DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

The role of severity perceptions and beliefs in natural infections in Shanghai parents’ vaccine decision-making: a qualitative study

Abstract Background China has reduced incidence of vaccine-preventable diseases through its Expanded Program on Immunization (EPI). Vaccines outside of the EPI are not provided for free by the government, however. This study explored how the stated importance of different disease and vaccine-related attributes interacted with beliefs about the immune system of a child to affect Chinese parents’ decision to obtain a non-EPI vaccine. Methods Mothers and fathers of young children at immunization clinics in Shanghai, China, were interviewed about vaccine decision-making and what attributes of a disease were important when making this decision. An inductive thematic analysis explored their beliefs about disease attributes and how these related to vaccination decisions. Results Among the 34 interviews, severity of the disease—particularly in causing long-term disability—was the most commonly cited factor influencing a parent’s decision to get a vaccine for their child. Many parents believed that natural infection was preferable to vaccination, as long as disease was not severe, and many were concerned that imported vaccines were inadequate for Chinese children’s physical constitutions. All these beliefs could influence the decision to vaccinate. Conclusions Many parents do not appear to understand how and why vaccines can support development of a healthy immune system. Because severity emerged as parents’ overriding concern when making decisions about vaccines, marketing for a childhood vaccine could focus on the severe condition that a vaccine can protect against.https://deepblue.lib.umich.edu/bitstream/2027.42/144525/1/12889_2018_Article_5734.pd

Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): Emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emissions (IVE) model with local emissions factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emissions factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. HTAP_v2.2 does not include the brick sector and we found that our sulfur dioxide (SO2) emissions estimates from brick kilns are comparable to 70 % of the total SO2 emissions considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emissions scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model\u27s boundary layer representation, to capture strong spatial gradients of air pollutant concentrations

GTC Simulation of Ideal Ballooning Mode in Tokamak Plasmas

U.S. Department of Energy (DOE) SciDAC GSEP Center; National Special Research Program of ChinaIn the present paper, we first derive the eigenmode equation of the ideal ballooning mode in tokamak plasmas using a gyrokinetic equation. It is shown that the gyrokinetic eigenmode equation can be reduced to the magnetohydrodynamic (MHD) form in the long wavelength limit when kinetic effects are ignored. Then, the global gyrokinetic toroidal code (GTC) is applied for simulations of the edge-localized ideal ballooning modes. The obtained mode structures are compared with the results of ideal MHD simulations. The observed scaling of the linear growth rate with the toroidal mode number is consistent with the ideal MHD theory. The simulation results verify the GTC capability of simulating MHD processes in toroidal plasmas

Neurexin Superfamily Cell Membrane Receptor Contactin-Associated Protein Like-4 (Cntnap4) Is Involved in Neural EGFL-Like 1 (Nell-1)-Responsive Osteogenesis

Contactin-associated protein-like 4 (Cntnap4) is a member of the neurexin superfamily of transmembrane molecules that have critical functions in neuronal cell communication. Cntnap4 knockout mice display decreased presynaptic gamma-aminobutyric acid (GABA) and increased dopamine release that is associated with severe, highly penetrant, repetitive, and perseverative movements commonly found in human autism spectrum disorder patients. However, no known function of Cntnap4 has been revealed besides the nervous system. Meanwhile, secretory protein neural EGFL-like 1 (Nell-1) is known to exert potent osteogenic effects in multiple small and large animal models without the off-target effects commonly found with bone morphogenetic protein 2. In this study, while searching for a Nell-1-specific cell surface receptor during osteogenesis, we identified and validated a ligand/receptor-like interaction between Nell-1 and Cntnap4 by demonstrating: 1) Nell-1 and Cntnap4 colocalization on the surface of osteogenic-committed cells; 2) high-affinity interaction between Nell-1 and Cntnap4; 3) abrogation of Nell-1-responsive Wnt and MAPK signaling transduction, as well as osteogenic effects, via Cntnap4 knockdown; and 4) replication of calvarial cleidocranial dysplasias-like defects observed in Nell-1-deficient mice in Wnt1-Cre-mediated Cntnap4-knockout transgenic mice. In aggregate, these findings indicate that Cntnap4 plays a critical role in Nell-1-responsive osteogenesis. Further, this is the first functional annotation for Cntnap4 in the musculoskeletal system. Intriguingly, Nell-1 and Cntnap4 also colocalize on the surface of human hippocampal interneurons, implicating Nell-1 as a potential novel ligand for Cntnap4 in the nervous system. This unexpected characterization of the ligand/receptor-like interaction between Nell-1 and Cntnap4 indicates a novel biological functional axis for Nell-1 and Cntnap4 in osteogenesis and, potentially, in neural development and function. © 2018 American Society for Bone and Mineral Research. © 2018 American Society for Bone and Mineral Researc

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

A generative AI model of urban spaces in the face of climate change

Urban land use structures have impact on local climate conditions. To shed light on the mechanisms of local climate w.r.t. urban land use, we present a data-driven, deep learning approach based on airborne LiDAR data statistics and the Landsat 8 satellite’s surface temperature product. Our study proposes a deep neural network architecture and data workflow to model the following question: How to vary a geospatial scene’s ambient temperature by modifying without bias its urban land use structures represented by LiDAR statistics? We model the phrase modify without bias by a constraint that allows all LiDAR statistics features to contribute to modelled temperature variations at same order of magnitude. In contrast to regular deep learning neural network optimization, we consider fixed model parameters, but variation of model input data, i.e. LiDAR statistics. The novelty of this thesis comprises of the introduction and evaluation of a deep neural network architecture that correlates vegetation and ambient temperatures from remote sensing modalities. The concept helps to approximate the climate resilience of urban areas. By analyzing numerous vegetation vs. temperature change pairs, we develop a statistical evaluation procedure to perform correlation analysis. The approach generates a qualitative answer to the question posed above: When statistically averaged over New York City with focus on the Queens borough, an increase in vegetation correlates with a decrease in ambient surface temperature with likelihood of 95%. Our contribution likes to inspire the development of urban heat island mitigation strategies in the face of climate change