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The future of the STEM workforce rests partly on the strength of the STEM teacher workforce to teach and nurture new generations of STEM graduates. However, the STEM teacher workforce is facing critical decline with the annual production dropping from about 31,000 a decade ago to around 20,000 in the last few years. This is concerning given the need for more STEM teachers to meet rising demands. Although production is decreasing, there are improvements in the diversity and qualifications of STEM teachers, including more female teachers and those with higher degrees in STEM fields. Investments in teacher salaries and financial support for STEM education can help address the shortage and improve the future STEM teacher workforce and STEM workforce. 

Abstract Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of the current UAVs stands as a significant obstacle to their broadscale deployment. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This paper details our investigation of a battery-free fixed-wing UAV, built from cost-effective off-the-shelf components, that takes off, remains airborne, and lands safely using only solar energy. In particular, we perform a comprehensive analysis and design space exploration in the contemporary solar harvesting context and provide a detailed accounting of the prototype’s mechanical and electrical capabilities. We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. Next, we evaluate the developed prototype in a bench-top setting using artificial light to demonstrate the feasibility of batteryless flight, followed by testing in an outdoor setting using natural light. Finally, we analyze the potential for scaling up the evaluation of batteryless UAVs across multiple locations and report our findings. 

Abstract Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)–based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory. This approach leverages the eigenvalues and eigenvectors of graph Laplacian and adjacency matrices to capture high-level details of specific atom pairwise interactions. Evaluated against benchmark datasets such as CASF-2016, CASF-2013, and the Cathepsin S dataset, the AGL-EAT-Score demonstrates notable accuracy, outperforming existing traditional and ML-based methods. The model’s strength lies in its comprehensive similarity analysis, examining protein sequence, ligand structure, and binding site similarities, thus ensuring minimal bias and over-representation in the training sets. The use of extended atom types in graph coloring enhances the model’s capability to capture the intricacies of protein-ligand interactions. The AGL-EAT-Score marks a significant advancement in drug design, offering a tool that could potentially refine and accelerate the drug discovery process. Scientific Contribution The AGL-EAT-Score presents an algebraic graph-based framework that predicts ligand-receptor binding affinity by constructing multiscale weighted colored subgraphs from the 3D structure of protein-ligand complexes. It improves prediction accuracy by modeling interactions between extended atom types, addressing challenges like dataset bias and over-representation. Benchmark evaluations demonstrate that AGL-EAT-Score outperforms existing methods, offering a robust and systematic tool for structure-based drug design. 

We present our study on AlInN-based ultraviolet (UV) core-shell nanowire light-emitting diodes (LEDs) utilizing a polarization-induced doping technique. Due to the formation of a core-shell structure, the non-radiative recombination on the nanowire surface is significantly reduced. Moreover, we have successfully fabricated AlInN/GaN-based core-shell nanowire UV LED employing polarization-engineered quantum barriers instead of conventional structures. The LED device exhibits significantly improved carrier concentration in the active region and decreased electron leakage due to the gradually raised effective conduction band barrier heights. At room temperature, the AlInN LEDs exhibit strong and stable emission at 296 nm. We provide a promising approach to fabricating high-performance light emitters.