Search Results (20)

Proton therapy has emerged as a highly precise and tissue-sparing radiotherapy technique, capitalizing on the unique energy deposition pattern of protons characterized by the Bragg peak. Ensuring treatment accuracy relies on calibration phantoms, often composed of tissue-equivalent polymeric materials. This study investigates the dosimetric behavior of four commonly used polymers—Parylene, Epoxy, Lexan, and Mylar—by analyzing their linear energy transfer (LET) values and Bragg curve characteristics across various proton energies. Experimental LET data were collected and used to train and evaluate the predictive power for Bragg peak of multiple artificial intelligence models, including kNN, SVR, MLP, RF, LWRF, XGBoost, 1D-CNN, LSTM, and BiLSTM. These algorithms were optimized using 10-fold cross-validation and assessed through statistical error and performance metrics including MAE, RAE, RMSE, RRSE, CC, and R². Results demonstrate that certain AI models, particularly RF and LWRF, accurately (in terms of all evaluation metrics) predict Bragg peaks in Epoxy polymers, reducing the reliance on costly and time-consuming simulations. In terms of CC and R² metrics, the LWRF model demonstrated superior performance, achieving scores of 0.9969 and 0.9938, respectively. However, when evaluated against MAE, RMSE, RAE, and RRSE metrics, the RF model emerged as the top performer, yielding values of 12.3161, 15.8223, 10.3536, and 11.4389, in the same order. Additionally, the SVR model achieved the highest number of statistically significant differences when compared pairwise with the other eight models, showing significance against six of them. The findings support the use of AI as a robust tool for designing reliable calibration phantoms and optimizing proton therapy planning. This integrative approach enhances the synergy between materials science, medical physics, and data-driven modeling in advanced radiotherapy systems. Full article

Puffballs, a group of macrofungi belonging to the Basidiomycota, pose taxonomic challenges due to their convergent morphological features, including spherical basidiocarps and similar peridial structures, which often hinder accurate species-level identification. This study proposes a deep learning-based classification framework for eight ecologically and taxonomically important puffball species: Apioperdon pyriforme, Bovista plumbea, Bovistella utriformis, Lycoperdon echinatum, L. excipuliforme, L. molle, L. perlatum, and Mycenastrum corium. A balanced dataset of 1600 images (200 per species) was used, divided into 70% training, 15% validation, and 15% testing. To enhance generalizability, images were augmented to simulate natural variability in orientation, lighting, and background. In this study, five different deep learning models (ConvNeXt-Base, Swin Transformer, ViT, MaxViT, EfficientNet-B3) were comparatively evaluated on a balanced dataset of eight puffball species. Among these, the ConvNeXt-Base model achieved the highest performance, with 95.41% accuracy, and proved especially effective in distinguishing morphologically similar species such as Mycenastrum corium and Lycoperdon excipuliforme. The findings demonstrate that deep learning models can serve as powerful tools for the accurate classification of visually similar fungal species. This technological approach shows promise for developing automated mushroom identification systems that support citizen science, amateur naturalists, and conservation professionals. Full article

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a Kolmogorov–Arnold Network (KAN) layer to enhance classification performance. The experimental results demonstrate significant improvements in classification metrics when using the CNN-SOM and CNN-KAN architectures. Additionally, advanced pretrained models such as MaxViT-S and ResNetV2-50 achieved high accuracy rates, with MaxViT-S achieving 98.9% accuracy. Statistical analyses using the chi-square test confirmed the reliability of the results, emphasizing the importance of validating evaluation metrics statistically. This research represents the first application of SOM in fungal classification and highlights the potential of deep learning in advancing fungal taxonomy. Future work will focus on optimizing the KAN architecture and expanding the dataset to include more fungal classes, further enhancing classification accuracy and ecological understanding. Full article

This study investigates the absorption of X-rays in mandibular tissues by comparing real tissues with tissue-equivalent materials using the PHITS Monte Carlo simulation program. The simulation was conducted over a range of X-ray photon energies from 50 to 100 keV, with increments of 5 keV, to evaluate the dose absorbed by different tissues. Real tissues, such as the skin, parotid gland, and masseter muscle, were compared with their tissue-equivalent polymeric materials, including PMMA, Parylene N, and Teflon. The results showed that the real tissues generally absorbed more X-rays than their corresponding equivalents, especially at lower energy levels. For instance, at 50 keV, differences in the absorbed doses reached up to 50% for the masseter muscle and its equivalent, while this gap narrowed at higher energies. The study highlights the limitations of current tissue-equivalent materials in accurately simulating real tissue behavior, particularly in low-energy X-ray applications. These discrepancies suggest that utilizing tissue-equivalent materials may lead to less accurate medical imaging and radiotherapy dose calculations. Future research should focus on improving tissue-equivalent materials and validating simulation results with experimental data to ensure more reliable dosimetric outcomes. This study provides a foundation for refining radiation dose calculations and improving patient safety in clinical applications involving X-rays. Full article

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species. Full article

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test. Full article

This study details the unique advantages and challenges associated with microreactors. Microreactors offer rapid installation and flexible application capabilities, meeting energy needs in remote and inaccessible areas. Unlike large nuclear power plants, they can be set up and start generating energy within a few days, resulting in significant time and cost savings. Their small size and modular design reduce capital and operational costs while enhancing economic competitiveness. However, some technical and regulatory challenges persist for the widespread adoption of microreactors. Licensing processes designed for large nuclear power plants may delay the widespread adoption of microreactors. Blockchain technology can play a crucial role in overcoming these challenges by providing transparency and reliability in the licensing processes. The operational settings of microreactors should be carefully considered, and regulatory authorities must be effectively designated. Collaboration and coordination are vital in this process. Consequently, the flexibility and innovative solutions offered by microreactors highlight the importance of future research to examine the optimal conditions for their use. Full article

The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas. Full article

This study evaluates the effectiveness of deep learning and canonical machine learning models for detecting diseases in crayfish from an imbalanced dataset. In this study, measurements such as weight, size, and gender of healthy and diseased crayfish individuals were taken, and at least five photographs of each individual were used. Deep learning models outperformed canonical models, but combining both approaches proved the most effective. Utilizing the ResNet50 model for automatic feature extraction and subsequent training of the RF algorithm with these extracted features led to a hybrid model, RF-ResNet50, which achieved the highest performance in diseased sample detection. This result underscores the value of integrating canonical machine learning algorithms with deep learning models. Additionally, the ConvNeXt-T model, optimized with AdamW, performed better than those using SGD, although its disease detection sensitivity was 1.3% lower than the hybrid model. McNemar’s test confirmed the statistical significance of the performance differences between the hybrid and the ConvNeXt-T model with AdamW. The ResNet50 model’s performance was improved by 3.2% when combined with the RF algorithm, demonstrating the potential of hybrid approaches in enhancing disease detection accuracy. Overall, this study highlights the advantages of leveraging both deep learning and canonical machine learning techniques for early and accurate detection of diseases in crayfish populations, which is crucial for maintaining ecosystem balance and preventing population declines. Full article

Ensuring safe and transparent tracking of nuclear materials in the modern era is critical for global security and compliance with international regulations. Blockchain technology, a decentralized and immutable ledger, offers a new approach to recording transactions, increasing trust without intermediaries. In this study, it was investigated whether nuclear material tracking was performed with advanced technology blockchain from past to present; it was seen that there needed to be a study on this subject in the literature, and that there was a gap. Search results proving this are presented. The authors present a model that can enable nuclear material tracking with blockchain technology, which will create a solid structure for recording and verifying every process step in the nuclear supply chain, from the creation of the first product to destruction. This model discusses how nuclear materials, which are very important to track from the beginning until they become waste, can be tracked with blockchain technology, and the contributions they can make nationally and internationally are explained. As a result of the research, it is shown that blockchain technology has the potential to pave the way for more resilient and precise nuclear supply chains by significantly increasing the security and efficiency of nuclear material tracking. Full article

The unique physical properties of heavy ion beams, particularly their distinctive depth–dose distribution and sharp lateral dose reduction profiles, have led to their widespread adoption in tumor therapy worldwide. However, the physical properties of heavy ion beams must be investigated to deliver a sufficient dose to tumors without damaging organs at risk. These studies should be performed on phantoms made of biomaterials that closely mimic human tissue. Polymers can serve as soft tissue substitutes and are suitable materials for building radiological phantoms due to their physical, mechanical, biological, and chemical properties. Extensive research, development, and applications of polymeric biomaterials have been encouraged due to these properties. In this study, we investigated the ionization, recoils, phonon release, collision events, and lateral straggle properties of polymeric biomaterials that closely resemble soft tissue using lithium-ion beams and Monte Carlo Transport of Ions in Matter simulation. The results indicated that the Bragg peak position closest to soft tissue was achieved with a 7.3% difference in polymethylmethacrylate, with an average recoils value of 10.5%. Additionally, average values of 33% were observed in collision events and 22.6% in lateral straggle. A significant contribution of this study to the existing literature lies in the exploration of secondary interactions alongside the assessment of linear energy transfer induced by the ⁷Li beam used for treatment. Furthermore, we analyzed the tissue-equivalent properties of polymer biomaterials using heavy ion beams, taking into account phonon release resulting from ionization, recoils, lateral straggle, and all other interactions. This approach allows for the evaluation of the most suitable polymeric biomaterials for heavy ion therapy while considering the full range of interactions involved. Full article

Helium ion beam therapy, one of the particle therapies developed and studied in the 1950s for cancer treatment, resulted in clinical trials starting at Lawrence Berkeley National Laboratory in 1975. While proton and carbon ion therapies have been implemented in research institutions and hospitals globally after the end of the trials, progress in comprehending the physical, biological, and clinical findings of helium ion beam therapy has been limited, particularly due to its limited accessibility. Ongoing efforts aim to establish programs that evaluate the use of helium ion beams for clinical and research purposes, especially in the treatment of sensitive clinical cases. Additionally, helium ions have superior physical properties to proton beams, such as lower lateral scattering and larger LET. Moreover, they exhibit similar physical characteristics to carbon, oxygen, and neon ions, which are all used in heavy ion therapy. However, they demonstrate a sharper lateral penumbra with a lower radiobiological absence of certainties and lack the degradation of variations in dose distributions caused by excessive fragmenting of heavier-ion beams, especially at greater depths of penetration. In this context, the status and the prospective advancements of helium ion therapy are examined by investigating ionization, recoil, and lateral scattering values using MC TRIM algorithms in mandible plate phantoms designed from both tissue and previously studied biomaterials, providing an overview for dental cancer treatment. An average difference of 1.9% in the Bragg peak positions and 0.211 mm in lateral scattering was observed in both phantoms. Therefore, it is suggested that the ⁴He ion beam can be used in the treatment of mandibular tumors, and experimental research is recommended using the proposed biomaterial mandible plate phantom. Full article

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar’s test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar’s test results found statistically significant differences between different Machine Learning approaches. Full article

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics. Full article

In this study, transfer learning has been used to overcome multilabel classification tasks. As a case study, movie genre classification by using posters has been chosen. Six state-of-the-art pretrained models, VGG16, ResNet, DenseNet, Inception, MobileNet, and ConvNeXt, have been employed for this experiment. The movie posters have been obtained from Internet Movie Database (IMDB). The dataset has been divided using an iterative stratification technique. A sequence of dense layers has been added on top of each model and these models have been trained and fine-tuned. All the results of the models compared considered accuracy, loss, Hamming loss, F1-score, precision, and AUC metrics. When the metrics used were evaluated, the most successful result regarding accuracy has been obtained from the modified DenseNet architecture at 90%. Also, the ConvNeXt, which is the newest model among all, performed quite satisfactorily, reaching over 90% accuracy. This study uses an iterative stratification method to split an unbalanced dataset which provides more reliable results than the classical splitting method which is the common method in the literature. Also, the feature extraction capabilities of the six pretrained models have been compared. The outcome of this study shows promising results regarding multilabel classification. As for future work, it is planned to enhance this study by using natural language processing and ensemble methods. Full article