Hamidreza Mortazavy Beni

Background: Accurate differentiation between benign and malignant breast tumors is critical for early diagnosis and treatment planning. Traditional approaches often rely on whole-image processing; however, the tumor contour contains rich morphological cues that can independently support malignancy assessment. Leveraging these contour-based features using artificial intelligence (AI) can enhance diagnostic specificity and interpretability. Objective: This study aims to evaluate the diagnostic potential of tumor outline features extracted from mammographic images using deep learning models, with a focus on interpreting their variations numerically and biologically. It also investigates whether combining deep features (ensemble approach) can improve classification accuracy. Methods: A public dataset of 100 mammography tumor contours was analyzed. Eight deep learning models (ResNet50, Xception65, VGG16, AlexNet, DenseNet, GoogLeNet, Inception-v3, and a feature-level ensemble) were used for feature extraction. These features were then classified using 5 machine learning algorithms: SVM, KNN, DT, Naive Bayes, and a shallow neural network. Performance metrics included accuracy, sensitivity, specificity, and precision. Results: Xception65 with Naive Bayes achieved 97.97% accuracy, while the feature ensemble with an ensemble classifier achieved 96.96% accuracy, 95.45% sensitivity, and 98.48% specificity. Naive Bayes consistently outperformed other classifiers in integrating deep contour features. Conclusion and Clinical Interpretation: Tumor contour-based analysis provides biologically meaningful indicators of malignancy—such as irregularity, spiculation, and shape complexity—without relying on full pixel intensity. The results demonstrate that outline-driven AI analysis can enhance breast cancer screening by offering a low-complexity, high-performance diagnostic tool. Future integration into clinical workflows may aid radiologists in real-time and reduce false positives in mammographic diagnosis.

This study numerically evaluates heat‐transfer enhancement in compact fin‐and‐tube heat exchangers by combining fin‐pitch optimization with surface‐mounted vortex generators (VGs). A preliminary scan of two fin pitches (15 and 25 mm) identifies 15 mm as the superior baseline, delivering ~7.4% higher convective coefficient. Building on this geometry, two VG shapes—triangular and semi‐cylindrical—are assessed for thermal‐hydraulic performance using validated 3‐D CFD. The semi‐cylindrical VG consistently outperforms the triangular one, increasing heat transfer by > 17% under comparable conditions owing to stronger, more persistent longitudinal vortices near the heat‐transfer surfaces. The model reproduces the published benchmarks with deviations < 5.5%. Additional parametric tests across Reynolds number and inlet‐air temperature confirm the robustness of these trends. Overall, coupling an optimized fin pitch with a semi‐cylindrical VG yields a compact exchanger with markedly improved heat‐transfer performance, offering practical guidance for high‐efficiency HVAC and automotive applications.

The human aortic arch is a system that is being organized by curvature–inertia coupling into cross-sectional flow that is organized into Dean-type vortices whose strength can be quantified using the Dean number. The current research re-formulated patient-specific arches based on contrast-enhanced computed tomography angiography and established four centerline-orthogonal planes of analysis between the distal ascending and proximal descending segments. Planes A and B are in the distal ascending part; Plane C is close to the apex of the arch, and Plane D is in the proximal descending aorta above the isthmus. Magnetic resonance imaging was used to get inflow waveforms/branch splits and through-plane velocities that were compared. Near-wall resolved, physiology-based computational fluid dynamics (Newtonian blood; unsteady Reynolds averaged Navier–Stokes shear stress transport; y+<1; Time scale: Δt≈T/100–T/200 with Courant number ≲1) was solved under primarily elastic walls with a limited two-way fluid–structure interaction (FSI) sensitivity check. We plotted a secondary-flow intensity (SFI) index vs Dean number (De) with an incipient and sustained threshold of 0.2 and 0.5. All the planes were lying on the shoulder-to-plateau of SFI(De) at peak systole; Plane C was resting on the plateau (SFI ≈ 1), which implies well-organized Dean cells, whereas the A–B planes exhibited strong and slightly weaker vortices. The early diastole rotated every plane to the left side of SFI(De), stratifying secondary flow (A in the transitional knee; C–D on the shoulder) and turning the axial pressure gradient around, with recirculation near branches. Shear measurements were in published envelopes, which facilitated numerical strength. The De lens clinically connects anatomy and hemodynamics to points susceptible to oscillatory shear, allowing plans to be made with curvature awareness, risks to be assessed in branch-specific ways, and longitudinal follow-up to be performed by monitoring the location of a patient on SFI(De).

Background and objective: Atmospheric aerosols from different industrial and natural sources enter the airways during inhalation. The smaller respirable aerosols enter the alveolar sacs and, depending on the residence time and toxicity, create severe respiratory health hazards. The physiological movement of the alveolar sacs is an important feature of breathing dynamics. Therefore, the knowledge of the dynamic behavior of the alveolar airways during airflow and aerosol transport is essential for the accurate health risk assessment of respiratory aerosols. Methods: This study analyzed the physiological movements of the alveolar sac and its impact on airflow and particle deposition in the acinar region. In the present study, the dynamic acinar model uses a Computational Fluid-Particle Dynamics (CFPD). The boundary condition of moving walls is presented by introducing a novel strategic motion function of the alveoli (Eq. 5) compatible with the physiological function of the lung. Results: The results of the present study indicated that particle density is a determining factor in increasing the percentage of particle pollution deposition lower than 3 µm. The study also reports that the air amplitude velocity (~0.01 vs. 0.00085 m/s) is a crucial index in the particle pollution deposition in alveoli. Conclusions: To date, several studies analyzed the airflow in acinar sections. However, a comprehensive analysis of the physiological behavior of the alveolar sacs is missing in the literature. The specific findings of this study would improve the knowledge of airborne particle transmission in the alveolar zone.

Magnetic Resonance Imaging (MRI) is a non-invasive imaging method that utilizes radio waves and magnetic fields. This study focuses on reducing the acoustic noise produced inside the cylindrical shell of the scanner, where the patient is located. Vibration modes are generated by eddy currents in the cylindrical shell induced by gradient magnetic fields. Additionally, the scanner wall is typically joined to the gradient spiral cylinder, causing vibrations to be transmitted to the wall and thereby producing extra sound waves. The present study investigates methods for mitigating noise from the scanner wall and reducing the transmission noise from the spiral gradient cylinder. Numerical methods and practical solutions for lowering acoustic noise in MRI gradient coils are explored. A 20 mm uniform absorber is demonstrated as an effective design for significantly reducing acoustic noise in the frequency range 0 to 3 kHz. Finally, numerical analysis of gradient cycles yields solutions that lower both vibration and noise levels.

Although SARS-COV-2 started in 2019, its losses are still significant, and it takes victims. In the present study, the epidemic patterns of SARS-COV-2 disease have been investigated from the point of view of mathematical modeling. Also, the effect of quarantine has been considered. This mathematical model is designed in the form of fractional calculations along with a model predictive control (MPC) to monitor this model. The fractional-order model has the memory and hereditary properties of the system, which can provide more adjustable parameters to the designer. Because the MPC can predict future outputs, it can overcome the conditions and events that occur in the future. The results of the simulations show that the proposed nonlinear model predictive controller (NMPC) of fractional-order has a lower mean squared error in susceptible people compared to the optimal control of fractional-order (~3.6e-04 vs. 47.4). This proposed NMPC of fractional-order can be used for other models of epidemics.