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The study of dislocation behavior by simulation methods is crucial due to its multiple roles in the prediction of the microstructural evolution of materials. Among various computational tools utilized for dislocation simulations, Molecular Dynamics (MD) method is widely used because of its satisfactory simulation results at the atomic scale. Many approaches have been introduced to create an edge dislocation in MD without standardizing basic procedures. This lack of knowledge motivated the present investigation to examine the potential functions and minimization algorithms’ impact on edge dislocation creation methods in FCC pure Aluminum by MD. It is elucidated that three methods: (1) removing two half-planes, (2) superimposing two crystals, and (3) displacement field, instead of removing one plane led to a more appropriate configuration of dislocations after static equilibrium. The results showed that initial atoms configurations shape the interatomic forces and play a significant role in the successful construction of edge dislocation. Graphical abstract

In this paper, we introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Although the disease is considered to be genetic, the highest rates of accurate diagnosis occur when the child is tested on behavioral characteristics and facial features. Patients have a common pattern of distinct facial deformities, allowing researchers to analyze only an image of the child to determine if the child has the disease. While there are other techniques and models used for facial analysis and autism classification on their own, our proposal bridges these two ideas allowing classification in a cheaper, more efficient method. Our deep learning model uses MobileNet and two dense layers to perform feature extraction and image classification. The model is trained and tested using 3,014 images, evenly split between children with autism and children without it; 90% of the data is used for training and 10% is used for testing. Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture. Additionally, there may be other diseases that are similarly diagnosable. Keywords: Deep Learning, Autism, Children, Diagnosis, Facial Image Analysis

The grain growth behavior of 304L stainless steel was studied in a wide range of annealing temperatures and times with emphasis on the distinction between normal and abnormal grain growth (AGG) modes. The dependence of AGG (secondary recrystallization) at homologous temperatures of around 0.7 upon microstructural features such as dispersed carbides, which were rich in Ti but were almost free of V, was investigated by optical micrographs, X-ray diffraction patterns, scanning electron microscopy images, and energy dispersive X-ray analysis spectra. The bimodality in grain-size distribution histograms signified that a transition in grain growth mode from normal to abnormal was occurred at homologous temperatures of around 0.7 due to the dissolution/coarsening of carbides. Continued annealing to a long time led to completion of secondary recrystallization and the subsequent reappearance of normal growth mode. Another noticeable abnormality in grain growth was observed at very high annealing temperatures, which may be related to grain boundary faceting/defaceting. Finally, a versatile grain growth map was proposed, which can be used as a practical guide for estimation of the resulting grain size after exposure to high temperatures.

To evaluate the frequency and prognosis of runt‐related transcription factor 1 (RUNX1) and additional sex combs like‐1 (ASXL1) mutations in acute myeloid leukaemia (AML) patients in northeastern Iran. This cross‐sectional study was performed on 40 patients with AML (including 35 patients with denovo AML and five patients with secondary AML) from February 2018 to February 2021. All patients were followed up for 36 months. We evaluated the frequency and survival rate of RUNX1 and ASXL1 mutations in AML patients. To detect mutations, peripheral blood samples and bone marrow aspiration were taken from all participants. One male patient (2.5%) had RUNX1 mutations and four cases (10%; 3 females vs. 1 male) had ASXL1 mutations. The survival rates of AML patients after 1, 3, 6, 9, 12, 24 and 36 months were 98%, 90%, 77%, 62%, 52%, 27% and 20%, respectively. There was a significant relationship between the occurrence of ASXL1 mutations and the survival of patients with AML (p = 0.027). Also, there was a significant relationship between the incidence of death and haemoglobin levels in patients with AML (p = 0.045). Thus, with an increase of one unit in patients' haemoglobin levels, the risk of death is reduced by 16.6%. Patients with AML had a high mortality rate, poor therapy outcome and low survival rate. ASXL1 and RUNX1 mutations are associated with a worse prognosis in patients with newly diagnosed AML. Also, we witnessed that the prevalence of ASXL1 to RUNX1 mutations was higher in northeastern Iran compared with other regions.

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines.

Purpose This paper aims to study the influence of the presence of steel and polyolefin (PO) fibers on the mechanical and durability properties of fiber and hybrid fiber-reinforced concrete (FRC and HFRC). Design/methodology/approach Hooked-end steel fibers having a length of 35 mm were applied at four different fiber content 1.0%, 1.5%, 2.0% and 2.5%, respectively. PO fibers having the length of 45 mm were also replaced with steel fibers at three different fiber content, 0.6%, 0.8% and 1.0%, to provide HFRC. The compressive, indirect tensile and flexural strengths; electrical resistivity; and water absorption were evaluated in this study. Findings The results showed that the addition of both steel and PO fibers led to improvements in the mechanical properties of FRC and HFRC. However, the replacement of steel fibers with PO fibers led to a slight loss in mechanical properties. Also, it was concluded that the addition of various types of fibers to concrete decreased both the electrical resistivity and water absorption compared with the control sample. Finally, distance-based approach analysis was used to select the most optimal mix designs. Originality/value According to this method, the HFRC specimen including 1.2% of steel and 0.8% of PO fibers was the most optimal mix design among all fiber-reinforced mix designs.

People’s eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose people to risky eating behaviors. Given the complexity of nutrition-related factors and the limitations of traditional statistical methods, the use of new approaches such as artificial intelligence and machine learning can play an effective role in analyzing multidimensional data and identifying complex patterns. This cross-sectional pilot study aimed to predict food addiction among university students by integrating demographic, anthropometric and personality data with machine learning methods. The data consisted of 210 samples, which were first preprocessed to ensure data quality and integrity. Tomek Links and SMOTE techniques were used to remove class imbalance. Feature selection was performed using the twelve different algorithms to identify the most important features related to food addiction prediction. Then, ten different machine learning models were implemented, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Classifier (SVC) with probability estimation, Decision Tree (DT), Random Forest (RF), AdaBoost, Gradient Boosting Classifier (GBC), CatBoost and LightGBM. The models were trained on the training dataset and their performance was evaluated using the accuracy, precision, recall, F1-Score and AUC metrics on the test dataset. In addition, the SHAP (SHapley Additive exPlanations) method was used to analyze the importance of features and interpret the advanced models to determine the impact of each psychological and behavioral feature on the prediction of food addiction. The results showed that more advanced models, especially ensemble methods such as Random Forest and CatBoost, have high power in identifying complex patterns and accurately predicting food addiction behaviors. SHAP analysis also showed that psychological characteristics such as feelings of worthlessness, impulsivity, anger, psychological distress, rigid cognitive styles, weight and height, body mass index (BMI) were related the most important factors affecting prediction. Although limitations such as small sample size, focusing on a specific student population, and the use of self-report instruments reduce the generalizability of the results, the innovation of this study in combining psychological and artificial intelligence approaches for early identification of high-risk individuals is remarkable. Overall, the integration of personality profiles with advanced computational models can form the basis for the development of artificial intelligence-based screening tools and targeted interventions to improve nutritional behaviors in young populations.

This paper aims to present and investigate a derivative-free design optimization methodology for continuum structures to maximize the eigenfrequencies with different constraints. The recently developed moving morphable component method is used as the parameterization technique which is so suitable for non-gradient optimization algorithms due to the lower numbers of design variables. Two objective functions are considered in this paper. In the first point of view, the aim is to maximize multiple eigenfrequencies. For the second strategy, the maximization of multiple frequency gaps is considered. Multiple constraints are considered in the problem definition. Instead of virtual mass, static stress and compliance are defined. Previous papers presented optimal designs only for eigenfrequency maximization and used a virtual mass to prevent discontinuity in the design domain. Herein, the topological design is performed for both static (stress and compliance) and eigenfrequency targets. Based on the results, the cooperative coevolutionary strategy coupled with MMC has a very good potential in solving eigenfrequency-related problems while needing no sensitivity analysis. Since this algorithm is so easy to implement and very successful in benchmark problems, industries can use it even for more complex cases. Also, it is observed that the algorithm can produce diverse and competitive outputs due to non-deterministic behavior. So it can be categorized as generative design tools.

Objective Antiplatelet drugs, such as Aspirin and Clopidogrel (Plavix) are effective in the primary prevention of thromboembolic events. They are commonly used to reduce the risk of recurrence of thromboembolism. The body’s hemostatic system responds differently to these drugs in different people. Resistance testing for aspirin and Clopidogrel is now recommended before starting antiplatelet therapy. Methods A systematic literature search was performed on May 12, 2021, using the medical search engines PubMed, Scopus, and Web of Science, and the local databases SID and Magiran. After data extraction, a meta-analysis was performed using Comprehensive Meta-Analysis (CMA2) software. The I2 statistic was used to measure heterogeneity between estimates. Results Among the 949 papers, Clopidogrel resistance was assessed in 136 patients and Aspirin resistance in 400 patients. The prevalence of Aspirin resistance was found to be 52.1% and the prevalence of Clopidogrel resistance was found to be 20.5%. Conclusion It seems that in Iran, the issue of Aspirin and Clopidogrel resistance is suboptimally addressed. This pattern could also occur in other developing countries in the Middle East region.

Breast cancer requires early detection for effective treatment. Although ultrasound offers high sensitivity and is less invasive, it still faces challenges such as speckle noise and complex tissue textures. This study aims to develop hybrid deep learning models to improve automatic breast tumor segmentation and classification, and to assess whether combining segmentation with classification models enhances performance compared to using these models individually. This study employed the publicly available dataset, containing 780 images from 600 patients, classified into normal, benign, and malignant lesions. In the initial step, image preprocessing involved resizing images, normalizing pixel intensities, and performing data augmentation. Pre-trained deep learning models-VGG-16, DenseNet-121, DenseNet-169, and ResNet-50-are fine-tuned for classification tasks. Models are evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). The U-Net model is used for generating segmentation masks to highlight regions of interest, with performance assessed through IoU and Dice coefficient. The study compares classification performance using original versus segmented images. On original breast ultrasound images, DenseNet-169 achieved 87% accuracy with an AUC of 0.99, while DenseNet-121 reached an AUC of 0.85. With segmented images, both DenseNet-121 and DenseNet-169 achieved high AUC values ([almost equal to]0.99-1.00), with DenseNet-121 also reaching 98% accuracy. VGG-16 obtained an AUC of 0.99 and 97% accuracy, and ResNet-50 achieved an AUC of 0.93 with 84% accuracy. Applying U-Net segmentation before classification improved model performance on the BUSI dataset, particularly for DenseNet-121 and DenseNet-169, by helping the classifiers focus on lesion-specific regions. However, as this work represents an initial technical evaluation using a single-center dataset, the findings should be considered preliminary. External validation on multicenter cohorts and prospective studies will be essential before any consideration of real-world deployment.