'Hero' who wrestled gun from Bondi shooter named as Ahmed al Ahmed A hero bystander who was filmed wrestling a gun from one of the Bondi Beach attackers has been named as 43-year-old Ahmed al Ahmed. Video verified by the BBC showed Mr Ahmed run at the gunman and seize his weapon, before turning the gun round on him, forcing his retreat. Mr Ahmed, a fruit shop owner and father of two, remains in hospital, where he has undergone surgery for bullet wounds to his arm and hand, his family told 7News Australia. Eleven people were killed in the shooting on Sunday night, as more than 1,000 people attended an event to celebrate Hanukkah. The attack has since been declared by police as a terrorist incident targeting the Jewish community.

Cancer is one of the leading health challenges for women, specifically breast and ovarian cancer. Early detection can help improve the survival rate through timely intervention and treatment. Traditional methods of detecting cancer involve manually examining mammograms, CT scans, ultrasounds, and other imaging types. However, this makes the process labor-intensive and requires the expertise of trained pathologists. Hence, making it both time-consuming and resource-intensive. In this paper, we introduce a novel vision transformer (ViT)-based method for detecting and classifying breast and ovarian cancer. We use a pre-trained ViT-Base-Patch16-224 model, which is fine-tuned for both binary and multi-class classification tasks using publicly available histopathological image datasets. Further, we use a preprocessing pipeline that converts raw histophological images into standardized PyTorch tensors, which are compatible with the ViT architecture and also help improve the model performance. We evaluated the performance of our model on two benchmark datasets: the BreakHis dataset for binary classification and the UBC-OCEAN dataset for five-class classification without any data augmentation. Our model surpasses existing CNN, ViT, and topological data analysis-based approaches in binary classification. For multi-class classification, it is evaluated against recent topological methods and demonstrates superior performance. Our study highlights the effectiveness of Vision Transformer-based transfer learning combined with efficient preprocessing in oncological diagnostics.

Chest X-ray (CXR) imaging remains one of the most widely used diagnostic tools for detecting pulmonary diseases such as tuberculosis (TB) and pneumonia. Recent advances in deep learning, particularly Vision Transformers (ViTs), have shown strong potential for automated medical image analysis. However, most ViT architectures are pretrained on natural images and require three-channel inputs, while CXR scans are inherently grayscale. To address this gap, we propose RepViT-CXR, a channel replication strategy that adapts single-channel CXR images into a ViT-compatible format without introducing additional information loss. We evaluate RepViT-CXR on three benchmark datasets. On the TB-CXR dataset,our method achieved an accuracy of 99.9% and an AUC of 99.9%, surpassing prior state-of-the-art methods such as Topo-CXR (99.3% accuracy, 99.8% AUC). For the Pediatric Pneumonia dataset, RepViT-CXR obtained 99.0% accuracy, with 99.2% recall, 99.3% precision, and an AUC of 99.0%, outperforming strong baselines including DCNN and VGG16. On the Shenzhen TB dataset, our approach achieved 91.1% accuracy and an AUC of 91.2%, marking a performance improvement over previously reported CNN-based methods. These results demonstrate that a simple yet effective channel replication strategy allows ViTs to fully leverage their representational power on grayscale medical imaging tasks. RepViT-CXR establishes a new state of the art for TB and pneumonia detection from chest X-rays, showing strong potential for deployment in real-world clinical screening systems.

Accurate and scalable cancer diagnosis remains a critical challenge in modern pathology, particularly for malignancies such as breast, prostate, bone, and cervical, which exhibit complex histological variability. In this study, we propose a transformer-based deep learning framework for multi-class tumor classification in histopathological images. Leveraging a fine-tuned Vision Transformer (ViT) architecture, our method addresses key limitations of conventional convolutional neural networks, offering improved performance, reduced preprocessing requirements, and enhanced scalability across tissue types. To adapt the model for histopathological cancer images, we implement a streamlined preprocessing pipeline that converts tiled whole-slide images into PyTorch tensors and standardizes them through data normalization. This ensures compatibility with the ViT architecture and enhances both convergence stability and overall classification performance. We evaluate our model on four benchmark datasets: ICIAR2018 (breast), SICAPv2 (prostate), UT-Osteosarcoma (bone), and SipakMed (cervical) dataset -- demonstrating consistent outperformance over existing deep learning methods. Our approach achieves classification accuracies of 99.32%, 96.92%, 95.28%, and 96.94% for breast, prostate, bone, and cervical cancers respectively, with area under the ROC curve (AUC) scores exceeding 99% across all datasets. These results confirm the robustness, generalizability, and clinical potential of transformer-based architectures in digital pathology. Our work represents a significant advancement toward reliable, automated, and interpretable cancer diagnosis systems that can alleviate diagnostic burdens and improve healthcare outcomes.

Accurate classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images is essential for early diagnosis and effective treatment planning. This study investigates the use of transfer learning with pretrained convolutional neural networks (CNNs) to improve diagnostic performance. To address the class imbalance in the dataset of 3,631 Hematologic and 7,644 ALL images, we applied extensive data augmentation techniques to create a balanced training set of 10,000 images per class. We evaluated several models, including ResNet50, ResNet101, and EfficientNet variants B0, B1, and B3. EfficientNet-B3 achieved the best results, with an F1-score of 94.30%, accuracy of 92.02%, andAUCof94.79%,outperformingpreviouslyreported methods in the C-NMCChallenge. Thesefindings demonstrate the effectiveness of combining data augmentation with advanced transfer learning models, particularly EfficientNet-B3, in developing accurate and robust diagnostic tools for hematologic malignancy detection.

User Interface (UI) and User Experience (UX) design are foundational components of the software development lifecycle, playing a very important role in shaping how users perceive, interact with, and derive value from digital products. UI design encompasses the visual and interactive elements of a system, including layout, typography, and on-screen components. In contrast, UX design encompasses the broader user journey, including the emotions, perceptions, and behaviors that emerge before, during, and after interaction with a product [34]. The quality of UI/UX design is a decisive factor in product success and user retention. Research consistently shows that poor UI/UX can drive users to abandon products altogether [9, 63].

An attorney with the American Civil Liberties Union (ACLU) who's overseeing a high-profile deportation case in Louisiana says she was stripped of her electronics moments before a pivotal hearing, preventing her from accessing evidence and court records that remained available to the three US government attorneys in the room, each of whom were allowed use of a laptop by the court. Louisiana immigration judge Jamee Comans ruled late last month that Columbia graduate student Mahmoud Khalil was eligible for deportation. During that hearing, however, Khalil's attorney Nora Ahmed says she was barred from bringing her laptop into the courtroom, despite having filed the proper paperwork in advance and being a frequent visitor to the immigration facility. "There should not be an advantage, no matter how small or how large, provided to a particular party over the other," says Ahmed. "Because that starts to infect the proceedings themselves and the notion of fundamental fairness that we all uphold in courtroom proceedings." The Justice Department did not respond to a request for comment.

One significant challenge in research is to collect a large amount of data and learn the underlying relationship between the input and the output variables. This paper outlines the process of collecting and validating a dataset designed to determine the angle of arrival (AoA) using Bluetooth low energy (BLE) technology. The data, collected in a laboratory setting, is intended to approximate real-world industrial scenarios. This paper discusses the data collection process, the structure of the dataset, and the methodology adopted for automating sample labeling for supervised learning. The collected samples and the process of generating ground truth (GT) labels were validated using the Texas Instruments (TI) phase difference of arrival (PDoA) implementation on the data, yielding a mean absolute error (MAE) at one of the heights without obstacles of 25.71 Indoor Positioning (IP), has the potential to enable several different technologies that could massively improve patient management in care homes, asset management and general automation in warehouses, automated inspection and maintenance, etc [1], [2]. IP assists in determining the device location by measuring location-dependent phenomena. These measurements could include the received signal strength (RSS) of wireless signals at the device's location, the angle of arrival (AoA) of wireless signals between the device and a base station, or the time of arrival (ToA) or time difference of arrival (TDoA) of wireless signals [3]. A wide range of wireless technologies are employable for RSS, AoA, ToA, and TDoA measurements. These include, but are not limited to, Bluetooth low energy (BLE) [4], [5], ultra-wideband radio (UWB) [6]-[8], wireless fidelity (WiFi) [9], [10] and millimeter (mm) wave radio [11]-[13]. UWB radio provides extremely accurate ToA and TDoA measurements due to the narrow width of its signals in the time domain, and more recently work has started on applying UWB to the task of AoA determination [14]. However, when considering a large-scale IP system, it is essential to consider the deployment cost of the technology.

In medieval Europe, barber-surgeons might cut your hair, shave your face, do a bit of blood-letting and tend to a broken limb. They might also pull a tooth out with a "pelican" – a crude beak-like shank – or lever it out with an iron "tooth key". By the 17th century they might just knock it out with a steel punch elevator. It's a winding, gruesome road from these early practitioners of dentistry to today's world of 3D printing, artificial intelligence and robots that can create dental implants. Wayne Sampson, a dental historian and emeritus professor at the University of Adelaide, says the history of dental work goes back much further than the barber-surgeons.

Agent-based simulation with a synthetic population can help us compare different treatment conditions while keeping everything else constant within the same population (i.e., as digital twins). Such population-scale simulations require large computational power (i.e., CPU resources) to get accurate estimates for treatment effects. We can use meta models of the simulation results to circumvent the need to simulate every treatment condition. Selecting the best estimating model at a given sample size (number of simulation runs) is a crucial problem. Depending on the sample size, the ability of the method to estimate accurately can change significantly. In this paper, we discuss different methods to explore what model works best at a specific sample size. In addition to the empirical results, we provide a mathematical analysis of the MSE equation and how its components decide which model to select and why a specific method behaves that way in a range of sample sizes. The analysis showed why the direction estimation method is better than model-based methods in larger sample sizes and how the between-group variation and the within-group variation affect the MSE equation.