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In situ expansion capacity evaluation of terminal alveoli in chronic obstructive pulmonary disease using an ultrathin optical coherence tomography catheter

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Biomedical Optics Express 2025年第5期16卷 2156-2170页

作者： Fang, Bingyan Cai, Yigen Zhang, Zhuopan Liang, Jiaxi Chen, Jiarui Chen, Xiaowei Huang, Xiangdi Zhang, Jian Wen, Xue Yang, Sihua Xiong, Kedi College of Biophotonics School of Optoelectronic Science and Engineering South China Normal University Guangzhou510631 China MOE Key Laboratory of Laser Life Science Institute of Laser Life Science South China Normal University Guangzhou510631 China The Key Laboratory of Advanced Interdisciplinary Studies The First Affiliated Hospital of Guangzhou Medical University School of Biomedical Engineering Guangzhou Medical University Guangzhou511436 China Shenzhen Photonstream Limited Shenzhen518117 China State Key Laboratory of Precision Electronics Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou510006 China

Morphological structural changes in the peripheral alveoli serve as critical indicators for the early pathological features of chronic obstructive pulmonary disease (COPD). Endoscopic optical coherence tomography (OCT) has emerged as a valuable diagnostic tool for COPD, enabling the assessment of airway mucosa, smooth muscle thickness, lumen diameter, lumen area, and airway wall area. However, conventional transbronchial OCT imaging catheters are limited in size, restricting their access to terminal bronchioles and thereby impeding the morphological and structural analysis of peripheral alveoli. In this study, we present a bronchial ultrathin OCT (Bronchial-uOCT) imaging catheter with an outer diameter of 380 µm, an axial resolution of 8.4 µm, and a lateral resolution of 9.3 µm. Using New Zealand rabbit COPD models, we achieved real-time in situ 3D volumetric imaging of terminal alveoli. Statistical analysis revealed a difference of 5 times in the alveolar area between normal and COPD pathological groups (P = 0.00018). Quantitative analysis of expansion compliance revealed significant differences in the growth rate and trend of the alveolar area between the normal group and the COPD pathology group. Furthermore, the opening direction of the curve obtained through power law fitting may help diagnose COPD. These findings highlight bronchial-uOCT as a clinically significant approach capable of in situ visualization of alveolar structures in the terminal bronchioles, offering a promising approach for the early diagnosis and assessment of COPD. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Catheters

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Influence of Heat on Fractionalized Nanofluid with Magnetic Field

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International Journal of Applied and Computational Mathematics 2025年第1期11卷 1-15页

作者： Ramzan, Muhammad Amir, Muhammad Abbas, Shajar Nazar, Mudassar Jan, Rashid Al Agha, Afnan AL Garalleh, Hakim Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan Department of Civil Engineering College of Engineering Institute of Energy Infrastructure (IEI) Universiti Tenaga National (UNITEN) Putrajaya Campus Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia Mathematics Research Center Near East University TRNC Nicosia 99138 Turkey Department of Mathematical Science College of Engineering Universty of Business and Technology Jeddah 21361 Saudi Arabia

Numerous researchers have been drawn to the rheology of non-Newtonian fluids because of their diverse uses in the engineering and manufacturing fields, such as lubrication, plastic processing, and mining. Additionally, the characteristics of magnetohydrodynamics non-Newtonian fluids permit its widespread application in computer hard drives, loudspeakers, magnetic resonance imaging, the administration of magnetic medicines, and magnetic hyperthermia. The novelty of present study is the use of fourier’s and fick’s laws. The current work is focused on the analysis of heat and mass transfer in a magnetohydrodynamics Maxwell nanofluid flows across an inclined vertical plate because of these possible uses. The values of angle of inclination is chosen in the range 0 to π3. From Table 1, the values of nanoparticles lies in the range of 0.012–0.18. Using suitable non-dimensional variables, ordinary differential equations are created from the modeling equations, and the Laplace transform method is used to solve these equations. Semi-analytical solutions for temperature, concentration, Bio-Convection, and velocity are found after employing the Laplace transform approach to address the issue. The study highlights several key outcomes likes velocity of fluid is increased with increasing values of Gr but decreased with increasing values of Pr, M, and maxwell parameter. The effect of increasing values of parameters R, L1, and N1 are seen to suppress the concentration profile. The bioconvection concentration profile increases with Φ but reduced with increasing values of L2. Furthermore, the comparison between ordinary and fractionalized maxwell fluid has been drawn. The application of the unsteady flow of nanofluid over a plate with the CPC operator has the potential to impact a wide range of solar energy-related technologies and innovations, leading to increased efficiency, sustainability, and the advancement of renewable energy solutions. © The Author(s), under exclusive licence

关键词： Chemical reaction CPC derivative Free convection Magnetic field Maxwell fluid

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Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Suganiya Murugan S. Pradeep Kumar Kalaiarasi G B. Saritha B. Rubini Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu Department of ACSE Vignan foundation for Science technology and Research Andhrapradesh India Department of Biomedical Engineering Erode Sengunthar Engineering College Thudupathi Erode Tamil Nadu India Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. Visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies.

关键词： Training Measurement Logistic regression Prevention and mitigation Data visualization Machine learning Predictive models Injuries Random forests Wearable sensors

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A Novel Smart Facial Features for Real-Time Motorists Sleepiness Prediction and Alerting System Using Hybrid Deep Convolutional Neural Network in Computer Vision 1st

A Novel Smart Facial Features for Real-Time Motorists Sleepi...

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1st International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Senthil, G.A. Prabha, R. Sridevi, S. Deepa, R. Shimona, S. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India

ISBN: (纸本)9783031808388

Motorists Sleepiness prediction is a process of detecting when an Operator is experiencing Sleepiness or fatigue while driving a vehicle. This is an important safety feature, as Sleepy driving can lead to accidents and injuries. There are several methods used to predict Operator Sleepiness, including physiological monitoring, behavioural monitoring, and hybrid methods. Physiological monitoring methods like CNN involve measuring the Operator's physiological signals, such as image or video frame processing from a camera. These frames can provide information about the Operator's level of alertness and can be used to detect Sleepiness. Behavioural monitoring methods that are DCNN on the other hand, involve observing the Operator's behaviour, such as comparing the frames with the processed dataset. This information is mainly used to detect Sleepiness. Hybrid methods combine physiological and behavioural monitoring methods of CNN and DCNN and added to the fuzzy logic algorithm makes an HDCNN (Hybrid Deep Convolutional Neural Network) to provide a more comprehensive assessment of the Operator's level of Sleepiness and improves the accuracy. This article explains techniques to spot the lips and eyes in a video taken during a research project by the Indian institute of Road Safety (IIROS). A footage of the transition from awake to fatigued to drowsy will be captured using the digital camera. The Proposed algorithm's function is to locate the face recognized in a captured video image. The face region is used mostly for its ability to act independently. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Murugan, Suganiya Kumar, S. Pradeep Kalaiarasi, G. Saritha, B. Rubini, B. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Eee Tamil Nadu Chennai India Vignan Foundation for Science Technology and Research Department of Acse Andhrapradesh India Erode Sengunthar Engineering College Thudupathi Erode Department of Biomedical Engineering Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Eee TamilNadu Chennai India

ISBN: (纸本)9798331521394

关键词： Logistic regression

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Hybrid Link Prediction Model with Explainable AI for Online Social Networks

Hybrid Link Prediction Model with Explainable AI for Online ...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Suruchi Sabherwal Adwitiya Sinha Department of Information Science & Engineering CMR institute of Technology Bengaluru India Department of Natural and Applied Science TERI School of Advanced Studies New Delhi India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Online social network systems are inherently complex due to the various types of connections that can form between any two nodes within a single network. These intricate structures enable users to engage in diverse interactions, resulting in constantly evolving behaviors. This results in cross dimensional evolution of complex networks with links forming between social entities with variable tie strength. This makes prediction of links in online networks quite significant for several higher tasks, like knowledge extraction, network learning, signed behavior analysis, etc. Though there exist several link prediction scores, however, the existing literature lacks hybrid link prediction score to compute the possible formation of strong or weak ties in a social network. In this paper, a hybrid prediction mechanism, namely GML-hybrid is proposed to estimate the possible links using network theory and machine learning. The proposed hybrid link prediction algorithm combines already existing distance metrics and extracts different thresholds across three different levels which comprise of first, second and third quantile used for dataset labelling. This proposed algorithm is applied on Ego Facebook dataset considering 15 classifiers for model building Further, the results obtained from 5 highest performing models are further analyzed using explainable AI using SHAP.

关键词： Measurement Analytical models Machine learning algorithms Social networking (online) Explainable AI Predictive models Prediction algorithms Classification algorithms Sustainable development Network theory (graphs)

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Enantioselective and divergent construction of chiral amino alcohols and oxazolidin-2-ones via Ir-f-phamidol-catalyzed dynamic kinetic asymmetric hydrogenation

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Chinese Chemical Letters 2025年第3期36卷 294-298页

作者： Ruixue Liu Xiaobing Ding Qiwei Lang Gen-Qiang Chen Xumu Zhang Department of Chemistry Medi-Pingshan and Shenzhen Grubbs InstituteSouthern University of Science and TechnologyShenzhen 518000China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518000China Chemistry and Chemical Engineering Guangdong Laboratory Shantou 515031China

The dynamic kinetic resolution(DKR)process remains a highly efficacious approach for constructing chiral amino alcohols via the catalytic asymmetric hydrogenation ofα-amino *** report herein a highly efficient and enantioselective anti-selective dynamic kinetic asymmetric hydrogenation ofα-amino ketones catalyzed by Ir-(S)-f-phamidol system,providing various chiral amino alcohols and chiral oxazolidin-2-ones divergently with high diastereo-and enantioselectivity(up to 99%yield,up to 99%ee and up to 99:1 dr).In addition,the reaction could be performed on the gram-scale,and the resulting chiral amino alcohols are key intermediates of norephedrine and metaraminol.

关键词： Asymmetric hydrogenation α-Amino ketone f-Phamidol Iridium-catalysed Divergent synthesis

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Applications of advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction

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Computer Modeling in engineering & Sciences 2025年第4期143卷 1249-1272页

作者： Rana Muhammad Adnan Mo Wang Adil Masood Ozgur Kisi Shamsuddin Shahid Mohammad Zounemat-Kermani College of Architecture and Urban Planning Guangzhou UniversityGuangzhou510006China Center for Global Health Research Saveetha Institute of Medical and Technical SciencesChennai600001India Department of Natural and Applied Sciences TERI School of Advanced StudiesNew Delhi110070India Department of Civil Engineering Lübeck University of Applied ScienceLübeck23562Germany Department of Civil Engineering School of TechnologyIlia State UniversityTbilisi0162Georgia School of Civil Environmental and Architectural EngineeringKorea UniversitySeoul02841Republic of Korea School of Civil Engineering Faculty of EngineeringUniversiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia Department of Civil Engineering Shahid Bahonar University of KermanKerman***Iran

Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental ***,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models *** study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting *** research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the *** input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)*** performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model *** results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other ***-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and *** findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological *** research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.

关键词： Suspended sediment load prediction neuro-fuzzy gradient-based optimizer ANFIS

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Effect of Cu content on microstructural evolution and mechanical behavior of Al–Cu–Mg–Ag alloys

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Journal of Materials Research and technology 2025年 36卷 5222-5236页

作者： Lianzhou Li Wentao Liu Namin Xiao Xingwu Li Haoyan Meng Jingjing Ruan Liang Jiang Lilong Zhu Institute for Advanced Studies in Precision Materials Yantai University Yantai Shandong 264005 China School of Materials Science and Engineering Shandong University of Science and Technology Qingdao Shandong 266590 China AECC Beijing Institute of Aeronautical Materials Beijing 100095 China

This study systematically investigates the effect of Cu content (2.77–6.80 wt%) on the microstructural evolution and mechanical behavior of Al–Cu–Mg–Ag alloys through comprehensive characterization using scanning electron microscope (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The relationship between microstructure and yield strength (YS) was quantitatively analyzed using multiple strengthening models. Experimental results demonstrate that increasing Cu content promotes eutectic phase formation (θ-Al 2 Cu, T-Al 20 Cu 2 Mn 3 and Ag-containing S-Al 2 CuMg) in as-cast microstructures, accompanied by a transition from dendritic to cellular dendritic grain morphology. Coarse surplus phases (CSPPs) facilitate particle-stimulated nucleation (PSN), enhancing recrystallization to a maximum of 88.91 % at 6.80 wt% Cu. The number density of the Ω phase exhibits a non-monotonic dependence on Cu content, reaching a maximum of 6.72E+22 m −2 at 4.68 wt% Cu, while θ′ phase precipitation is significantly suppressed. Mechanical properties and grain boundary characteristics show strong Cu content dependence: increasing Cu content reduces precipitate-free zone (PFZ) width and transforms grain boundary precipitates (GBPs) from discontinuous to continuous distributions. Yield strength demonstrates a positive correlation with Cu content, while ductility follows an inverse trend. Strengthening mechanism analysis reveals that Ω phase precipitation dominates the yield strength enhancement.

关键词： Al–Cu–Mg–Ag alloys Microstructural evolution Precipitation Mechanical properties Strengthening mechanism

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MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

MPEC: Manifold-Preserved EEG Classification via an Ensemble ...

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International CSI Computer Conference, CSICC

作者： Shermin Shahbazi Mohammad–Reza Nasiri Majid Ramezani Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Accurate classification of EEG signals is crucial for brain-computer interfaces (BCls) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.

关键词： Manifolds Geometry Support vector machines Technological innovation Accuracy Sensitivity Brain modeling Electroencephalography Brain-computer interfaces Reliability

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