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The "PhysiSens" dataset contains 5,185 annotated images of physics instrument sourced from the Physics Lab Support Room, the United International University (UIU). This dataset was specifically designed to facilitate the development and validation of machine learning models for the real-time detection of physics instrument. To mimic real-world scenarios and enhance the robustness of models trained on this data, images were captured under varied lighting conditions and against diverse backgrounds. Each electronic component was photographed from multiple angles, and following collection, images were standardized through auto-orientation and resized to 640x640 pixels, introducing augmentation increases brightness (0 to 20%) . The dataset is organized into 20 distinct classes of commonly used physics instrument. The dataset were split into training (70%), validation (20%), and test (10%) sets.

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This data contains images about Illustrates the conceptual framework model hypothesis, Results of the path analysis, Word Cloud Analysis, Cluster Analysis

This dataset was created to support a research idea that transportation-related objects in satellite imagery can be detected with steady accuracy when they are labeled with clear and consistent annotations. It focuses on four object classes: airplane, boat, car, and ship, and includes scenes from airports, highways, harbors, marinas, and coastal regions. These locations were selected to cover different backgrounds, object scales, and environmental conditions, which helps in studying model performance in realistic satellite settings. Images were collected using Google Earth Pro under fair use and academic research guidelines. Candidate regions were explored manually and exported in high resolution through the built-in tools of the software. After collection, each image was reviewed for clarity, relevance, and presence of target objects. Files with heavy noise, strong cloud cover, or duplicate viewpoints were removed. When needed, basic preparation such as cropping and resizing was applied to keep the focus on relevant areas while maintaining visual quality. Annotation was carried out using CVAT and Roboflow. Each object instance was marked with a bounding box and assigned one of the four class labels. A separate verification pass was performed to maintain consistent box placement and correct class assignment across the dataset. For this release, all final annotations are stored in a single COCO format JSON file that follows the standard object detection structure. The images are organized into separate folders for training, validation, and testing. A Roboflow project link is provided so that users can view the dataset online, apply their own preprocessing pipeline, and export the same annotations into formats such as YOLO or Pascal VOC if required. The dataset can be interpreted by loading the COCO JSON file, reading the category identifiers, and mapping them to airplane, boat, car, and ship. Each annotation entry provides bounding box coordinates and class information that can be used directly with common computer vision libraries. Researchers can use this dataset to test model generalization across land and sea scenes, evaluate multi-class detection performance, study small object behavior, compare detection architectures, and explore transfer learning strategies in aerial imagery.

Its a review work

This dataset contains the completed CARE checklist (Case Report guidelines) for the manuscript entitled "Clinical characteristics and pathological analyses of intra-abdominal aggressive fibromatosis: A case report and literature review". The checklist details how our case report adheres to each item of the CARE guidelines, with corresponding citations and locations within the manuscript.

Botulinum toxin (BoNT), derived from Clostridium botulinum, is widely recognized for its cosmetic applications and therapeutic efficacy in various neurological and dermatological disorders. In recent years, its potential role in treating hair related disorders has garnered increasing attention, including androgenetic alopecia (AGA) alopecia areata (AA), telogen effluvium (TE), and trichodynia-associated alopecia. Our own prior research corroborates these findings, indicating that for AGA patients with vertex hair loss, both intradermal injection within the alopecic area and intramuscular injection around the head effectively increase hair diameter, with the latter showing greater efficacy in improving hair density. For AGA patients with male pattern (male pattern hair loss, MPHL) and patient with congenital high hairline (CHHL), hair transplantation constitutes a commonly employed treatment modality. While significant technical advancements have been made in transplantation techniques, persistent challenges include graft survival and preservation. Ischemia during the immediate post-implantation period is a primary contributor to transplantation failure. BoTN can improve the scalp microenvironment by enhancing local oxygenation and blood flow through vasodilation and reduced vascular pressure. Given the proposed mechanism of BoNT, we hypothesize that perioperative BoNT administration may offer a novel approach to enhance hair transplantation outcomes by mitigating ischemia-induced graft failure. To rigorously evaluate this hypothesis, we designed and registered the present randomized split-scalp controlled trial investigating the impact of preoperative BoNT on hair transplantation outcomes.

Petrographic descriptions, representative petrographic photographs, and analytical methods are provided in Supplementary Text S1, whereas the sample locations, U–Pb dating results, Hf isotopic data and whole-rock geochemical data are presented in Supplementary Tables S1-S3.

It includes the basic information of 143 children with hypospadias and their CONNERS scale scores.

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