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Facile engineering of aptamer-coupled silk fibroin encapsulated myogenic gold nanocomposites: investigation of antiproliferative activity and apoptosis induction
Nanocomposites selectively induce cancer cell death, holding potential for precise liver cancer treatment breakthroughs. This study assessed the cytotoxicity of gold nanocomposites (Au NCs) enclosed within silk fibroin (SF), aptamer (Ap), and the myogenic Talaromyces purpureogenus (TP) against a human liver cancer cell (HepG2). The ultimate product, Ap-SF-TP@Au NCs, results from a three-step process. This process involves the myogenic synthesis of TP@Au NCs derived from TP mycelial extract, encapsulation of SF on TP@Au NCs (SF-TP@Au NCs), and the conjugation of Ap within SF-TP@Au NCs. The synthesized NCs are analyzed by various characteristic techniques. Ap-SF-TP@Au NCs induced potential cell death in HepG2 cells but exhibited no cytotoxicity in non-cancerous cells (NIH3T3). The morphological changes in cells were examined through various biochemical staining methods. Thus, Ap-SF-TP@Au NCs emerge as a promising nanocomposite for treating diverse cancer cells. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Development and validation procedure of the higher educational facilities scale (HEFS)
Purpose: The purpose of this study was to develop and validate a scale to assess the influence of Higher Educational Facilities for the growth of education in higher education institutions. Design/methodology/approach: The first step in the process of scale development is to generate an item pool containing as many items as possible which captures the construct of interest. A total of 111 items were constructed for the initial try-out of the scale measuring the construct of higher educational facilities. This rating scale was based on the Likert-type was designed, where each item had to be rated on a five-point scale. The scale consisted of a few items involving the dimensions of infrastructure, quality assessment and quality assurance regard to the vision actualization. Findings: Higher Educational Facilities Scale (HEFS) was developed by the investigator and designed in the format of a 5-point rating scale of the Likert type. There are different phases identified for the scale construction. In the first phase, items are created and the contents validity is determined. The scale is constructed in the second phase. Pre-testing the questions, administering the survey, reducing the number of items and determining how many factors the scale captures are all steps in the scale construction process. The number of dimensions, reliability and validity are all verified in the third phase, scale evaluation. In developing the scale, the content and face validity was ascertained. The reliability of the scale and its three subscales were established. This scale has potential value for policymakers to assess the perception held by the religious faculty members working in higher education institutions. Originality/value: The research is part of the doctoral thesis by Dr Deepa Thomas under the supervision of Dr Fr. Joseph C. C. and the co-supervision of Dr Kennedy Andrew Thomas. The purpose of the scale is to assess the higher educational facilities of in institutions of higher Education. Quality, excellence and service are the vision and purpose of higher education institutions to provide ample opportunities and good facilities for their beneficiaries, thus creating tremendous changes in the Indian education scenario. 2024, Emerald Publishing Limited. -
The feasibility analysis of load based resource optimization algorithm for cooperative communication in 5G wireless ad-hoc networks
Efficient allocation of resources is crucial in wireless ad hoc networks (WANETs) as spectrum assets are costly. Cooperative communications were introduced as a solution to the problem of limited spectrum availability. In this approach, numerous nodes share their resources and increase the bandwidth available to end-users. This research investigates the practicality of a new algorithm that optimizes resources based on load for Cooperative Communications in 5 G WANETs. The algorithm consists of two components. Initially, a distributed algorithm for forming a topology is suggested. This algorithm employs a load-based approach to explore network conditions and efficiently choose the most suitable topology. An optimization algorithm that relies on a greedy strategy is suggested. In this approach, the chosen nodes send their bits to the receiver to maximize the attainable system throughput. A thorough simulation study is conducted to evaluate the overall performance of the proposed algorithm in assessing existing methods. The proposed model obtained 94.72 % energy efficiency, 91.69 % network throughput, 94.72 % spectrum utilization, 27.47 % network delay, 24.08 % packet loss rate, 94.38 % signal-to-noise ratio, 93.91 % data transfer rate, 95.87 % error detection rate, and 94.28 % link reliability rate. The results demonstrate that the suggested algorithm significantly enhances the system and the overall network performance compared to existing approaches. The proposed approach is feasible and environmentally friendly for optimizing bandwidth in 5 G wireless ad hoc Networks. 2024 The Authors -
Elucidating the interplay of PPAR gamma inhibition and energy demand in adriamycin-induced cardiomyopathy: In Vitro and In Vivo perspective
Adriamycin is an anticancer anthracycline drug that inhibits the progression of topoisomerase II activity and causes apoptosis. The effective clinical application of the drug is very much limited by its adverse drug reactions on various tissues. Most importantly, Adriamycin causes cardiomyopathy, one of the life-threatening complications of the drug. Altered expression of PPAR? in adipocytes inhibited the glucose and fatty acids uptake by down regulating GLUT4 and CD36 expression and causes cardiotoxicity. Therefore, the influence of Adriamycinin cardiac ailments was investigated in vivo and in vitro. Adriamycin treated rats showed altered ECG profile, arrhythmic heartbeat with the elevated levels of CRP and LDH. Dysregulated lipid profiles with elevated levels of cholesterol and triglycerides were also observed. Possibilities of cardiac problems due to cardiomyopathy were analyzed through histopathology. Adriamycin treated rats showed no signs for atheromatous plaque formation in aorta but disorganized cardiomyocytes with myofibrillar loss and inflammation in heart tissue, indicative of cardiomyopathy. Reduced levels of antioxidant enzymes confirmed the incidence of oxidative stress. Adriamycin treatment significantly reduced glucose and insulin levels, creating energy demand due to decreased glucose and insulin levels with increased fatty acid accumulation, ultimately resulting in oxidative stress mediated cardiomyopathy. Since PPARs play a vital role in regulating oxidative stress, the effect of Adriamycin on PPAR? was analyzed by western blot. Adriamycin downregulated PPAR? in a dose-dependent manner in H9C2 cells in vitro. Overall, our study suggests that Adriamycin alters glucose and lipid metabolism via PPAR? inhibition that leads to oxidative stress and cardiomyopathy that necessitates a different therapeutic approach. 2024 Wiley Periodicals LLC. -
Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license. -
Serverless Data Processing System and its Design Space Consideration
Serverless computing is becoming increasingly important in data-processing applications in science and business. The scheduler is at the centre of serverless data-processing systems, allowing for dynamic decisions on job and data placement. The complex design space, which is influenced by various user, cluster, and workload variables, presents problems for developing high-performance and cost-effective scheduling structures and processes. To make this exploration easier, we present Sched-Probe, a framework that includes a conceptual model and simulator for systematic design space exploration. Using the Sched-Probe framework, we evaluate the performance of three scheduling systems and two techniques using real-world workloads. Our open-source software is now available on ExDe, allowing system designers to collaborate on delving into the complexity of serverless scheduling, paving the way for optimised and efficient data-processing systems. 2024, Iquz Galaxy Publisher. All rights reserved. -
Moderating role of firm characteristics on the relationship between corporate social responsibility and financial performance: evidence from India
Purpose: The effect of corporate social responsibility (CSR) on corporate financial performance (CFP) is shown to depend on both firm-specific and external factors. This study investigates the moderating role of two firm-specific factors the firm life-cycle stage and ownership structure on the CSRCFP relationship in a developing economy setting India. Design/methodology/approach: The study covers 1,419 listed companies in India during 201521. The firm lifecycle is represented using firm age and future growth prospects. Ownership is represented through a dummy variable and promoters holding percentages. Return on assets (RoA) is used as a measure of CFP, while CSR intensity, i.e. the ratio of CSR expenditure to profit after tax (PAT), is used to represent CSR. Fixed effect panel regression and generalized method of moments (GMM) models are used for data analysis. Findings: CSR expenditure has a significant negative impact on CFP. Firm age and future growth prospects amplify this negative impact, indicating that the firm life-cycle has a significant negative moderating effect on the CSRCFP relationship. Furthermore, the impact of CSR on CFP is worse for government companies than private ownership. Promoters holdings have a positive impact on the CSRCFP relationship. Research limitations/implications: The results question the validity of mandatory CSR expenditure on companies operating in developing countries and call for a differentiated policy approach to CSR expectations based on firm characteristics. This study also enhances the existing literature on CSRCFP. Originality/value: The growing research on CSRCFP has limited coverage of firm characteristics as contributing factors. Hence, this paper helps in enhancing the existing literature on CSRCFP and makes it more relevant to firms with specific characteristics. 2024, Nisha Prakash and Aparna Hawaldar. -
Weakly Non-linear Stability Analysis of Triple-Diffusive Convection in a Bi-viscous Bingham Fluid Layer with Cross-Diffusion Effects
The paper investigates the impact of cross-diffusion on triple-diffusive convection in a bi-viscous Bingham fluid layer. Non-linear stability analysis is performed, and the expression of the critical-Rayleigh-number is obtained, resulting in an analytical solution of the Ginzburg-Landau model (GLM). The coefficients in the GLM involve the scaled Rayleigh-number, the solutal Rayleigh-numbers, the solutal diffusivity rates, the bi-viscous Bingham fluid parameter, and the cross-diffusion parameters. The solutal Rayleigh-numbers, the solutal diffusivity rates, and the bi-viscous Bingham fluid parameter alone determine the critical-Rayleigh-number, which provides the condition for the stationary onset. The neutral curves for the stationary mode are examined. It is found that the solutal diffusivities and bi-viscous Bingham fluid parameter advance the onset of convection, whereas the solutal Rayleigh-numbers delay it. The Nusselt number, Nu, and the Sherwood numbers, Sh1 and Sh2, determine the heat- and mass-transfer rates obtained for the convection system. We see that Nu, Sh1 and Sh2 increase with an increase in the values of the bi-viscous Bingham fluid parameter. Also, we observe that increase in the Prandtl number effect increases them, and the same is true of the solutal Rayleigh-numbers, whereas the opposite impact on Nu, Sh1 and Sh2 is seen for solutal diffusivities, Soret and cross-diffusion parameters. In general, we observe that mass-transfer is more than the heat-transfer (Sh1>Sh2>Nu) depending on the value of diffusivities. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
Photocatalytic and eco-emission applications of green synthesized ZnO-CB nanoparticles
Herein, we report the synthesis of ZnO nanoparticles (ZnO-CB NPs) by employing the solution combustion method using an aqueous extract of brinjal calyxes as fuel. Characterization techniques, such as X-ray diffraction (XRD), Fourier transform Infrared spectroscopy (FTIR), UVvisible spectroscopy, and Scanning electron microscopy (SEM), were used to investigate the structural, optical, and morphological properties of synthesized nanoparticles, respectively. Highly porous hexagonal crystalline ZnO-CB NPs with less than 7 nm particle size were obtained. The photocatalytic performance of synthesized material is measured with Malachite green (MG), Basic brown 1 (BB1), and Acid orange 36 (AO36) as benchmark dyes. It showed that the synthesized material worked effectively under pH 10 with UV light irradiation. The synthesized ZnO-CB NP shows good removal effectiveness of the MG, BB1, and AO36 dyes with 99.3 %, 99.6 %, and 99.5 %, respectively, which can be promising photocatalysts for ecological applications such as wastewater remediation. Further, the synthesized ZnO-CB NP was used as blends in the methyl ester of Millettia pinnata oil (MPME), which is blended 20 % with commercial diesel (MPME20). The synthesized ZnO-CB NP was added to the MPME20 in varying amounts to ascertain its effects on the quality of emissions of various greenhouse gases such as hydrocarbons, COx, and NOx. Moreover, brake thermal efficiency (BTHE) and brake-specific fuel consumption (BSFC) were studied for the blends. The blend MPME20 with 25 mg of ZnO-CB NP, i.e., MPME20-25 mg, ZnO-CB, displays the best performance and reduced emissions. 2024 The Author(s) -
Spiking neural network with blockchain for tampered image detection using forensic steganography images
Accurate tools are required to acknowledge misleading images in order to maintain image legitimacy, and these tools must allow for legal operations on images. Additionally, after posting their images to the Internet, image owners lose rights over the images because there are no measures in place to safeguard them from misuse. One of the most well-liked techniques for addressing copyright disputes is the use of steganography technologies. The embedded steganography images can, sadly, be easily altered or deleted. To address this problem, this work presents the spiking neural network (SNN) with blockchain for tampered image detection utilizing forensic steganography images. Forensic steganography images that have been altered can be found with this SNN. Using steganography images from the database, SNN is trained in this model. The blockchain stores the owners access policies. The Python platform is used to implement the proposed strategy. F-measure, specificity, accuracy, precision, recall false positive rate (FPR), and false negative rate (FNR) are used to gauge how well the proposed approach performs. When compared to state-of-the-art approaches, the proposed approach obtained an impressive rise of 98.65%, in classification accuracy. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Mapping the Landscape of Financial Mindfulness Research: A Bibliometric Study
Financial mindfulness is an emerging concept in personal finance that promotes a transformative mindset to encourage rational decision-making and mitigate emotional biases. This study explores the evolution of research on financial mindfulness through a systematic literature review and bibliometric analysis of 58 relevant articles. The bibliometric analysis reveals the developmental trajectory of this literature, identifying notable authors, journals, countries involved, and international collaborations. Financial mindfulness originated from integrating finance with mindfulness principles, building on earlier concepts like money attitudes and behavioural finance. The study highlights the interdisciplinary nature of financial mindfulness, with significant contributions from finance, economics, and psychology. This research is novel in that it is the first attempt to systematically analyse the literature on financial mindfulness in this comprehensive manner. It establishes a foundational understanding for future researchers in the field, mapping out the existing landscape and highlighting the interdisciplinary collaboration that characterizes this area of study. By charting the developmental path and identifying key contributors and trends, this research sets the stage for continued exploration and a deeper understanding of financial mindfulness. Ultimately, it encourages further interdisciplinary research and practical applications in personal finance management, ensuring that future studies build on a robust framework of existing knowledge. This research not only explores past developments but also provides a clear direction for future inquiries and innovations in the field. 2024, Iquz Galaxy Publisher. All rights reserved. -
Characterisation of Sn-Cl co-doped ?-Ga2O3 thin films deposited via spray pyrolysis and their application in UV detector devices
Ga2O3, an ultrawide bandgap semiconducting oxide, is currently emerging as a promising candidate for various applications, such as power devices, solar-blind UV detectors, high temperature oxygen sensors and biomedical imaging. One significant limitation hindering the application of Ga2O3 as a wide-bandgap semiconductor is its poor conductivity. In this work, we investigate whether doping with tin and chlorine can mitigate this condition. Sn-Cl co-doped ?-Ga2O3 thin films are deposited on glass substrates using spray pyrolysis technique. The deposited films are subjected to comprehensive analysis, including structural, optical and morphological measurements using techniques like X-ray diffraction, UV-Vis-NIR spectroscopy, X-ray photoelectron spectroscopy and EDX studies. Electrical properties are assessed using the four-probe method and Hall measurements. The best conductivity of 8.86 ??1m?1 is observed when 8.68 at% of Sn and 3.37 at% of Cl were co-doped into Ga2O3 (S(3)) and its optical band gap is calculated to be 4.65 eV. This is about five orders of improvement in conductivity as compared to that of pure Ga2O3 thin film deposited by the same method. Furthermore, we have constructed a deep UV detector utilizing doped ?-Ga2O3 thin films as the semiconducting absorbing layer. The detector demonstrated the highest responsivity of 2.54 10?4 A/W at 260 nm and the corresponding specific detectivity is 1.4 109 Jones. The current research validates the potential of Sn-Cl co-doped ?-Ga2O3 thin film as an excellent choice for UV detector application. 2024 Elsevier B.V. -
Leveraging Machine Learning: Advanced Algorithms for Soil Data Analysis and Feature Extraction in Arid and Semi-arid Regions with Expert Systems
India is culturally diverse nation at large. There are two words of symphony one is tradition and second one is inherited agriculture. India has long historical advantage having conventional agricultural practices and the scope for it to dive into day to day life as agriculturist. Happiness shrinks as people grow into modern world current trend of agriculture faces a monument challenge and needs immediate address to survive. Now withstanding with this phrase of human life on earth its necessary to give more importance to soil rather than the existence. Soil health is more paramount in this equation, as it directly influences crop growth and yield. Traditionally, analysing a few key soil properties has been the cornerstone of soil treatment practices. However, this approach often overlooks the complex interplay between various soil characteristics. To overcome the above hurdle present research incorporates the method of multivariate data analysis with selective advanced algorithms in machine learning to find suitability to predict best fit algorithm in real time data sets in arid and semi-arid zones of kolar district in Karnataka. The purpose is to draw the attention of stake holders to leveraging the new technology to deploying them into effective assessment in building expert system to incorporate in regular use on handy devices. This penetrates the results by two extremely good classifications algorithms Decision Tree and Gradient Boosting emerged as winner with 99% accuracy. In contrast, Passive Aggressive and Linear SVC produced below average of 36% accuracy. The ensemble algorithms of SMOTE on Random Forest and Stochastic Decent Gradient produced the acceptable accuracy of 83%. This input helped dynamically to build ready to use expert systems for farmers. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
The efficiency of any cryptosystem not only depends on the speed of the encryption and decryption processes but also on its ability to produce different ciphertexts for the same plaintext. RSA, the public key cryptosystem, is the most famous and widely accepted cryptosystem, but it has some security vulnerabilities because it produces the same ciphertext for identical plaintexts occurring in several places. To enhance the security of RSA, magic square-based encoding models have been proposed in the literature. Although magic square-based encoding models have been proposed, they are static. Thus, this paper introduces a dynamic-based magic square with RSA, where encryption and decryption are performed using numbers generated from the magic square instead of ASCII values. Unlike the static magic square, the proposed dynamic magic square allows users to specify the starting and ending numbers in any position rather than fixed positions. In the proposed dynamic magic square generation, different 4 4 magic square templates are created, and 16 16 magic squares are generated from them. Experimental results clearly demonstrate the improved security of RSA. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Appraisal of the potential of endophytic bacterium Bacillus amyloliquefaciens from Alternanthera philoxeroides: A triple approach to heavy metal bioremediation, diesel biodegradation, and biosurfactant production
Endophytic microbes have been associated with many positive traits due to their endurance mechanisms. The current study was designed at exploring the potential of the endophytic bacterium Bacillus amyloliquefaciens MEBAphL4 isolated from Alternanthera philoxeroides for biosurfactant production and bioremediation efficiency. This endophyte, isolated from the polluted Madiwala lake in Bangalore, displayed elevated resistance to Cr and Pb till 2000 mg/L. The metal removal efficiency was found to be higher for Cr (25.7 %) at pH 6 and for Pb (92.3 %) at pH 9. Further, the present study also describes biosurfactant production with good emulsification ability (E24-52 %) and stability over a range of pH (8?12), temperature (2040C) and salinity (515 %). Biosurfactant production was enhanced 1.18-fold using the Response Surface Methodology approach and characterised by Fourier Transformation Infra-red Spectroscopy and Ultra-Performance Liquid Chromatography- Mass Spectrometry showing the presence of lipopeptides, fengycin, iturin and surfactin of molecular weights 1463.65, 1043.44 and 1012.56 Da respectively. The potential application of the biosurfactant in degrading various hydrocarbons was evaluated, demonstrating its effectiveness in bioremediation of oil-contaminated sites. Specifically, diesel biodegradation was measured at 56.460.95 %. These findings underscore the potential of B. amyloliquefaciens in environmental applications such as heavy metal biosorption and the bioremediation of contaminated sites, particularly those affected by oil spills and correlates to UN SDG6 of clean water and sanitation. 2024 Elsevier Ltd -
The Effect of Customer Satisfaction on Use Continuance in Bank Chatbot Service
Chatbots are text-based conversational agents that use Natural Language Processing to converse with customers. Chatbot has revolutionized the service industry by providing a customer-centric environment and a cost-effective business pattern to service providers. This technology is still maturing and has already influenced a lot of businesses due to its effective human-like interaction in different sectors. The banking industry too has adopted this very well. However, the acceptance level of this service is relatively slow among banking customers when compared to other sectors. This study focuses on the role of customer satisfaction factors that influence the use continuance of Chatbot services in the banking sector. A quantitative research design, using a purposive sampling method with a sample size of 422 respondents was considered. The data was analysed using SPSS and JMP. The results gave some new perspectives that will help the service providers to identify the antecedents that influence the use continuance of Chatbot service. IJCESEN. -
Influence of organization ethos on research competence of teachers in higher education institutions
The standards of research depend on the maintenance and coordination of research activities that are conducted by the teachers in higher education institutions. The flexibility in ordinances and statutes empowers the higher education institutions to frame the guidelines that empower the research competence of the teachers. This descriptive research has collected the data from 451 regular teachers of higher education institutions from different areas of discipline for the research. The results of the study show that there is a significant difference in measures of the perceptions of the teachers towards the relationship between organization ethos and research competence in higher education institutions. The study indicates the practical and academic importance for teachers to enhance research performance of higher education institutions. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Affordable Two-Dimensional Layered Cd(II) Coordination Polymer: High-Performance Pseudocapacitor Electrode Behavior
In recent years, pseudocapacitive materials have been investigated rigorously as they provide a unique pathway for realizing high-energy and high-power densities. However, innovative approaches involving rational design and synthesis of new materials are still vital to address concerns such as degradation, low conductivity, low cycling performance, high resistance, production cost, etc. Working in this direction, we report the cost-effective synthesis, characterization, and excellent pseudocapacitive behavior of a Cd(II)-based coordination polymer (COP) abbreviated as Cd(DAB). It has been realized in quantitative yield through a facile one-pot reaction occurring among the N4-ligand, 3,3?-diaminobenzidine (DAB), and Cd(II) ions, derived from Cd(OAc)22H2O, at room temperature. The proposed structure of the COP was ascertained by subjecting it to various standard spectroscopic and electron microscopic studies; these techniques reveal the self-assembly of indefinitely long coordination strands into a two-dimensional (2D) layered structure. The electrochemical performance of Cd(DAB) was evaluated as an electrode material for supercapacitors. Owing to its high conductivity, it portrayed remarkable energy storage (pseudocapacitor) behavior; it exhibited a high specific capacitance of 1341.6 F g-1 and a long cycle life with 81% retention over 10,000 cycles at 20 A g-1. Additionally, an asymmetrical supercapacitor device was fabricated, which exhibited a specific capacitance of 428.5 F g-1 at a current density of 1 A g-1 2024 The Authors. Published by American Chemical Society. -
Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review
Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of postoperative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Exploring the Central Region of NGC 1365 in the Ultraviolet Domain
Active galactic nuclei (AGN) feedback and its impact on their host galaxies are critical to our understanding of galaxy evolution. Here, we present a combined analysis of new high resolution ultraviolet (UV) data from the Ultraviolet Imaging Telescope (UVIT) on AstroSat and archival optical spectroscopic data from the Very Large Telescope/MUSE, for the Seyfert galaxy, NGC 1365. Concentrating on the central 5 kpc region, the UVIT images in the far- and near-UV show bright star-forming knots in the circumnuclear ring as well as a faint central source. After correcting for extinction, we found the star formation rate (SFR) surface density of the circumnuclear 2 kpc ring to be similar to other starbursts, despite the presence of an AGN outflow, as seen in [O iii] 5007 On the other hand, we found fainter UV and thus lower SFR in the direction southeast of the AGN relative to northwest in agreement with observations at other wavelengths from JWST and Atacama Large Millimeter/submillimeter Array. The AGN outflow velocity is found to be lesser than the escape velocity, suggesting that the outflowing gas will rain back into the galaxy. The deep UV data have also revealed diffuse UV emission in the direction of the AGN outflow. By combining [O iii] and UV data, we found the diffuse emission to be of AGN origin. 2024. The Author(s). Published by the American Astronomical Society.