7940 research outputs found
Sort by
Development of Efficient Movie Recommender System for a Group of Users based on Their Preferences
No full textRecommender Systems have gained popularity recently due to their ability to expedite users’ selection processes. Traditional recommender systems mainly focus on providing recommendations to a user. It is not suitable for recommending an item to groups of users. A group recommendation system (GRS) addresses this issue of recommendation. Group recommender system is popular in a few domains such as parties, tourism, movies, etc. Movie group recommender system is a special kind of GRS that is designed for movie recommendations to the group of users. In contrast to traditional Recommender Systems (RS), movie GRS has gained prominence for its ability to cater to the collective preferences of groups. The task of GRS is divided into two tasks such as group item preference prediction and group item recommendation. Nowadays, group satisfaction has become a major issue in the area of GRS. Group user satisfaction plays a vital role in collective decision-making in a group. Many researchers have developed various algorithms to address this issue. However, prior GRS techniques failed to address the important issue of group satisfaction. The efficacy of GRS relies heavily on understanding group preferences, encompassing factors like trust, influence, and likeness among group members. An ongoing challenge in the movie GRS pertains to member relationships and group satisfaction. However none of the researchers provide a good user satisfaction with less error rate. This thesis tries to address the issue of group user satisfaction of movie GRS in three contributory chapters. The first contributory chapter uses the collaborative filtering approach and tries to enhance group satisfaction. It explores member inclination and item usefulness within a group to address this issue. The proposed methodology employs an aggregate prediction technique to calculate the final group score. It computes user inclination and item usefulness at both the individual and group levels. A novel aggregation strategy, Popularity and Likeness-based Aggregation (PLAS) is developed to aggregate individual predictions into a complete group score. Another collaborative filtering (CF) approach, revised slope one, is proposed to predict group member preferences. Existing average modelling combines the preference to get a final group preference. Experimental outcomes show the superiority of the proposed method. The second contributory chapter uses the content information with the user profile to improve group satisfaction further. It employs cluster validation metrics for the selection of appropriate clusters and cluster sizes in the group formation stage. Later, a novel technique is proposed for predicting individual member ratings considering user characteristics or genre information to optimize group user satisfaction. Based on user and item characteristics, the newly introduced user inclination and item usefulness-based aggregation function aims to enhance the aggregation process. Later, the study further gives another approach to improve group user satisfaction with less error. The study introduces novel similarity methods to predict group user ratings individually. A linear neural network model is introduced to aggregate individual ratings into a group score effectively. Finally, the performance is evaluated on standard datasets. Experimental outcomes show the superiority of the newly introduced approach. Finally, this study contributes to the evolving field of movie GRS by addressing the challenges of group user satisfaction. Also, this chapter explores the fusion of sequential and group recommendations through the ”Dynamic Group Recommendation using Sequential Recommendation” (DGRSR) method, showing improved group predictions and recommendations in a linear approach. The experiments are performed on the standard datasets. Results show the superiority of the state of art techniques
Development of a Novel Fluorescent and Injectable Thermosensitive Hydrogel as Multifunctional Modalities for Imaging and Glioblastoma Therapy
No full textGlioblastoma multiforme (GBM) is a highly aggressive intracranial tumor characterized by uncontrolled proliferation, neurodegeneration, and tissue infiltration. Surgical debulking, the mainstay of treatment for GBM, frequently suffers from incomplete excision owing to the tumor’s invasive nature. This incomplete removal ultimately paves the way for inevitable tumor recurrence within 6-12 months post-resection. An innovative approach to enhance treatment efficacy involves localized drug delivery systems, which administers high concentration of drugs directly into the resection cavity; thereby enhancing the therapeutic efficacy and significantly mitigating systemic exposure. In addition, the diagnosis and treatment of GBM are extremely challenging due to its intratumoral heterogeneity. Fortunately, the identification of specific molecular signatures of GBM has emerged as a better-suited technique for its effective diagnosis. The discovery of approved drugs targeting these biomarkers can effectively conquer GBM. In parallel, fluorescence imaging (FL) techniques offer substantial advantages in clinical diagnosis, including high contrast and sensitivity, non-invasiveness, and low cost. Thus, there is a growing demand for the development of multifunctional injectable systems that integrate diagnostic and therapeutic capabilities for effective detection, imaging, and targeting of GBM lesions. In this study, we systematically discovered 2734 overlapping genes that were differentially expressed between GBM and noncancerous brain tissues through meta-analysis of transcriptomic data. The relevant functions and signaling pathways of differentially expressed genes (DEGs) were detected through enrichment analysis. Subsequently, AURKA, AURKB, CDK1, CDK2, CCNB1, CCNB2, CDC20, BUB1, PLK1, and BIRC5 were screened as hub genes via maximum neighborhood component (MNC) algorithm. Furthermore, a drug-gene interaction network predicted paclitaxel (PTX) as a potential therapeutic candidate to neutralize the dysregulated effects of oncogenes. Molecular docking studies showed a stronger binding affinity of PTX with AURKA [ΔG = -9.06 kcal/mol], CDK2 [ΔG = -8.93 kcal/mol], and BIRC5 [ΔG = -7.55 kcal/mol]. We further encapsulated PTX within the mesopores of hematite (α-Fe2O3) nanoparticles (HPTX). The encapsulation efficiency (EE) and loading capacity (LC) of PTX were estimated to be 99.2 ± 0.2 % and 10.3 ± 0.08 %, respectively. The MTT assay reflected superior toxicity of HPTX (IC50 = 39.2 ng/mL) against LN229 cells compared to free PTX (IC50 = 100 ng/mL) post-24-hour treatment. Moreover, we developed ultra-small (~ 3 nm) nitrogen-doped carbon quantum dots (NCQDs) with high quantum yield (QY). NCQDs demonstrated remarkable antijamming performance and high photostability, ideal for bioimaging. Moreover, the water-soluble luminous pearls served as a biocompatible trident in cancer biology, achieving a three-pronged action of in vitro cell photostability, multi-color imaging, and migration without hampering the biological system. The histological and biochemical analysis demonstrated no overt toxicity of NCQDs in mice, even under multi-dosing situations. Further, nanofibrous polyelectrolyte (PEC) complex of chitosan (CH) and sodium alginate (SAlg) was synthesized at different volumetric ratios (CHAlg50 and CHAlg70). These nanofibers were incorporated into CH-based injectable thermoresponsible hydrogel to enhance mechanical properties, achieve sustained drug release, and ensure long-term therapeutic efficacy. Subsequently, the optimal concentrations of NCQDs and HPTX were then embedded within the thermogel matrix, enabling simultaneous diagnosis and therapy of GBM. Hydrogels formulated with CHAlg50 demonstrated favorable swelling ratio (12.9), minimal degradability, and sustained PTX release (39.14%) compared to the CHAlg70 counterparts. Mechanical characterization revealed a Young’s modulus (YM) of 12 kPa, closely mimicking the softness of human tissues (1-100 kPa). The HPTX-loaded hydrogel variant, by virtue of its slow and sustained release of PTX, exerted pronounced cytotoxicity against LN229 cells, as evidenced by MTT assay and live/dead staining. Moreover, in vitro studies highlighted the multifaceted properties of the optimized hydrogel variant in monitoring cellular uptake and inducing apoptosis in LN229 cells. The green FL of NCQDs facilitated the detection of PTX-induced apoptosis, obviating the need for multiplexed dyes. Additionally, the optimized hydrogel variant significantly downregulated the expression of the AURKA, BIRC5, and CDK2 oncogenes. In conclusion, the amalgamation of diagnostic and therapeutic moieties within a single system provides a new dimension for the potential application of injectable thermogels in cancer theranostics
Bi/Sb Based Halide Perovskite Like Compounds: Effect of Substitution on Structural, Optical and Electronic Properties
No full textHalide perovskites and related compounds are gaining significant attention from the broader scientific community across the world because of their intrinsic optoelectronics properties as well as potential alternative to the renewable energy sources. These compounds usually have some unique properties like, long carrier diffusion length, high absorption coefficient, excellent charge transport, low deep level trap states and high photoluminescence efficiencies. Such properties make these compounds suitable for potential high performance optoelectronic devices such as solar cell and light emitting diodes (LEDs). Moreover, higher power conversion efficiencies (PCE), cheaper constituent elements, easy fabrication of the thin films for solar cells stimulate substantial research activity in these compounds making them a game changer in the field of photovoltaics (PVs). Among the halide perovskites, lead based perovskites (having formula APbX3) are extensively studied because of their excellent photovoltaic properties. However, their poor stability towards light, heat, atmospheric moisture and the toxicity of Pb hinders it, from the large scale applicability of these materials. Search for Pb free materials lead to exploration of Bi/Sb–based halide perovskite related compounds, which are more stable and relatively less toxic as compared to Pb. In order to maintain charge neutrality, three moles of Pb2+ can be replaced with two moles of (Bi/Sb)3+ forming a vacancy ordered structure. Bi/Sb metals are chosen to replace lead because of their similar electronic configuration having 6s/5s lone pairs. In this thesis several lead free Bi/Sb– based halide perovskite like compounds with A site cations varying from inorganic (Cs) to organic (methyl ammonium, ethyl ammonium, propyl ammonium etc.) and the halides from Cl to I are synthesized and their structural stability as well as the optoelectronic properties are studied. Efforts are made to understand the effect of substitution on the structure and optical properties with substitution at various sites. This thesis is majorly divided into four parts like introduction, materials method, working chapters and the summary and future work. The chapter one is introduction to halide perovskite and a brief description to the fundamentals and the types of halide perovskites. Second chapter is about materials and methods used to synthesize and characterize the compounds. In this chapter, the complete synthesis methods as well as different techniques used to characterize samples are discussed. Chapters 3–7 are about understanding the effect of substitution at various sites in the Bi/Sb– based halide perovskite related compounds, for their structural stability as well as optoelectronic properties. Chapter 3 describes the stability of different polymorphs of Cs3Sb2Cl9 phase and the effect of Bi substitution on structure, optical and electronic properties of Cs3Sb2Cl9. Cs3Sb2Cl9 has two polymorphs, thermodynamically stable trigonal phase and a metastable orthorhombic phase. It is observed that the activation energy for the formation of metastable orthorhombic phase in the solution is higher than the trigonal phase. Heat treatment on both orthorhombic and trigonal Cs3Sb2Cl9 phases at 300 °C confirms that trigonal phase is the thermodynamically stable phase while orthorhombic phase is metastable. Further, Bi substitution on the Cs3Sb2Cl9 leads to mixture of trigonal and orthorhombic phases till x < 0.1 and for higher substitution pure orthorhombic phase is obtained. The structural phase transition from trigonal to orthorhombic was evident from SCXRD, PXRD and Raman studies. Both of the pristine compounds, Cs3Sb2Cl9 and Cs3Bi2Cl9 are indirect band gap type in nature. Further in chapter 4, halide site substitution in Cs3Sb2Cl9–xBrx gives rise to a band type transition from indirect to direct beyond x = 2 without any structural phase transition. This band gap type transition is attributed to the site specific substitution of Br at the terminal Cl site. Preferential substitution of Br at the terminal position results in splitting of p–states in the conduction band along with significant change in the terminal Cl/Br–Sb–Cl/Br bond angle in the SbX6 polyhedra, which are responsible for the band gap type transition in Cs3Sb2Cl9–xBrx. The band gap is further tuned by substitution of iodine at chlorine site in the layered phase of Cs3Sb2Cl9–xIx and discussed in the chapter 5. Cs3Sb2Cl9–xIx compounds have two different structures, thermodynamically stable trigonal phase and metastable hexagonal phase. At room temperature, Cs3Sb2Cl9–compounds form a mixture of hexagonal and trigonal phases. Heating these mixed phase compounds at high temperature (600 °C) in a sealed tube gives pure trigonal phase for all the compositions. The hexagonal phase (dimer phase) Cs3Sb2Cl9–xIx compounds are of indirect band gap type, whereas the trigonal phase (layered phase) compounds are of direct band gap type except for pristine chlorine compound. Theoretical calculation shows a band gap type transition from indirect to direct with one mole of iodine substitution at terminal chlorine site of the trigonal Cs3Sb2Cl9, which supports the experimental observation. Suitable compositions with required band gap and band type has been chosen for the preliminary photodetector applications of these compounds. In chapter 6, the A site cation is replaced with organic cation (ethylamine) as well as the halide is taken as iodine to synthesize a series of (C2H5NH3)3Bi2–2xSb2xI9 compounds to understand its structural stability and optical properties. Three different phases in the (C2H5NH3)3Bi2–2xSb2xI9 series have been successfully synthesized i.e. hexagonal phase with isolated dimeric [(Bi/Sb)2I9]3− units for 2x = 0–0.6, a new monoclinic phase (1–1–4 phase) containing isolated trimeric [(Bi/Sb)3I12]3− units for 2x = 0.8–1.6 and [∞ 1 (Bi/Sb)2I93−] 1D orthorhombic phase for 2x = 1.9–2.0. The structural transition from hexagonal to monoclinic phase is associated with symmetry reduction and large distortion in the trimeric [(Bi/Sb)3I12]3− units. Finally, at higher Sb content, the orthorhombic phase is further relaxed and contains corner shared infinite [∞1 (Bi/Sb)2I93−] 1D chains. The 1D orthorhombic phase shows enhanced PL intensity as a result of less structural defects and more relaxed structure as compared to 0D hexagonal and monoclinic phases. It may be noted that, the new 1–1–4 phase is only stable in the intermediate composition and trying to synthesize the pristine compound remains a futile attempt. Further keeping the organic cation as the ethylamine, Bi as the metal centre and varying the ratio of Cl:Br leads to a series of composition (C2H5NH3)2BiCl5–xBrx and discussed in chapter 7. This perovskite related compounds containing 1D zigzag chain of BiX6 polyhedra are synthesized and studied at room temperature and low temperature. A structural transition is observed from Cmca space group at RT (293K) to a polar space group, Aba2, at low temperature (90K) for all compositions. The phase transition from Cmca space group at RT to polar Aba2 space group at LT is associated with the ordering of the ethylamine (removal of mirror plane) unit, as well as the formation of stronger H–bond in the LT phase. Optical study shows all the compounds are indirect band gap type. Theoretical calculation does not show any significant distortion of density of states of the Bi–p states (as shown in the chapter 4) with Br substitution suggesting no apparent change in the electronic structure and corroborate with the observed indirect band gap type for all the compounds in (C2H5NH3)2BiCl5–xBrx. Finally, the chapter 8 summarizes all the works done in the doctoral thesis and extension of some of the work have been proposed to carry out in future
Investigation of Structural Phase Transitions in K0.5Na0.5NbO3 Based Ferroelectric Systems due to Chemical Modifications
No full textPerovskite-based ferroelectric materials have strong potential to be utilized in numerous electrical and electronic devices due to their high piezoelectric coefficients, excellent dielectric and ferroelectric properties, and strong electromechanical coupling. These outstanding functional properties make ferroelectric oxides highly suitable for device applications including piezoelectric sensors, electrostrictive actuators, electromechanical transducers, capacitors, underwater acoustic devices, ultrasonic medical imaging, non-volatile memory, energy harvesting, and energy storage devices. At the same time, the functional properties of perovskite-based ferroelectric oxides can be easily tuned as per the requirement of device applications. The exploration of lead-free ferroelectric has significantly expanded in recent years due to the toxicity of lead (Pb), with a focus on three main groups of materials currently under consideration: BaTiO3 (BT)-based, (Na0.5Bi0.5)TiO3 NBT-based, and K0.5Na0.5NbO3 (KNN)-based piezoelectrics. Among these, KNN-based lead-free ferroelectric systems received significant attention since 2003 after the famous work on textured (Li, Ta, Sb) modified KNN ceramic by Saito et al. with excellent piezoelectric properties. Although pure KNN exhibits high Curie temperature (TC ~ 420°C) and high remanent polarization (Pr = 33 μC/cm2), synthesizing high-quality KNN with precise stoichiometry, material stability, and reproducibility using ordinary synthesis conditions is a challenge. Additionally, KNN has limitations such as moderate piezoelectric property, higher coercive field, and poor electromechanical coefficient. To enhance its density, microstructure and piezoelectric properties, various strategies have been adopted by different research groups. The fabrication of solid solutions with different perovskites and chemical modifications at different sites are the most effective ways to improve the density and functional properties of KNN ceramics. The 1st part of this work is mainly focused on the fabrication of solid solutions of KNN with Ba0.5Sr0.5TiO3 (BST) and CaTiO3 (CT). In the second part, we focus on the chemical substitution, such as Sm substituted KNN and Li/Ta substituted KNN. The lead- free ferroelectric solid solution of (1–x)(K0.5Na0.5)NbO3-x(Ba0.5Sr0.5)TiO3 (KNN-xBST, where x = 0.00, 0.025, 0.05, 0.10, 0.15, 0.20, 0.30) were synthesized by solid-state reaction technique. The Rietveld refinement of the XRD data and Raman spectroscopic studies suggest a compositional-driven structural phase transition from an orthorhombic (Amm2) phase for x = 0.00 to the orthorhombic+tetragonal (Amm2+P4mm) dual-phase for 0.025 ≤ x ≤ 0.15, then to the tetragonal+cubic (P4mm+ 3̅) dual-phase (x = 0.20) and finally to cubic (3̅ ) phase with increase in BST concentration at room RT. FESEM micrograph for pure KNN consists of well-defined grains of different sizes due to abnormal grain growth process. However, with the increase in BST concentration, the average grain size decreases, resulting in a compact microstructure and uniform distribution of grains. The temperature-dependent dielectric properties show two dielectric anomalies correspond to orthorhombic to tetragonal (TO-T) and tetragonal to cubic phase transition (TC) for pure KNN. However, with increasing BST concentration, both the transition temperatures decrease, the transition peaks become broaden, and for x ≥ 0.10, TO-T is expected to be below the RT. The dielectric constant at RT increases with BST concentration up to x = 0.10 and after that, it decreases. The P-E hysteresis loops show ferroelectric behavior up to x = 0.15 and paraelectric behavior for x = 0.20 and 0.30. With increasing BST concentration, the EC value decreases, is a minimum for x = 0.10 and subsequently increases. Similarly, the remanent polarization initially decreases with increasing composition, then increases, becomes maximum for x = 0.10, and after that, it decreases. The d33 increases with increasing BST concentration and the maximum value is found for x = 0.025. A phase diagram has been presented based on the temperature-dependent dielectric, RT XRD, and Raman data. Lead-free piezoelectric ceramics of (1-x)K0.5Na0.5NbO3-xCaTiO3 (KNN-xCT, where x = 0.00, 0.02, 0.03, 0.04, 0.05, 0.06, 0.08, 0.10, and 0.15) were fabricated using solid-state synthesis technique. The X-ray diffraction and Raman spectroscopic analysis revealed a composition-dependent structural phase transitions: three phase transitions, namely from a pure orthorhombic (Amm2) phase for x ≤ 0.02 to a mixed phase of orthorhombic and tetragonal (Amm2+P4mm) phases (0.03 ≤ x ≤ 0.08), and finally another mixed phase of tetragonal+cubic (P4mm+3̅) for x = 0.10 and 0.15 at RT. The morphological study reveals a decrease in grain size along with a more uniform distribution of grains as the concentration of CaTiO3 (CT) increases. The temperature- dependent dielectric properties show that, with an increase in CT substitution, both the phase transition temperatures (TO-T and TC) decrease, the transition peaks broaden, and for x > 0.06, the TO-T shifted below RT. Among the prepared samples, the 5 mol.% CT modified KNN shows the optimum electrical properties (d33 = 114 pC/N, Ɛr = 412, 2Pr = 15.25 μC/cm2) at RT. A phase diagram has been constructed based on the information gathered from the temperature-dependent dielectric measurements, RT X-ray diffraction, and Raman spectroscopy data. Substitution of suitable metal ions in ferroelectric oxides modifies the physical properties and can induce additional functionalities. Sm (samarium) substitution in ((K0.5Na0.5)1-3xSmx)NbO3 (KNSN) ceramics is expected to alter the crystal structure and induce local structural heterogeneity, influencing dielectric, ferroelectric, and piezoelectric properties. High-density KNSN (0.00 ≤ x ≤ 0.02) ceramics were fabricated by the conventional solid-state reaction route. X-ray diffraction and Raman spectroscopic analysis indicate that KNSN ceramics exhibit the single-phase orthorhombic (Amm2) structure for x = 0.00 and the coexistence of orthorhombic and tetragonal (Amm2+P4mm) structure for the composition range 0.005 ≤ x ≤ 0.02. Increasing Sm concentration leads to a slight increase in the values of TO-T, while the TC value remains constant. However, the broadening of the TO-T peak at the phase transition is observed as Sm concentration increases. We observed dielectric relaxation behavior in KNSN at orthorhombic to tetragonal (TO-T) phase transition temperature, and its origin can be attributed to the structural heterogeneity at the inter-ferroelectric phase boundary. With an increase in Sm concentration, the dielectric constant at RT increases, reaches a maximum at x = 0.005 (Ɛr = 583), and then decreases. The ceramic with x = 0.005 exhibits the maximum ferroelectric properties (2Pr = 57.02 μC/cm2) and the highest piezoelectric coefficient (d33 = 94 pC/N) at RT among the prepared samples of KNSN. The enhanced piezoelectric property for the critical composition x = 0.005 is due to the increase in the degree of local structural heterogeneity caused by Sm substitution. Lead-free ferroelectric ceramic of (K0.48Na0.48 Li0.04)(Nb1-xTax)O3 (KNLNT-x, where x = 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.40) were prepared using solid-state synthesis technique. The XRD analysis using Rietveld refinement and Raman spectroscopic studies suggests a composition-driven structural phase transition from the orthorhombic (Amm2) phase for x 0.20. SEM micrograph of KNLN (x = 0.00) shows a highly dense ceramic with inhomogeneous distribution of grains of different sizes. However, with an increase in Ta concentration, the grain size decreases, resulting in a more compact microstructure with a uniform distribution of grains. With increasing Ta concentration, both TO-T and TC systematically decrease, the transition peaks broaden, and TO-T shifts below RT for x > 0.15. For all the compositions, the P-E loops show well defined, nonlinear and saturated loops suggesting good ferroelectric nature. The 2Pr value intially decreases, then increases with increasing Ta concentration, becomes maximum for x = 0.20 (40.62 μC/cm2) and decreases after that. On the other hand, the coercive field EC ecreases with increasing x, is a minimum for x = 0.20 ( 9.3 kV/cm) and subsequently increases. However, the dielectric constant and piezoelectric coefficient at RT increase with increasing x, reaches maxima at x = 0.20 and subsequently decreases. Among the Ta- modified samples, the ceramic with x = 0.20 shows the highest dielectric constant (Ɛr = 556) and piezoelectric coefficient (d33 = 159 pC/N). The enhanced piezoelectric property is attributed to the morphotropic phase boundary (MPB) composition. Based on the RT XRD data, Raman spectra, and temperature-dependent dielectric properties, a phase diagram has been constructed
Towards Designing of Wireless Device Fingerprinting Systems
No full textThe current communication era prefers wireless networks over wired networks. The advantages of seamless connectivity, ease of use, and cost effectiveness have led to a large-scale migration from wired infrastructure to wireless infrastructure. Providing foolproof security is one of the major concerns of a wireless network. Message confidentiality can be assured by Wi-Fi encryption protocols such as Wired Equivalent Privacy (WEP), Wi-Fi Protected Access (WPA), Wi-Fi Protected Access II (WPA2), and the latest Wi-Fi Protected Access 3 (WPA3). Authentication of users is performed through Remote Authentication Dial-In User Service (RADIUS). There is, however, a lack of device authentication in the current wireless network infrastructure. Filtering based on physical addresses can easily be circumvented. The need for a device authentication mechanism has become increasingly important in order to identify devices connected to a network. The process of generating the unique profile for each device by extracting the discriminating pattern imbibed in their network characteristics is known as device fingerprinting. Over the past decade, device fingerprinting systems have made remarkable advancements. In spite of this, there are still a number of open challenges in the field of device fingerprinting. In this thesis, we attempt to design a device and device type fingerprinting system within a wireless network infrastructure based upon IEEE 802.11. A primary objective of this research is to propose various methods for selecting features, extracting features, generating fingerprints and developing classifiers for fingerprinting devices and types. As part of this thesis, a method of device type fingerprinting is employed in order to determine how to categorize devices based on their make. Inputs to the proposed model come from the probe requests that are emitted from the target device. Feature selection is based on correlation among the various fields in the packet header in order to choose the most suitable one. To generate fingerprints that are distinctive for each device, statistical techniques have been employed. The classification of device types is based on distance similarity techniques. This technique uses a set of features extracted from each packet request to create a fingerprint. The fingerprints are then compared to a database of known fingerprints from other devices in order to determine the class of the device. The classification is based on how well the features of each fingerprint match those in the database. Detecting a device’s make/vendor can only be accomplished by fingerprinting its type. In contrast, it is more challenging to identify each and every device connected to a wireless network. Subsequent contributions of the thesis focus on the identification of end user devices in networks using device fingerprinting techniques. It has been discussed how device fingerprinting can be used for identifying end-user devices. A technique based on packet sequence number was used prior to device fingerprinting to remove outliers. Due to heterogeneity in devices, the time-variant behavior of network traffic stemming from different devices has been utilized to create distinct, reproducible device fingerprints. For feature extraction the built-in scale localization and multi resolution ability of biorthogonal wavelets has been applied. The wavelet algorithm is ideal for the detection of hidden, but highly regular/irregular traffic patterns in captured network parameters. To generate the device fingerprint, the energy, variance, and entropy present in the detailed coefficients are calculated. A classifier that is based on ensembles is used for the classification of devices. Biorthogonal wavelets have two orthogonal wavelet functions that are combined in a wavelet transform, which makes them suitable for multiresolution feature extraction. The energy is a measure of the strength of the signal, the variance is a measure of the signal’s regularity, and entropy is a measure of the signal’s randomness. The combination of these parameters in the wavelet transform allows for the detection of hidden traffic patterns, and the device classification is based on an ensemble of classifiers, which increases the accuracy of the device identification. Although wavelet transformation and ensemble classifier produces fair results, they are computationally intensive because of the complexities involved in them. Therefore, the objective is to develop a device fingerprinting method that is computationally efficient and provides better accuracy than the previous method. For the generation of device fingerprints, an optimized histogram-based feature extraction technique has been used. In order to minimize the expected L2 loss between the histogram and the underlying density function, the optimized histogram method estimates a bin-width which minimizes the expected loss. To classify devices, a single hidden layer feed forward neural network known as Extreme Learning Machine (ELM) was used. Despite being derived from an artificial neural network, ELM is much faster than conventional neural networks, resulting in improved performance. Since ELM is not iterative, it facilitates faster decision-making since it does not require weights and biases to be adjusted. Additionally, the Jaya optimization algorithm is used to fine tune the parameters. ELM has fewer parameters than other neural networks, which makes it more computationally efficient. This also reduces the complexity of the model, making it easier to interpret and understand. The last contribution uses deep learning algorithms to address the problem of selecting and extracting appropriate features from input data. The thesis concludes with the development of a deep convolution neural network model for fingerprinting multiple devices. This deep neural network architecture facilitates end-to-end learning and is capable of achieving promising results. The deep learning algorithms allow for the extraction of features that are not immediately apparent, such as patterns in the data that can be difficult to detect by hand. Furthermore, the deep neural network architecture allows for the efficient training and testing of models, as well as the ability to generalize to new data. This enables the model to accurately detect and identify multiple devices, even when presented with novel data. An extensive set of experiments has been conducted separately for device fingerprinting and type fingerprinting. Each proposed framework has been evaluated using benchmark datasets. Our proposed frameworks have been compared with the state-of-the-art fingerprinting schemes based on their accuracy, frame count, and number of devices involved. Compared to their counterparts, the proposed methods perform better. Our proposed frameworks are more efficient in terms of accuracy, frame count, and the number of devices involved. The benchmarks used to evaluate the approaches are more stringent, which ensures that the results are representative of real-world scenarios. Furthermore, the experiments we conducted independently gave us more insight into the strengths and weaknesses of each framework
Studies on Characterization of Properties and Reduction Behaviours of Iron Ore Pellets in Non-Coking Coal for Application in Sponge Iron Making
No full textIn view of the increasing demand for steel and the depletion of high-grade iron ores day by day, research works on low-grade and medium-grade iron ores for sponge iron industries have become the needs of today. In the present work, pellets were made from hematite iron ores of Ramallakota and Veldurthi mines, Andhra Pradesh, and Bolani mine of Odisha, India, by the addition of 1.5 to 4.5 wt. % of bentonite and molasses binders separately. Dried iron ore pellets were then fired at three different temperatures of 1173, 1373 and 1573K for 1, 2 and 3 h. The aims of this research work have been to determine the effects of firing temperature and time, type of iron ore and binder addition on the chemical, physical and mechanical properties of resultant dried and fired iron ore pellets. Chemical compositions and XRD patterns of selected iron ores revealed Ramallakota and Veldurthi to be low-grade hematite iron ores having 27 – 46% Fe, 38 – 66 % Fe2O3, 9 - 27% SiO2 and 13 - 23 % Al2O3, while Bolani iron ore was identified as high-grade hematite ore with 62.80 % Fe and total gangue of about 7 %. The Ramallakota iron ore fine was found to have a higher BET surface area than those of Veldurthi and Bolani iron ore fines. The drop number of dried pellets increased with an increase in their binder contents. All types of fired iron ore pellets exhibited an increase in their crushing strengths and a decrease in apparent porosity values with the rise of firing temperature and time due to an increase in the extent of sintering of particles, as revealed by SEM micrographs. Iron ore pellets produced by the addition of molasses demonstrated relatively lower crushing strengths and higher porosity values than bentonite-added pellets. Fired iron ore pellets prepared with Ramallakota iron ore exhibited highest crushing strengths and apparent porosity values. The crushing strengths of fired iron ore pellets were found to be linked to their alumina/silica ratio, while the apparent porosity values seem to be associated with alumina contents in ore. XRD patterns revealed no any phase changes in the investigated fired iron ore pellets. The reduction behaviours of fired Ramallakota, Veldurthi and Bolani iron ore pellets were studied isothermally at temperatures of 1073, 1123, 1173 and 1223K for varying time periods in non-coking coal and the effects of different process variables were investigated. A rise in reduction temperature considerably improved the reduction rate, and this was found to be sharper up to about 45 – 55 % reduction in the first 15 – 45 min due to speedy emission of volatile matter from coal. Ramallakota-fired iron ore pellets exhibited relatively higher reduction rates than Veldurthi and Bolani-fired iron ore pellets reduced under identical conditions due to their higher porosity and gangue contents. Comparison of XRD patterns confirmed almost complete reduction in fired Ramallakota iron ore pellets at all the studied reduction temperatures in a much shorter period. An increase in firing time from 1 – 3 h at 1573 K decreased the degree of reduction in resulting fired iron ore pellets due to development of denser structure in the pellet matrices which hinder the flow of reducing gases. Fired iron ore pellets made with molasses have exhibited slightly higher % of reduction values than those prepared under identical conditions by the addition of bentonite, and this is believed to be due to variation in their porosity values. Variations in binder content have no noticeable effect on the reduction of fired iron ore pellets. Fired iron ore pellets reduced in coal char have exhibited relatively lower reduction rates owing to relatively lower volatile matter content in coal char. XRD patterns revealed fayalite and hercynite formations in all the types of reduced iron ore pellets after the conversion of Fe2O3 to FeO. The intensities of these phases were relatively higher in the reduced pellets of Ramallakota iron ore. SEM images of reduced iron ore pellets revealed an increase in porous structure with rise of reduction temperature and formation of iron whiskers at reduction temperatures of 1173 and 1223 K in all the types of reduced iron ore pellets. The change in morphology and formation of compact iron layers in the reduced pellet matrices with an increase in reduction time at 1223 K was observed clearly in the SEM images. SEM images indicated relatively more growth of iron whiskers in reduced Bolani high-grade iron ore pellets. Kinetic model equation, 1 − (1 − )1/3 = kt was noticed to fit well for the experimental results and computed apparent activation energy values were found to be in the range 14 – 21 kJmol-1, indicating overall reduction rate to be controlled by both the chemical reaction and gaseous diffusion processes. Fired iron ore pellets reduced in coal char under identical conditions exhibited relatively higher activation energy values owing to their slower reduction rates
Design and Analysis of Nonlinear-Stiffness-Based Metastructures for Vibration Isolation Applications
No full textThe increasing demand of small-scale precision instruments for application in microscopes, balancing and scaling instruments, robot grippers, etc., requires a robust vibration isolation device to effectively isolate unwanted low-frequency excitations which can affect the functionality and accuracy of the system. The vibration isolation is generally achieved using active and passive control system; however, the cost limitation of active isolator leads to the wide application of passive isolators. The effective frequency range of a linear passive vibration isolator is often limited by the static stiffness required to support a load. This limitation is improved in this study by incorporating nonlinearity in the form of stiffness to obtain quasi-zero-stiffness (QZS) characteristics for enhancing vibration isolation in lowfrequency ranges. The QZS characteristics works on the high static and low dynamic stiffness (HSLDS) mechanism, where the low dynamic stiffness leads to low-natural frequency of system with high static stiffness for supporting the mass. This requirement of obtaining QZS behavior using a deformable material that can effectively dissipate undesired vibrations in low-frequency ranges paved the way for the application of mechanical metamaterials in the vibration isolation field. Mechanical metamaterials are artificially engineered composites designed by varying the geometrical configuration to obtain the required properties. In this research work, the unit cell of metamaterials exhibiting QZS characteristic is achieved using two techniques- (i) designing negative and positive stiffness elements separately and combining them to form a single unit cell exhibiting QZS, and (ii) designing single element metamaterial exhibiting QZS. Four different designs are proposed in this study. The first design is a combination of bistable inclined beams and semicircular arches. The second design is the combination of a bistable cosine beam system and semicircular arches. The third design is the tunable single-beam element for fixed-guided boundary conditions. The fourth design is the tunable bottom reinforced cosine beam element for fixed-fixed boundary conditions. All the developed designs are modeled in SOLIDWORKS and fabricated using a 3D Printing technique. The deformable elements are printed using Thermoplastic polyurethane (TPU) material, and stiffer walls are printed using Polylactic acid (PLA). The static study is performed analytically, numerically using finite element simulation software (ANSYS) and validated using experimental results. The designed unit cells are then arranged in a parallel direction to form a cubic-like structure, which acts as a platform to mount the QZS payload on top and mitigate the incoming vibration due to base excitation. Based on the static results, the nonlinear force-displacement relation is obtained by curve-fitting the experimentally obtained results using seventh-order nonlinear regression analysis. Further, the dynamic behavior of the proposed designs is studied by deriving the approximate nonlinear dynamic equation and solved using the Harmonic Balance Method (HBM) technique. The obtained frequency response and transmissibility curve for all the designs show bending to the right depicting the jump phenomenon and validating the nonlinear behavior. A parametric study is also performed by varying excitation amplitude and damping ratio; the results suggest that the structure should be designed for low excitation amplitude with optimum damping. The stability of the steadystate response is also studied to explore the unstable region of the proposed designs. Further, a dynamic experiment is performed on all four designs using an electrodynamic shaker setup to validate the proof-of concept. The results suggest a low transmissibility peak, low resonance frequency, and wide isolation range for the QZS payload compared to the linear payload. A comparative study is performed to study the isolation effectiveness by designing all the proposed metamaterials for a common QZS payload. Finally, this research presented a proof of concept where the mass can be customized based on the frequency requirement. It is also observed that the QZS characteristic gets influenced by the geometrical parameters, which can be used to tune the design as per the mass requirement for the practical application
Development of Decellularized Xenogeneic Scaffold for Cardiac Tissue Engineering Applications
No full textRecent advancements in tissue engineering have paved the way to fabricate varieties of scaffolds from xenogeneic sources to provide improvement in cardiovascular applications. The main objective of the study was to develop the scaffold from a xenogeneic source and explore the possibility of using caprine pericardium in the field of tissue engineering applications. The first part of the study focuses on the optimization of the decellularization of the caprine pericardium with various concentrations of a non-ionic surfactant and anionic detergent. Three different decellularization procedures based on anionic detergent, non-ionic surfactant, and a combination of both were used to achieve the decellularized caprine pericardium (DCP). The efficiency of decellularization protocol was assessed by protein estimation, histology, and DNA quantification. Further, to identify the impact of decellularization on extracellular matrix (ECM) damage, the biomechanical properties of the native pericardium and DCP were investigated. The result showed that optimal concentration and combinations of anionic detergent and non-ionic surfactants play a significant role in gaining decellularized tissues with intact ECM. In the next part of the study, hemocompatibility and physicochemical properties of the DCP were obtained from the optimized concentration of anionic detergent and non-ionic surfactant. were explored. Hemolysis, thrombotic, and platelet assay showed that the DCP was compatible with blood. The successful sterilization of DCP was obtained from antibiotics/antimycotic treatment when compared to the ethanol sterilization technique. Next part of the study focused on analyzing the biological interaction of the serous and fibrous side of the DCP with valvular interstitial cells (VIC) and mimicking the aortic valve calcification process by recellularization with VIC. The biocompatibility studies show that the DCP was capable of supporting cell binding, adhesion, and proliferation of VIC. In particular, the serous side of DCP promotes cell binding and proliferation, and the fibrous side supports cell infiltration. The aortic valve calcification process was mimicked in the in vitro conditions by changing the medium with the different concentrations of calcium and phosphate with DCP. The atomic absorption spectroscopy and alizarin red staining results show that the combination of calcium and phosphate has a critical role in the accelerated calcification of DCP. Final part of the study was intended to develop a biohybrid scaffold of DCP and graphene oxide (GO) by an immersion coating technique. Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and scanning electron microscope (SEM) were performed to characterize GO-DCP. The presence of GO on the surface of the GO-DCP biohybrid was confirmed by SEM analysis. The existence of glycosaminoglycan, elastin, and collagen in the DCP and GO-DCP was inferred from the FTIR. The antimicrobial activity of GO was evaluated against E. coli and showed minimum inhibitory concentration at 125 μg/ml and minimum bactericidal concentration at 250 μg/ml. The biocompatibility of GO-DCP results shows that GO coating supports cell adhesion on the serous and fibrous sides of the DCP. Further, the biomechanical response of DCP was unaltered by the presence of GO. Thus, the scaffold developed from the caprine model was explored in this study, and the result indicates that the DCP is hemocompatible, noncytotoxic, and biocompatible. These properties indicate that the caprine pericardium can be utilized for cardiac tissue engineering applications
Analytic Primary Scalar hair Charged Black Holes and their Thermodynamics in AdS Space in D Dimensions
No full textBlack holes are the simplest and yet the most mysterious objects in our Universe that have brought to light strong hints of a profound and fundamental connection between gravity, thermodynamics, and quantum theory. Classical black holes are believed to follow the famous no-hair theorem, which states that black holes are characterized by only three parameters: their mass, charge, and angular momentum. The main aim of this thesis is first to present and discuss new families of primary hair-charged black hole solutions in asymptotically anti–de Sitter (AdS) spaces in various dimensions and then study their thermodynamic properties. For this purpose, we consider the Einstein-Maxwell-scalar gravity system that carries the nontrivial coupling between the scalar and Maxwell fields. We analytically solve the coupled Einstein-Maxwell-scalar field equations and obtain exact hairy charged black hole solutions in various dimensions. The analytic hairy black hole solutions are obtained for various horizon topologies. This includes planar, spherical, and hyperbolic horizon topologies. The thesis work carried out with reference to hairy black holes provides several interesting novel results and observations. Particularly, the constructed hairy solutions exhibit many desirable features, including the well-behaved nature of the scalar field and curvature scalars everywhere outside the horizon. In addition, analytic expressions of regularized action, stress tensor, conserved charges, and free energies are obtained. We analyze the thermodynamics of the hairy black holes in canonical and grand-canonical ensembles and find that the scalar hair significantly modifies the thermodynamic structure of the black hole. This includes the occurrence of small/large black hole phase transition not only in the grand-canonical ensemble but also in lower three-dimensional gravity systems. Similarly, there also occurs an interesting hairy/nonhairy phase transition, with thermodynamically preferred and stable hairy phases at low temperatures. Moreover, we also analyze the dynamical stability of the massless scalar field perturbation in the constructed hairy backgrounds and find that the scalar perturbations are stable. Our analysis further suggests that the corresponding scalar quasi-normal modes can probe the small/large hairy black hole phase transition
Deep Learning Methods for Vehicle Detection in Mixed and Undisciplined Traffic Environments
No full textNowadays, an intelligent traffic management system (ITMS) is essential for mixed and undisciplined traffic scenarios. It uses intelligent vehicle detection (IVD) approaches that comprise advanced sensors and computer vision algorithms for detecting and tracking vehicles. These are also used to improve traffic flow, reduce congestion, enhance safety, and support autonomous driving. Implementing IVD in disciplined traffic conditions is not complicated but becomes challenging in mixed and undisciplined traffic conditions. The major issue is due to multi-scale vehicles traveling close to each other on the roads and not following lane discipline. In recent years, convolutional neural network (CNN)-based deep learning (DL) methods have attained incredible progress in implementing IVD for disciplined traffic. However, most CNN-based DL methods do not consider mixed and undisciplined traffic environments. Also, these methods have difficulties in the extraction of the multi-scale features due to existing CNN backbones. In the case of multi-scale feature extraction, the main challenge is to accurately identify and extract the relevant features from an image or data across multiple scales. These features are necessary to capture different levels of detail in an image or to identify vehicles of different sizes. This research work focuses on designing DL methods to implement IVD in mixed and undisciplined traffic environments, which helps to overcome the issues of multi-scale feature extraction. Different approaches have been adopted to implement IVD, which are summarized below: • Initially, a vast, diverse traffic labeled dataset (DTLD) is collected and labeled for mixed and undisciplined vehicles. Also, an advanced visual computing deep learning (AVCDL) method is designed to implement IVD under diverse traffic conditions. AVCDL method ensembles features of two CNN architectures and combines them on a single channel via a feature concatenation to overcome the multi-scale feature extraction problem. It also uses an improved multi-stage vehicle detection head (MSVDH) that classifies the target vehicles into respective categories. In this scenario, the detection accuracy needs to be improved because AVCDL uses convolution operations that are locally constrained to a small area of an image. In the following contribution, a transformer-based self attention mechanism is used, which is globally constrained to the whole image. • A swin transformer-based vehicle detection (STVD) framework in an undisciplined traffic environment is designed. Swin transformer (ST) is a backbone that exchanges information within and between image patches and provides hierarchical feature maps. It uses the shifted window mechanism with self-attention blocks that are globally constrained to the whole image. Additionally, a bi-directional feature pyramid network (BIFPN) is connected to the output stages of the ST backbone for combining low-resolution features with high-resolution features bi-directionally, which provides more robust multi-scale features with different scales and resolutions. STVD effectively alleviates the multi-scale feature extraction problem. However, it runs slowly compared to the AVCDL method due to exponential parameter generation. The following contribution considers both speed and accuracy performance metrics and provides a robust vehicle detection method. • To consider speed and accuracy, a multi-class vehicle detection (MCVD) model is designed to detect vehicles in heterogeneous traffic using a realistic traffic dataset. MCVD is designed with a CNN backbone named VDnet, a light fusion bi-directional feature pyramid network (LFBFPN) and a modified vehicle detection head (MVDH). All the components of MCVD are designed using a depth-wise separable convolution (DWSC) to reduce the parameters in the detection model. VDnet extracts multi-scale features from the traffic input images using feature reuse techniques to enhance the feature extraction at multiple scales. LFBFPN combines these features bi-directionally and provides robust feature maps. Finally, MVDH is applied to detect multi-class vehicles and classify them into respective categories. The above methods are analyzed, experimented and measured over realistic traffic scenarios on the Quadro P6000 GPU system. These are also compared with the existing state-of-the-art IVD methods. The simulation results state that AVCDL achieves 86.17% accuracy with 27 frames per second (FPS), STVD achieves 91.32% accuracy with 17 FPS, and MCVD achieves 91.45% accuracy with 35 FPS. Also, the real-time performance of AVCDL and MCVD methods is validated using NVIDIA Jetson Tx2 and Nano boards. In addition, these methods are tested over other objects by including standard object detection atasets. Ultimately, the outcomes of IVD methods are used to estimate traffic parameters for implementing an efficient traffic management system