Hybrid Sensorless Field Oriented and Direct Torque Control for Variable Speed Brushless DC Motors

The objective of this thesis is to design a hybrid sensorless closed-loop motor controller using a combination of Field-Oriented Control (FOC) and Direct Torque Control (DTC) for brushless DC motors used in multi-rotor aerial vehicles. The primary challenge is the wide range of desired working speeds, which can quickly vary from low speed to high speed. For this range, the control approach must be efficient, effective, and low-cost in order to provide fast response times during initial startup, steady-state, and transient operation. Additional design challenges include minimal response time to desired speed changes and small steady-state speed errors. Finally, the control approach must be robust to motor parameter uncertainties or variations and the operation of the final design must be robust to measurement noise

Hybrid Field Oriented and Direct Torque Control for Sensorless BLDC Motors Used in Aerial Drones

In this study, a sensorless hybrid control scheme for brushless direct current (BLDC) motors for use in multirotor aerial vehicles is introduced. In such applications, the control scheme must satisfy high-performance demands for a wide range of rotor speeds and must be robust to motor parameter uncertainties and measurement noise. The proposed controller combines field-oriented control (FOC) and direct torque control (DTC) techniques to take benefit of the advantages offered by each of these techniques individually. Simulation results demonstrate the effectiveness of the proposed control scheme over a wide range of rotor speeds as well as good robustness against parameter uncertainties within -5to + 10% for inductance and -5to + 5% for resistance parameters. The proposed hybrid controller is robust also against noise in voltage and current measurements. In order to verify the results from simulation, the proposed hybrid controller is implemented in hardware using the TI C2000 Piccolo Launchpad and TI BOOSTXL-DRV8305EVM BoosterPack. Testing is done with a Bull Running motor typically used in aerial drones. Testing experiments demonstrate that the hybrid controller reduces the rotor speed ripple when compared to DTC while operating in steady-state mode and decreases the response time to desired speed changes when compared to FOC

A Phenotypic Mouse Model of Basaloid Breast Tumors

Chemotherapeutic strategies that target basal-like breast tumors are difficult to design without understanding their cellular and molecular basis. Here, we induce tumors in mice by carcinogen administration, creating a phenocopy of tumors with the diagnostic and functional aspects of human triple negative disease (including EGFR expression/lack of erbB, estrogen-independent growth and co-clustering of the transcriptome with other basaloid models). These tumor strains are a complement to established mouse models that are based on mutations in Brca1 and/or p53. Tumors comprise two distinct cell subpopulations, basal and luminal epithelial cells. These cell fractions were purified by flow cytometry, and only basal cell fractions found to have tumor initiating activity (cancer stem cells). The phenotype of serially regenerated tumors was stable, and irrespective of tumor precursor cell. Tumors were passaged entirely in vivo and serial generations tested for their phenotypic stability. The relative chemo-sensitivity of basal and luminal cells were evaluated. Upon treatment with anthracycline, tumors were effectively de-bulked, but recurred; this correlated with maintenance of a high rate of basal cell division throughout the treatment period. Thus, these tumors grow as robust cell mixtures of basal bipotential tumor initiating cells alongside a luminal majority, and the cellular response to drug administration is dominated by the distinct biology of the two cell types. Given the ability to separate basal and luminal cells, and the discovery potential of this approach, we propose that this mouse model could be a convenient one for preclinical studies

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense RFI are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1e13 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5e16 W) is at most 3e-4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023).Comment: 22 pages, 9 figures, submitted to AJ, revise

MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people

While an estimated 14-20% of young adults experience mental health conditions worldwide, the best strategies for prevention and management are not fully understood. The ubiquity of smartphone use among young people makes them excellent candidates for collecting data about lived experiences and their relationships to mental health. However, not much is known about the factors affecting young peoples’ willingness to share information about their mental health. OBJECTIVE: We aim to understand the data governance and engagement strategies influencing young peoples’ (aged 16-24) participation in app-based studies of mental health. We hypothesize that willingness to participate in research is influenced by involvement  in how their data is collected, shared, and used. METHODS: Here, we describe the MindKind Study, which employs mixed methods to understand the feasibility of global, smartphone-based studies of youth mental health. A pilot 12-week app-based substudy will query participants’ willingness to engage with remote mental health studies. Participants will be randomized into one of four different data governance models designed to understand their preferences, as well as the acceptability of models that allow them more or less control over how their data are accessed and used. Enrolees will receive one of two different engagement strategies. A companion qualitative study will employ a deliberative democracy approach to examine the preferences, concerns and expectations of young people, with respect to remote mental health research. We also detail our engagement with young people as co-researchers in this study. This pilot study is being conducted in India, South Africa and the United Kingdom. CONCLUSION: This study is expected to generate new insights into the feasibility of, and best practices for, remote smartphone-based studies of mental health in youth and represents an important step toward understanding which approaches could help people better manage their mental health

How to Optimize Cancer Treatment in Older Patients

Cancer is a disease of older people, but this age group has often been excluded from clinical trials of cancer, which leads to poor transportability of standardized treatments in older cancer patients. One of the main reasons for the exclusion is the heterogeneity of older people in several domains: social environment, comorbidities, dependency, functional status, nutritional status, cognition status and mood status. Comprehensive geriatric assessment (CGA) aims to assess this heterogeneity and has identified frequent health problems often unknown before therapeutic decisions, which allows for targeted geriatric interventions with or without followup and appropriate cancer treatment selection. Several tools and scores have been developed for a complementary approach. These tools screen for vulnerability to select patients who may benefit from a CGA; are predictive tools for survival, post-operative complications, or chemotherapy-related toxicity; are decisional algorithms for cancer treatment; or define a core set of geriatric data to be collected in clinical cancer trials. Here, we present an overview of the geriatric tools that were published in PubMed from 2000 to 2017, that could help in the therapeutic decision-making for older cancer patients