Single Unit Monomer Insertion: A Versatile Platform for Molecular Engineering through Radical Addition Reactions and Polymerization

Single unit monomer insertion (SUMI) is an emerging technology for precise polymer synthesis through a radical addition reaction mechanism with monomer additions occurring one at a time. It possesses the capability of highly efficient and economical chemical conversion for carbon-carbon bond formation. Originating from reversible deactivation radical polymerization (RDRP), SUMI retains the virtues of mild reaction conditions and remarkable tolerance toward functionalities. Additionally, SUMI can provide a versatile platform for the engineering of stereochemistry as well as mechanistic and kinetic studies of radical addition reactions and RDRP. Herein, the history and development of SUMI are reviewed, and the advances since its advent in organic transformation and sequence-defined polymer synthesis are highlighted. Current challenges are discussed, and a perspective on future opportunities for this promising synthetic technology is also presented

On generalizations of Iwasawa's theorem

Iwasawa's theorem indicates that a finite group $G$ is supersolvable if and only if all maximal chains of the identity in $G$ have the same length. As generalizations of Iwasawa's theorem, we provide some characterizations of the structure of a finite group $G$ in which all maximal chains of every minimal subgroup have the same length. Moreover, let $\delta(G)$ be the number of subgroups of $G$ all of whose maximal chains in $G$ do not have the same length, we prove that $G$ is a non-solvable group with $\delta(G)\leq 16$ if and only if $G\cong A_5$

MODELING THE PHYSICAL, OPTICAL AND BIOLOGICAL PROPERTIES OF CHESAPEAKE BAY

This thesis describes a relatively simple biogeochemical model that I developed and coupled with a three-dimensional circulation model of the Chesapeake Bay. To improve the performance of the physical model I attempted to assimilate high-resolution salinity data using a Newtonian relaxation scheme. In general, the simple assimilation scheme leads to visibly improved density structures in the Bay. However, the injection of high-resolution salinity data produces transient gravitational readjustment, which can have a significant impact on the biogeochemical properties and processes in the estuary. Therefore, this approach cannot be directly applied in biogeochemical modeling studies. Instead, I show that adjusting the salinity at open-ocean boundaries is also able to improve the density structure of the inner estuary. To obtain a relatively simple but effective way to model light attenuation variability in the coupled physical-biological model, I adopted a simple, non-spectral empirical approach. Surface water quality data and light measurements from the Chesapeake Bay Program were used to determine the absorption coefficients in a linear regression relationship. The resulting model between light attenuation coefficient (Kd) and water quality concentrations (chlorophyll, TSS and salinity as a proxy for CDOM) gives generally good estimates of Kd in most parts of Chesapeake Bay. I also discuss the feasibility and caveats of using Kd converted from Secchi depth in the empirical method. To develop the relatively simple biogeochemical model for Chesapeake Bay, I adopted a simple NPZD-type biological model and added in necessary additional components and simple parameterizations of the important processes for estuarine applications. The coupled model is then run under very different conditions: a dry year (1995) and a very wet year (1996). Observations of DIN, chlorophyll, total suspended solids (TSS), dissolved oxygen (DO), and light attenuation coefficient (Kd) obtained from Chesapeake Bay Program are used to validate the model. I demonstrate that this simple biological model is capable of reproducing the major features in nutrient, phytoplankton, DO, TSS and Kd distributions in a complex ecosystem like Chesapeake Bay, and the model is robust enough to generate reasonable results under both wet and dry conditions. Sensitivity studies on selected parameters are also reported

Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition

Self-supervised learning (SSL) for WiFi-based human activity recognition (HAR) holds great promise due to its ability to address the challenge of insufficient labeled data. However, directly transplanting SSL algorithms, especially contrastive learning, originally designed for other domains to CSI data, often fails to achieve the expected performance. We attribute this issue to the inappropriate alignment criteria, which disrupt the semantic distance consistency between the feature space and the input space. To address this challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency (ARC) as a solution to define proper alignment criteria. ARC is designed to retain semantic information from the input space while introducing robustness to real-world noise. Moreover, we substantiate the effectiveness of ARC through a comprehensive set of experiments, demonstrating its capability to enhance the performance of self-supervised learning for WiFi-based HAR by achieving an increase of over 5\% in accuracy in most cases and achieving a best accuracy of 94.97\%

Research on the Frequency Aliasing of Resistance Acceleration Guidance for Reentry Flight

According to the special response of resistance acceleration during hypersonic reentry flight, different guidance frequency will result to very different flight and control response. The analysis model for the response of resistance acceleration to the attack angle and dynamic press is put forward respectively in this paper. And the frequency aliasing phenomenon of guidance is revealed. The simulation results to the same vehicle sufficiently substantiate the frequency aliasing of resistance acceleration during reentry guidance

Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition

Self-supervised learning (SSL) for WiFi-based human activity recognition (HAR) holds great promise due to its ability to address the challenge of insufficient labeled data. However, directly transplanting SSL algorithms, especially contrastive learning, originally designed for other domains to CSI data, often fails to achieve the expected performance. We attribute this issue to the inappropriate alignment criteria, which disrupt the semantic distance consistency between the feature space and the input space. To address this challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency (ARC) as a solution to define proper alignment criteria. ARC is designed to retain semantic information from the input space while introducing robustness to real-world noise. Moreover, we substantiate the effectiveness of ARC through a comprehensive set of experiments, demonstrating its capability to enhance the performance of self-supervised learning for WiFi-based HAR by achieving an increase of over 5\% in accuracy in most cases and achieving a best accuracy of 94.97\%

Machine Learning Approaches for Region-level Prescription Demand Forecasting

Region-level prescription demand is closely intertwined with the incidence of diseases within a given area. However, conventional forecasting methods primarily rely on historical data, and ignore the spatial correlation in prescription data. In this study, we employ graph structures to capture the interactions among drug demand in different regions. By leveraging two popular graph neural network-based models, our objective is to harness the power of spatial-temporal correlation to enhance the accuracy of predictions. To assess the effectiveness of the graph neural network-based model, we conduct extensive experiments on a comprehensive real world dataset. The results demonstrate that the performance of the graph neural network consistently surpasses that of statistical learning-based methods and traditional deep learning-based methods.</p