Human lifespan continues to extend as an unprecedented number of people reach their seventh and eighth decade of life, unveiling chronic conditions that affect the older adult. Such skin conditions include senile purpura, seborrheic keratoses, pemphigus vulgaris, bullous pemphigoid, diabetic foot wounds, and skin cancer. Current methods of drug testing prior to clinical trials require the use of pre-clinical animal models, which are often unable to adequately replicate human skin response. A reliable model for aged human skin is needed. Current challenges in developing an aged human skin model include the intrinsic variability in skin architecture from person-to-person. An ideal skin model would incorporate innate functionality such as sensation, vascularization, and regeneration. The advent of 3D bioprinting allows us to create human skin equivalent for use as clinical-grade surgical graft, for drug testing, and other needs. In this review, we describe the process of human skin aging and outline the steps to create an aged skin model with 3D bioprinting using skin cells (i.e., keratinocytes, fibroblasts, and melanocytes). We also provide an overview of current bioprinted skin models, associated limitations, and direction for future research.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (MobileNet) was trained to identify key features of various satellite images that contained fire or without fire. Then, the trained system is used to classify new satellite imagery and sort them into fire or no fire classes. A cloud-based development studio from Edge Impulse Inc. is used to create a NN model based on the transferred learning algorithm. The effects of four hyperparameters are assessed: input image resolution, depth multiplier, number of neurons in the dense layer, and dropout rate. The computational cost is evaluated based on the simulation of deploying the neural network model on an Arduino Nano 33 BLE device, including Flash usage, peak random access memory (RAM) usage, and network inference time. Results supported that the dropout rate only affects network prediction performance; however, the number of neurons in the dense layer had limited effects on performance and computational cost. Additionally, hyperparameters such as image size and network depth significantly impact the network model performance and the computational cost. According to the developed benchmark network analysis, the network model MobileNetV2, with 160 × 160 pixels image size and 50% depth reduction, shows a good classification accuracy and is about 70% computationally lighter than a full-depth network. Therefore, the proposed methodology can effectively design an ML application that instantly and efficiently analyses imagery from a spacecraft/weather balloon for the detection of wildfires without the need of an earth control centre.