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Foetal brain extraction using mathematically modelled local foetal minima
This paper proposes segmentation techniques to separate brain parcel from the MRI of the human embryo and also determines the abnormality of the foetal brain at various gestational weeks. These strategies mean to characterise areas of the premium of various granularities: brain, tissue types, or constructions that are more limited. Various philosophies have been applied for this division task and can be grouped into the solo, parametric, characterisation, atlas combination, and deformable models. Brain atlases are usually used as preparing information in the division interaction. Difficulties identifying using pictures secured, the quick mental health, and the restricted accessibility of imaging information thwart this division task. This paper discusses foetal brain segmentation using mathematically modelled foetal brain minima by using a curve fitting segmentation technique. Broad tests show that the proposed approach beats the ebb and flow of various segmentation techniques and the results gained are significant. Copyright 2023 Inderscience Enterprises Ltd. -
Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies
In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies; clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. 2021 IEEE. -
Solid-State Organic Fluorophore for Latent Fingerprint Detection and Anti-Counterfeiting Applications
A highly fluorescent material exhibiting solid-state fluorescence is particularly important in detecting latent fingerprints (LFPs) and anti-counterfeiting applications. Herein, we have synthesized a coumarin-benzothiazole moiety 3-(benzo[d]thiazol-2-yl)-2H-chromen-2-one (3-BTC) to inspect its capability to visualize LFPs and work as an anti-counterfeiting ink. The compound showed yellow-greenish emission under UV excitation and good covertness under visible light conditions. With the help of the powder dusting method, the latent fingerprints were coated with 3-BTC powder and images of the LFPs developed over various substrates including plastic, steel, aluminium plate, rubber, etc. under UV 365 nm light displayed good resolution be able to discern the patterns of all the levels 13. Apart from fresh fingerprints (taken within 10 seconds), aged (over 60 days) and incomplete eccrine LFPs were successfully visualized using 3-BTC powder. Anti-counterfeiting ink prepared using 3-BTC also proved to be a promising candidate as an anti-counterfeiting ink. Various types of paper materials, including tissue paper, printing paper, newspaper, etc. were used for evaluating 3-BTC as a satisfactory anti-counterfeiting ink. 2024 Wiley-VCH GmbH. -
Design, Synthesis, Molecular Docking, and In Vitro Antiproliferative Evaluation of QuinolinePyranocoumarin Hybrids as Potent DNA Topoisomerase II Inhibitors
A series of novel quinolinepyranocoumarin hybrids incorporating quinoline and coumarin moieties were designed, synthesized, and evaluated as DNA topoisomerase II inhibitors. The key scaffold, 2-amino-5-oxo-4-(quinolin-4-yl)-4,5-dihydropyrano[3,2-c]chromene-3-carbonitrile, was obtained via a one-pot reaction in 92% yield at room temperature. Ten derivatives (4a4j) were characterized by 1H/13C NMR and mass spectrometry. Docking studies against TOPO II? (PDB ID: 5GWK) revealed compound 4b with the best binding affinity (?8.02kcal/mol), outperforming doxorubicin. In vitro cytotoxicity on MCF-7 and K562 cells showed lower IC50 values for MCF-7, and cell cycle analysis indicated G2/M arrest consistent with TOPO II inhibition. 2026 Wiley-VCH GmbH. -
A new benzothiazoloacetonitrile-derived fluorescent probe for selective hydrazine detection and its applications in bioimaging and cotton swab analysis
Hydrazine (N2H4) is extensively utilized in various chemical industries. However, it is a highly toxic and explosive chemical posing a serious risk to human health and the environment, which warrants its quick and selective detection. To address this issue, we introduce a benzothiazoloacetonitrile-based fluorescent probe containing a recognition site for hydrazine detection. Adding the benzothiazoloacetonitrile group to the phenanthroline-based imidazole fluorophore increased BTN's electrophilicity, aiding the nucleophilic attack by hydrazine. This led to a rapid fluorescence change from orange to green within one minute, with a limit of detection (LOD) of 0.21 M, resulting from the cleavage of the olefinic bond between the donor and acceptor units. The probe's selective response to hydrazine was supported by a specific reaction mechanism, confirmed by LC-MS and DFT studies. Additionally, the probe can detect hydrazine using cotton swabs for quick, on-site testing. It also allows for clear visualisation in living cells through different fluorescence channels. Overall, these results demonstrate that the probe exhibits significant potential for the detection of hydrazine in environmental and biological samples. This journal is The Royal Society of Chemistry, 2026. -
Highly emissive dibenzofuranfluorophores with aggregation-induced emission for bioimaging in HeLa cell lines
Dibenzo[b,d]furans, structural analogs of furan, represent an emerging class of promising molecules whose solid-state emission properties remain largely unexplored. The aim of the present work is to design and synthesize new dibenzo[b,d]furan-based organic fluorophores for bioimaging applications. The synthesis involved a single-step Schiff base reaction of 4-(dibenzo[b,d]furan-4-yl)aniline with two different ortho-hydroxy aldehydes, furnishing DBF1 and DBF2 in high yields. Both fluorophores DBF1 and DBF2 exhibited high fluorescence in their solid and aggregated states. The photophysical properties in solution, solid, and aggregated states were investigated using absorbance and emission spectroscopy. The aggregation process was confirmed by the particle size analysis using dynamic light scattering (DLS). The cytotoxicity of the molecules was investigated against HeLa cell lines by the standard MTT assay. DBF1 and DBF2 demonstrated exceptional photoluminescence with quantum yields reaching up to 17.89% and 2.26% respectively, highlighting their potential as excellent materials for imaging applications. Both DBF1 and DBF2 exhibited significant toxicity towards HeLa cells with IC50 values of 42.08 ?g ml?1 and 39.74 ?g ml?1, respectively, showcasing notable anti-proliferative activity against HeLa cells. Both fluorophores exhibited excellent emission in HeLa cells with mean relative fluorescence intensities of 1.008 0.77 a.u. and 1.44 0.65 a.u. for DBF1 and DBF2, respectively. Thus, this work presents the lesser explored dibenzo[b,d]furan-based organic fluorophores for bioimaging applications with potential inhibitory activity against HeLa cells. 2025 The Royal Society of Chemistry. -
WOMEN'S SENSE OF BELONGING: MODERATING ECONOMIC, SOCIOCULTURAL, AND ENVIRONMENTAL IMPACTS ON COMMUNITY-BASED TOURISM DEVELOPMENT
Despite the tremendous development in the literature on sustainable tourism, the focus remains especially on government regulation and international cooperation. As will be seen, the present study is rare in adopting a bottom-up approach that focuses on the perceptions, attitudes, and beliefs of the communities. Therefore, this study aims to explore the positive impacts of community-based tourism, including economic, sociocultural, and environmental factors, on sustainable tourism development. Furthermore, it investigates the moderating role of women's sense of belonging in such relationships. Partial Least Squares Structural Equation Modelling (PLS-SEM) was adopted to analyze the survey results from 183 Omani women. The participants were selected via convenience sampling, therefore facilitating efficient data gathering over the course of the investigation. Convenience sampling was approved given the exploratory nature of the study and the need to gain understanding from a particular demographic group - Omani women - who significantly participate in community and tourism-related activities in Oman. Data gathering ran from January 21 to the end of February 2024. The authors and research assistants distributed the questionnaires to local markets, cultural institutions, university students and community gatherings, among other places, in order to get a diverse sample of Omani women. The study findings illustrate that economic and environmental variables significantly influence sustainable tourism development within the community-based tourism framework; however, sociocultural aspects show a lesser association. Interestingly, women's sense of belonging functions as a crucial moderating factor, amplifying the positive outcomes of economic and environmental factors on tourism sustainability. The results of the study considerably progress the theoretical knowledge of community-based tourism (CBT) by defining the complex connections between economic, social, and environmental components as well as the moderating function of a feeling of belonging (PS). This research delivers important practical uses to policymakers and tourist planners as well as community leaders in Oman who aim to develop community-based tourism. Environmental stewardship holds significant importance because its positive moderation effects strengthen the connection between ENV and STD, according to study findings. 2025 Editura Universitatii din Oradea. All rights reserved. -
Smart therapist: The mental health detector
In this fast-moving world, mental health disorder has turned to be one of the important issues which need utmost care and concern. According to the National Mental Health Survey Report, at least 14% of the India's population needs attention for mental disorders, of which stress, depression, and anxiety are the most common. An automated system that helps in detecting these mental disorders and thereby helps us to lead a better life by moulding our lifestyle is desirable. This chapter proposes an Intelligent Therapist based on machine learning which diagnoses a person's mental state through a set of questionnaires that helps in determining his/her mental state. The chatbot analyses the response and classifies them into different categories as per the severity of the disorder. The model is trained and tested on varying datasets to provide better accuracy in the understanding of human behaviour. This web-based chatbot will be accessible to anyone having access to the internet and will act as a preliminary self-check-up point. 2025 selection and editorial matter, Neha Goel and Ravindra Kumar Yadav; individual chapters, the contributors. -
Corrosion behavior of AlCuFeMn alloy in aqueous sodium chloride solution
Medium Entropy Alloy AlCuFeMn possesses high room temperature strength and oxidation endurance. In present work, the aqueous corrosion resistance of the as-cast as well as low temperature oxidized AlCuFeMn alloy in 3.5 wt% NaCl solution, is explored. Equimolar proportions of high purity copper, manganese, iron, and aluminum were arc melted and cast in a copper mold. The alloy primarily consists of a face-centered cubic and a body-centered cubic phase. Potentiodynamic polarization tests on the alloy after low temperature surface oxidation reveal an aqueous corrosion resistance comparable to AISI 304 steel and CoCrFeMnNi high entropy alloy. The X-ray photoelectron spectroscopic studies confirmed that the free surface in the as-cast alloy is in partially oxidized state. The same completely oxidizes after low-temperature surface oxidation. Such low temperature surface oxidation improves pitting corrosion resistance in AlCuFeMn alloy due to increased metal/oxide layer resistance. The electrochemical impedance spectroscopy tests coupled with microscopy confirmed that the principal corrosion mechanisms in the alloy are of the uniform and pitting type. The energy dispersive spectroscopy experiments indicate that a copper oxide enriched layer is formed on the surface oxidized specimen during corrosion. 2021 Elsevier B.V. -
Comparative Analysis of Various Ensemble Approaches for Web Page Classification
The amount of data available on web pages is enormous, and extracting the relevant information and classifying them is an important task. Web page classification finds applications in web content filtering, maintaining and expanding web directories, building efficient crawlers, etc. Machine Learning methods known for their well-established classification approaches have proved to be effective in web page classification. The present work uses ensemble methods like Bagging Meta Estimator, Random Forest, Adaptive boosting, Gradient Tree boosting, Extreme Gradient boosting and stacking to improve single classifiers results. One dataset is manually created to classify web pages into IoT projects and non-IoT projects. Another publicly available dataset is used to classify publications- and conference-related web pages. The advantage of the Ensemble methods over single classifiers has been validated, and various parameters to tune the Ensemble classifiers have been presented and analysed, with accuracy being the metric for performance. Features like learning rate, number of estimators, and maximum number of features have been tuned besides other parameters, and a comparison has been presented. 2023 Scrivener Publishing LLC. -
Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification
Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers. The Gradient Boosting and Extreme Boosting ensemble models are used in this work for binary classification. The dataset containing URLs of web pages have been collected manually. The comparison between the two boosting algorithms validated the improvement in accuracy and speed obtained through Extreme boosting. Extreme boosting has been found to be around ten times faster than Gradient boosting and also shows improvement in accuracy. The effect of three preprocessing techniques; lemmatization, stop words removal and regular expressions shows that these preprocessing techniques improves the accuracy of the results but not significantly. 2020 IEEE. -
Spatial variations of landslide severity with respect to meteorological and soil related factors
Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Decolonizing Open Science: Southern Interventions
Hegemonic Open Science, emergent from the circuits of knowledge production in the Global North and serving the economic interests of platform capitalism, systematically erase the voices of the subaltern margins from the Global South and the Southern margins inhabiting the North. Framed within an overarching emancipatory narrative of creating access for and empowering the margins through data exchanged on the global free market, hegemonic Open Science processes co-opt and erase Southern epistemologies, working to create and reproduce new enclosures of extraction that serve data colonialism-capitalism. In this essay, drawing on our ongoing negotiations of community-led culture-centered advocacy and activist strategies that resist the racist, gendered, and classed structures of neocolonial knowledge production in the metropole in the North, we attend to Southern practices of Openness that radically disrupt the whiteness of hegemonic Open Science. These decolonizing practices foreground data sovereignty, community ownership, and public ownership of knowledge resources as the bases of resistance to the colonial-capitalist interests of hegemonic Open Science. The Author(s) 2021. -
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M. -
Detection of faces from video files with different file formats
Face detection is the primary approach of all fundamental problems of human computer interaction system (HCIS). This paper evaluates the performance of detection system on single face from stored videos that are stored in different file formats. Stored videos contain raw homemade datasets as well as ready-made datasets. This proposed work concludes detection percentage of face detection system in different video formats. The implementation is done in two phases. The raw homemade dataset is tested on.3gp,.avi,.mov,.mp4 and a ready-made dataset is tested on.wmv,.m4v,.asf,.mpg file formats. The coding part for face detection has been done in MATLAB R2013a. The detection of faces from video file was 72.79 % for homemade dataset and 82.78% for ready-made dataset. 2016 IEEE. -
Extraction of features from video files using different image algebraic point operations
In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a. 2019 Association for Computing Machinery. -
Data Analytics for Social Microblogging Platforms
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. 2023 Elsevier Inc. All rights reserved. -
Photocatalytic driven self-cleaning IPN membranes infused with a host-guest pair consisting of metal-organic framework encapsulated anionic nano-clusters for water remediation
Traditional water treatment membranes frequently encounter challenges in attaining an ideal equilibrium between permeability and selectivity. The performance of membranes is further hampered by hydrophobicity, scalability, and fouling problems, as well as excessive energy consumption. Hence, the current research is dedicated to the development of highly effective antifouling membranes, aiming for a significant balance between water permeance and separation efficiency, and featuring exceptional photocatalytic self-cleaning properties to ensure the sustainable reuse of membranes. In this study, a unique nanocomposite-based membrane is designed containing metal-organic frameworks (MOFs) MIL-101 (Fe) encapsulated copper-containing polyoxometalate (Cu-POM) incorporated into an interpenetrating polymer networks (IPNs) membrane. POMs are highly electronegative, oxo-enriched nanosized metal-oxygen cluster species and when composited with MOF yields POMOF which can help in the removal of pollutants from water through electrostatic site-specific binding. The IPN membrane designed by polymerizing aniline in the presence of polyvinylidene fluoride (PVDF) offers tunable pores of the membrane. The infusion of POMOF imparts a strong negative charge to the membrane surface, improving membrane hydrophilicity. This enhances pollutant removal through the Donnan exclusion principle and adds anti-fouling properties. Furthermore, the reduced pore size achieved by the IPN architecture in the POMOF@IPNs membrane effectively sieves out both cationic and anionic dyes, as well as pharmaceutical pollutants. Additionally, POMOF enhances the photocatalytic degradation of CR and MB dyes, coupled with essential self-cleaning attributes vital for separation processes. The IPNs structure, apart from housing POMOF, fortifies the membrane's mechanical strength with its distinctive network-like configuration. Furthermore, these advanced membranes showcase robust antibacterial and antiviral characteristics, while remaining non-cytotoxic to mammalian cells. Our findings indicate that the state-of-the-art POMOF@IPNs membrane is scalable and holds substantial promise for industrial wastewater treatment. 2024 Elsevier B.V. -
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE. -
Border Collie Optimization
In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test. 2013 IEEE.