. 43.39 and 40.56% respectively among all the heavy metal used. The obtained GC data confirmed that R. toruloides is capable of producing biodiesel that falls within the range specified by the ASTM standards, and the biodiesel properties are similar to vegetable oil. These results serve as evidence of the high quality of the biodiesel produced by R. toruloides. The optimization of phenol degradation and lipid production was then investigated using the Design Expert software in objective four. Using the Placket-Burman design, an initial screening of the most significant factors (pH, temperature, agitation speed, incubation period, and inoculum size) was conducted. Four factors are chosen for optimization by RSM (CCD experiment) out of a total of five. An optimized condition was obtained after the validation was incubation duration 92.145 h, temperature 29.46 ℃, inoculum size 14.68% v/v, and pH 6.07 and the 100% phenol was removed with an increase in lipid production 0.915 g/L. Again the RSM data was validated by ANN MOO-GA confirming the optimization of phenol degradation and lipid production was properly done. A study has been conducted in the 5 L reactor subsequent to the optimization study as the final objective. A batch mode reactor study was performed to treat synthetic paper pulp industry wastewater. The results of the study confirmed that R. toruloide exhibited phenol degradation (specifically, removal of 0.75 g/L from black liquor within 72 h of incubation), lignin degradation at a rate of 300 mg/L, and complete adsorption of heavy metals. The whole production process was analysed for the environmental impact and techno-economic feasibility and the obtained data confirmed that after 3 years the production system are economically viable with less environmental impact. Based on the comprehensive findings of the study, it can be concluded that R. toruloides is among the most effective oleaginous yeasts for biodiesel production and effluent valorization
Impact of Climate Change on River Flow from Basin Scale to Micro Watershed in Brahmani River Basin India
No full textAmid the increasing concerns of global climate change, the Brahmani River Basin in India stands out as a significant hydrological system undergoing notable variations in rainfall patterns. Utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study accurately forecasted these rainfall patterns, achieving significant closness during the training and testing phases. Subsequently, these projections were incorporated into the North American Mesoscale (NAM) and the Variable Infiltration Capacity (VIC) models. Notably, the VIC model demonstrated robustness with high R-values both during training and testing phases. Using this forecasted data, a comprehensive water budget analysis was performed for the Gomlai and Santrabandha micro catchment, revealing a discerning trend of declining water discharge at the Gomlai station. This assessment underscored a pronounced water demand-supply discrepancy in the Gomlai and Santrabandha micro-catchment, highlighted by a shortfall of 1.74 M m3 and a surplus of 0.46 M m3 annually in the respected micro watershedes. Additionally, the Analytical Hierarchy Process (AHP) was employed to delineate the spatial hydrological dynamics of the basin,that identified the specific discharge and recharge zones. Collectively, the findings elucidate the hydrological implications of climate change on the Brahmani River Basin, proposing pertinent interventions and strategies. This research not only emphasizes the challenges posed to the Brahmani River Basin but also offers a roadmap for sustainable water management, showcasing its potential applicability in similar global contexts