Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Effect of typical low-impact development measures on control of thermal loads from urban stormwater runoff.

Ground hardening in urban areas increases the risk of thermal enrichment in surface rainwater runoff. Moreover, the thermal pollution from rainwater runoff has become an important problem that damages the urban aquatic environment. Current studies have focused mainly on the potential hazard caused by runoff thermal pollution to aquatic microorganisms. However, there are few studies on the efficacy of controlling runoff thermal load through low-impact development (LID) and renovation in urban areas. The effects of LID modification were evaluated by monitoring the characteristics of the runoff thermal load on each underlying surface in the study area and conducting laboratory-scale bioretention experiments. The results showed that the initial thermal effect of each underlying surface was significant after the start of rainfall, based on the thermal load. Ceramic granules are remarkable bioretention fillers. Their average heat load and volume reduction rates are 55.6 % and 32.7 %, respectively. After LID modification, the thermal load of surface runoff in the study area decreased to 73.42 % under similar rainfall conditions. After the secondary treatment of the bioretention facility, the total thermal load of the outflow facility was 31.40 % of that before renovation. The peak thermal load reduced by 69.15 % and was delayed for 10 min. The control effect differed statistically.

Development of novel polymorphic microsatellite markers for Picea brachytyla.

BACKGROUND: Picea brachytyla is a unique tree species in China. Due to being extensively exploited in the past, it is listed as Vulnerable in the IUCN Red List. It is mainly distributed across the Hengduan and Daba-Qinglin mountains and has been found in other areas including Sichuan Province and Qinghai Province, China. Microsatellites, or simple sequence repeats (SSRs), are widely used in correlational studies of genetic protection. Few markers have been developed for P. brachytyla because of the small number of trees and scholarly resources available for study. METHODS AND RESULTS: The genomic DNA of P. brachytyla was sequenced using the DNBSEQ platform, and unigenes were obtained after assembly and deredundancy. Of the 100 primer pairs screened, we isolated 10 useful microsatellite loci from P. brachytyla genes. The observed and expected heterozygosity values ranged from 0.173 (P24) to 0.788 (P79; mean 0.469) and 0.199 (P87) to 0.911 (P79; mean 0.700), respectively. Polymorphism-information content (PIC) ranged from 0.190 (P84) to 0.904 (P79; mean 0.666). Only P84 and P72 were in a Hardy-Weinberg equilibrium (P > 0.05) in the different P. brachytyla populations. All the levels of linkage disequilibrium (LD) were high for the 10 SSR loci indicating that there were no autocorrelations among the 10 SSR loci. CONCLUSIONS: The novel polymorphic microsatellite markers showed high polymorphism for P. brachytyla. These polymorphic microsatellites can provide a basis for future conservation and genetic research on this rare plant species.

Effects of the Structure of TiO2 Nanotube Arrays on Its Catalytic Activity for Microbial Fuel Cell.

To enhance the microbial fuel cell (MFC) for wastewater treatment and chemical oxygen demand degradation, TiO2 nanotubes arrays (TNA) are successfully synthesized on Ti foil substrate by the anodization process in HF and NH4F solution, respectively (hereafter, denoted as TNA-HF and TNA-NF). The differences between the two kinds of TNA are revealed based on their morphologies and spectroscopic characterizations. It should be highlighted that 3D TNA-NF with an appropriate dimension can make a positive contribution to the high photocatalytic activity. In comparison with the TNA-HF, the 3D TNA-NF sample exhibits a significant enhancement in current generation as the MFC anode. In particular, the TNA-NF performs nearly 1.23 times higher than the TNA-HF, and near twofold higher than the carbon felt. It is found that the two kinds of TiO2-based anodes have different conductivities and corrosion potentials, which are responsible for the difference in their current generation performances. Based on the experimental results, excellent stability, reliability, and low cost, TNA-NF can be considered a promising and scalable MFC bioanode material.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer's disease.

Alzheimer's disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions. Although connections between changes in brain networks of Alzheimer's disease patients have been established, the mechanisms that drive these alterations remain incompletely understood. This study, which was conducted in 2018 at Northeastern University in China, included data from 97 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset covering genetics, imaging, and clinical data. All participants were divided into two groups: normal control (n = 52; 20 males and 32 females; mean age 73.90 ± 4.72 years) and Alzheimer's disease (n = 45, 23 males and 22 females; mean age 74.85 ± 5.66). To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer's disease patients, we proposed a local naïve Bayes brain network model based on graph theory. Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined, including clustering coefficient, modularity, characteristic path length, network efficiency, betweenness, and degree distribution compared with empirical methods. This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer's disease patients. Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions. The ADNI was performed in accordance with the Good Clinical Practice guidelines, US 21CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards (IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards (IRBs)/Research Ethics Boards (REBs).