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关键词： Shape optimization  

摘要： Modern turbomachinery is required to maintain high aerodynamic performance over a wide operating range. To address this requirement, a multipoint discrete-adjoint optimization framework is developed based on the open-source SU2 platform and blade-profile parameterization based on computer-aided design (CAD). Two representative operating points, corresponding to transonic and subsonic flow conditions, are considered in the multipoint optimization, while two corresponding single-point optimizations are also performed for comparison. The results show that the multipoint optimized blade achieves a 17.3% reduction in weighted total pressure loss relative to the baseline blade, with reductions of 20.4% and 12.0% at the two operating points, respectively. Although the single-point optimized designs show better performance at their respective design points, the subsonic single-point optimized blade suffers suction-side trailing-edge separation under the transonic condition, leading to an enlarged vortex region, intensified wake mixing, and degraded aerodynamic performance. Geometrically, the optimized blades exhibit a reduced geometric chord length through a decrease in stagger angle under the fixed axial-chord constraint, and the multipoint optimized profile lies between the transonic and subsonic single-point design tendencies near the suction-side leading edge. The corresponding flow-field changes include an upstream shift of the high-Mach-number region, smoother postpeak deceleration, weakened shock intensity, and reduced flow loss. In addition, the multipoint optimized blade shows lower turbulent kinetic energy levels near the trailing edge, increased trailing-edge base pressure, and reduced wake-vortex size, indicating weakened wake mixing and lower local energy dissipation. These results demonstrate the effectiveness of the proposed framework for multipoint aerodynamic optimization of turbine cascades.

关键词： AUC   Area Under the Curve   BMI   Body Mass Index   CAD   Coronary Artery Disease   CI   Confidence Interval   FDR   False Discovery Rate   GWAS   Genome-Wide Association Study   IDI   Integrated Discrimination Improvement   ICD-10   International Classification of Diseases 10th Revision   KNN   K-Nearest Neighbors   LD   Linkage Disequilibrium   MR   Mendelian Randomization   NMR   Nuclear Magnetic Resonance   NPX   Normalized Protein Expression   NRI   Net Reclassification Improvement   OR   Odds Ratio   PAF   Population Attributable Fraction   PRS   Polygenic Risk Score   ROC   Receiver Operating Characteristic   SD   Standard Deviation   SNF   Similarity Network Fusion   TRIPOD   Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis   UKB-PPP   UK Biobank Pharma Proteomics Project   Coronary artery disease   Multi-omics integration   Polygenic risk score   UK Biobank   Risk stratification   Precision medicine   Internal validation  

摘要：

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  Multi-omics integration improves CAD risk prediction over clinical and genomic models.
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  Four major biological modules identified: vascular dysfunction, lipid metabolism, inflammation, remodeling.
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  High-risk individuals exhibit 4.52-fold increased CAD odds with 11.24% incidence.

关键词： CAD   cinnamyl alcohol dehydrogenase   C4H   cinnamate 4-hydroxylase   CCoAOMT   caffeoyl-CoA O-methyltransferase   CCR   cinnamoyl-CoA reductase   CDS   coding sequences   CE   catechin equivalent   COMT   catechol-O-methyltransferase   4CL   4-coumarate-CoA ligase   DLR   dual-luciferase report   EMSA   electrophoretic mobility shift assay   ERF   ethylene response factor   GA   gallic acid   PAL   phenylalanine ammonia-lyase   PCR   polymerase chain reaction   P. digitatum   Penicillium digitatum   P. longanae   Phomopsis longanae Chi   POD   peroxidase   PPO   polyphenol oxidase   RT-qPCR   real-time quantitative PCR   SA   salicylic acid   SAR   systemic acquired resistance   TF   transcription factor   Y2H   yeast two-hybrid   Dimocarpus longan Lour   Salicylic acid   Disease resistance   Phenylpropanoid pathway   ERF transcription factor   Molecular mechanism  

摘要：

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  ● Salicylic acid (SA) treatment reduces disease occurrence in longan fruit.
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  ● SA treatment induces longan resistance by activating phenylpropanoid metabolism.
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  ● DlERF98 expression is upregulated by SA treatment.
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  ● DlERF98 targes phenylpropanoid metabolism-relates genes.
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  ● DlERF98 improves disease resistance in longans against Phomopsis longanae Chi.

关键词： Fault detection and diagnosis   Calibration   Energy audit   Calibration as Diagnosis   White-box model   Ventilation   EnergyPlus  

摘要：

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  Calibration as Diagnosis reframes model calibration as a fault detection tool.
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  Calibrated parameters act as physically interpretable fault indicators.
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  Severe outdoor airflow deficits are revealed despite compliant indoor CO₂ levels.
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  Method provides numerical reference for quantifying fault magnitude.

关键词： 3D reconstruction   3D Gaussian Splatting   Computer-aided design (CAD)   Editable reconstruction   Industrial assembly   Assembly preview  

摘要：

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  Proposed a CAD-guided editable 3D Gaussian framework for assembly preview.
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  Used ALIKED-NN-assisted SfM to improve sparse reconstruction stability.
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  Built a part-level 3DGS pipeline with segmentation and point-cloud filtering.
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  Enabled editable assembly previews from CAD priors and real-world images.

关键词： JT60-SA   VUV   Plasma diagnostic   MCNP   GEOUNED   RadModeling  

摘要： The accurate conversion of CAD models into MCNP geometry is fundamental to the design and development of nuclear fusion facilities such as ITER, DEMO, JT-60SA, and IFMIF-DONES, where radiation transport simulations play a critical role. As system geometries grow increasingly complex, advanced computational tools are essential for generating reliable MCNP models. This study evaluates the performance of the GEOUNED code and RadModeling tools for CAD-to-MCNP conversion. The CAD model of the Vacuum Ultra Violet (VUV) spectrometer, developed to monitor the divertor region of JT-60SA, diagnostic system for JT-60SA has been simplified and processed using these tools. The validity of the resulting MCNP model has been confirmed through stochastic volume calculations and benchmarked against results obtained using the SuperMC code. Preliminary nuclear response simulations, including neutron and proton fluxes, total dose, total heating, and neutron flux difference (%), have been performed for the integrated JT-60SA model with VUV diagnostic, demonstrating the effectiveness of the proposed conversion workflow.

关键词： BEM   Building Energy Modeling   CNN   Convolutional Neural Network   GCN   Graph Convolutional Network   XAI   Explainable Artificial Intelligence   CAM   Class Activation Mapping   EPC   Energy Performance Certificate   EUI   Energy Use Intensity   MLP   Multi-Layer Perceptron   RMSE   Root Mean Squared Error   CVRMSE   Coefficient of Variation of the Root Mean Squared Error   MAE   Mean Absolute Error   MLR   Multiple Linear Regression   RF   Random Forest   XGBoost   eXtreme Gradient Boosting   SHAP   SHapley Additive exPlanations   S/V   Surface-to-Volume Ratio   Energy modeling   Floor-plan morphology   Multimodal learning   Explainable diagnostics   Residential energy performance  

摘要： Early-stage energy modeling is crucial for optimizing residential building design. However, current approaches often face challenges related to design uncertainty, incomplete input parameters, and limited computational efficiency. This study develops a multimodal surrogate modeling framework for early-stage building energy modeling and diagnostics by integrating architectural floor-plan images with numerical energy-efficiency features. A convolutional neural network (CNN) is used to extract visual spatial features directly from floor-plan layouts, while a multi-layer perceptron (MLP) processes numerical design parameters. These two feature streams are then fused within a hybrid CNN–MLP architecture for energy use intensity prediction. The proposed model adopts a multi-scale convolutional structure without pooling operations, allowing it to preserve fine-grained spatial information in floor-plan images. Validation using more than 3,000 residential design samples shows that the proposed model outperforms single-modal machine learning baselines, including Random Forest and XGBoost, as well as multimodal deep learning models based on ViT and EfficientNet feature extractors. To enhance model interpretability and provide design-oriented feedback, SHAP and class activation mapping (CAM) techniques are incorporated. The results indicate that external boundaries, corner geometries, and geometric complexity play important roles in residential energy performance. Geometry-based metrics, including a modified compactness ratio (O/A) and an auxiliary external boundary ratio (aux_O/A), are further introduced to support spatial diagnostics. To examine model transferability, this study conducts a transfer learning experiment using residential floor-plan data from Birmingham. The results show that fine-tuning the model with a small subset of target-city samples can effectively correct cross-city prediction bias, suggesting the potential of the proposed framework for adaptation to dif

关键词： Geometry post-processing   Iso-geometric analysis   Multi-source uncertainties   Non-probabilistic reliability-based topology optimization   NURBS   Parameterized level set method  

摘要： This paper proposes an integrated framework for non-probabilistic reliability-based topology optimization (NRBTO) and automated geometry post-processing based on iso-geometric analysis (IGA) and parameterized level set method (PLSM), enabling displacement reliability topology optimization. In this study, the level set function is parameterized using Greville points and non-uniform rational B-spline (NURBS) basis functions, transforming the Hamilton-Jacobi partial differential equation into a system of ordinary differential equations. Then, considering the unknown-but-bounded uncertainties of material and external loads, uncertainty quantification is performed using interval models, and the optimization feature distance is selected as the index for evaluating displacement reliability. The evolution velocity field at Greville points is then derived via shape derivatives with the interval parametric vertex method. Furthermore, to convert the implicit level set field into explicit CAD models, this paper establishes a comprehensive automated geometry post-processing workflow. The process includes boundary extraction from the level set scalar field, boundary repair, and identification and control of various geometric features. Throughout the entire framework, the geometric model, analysis model, and level set function are constructed by the same NURBS basis functions, effectively minimizing errors. Finally, the efficacy of this approach is verified through several numerical examples, yielding smooth, reliable and highly manufacturable geometry structures.

关键词： Industrial optical metrology   Multi-view stereo vision   CAD-guided measurement   Chamfer-edge extraction   Circular-holecontourreconstruction  

摘要： High-precision three-dimensional measurement of chamfered circular holes remains challenging in industrial environments due to chamfer interference, edge ambiguity, reflective surfaces, and multi-view reconstruction errors. To address these issues, this paper proposes a CAD-guided robotic multi-view stereo vision framework for automated 3D measurement and localization of chamfered circular holes. First, a photogrammetry-based multi-view pose estimation method is developed to establish accurate geometric relationships among different viewpoints. Then, an efficient viewpoint-planning strategy with virtual visual feedback is introduced to optimize robot poses and acquisition trajectories using CAD prior information. To achieve reliable chamfer-edge extraction, a multi-stage edge detection and layered clustering-based filtering framework is proposed. Furthermore, an ellipse matching method combining prior projection and multi-constraint verification is designed to improve correspondence robustness across multiple views. Finally, a unified multi-view 3D contour reconstruction and optimization method is proposed to jointly reconstruct hole geometry from all edge points, thereby reducing cumulative reconstruction errors and improving global geometric consistency. Experimental results demonstrate that the proposed framework achieves high accuracy, robustness, and efficiency under different chamfer angles, hole sizes, materials, and illumination conditions, showing strong potential for complex industrial hole measurement applications.

关键词： Alzheimer’s disease   Grad-CAM   MRI   Poly-Focal Loss   Vision Transformer  

摘要： Early classification of Alzheimer’s disease (AD) stages remains challenging because of subtle morphological differences, overlapping cognitive symptoms, and class imbalance across diagnostic groups. This study presents ADViTX++, a Vision Transformer (ViT)-based framework for five-class AD classification using subject-level axial MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The preprocessing pipeline includes HD-BET-based skull stripping, denoising, unsharp masking, background cropping, resizing, and normalization. Three loss functions, namely Sparse Categorical Cross-Entropy (SCE), Polynomial Cross-Entropy (Poly-CE), and Polynomial Focal Loss (Poly-Focal), were evaluated to examine class-imbalance handling at the loss-function level. In the 80:20 subject-level hold-out experiment, Poly-Focal achieved 98.31 % accuracy, 98.35 % weighted precision, 98.31 % weighted F1-score, 98.63 % average sensitivity, and 99.57 % average specificity. Additional subject-level 5-fold cross-validation with five random seeds was conducted to assess stability across data partitions. Grad-CAM++ was adapted to ViT patch-token representations and applied after inference to provide qualitative visual explanations of image regions contributing to model predictions. The results indicate that ADViTX++ provides competitive performance for this dataset, while further external validation is required before clinical use.