Single Chain Reversible Addition-Fragmentation Chain Transfer (RAFT) Poly(N-isopropylacrylamide) for Chemical Sensing

An approach to single chain poly-N-isopropyl acrylamide (polyNIPAM) with a redox tag was polymerized by the reversible addition fragmentation-chain transfer (RAFT) method. Molecularly imprinted polymers (MIPs) technology is the design of an artificial receptor with high selectivity for a specific analyte. The synthesized RAFT polymers were devised to develop conformation-based electrochemical MIP sensors.The material polyNIPAM is attractive as a receptor of a chemical sensor. Due to its thermosensitive properties, polyNIPAM collapses above the lower critical solution temperature (LCST) and returns to its original state when the temperature is reduced below LCST. This reversible aggregation behavior shows that polyNIPAM has a flexible structure vital to generating a conformational change with stimulus and molecular recognition. Beyond the aggregation behaviors, the isopropyl group of these monomers can form hydrophobic interactions, which helps create non-covalent interactions, the same as the use of acidic and basic functional monomers in MIP synthesis. This non-covalent crosslinking can reduce the number of covalent crosslinkers, increasing the binding affinity of the MIPs. Another approach to minimize binding blockage and increase the binding affinity is untangling in THF, which takes approximately one day to reach an equilibrium state. The polyNIPAM size measurements by dynamic light scattering (DLS) were conducted because it confirmed that the reversible aggregation behavior of polyNIPAM was not influenced by an applied voltage. The success of synthesis and characterization of ferrocene contained polyNIPAM illustrates that modifying a redox tag on the RAFT agent is feasible. However, ferrocene was found to not be stable with vinyl-pyridine, which is the basic functional monomer used in the current MIP recipe. Due to this, exploration of other redox tags such as triphenylamine (TPA) and anthraquinone (AQ) were tested. These redox tags were found not to be adequate for our application. As a result, they were declared to no longer be candidates for the project. However, methylene blue (MB), another redox tag option, was studied. It is believed that MB has the potential to make this approach of conformational-based electrochemical MIP sensors work, but it will require more research. For this reason, future work should focus on this redox tag

Research on Spatial Optimization of Shopping Center under the Background of Epidemic Situation : A Case Study of Weilaishi Shopping Center in Handan

In the current social background of frequent epidemics, shopping centers, as commercial buildings with a large number of people, it is very important to reduce the risk of environmental infection. This paper takes the Weilaishi Shopping Center in Handan as an example, summarizes the epidemic prevention status of five types of spatial nodes through field investigation, and analyzes the shortcomings of the existing emergency design of the Weilaishi Shopping Center. Based on the analysis results, this paper puts forward the optimization strategy from the spatial level, which provides a certain reference value for the research on the improvement of shopping center space environment emergency capability

Quantifying the Impacts of Flash Flooding on Dominica’s Material Stocks in Buildings: A GIS-based methodological framework for Small Island States

Economic growth is usually accompanied by extensive extraction of natural resources, especially in developing countries. From a “material-stock-flow-service” perspective, the substantial part (e.g., construction materials) of the extracted natural resources as inflows to a society get accumulated in the built environment as “material stocks” (MS). Depending on the end-use types of their containers, MS provide essential services to a society such as housing, education and transportation. When an environmental hazard strikes, MS lose their functionality due to the destruction of the physical structure of their carriers, resulting in extra construction waste that then must be cleared for recovery. To make a society more resilient to environmental hazards, which is especially important in small island states with limited natural and human resources, the knowledge of exposure of MS to hazard risk is critical. This research focuses on the quantity and spatial distribution of MS in buildings in the context of intense rainfall-triggered flash flooding in Dominica, a small island state in the Caribbean region. A Geographical Information System (GIS)-based stock-driven methodology is used to quantify four typical types of construction materials: concrete, aggregates, timber, and steel. To quantify exposed MS in buildings to flash flooding, an event-based flood model is used to generate flood inundation extents at the national scale. To investigate the degrees to which the exposed households are susceptible to the impacts of environmental hazards, this research also designs a resident survey to collect social factors contributing to household vulnerability to hazards. For 2020, the total MS in the building sector is estimated at 6,574 kt, equivalent to 91 t per capita, given Dominica’s population of the year. In terms of the distributions of MS in different material categories, concrete accounts for 86% of the total MS in buildings, followed by aggregate at 7%, timber at 4% and steel at 3%. Examining the exposure of MS in buildings to flash flooding, it is found that flood events of larger magnitudes would result in more MS contained in the exposed buildings. For flash flood events with 5-year, 10-year, and 20-year return periods, the numbers of exposed buildings are 2,781, 3,030, and 3,274, respectively, which contain 17%, 18%, and 19% of the total MS in buildings in Dominica. This research demonstrates how to link the results of material stock accounting to flash flood modelling, approaching the concept of socio-economic metabolism from an environmental hazard risk perspective. Knowledge of the quantity and spatial distribution of the exposed MS in buildings can assist local governments in making cost-effective mitigation plans before a hazard event. Although the designed survey was not implemented due to travel restrictions, it is a valuable instrument to collect the information about household vulnerability to environmental hazards, which can help hazard response agencies with more-efficient rescue operations during a hazardous event

DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.Comment: 13 pages, 5 figures, and 2 table

GWAI: Harnessing Artificial Intelligence for Enhancing Gravitational Wave Data Analysis

Gravitational wave (GW) astronomy has opened new frontiers in understanding the cosmos, while the integration of artificial intelligence (AI) in science promises to revolutionize data analysis methodologies. However, a significant gap exists, as there is currently no dedicated platform that enables scientists to develop, test, and evaluate AI algorithms efficiently. To address this gap, we introduce GWAI, a pioneering AI-centered software platform designed for gravitational wave data analysis. GWAI contains a three-layered architecture that emphasizes simplicity, modularity, and flexibility, covering the entire analysis pipeline. GWAI aims to accelerate scientific discoveries, bridging the gap between advanced AI techniques and astrophysical research.Comment: 10 pages, 5 figure