U radu se donose rezultati istraživanja nezavisne kulturne scene u Zadru, pri čemu su
u fokusu rada prostorni aspekti djelovanja u polju nezavisne kulture. Kontinuirana nastojanja
aktera scene da ...ostvare pravo na prostor, a time i pravo na grad, u ovom se radu sagledavaju
iz teorijske perspektive kritičke urbane teorije. Sukladno njezinom kulturno-umjetničkom i
društvenom djelovanju, a koje nerijetko odlikuju aktivističke i progresivne prakse, nezavisnu
kulturnu scenu u ovom se radu sagledava kao specifičan tip civilnih urbanih aktera. Svrha istraživanja
bila je steći uvid u različite aspekte borbe za ostvarivanje prava na prostor djelovanja,
i to iz perspektive predstavnika scene. Istraživanje je provedeno tehnikom polustrukturiranog
intervjua te je obuhvatilo dvadeset i jednog sugovornika. Na temelju kodiranja podataka, koje
se odvijalo na deskriptivnoj, tematskoj i analitičkoj razini, rezultati istraživanja organizirani su
u tri tematske cjeline: prostor kao upotrebni resurs, prostor kao tržišni resurs te uloga javnih
politika. Rezultati istraživanja pokazuju da se kroz prostornu problematiku nezavisne kulture
ocrtavaju širi mehanizmi odnosa prema prostornim resursima i prostoru kao javnom dobru,
što dodatno potvrđuje njezinu društvenu relevantnost.
Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and ...are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.
We describe a new scheme for a consistent linkage between the transport and equilibrium solvers in the integrated transport code TOPICS for a transport simulation in tokamaks during a plasma volume ...ramp-up phase. Although the flux conserving tokamak (FCT) equilibrium solver implemented in TOPICS is well consistent with the transport solver, it cannot evaluate a toroidal magnetic flux associated with an evolving equilibrium by itself. The evolution of the equilibrium is then modeled with a combination of the determination of the toroidal magnetic flux by the iterative solution of the Grad–Shafranov equation and the FCT scheme. The new scheme developed is applied to a predictive simulation with the volume expansion in a tokamak comparable to JT-60U for both the limiter and the divertor cases.
This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has ...shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.
The cultural heritage buildings (CHB), which are part of mankind’s history and identity, are in constant danger of damage, or in extreme cases, complete destruction. Thus, it’s of utmost importance ...to preserve them by identifying the existent, or presumptive, defects using novel methods so that renovation processes can be done in a timely manner and with higher accuracy. The main goal of this research is to use new Deep Learning (DL) methods in the process of preserving CHBs (situated in Iran); a goal that has been neglected especially in developing countries such as Iran, as these countries still preserve their CHBs using manual, and even archaic, methods that need direct human supervision. Having proven their effectiveness and performance when it comes to processing images, the Convolutional Neural Networks (CNNs) are a staple in computer vision (CV) literacy and this article is not exempt. When lacking enough CHB images, training a CNN from scratch would be very difficult and prone to overfitting; that’s why we opted to use a technique called transfer learning (TL) in which we used pre-trained ResNet, MobileNet, and Inception networks, for classification. Even more, the Grad-CAM was utilized to localize the defects to some extent. The final results were very favorable, compared to similar articles. We reached 94% in Precision, Recall, and F1-Score with our fine-tuned MobileNetV2 model, which showed a 4%–5% improvement over other similar works. The final proposed model can pave the way for moving from manual to unmanned CHB conservation, hence an increase in accuracy and a decrease in human-induced errors.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate ...diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer's disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer's diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model's exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer's disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.
Using the finite element method and the heat conduction equation, the temperature, stress, and end-face deformation in Tm:YAP crystal under high pump power were analyzed. Combined with gradient ...doping technology, an effective way to improve the internal heat distribution of the crystal was studied. The results showed that when the total pump power was 200 W, under the same cooling conditions, the maximum temperature difference inside Tm:YAP decreased from 58 K to 25 K after gradient doping. The thermal stress and end-face thermal deformation were also significantly improved. In addition, a reasonable micro-channel structure also effectively removed the heat generated inside the crystal.
The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase ...Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97% in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.
The authors suggest a novel detection method for ceiling damage using image differencing and Grad-CAM. The proposed method requires the latest image and the past image of the facility to compare. It ...adjusts the difference due to camera position, sunlight condition, etc. The authors show validity of this method showing four pairs of input images. The proposed method can appropriately detect damaged areas with better accuracy for facility inspection by narrowing the areas of inspection using image differencing and Grad-CAM procedures.
The authors suggest a novel detection method for ceiling damage using image differencing and Grad-CAM. The proposed method requires the latest image and the past image of the facility to compare. It ...adjusts the difference due to camera position, sunlight condition, etc. The authors show validity of this method showing four pairs of input images. The proposed method can appropriately detect damaged areas with better accuracy for facility inspection by narrowing the areas of inspection using image differencing and Grad-CAM procedures.
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