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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. -
Adolescents considering approval from others as a contributor to self-worth
Adolescence is a period that starts from biological puberty till the individual is legally adult. This stage of life is filled with confusion, energy, and curiosity. As much as education is going to determine their future, the self-worth they have plays a critical role in choosing the future path. In India, the adolescents are dependent on their parents and guardians financially. They need approval from others to be acceptable. This study focuses on the effective support from others have on their self-worth. RJPT All right reserved. -
Adoption and Usage of Digital Financial Services in Karnataka, India: Spatial, Gender and Age Disparities
Financial services are digitized to widen access to finance and ensure transparency in financial transactions using technologies such as "Artificial Intelligence" (AI), "Machine Learning" (ML), "Big Data Analytics" (BDA), and "Blockchain Technology" (BT). Digital Financial Services (DFS) have increasingly been adopted by customers as the DFS are safe and secure, affordable, faster, convenient, and accessed anytime. The government and the RBI have taken many initiatives to promote DFS. Further, Digital Financial Services Providers (DFSP)-FinTech companies and formal financial institutions-come up with innovative DFS to suit the needs of the customers. As a result, DFS adoption and usage have grown multifold. The pandemic indirectly enhanced the adoption and usage of DFS. India has been the number one country in the world in real-time payments with 20.5 billion transactions in the year 2020 and DFS has not penetrated uniformly across gender, ages, and regions. Indian Institute of Finance. -
Adoption Laws in India : A critical Analysis through a Sociological Lens
Golden Research Thoughts Vol.2, Issue 6, pp.1-7 ISSN No. 2231-5063 -
Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. 2023 -
Adsorption and storage of hydrogen- A computational model approach
Due to the imperative global energy transition crisis, hydrogen storage and adsorption technologies are becoming popular with the growing hydrogen economy. Recently, complex hydrides have been one of the most reliable materials for storing and transporting hydrogen gas to various fuel cells to generate clean energy with zero carbon emissions. With the ever-increasing carbon emissions, it is necessary to substitute the current energy sources with green hydrogen-based efficient energy-integrated systems. Herein, we propose an input-output model that comprehends complex hydrides such as lithium and magnesium alanates, amides and borohydrides to predict, estimate, and directly analyse hydrogen storage and adsorption. A critical and thorough comparative analysis of the respective complex hydrides for hydrogen adsorption and storage is discussed, elucidating the storage applications in water bodies. Several industrial scale-up processes, economic analysis, and plant design of hydrogen storage and adsorption approaches are suggested through volumetric and gravimetric calculations. 2024 Elsevier Inc. -
Adsorptive capacity of PANI/Bi2O3 composite through isotherm and kinetics studies on alizarin red
Adsorption offers numerous advantages for eliminating organic pollutants such as dyes, making it a valuable method for water treatment. Polyaniline/Bi2O3 (PANI/Bi2O3) nanocomposite is synthesized from aniline by the chemical oxidative polymerization method. The sample shows a high positive surface charge density as seen from zeta potential analysis. X-ray Diffraction analysis, FTIR analysis, UVvis spectroscopy technique, thermogravimetric analysis, BET N2 Adsorption-desorption analysis, DLS, and zeta potential analysis are the tools employed to characterize the PANI/Bi2O3 nanocomposite. The impact of PANI/Bi2O3 on the outcome of adsorption is confirmed by comparing the composite with pristine Bi2O3 and PANI. The effect of various factors like time, temperature, initial dye concentration, and varying pH on the adsorption efficiency is studied. A maximum adsorption efficiency of 95 % is observed when 100 mg of PANI/Bi2O3 nanocomposite is utilized for a duration of 100 min. The adsorption efficiency increases at higher temperatures, and a maximum adsorption efficiency is observed at a pH of 11.4. The adsorption isotherms proposed by Freundlich and Langmuir are examined to confirm the adsorption mechanism, which entails the creation of a single layer of dye molecules on the adsorbent's surface. Analysis of kinetic parameters indicates that the reaction follows pseudo-second-order adsorption kinetics. The composite produced demonstrates effectiveness as an adsorbent for removing harmful organic pollutants from water sources. 2024 Elsevier B.V. -
Adsorptive removal of Cr (VI) using mesoporous iron-aluminum oxyhydroxide-polyvinyl alcohol self-supporting film: Kinetics, optimization studies and mechanism
Over the past decades, the disposal of heavy metals like Cr(VI) from industries had an adverse effect on the environment, thus making it a topic of particular interest. In this context, mesoporous Aluminum oxyhydroxide-polyvinyl alcohol self-supporting films were synthesized, and different transition metals (V, Fe, Co, Ni and Cu) were incorporated by an eco-friendly route, and their adsorptive capacity towards Cr (VI) was studied. The composite mesoporous film with iron, aluminum oxyhydroxide and PVA was more efficient adsorbent than other transition metal incorporated aluminum oxyhydroxide films. The surface and chemical properties of the film were confirmed by pXRD, FTIR, Raman Spectra, BET-Surface area, BJH, SEM and Optical Profilometry. Furthermore, the effect of different parameters that impact the adsorption capacity towards Cr (VI) is discussed, including adsorbent load, contact time, solution pH, temperature, and initial concentration. A detailed investigation of the film before and after the adsorption of Cr (VI) using different characterization techniques is investigated in detail. The kinetic studies and adsorption isotherms are studied, and a suitable mechanism has been proposed for Cr (VI) removal. The synthesized films possess potential advantages like cost-effectiveness, eco-friendly nature, reusability, and higher removal efficiency towards the removal of Cr (VI) from an aqueous solution. 2023 Elsevier Ltd -
Adsorptive removal studies of Rhodamine B by PEG capped polyaniline/TiO2/CuO composite
The availability of pure and fresh water is the prime need of human beings for survival. The fact that the rate of water pollution is alarmingly increasing is making scientists think of ways to minimize the pollution effects. Among the numerous techniques, adsorption is the most cost-effective and easy method for removing pollutants from water bodies. In this study, a ternary PEG capped polyaniline/TiO2/CuO composite with excellent surface area is synthesized, and its adsorption studies carried out using Rhodamine B, one of the strongest organic pollutants. The characterization studies of the prepared composites have been done using XRD, FT-IR, HR-TEM, DLS, zeta potential, BET, and XPS analyses. Isotherm, kinetics, and thermodynamic studies of adsorption have been done for the prepared composite to evaluate its adsorptive efficiency. The adsorption studies of RhB dye using the synthesized composite followed the Langmuir adsorption isotherm. The kinetics study for the adsorption process indicates that pseudo second order kinetics fits best with the adsorption process taking place on the PANI/TiO2/CuO composite. The thermodynamic studies reveal the spontaneity of the process and the exothermic behavior at lower temperatures. The results prove the efficiency of the synthesized adsorbent towards environmental remediation studies. 2023 Elsevier Ltd -
Advanced Computational Method to Extract Heart Artery Region
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal. 2022 Seventh Sense Research Group. -
Advanced Electrochemical Detection of 2,4-dichlorophenol in Water with Molecularly Imprinted Chitosan Stabilized Gold Nanoparticles
2,4-Dichlorophenol (2,4-DCP) is a hazardous chemical that can be passed down to offspring. Because 2,4-DCP degrades slowly and can be passed down to future generations, its a pesticide that needs to be continuously monitored and managed. With the use of chitosan-stabilized AuNPs on a glassy carbon electrode and the molecular imprinting technique, an effective electrochemical sensor has been built for the selective determination of 2,4-DCP in different aqueous samples. The analytes electroactive surface area and number of interaction sites are both increased by the AuNPs. The formulated AuNPs were characterized using several material characterization techniques. Molecularly imprinted nanomaterials provided the selectivity against other interfering chlorophenols. With a detection limit of 6.33 nM and a broad linear dynamic range of 21.09 to 310 nM, 2,4-DCP was found using differential pulse voltammetry. Without interference from structural analogs, the sensor was effectively evaluated in a variety of contaminated water samples. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Advanced electrochemical performance of N-Ti3C2/MnO2 MXene as a promising electrode for energy storage
In this study, we demonstrate a simple and efficient two-step synthetic strategy to design a high-performance N-Ti3C2/MnO2 composite for energy storage application. Nitrogen doping alters the electronic structure of electrode materials and enhances pseudocapacitance. N-Ti3C2 serves as a supporting substrate for MnO2, boosting the active surface area by preventing Ti3C2 layer stacking. Benefitting from the collaborative contribution and synergistic interaction within this multicomponent system, N-Ti3C2/MnO2 results in exceptional specific capacitance of 2107.1 Fg?1 at 1 Ag?1. It also exhibits a low internal resistance and maintains a capacitive retention of 94% over 3000 cycles. The asymmetric capacitor also delivers an energy density of 117.1 Whkg?1 at a power density of 1290.1 Wkg?1. This work presents a straightforward method for modifying Ti3C2 through nitrogen doping and the insertion of MnO2 as an interlayer spacer to enhance electrochemical performance. Qatar University and Springer Nature Switzerland AG 2024. -
Advanced hybrid SVPWM techniques for two level VSI
This paper brings an advanced class of hybrid SVPWM techniques for medium voltage drive applications with two-level inverter which employs multiple division of active vector time (MDAVT) switching sequences to reduce total harmonic distortion (THD) and switching loss. The proposed hybrid SVPWM techniques are categorised based on the principle of bus-clamping strategies. Multiple division active vector time (MDAVT) switching sequences are used in the proposed strategies. The newly developed MDAVT switching strategies produce PWM waveform for all odd and even pulse number and maintain the symmetry of the voltage waveform. This work compares different MDAVT switching sequences based on modulation index and location of the clamping position (zero vector changing angle) of a phase in a line cycle. The proposed techniques lead to the reduction in weighted total harmonic distortion of line voltage (Vwthd) as well as switching loss. The results point to the superior order of performance of the developed MDAVT sequences in the various ranges of operation of modulation index and power factor values. The superior harmonic performance and switching loss characteristics of the MDAVT PWM techniques over the conventional SVPWM is experimentally verifiedona415 V, 2 hp induction motor drive. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection
Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company. -
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. -
Advancements in optical steganography for secure medical data transmission in telehealth systems
Secure medical data transfer technologies have advanced as a result of the brisk growth of telehealth services. This study provides a thorough review of the most up-to-date research on using optical steganography to conceal medical records from prying eyes. Data concealing capacity has been increased without sacrificing picture quality using new techniques that make it difficult for unauthorised parties to access hidden information. Using adaptive steganography methods, medical data may be encoded in images in a way that makes it impossible to detect or extract by prying eyes. By concealing information over many picture layers, multi-layer steganography adds an extra degree of protection from prying eyes. The development of steganographic techniques has been spurred on by the use of machine learning and artificial intelligence to enhance steganalysis and the use of quantum characteristics to offer an extra layer of security in quantum steganography. Combining this with cryptographic safeguards like encryption provides an additional layer of security. In order to successfully safeguard sensitive medical data during transmission, standardisation and compliance in optical steganography are becoming more important as telehealth systems become more widespread. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Advancing Brain Tumor Segmentation in MRI Scans: Hybrid Attention-Residual UNET with Transformer Blocks
Accurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre-and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the models superior segmentation performance, marking a significant advancement in brain tumor analysis across pre-and post-operative MRI scans. 2024 by the authors of this article. -
Affective geographies and the anthropocene: Reading shubhangi swarups latitudes of longing
This paper is a critical reading of the affective and emotional geographies imagined in the Islands plot-line of Shubhangi Swarups novel Latitudes of Longing (2018). The paper argues that Swarup presents the case of a rethinking environmental aesthetics that conveys a deeper sense of space, time, and place. By creating an ambient poetics to negotiate human and non-human interconnectedness, the paper demonstrates the strength of novelistic traditions and their potential to generate an idea of affect that is transcorporeal as one not located only in the site of the human body, instead, emanating from a more nuanced interconnectedness between the human and the non-human world. Informed by affective ecocriticism and Zayin Cabots multiple ontologies approach that generates ecologies of participation, the paper closely reads the Islands section to establish how literary illustrations provide an instance to widen the horizons of environmental engagement and generate a narrative imagination that encompasses a larger ecosystem cutting across geological spacetimes in the Anthropocene. Swarups use of fiction is critically used to generate an ecoaesthetics that leads to a more informed ethical action towards recognizing the interconnectedness of living and non-living forms that create sustainable ecologies. 2021 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
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. -
After-sale service experiences and customer satisfaction: An empirical study from the Indian automobile industry
For the growth of any industry, services play an essential role. Customers are more aware of the type of services they receive, and the expectations from the service providers are very high. Twenty-two percent total Gross Domestic Product (GDP) of the country is generated through the automotive industry. Global automotive majors have entered India and have dramatically changed the country's car production scenario. Changes to international technology design and adaptation have helped Indian car manufacturing compete globally, facing worldwide challenges. Considering services' high significance and essential role in the automobile industry, this study examined customer satisfaction with after-sales service experiences in the automobile sectorthis paper analyses customer satisfaction concerning automotive service interactions. The conceptual framework explains the impact on customer satisfaction in various car industries from various experiences, including employee behaviour, service lead time, service quality, service processes, and service costs. The respondents from Bangalore were selected. The data collection sampling approach used was convenience sampling. In a standardized questionnaire, data is collected from 400 respondents. The results demonstrate the substantial influence of service interactions on customer satisfaction. 2022 Elsevier Ltd