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Modelling and optimization of Rhodamine B degradation over Bi2WO6Bi2O3 heterojunction using response surface methodology
The Bi2O3/Bi2WO6 heterostructures of various compositions are prepared via the surfactant-assisted solgel method, which exhibits enhanced and synergistic photocatalytic activity towards the degradation of Rhodamine B (Rh B) using visible light irradiation. Characterization of these heterostructures has been done using X-ray diffraction, microscopic and spectroscopic methods. The 50% tungstate in bismuth oxide (BWO) nanocomposites having band gap of 2.85eV and an average size of 4080nm shows maximum dye removal up to 87% in 4h compared to pure Bi2O3 and other heterostructures of Bi2O3/Bi2WO6. The reusability studies demonstrate the excellent retention of photocatalytic activity without much loss in activity, implying the stability and efficiency of the prepared catalyst. The degradation of the Rh B dye is modeled mathematically to analyze the interactive effects of the key parameters like the time, amount of catalyst, and dye concentration, and to determine the optimal setting of these parameters to optimize the degradation process using the face-centered Central Composite Design (FC-CCD) of the Response Surface Methodology (RSM) analysis. An accurate full quadratic model has been developed with R2 = 99.41%. The sensitivity of the degradation was evaluated at all levels of the key parameters. At 0.1g of catalyst amount, it was found that the increment of the catalyst amount would be suitable for improved degradation as compared to allowing more time for the degradation. The maximum degradation was obtained for a dye concentration of 5ppm, and 0.1g catalyst for 4h. 2022, King Abdulaziz City for Science and Technology. -
Water Purification Using Subnanostructured Photocatalysts
Visible light is an abundant resource, and photocatalysts absorb this light and use it to energize chemical reactions. Of the many types of reactions that are catalyzed by photocatalysts, wastewater purification is an important area. Photocatalysis is an economical, eco-friendly, and sustainable method of purifying water, a precious resource for which need is increasing while availability is shrinking. Of the several types of photocatalytic materials available, atomically dispersed metals and metal oxides appear to be the most promising. In conventional materials, the efficiency of utilization of active photocatalytic material is rather poor because only a small fraction of those present on the surface can serve as active materials. As the particle size decreases, this efficiency increases. In this respect, subnanometric catalysts such as single-site heterogeneous catalysts, atomically dispersed catalysts, and single-atom catalysts have distinct advantages when compared with their bulk and nanometric counterparts. The challenges in preparing stable single-atom catalysts have largely been overcome, and several methods are now available for their preparation. Many atomically dispersed photocatalytic materials have been synthesized, and many new insights have been gained, unlocking the tremendous potential in purifying wastewater by utilizing solar radiation. The aspects of higher activity, improved selectivity, economical use of materials, and a better understanding of the structure-activity relationship offered by subnanometric photocatalysts have been explored in this chapter. 2020 American Chemical Society. -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC. -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
A Study on the Influence of Personality Traits on Entrepreneurial intentio
Pacific Business Review International, Vol. 9, Issue 5. pp. 12-19, ISSN No. 0974-438X -
A Study on the Influence of personality traits on entrepreneurial intention among working professionals in the Indian technical organizations
Pacific Business Review International, Vol. 9, Issue 5, pp. 12-19, ISSN 097X-438X -
Efficient Ultra Wideband Radar Based Non Invasive Early Breast Cancer Detection
Ultra Wideband radar systems have emerged as a good alternative for non-invasive and harmless breast cancer detection. In this paper, bistatic and monostatic radar systems are proposed, which detects the deep-rooted and smallest formation of the tumor in the breast. The source signal for transmission through the breast is a seventh derivative Gaussian Ultra Wideband pulse. This pulse is shaped using the proposed sharp transition bandpass Finite Impulse Response filter. The pulse shaper filter design has a sharp transition, hence efficient for shaping very short-duration pulses, achieving higher data rate and less interference issues. Also, the pulse tightly fits the Federal Communication Commission spectral mask, thus achieving higher spectral utilization efficiency and meeting the signal safety standards for transmission through the breast. The shaped pulse fed to the antenna of the radar system provides higher antenna radiation efficiency and radiating power due to the concentration of power in the main lobe, sidelobe suppression, and less channel loss. Tumor detection is based on the time and frequency domain analysis of the backscattered signals from the tumor. These signals have higher amplitude, higher electric field intensity variations, and an increase in the scattering parameter values due to the presence of tumor. Simulation results show significant changes in the electric field intensity for normal and malignant breast tissue for tumor sizes ranging from 4 mm to 0.5 mm. To accurately detect the location of tumor inside the breast, Specific Absorption Rate (SAR) analysis is carried out. It is observed that the energy absorption in the cancerous breast is higher than that of the normal breast, thereby aids to detect the location of the tumor accurately by identifying the coordinates of the maximum value of SAR. The results obtained with an experimental setup consisting of fabricated heterogeneous breast phantom with tumor and monostatic radar closely confirms with the simulation results. 2013 IEEE. -
Pulse Shaper Design for UWB-Based Medical Imaging Applications
In this paper, a pulse shaping filter is designed to shape the higher-order derivatives of the basic UWB Gaussian pulse for efficient pulse transmission through human tissues for medical imaging applications. The shaped pulse for the desired center frequency fits the FCC mask and power spectral density (PSD) specifications with higher spectral efficiency being achieved. It is observed that the ringing effect of Gaussian pulse is reduced by using the proposed bandpass FIR shaping filter. The low ringing effect observed in the shaped pulse ensures better antenna power distribution and improved location accuracy which is critical factor for medical imaging applications. The pulses synthesized are highly orthogonal which aids in multi-access communication, improved bit error rate (BER) performance and short duration UWB pulses leading to higher data rate transmission. The drooping frequency response characteristics of the synthesized pulse have reduced clutter hence tightly focused image obtained for imaging applications. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis of UWB Pulse Shaper for Efficient Pulse Propagation in Human Tissue
In this paper, a filter based pulse shaper is proposed for efficient Ultra-wideband (UWB) pulse transmission through human tissues. A bandpass Finite Impulse Response (FIR) filter is synthesized and its closed form expression for the impulse response coefficients is obtained. The filter shapes the basic UWB pulse, to closely fit the desired Federal Communication Commission (FCC) mask specifications, to achieve high spectral utilization efficiency. In this approach, the effects due to Gibb's phenomenon are minimized thereby resulting in lower dominant sidelobe of the resultant UWB pulse. The interference between adjacent pulses of the UWB data stream is minimized thus it allows shorter duration UWB pulses to be synthesized leading to higher data rate transmission compared to some techniques in literature. 2020 IEEE. -
Kernel granulometric texture analysis and light res-aspp-unet classification for covid-19 detection
This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. 2022 Tech Science Press. All rights reserved. -
New Insights in the Bubble Wall of NGC 3324: Intertwined Substructures and a Bipolar Morphology Uncovered by JWST
We report the discovery of intertwined/entangled substructures toward the bubble wall of NGC 3324 below a physical scale of 4500 au, which is the sharp edge/ionization front/elongated structure traced at the interface between the H ii region and the molecular cloud. The sharp edge appears wavy in the Spitzer 3.6-8.0 ?m images (resolution ?2?). Star formation signatures have mostly been traced on one side of the ionization front, which lies on the molecular clouds boundary. The James Webb Space Telescopes (JWST) near- and mid-infrared images (resolution ?0.?070.?7) are employed to resolve the sharp edge, which has a curvature facing the exciting O-type stars. The elongated structures are associated with the 3.3 ?m polycyclic aromatic hydrocarbon (PAH) emission, the 4.05 ?m ionized emission, and the 4.693 ?m H2 emission. However, the PAH-emitting structures are depicted between the other two. The H2 emission reveals numerous intertwined substructures that are not prominently traced in the 3.3 ?m PAH emission. The separation between two substructures in the H2 emission is ?1.?1 or 2420 au. The intertwined substructures are traced in the spatial areas associated with the neutral to H2 transition zone, suggesting the origin of these structures by thin-shell instability. Furthermore, an arc-like feature traced in the Spitzer 3.6-8.0 ?m images is investigated as a bipolar H ii region (extent ?0.35 pc) at T d ?25-28 K using the JWST images. A massive-star candidate VPHAS-OB1 #03518 seems to be responsible for the bipolar H ii region. 2023. The Author(s). Published by the American Astronomical Society. -
Quantum inspired meta-heuristic approaches for automatic clustering of colour images
In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, (Formula presented.) -test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics. 2021 Wiley Periodicals LLC -
Novel quantum inspired approaches for automatic clustering of gray level images using Particle Swarm Optimization, Spider Monkey Optimization and Ageist Spider Monkey Optimization algorithms
This paper is intended to identify the optimal number of clusters automatically from an image dataset using some quantum behaved nature inspired meta-heuristic algorithms. Due to the lack of sufficient information, it is difficult to identify the appropriate number of clusters from a dataset, which has enthused the researchers to solve the problem of automatic clustering and to open up a new era of cluster analysis with the help of several natures inspired meta-heuristic algorithms. In this paper, three quantum inspired meta-heuristic techniques, viz., Quantum Inspired Particle Swarm Optimization (QIPSO), Quantum Inspired Spider Monkey Optimization (QISMO) and Quantum Inspired Ageist Spider Monkey Optimization (QIASMO), have been proposed. A comparison has been outlined between the quantum inspired algorithms with their corresponding classical counterparts. The efficiency of the quantum inspired algorithms has been established over their corresponding classical counterparts with regards to fitness, mean, standard deviation, standard errors of fitness, convergence curves (for benchmarked mathematical functions) and computational time. Finally, the results of two statistical superiority tests, viz., t- test and Friedman test have been provided to prove the superiority of the proposed methods. The superiority of the proposed methods has been established on five publicly available real life image datasets, five Berkeley image datasets of different dimensions and four benchmark mathematical functions both visually and quantitatively. 2019 Elsevier B.V. -
Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Unveiling the realm of AI governance in outer space and its importance in national space policy
This article explores the notable legal concerns that may arise from the growing utilisation of artificial intelligence and machine learning in outer space. Whether it is conducting space exploration, clearing orbital debris, or extracting resources from specific areas in space, these activities are becoming more popular. Therefore, it is necessary to establish a regulatory framework to ensure consistency and objective standards. In order for national space legislation to effectively address the challenges presented by activities involving robots with different levels of autonomy and numerous objectives, it is essential to appraise the nature of these challenges. The article aims to investigate the relationship between the Montreal Declaration for a Responsible Development of Artificial Intelligence, 2017, and outer space laws and principles. It also examines the legal status of autonomous space objects, such as planetary rovers that are currently in operation or will be in the near future. Ultimately, the article highlights the importance of national space policy in addressing the appropriate regulation of artificial intelligence in outer space. In conclusion, this article has also discussed the potential effectiveness of utilising artificial intelligence-based methodologies and strategies to enhance current space policy. 2024 IAA -
A common law in space for public health
Beyond the gravitational pull of Earth, space travel poses substantial public health hazards pertaining to the physical and mental well-being of astronauts and passengers, in addition to a possible threat to the populace of Earth upon re-entry. Exposure to cosmic radiation, cranial pressure from microgravity, weakened immunity to contagion, and the potential for depression and psychosis are all risks. Public health crises of this nature are to be expected as the duration of missions extends, as is the case with Mars settlement. In contrast to national space programmes, which have regarded these obstacles as human factors effecting the mission, public health law in common law British nations approaches them from the perspective of social justice and the preservation of human life and societal welfare. Countries including Australia, Canada, the United States, and the United Kingdom continue to apply traditional common law principles of public health law, which provide a sensible and enduring method for reconciling competing public and private interests. Common law permits the violation of civil liberties through the use of force in public health restraint, forced medication, and quarantine, but only if necessary, reasonable, and equitable. While the understanding of the health challenges associated with long-duration spaceflight may be in its infancy for national space programmes and civilian space ventures, the application of common law public health principles could aid in the establishment of health and safety protocols in which human reactions to crises in space resemble those observed on Earth. This may, nevertheless, necessitate the enactment of a more comprehensive federal public health statute. Embedded in both public health common law and international space law, the pre-eminence of preserving and respecting human life and well-being continues to be a cornerstone of humane justice despite the perilous conditions of space. 2024 IAA -
Study on Space Debris Mitigation Under the National Space Laws
The international community is beginning to focus on the issue of space debris. Space debris has increased in the low Earth orbit due to accidental collisions between various space objects such as operational satellites. In China, the destruction of the FengYun - 1C weather satellite by an anti-satellite device caused an exponential increase in space debris. During the Ukraine war in 2022, Russia destroyed a defunct satellite which created space debris. This act put astronauts on the International Space Station at risk. Collisions have also happened between American satellites that are widely used for research or to provide communication facilities. Two unmanned European Space Agency (E.S.A.) satellites - the European Remote Sensing satellite (E.R.S.) and the Environmental Satellite (Envisat) - are currently in orbit reviving the debate over whether or not to engage in active debris removal. Despite gaining the interest of the international space community, efforts to reduce space debris have received scant legal recognition. Recent years have seen a dramatic decrease in launch costs, making space travel more affordable and feasible for the general public. As a result, smaller satellites can now be placed in low Earth orbit. Mega-constellations like SpaceX, OneWeb, Starlink, and Amazon Kuiper have also been launched or will be launched into space. It is predicted that about five per cent of all satellites will fail to be disposed of at the end of their lives, either because of technical difficulties or a lack of proper planning for the disposal phase. As a result, there is a greater possibility of collision with other celestial bodies. The problem of orbital pollution is made much worse by the fact that each collision can produce a large number of new pieces of debris. The inoperable satellites can only be retrieved from orbit with the active participation of the international community. The space sector is in the midst of a period of profound change. As a result of recent developments in microelectronics, materials, and battery technology, multiple constellations are now able to function in low Earth orbit, at altitudes of less than 1,000 kilometres. When it comes to domestic space regulation, the International Law Association (I.L.A.) Model marked a significant shift. As a result, many nations with space programmes have adopted national space laws that include provisions for dealing with space debris. Guidelines included in soft-law instruments have provided impetus in the absence of a mandatory international regime on space debris. 2024 University of Bologna. All rights reserved. -
Physical framework for a counselling environment in India: Thematic analysis of counsellors' perceptions
The influence of the physical environment on the counselling process is an inevitable part of a counselling session. However, there is little insight in Indian research into the desirable elements of the physical environment of the counselling room that helps facilitate a counselling session. Interviews were conducted with 10 professional counsellors in India. Thematic analysis of the databrought out various Basic and Organising themes under the following three Global themes: (a) Elements of the physical framework; (b) Counsellor's perspectives about the physical framework in counselling; and (c) Motivation to build a framework. The findings show how counsellors can systematise the physical framework to help construct the counselling session effectively. 2020 British Association for Counselling and Psychotherapy -
Decoding boomerang hiring: A suggestive framework to improve organizational efficiency
In an ever changing, volatile and dynamic business environment, efforts put by the human resources reflect the organizational efficiency. Organizations should always focus on maintaining smooth relations with the Alumni and Boomerangs as they play a crucial role in the expanding horizons of business. A positive word of mouth also helps in improving the goodwill and image of the company. It will encourage the prospective employees to view the organization in a positive light. Rehiring former employees is one of the mechanisms for recruitment used by a large number of corporations primarily because of the inherent advantage of added experience as well as savings in terms of cost of recruitment and training. The present study attempts to give an overview of Boomerang Hiring, the possible value additions being made in terms of Human Capital and Social Capital on basis of the type of respective organizations they are returning from. Additionally, the perspective of the rehired employee is also presented. The study is further enriched by quoting a few instances from the corporate world. The Rehiring Strategies tailored as per organizational requirements will lead towards holistic growth and development of the entity. 2020 SERSC. -
Approach for Preprocessing in offline Optical Character Recognition (OCR)
offline optical character recognition (offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR. 2022 IEEE.