Industrial plastics have seen considerable progress recently, particularly in manufacturing non-lethal projectile holders for shock absorption. In this work, a variety of percentages of alumina (Al2O3) and carbon black (CB) were incorporated into high-density polyethylene (HDPE) to investigate the additive material effect on the consistency of HDPE projectile holders. The final product with the desired properties was controlled via physical, thermal, and mechanical analysis. Our research focuses on nanocomposites with a semicrystalline HDPE matrix strengthened among various nanocomposites. In the presence of compatibility, mixtures of variable compositions from 0 to 3% by weight were prepared. The reinforcement used was verified by X-ray diffraction (XRD) characterization, and thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used for thermal property investigation. Alumina particles increased the composites’ thermal system and glass transition temperature. Mechanical experiments indicate that incorporating alumina into the matrix diminishes impact resistance while augmenting static rupture stress. Scanning electron microscopy (SEM) revealed a consistent load distribution. Ultimately, we will conduct a statistical analysis to compare the experimental outcomes and translate them into mathematical answers that elucidate the impact of filler materials on the HDPE matrix.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

In the present research work, we investigated the use of the image intensifier in the extraction of radiopaque foreign bodies in traumatology. First of all, it is necessary to clarify that this method constitutes an essential component of practically generalized use, in which low current level radiation is used, that is, fluoroscopic radiation, so that it can be applied for a considerably longer time than that of the longest radiographic exposure. This tool works with a tube intended for this purpose, which is known as fluoroscopy. The radiations from the tube pass through the patient and reach the serigraph, on which the image intensifier or fluoroscopic screen is mounted. In the latter case, this is where the chain ends, since it is on this screen that the image is formed and where the physician directly observes the region to be studied. It is also necessary to define that a foreign body is any element foreign to the body that enters it, either through the skin or through any natural orifice such as the eyes, nose, throat, preventing its normal functioning. It was possible to obtain as a result that the advantages of fluoroscopic navigation are the reduction of surgical time and the amount of irradiation, which goes from about 140 seconds without navigation to only 8 seconds, which is a substantial difference. Among the conclusions, it was possible to highlight that in the case of a radiopaque object, it is essential to have an image intensifier for localization of the foreign body during surgery; while in the case of a radiolucent foreign body, it is more advisable to locate it through the clinic, since these tend to form granulomas.

Introduction: Given the heterogeneous nature and inherent complexity of forensic medical expertise, the expert (medical professional or related areas) must make the best use of the technical and technological tools at his disposal. Imaging, referring to the set of techniques that allow obtaining images of the human body for clinical or scientific purposes, in any of its techniques, is a powerful support tool for establishing facts or technical evidence in the legal field. Objective: To analyze the use of magnetic resonance and computed tomography in postmortem diagnosis. Methodology: information was searched in the databases PubMed, Science Direct, Springer Journal and in the search engine Google Scholar, using the terms “X-Ray Computed Tomography”, “Magnetic Resonance Spectroscopy”, “Autopsy” and “Forensic Medicine” published in the period 2008–2015. Results: MRI is useful for the detailed study of soft tissues and organs, while computed tomography allows the identification of fractures, calcifications, implants and trauma. Conclusions: In the reports found in the literature search, regarding the use of nuclear magnetic resonance and computed tomography in postmortem cases, named by the genesis of the trauma, correlation was found between the use of imaging and the correct expert diagnosis at autopsy.

In Costa Rica, there is no explicit recommendation from the competent authorities for the use of a specific phantom, so experts must explore what suppliers offer, among which the Normi Mam Digital phantom from PTW stands out. This article presents the results of the dosimetry and image quality control applied to the Normi Mam Digital phantom to validate it as equipment that complies with the recommendations of the Human Health Series No. 17. The results obtained were satisfactory, proving that the equipment complies with the tolerances recommended by international health bodies.

In the process of X-ray transmission imaging, the mutual occlusion between structures will lead to the image information overlap, and the computed tomography (CT) method is often required to obtain the structure information at different depths, but with low efficiency. To address these problems, an X-ray focused on imaging algorithm based on multi-line scanning is proposed, which only requires the scene target to pass through the detection area along a straight line to extract multi-view information, and uses the optical field reconstruction theory to achieve the de-obscured reconstruction of the structure at a specified depth with high real-time. The results of multi-line scan and X-ray reconstruction of the target show that the proposed method can reconstruct the information of any specified depth layer, and it can perform fast imaging detection of the mutually occluded target structures and improve the recognition of the occluded targets, which has a good application prospect.