Browse Items (5511 total)
Sort by:
-
Thermoluminescence glow curve analysis and trap parameters calculation of UV-induced La2Zr2O7 phosphor doped with gadolinium
Thermoluminescence (TL) glow curve analysis and calculation of trap parameters are reported for gadolinium (Gd3+)-doped La2Zr2O7 (LZO) phosphor. Phosphors were prepared by modified solid-state reaction method with varying concentration of Gd3+ (0.12.5mol%) including proper calcination and sintering temperature. Structural analysis of prepared phosphor for optimized TL concentration was recorded by X-ray diffraction analysis technique. Morphology was analyzed by scanning electron microscopic technique. The UV ray induced to the phosphor and effect of dose response recorded for variable dose rates of UV and TL glow curve were observed. The experimental and theoretical comparison was done by computerized glow curve deconvolution technique which determines the trap parameters such as trap depth, order of kinetics, and frequency factor for optimized concentration of dopant. The trap parameters and trap model are discussed in detail. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Analysis of supervised and unsupervised technique for authentication dataset
Traditional methods of data storage vary from the present. These days data has become more unstructured and requires to be read contextually. Data Science provides a platform for the community to perform artificial intelligence and deep learning methodologies on large volumes of structured and unstructured data. In the era of artificial intelligence, AI is showing it's true potential by addressing social causes and automation in various industries such as automobile, medicine and smart buildings, healthcare, retail, banking, and finance service are some of the deliverables. From a variety of sources and flooding data, AI and machine learning are finding real-world adoption and applications. The nature of the data models is trial and error and is prone to change with their discoveries for the specific problem and this is the case with the different algorithms used. In this paper, we apply machine learning algorithms such as unsupervised learning k-means, bat k-means and supervised learning decision tree, k-NN, support vector machine, regression, discriminant analysis, ensemble classification for data set taken from UCI repository, phishing website, website phishing, Z- Alizadeh Sani and authentication datasets. Authentication dataset is generated for testing Single Sign-on which learns from data by training to make predictions. 2018Rahul K. Dubey, P. K. Nizar Banu. -
Journeying through the Indian railways in around India in 80 trains (2012) by monisha rajesh and chai, chai: Travels in places where you stop but get never off (2009) by bishwanath ghosh
An Indian train is a space that exemplifies a true sense of transient cultural pattern as it travels through different states of India constantly assimilating people of diverse cultures. In this liminal space, a passenger travels from known to unknown in terms of geography, culture, language, cuisine, sartorial configuration and psychological makeup. Indian Railways offers an insightful analysis of cohabitation - the conflict and the coexistence of people amidst cultural differences.An Indian train is an exemplar of an accurate secular structure, blurring the lines of discrepancies based on religion, caste, gender, sex and sexuality. Prejudices that are evident in spaces relatively marked by certain spatial permanence dilute in a train. A provisional spatial arrangement of a train therefore questions the idea of tolerance and intolerance compared to that of permanent arrangement. As the Indian train incorporates people of all ages and territories, the train is a specimen of the concept of Bakhtinian polyphony, wherein the dialogues occurring between passengers represent varied consciousness. Thus, a train travelogue encompasses unmerged voices, each carrying a unique conscious design. The people travelling in an Indian train are separated on one single ground: economy. Therefore, economic factor becomes an overarching pattern of base to assign a certain culture in a superstructure to each class and each offers a unique perspective to the travelogue. This paper will analyze the trope of the train in two Indian travelogues based on culture, Marxist economic structure, Bakhtinian concept of polyphony, secularism and the idea of tolerance. AesthetixMS 2020. This Open Access article is published under a Creative Commons Attribution Non-Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For citation use the DOI. For commercial re-use, please contact editor@rupkatha.com. -
Socio-economic development of Darjeeling Himalayas: Categorical principal component analysis (CATPCA) and ordinal logistic regression (OLR)
The measurement of regional development plays a crucial role in improving the quality of life of local communities. However, the process of analyzing the regional progress was challenging as regional development was presented as a multidimensional concept. Nonetheless, the study's primary objective was to understand the indicators that genuinely reflect the development process's various dimensions in the northernmost district of West Bengal, Darjeeling Himalayas. Seven dimensions of development, namely psychological well-being, health, education, governance, safety and crime, energy and environment and standard of living were identified for analyzing the socio-economic development of the Darjeeling Himalaya. A questionnaire was framed and circulated in the region for the collection of data. By applying Categorical Principal Component Analysis (CATPCA), the data collected was aggregated into the above mentioned seven dimensions of development and analyzed the relationship between these development indicators through the Ordinal Logistic Regression model (OLR). The results showed that education and governance indicators had a significant impact on psychological wellbeing. Governance was affected by psychological wellbeing, while the standard of living was affected by psychological wellbeing and health indicators in the region. 2021 The Society of Economics and Development, except certain content provided by third parties. -
Analytical Estimation and Experimental Validation of the Bending Stiffness of the Transmission Line Conductors
The bending stiffness of transmission line conductors can vary significantly, ranging from maximum stiffness when behaving monolithically to minimum stiffness when wires behave loosely. This large range makes it challenging to estimate stiffness accurately at intermittent bending stages. To address this issue, a mathematical model that accounts for both frictional forces between wires in the same layer and the clenching effects of helical wires from preceding layers is proposed in this paper. The proposed model estimates cable bending stiffness as a function of axial load and curvature for multilayered strands by considering slip caused by wire behavior. To evaluate the bending stiffness, experiments were conducted on Panther and Moose Indian Power Transmission line conductors. The proposed slip model considers Coulomb frictional effects and clenching effects caused by Hertzian contact forces, filling the void in the estimation procedure. Additionally, the model considers the wire stretch effect, a parameter not previously accounted for in cable research. The predicted numerical results of the proposed model were found to vary within a maximum of 7% from the experimental tests. The proposed mathematical model thus offers a more accurate and comprehensive way of estimating the bending stiffness of transmission line conductors, addressing the existing limitations in the estimation procedure. 2024 College of Engineering, Universiti Teknologi MARA (UiTM), Malaysia. -
Numerical analysis and finite element simulation of axial stiffness of overhead transmission line conductor
Cables, overhead electrical conductors, and ropes are flexible structural assemblies made out of a central core and number of wires which are twisted together to form a complex helical structure. In the majority, cables are subjected to axial loading primarily, followed by the associated twisting. Depending upon the application, they are additionally loaded in bending also. The mechanical behavior of the cables can be predicted by various mathematical models reported in the literature. The mathematical model can predict the overall global behavior of the cable well. However, the local behavior of the cable must be included to have intricate realistic studies. In this paper, an attempt is made to predict the response of the cable considering all the local effects under axial loading. A core with a single layer of six wires is modelled using the helical rod concept and its mechanical behavior is investigated by means of Finite Element Analysis (FEA). The effect of axial loading on the cable is proposed to be studied as a function of various cable axial strains. The core-wire and the wire-wire contact mode of the cable assembly have been considered with due consideration of the contact forces and the associated frictional effects. The reduction in cable stiffness has been studied under various slip modes. The analytical and FEA results are validated with experimental tests on a single-layered transmission line conductor. TJPRC Pvt. Ltd. -
Hockey among the Indigenous Tribes of Sundargarh, Odisha
[No abstract available] -
A Facile One-Pot Solvent-Free Synthesis, in Vitro and in Silico Studies of a Series of Tetrahydropyridine Derivatives as Breast Cancer Inhibitors
Ammonium trifluoroacetate (ATA) catalysed synthesis of 1,2,5,6-tetrahydropyridine (THP) derivatives, under eco-friendly conditions via a facile one-pot strategy. We have synthesized fifteen THP derivatives, and docked into the crystal structure of Phosphatase and Tensin Homolog deleted on Chromosome 10 (PTEN) tumour suppressor protein (PDB ID: 1D5R) based on drug-likeness prediction and pharmacokinetic properties. Molecular docking simulation studies reveal that four of our synthesised compounds are potential hit candidates because they bound to the receptor through 57 conventional hydrogen bonds with ?9.7 to ?8.6 kcal/mol of binding energy. The compounds were evaluated using the in vitro inhibitory activity of MCF-7 breast cancer cell lines. Identified hit compounds showed moderate inhibition at (160320 ?g/mL) and inhibitory concentration IC50 values in the low micromolar range of 171.062, 189.803, 195.469 and 181.272 ?g/mL respectively. The results obtained are very promising; therefore fine-tuning the substituents of hit molecules with appropriate bioisosteres can lead to the development of potential leads. 2023 Wiley-VCH GmbH. -
A novel SIW based dual-band power divider using double-circular complementary split ring resonators
This article presents a novel design of substrate integrated waveguide (SIW) dual-band power divider loaded with double-circular complementary split-ring resonators (CSRRs). The double-circular CSRRs are etched on the top layer of the proposed structure to obtain the dual-band characteristic. The proposed geometry provides a passband frequency below the cut-off frequency of the SIW due to the electric dipole nature of the CSRRs. By changing the dimensions of the CSRRs, various passband characteristics are studied. To validate the design idea, a compact dual band power divider with equal power division operating at 8.4 and 11.7 GHz is designed, fabricated, and tested. A good steadiness is found between simulated and tested results. The proposed idea provides features of compact size, dual-band operation, and good isolation. The size of the fabricated prototype excluding microstrip transition is 0.473?g 0.284?g, where ?g is the guided wave length at the center frequency of first band. 2019 Wiley Periodicals, Inc. -
Foetal brain extraction using mathematically modelled local foetal minima
This paper proposes segmentation techniques to separate brain parcel from the MRI of the human embryo and also determines the abnormality of the foetal brain at various gestational weeks. These strategies mean to characterise areas of the premium of various granularities: brain, tissue types, or constructions that are more limited. Various philosophies have been applied for this division task and can be grouped into the solo, parametric, characterisation, atlas combination, and deformable models. Brain atlases are usually used as preparing information in the division interaction. Difficulties identifying using pictures secured, the quick mental health, and the restricted accessibility of imaging information thwart this division task. This paper discusses foetal brain segmentation using mathematically modelled foetal brain minima by using a curve fitting segmentation technique. Broad tests show that the proposed approach beats the ebb and flow of various segmentation techniques and the results gained are significant. Copyright 2023 Inderscience Enterprises Ltd. -
Solid-State Organic Fluorophore for Latent Fingerprint Detection and Anti-Counterfeiting Applications
A highly fluorescent material exhibiting solid-state fluorescence is particularly important in detecting latent fingerprints (LFPs) and anti-counterfeiting applications. Herein, we have synthesized a coumarin-benzothiazole moiety 3-(benzo[d]thiazol-2-yl)-2H-chromen-2-one (3-BTC) to inspect its capability to visualize LFPs and work as an anti-counterfeiting ink. The compound showed yellow-greenish emission under UV excitation and good covertness under visible light conditions. With the help of the powder dusting method, the latent fingerprints were coated with 3-BTC powder and images of the LFPs developed over various substrates including plastic, steel, aluminium plate, rubber, etc. under UV 365 nm light displayed good resolution be able to discern the patterns of all the levels 13. Apart from fresh fingerprints (taken within 10 seconds), aged (over 60 days) and incomplete eccrine LFPs were successfully visualized using 3-BTC powder. Anti-counterfeiting ink prepared using 3-BTC also proved to be a promising candidate as an anti-counterfeiting ink. Various types of paper materials, including tissue paper, printing paper, newspaper, etc. were used for evaluating 3-BTC as a satisfactory anti-counterfeiting ink. 2024 Wiley-VCH GmbH. -
Corrosion behavior of AlCuFeMn alloy in aqueous sodium chloride solution
Medium Entropy Alloy AlCuFeMn possesses high room temperature strength and oxidation endurance. In present work, the aqueous corrosion resistance of the as-cast as well as low temperature oxidized AlCuFeMn alloy in 3.5 wt% NaCl solution, is explored. Equimolar proportions of high purity copper, manganese, iron, and aluminum were arc melted and cast in a copper mold. The alloy primarily consists of a face-centered cubic and a body-centered cubic phase. Potentiodynamic polarization tests on the alloy after low temperature surface oxidation reveal an aqueous corrosion resistance comparable to AISI 304 steel and CoCrFeMnNi high entropy alloy. The X-ray photoelectron spectroscopic studies confirmed that the free surface in the as-cast alloy is in partially oxidized state. The same completely oxidizes after low-temperature surface oxidation. Such low temperature surface oxidation improves pitting corrosion resistance in AlCuFeMn alloy due to increased metal/oxide layer resistance. The electrochemical impedance spectroscopy tests coupled with microscopy confirmed that the principal corrosion mechanisms in the alloy are of the uniform and pitting type. The energy dispersive spectroscopy experiments indicate that a copper oxide enriched layer is formed on the surface oxidized specimen during corrosion. 2021 Elsevier B.V. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Decolonizing Open Science: Southern Interventions
Hegemonic Open Science, emergent from the circuits of knowledge production in the Global North and serving the economic interests of platform capitalism, systematically erase the voices of the subaltern margins from the Global South and the Southern margins inhabiting the North. Framed within an overarching emancipatory narrative of creating access for and empowering the margins through data exchanged on the global free market, hegemonic Open Science processes co-opt and erase Southern epistemologies, working to create and reproduce new enclosures of extraction that serve data colonialism-capitalism. In this essay, drawing on our ongoing negotiations of community-led culture-centered advocacy and activist strategies that resist the racist, gendered, and classed structures of neocolonial knowledge production in the metropole in the North, we attend to Southern practices of Openness that radically disrupt the whiteness of hegemonic Open Science. These decolonizing practices foreground data sovereignty, community ownership, and public ownership of knowledge resources as the bases of resistance to the colonial-capitalist interests of hegemonic Open Science. The Author(s) 2021. -
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M. -
Photocatalytic driven self-cleaning IPN membranes infused with a host-guest pair consisting of metal-organic framework encapsulated anionic nano-clusters for water remediation
Traditional water treatment membranes frequently encounter challenges in attaining an ideal equilibrium between permeability and selectivity. The performance of membranes is further hampered by hydrophobicity, scalability, and fouling problems, as well as excessive energy consumption. Hence, the current research is dedicated to the development of highly effective antifouling membranes, aiming for a significant balance between water permeance and separation efficiency, and featuring exceptional photocatalytic self-cleaning properties to ensure the sustainable reuse of membranes. In this study, a unique nanocomposite-based membrane is designed containing metal-organic frameworks (MOFs) MIL-101 (Fe) encapsulated copper-containing polyoxometalate (Cu-POM) incorporated into an interpenetrating polymer networks (IPNs) membrane. POMs are highly electronegative, oxo-enriched nanosized metal-oxygen cluster species and when composited with MOF yields POMOF which can help in the removal of pollutants from water through electrostatic site-specific binding. The IPN membrane designed by polymerizing aniline in the presence of polyvinylidene fluoride (PVDF) offers tunable pores of the membrane. The infusion of POMOF imparts a strong negative charge to the membrane surface, improving membrane hydrophilicity. This enhances pollutant removal through the Donnan exclusion principle and adds anti-fouling properties. Furthermore, the reduced pore size achieved by the IPN architecture in the POMOF@IPNs membrane effectively sieves out both cationic and anionic dyes, as well as pharmaceutical pollutants. Additionally, POMOF enhances the photocatalytic degradation of CR and MB dyes, coupled with essential self-cleaning attributes vital for separation processes. The IPNs structure, apart from housing POMOF, fortifies the membrane's mechanical strength with its distinctive network-like configuration. Furthermore, these advanced membranes showcase robust antibacterial and antiviral characteristics, while remaining non-cytotoxic to mammalian cells. Our findings indicate that the state-of-the-art POMOF@IPNs membrane is scalable and holds substantial promise for industrial wastewater treatment. 2024 Elsevier B.V. -
Border Collie Optimization
In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test. 2013 IEEE. -
Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding
A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu's criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and SensenDice Similarity Index. The KruskalWallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality. 2021 Elsevier B.V. -
Hyperspectral multi-level image thresholding using qutrit genetic algorithm
Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and Sensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. 2021 Elsevier Ltd -
Photocatalytic nanomaterials: Applications for remediation of toxic polycyclic aromatic hydrocarbons and green management
Nanomaterials (NMs) have piqued the attention of scientists and researchers across many biomedical sciences due to their superior physical, chemical, and magnetic properties. The efficacy and efficiency of NMs depend on adapting to specific site conditions and soil composition. NMs have lately received much attention in the context of polycyclic aromatic hydrocarbons (PAHs) polluted soil remediation and water mitigation because of their unique properties resulting from their nanoscale sizes. The remediation of hazardous PAHs in water and soil is a hot research subject. Because the exposure of PAHs in water and soil results in pollution, which raises major human health concerns. The current review reports novel advancements in NMs that subsidize enhancement for degradation of PAHs. Challenges to the fabrication of high activity-based photocatalytic materials are also discussed. Furthermore, this review delivers exclusive and wide-ranging perspectives on the fabrication of nanomaterial-based photocatalytic systems. The knowledge of both soil remediation and water mitigation is also updated. 2022