Constructing a resilient ecological network by considering source stability in the largest Chinese urban agglomeration.

The dynamics of ecological sources and their impact on the resilience of ecological networks (ENs) have attracted increasing attention from both researchers and managers. Although a couple of studies have recognized the source-loss effects on network resilience, there is a knowledge gap in integrating spatiotemporal changes of the sources while constructing resilient ENs. Here, we propose the concept of dynamic stability (DS) to explore the sources' changes over a certain period and improve source identification by grading the DS in the largest urban agglomeration located in the middle reaches of the Yangtze River in China. An investigation of the five selected ecosystem service (ES) indicators in 2000, 2010, and 2018 identified 49, 54, and 68 preliminary sources, respectively, from which 11, 14, and three sources were extracted, respectively, with high, moderate, and low levels of DS, respectively. A three-tier EN was constructed by considering both the ESs and DSs of the extracted sources. The constructed network was scale-free and featured in small world in topology analysis. Moreover, a carefully designed attack test found that this EN was of good resilience as the three critical nodes that might cause a marked decay of resilience were in high or moderate DSs and were preferentially protected by the Ecological Conservation Red Line policy in China. In conclusion, the improved approach of considering the DSs of sources may help to precisely identify and protect the critical nodes threatening network resilience, which is highly desired in constructing ENs facing various rapid changes, especially in large-scale urbanized areas.

Monolithic 6 × 6 transmitter-router with simultaneous sub-nanosecond port and wavelength switching.

A monolithic 6 × 6 transmitter-router with both port and wavelength switching at sub-nanosecond speed is proposed and experimentally demonstrated. Based on an intra-cavity cyclic echelle diffraction grating router (EDGR) and semiconductor optical amplifier (SOA) arrays, each selectable output port can realize a selected multi-wavelength laser (MWL) output. The measurement results show that all 36 input-output combinations have a single-mode emission spectrum with a sidemode suppression ratio (SMSR) over 30 dB. Simultaneous switching of six laser wavelengths is achieved together with the switching of the output port by a single electrode selection. The switching time is less than 1 ns. It can offer a cost-effective solution to multi-wavelength multi-port optical transmitter-routers for fast distributed optical switching in datacenters and high-performance computers (HPCs).

Assessment of PM2.5-related health effects: A comparative study using multiple methods and multi-source data in China.

In China, PM2.5 pollution has caused extensive death and economic loss. Thus, an accurate assessment of the spatial distribution of these losses is crucial for delineating priority areas for air pollution control in China. In this study, we assessed the PM2.5 exposure-related health effects according to the integrated exposure risk function and non-linear power law (NLP) function in 338 prefecture-level cities in China by utilizing online monitoring data and the PM2.5 Hindcast Database (PHD). Our results revealed no significant difference between the monitoring data and PHD (p value = 0.66 > 0.05). The number of deaths caused by PM2.5-related Stroke (cerebrovascular disease), ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer at the national level estimated through the NLP function was 0.27 million (95% CI: 0.06-0.50), 0.23 million (95% CI: 0.08-0.38), 0.31 million (95% CI: 0.04-0.57), and 0.31 million (95% CI: 0.16-0.40), respectively. The total economic cost at the national level in 2016 was approximately US$80.25 billion (95% CI: 24.46-132.25). Based on a comparison of Z statistics, we propose that the evaluation results obtained using the NLP function and monitoring data are accurate. Additionally, according to scenario simulations, Beijing, Chongqing, Tianjin, and other cities should be priority areas for PM2.5 pollution control to achieve considerable health benefits. Our statistics can help improve the accuracy of PM2.5-related health effect assessments in China.

Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data.

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R 2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the "gate" concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

Comprehensive dynamic analysis of proteins in the spermatheca of female Haemaphysalis longicornis after copulation.

Ticks are obligate blood-sucking parasitic arthropods. When sucking the blood of hosts, they can also transmit a variety of pathogens to hosts that severely endanger the health of humans and animals. The spermatheca is an organ for the storage and protection of sperm and an important component of the reproductive system of female ticks. The spermatheca content changes dramatically over time after copulation. In particular, some proteins and polypeptide substances can influence the physiological functions of female ticks and promote blood feeding and egg laying by female ticks. To investigate the molecular mechanisms underlying the productive process of Haemaphysalis longicornis, data-independent acquisition (DIA) quantitative proteomics technology was used to perform in-depth research of the dynamic changes in all proteins in the spermatheca of ticks within a short time after copulation to look for key proteins in the spermatheca contents after copulation that affect the reproduction of female ticks in order to provide meaningful information for the comprehensive prevention and control of ticks.