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Engineered biocorona on microplastics as a toxicity mitigation strategy in marine environment: Experiments with a marine crustacean Artemia salina
The marine environment has become a major sink for microplastics (MPs) wastes. When MPs interact with biological macromolecules, the biocorona forms on their surface, which can alter their biological reactivity and toxicity. In this study, we investigated the impact of biocorona formation on the toxicity of aminated (NH2) and carboxylated (COOH) polystyrene MPs towards the marine crustacean Artemia salina. Biocoronated MPs were prepared using cell-free extracts (CFEs) from microalgae Chlorella sp. (phytoplankton) and the brine shrimp Artemia salina (zooplankton). The results revealed that biocorona formation effectively reduced the toxicity of MPs. Pristine NH2-MPs exhibited higher reactive oxygen species production (ROS) (182%) compared to COOH-MPs (162%) in Artemia salina. Notably, NH2-MPs coronated with brine shrimp CFE exhibited a substantial reduction in ROS production (127%) than those coronated with algal CFE, with COOH-MPs showing a similar trend (120%). Biocorona formation also significantly decreased malondialdehyde (MDA) levels and antioxidant activity compared to pristine MPs. Molecular docking and dynamics simulations demonstrated a strong binding between polystyrene and acetylcholinesterase (AChE). In vitro studies indicated that pristine NH2-MPs exhibited more reduction in AChE activity (84%) compared to COOH-MPs (95%). However, no significant reduction in AChE activity was observed upon exposure to MPs coronated with either algal or brine shrimp cell-free extracts. Independent action modeling indicated an antagonistic interaction for MPs coronated with both the CFEs. Pearson correlation and cluster heatmap analysis suggested that the toxicity reduction in Artemia salina might be driven by decreased oxidative stress followed by the corona formation. Overall, this study provides valuable insights into the potential of biomolecules from phytoplankton and zooplankton to reduce MPs toxicity in Artemia salina, while highlighting their role in modulating the toxicity of other marine pollutants. 2024 The Author(s) -
Role of mixed molecular weight PEO-PVDF polymers in improving the ionic conductivity of blended solid polymer electrolytes
Blended solid polymer electrolytes (BSPE) were prepared by mixing different molecular weight polymers PEO6 (Mw = 1 106 g/mol), PEO5 (Mw = 1 105 g/mol), and PVDF (Mw = 5.25 105 g/mol) complexed with lithium salt. Conductivity and dielectric studies at different temperatures were carried out on these BSPE systems by varying the wt% of PEO5 and PVDF with respect to PEO6, keeping the wt% of lithium salt constant. The electrical characterizations of BSPE systems have been investigated using impedance spectroscopy in the frequency range 0.1106 Hz. The conductivity data shows that inclusion of PEO5 and PVDF into the PEO6 matrix improved the overall lithium-ion dynamics in the polymer matrix. The composition, PEO6 (94 wt%)-PEO5 (3 wt%)/PVDF (3 wt%)-LiClO4, exhibited maximum conductivity of 6.44 10?4 Scm?1 at 303 K. TheDC conductivity variation with temperature of BSPE systems follows Arrhenius relation and variation of AC conductivities with frequency obeys Jonschers power law. The real and imaginary part of dielectric constant and the dielectric relaxation were also investigated. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
A modified invasive weed optimization for MPPT of PV based water pumping system driven by induction motor
A novel approach called Modified Invasive Weed Optimization (MIWO) technique has been developed and combined with the Perturb and Observes (P&O) algorithm to enhance the extraction of maximum power from photovoltaic (PV) panels in the presence of partial shading conditions. The conventional P&O algorithm falls short in extracting the maximum power from PV systems under partial shading conditions due to the existence of multiple maximum points. In such scenarios, optimization techniques can be employed to search for the global maximum point. The proposed MIWO-based P&O algorithm updates the reference voltage to ensure that the PV system operates at the Maximum Power Point (MPP) based on the prevailing weather conditions. This MIWO based PV system is further fed to water pumping system. A PV-based water pumping system is utilized for both irrigation and domestic purposes. Additionally, a sensorless vector control-based induction motor is employed in this study to drive the pump. The objective of this research is to demonstrate the achievement of an efficient PV-based water pumping system without the need for battery storage. Various results based on MIWO are compared with PSO and GWO. The results are presented based on various water pumping applications and the availability of solar irradiance during rapid climate changes. MATLAB/Simulink simulations, along with hardware-based experiments, are provided to validate the effectiveness of the proposed method under both transient and steady-state conditions. 2024 IOP Publishing Ltd. -
Assessing Opportunities and Constraints of Community Engagement in Tourism Development at Hampi
Community Based Tourism will create a momentous milestone in improving the participation level of local communities in the arena of Heritage tourism as well as all the various branches of the travel and expedition industry. Whose heritage is being preserved, by whom, and for what purposes? (Shepherd 2006). This study particularly gives due emphasis on evaluating the level of participation from the resident???s part by assessing various development strategies offered by the respective authorities. The study also tries to address all the possible streams where the community can easily thrive and it also addresses the major obstacles which restricts their entry, indicating an inclusive approach towards community participation through community based tourism (CBT). Lack of cooperation among stakeholders was identified as challenges of CBT, (Tamir 2015). Tourism market of Hampi is developing year by year but the standard of living of the underprivileged community is always remains the same without any improvement and this is the core problem mentioned in the study. Quantitative research technique was used for the study with the help of a structured questionnaire which was used for collecting data. The collected data was analyzed with the help of quantitative data analysis software, SPSS. This research tries to bring out the various reasons behind the low level of community involvement in tourism industry with relevant evidence from the analyzed data. The study indicates the hopelessness of the community towards the major development strategies forwarded by the authorities especially Hampi Master Plan. It also addresses the poor level of education and lack of information about the potential of tourism employability and its scope in the field of community development. A unique relic of medieval commerce and religious faith into a lifeless ruin, (John and Michell2012) this study pinpoints the inefficiency of the tourism boards and poor functioning of other responsible authorities too. It VII also invokes the possibilities of promoting Agro-Tourism along with the existing Heritage tourism. There is enough scope for further study in the light of execution of CBT model as well as assessing the scope of Agro tourism in Hampi. This suggestion is propounded mainly because of the structural phase of the economy that is more than 70% of the working population is involved in agriculture and its allied sectors. There is a noticeable gap between the expectations of the local residents and what they are actually receiving from the authorities moreover, these entire gaps may hamper the idea of inclusive approach. If effective training and regular awareness creation program is initiated by the responsible authorities, it will create a better platform for inclusive approach. -
A Novel Approach to Automatic Ear Detection Using Banana Wavelets and Circular Hough Transform
Ear is an attractive biometric trait that maintain their structure with increasing age. Because of the complex geometry of ear, its detection is very difficult. This paper proposes a modified algorithm for automatic detection of 2D ear images using Banana wavelets and Hough transform. Banana wavelets derived from bank of stretched and curved Gabor wavelets are used to identify curvilinear ear structure. Addition of a preprocessing stage, prior to application of banana wavelets is found to improve the detection results further. The proposed algorithm is brought in to comparison with three existing algorithms and evaluated on standard databases. In addition to manual detection accuracy, this paper also calculates the efficiency of the proposed method using automatic classification techniques. The features like LBP and Gabor extracted from segmented ear image is used by different classifiers to determine whether the segmented portion of the image is class Ear or Non ear. 2019 IEEE. -
Ear Recognition Using Pretrained Convolutional Neural Networks
Ear biometrics, which involves the identification of a person from an ear image, is challenging under unconstrained image capturing scenarios. Studies in Ear biometrics reported that the Convolutional Neural Network is a better alternative to classical machine learning with handcrafted features. Two major concerns in CNN are the requirement of enormous computing resources and large datasets for training. The pretrained network concept helps to use CNN with smaller datasets and is less demanding on hardware. In this paper, three pre-trained CNN models, AlexNet, VGG16, and ResNet50 are used for ear recognition. The fully connected classification layers of the nets are trained with AWE, an unconstrained ear dataset. Alternatively, the CNN layers output (the CNN features) are extracted, and an SVM classification model is built. To improve the classification accuracy, the training dataset size is increased through data augmentation. Data augmentation improved the classification accuracy drastically. The results show that ResNet50, with the fully connected classification layer, results in higher accuracy. 2021, Springer Nature Switzerland AG. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Vapour growth and characterization of beta indium sesquitelluride crystals
Physical Vapour Deposition (PVD) provides stoichiometric crystals of different morphology, depending upon the materials, geometry of ampoules, temperature profiles, growth parameters and kinetics of crystallization. The crystal forms such as needles, platelets and spherulites of beta indium sesquitelluride (?-In2Te3) were produced by controlling the temperature of source and growth zones. The X-Ray Diffraction (XRD) and chemical analysis of the spherulitic crystals confirmed zinc blende structure with beta phase. Their resistivity (135.16 ? cm) at room temperature (300 K) was determined by van der Pauw method. The temperature dependence of DC conductivity was investigated using the conventional two-probe technique. The variation of dielectric constant (?1) and dielectric loss (tan ?) with temperature has been studied for different frequencies (1 kHz-1 MHz). The AC conductivity, ?ac(?) was found to vary with angular frequency as ?s, where s is the frequency exponent. The values of s lie very close to unity and show a slight decrease with increase in temperature, which indicate a Correlated Barrier Hopping (CBH) between centres forming Intimate Valence Alternation Pairs (IVAP). The activation energy for conduction ranges from 0.187 eV to 0.095 eV. The microhardness of ?-In2Te3 spherulites is found to be 353.5 kg/mm2, which is higher than that of other semiconducting chalcogenides. The results thus obtained on crystals grown from vapour phase open up ample possibilities for radiation detector applications. 2014 Elsevier B.V. -
Growth and characterization of chalcogenide crystals by vapour method
A horizontal linear gradient two zone furnace was designed and employed to grow single crystals of indium telluride by Physical Vapour Deposition (PVD) method. It was calibrated for various trials including, series and parallel combinations of coils, and set temperatures. Systematic growth runs for chalcogenide crystals were performed by varying the source and growth temperatures. Crystals of different sizes and morphologies were obtained. The morphology and chemical analysis of the grown crystals were investigated by Scanning Electron Microscope (SEM) and Energy Dispersive Analysis using X-rays (EDAX). The hardness of the crystals was estimated using a Vickers microhardness tester. 2011 American Institute of Physics. -
Spherulitic crystallization of ?-In2Te3 by physical vapour deposition
Different morphologies of indium telluride (In2Te3) including novel spherulites were crystallized using the physical vapour deposition (PVD) method, by varying the difference in the growth and source zone temperature (?T) of a dual zone horizontal furnace assembled indigenously. Whiskers and kinked needles of In2Te3were grown at ?T = 250 K and 300 K respectively, maintaining the growth zone at 500 C. At high supersaturation (? T = 400 K), spherulitic crystals were obtained. The stoichiometric composition of these crystals has been confirmed using energy dispersive analysis by x-rays (EDAX). The structure of ?-In2Te3 spherulitic crystals is identified as zinc blende with lattice parameter a = 6.159 from x-ray diffraction (XRD) studies. The scanning electron microscope (SEM) images revealed the radial structure of the grown spherulites. The growth mechanism for the spherulitic crystallization of ?-In2Te3 crystals has been discussed based on the theoretical models. Copyright 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. -
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
Vapour Growth and Characterization of Beta Indium Sesquitelluride Crystals
Journal of Crystal Growth, Vol-394, pp. 1-6. ISSN-0022-0248 -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Ultraviolet Flux and Spectral Variability Study of Blazars Observed with UVIT/AstroSat
Blazars, the peculiar class of active galactic nuclei, are known to show flux variations across the accessible electromagnetic spectrum. Though they have been studied extensively for their flux variability characteristics across wavelengths, information on their ultraviolet (UV) flux variations on timescales of hours is very limited. Here, we present the first UV flux variability study on intraday timescales of a sample of ten blazars comprising two flat-spectrum radio quasars (FSRQs) and eight BL Lacertae objects (BL Lacs). These objects, spanning a redshift (z) range of 0.034 ? z ? 1.003, were observed in the far-UV (1300?1800 and near-UV (2000?3000 wavebands using the ultraviolet imaging telescope on board AstroSat. UV flux variations on timescales of hours were detected in nine sources out of the observed ten blazars. The spectral variability analysis showed a bluer-when-brighter trend with no difference in the UV spectral variability behavior between the studied sample of FSRQs and BL Lacs. The observed UV flux and spectral variability in our sample of both FSRQs and BL Lacs revealed that the observed UV emission in them is dominated by jet synchrotron process. 2024. The Author(s). Published by the American Astronomical Society. -
Grading of Red Chilli, Cardamom and Coriander Using Image Processing
Indian cuisine is known for its wide range of spices. Spices are known as the heart and soul of Indian food. Traditionally, categories are identified based on certain chemical technology or with the help of senses gifted to mankind. In this paper, an image processing technique used to extract multiple features is presented to determine the various categories of spices consumed. This proposed work uses different varieties of common Indian spices such as Capsicum annuum (dry red chilli), Elettaria cardamomum (cardamom) and Coriandrum Sativum (coriander). While creating the image dataset, different categories of all spices were taken from southern region of India. Features are extracted from the manually created image dataset, which forms the base for classification. The result obtained using Multilayer Perceptron (MLP), Naive Bayes and Random Forest classifier is found to be optimal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing
Crowdsourcing is a procedure of outsourcing the data to an abundant range of individual workers rather than considering an exclusive entity or a company. It has made various types of chances for some difficult issues by utilizing human knowledge. To acquire a worldwide optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this procedure, there is a major security concern; i.e., the platform may not be trustworthy, and so, it brings about a threat to workers location privacy. Recently, many distinguished research papers are published to address the security and privacy issues in mobile crowdsourcing. According to our knowledge, the security issues that occur in terms of data reporting were not addressed. Secure and efficient data aggregation and data reporting are the critical issue in Mobile Crowdsourcing (MCS). Cluster-based mobile crowdsourcing (CMCS) is the efficient way for data aggregation and data reporting. In this paper, we propose a novel procedure, the efficient fuzzy logic cluster formation protocol (EFLCFP) for cluster formation, and use cluster cranium (CC) for data aggregation and data reporting. We recommend a couple of secure and efficient data transmission (SET) protocols for CMCS, (i) SET-IBE uses additively homomorphic identity-based encryption system and (ii) SET-IBOOS uses the identity-based online/offline digital signature system, respectively. Then, we have widen the features of cluster cranium by increasing the propensity to achieve aggregation and reporting on the data yielded by the requesters without scarifying their privacy. Also, considering query optimization using cost and latency. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.