Which Factors Can Contribute to the Success of Environmental and Animal Protection Projects in Donation-based Crowdfunding? A Neural Network Model

The crowdfunding industry has developed rapidly in recent years, the existing research shows that crowdfunding can help in many fields such as entrepreneurship, creative products, or donations. Due to global meteorological issues, more and more people are paying attention to the environment and animal protection. However, fundraising in these areas has been the biggest problem, the emergence of donation crowdfunding (DCF) can alleviate this dilemma. Currently, in academia, there is still less research focused on crowdfunding for environmental and animal protection. This paper aims to study the factors influencing the successful financing of environmental and animal protection projects in the DCF. This paper analyses 700 DCF environmental and animal protection projects in China as samples, and creatively introduces financial transparency scoring indicators. Through binary logistic regression, financial transparency was found to be the most critical positive factor affecting project success. At the same time, donors receive NPO-initiated projects well, and the number of donors can also positively impact the results. However, the excessive description of the projects can have the opposite effect. This study also introduced a neural network model, and found that the neural network model can optimize the discriminant accuracy of the traditional binary logistic regression model

Dietary glycaemic index, glycaemic load and head and neck cancer risk: A pooled analysis in an international consortium

High dietary glycaemic index (GI) and glycaemic load (GL) may increase cancer risk. However, limited information was available on GI and/or GL and head and neck cancer (HNC) risk. We conducted a pooled analysis on 8 case-control studies (4081 HNC cases; 7407 controls) from the International Head and Neck Cancer Epidemiology (INHANCE) consortium. We estimated the odds ratios (ORs) and 95% confidence intervals (CIs) of HNC, and its subsites, from fixed- or mixed-effects logistic models including centre-specific quartiles of GI or GL. GI, but not GL, had a weak positive association with HNC (O

Generating Multimode Entangled Microwaves with a Superconducting Parametric Cavity

In this Letter, we demonstrate the generation of multimode entangled states of propagating microwaves. The entangled states are generated by parametrically pumping a multimode superconducting cavity. By combining different pump frequencies, applied simultaneously to the device, we can produce different entanglement structures in a programable fashion. The Gaussian output states are fully characterized by measuring the full covariance matrices of the modes. The covariance matrices are absolutely calibrated using an in situ microwave calibration source, a shot noise tunnel junction. Applying a variety of entanglement measures, we demonstrate both full inseparability and genuine tripartite entanglement of the states. Our method is easily extensible to more modes.Comment: 5 pages, 1 figures, 1 tabl

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine

Prospects for a Multi-TeV Gamma-ray Sky Survey with the LHAASO Water Cherenkov Detector Array

The Water Cherenkov Detector Array (WCDA) is a major component of the Large High Altitude Air Shower Array Observatory (LHAASO), a new generation cosmic-ray experiment with unprecedented sensitivity, currently under construction. The WCDA is aimed at the study of TeV $\gamma$-rays. In order to evaluate the prospects of searching for TeV $\gamma$-ray sources with the WCDA, we present in this paper a projection for the one-year sensitivity of the WCDA to TeV $\gamma$-ray sources from TeVCat (footnote: http://tevcat.uchicago.edu) using an all-sky approach. Out of 128 TeVCat sources observable to the WCDA up to a zenith angle of $45^\circ$, we estimate that 42 would be detectable for one year of observations at a median energy of 1 TeV. Most of them are Galactic sources, and the extragalactic sources are Active Galactic Nuclei (AGN)

Conceptual planning of urban–rural green space from a multidimensional perspective: A case study of Zhengzhou, China

The structure and function of green-space system is an eternal subject of landscape architecture, especially due to limited land and a need for the coordinated development of PLEs (production, living, and ecological spaces). To make planning more scientific, this paper explored green-space structure planning via multidimensional perspectives and methods using a case study of Zhengzhou. The paper applies theories (from landscape architecture and landscape ecology) and technologies (like remote sensing, GIS—geographic information system, graph theory, and aerography) from different disciplines to analyze current green-space structure and relevant physical factors to identify and exemplify different green-space planning strategies. Overall, our analysis reveals that multiple green-space structures should be considered together and that planners and designers should have multidisciplinary knowledge. For specific strategies, the analysis finds (i) that green complexes enhance various public spaces and guide comprehensive development of urban spaces; (ii) that green ecological corridors play a critical role in regional ecological stability through maintaining good connectivity and high node degree (Dg) and betweenness centrality index (BC) green spaces; (iii) that greenway networks can integrate all landscape resources to provide more secured spaces for animals and beautiful public spaces for humans; (iv) that blue-green ecological networks can help rainwater and urban flooding disaster management; and (v) that green ventilation corridors provide air cleaning and urban cooling benefits, which can help ensure healthy and comfortable urban–rural environments. In our view, this integrated framework for planning and design green-space structure helps make the process scientific and relevant for guiding future regional green-space structure

Dark Energy Survey Year 3 results: Cosmology with moments of weak lensing mass maps - Validation on simulations

We present a simulated cosmology analysis using the second and third moments of the weak lensing mass (convergence) maps. The second moment, or variances, of the convergence as a function of smoothing scale contains information similar to standard shear two-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The analysis is geared towards the third year (Y3) data from the Dark Energy Survey (DES), but the methodology can be applied to other weak lensing data sets. We present the formalism for obtaining the convergence maps from the measured shear and for obtaining the second and third moments of these maps given partial sky coverage. We estimate the covariance matrix from a large suite of numerical simulations. We test our pipeline through a simulated likelihood analyses varying 5 cosmological parameters and 10 nuisance parameters and identify the scales where systematic or modelling uncertainties are not expected to affect the cosmological analysis. Our simulated likelihood analysis shows that the combination of second and third moments provides a 1.5 per cent constraint on S8 σ8(ωm/0.3)0.5 for DES Year 3 data. This is 20 per cent better than an analysis using a simulated DES Y3 shear two-point statistics, owing to the non-Gaussian information captured by the inclusion of higher order statistics. This paper validates our methodology for constraining cosmology with DES Year 3 data, which will be presented in a subsequent paper. © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

San Francisco Hep B Free: A Grassroots Community Coalition to Prevent Hepatitis B and Liver Cancer

Chronic hepatitis B is the leading cause of liver cancer and the largest health disparity between Asian/Pacific Islanders (APIs) and the general US population. The Hep B Free model was launched to eliminate hepatitis B infection by increasing hepatitis B awareness, testing, vaccination, and treatment among APIs by building a broad, community-wide coalition. The San Francisco Hep B Free campaign is a diverse public/private collaboration unifying the API community, health care system, policy makers, businesses, and the general public in San Francisco, California. Mass-media and grassroots messaging raised citywide awareness of hepatitis B and promoted use of the existing health care system for hepatitis B screening and follow-up. Coalition partners reported semi-annually on activities, resources utilized, and system changes instituted. From 2007 to 2009, over 150 organizations contributed approximately $1,000,000 in resources to the San Francisco Hep B Free campaign. 40 educational events reached 1,100 healthcare providers, and 50% of primary care physicians pledged to screen APIs routinely for hepatitis B. Community events and fairs reached over 200,000 members of the general public. Of 3,315 API clients tested at stand-alone screening sites created by the campaign, 6.5% were found to be chronically infected and referred to follow-up care. A grassroots coalition that develops strong partnerships with diverse organizations can use existing resources to successfully increase public and healthcare provider awareness about hepatitis B among APIs, promote routine hepatitis B testing and vaccination as part of standard primary care, and ensure access to treatment for chronically infected individuals

Optimisation of biomass, exopolysaccharide and intracellular polysaccharide production from the mycelium of an identified Ganoderma lucidum strain QRS 5120 using response surface methodology

Wild-cultivated medicinal mushroom Ganoderma lucidum was morphologically identified and sequenced using phylogenetic software. In submerged-liquid fermentation (SLF), biomass, exopolysaccharide (EPS) and intracellular polysaccharide (IPS) production of the identified G. lucidum was optimised based on initial pH, starting glucose concentration and agitation rate parameters using response surface methodology (RSM). Molecularly, the G. lucidum strain QRS 5120 generated 637 base pairs, which was commensurate with related Ganoderma species. In RSM, by applying central composite design (CCD), a polynomial model was fitted to the experimental data and was found to be significant in all parameters investigated. The strongest effect (p lt 0.0001) was observed for initial pH for biomass, EPS and IPS production, while agitation showed a significant value (p lt 0.005) for biomass. By applying the optimized conditions, the model was validated and generated 5.12 g/L of biomass (initial pH 4.01, 32.09 g/L of glucose and 102 rpm), 2.49 g/L EPS (initial pH 4, 24.25 g/L of glucose and 110 rpm) and 1.52 g/L of IPS (and initial pH 4, 40.43 g/L of glucose, 103 rpm) in 500 mL shake flask fermentation. The optimized parameters can be upscaled for efficient biomass, EPS and IPS production using G. lucidum

The impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys

Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distributions is to apply weights to a spectroscopic sample, so that their weighted photometry distribution matches the target sample. In this work, we estimate the selection bias in redshift that is introduced in this procedure. We do so by simulating the process of assembling a spectroscopic sample (including observer-assigned confidence flags) and highlight the impacts of spectroscopic target selection and redshift failures. We use the first year (Y1) weak lensing analysis in Dark Energy Survey (DES) as an example data set but the implications generalize to all similar weak lensing surveys. We find that using colour cuts that are not available to the weak lensing galaxies can introduce biases of up to Δz ∼ 0.04 in the weighted mean redshift of different redshift intervals (Δz ∼ 0.015 in the case most relevant to DES). To assess the impact of incompleteness in spectroscopic samples, we select only objects with high observer-defined confidence flags and compare the weighted mean redshift with the true mean. We find that the mean redshift of the DES Y1 weak lensing sample is typically biased at the Δz = 0.005−0.05 level after the weighting is applied. The bias we uncover can have either sign, depending on the samples and redshift interval considered. For the highest redshift bin, the bias is larger than the uncertainties in the other DES Y1 redshift calibration methods, justifying the decision of not using this method for the redshift estimations. We discuss several methods to mitigate this bias