Search

There is ample evidence of dark matter (DM), a phenomenon responsible for ≈ 85% of the matter content of the Universe that cannot be explained by the Standard Model (SM). One of the most compelling hypotheses is that DM consists of beyond-SM particle(s) that are nonluminous and nonbaryonic. So far, numerous efforts have been made to search for particle DM, and yet none has yielded an unambiguous observation of DM particles. We present in Chapter 2 the SuperCDMS HVeV Run 2 experiment, where we search for DM in the mass ranges of 0.5--10⁴ MeV/c² for the electron-recoil DM and 1.2--50 eV/c² for the dark photon and the Axion-like particle (ALP). SuperCDMS utilizes cryogenic crystals as detectors to search for DM interaction with the crystal atoms. The interaction is detected in the form of recoil energy mediated by phonons. In the HVeV project, we look for electron recoil, where we enhance the signal by the Neganov-Trofimov-Luke effect under high-voltage biases. The technique enabled us to detect quantized e⁻h⁺ creation at a 3% ionization energy resolution. Our work is the first DM search analysis considering charge trapping and impact ionization effects for solid-state detectors. We report our results as upper limits for the assumed particle models as functions of DM mass. Our results exclude the DM-electron scattering cross section, the dark photon kinetic mixing parameter, and the ALP axioelectric coupling above 8.4 x 10⁻³⁴ cm², 3.3 x 10⁻¹⁴, and 1.0 x 10⁻⁹, respectively. Currently every SuperCDMS detector is equipped with a few phonon sensors based on the transition-edge sensor (TES) technology. In order to improve phonon-mediated particle detectors' background rejection performance, we are developing highly multiplexed detectors utilizing kinetic inductance detectors (KIDs) as phonon sensors. This work is detailed in chapter 3 and chapter 4. We have improved our previous KID and readout line designs, which enabled us to produce our first ø3" detecto

The precision and productivity requirements for modern manufacturing process drive precision systems to their physical limitations, such as the limitation of sensor resolution, actuator saturation, and traversing speed. Incorporating these restrictions, constrained optimization has been an active research area that can explicitly handle the physical constraints. This dissertation covers three important topics in productivity and precision improvements through constrained optimization: sub-count sensor estimation, constraint violation avoidance, and cycle-timeminimization.To solve sensor quantization and measurement synchronization problem, amodel-based encoder-triggered estimation method was developed addressing state continuity. By enforcing smooth estimation in important physical states such as position and velocity, this method avoids abrupt estimation change when encoder trigger occurs. Furthermore, because continuous-time open-loop model is used to predict inter-trigger behavior, the developed sub-count estimator can be applied to asynchronous systems with untimely measurement updates. By applying this sub-count estimation method, the precision system can improve tracking performancewhen only quantized and untimely sensor measurement is available.When improving tracking performance, the precision system is often pushed to its limitation. This often causes the precision system to violate system constraints (such as control signal saturation and excessive acceleration), which results in unexpected vibration and sacrifices the tracking performance. To avoid such kind of constraint violation, the system model can be used to predict the dynamic behavior of the plant given a known trajectory and plant model. A model predictive control based optimal feed-forward tracking method is proposed to be integrable with any feedback controller.Besides tracking performance, cycle time is also an important index for modern precision motion control. The shorter the cycle time is

The prevalence of obesity among US children and adolescents has rapidly increased in the past several decades, and the epidemic of childhood obesity is currently a serious public health concern in the United States. This dissertation consists of three studies examining individual- and neighborhood-level determinants of childhood obesity. The study area was Los Angeles County in California. Our first study examined the effects of maternal employment, individual socioeconomic status (SES), and neighborhood SES on childhood obesity. The second study not only investigated the independent effect of neighborhood food environment on childhood obesity, but also examined the mediation and moderation effects of household grocery shopping distance on the relationship between neighborhood food environment and childhood obesity. The third study assessed the comparability of two commercial (i.e., InfoUSA and Dun & Bradstreet) and one government (i.e., Los Angeles County Department of Public Health) food environment databases which can be used to measure neighborhood food environment.Our main findings indicated that maternal part-time employment was associated with increased child's BMI, and children's TV-watching time mediated this relationship. In addition, both individual- and neighborhood-level SES measures were inversely associated with childhood obesity. We also found that children living in neighborhoods with lower density of supermarkets and grocery stores or living in neighborhoods with higher density of convenience stores were more likely to have higher BMI. Findings from our comparison study reported that for the InfoUSA vs. Dun & Bradstreet comparison, similarity of counts was high for supermarkets & grocery stores and convenience stores. For Los Angeles County Department of Public Health vs. Dun & Bradstreet comparison, similarity of counts was high for chain supermarkets, independent grocery stores, meat and fish markets, sweets stores, and bakeries