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Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Alzubaidi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Neural Particle Method (NPM) is a newly proposed Physics-Informed Neural Network (PINN) based, truly meshfree method for hydrodynamics modeling. In the NPM, PINN is applied to globally approximate field variables, and the high-order Implicit Runge–Kutta (IRK) method is used to treat the time integration. The NPM can easily achieve incompressibility and deal with the free-surface problem. However, the NPM is currently limited to inviscid hydrodynamics problems and is computationally expensive. In this work, we developed a general NPM (gNPM) for viscous hydrodynamics modeling. In the gNPM, a single pressure is output as the predicted pressure field rather than the multiple pressures in the original NPM. Thus, the size of the neural network is greatly reduced, making the gNPM computationally more efficient. Besides, the spatial derivatives in the governing equations are calculated with respect to the current spatial coordinates rather than the predicted space. In this manner, the gNPM is more straightforward to be implemented. Furthermore, by considering the viscous term in the conservation of momentum, the gNPM can be applied for viscous hydrodynamics modeling. The effectiveness and robustness of the gNPM have been demonstrated through several hydrodynamics benchmark cases with different boundary conditions. We highlight that the proposed gNPM is able to cope with highly uneven particle distributions, while the traditional meshfree method can produce severe failure. • A general Neural Particle Method (gNPM) is proposed for hydrodynamics modeling. • The gNPM is computationally more efficient and easier to be implemented than the NPM. • The effectiveness of the gNPM has been demonstrated through benchmark cases. • The gNPM is more robust than the SPH for uneven particle distributions. [ABSTRACT FROM AUTHOR]

Physics-Informed Neural Networks (PINNs) have recently gained increasing attention in the field of topology optimization. The fusion of deep learning and topology optimization has emerged as a prominent area of insightful research, where minimization of the loss function in neural networks can be comparable to minimization of the objective function in topology optimization. Inspired by concepts of PINNs, this paper proposes a novel framework, ' Complete Physics-Informed Neural Network-based Topology Optimization (CPINNTO) ', to address various challenges in topology optimization, particularly related to structural optimization. The key innovation of the proposed framework lies in introducing the first complete machine-learning-based topology optimization framework through integration of two distinct PINNs. Herein, the Deep Energy Method (DEM) PINN is implemented to determine the deformation state of corresponding structures numerically. In addition, derivation of the objective function with respect to design variables is replaced with automatic differentiation in sensitivity-analysis PINN (S-PINN). The feasibility and potential of the CPINNTO framework have been assessed through several case studies while highlighting strengths and limitations of utilizing PINNs in topology optimization. Subsequent findings indicate that CPINNTO can achieve optimal topologies without labeled data nor FEA. The numerical examples demonstrate that CPINNTO is capable of stably obtaining optimal structures for various topology optimization applications, including compliance minimization problems, multi-constrained problems, and three-dimensional problems. Resulting designs exhibit favorable compliance values comparable to the designs obtained via density-based topology optimization. In summary, the proposed CPINNTO framework opens up novel and interesting possibilities for structural topology optimization. • A novel Complete PINN-based topology optimization framework (CPINNTO). • Two PINNs to replace structural and sensitivity analyses in topology optimization. • CPINNTO does not require any labeled data or FEA to train PINNs. • CPINNTO can effectively optimize problem domains of both 2-D and 3-D structures. [ABSTRACT FROM AUTHOR]

The degree of compaction in the levee building materials is a crucial factor that affects the piping phenomena. The density and compaction of the soil strata determine the structural soundness of the levee. Segments with reduced density or compaction can become weak spots during floods. To assess part of the Helena levee (2,500 m) in Arkansas (AR), the United States, an extensive ground-penetrating radar (GPR) fieldwork was conducted. This reliable method will undoubtedly improve the assessment procedure of the levee structure by identifying the weak spots within the structure that result from poor compaction of the levee core layers in a short time with high accuracy. However, interpreting the GPR data can be challenging and requires specialised knowledge. Obtaining meaningful insights typically involves a time-consuming process of extensive manual processing and visual inspection. To address this issue, this article proposes a novel, reliable deep-feature fusion framework for GPR data to identify horizontal variation in the soil compaction of a levee. To address data scarcity, a new type of transfer learning in the same domain is adopted, and four deep learning models (Xception, Inception, EfficientNet and MobileNet) are used to extract features. The combined features are then used to train and test five machine learning classifiers (Neural Network, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, and Naive Bayes). The best combination of deep Learning and machine learning is four models with the neural network classifier which achieved the highest results by obtaining an accuracy of 98.2%, an F1 score of 97.6%, and an area under the curve of 99.9%. The proposed framework faced an additional challenge when subjected to an unseen dataset of 1,511 images reserved primarily for testing. Remarkably, it achieved an accuracy rate of 95.7% with the neural network classifier. This article presents a new research direction that has substantial potential in various domains, including civil engineering, the petroleum sector, road safety, agriculture, and more. • A Reliable deep feature fusion technique for the Ground Penetrating Radar (GPR) Data. • A new type of transfer learning is adopted to address training data scarcity issues. • Feature fusion is performed on four DL models. • Five machine learning classifiers have been employed. • The proposed framework achieved an accuracy of 98.2%, an F1 score of 97.6% and an AUC of 99.9 %. [ABSTRACT FROM AUTHOR]