Thermal-porosity characterization data of additively manufactured Ti-6Al-4V thin-walled structure via laser engineered net shaping.

In-process thermal melt pool images and post-fabrication porosity labels are acquired for Ti-6Al-4V thin-walled structure fabricated with OPTOMEC Laser Engineered Net Shaping (LENS™) 750 system. The data is collected for nondestructive thermal characterization of direct laser deposition (DLD) build. More specifically, a Stratonics dual-wavelength pyrometer captures a top-down view of the melt pool of the deposition heat-affected zone (HAZ), which is above , and Nikon X-Ray Computed Tomography (XCT) XT H225 captures internal porosity reflective of lack of fusion during the fabrication process.

A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images.

Background: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients.

Preoperative Prediction and Identification of Extracapsular Extension in Head and Neck Cancer Patients: Progress and Potential.

Background This study aimed to demonstrate both the potential and development progress in the identification of extracapsular nodal extension in head and neck cancer patients prior to surgery. Methodology A deep learning model has been developed utilizing multilayer gradient mapping-guided explainable network architecture involving a volume extractor. In addition, the gradient-weighted class activation mapping approach has been appropriated to generate a heatmap of anatomic regions indicating why the algorithm predicted extension or not.

Effectiveness of facemasks for opening a university campus in Mississippi, United States - a modelling study.

Universities are at risk for COVID-19 and Fall semester begins in August 2020 for most campuses in the United States. The Southern States, including Mississippi, are experiencing a high incidence of COVID-19. The objective of this study is to model the impact of face masks and hybrid learning on the COVID-19 epidemic on Mississippi State University (MSU) campus.

Alarm-Related Workload in Default and Modified Alarm Settings and the Relationship Between Alarm Workload, Alarm Response Rate, and Care Provider Experience: Quantification and Comparison Study.

Background: Delayed or no response to impending patient safety-related calls, poor care provider experience, low job satisfaction, and adverse events are all unwanted outcomes of alarm fatigue. Nurses often cite increases in alarm-related workload as a reason for alarm fatigue, which is a major contributor to the aforementioned unwanted outcomes. Increased workload affects both the care provider and the patient.