In this paper, a review of natural fibers composites used in protective armors for ballistic applications like bulletproof vests is presented. It seeks to compile the results and establish natural fiber composites as an alternative to absolute synthetic composites used for the same application. The review assesses the latest articles under two categories viz. only natural fibers and hybrid composites with natural and synthetic fibers. It covers a range of fibers from flax, basalt, curaua, kenaf, aramid, jute, coir, and hemp. The study systematically classifies the composites based on the ballistic limit they exhibit and the back face signature measured during standardized ballistic testing. In accordance with the results, the findings are compared against standard materials such as Kevlar used in ballistic armors. The hybrid composites are seen to present superior properties in comparison to plain natural fiber composites. Natural fibers provide an ecological and economical alternative to existing composites. The review shows promising results and immense future scope for natural fibers in the area of ballistic applications and they must be actively considered for replacing synthetic fibers in armor.

The Hip joint is the primary joint which gives stability to the human body. The wear and tear associated with age and other factors, require these joints to be replaced by implants using hip arthroplasty surgeries. Cobalt chromium alloy (CoCr), titanium alloy, stainless steel are some of the most common hip joint materials used for hip implants. The design requirement for hip joint implants are very stringent to avoid revision joint surgeries due to aseptic loosening. There are various choices in shapes and materials used for stem and acetabular designs. This makes it more difficult to make an informed decision on the type of design and material that can be used for hip implants. Circular, Oval, ellipse and trapezoidal designs with three individual cross sections (defined as profile 1, profile 2 and profile 3) are considered for the study. All models are modeled using CATIA V-6. Static structural analysis is performed using ANSYS R-19 to arrive at the best possible design and material combination for stem and acetabular cup. It was found that, profile 2 of all the four designs has the lowest possible deformation and von Mises stress when compared to profile 1 and profile 2. In general, profile 2 with trapezoidal stem has best outcomes in terms of its mechanical properties. Besides, stem designed with material CoCr and its associated acetabular cup with CoC (ceramic on ceramic) material can produce an implant having better properties and longer durability. CoCr was found to be the preferred choice of material for stem design. It was also observed that, irrespective of material considered for the analysis profile 2 with trapezoidal stem showcased lesser deformation and von Mises stress over the other eleven models. For analysis involving acetabular cups, CoC implants exhibited better mechanical properties over the conventional CoPE (Ceramic on polyethylene) materials such as Ultra-high molecular weight polyethylene (UHMWPE). It is inferred from the findings of this study that, the profile 2 with trapezoidal stem design made of CoCr material and acetabular cup made of CoC material is best suited for hip joint implants.

The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.