Search for: All records

Digital controls is a topic often learned through a highly theoretical, almost purely mathematical approach which students struggle to master. Project-based learning is one potentially effective way to address this issue, and hands-on learning as a component of projects can make it even more effective. However, access to equipment for hands-on learning can present significant challenges. To address this issue, we have designed and developed two novel prototypes of hands-on equipment for learning controls that are open-source, inexpensive to produce, and portable. They are suitable for use in undergraduate and graduate-level digital embedded control systems courses. These newly developed devices are a pendulum driven by a dc motor, and a straight-line mechanism consisting of a board, two links, and a dc motor. Control of the devices was used as the primary basis for a class project given to students. 

Drone simulators are often used to reduce training costs and prepare operators for various ad-hoc scenarios, as well as to test the quality of algorithmic and communication aspects in collaborative scenarios. An important aspect of drone missions in simulated (as well as real life) environments is the operational lifetime of a given drone, in both solo and collaborative fleet settings. Its importance stems from the fact that the capacity of the on-board batteries in untethered (i.e., free-flying) drones determines the range and/or the length of the trajectory that a drone can travel in the course of its surveilance or delivery missions. Most of the existing simulators incorporate some kind of a consumption model based on different parameters of the drone and its flight trajectory. However, to our knowledge, the existing simulators are not capable of incorporating data obtained from actual physical measurements/observations into the consumption model. In this work, we take a first step towards enabling the (users of) drones simulator to incorporate the speed and direction of the wind into the model and monitor its impact on the battery consumption as the direction of the flight changes relative to the wind. We have also developed a proof-of-concept implementation with DJI Mavic 3 and Parrot ANAFI drones. 

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients. 

Commercialized bumble bees (Bombus) are primary pollinators of several crops within open field and greenhouse settings. However, the common eastern bumble bee (Bombus impatiens Cresson, 1863) is the only species widely available for purchase in North America. As an eastern species, concerns have been expressed over their transportation outside of their native range. Therefore, there is a need to identify regionally appropriate candidates for commercial crop pollination services, especially in the western U.S.A. In this study, we evaluated the commercialization potential of brown-belted bumble bees (Bombus griseocollis De Geer, 1773), a broadly distributed species throughout the U.S.A., by assessing nest initiation and establishment rates of colonies produced from wild-caught gynes, creating a timeline of colony development, and identifying lab-reared workers’ critical thermal maxima (CTMax) and lethal temperature (ecological death). From 2019 to 2021, 70.6% of the wild-caught B. griseocollis gynes produced brood in a laboratory setting. Of these successfully initiated nests, 74.8% successfully established a nest (produced a worker), providing guidance for future rearing efforts. Additionally, lab-reared workers produced from wild-caught B. griseocollis gynes had an average CTMax of 43.5°C and an average lethal temperature of 46.4°C, suggesting B. griseocollis can withstand temperatures well above those commonly found in open field and greenhouse settings. Overall, B. griseocollis should continue to be evaluated for commercial purposes throughout the U.S.A.

 

A novel optical frequency division technique, called regenerative harmonic injection locking, is used to transfer the timing stability of an optical frequency comb with a repetition rate in the millimeter wave range ($\sim <\#comment/>300\mathrm{G}\mathrm{H}\mathrm{z}$) to a chip-scale mode-locked laser with a$\sim <\#comment/>10\mathrm{G}\mathrm{H}\mathrm{z}$repetition rate. By doing so, the 300 GHz optical frequency comb is optically divided by a factor of$30\times <\#comment/>$to 10 GHz. The stability of the mode-locked laser after regenerative harmonic injection locking is$\sim <\#comment/>{10}^{-<\#comment/>12}$at 1 s with a$1/\mathrm{\tau <\#comment/>}$trend. To facilitate optical frequency division, a coupled opto-electronic oscillator is implemented to assist the injection locking process. This technique is exceptionally power efficient, as it uses less than$100\text{µ<\#comment/>}\mathrm{W}$of optical power to achieve stable locking.

 

Abstract Background Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. Results We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. Conclusion Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.