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Qualitative and quantitative test of digital micromirror device for next generation UV multi-object spectroscopy
The coming decade in astronomy focuses on large wide field imaging and spectroscopic surveys. No wide field imaging facility extends to the UV region, which represents an important window into a wide variety of astrophysical problems. Also, spectroscopy would be essential to understand the physical and chemical properties of several stars, star forming regions and galaxies. Multi object spectroscopy (MOS) would be an efficient way to obtain these parameters for a large number of objects at a much shorter timescale. Digital Micromirror Device (DMD) acts as a programmable slit mask and can be used to achieve this goal in an MOS. This paper discusses different ground tests conducted on DMD to be used for the above said application. Numerical simulations for the diffraction effects on DMD is also carried out and the results are shared in this paper. 2020 SPIE -
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry. -
Development of a fluorescent scaffold by utilizing quercetin template for selective detection of Hg2+: Experimental and theoretical studies along with live cell imaging
Quercetin is an important antioxidant with high bioactivity and it has been used as SARS-CoV-2 inhibitor significantly. Quercetin, one of the most abundant flavonoids in nature, has been in the spot of numerous experimental and theoretical studies in the past decade due to its great biological and medicinal importance. But there have been limited instances of employing quercetin and its derivatives as a fluorescent framework for specific detection of various cations and anions in the chemosensing field. Therefore, we have developed a novel chemosensor based on quercetin coupled benzyl ethers (QBE) for selective detection of Hg2+ with naked-eye colorimetric and turn-on fluorometric response. Initially QBE itself exhibited very weak fluorescence with low quantum yield (? = 0.009) due to operating photoinduced electron transfer (PET) and inhibition of excited state intramolecular proton transfer (ESIPT) as well as intramolecular charge transfer (ICT) within the molecule. But in presence of Hg2+, QBE showed a sharp increase in fluorescence intensity by 18-fold at wavelength 444 nm with high quantum yield (? = 0.159) for the chelation-enhanced fluorescence (CHEF) with coordination of Hg2+, which hampers PET within the molecule. The strong binding affinity of QBE towards Hg2+ has been proved by lower detection limit at 8.47 M and high binding constant value as 2 104 M?1. The binding mechanism has been verified by DFT study, Cyclic voltammograms and Jobs plot analysis. For the practical application, the binding selectivity of QBE with Hg2+ has been capitalized in physiological medium to detect intracellular Hg2+ levels in living plant tissue by using green gram seeds. Thus, employing QBE as a fluorescent chemosensor for the specific identification of Hg2+ will pave the way for a novel approach to simplifying the creation of various chemosensors based on quercetin backbone for the precise detection of various biologically significant analytes. 2024 Elsevier B.V. -
A dual-functional rhodamine B and azo-salicylaldehyde derivative for the simultaneous detection of copper and hypochlorite: synthesis, biological applications and theoretical insights
A multifunctional rhodamine derivative containing azo-salicylaldehyde (BBS) was designed and synthesized as a colorimetric and fluorescence turn-on probe for the selective detection of copper cations (Cu2+) and hypochlorite anions (OCl?) in aqueous media. In the presence of Cu2+, the probe BBS exhibited turn-on absorption and fluorescence change at 554 nm and 585 nm, respectively. The binding mechanism of BBS with Cu2+ induces the opening of a spirolactam ring in the rhodamine moiety by the formation of a metal-ligand complex, achieving 10-fold enhancement in fluorescence and quantum yield, along with a binding constant of 1 104 M?1 and a detection limit of 2.61 ?M. Addition of OCl? enhanced the absorbance and fluorescence intensities at 520 nm and 575 nm, respectively. The probe BBS underwent hypochlorite-mediated oxidation, followed by hydrolysis, resulting in the formation of rhodamine B itself, which is detectable by the naked eye via the color and fluorescence enhancement by 11-fold with a high quantum yield and a detection limit of 1.96 ?M. For practical applications, sensor BBS can be used to detect Cu2+ in water samples and on cotton swabs. For biological applications, the interaction of the BBS-Cu(ii) complex with transport proteins such as bovine serum albumin (BSA) and ct-DNA was investigated through UV-vis and fluorescence titration experiments. Additionally, the structural optimization of BBS and the BBS-Cu(ii) complex was demonstrated using DFT, and the interactions of the BBS-Cu(ii) complex with BSA and ct-DNA were analysed through theoretical docking studies. Bioimaging studies were conducted by capturing fluorescence images of BBS with Cu2+ and OCl? in a physiological medium containing living plant tissue using green gram seeds. 2024 The Royal Society of Chemistry. -
Construct modelling, statistical analysis and empirical validation using PLS-SEM: a step-by-step guide of the analysis procedure
Partial least square-structured equation modelling (PLS-SEM) is a widely accepted tool for statistical analysis in social science research. The complex architecture of PLS-SEM sometimes makes it difficult for users to understand the taxonomy, nomenclature, or process of statistical analysis. This research study proposes summarising the procedure adopted in PLS-SEM for data analysis. Measurement evaluation and structural model was the subject of discussion, with a focus on the statistical techniques employed. Furthermore, the threshold values corresponding to statistical tools under measurement and structural model were also provided. The inference of these threshold values were also discussed with an eye on improving researchers awareness and understanding. The discussion about the methodology adopted in statistical analysis with the help of PLS-SEM is also reported. Finally, the limitations of the research work were presented, and further study directions were streamlined. 2024 Inderscience Enterprises Ltd. -
Improvising data security measures using rajan transform
Data security has always been a concern with the use of a large amount of data in our day-to-day life. There are many methods suggested and presented to secure data during the stages of its preprocessing and post-processing. However, many of them are not following the process of Homomorphism. During the study of Fast Fourier transform (FFT), Hadamard transform (HT) and Rajan transform (RT), this research work encountered a method that uses the cyclic, dyadic and graphical inverse properties of data and encrypts them which makes them homomorphic. This paper is targeting to improvise the data security measures using Homomorphism-based Rajan Transform, a method, which can help in securing data while data processing. The proposed methodology works in such a way that the encrypted data is available for processing without decrypting data into the original form. The performance of the proposed method is described by the efficiency of the algorithm, key size, Block size, and no of rounds required to complete the encryption. It has been found, if we take 512 bits of input data to get 512-bit ciphertext, it takes 9 rounds and generates a 4608-bit key. 2021 Taylor's University. All rights reserved. -
Eye blink detection using CNN to detect drowsiness level in drivers for road safety
Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the Xiaoyang Tan of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety. 2021 Institute of Advanced Engineering and Science. All rights reserved. -
Engineering a low-cost diatomite with Zn-Mg-Al Layered triple hydroxide (LTH) adsorbents for the effectual removal of Congo red: Studies on batch adsorption, mechanism, high selectivity, and desorption
In this work, naturally occurring, low-cost diatomite (De) or diatomaceous earth (DE) adsorbent was treated with various molar concentrations (0.05, 0.1, and 0.2 M) of Zn-Mg-Al layered triple hydroxide (LTH) using a co-precipitation approach. The DE-modified samples were named 0.05 LDE, 0.1 LDE, and 0.2 LDE and employed to remove Congo Red (CR) dye from an aqueous solution. The adsorbents were examined using XRD, BET-N2 adsorption-desorption method, ATR-IR, FESEM-EDX, and XPS, and also analyzed for zeta potentials of adsorbents at pH values between 5 and 11 to observe their surface charges. The removal efficiencies of CR were 96.5%, 99.7%, and 94.5% for 20 mg of 0.05 LDE, 0.1 LDE, and 0.2 LDE, respectively, at pH 7. A bare DE, however, showed a removal efficiency of only 7.4%. After CR adsorption, the maximum adsorption capacities (qmax) of the adsorbents were examined using the Langmuir isotherm, and the results showed that 0.1 LDE-CR (44.0 mgg?1) had a higher qmax than 0.05 LDE-CR (35.6 mgg?1), 0.2 LDE-CR (27.9 mgg?1), and DE-CR (0.9 mgg?1). The optimal adsorbent of 0.1 LDE was utilized for the selectivity and salt effects based on the investigation's efficiency in removing contaminants. 0.1 LDE has been studied for reusability of up to five cycles and can remove CR up to three cycles with 77.7% and 79.9% efficiency using NaCl and NaOH, respectively. The adsorbents may therefore be particularly effective at removing CR from water and beneficial in industrial settings where dye is often used. 2023 Elsevier B.V. -
RADON in GROUNDWATER of MAGADI TALUK, RAMANAGARA DISTRICT in KARNATAKA
Radon is a water-soluble radioactive noble gas produced from the alpha decay of 226Ra in uranium series. Its presence in drinking water and open air increases the risk of lung and intestinal cancers in human beings. In view of this, radon concentration in groundwater and its dose due to inhalation and ingestion to the population of Magadi taluk of Ramanagara district in Karnataka state, India was studied. The groundwater samples were analyzed for radon concentration using emanometry technique. The study showed that the radon concentration in this area varied from 27.4 1.0 to 167.5 3.9 Bq/L and the effective dose ranged from 104.2 2.7 to 636.2 11.0 ?Sv/a. The study also revealed that 95% of the 37 samples studied showed higher radon concentration compared to the UNSCEAR recommendation (40 Bq/L) and all the samples showed higher than the USEPA recommendation (11.1 Bq/L). Ten samples have concentration above the maximum permissible level prescribed by WHO (100 Bq/L). The groundwater samples are found to be slightly alkaline within the permissible limit of Indian Standards. 2018 The Author(s). Published by Oxford University Press. All rights reserved. -
Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis
More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks
In recent years, WiFi offloading provides a potential solution for improving ad hoc network performance along with cellular network. This paper reviews the different offloading techniques that are implemented in various applications. In disaster management applications, the cellular network is not optimal for existing case studies because the lack of infrastructure. MANET Wi-Fi offloading (MWO) is one of the potential solutions for offloading cellular traffic. This word combines the cellular network with mobile ad hoc network by implementing the technique of Wi-Fi offloading. Based on the applications requirements the offloading techniques implemented into mobile-to-mobile (M-M), mobile-to-cellular (M-C), mobile-to-AP (M-AP). It serves more reliability, congestion eliminated, increasing data rate, and high network performance. The authors also identified the issue while implementing the offloading techniques in network. Finally, this paper achieved the better performance results compared to existing approaches implemented in disaster management. Copyright 2021, IGI Global. -
Secure biometric authentication with de-duplication on distributed cloud storage
Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes. 2021 M et al. All Rights Reserved. -
A novel multi functional multi parameter concealed cluster based data aggregation scheme for wireless sensor networks (NMFMP-CDA)
Data aggregation is a promising solution for minimizing the communication overhead by merging redundant data thereby prolonging the lifetime of energy starving Wireless Sensor Network (WSN). Deployment of heterogeneous sensors for measuring different kinds of physical parameter requires the aggregator to combine diverse data in a smooth and secure manner. Supporting multi functional data aggregation can reduce the transmission cost wherein the base station can compute multiple statistical operations in one query. In this paper, we propose a novel secure energy efficient scheme for aggregating data of diverse parameters by representing sensed data as number of occurrences of different range value using binary encoded form thereby enabling the base station to compute multiple statistical functions over the obtained aggregate of each single parameter in one query. This also facilitates aggregation at every hop with less communication overhead and allows the network size to grow dynamically which in turn meets the need of large scale WSN. To support the recovery of parameter wise elaborated view from the multi parameter aggregate a novelty is employed in additive aggregation. End to end confidentiality of the data is secured by adopting elliptic curve based homomorphic encryption scheme. In addition, signature is attached with the cipher text to preserve the data integrity and authenticity of the node both at the base station and the aggregator which filters out false data at the earliest there by saving bandwidth. The efficiency of the proposed scheme is analyzed in terms of computation and communication overhead with respect to various schemes for various network sizes. This scheme is also validated against various attacks and proved to be efficient for aggregating more number of parameters. To the best of our understanding, our proposed scheme is the first to meet all of the above stated quality measures with a good performance. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Discrete Integrity Assuring Slice-Based Secured Data Aggregation Scheme for Wireless Sensor Network (DIA-SSDAS)
In a wireless sensor network, data privacy with a minimum network bandwidth usage is addressed using homomorphic-based data aggregation schemes. Most of the schemes which ensure the end-to-end privacy provide collective integrity verification of aggregated data at the receiver end. The presence of corrupted values affects the integrity of the aggregated data and results in the rejection of the whole data by the base station (BS) thereby leading to the wastage of bandwidth and other resources of energy constraint wireless sensor network. In this paper, we propose a secured data aggregation scheme by slicing the data generated by each sensor node deployed in layered topology and enabling en route aggregation. Novel encoding of data and hash slices based on child order is proposed to enable concatenation-based additive aggregation and smooth extraction of slices from the aggregate by the BS. Elliptic curve-based homomorphic encryption is adopted to ensure end-to-end confidentiality. To the best of our knowledge, the proposed scheme is the first which facilitates the BS to perform node-wise integrity verification, filter out only the corrupted portion, and implement dynamic query over the received data. Communication- and computation-based performance analysis shows the efficiency of the proposed scheme for varied network sizes. The scheme can resist eavesdropping attack, node compromising attack, replay attack, malleability attack, selective dropping attack, and collusion attack. 2021 D. Vinodha and E. A. Mary Anita. -
Integrity assured multi-functional multi-application secure data aggregation in wireless sensor networks (IAMFMA-SDA)
Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results. 2023 - IOS Press. All rights reserved. -
Theoretical Framework for Blockchain Secured Predictive Maintenance Learning Model Using Digital Twin
The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and exciting technology that has the potential to offer DTs monitoring capabilities, strengthening security and enhancing DTs transparency, dependability, and immutability. Intelligent behavior can be integrated into blockchain-based DTs to foresee important maintenance tasks and successfully manage machine functions. Our research involves creating a theoretical framework that leverages emerging technologies such as blockchain, artificial intelligence and DTs to facilitate resolution in the predictive maintenance of industry machines with minimised governing cost. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Framework based on IoT, AI, and blockchain for smart access to government agricultural schemes
Agriculture plays an important part in most countries, such as India. A survey says that 54.6% of the total labor force of India is engaged in agriculture and its connected activities. The government is announcing many schemes to facilitate agriculture and support farmers. But most of the farmers are from poor families and are not able to reach the government schemes when they are really in need. Also, it is required to observe and measure the inter and intra-field variability in crops to enjoy the complete benefits of government schemes. This can be done with the advancements in the field of the Internet of Things. Information related to the impact of natural calamities on the agricultural field, malfunctions in the machinery used for cropping, yielding level, and health status of crops can be measured using the technology of IoT (Internet of Things) and analyzed using AI (Artificial Intelligence). Blockchain plays a critical role in replacing traditional means of data storage and exchanging agricultural data with a more trustworthy, immutable, transparent, and decentralized approach. By keeping all the transactions related to government schemes in blockchain, the possible crimes in the form of false data by the intermediate dealers acting between the farmers and the government can be addressed. This, in turn, allows useful government schemes to reach the farmer in time. We propose to develop a theoretical model using IoT, AI, and blockchain, which can assist the farmers in benefitting from the appropriate schemes announced by the government in time and achieving precise agriculture. 2024 Bentham Science Publishers. All rights reserved. -
Vimana /
Patent Number: 202241030155, Applicant: Ramesh Chandra Poonia.
Drone navigation works by building a map of its surroundings while tracking its position within the map. This allows the drone to demonstrate positional accuracy (the global average URE (User error rate) across all satellites) of < 0.643 m (2.1 ft.) 95% of the lime using the Global Positioning System (GPS). The problem with this technology is twofold. It deploys only L band communication in practice. -
A study on the role of academic web application among children an analysis through parents /
The education systems in today’s world have greatly changed with the emergence of new technologies. With the advent of digital media, variety of applications are developed which aid the children to step into their primary education. This research paper aims at finding out the role of such academic web applications among children.