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Since improving floodwater utilization may increase flood risk, flood risk control methods for trade-offs between these factors have research value. This study presented a flood risk control method oriented towards floodwater utilization which considers multiple main flood risk factors. The proposed method not only achieves the boundaries of the flood limited water level (FLWL) under various acceptable risks but also dynamically controls the water level to enhance floodwater utilization. A case study conducted on the Danjiangkou reservoir yielded the following results: (1) The proposed method provides FLWL dynamic control boundaries under various acceptable risks. (2) The proposed method reveals the potential to raise the FLWL, with a possibility to raise it by 1.00 m above the present FLWL under the absence of flood risk. (3) The available flood resources in both the wet and dry seasons increase, on average, by 0.83 and 0.81 billion m3, and the flood risk remains within the acceptable range after raising the FLWL by 1.00 m, which contributes to enhancing floodwater utilization.

Interest in edible and medicinal macrofungi is millennial in terms of their uses in health and food products in Central Asia, while interest in inedible and medicinal macrofungi has grown in popularity in recent years. Edible and inedible medicinal basidiomycetes were collected during field surveys from different regions of Uzbekistan. The morphological characters and similarity assessment of rDNA-Internal Transcribed Spacer sequence data were used to measure diversity and habitat associations. A number of 17 species of medicinal macrofungi of ethnomycological and medicinal interest was found associated with 23 species of trees and shrubs belonging to 11 families and 14 genera. Polyporaceae and Hymenochaetaceae were represented by the highest number of species followed by Ganodermataceae, Fomitopsidaceae, Auriculariaceae, Cerrenaceae, Grifolaceae, Phanerochaetaceae, Laetiporaceae, Schizophyllaceae, and Stereaceae. The highest number of medicinal basidiomycete species was reported in the following host genera: Acer, Betula, Celtis, Crataegus, Juglans, Juniperus, Lonicera, Malus, Morus, Platanus, Populus, Prunus, Quercus, and Salix. An updated list of edible and inedible medicinal mushrooms identified in Uzbekistan, their morphological characteristics, and phylogenetic placement are given for the first time. Information is provided on their uses in traditional and modern medicine. Their bioactive compounds and extracts can be applied as medicines, as well as food and cosmetic ingredients.

Sanghuangporus sanghuang is a large wood-decaying mushroom highly valued in traditional Chinese medicine due to its medicinal properties, including hypoglycemic, antioxidant, antitumor, and antibacterial properties effects. Its key bioactive compounds include flavonoids and triterpenoids. Specific fungal genes can be selectively induced by fungal elicitors. To investigate the effect of fungal polysaccharides derived from Perenniporia tenuis mycelia on the metabolites of S. sanghuang, we conducted metabolic and transcriptional profiling with and without elicitor treatment (ET and WET, respectively). Correlation analysis showed significant differences in triterpenoid biosynthesis between the ET and WET groups. In addition, the structural genes associated with triterpenoids and their metabolites in both groups were verified using quantitative real-time polymerase chain reaction (qRT-PCR) and high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS). Through metabolite screening, three triterpenoids were identified: betulinol, betulinic acid, and 2-hydroxyoleanolic acid. Excitation treatment increased the level of betulinic acid by 2.62-fold and 2-hydroxyoleanolic acid by 114.67-fold compared to WET. The qRT-PCR results of the four genes expressed in secondary metabolic pathways, defense gene activation, and signal transduction showed significant variation between the ET and WET groups. Overall, our study suggests that the fungal elicitor induced the aggregation of pentacyclic triterpenoid secondary metabolites in S. sanghuang.

Obtaining more accurate flood information downstream of a reservoir is crucial for guiding reservoir regulation and reducing the occurrence of flood disasters. In this paper, six popular ML models, including the support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), multilayer perceptron (MLP), long short-term memory (LSTM) and gated recurrent unit (GRU) models, were selected and compared for their effectiveness in flood routing of two complicated reaches located at the upper and middle main stream of the Yangtze River. The results suggested that the performance of the MLP, LSTM and GRU models all gradually improved and then slightly decreased as the time lag increased. Furthermore, the MLP, LSTM and GRU models outperformed the SVR, GPR and RFR models, and the GRU model demonstrated superior performance across a range of efficiency criteria, including mean absolute percentage error (MAPE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), Taylor skill score (TSS) and Kling–Gupta efficiency (KGE). Specifically, the GRU model achieved reductions in MAPE and RMSE of at least 7.66% and 3.80% in the first case study and reductions of 19.51% and 11.76% in the second case study. The paper indicated that the GRU model was the most appropriate choice for flood routing in the Yangtze River.

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC.

Recent advances in deep learning, especially the long short-term memory (LSTM) networks, provide some useful insights on how to tackle time series prediction problems, not to mention the development of a time series model itself for prediction. Runoff forecasting is a time series prediction problem with a series of past runoff data (water level and discharge series data) as inputs and a fixed-length series of future runoff as output. Most previous work paid attention to the sufficiency of input data and the structural complexity of deep learning, while less effort has been put into the consideration of data quantity or the processing of original input data—such as time series decomposition, which can better capture the trend of runoff—or unleashing the effective potential of deep learning. Mutual information and seasonal trend decomposition are two useful time series methods in handling data quantity analysis and original data processing. Based on a former study, we proposed a deep learning model combined with time series analysis methods for daily runoff prediction in the middle Yangtze River and analyzed its feasibility and usability with frequently used counterpart models. Furthermore, this research also explored the data quality that affect the performance of the deep learning model. With the application of the time series method, we can effectively get some information about the data quality and data amount that we adopted in the deep learning model. The comparison experiment resulted in two different sites, implying that the proposed model improved the precision of runoff prediction and is much easier and more effective for practical application. In short, time series analysis methods can exert great potential of deep learning in daily runoff prediction and may unleash great potential of artificial intelligence in hydrology research.

In order to study the crystallinity of different density polyethylenes, this paper conducts an experimental study on the transformation of the conductance mechanism under a high electric field. In this experiment, X-ray diffraction (XRD), differentials scanning calorimetry (DSC), direct current (DC) breakdown of low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE), medium-density polyethylene (MDPE) and high-density polyethylene (HDPE), as well as the conductivity characteristics under an electric field of 5−200 kV/mm are tested. In addition, the electric field−current density curves of the four kinds of polyethylene are fitted to analyze their conductance transition in non-ohmic regions under different high field strengths, through applying the mathematical formula of a variety of conductance mechanisms. The experimental results are as follows: as the density of polyethylene increases, the crystallinity increases continuously. Moreover, the continuous increase of crystallinity causes the electric conduction flow under the same field strength to decrease significantly. The field strength corresponding to the two turning points in the conductance characteristic curve increases simultaneously, and the breakdown field strength increases accordingly; through analysis, it is found that in the high field, as the electric field increases, the conductance mechanism develops from the ohmic conductance of the low field strength region to the bulk effect of the high field strength region (Poole−Frenkel effect). Then, it develops into the electrode effect to the high field strength (Schottky effect), although the threshold field strength of this conductance mechanism transition increases with the increase of crystallinity.

In order to study the effects of the crystallinity of polyethylene with different densities on breakdown strength and conductance properties, this paper mainly tests the X-ray diffraction (XRD), different scanning calorimeter (DSC), direct current (DC) breakdown and conductance properties of low-density polyethylene (LDPE), linear low density polyethylene (LLDPE), medium density polyethylene (MDPE), and high-density polyethylene (HDPE), and further analyzes the experimental results separately. The results show that an increase in the density of polyethylene leads to the continuous improvement of crystallinity, and an increase in crystallinity causes a significant decrease in the conduction current at the same field strength. The field strength corresponding to the two turning points in the conductance characteristic curve increases simultaneously.

Electric potential plays an indispensable role in tissue engineering and wound healing. Piezoelectric nanogenerators based on direct piezoelectric effects can be self-powered energy sources for electrical stimulation and have attracted extensive attention. However, the accuracy of piezoelectric stimuli on piezoelectric polymers membranes in vitro during the dynamic condition is rarely studied. Here, a self-powered tunable electrical stimulation system for assisting the proliferation of preosteoblasts was achieved by well-aligned P(VDF-TrFE) piezoelectric nanofiber membrane (NFM) both as a nanogenerator (NG) and as a scaffold. The effects of electrospinning and different post-treatments (annealing and poling) on the surface wettability, piezoelectric β phase, ferroelectric properties, and sensing performance of NFMs were evaluated here. The polarized P(VDF-TrFE) NFM offered an enhanced piezoelectric value (d31 of 22.88 pC/N) versus pristine P(VDF-TrFE) NFM (d31 of 0.03 pC/N) and exhibited good sensing performance. The maximum voltage and current output of the P(VDF-TrFE) piezoelectric nanofiber NGs reached −1.7 V and 41.5 nA, respectively. An accurate electrical response was obtained in real time under dynamic mechanical stimulation by immobilizing the NGs on the flexible bottom of the culture plate, thereby restoring the real scene of providing electrical stimulation to the cells in vitro. In addition, we simulated the interaction between the piezoelectric nanofiber NG and cells through an equivalent circuit model. To verify the feasibility of P(VDF-TrFE) nanofiber NGs as an exact electrical stimulation, the effects of different outputs of P(VDF-TrFE) nanofiber NGs on cell proliferation in vitro were compared. The study realized a significant enhancement of preosteoblasts proliferation. This work demonstrated the customizability of P(VDF-TrFE) piezoelectric nanofiber NG for self-powered electrical stimulation system application and suggested its significant potential application for tissue repair and regeneration.

Self-powered operation, flexibility, excellent mechanical properties, and ultra-high sensitivity are highly desired properties for pressure sensors in human health monitoring and anthropomorphic robotic systems. Piezoelectric pressure sensors, with enhanced electromechanical performance to effectively distinguish multiple mechanical stimuli (including pressing, stretching, bending, and twisting), have attracted interest to precisely acquire the weak signals of the human body. In this work, we prepared a poly(vinylidene fluoride-trifluoroethylene)/ multi-walled carbon nanotube (P(VDF-TrFE)/MWCNT) composite by an electrospinning process and stretched it to achieve alignment of the polymer chains. The composite membrane demonstrated excellent piezoelectricy, favorable mechanical strength, and high sensitivity. The piezoelectric coefficient d33 value was approximately 50 pm/V, the Young’s modulus was ~0.986 GPa, and the sensitivity was ~540 mV/N. The resulting composite membrane was employed as a piezoelectric pressure sensor to monitor small physiological signals including pulse, breath, and small motions of muscle and joints such as swallowing, chewing, and finger and wrist movements. Moderate doping with carbon nanotubes had a positive impact on the formation of the β phase of the piezoelectric device, and the piezoelectric pressure sensor has the potential for application in health care systems and smart wearable devices.

Several viruses within the Coronaviridae family have been categorized as either emerging or re-emerging human pathogens, with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) being the most well known. The NIAID-sponsored Virus Pathogen Database and Analysis Resource (ViPR, www.viprbrc.org) supports bioinformatics workflows for a broad range of human virus pathogens and other related viruses, including the entire Coronaviridae family. ViPR provides access to sequence records, gene and protein annotations, immune epitopes, 3D structures, host factor data, and other data types through an intuitive web-based search interface. Records returned from these queries can then be subjected to web-based analyses including: multiple sequence alignment, phylogenetic inference, sequence variation determination, BLAST comparison, and metadata-driven comparative genomics statistical analysis. Additional tools exist to display multiple sequence alignments, view phylogenetic trees, visualize 3D protein structures, transfer existing reference genome annotations to new genomes, and store or share results from any search or analysis within personal private ‘Workbench’ spaces for future access. All of the data and integrated analysis and visualization tools in ViPR are made available without charge as a service to the Coronaviridae research community to facilitate the research and development of diagnostics, prophylactics, vaccines and therapeutics against these human pathogens.

The decoration of noble metal nanoparticles (NPs) on the surface of metal oxide semiconductors to enhance material characteristics and gas-sensing performance has recently attracted increasing attention from researchers worldwide. Here, we have synthesized porous silicon (PS)/WO3 nanorods (NRs) functionalized with Pd NPs to enhance NO2 gas-sensing performance. PS was first prepared using electrochemical methods and worked as a substrate. WO3 NRs were synthesized by thermally oxidizing W film on the PS substrate. Pd NPs were decorated on the surface of WO3 NRs via in-situ reduction of the Pd complex solution by using Pluronic P123 as the reducing agent. The gas-sensing characteristics were tested at different gas concentrations and different temperatures ranging from room temperature to 200 °C. Results revealed that, compared with bare PS/WO3 NRs and Si/WO3 NRs functionalized with Pd NPs, the Pd-decorated PS/WO3 NRs exhibited higher and quicker responses to NO2, with a detection concentration as low as 0.25 ppm and a maximum response at room temperature. The gas-sensing mechanism was also investigated and is discussed in detail. The high surface area to volume ratio of PS and the reaction-absorption mechanism can be explained the enhanced sensing performance.

Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the distribution functions and the cumulative distribution functions of each parameter, which are applied to dispersing the parameters and calculating the differential warning values, are calculated by using the two-parameter Weibull model. To calculate the membership functions of parameters for each failure mode, the Apriori algorithm is chosen to mine the association rules between parameters and failure modes. After that, the failure probability of each failure mode is obtained by integrating the membership functions of different parameters by a weighted method, and the important weight of each parameter is calculated by the differential warning values. According to the failure probability calculation result, the series model is established to estimate the failure probability of the equipment. Finally, an application example for two 220 kV transformers is presented to show the detailed process of the method. Compared with traditional fault diagnosis methods, the calculation results not only identify the failure modes correctly, but also reflect the failure probability changing trend of the equipment accurately.