scholarly journals The spatio-temporal distribution of Oncomelania hupensis along Yangtze river in Jiangsu Province, China after implementation of a new, integrated schistosomiasis control strategy

2016 ◽  
Vol 11 (3) ◽  
Author(s):  
Jian He ◽  
Wei Li ◽  
Robert Bergquist ◽  
Jian-Feng Zhang ◽  
Liang Shi ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Control Strategy ◽  
Yangtze River ◽  
High Density ◽  
Jiangsu Province ◽  
Oncomelania Hupensis ◽  
Risk Area ◽  
Schistosomiasis Control ◽  
Spatio Temporal ◽  
Strong Control

This study concerns an integrated strategy of schistosomiasis control, focusing on the management and elimination of the main transmission cycles and reservoirs along Yangtze river, Jiangsu Province (China), instituted in 2004. Our analysis, including mapping, spatial autocorrelation and spatio-temporal scanning, was implemented between 2001 and 2013 to explore the changes in the distribution of <em>Oncomelania hupensis</em>, the intermediate host of <em>Schistosoma japonicum</em>. Two high-density snail locations in the upper and middle reaches of the river were observed along with one high-risk area due to infected snails in the upper reaches at the beginning of the study period. The number of high-density snail habitats declined sharply after 2004 and infected snails disappeared completely by 2010. Global spatial autocorrelation showed spatial clustering of snails in general, as well as of infected ones when snail densities were relatively high, while local spatial autocorrelation showed the number of specific clusters declining and switching spatially from the upper to the middle reaches of the Yangtze river in Jiangsu Province during the study period. The integrated snail control strategy was found to be effective, but the middle reaches of the river will require continued strong control resources.

scholarly journals Effectiveness of the new integrated strategy to control the transmission of Schistosoma japonicum in China: a systematic review and meta-analysis

Parasite ◽  
2018 ◽  
Vol 25 ◽  
pp. 54 ◽  
Author(s):  
Chunyan Qian ◽  
Yuefeng Zhang ◽  
Xinyan Zhang ◽  
Chao Yuan ◽  
Zhichao Gao ◽  
...  
Keyword(s):  
Schistosoma Japonicum ◽  
Control Strategy ◽  
Meta Analysis ◽  
Infection Risk ◽  
Oncomelania Hupensis ◽  
Local Conditions ◽  
Schistosomiasis Control ◽  
Integrated Strategy ◽  
New Strategy ◽  
Morbidity Control

Since 2004, the national schistosomiasis control strategy in China has shifted from the morbidity control strategy (conventional strategy) to an integrated strategy (new strategy). We investigated the effectiveness of the new strategy and compared it against the conventional strategy. We retrieved from electronic databases the literature regarding the new strategy published from 2000 to 2017. The effect of the new or conventional strategy on infection by Schistosoma japonicum of humans and snails (Oncomelania hupensis) was evaluated with pooled log relative risk (logRR). A total of only eight eligible publications were included in the final meta-analysis. The results showed that implementation of the new strategy reduced the infection risk by 3–4 times relative to the conventional strategy. More specifically, the conventional strategy caused a reduction in both human (logRR = 0.56, 95% CI: 0.12–0.99) and snail infections (logRR = 0.34, 95% CI: −0.69–1.37), while the new strategy also significantly reduced both human (logRR = 1.89, 95% CI: 1.33–2.46) and snail infections (logRR = 1.61, 95% CI: 1.06–2.15). In contrast to the conventional strategy, the new strategy appeared more effective to control both human (logRR difference = 1.32, 95% CI: 0.78–1.86) and snail infections (logRR difference = 1.53, 95% CI: 0.76–2.31). Our data demonstrate that the new integrated strategy is highly effective to control the transmission of S. japonicum in China, and this strategy is recommended for schistosomiasis elimination in other affected regions across the world, with adaptation to local conditions.

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

scholarly journals Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1150
Author(s):  
Yang Zhong ◽  
Aiwen Lin ◽  
Chiwei Xiao ◽  
Zhigao Zhou
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Electrical Power ◽  
Random Forest Algorithm ◽  
Temporal Dynamic ◽  
Urban Agglomerations ◽  
Evolution Characteristics ◽  
Spatio Temporal ◽  
Electrical Power Consumption ◽  
Gwr Model

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

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