Variety Of Features Research Articles

Elucidation of the solution structure of macrocyclic paritaprevir and exploration of its antitumor potential through molecular docking

Macrocyclic molecules have emerged as significant contenders in drug development and supramolecular chemistry. Paritaprevir is a 15-membered macrocycle approved for the treatment of chronic Hepatitis C virus infection. The structural flexibility inherent in paritaprevir enables the presentation of diverse conformational features in solutions, thereby influencing its binding pattern with proteins. In this paper, we investigated the structural characteristics of paritaprevir in protic solvents (methanol and trifluoroethanol) as well as an aprotic solvent (acetonitrile) by using experimental electronic circular dichroism spectra combined with density functional theory calculations. Our study reveals that paritaprevir can adopt different molecular conformations due to its flexible carbon chain and the solvent environment. Conformer B is particularly stable in polar solvents because of the planar hydrophobic interactions between the phenanthridine and methylpyrazine groups. Density functional theory calculations-assisted structural analyses further disclosed the weak noncovalent interactions within the stable conformations of paritaprevir. Molecular docking of paritaprevir on different cancer proteins showed that the macrocycle was highly selective against lymphoma, endometrial carcinoma, colorectal cancer, and breast cancer. This suggests that paritaprevir could potentially serve as a promising lead compound for drug design targeting these specific types of cancers. The comparison between the low energy conformation and pharmacophore conformation (docking conformation) indicates that conformer B is the primary pharmacophore conformation of paritaprevir, providing important macrocyclic structural characteristics for future macrocyclic drug development.

3D Printing Technologies in Metallic Implants: A Thematic Review on the Techniques and Procedures.

Additive manufacturing (AM) is among the most attractive methods to produce implants, the processes are very swift and it can be precisely controlled to meet patient’s requirement since they can be produced in exact shape, dimension, and even texture of different living tissues. Until now, lots of methods have emerged and used in this field with diverse characteristics. This review aims to comprehensively discuss 3D printing (3DP) technologies to manufacture metallic implants, especially on techniques and procedures. Various technologies based on their main properties are categorized, the effecting parameters are introduced, and the history of AM technology is briefly analyzed. Subsequently, the utilization of these AM-manufactured components in medicine along with their effectual variables is discussed, and special attention is paid on to the production of porous scaffolds, taking pore size, density, etc., into consideration. Finally, 3DP of the popular metallic systems in medical applications such as titanium, Ti6Al4V, cobalt-chromium alloys, and shape memory alloys are studied. In general, AM manufactured implants need to comply with important requirements such as biocompatibility, suitable mechanical properties (strength and elastic modulus), surface conditions, custom-built designs, fast production, etc. This review aims to introduce the AM technologies in implant applications and find new ways to design more sophisticated methods and compatible implants that mimic the desired tissue functions.

The Utilization of Spotify Application to Improve English Listening Skill: Students’ experience

Background. Listening is a complex skill involving attentive listening, understanding oral symbols, appreciation, content capture, and comprehension of communication through spoken language. It is a fundamental skill in language learning that supports other language abilities. Purpose. This study aims to investigate the utilization of the Spotify application in improving English listening skills among sixth-semester English Tadris students at the Faculty of Tarbiyah and Keguruan, Universitas Islam Negeri Mahmud Yunus Batusangkar. Method. The research employs a quantitative approach with a survey design. Data collection was conducted using a questionnaire comprising 24 statements, distributed to each student in the class. The population consisted of sixth-semester English Tadris students at the university, with a sample size of 37 participants. The questionnaire's reliability was established with an RPB for each item greater than the r table (0.325) and a Cronbach's alpha of 0.831. Results. The assessment results indicate a positive response from students regarding the use of the Spotify application for improving their listening skills. Students reported that various features available on Spotify, along with its accessibility, contributed to enhancing their listening abilities. Conclusion. The study concludes that the Spotify application is a valid and reliable tool for improving students' English listening skills. Its effectiveness is attributed to the diverse features offered by the application and its convenience of use. These findings suggest that Spotify can be a valuable resource for language learners seeking to enhance their listening proficiency.

Design on temporal-spatial Transformer model for air target intention recognition

The battlefield changes rapidly in information warfare. The battlefield situation data presents massive and diversified characteristics, which makes it increasingly difficult to identify the operational intent of air targets based on expert experience. In combination with the current state-of-the-art intelligent methods, the Transformer model is studied and introduced into the field of air target intent recognition for the first time, and a new intent recognition method temporal-spatial transformer(TST) is designed, which can effectively mine the deep feature information in the temporal and spatial domains of battlefield situational data to improve the accuracy of air target combat intention recognition. In addition, a comparative study of the four currently advanced neural network intention recognition methods shows that TST achieved outstanding performance in all indicators and outperformed all compared neural network models. TST method not only has excellent accuracy but also extremely fast convergence rate, which allows it to quickly capture key information from battlefield situation data for intention recognition.

Pharmacological management of polycystic ovary syndrome.

Polycystic ovary syndrome is a common and frequently undiagnosed female endocrine disorder that is associated with diverse symptoms and features, and an increased risk of long-term chronic diseases such as type 2 diabetes and cardiovascular disease. Pharmacotherapy for polycystic ovary syndrome should be directed at the key concerns of the individual patient. The combined oral contraceptive pill or metformin may be prescribed for irregular periods. The combined oral contraceptive pill is preferred over antiandrogens for treatment of hirsutism and acne. Metformin is of benefit for reducing excess body weight and improving hormonal and metabolic outcomes in those with high metabolic risk (e.g. body mass index greater than 25 kg/m2). Inositol appears to have limited benefits for metabolic outcomes, although it is associated with fewer adverse effects than metformin. Modification of lifestyle factors is important as part of a holistic approach to managing polycystic ovary syndrome. Anti-obesity drugs may be considered for weight management in addition to lifestyle interventions.

Unveiling Structure-Enhanced Positional Embedding Module in Transformer-Based 3D Human Pose Estimation

Abstract With the advancement of computational capabilities, the realm of deep learning has expanded from 2D image tasks to encompass 3D video tasks. Human action recognition stands as a typical 3D video task, which emphasizes the accurately capture of local body structures. However, existing methods encounter limitations in precisely capturing these structures, especially in joints within the same body region that exhibits diverse static and dynamic features. To address this complexity, our study innovatively introduces a Structure-Enhanced Positional Embedding Module seamlessly integrated into the PoseFormer model, resulting in the innovative HStruPE-Former model. Experimental validation on the Human3.6M dataset demonstrates a significant performance improvement achieved by the HStruPE-Former model in the task of 3D human pose estimation. Particularly in scenarios involving intricate movements and postures, the module demonstrates improved stability and accuracy. Although the improvements are modest, this technology is expected to facilitate the exploration of untapped potential of structure-enhanced postion embedding modules in diverse neural network applications, thereby promoting further exploration in neural network research. This research outcome not only enhances the performance in current 3D video tasks but also paves the way for novel research directions in the field of deep learning.

Medulloblastomas Initiated by Homologous Recombination Defects in Mice

Germline mutations of homologous-recombination (HR) genes are among the top contributors to medulloblastomas. A significant portion of human medulloblastomas exhibited genomic signatures of HR defects. We asked whether ablation of Brca2, Palb2, and their related Brca1 and Bccip genes, in the mouse brain can differentially initiate medulloblastomas. We established conditional knockout mouse models of these HR-genes, and a conditional knockdown of Bccip (shBccip-KD). Deletion of any of these genes led to microcephaly and neurological defects, with Brca1- and Bccip- producing the worst. Trp53 co-deletion significantly rescued the microcephaly with Brca1, Palb2, and Brca2 deficiency, but it exhibited limited impact on Bccip- mice. For the first time, inactivation of either Brca1 or Palb2 with Trp53 was found to induce medulloblastomas. Bccip/Trp53 deletions failed, despite that shBccip-CKD was highly penetrative, to induce medulloblastomas. The tumors displayed diverse immunohistochemical features and chromosome copy number variation. While there were widespread upregulations of cell proliferative pathways, most of the tumors expressed biomarkers of the Sonic Hedgehog subgroup. The MBs developed from Brca1-, Palb2-, and Brca2- mice were highly sensitive to a PARP inhibitor, but not the ones from shBccip-CKD mice. Our models recapitulate the spontaneous medulloblastoma development with high penetrance and narrow time-window, providing ideal platforms to test therapeutic agents with the ability to differentiate HR defective and proficient tumors.

Aggregated Pattern Classification Method for improving neural disorder stage detection.

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages. The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Source domain prior-assisted segment anything model for single domain generalization in medical image segmentation

Deep learning-based medical image segmentation models often suffer from performance degradation across domains due to domain discrepancies arising from data collected by various healthcare centers. Recent advancements, particularly the Segment Anything Model (SAM), have shown promising generalization abilities in this field, inspiring the development of several SAM-based approaches to address domain discrepancy issue. Nevertheless, these methods seldom exploit the full potential of the rich source domain knowledge to improve segmentation accuracy in unseen target domains. In this paper, we propose a source domain prior-assisted module for generalizable medical image segmentation based on SAM. Specifically, we store diverse features of the source domain data in a memory bank. When applying the model to the target domain, the features of the target domain first match with the features in the memory bank to obtain invaluable prior information. This strategy enables the model to utilize the prior information from the source domain to adapt to the target domain. We validate the proposed method on two widely used medical image segmentation tasks across multiple domains and achieve state-of-the-art performance.

Research on precise lithium battery state of charge estimation method based on CALSE-LSTM model and pelican algorithm

This paper presents an innovative fusion model called “CALSE-LSTM,” which integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), self-attention mechanisms, and squeeze-and-excitation attention mechanisms to optimize the estimation accuracy of the State of Charge (SoC). The model incorporates battery historical data as input and employs a dual-attention mechanism based on CNN-LSTM to extract diverse features from the input data, thereby enhancing the model's ability to learn hidden information. To further improve model performance, we fine-tune the model parameters using the Pelican algorithm. Experiments conducted under Urban Dynamometer Driving Schedule (UDDS) conditions show that the CALSE-LSTM model achieves a Root Mean Squared Error (RMSE) of only 1.73 % in lithium battery SoC estimation, significantly better than GRU, LSTM, and CNN-LSTM models, reducing errors by 31.9 %, 31.3 %, and 15 %, respectively. Ablation experiments further confirm the effectiveness of the dual-attention mechanism and its potential to improve SoC estimation performance. Additionally, we validate the learning efficiency of CALSE-LSTM by comparing model training time with the number of iterations. Finally, in the comparative experiment with the Kalman filtering method, the model in this paper significantly improved its performance by incorporating power consumption as an additional feature input. This further verifies the accuracy of CALSE-LSTM in estimating the State of Charge (SoC) of lithium batteries.

Side-channel attacks based on attention mechanism and multi-scale convolutional neural network

One widely utilized attack technique for side-channel attacks is deep learning. However, the efficiency of deep learning attacks can be impacted by the differences in data collected from various devices or environmental conditions. These differences arise from factors such as hardware discrepancies, environmental noise, and countermeasures. Such differences can disrupt the correlation between the leaked information from the target device during operations and sensitive data, leading to failures in the attacks. In order to better extract the feature information and temporal information of traces in this challenge, we propose an Attention Mechanism and Multi-Scale Convolutional Neural Network (AMCNNet). The critical components of the AMCNNet are the multi-scale convolution module and the feature extraction module. The multi-scale convolution module extracts feature of different scales from traces, improving the network's ability to capture high-level meanings and details. Then, the feature extraction module dynamically weights and rescales the features to better captures the long-term dependencies in the traces.. Through experiments conducted on publicly available datasets such as DPA Contest v4, ASCAD, and AES_HD, as well as analyzing the guessing entropy, the experimental results demonstrate that the network exhibits strong generalization capability and robustness across datasets with diverse features.

Reshaping Multiculturalism through Accommodation, Encouragement and Respect: An Analytical Study of Zadie Smith’s White Teeth

Multiculturalism is the idea of distinct identities. It seeks cultural groups to be differently acknowledged, preserved, and supported in society. Zadie Smith’s White Teeth tells the story of subtle racial tensions in contemporary British society. These tensions are reflected in several ways: race, gender, ethnicity, religion, generation, citizenship, and immigrant status. In particular, immigrants face an identity crisis and do not find favorable circumstances to establish their values. There is a gap in understanding between families of different racial identities and even within a family. Smith tries to fill these tensions and multiple levels of gaps orienting the characters to the ideals of racial harmony and mutual understanding. The objective of the study is to reshape multiculturalism applying the theoretical concepts of Chris Barker and Conrad William Watson, Will Kymlicka, Keith Banting, and John Hotronto to analyze Smith’s portrayal of “the multicultural problem.” Smith’s White Teeth has attempted to offer a multicultural ethos formed around the ethics of cultural plurality, care, and respect for others and their differences in their terms. Smith's novel reflected the multiculturalism through different lived experiences of diverse characters. The study concludes that accommodation rather than assimilation, encouragement rather than mere tolerance of difference or a form of white paternalism, and respect for others in their terms are some of the ways to reshape multiculturalism.

Spiral-refraction mutation prairie dog algorithm: Optimization framework for engineering design of interconnected multimachine power system

Many real-world engineering problems, characterized by high-dimensionality, nonlinearity, nonconvexity, and multi-modality, demand advanced optimization methods. Traditional algorithms may struggle with these challenges. Prairie dog optimization (PDO) was proposed for function optimization but faces limitations in balancing exploration and exploitation due to two reasons. The first one is related to diversity features which may not be efficient, and the second one is related to having no leading mechanism for direct search feature towards the promising regions. With that in mind, this study introduces an enhanced PDO variant, improved PDO (IPDO), addressing PDO's drawbacks through two key strategies: refraction-based learning (RBL) and spiral search learning (SSL). RBL enhances exploration by generating refraction solutions, while SSL conducts deep local searches around the best solution, strengthening exploitation. IPDO's performance is evaluated on benchmarks, IEEE CEC2017/CEC2020 test suites, and real-world constraint engineering optimization problems. Comparative analyses, including statistical measures, convergence analysis, and boxplot analysis, demonstrate IPDO's superiority. Besides, the IPDO is applied to estimate more promising parameters of power system stabilizer utilized in an interconnected multimachine power system using Western System Coordinating Council three-machine, nine-bus power system. Results illustrate that the IPDO harvests the better estimation among the other methods, making it to be an efficient and powerful tool for dealing with the efficient operation of an interconnected multimachine power system which is a challenging real-world engineering problem.

Initiation Of Pancasila Student Profile In Upper Class Cultural Diversity Of Elementary School Students

The purpose of this study was to find out and describe the profile initiation of Pancasila students in the cultural diversity of the upper grades of elementary schools at SD N Demakan 01 Sukoharjo. This research is a qualitative research. This research was conducted at SD N Demakan 01. The determination of informants in this study used a purposive sampling technique. Informants in this study were class IV teachers at SD N Demakan 01 Mojolaban. Data collection techniques in this study are through interviews and documentation. Test its validity using source triangulation technique. Researchers present data using narrative text in the form of interactive data analysis. The results of the study show that the value of diversity or diversity is taught to participants at SD N Demakan 01 Sukoharjo, namely to get used to living in Indonesian society which has a diversity of ethnicities and cultures. Learning activities that can foster a global diversity character at SD N Demakan 01 Sukoharjo include not being picky with friends at school, associating with anyone regardless of religion, ethnicity and race, implementing tolerance, namely teachers and students during group discussions do not blame each other and justifying but respecting differences of opinion when discussing in class, respecting friends at school who are practicing prayer and fasting, studying and preserving cultures from other regions, and efforts to strengthen the climate of diversity in schools through storytelling.

Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning

Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human–robot interaction, and cross-cultural communication.

Immunohistochemistry Markers in Ovarian and Fallopian Tube Neoplasms: a Comprehensive Review.

Immunohistochemistry (IHC) has emerged as a crucial tool in diagnosing and managing ovarian cancer, offering invaluable insights into tumor biology and guiding therapeutic decisions. The intricate histopathological landscape of ovarian cancer presents challenges in accurate diagnosis and classification. IHC offers a complementary approach, aiding in the characterization of tumor subtypes, prognostication, and prediction of treatment response. By targeting specific biomarkers, IHC enables the identification of diverse histological features and molecular alterations associated with ovarian malignancies. The integration of IHC into routine diagnostic workflows enhances diagnostic accuracy, aids in the subclassification of ovarian tumors, and facilitates personalized treatment strategies. Emphasis is placed on the judicious selection of antibody panels tailored to specific clinical scenarios, ensuring optimal utilization of resources and minimizing diagnostic pitfalls. Overall, this review underscores the pivotal role of IHC in refining the diagnosis, prognostication, and management of ovarian cancer, highlighting its significance in the era of precision medicine. By leveraging the molecular insights provided by IHC, clinicians and pathologists can optimize patient care and improve outcomes in ovarian cancer management.

STRUCTURAL INSIGHTS AND ANTICANCER POTENTIAL OF MELITTIN IN CD147 INTERACTION

Objective: This study investigates the interaction between melittin (PDB ID: 2MLT), a bioactive peptide from honeybee venom, and CD147 (PDB ID: 5XF0), a glycosylated transmembrane protein implicated in tumor progression. Material and Method: Employing molecular docking and bioinformatics tools, our structural analysis reveals diverse binding features, including hydrogen bonds, salt bridges, and non-bonded contacts, between the CD147 complex and melittin. Result and Discussion: Non-bonded interactions between 2MLT and specific amino acids (Gly181 and Arg201) of CD147 are highlighted, resembling aspects of the CypA/CD147 binding mechanism (Pro180-Gly181 and Arg201). The elevated anticancer potential of 2MLT was substantiated by utilizing the AntiCP 2.0 server and the ENNAACT server, employing machine learning and artificial neural network algorithms. Additionally, hydrophobicity analysis aligns with characteristics associated with anticancer peptides. Notably, thermodynamic stability variations with temperature underscore the robust binding affinity of 2MLT to the 5XF0 receptor. While our study comprehensively explores molecular interactions and predictive analyses, further in vitro and in vivo investigations are crucial to validate these findings for potential therapeutic applications.

SMIAltmetric: A comprehensive metric for evaluating social media impact of scientific papers on Twitter (X)

The rise of social media has significantly influenced scholarly communication, knowledge dissemination, and research evaluation, leading to the enrichment of alternative metrics (altmetrics) for evaluating academic papers’ social impact, which assesses the social impact of academic papers through online activities, including reading, bookmarking, downloading, and commenting. However, these altmetrics often focus on the number of mentions on social media rather than thoroughly evaluating the source, content, and dissemination of these mentions. To address this gap, this study introduces the social media impact altmetric (SMIAltmetric), which is based on 44,087 publications and 860,680 tweets (now “posts”), a comprehensive scoring system for evaluating scientific papers on Twitter (now “X”), using diverse features, including literature-related, social media engagement-related, user-related, and content-related features. Employing Altmetric Attention Acores (AAS) as labels, we tested eight machine learning algorithms, with XGBoost demonstrating the highest accuracy at 0.8672. Crucial factors influencing SMIAltmetric, as identified by the SHAP value, were followers, retweets, mentions, and citation. Furthermore, consistency analysis and convergent validation between the proposed SMIAltmetric and AAS confirm the reliability and finer differentiation of SMIAltmetric. The proposed SMIAltmetric provides a more comprehensive understanding of a paper’s social media impact, enhancing the evaluation of scientific discourse and its engagement with society.

Role of Magnetic Resonance Imaging in evaluating T2-weighted hyperintensities in spinal cord in a tertiary care hospital in central India

Background Hyperintensities on T2-weighted images in the spinal cord are a complex and diagnostically challenging entity that can present with diverse clinical features. This study protocol outlines a comprehensive investigation to understand the causes, clinical and imaging characteristics, and correlation with pathological findings of hyperintensities on T2-weighted images in the spinal cord. By establishing a systematic assessment approach, this study seeks to provide valuable insights into these abnormalities’ diagnostic and prognostic implications. Methods The study will be conducted as a prospective observational design. Patients with clinically diagnosed or suspected spinal cord lesion presenting with intramedullary T2-weighted hyperintensity and referred for MRI evaluation will be included. Data collection will encompass patient demographics, clinical features, and extensive imaging parameters. Pathological data, when available, will be correlated with imaging findings. Various statistical methods will be employed to analyse the data, including frequency analysis, comparative tests, logistic regression, and survival analysis. Expected Results The study anticipates elucidating the spectrum of etiologies underlying hyperintensities on T2-weighted images in the spinal cord and their clinical and imaging profiles. The systematic approach will offer a structured diagnostic method, while correlations with pathological data will provide an enhanced understanding of these conditions. The results are expected to provide clinicians with valuable insights into diagnosing, treating, and prognosticating patients with spinal cord hyperintensities on T2-weighted images.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.