In this article, the authors created CuSbS₂ solar cells. They discovered that the cells' efficiency was affected by the formation of MoS₂. By incorporating a layer of single-walled carbon nanotubes, however, they were able to prevent MoS₂ formation and increase the device's efficiency.

Here the authors investigated the optical possibilities of gelatin and acrylic in regards to potential implementations at soft contact lenses. They fabricated lenses of different shapes and evaluated the refraction of laser light finding that gelatin needed to be thickened or increased in curvature to account for its lower refractive index compared to plastics, or used in a mixture to strengthen the lens.

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

At a crime scene, the presence and pattern of gunshot residue can help forensic scientists piece together the events that occurred. To assist this, the authors of this paper determined the relationship between shooting distance and nitrite residue patterns left on fabric targets.

In this study, the authors examined how Sri Lankan Americans (SLAs) view racial issues in the U.S. The main hypothesis is that SLAs, as a minority in the U.S., are supportive of the Black Lives Matter movement and its political goal, challenging the common notion that SLAs are anti-Black. The study found that a majority of SLAs believe the U.S. has systemic racism, favor BLM, and favor affirmative action. IT also found that Tamil SLAs have more favorable views of BLM and affirmative action than Sinhalese SLAs.

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We hypothesized that the thermophilic temperature (45-60^oC) would increase biogas production. The production process is much faster and more abundant at temperatures around 55-60^oC.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.