Diagnosis and detection of bone fracture in radiographic images using deep learning approaches.

Introduction: Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deep-learning (DL) algorithms.

Assessing the potential of solubility trapping in unconfined aquifers for subsurface carbon storage.

Carbon capture and storage projects need to be greatly accelerated to attenuate the rate and degree of global warming. Due to the large volume of carbon that will need to be stored, it is likely that the bulk of this storage will be in the subsurface via geologic storage. To be effective, subsurface carbon storage needs to limit the potential for CO leakage from the reservoir to a minimum.

Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz.

Emission analysis of recycled tire rubber modified asphalt in hot and warm mix conditions.

The hazardous emissions of crumb rubber (CR) modified asphalt during construction has been a concern for a long period. This study aims to identify the emission components in the CR modified asphalt in traditional hot mix asphalt (HMA) and with recently developed warm mix asphalt (WMA). The dynamic headspace gas chromatography-mass spectrometry (GCMS) was employed for identifying the emission of asphalt binders at 120°C, 140°C and 160°C.