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Essays on Job Search and Labor Markets
No full textThis dissertation contains three essays on the topics of job search and labor markets. The first essay studies effects of occupational credentials on unemployment duration. Using a random search model we theorize that occupational credentials in the form of licenses and certificates reduce unemployment duration through improvements in human capital, provision of better signalling, or both. Whichever credential has the stronger effect remains an empirical question. Since the questions about occupational credentials have recently been added to large population surveys in the US, the topic is relatively nascent; and we find that certificates issued by businesses as well as licenses decrease unemployment duration while the former has the stronger effect that the latter. The second and third essays study effects of immigration and linguistic assimilation on growth in native employment. The second essay studies those effects in the context of five European countries whereas the third essay studies those effects in the US case. The second essay derives a search and matching model to demonstrate that immigrants having higher search costs than natives, join the host country economy and boost native employment, accepting lower wages and driving firms\u27 costs down. The second essay also develops a novel index to measure linguistically assimilated and linguistically unassimilated areas using Google Trends search data whereas the third essay relies on self-reported data on linguistically isolated households. Findings from both essays are similar: inflows of new immigrants into linguistically assimilated areas increase growth in native employment. However, the second essay, with the exception of France, demonstrates that inflows into linguistically unassimilated areas do not show evidence of the effects in contrast to the case of the US. However, further applications of the index derived in the second essay to the case of the US will shed some light on the comparability of the results
Computational Studies of Biological Systems and Nanomaterials and Their Applications
No full textMolecular simulation is an invaluable tool across multiple research fields, encompassing biological systems, drug discovery, nanomaterial science, and more. Scientists employ diverse simulation methods to address the challenges within these fields. However, progress is limited by the complexities of the systems. I have utilized molecular modeling, molecular dynamics (MD) simulation, and steered molecular dynamics (SMD) simulation to address complex problems in chemistry, biophysics, drug discovery, and nanoscience. This thesis covers three primary research topics: biophysical characterization of protein-protein interaction between SARS-CoV-2 spike receptor binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2) using SMD, CHARMM-GUI Nanomaterial Modeler and its applications to various nanomaterial/nano-bio systems, and development of Bicelle Builder in CHARMM-GUI
Hardware Acceleration of Multiple-Input Multiple-Output Underwater Acoustic Communication Systems
No full textThis Ph.D. dissertation investigates the hardware acceleration of a Single-Carrier Coherent Modulation (SCCM) Multiple-Input and Multiple-Output (MIMO) underwater acoustic (UWA) communication system using the System on Chip (SoC) architecture of Xilinx Field Programmable Gate Arrays (FPGAs) that contains Programmable Logic (PL) Units and an embedded ARM-based Processing System (PS). The proposed FPGA acceleration speeds up the high-complexity MIMO algorithms in three aspects. First, a hardware testbed is proposed for an SCCM-based high-frequency multi-channel transmitter and receiver. The transmitter modules, including the Forward Error Correction encoder, interleaver, bit-to-symbol mapper integrated with pulse-shaping filter, and carrier modulator, are implemented fully in the PL with flexible parameters adjusted on the fly by the PS. The multi-channel receiver utilizes external Analog-to-Digital Converters (ADC) and implements the digital down converters (DDC) on the PL. The resulting baseband signals are transferred to the PS through a high-speed AXI bus and then to a laptop through the Ethernet port. The proposed FPGA testbed is integrated with analog front-end circuits for a 4 X 4 MIMO with carrier frequencies of 115 kHz -- 400 kHz. Extensive field experiments are conducted to verify different UWA signaling schemes, including a fast-mode JANUS standard.Second, the multi-channel MIMO transmitter is expanded to a massive MIMO transmitter with up to 128 channels. An all-digital baseband and passband precoder is proposed and implemented by a hybrid beamforming (HBF) architecture where the baseband precoder maps user signals into different sub-array groups by small-size matrix multiplication. The passband precoder up-converts each group signal via a Digital-Up-Converters (DUC), then expands the passband signal to multiple branches by varying the duty cycle and time delay of the passband Pulse-Width-Modulation (PWM) signal. The resulting 128 transmit branches serve multi-users with reduced pilots for channel estimation and improve the signal-to-noise ratio at the user receiver. Third, several high-complexity modules in the baseband Turbo receiver are accelerated for severe multipath UWA channels, including Improved Proportionate NLMS (IPNLMS) channel estimation, Successive Interference Cancellation (SIC), and Turbo equalization, where matrix inversion and multiplication are required for large-size matrices up to 300 X 300 for a 4 X 4 MIMO. The IPNLMS channel estimation is accelerated by parallel computing and pipelining. As a result, the number of clock cycles required for one iteration of updating one channel impulse response (CIR) of length L=100 is reduced to 20% of that achieved by existing systems in the literature. In addition, the proposed multi-channel IPNLMS architecture is able to update 16 channel CIRs of length L=100 within twice the clock cycles for one channel, with only 16 more square root operators and four more multipliers. The proposed two-stage pipelined architecture for SIC best utilizes the block RAM and the convolution nature of the shifted data blocks, resulting in a 40 times speed up over the implementation on a powerful CPU platform. The matrix arithmetic in the Turbo equalizer is accelerated by leveraging the Hermitian symmetric property of the matrix and the proposed matrices storage strategy to reduce the complexity of LU-decomposition-based block matrix inversion. The proposed implementation of the large-size matrix inversion on the order of 300 X 300 achieves a 10, 53, and 222 times speed up over the GPU, Xeon CPU, and Arm Cortex-A53 implementations, respectively
The Commercial Determinants of Health in the Context of COVID 19
No full textOne of the major public health side effects of the COVID-19 pandemic was its contribution to non-communicable diseases (NCDs) and their associated risk factors such as obesity. At the same time, public health researchers became increasingly cognizant of how the commercial determinants of health contributed to this challenge. In this article, we contribute to this literature by discussing how major beverage and fast-food companies took advantage of this situation through a variety of strategies that essentially increased their profits at a time of worsening COVID-19 and NCD conditions. Despite overwhelming data highlighting worsening NCD and obesity problems, governments did not recognize this situation and introduce policies limiting industries from taking advantage of the pandemic situation. We conclude by providing several concrete political and policy actions that political leaders can take to avoid this situation in the future
Isomorphism classes of cut loci for a cube
No full textWe prove that a face of a cube can be optimally partitioned into connected
193 sets on which the cut locus, or ridge tree, is constant up to isomorphism
as a labeled graph. These are 60 connected open sets, curves bounding them, and
intersection points of curves. Polynomial equations for the curves are
provided. Sixteen pairs of sets support the same cut locus class. We present
the 177 distinct cut locus classes
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
No full textMolecular dynamics (MD) has served as a powerful tool for designing materials
with reduced reliance on laboratory testing. However, the use of MD directly to
treat the deformation and failure of materials at the mesoscale is still
largely beyond reach. Herein, we propose a learning framework to extract a
peridynamic model as a mesoscale continuum surrogate from MD simulated material
fracture datasets. Firstly, we develop a novel coarse-graining method, to
automatically handle the material fracture and its corresponding
discontinuities in MD displacement dataset. Inspired by the Weighted
Essentially Non-Oscillatory scheme, the key idea lies at an adaptive procedure
to automatically choose the locally smoothest stencil, then reconstruct the
coarse-grained material displacement field as piecewise smooth solutions
containing discontinuities. Then, based on the coarse-grained MD data, a
two-phase optimization-based learning approach is proposed to infer the optimal
peridynamics model with damage criterion. In the first phase, we identify the
optimal nonlocal kernel function from datasets without material damage, to
capture the material stiffness properties. Then, in the second phase, the
material damage criterion is learnt as a smoothed step function from the data
with fractures. As a result, a peridynamics surrogate is obtained. Our
peridynamics surrogate model can be employed in further prediction tasks with
different grid resolutions from training, and hence allows for substantial
reductions in computational cost compared with MD. We illustrate the efficacy
of the proposed approach with several numerical tests for single layer
graphene. Our tests show that the proposed data-driven model is robust and
generalizable: it is capable in modeling the initialization and growth of
fractures under discretization and loading settings that are different from the
ones used during training
Formation of Amorphous Carbon Multi‐Walled Nanotubes from Random Initial Configurations
No full textAmorphous carbon nanotubes (a‐CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential. The important variables (like density, height, and diameter) required to successfully simulate a‐CNTs were predicted with the machine learning random forest technique. The width of the a‐CNT models ranged between 0.55–2 nm with an average inter‐wall spacing of 0.31 nm. The topological defects in a‐CNTs were analyzed and new defect configurations were observed. The electronic density of states and localization in these phases were discussed and delocalized electrons in the π subspace were identified as an important factor for inter‐layer cohesion. Spatial projection of the electronic conductivity favors axial transport along connecting hexagons, while non‐hexagonal parts of the network either hinder or bifurcate the electronic transport. A vibrational density of states was calculated and is potentially an experimentally comparable fingerprint of the material. The appearance of a low‐frequency radial breathing mode was discussed and the thermal conductivity at 300 K was estimated using the Green‐Kubo formula
The Role of Primary Care in Improving Population Health
No full textPolicy Points Systems based on primary care have better population health, health equity, and health care quality, and lower health care expenditure. Primary care can be a boundary‐spanning force to integrate and personalize the many factors from which population health emerges. Equitably advancing population health requires understanding and supporting the complexly interacting mechanisms by which primary care influences health, equity, and health costs