•A CNN-based approach was applied to map the landslide susceptibility.•A novel multiscale sampling strategy was proposed to generate the training data.•Three machine learning methods were applied for ...comparison.•CNN trained with multiscale fusion data can generate accurate and reliable results.
Landslides are one of the most widespread natural disasters and pose severe threats to people, properties, and the environment in many areas. Landslide susceptibility mapping (LSM) has proven useful in designing landslide mitigation strategies for reducing disaster risk and societal and economic losses, which are essential for land use planning, hazard prevention, and risk management. Recent efforts for improving accuracies of LSM have focused on the utilization of convolutional neural network (CNN) in some image-related tasks, however, due to the inconsistency of data representation, CNN-related studies need to be further explored. In this study, a CNN-based approach for LSM was proposed and experimentally applied in a Jiuzhaigou region where a catastrophic earthquake taken place on 8 August 2017, in Sichuan, China. To address the issue of data representation in the CNN model, we proposed a multiscale sampling strategy which to our knowledge is novel in LSM. In this way, the multiscale training samples (i.e., small scale, medium scale and large scale) were generated from the selected eleven landslide causative factors. The success-rate curve (SRC) and prediction-rate curve (PRC) were applied to validate the LSM results, and three conventional machine learning algorithms, i.e., logistic regression, multi-layer perceptron (MLP) neural network and radial basis function (RBF) neural network, were attempted for comparison. Specifically, MLP neural network achieved the best performance among three machine learning methods, with the area under the SRC (AU-SRC) and PRC (AU-PRC) values of 81.18% and 82.84%, respectively. Nevertheless, the AU-SRC and AU-PRC values of CNN-based approach reached to 97.45% and 88.02%, which were about 16% and 6% higher than that of the MLP neural network, respectively. The present study demonstrated both the excellent goodness-of-fit and strong prediction ability of CNN-based approach for LSM, which also showed the effectiveness and feasibility of the proposed multiscale sampling strategy. Additionally, present study revealed that the spatial data close to the landslide location might be more suitable to predict the probability of the landslide occurrence. Finally, we expect that the deep learning method based on multiscale data representation will advance our ability to assess the landslide susceptibility and raise the awareness of landslide disasters.
In order to solve the problem that the existing LoRaWAN adaptive data rate control algorithm leads to low data transmission efficiency in the case of network congestion, a method combining a fuzzy ...logistic regression classifier and an improved adaptive data rate controller adjusting the avoidance time was proposed. The classifier could obtain the predicted congestion state by logistic regression learning. The data rate controller determined the data rate adjustment scheme according to the predicted congestion state. The experimental results showed that when the network congestion occurred in about 12s, the number of packet loss by the LoRaWAN default method was higher than that by the method in the research. The value of ADR_ MSG_CNT of the 15 source nodes in the method was 30 within 0–10 s, while the RCV_ACK_CNT of some nodes was 0. It proved that the method was more efficient than the original LoRaWAN adaptive data rate control algorithm.
In Punjab (Pakistan), the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources. Intensive pumping has resulted in a rapid decline ...in the level of the water table as well as its quality. Better management practices and artificial recharge are needed for the development of sustainable groundwater resources. This study proposes a methodology to delineate favorable groundwater potential recharge zones (FPRI) by integrating maps of groundwater potential recharge index (PRI) with the DRASTIC-based groundwater vulnerability index (VI). In order to evaluate both indexes, different thematic layers corresponding to each index were overlaid in ArcGIS. In the overlay analysis, the weights (for various thematic layers) and rating values (for sub-classes) were allocated based on a review of published literature. Both were then normalized and modified using the analytical hierarchical process (AHP) and a frequency ratio model respectively. After evaluating PRI and FPRI, these maps were validated using the area under the curve (AUC) method. The PRI map indicates that 53% of the area assessed exists in very low to low recharge zones, 22% in moderate, and 25% in high to excellent potential recharge zones. The VI map indicates that 38% of the area assessed exists in very low to low vulnerability, 33% in moderate, and 29% in high to very high vulnerability zones. The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential. During the validation process, it was found that the AUC estimated with modified weights and rating values was 79% and 67%, for PRI and VI indexes, respectively. The AUC was less when evaluated using original weights and rating values taken from published literature. Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.
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•Remotely derived maps and conventional data are helpful in mapping recharge zones.•Probability frequency ratio is preferred to allocate highly validated rating values.•Analytical hierarchical process is multi-criterion approach for weightage allocation.•Recharge potential and vulnerability help evaluate favorable recharge zones.•The area under the curve approach can be used to validate maps.
Dynamics of associative polymers Zhang, Zhijie; Chen, Quan; Colby, Ralph H
Soft matter,
2018, Letnik:
14, Številka:
16
Journal Article
Recenzirano
Current progress in understanding the dynamics of associating polymers is reviewed, with examples including both ionic and hydrogen bonding associations. A particular emphasis is placed on ...quantification of the strength of the interaction that sets the association lifetime. Knowledge of the interaction energy and the number density of associating groups allows a rational understanding of the linear viscoelastic response of many associating polymers.
Current progress in understanding the dynamics of associating polymers is reviewed, with examples including both ionic and hydrogen bonding associations.
Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern ...agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.
Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make ...accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.
Summary Background The avian influenza A H7N9 virus has caused infections in human beings in China since 2013. A large epidemic in 2016–17 prompted concerns that the epidemiology of the virus might ...have changed, increasing the threat of a pandemic. We aimed to describe the epidemiological characteristics, clinical severity, and time-to-event distributions of patients infected with A H7N9 in the 2016–17 epidemic compared with previous epidemics. Methods In this epidemiological study, we obtained information about all laboratory-confirmed human cases of A H7N9 virus infection reported in mainland China as of Feb 23, 2017, from an integrated electronic database managed by the China Center for Disease Control and Prevention (CDC) and provincial CDCs. Every identified human case of A H7N9 virus infection was required to be reported to China CDC within 24 h via a national surveillance system for notifiable infectious diseases. We described the epidemiological characteristics across epidemics, and estimated the risk of death, mechanical ventilation, and admission to the intensive care unit for patients admitted to hospital for routine clinical practice rather than for isolation purpose. We estimated the incubation periods, and time delays from illness onset to hospital admission, illness onset to initiation of antiviral treatment, and hospital admission to death or discharge using survival analysis techniques. Findings Between Feb 19, 2013, and Feb 23, 2017, 1220 laboratory-confirmed human infections with A H7N9 virus were reported in mainland China, with 134 cases reported in the spring of 2013, 306 in 2013–14, 219 in 2014–15, 114 in 2015–16, and 447 in 2016–17. The 2016–17 A H7N9 epidemic began earlier, spread to more districts and counties in affected provinces, and had more confirmed cases than previous epidemics. The proportion of cases in middle-aged adults increased steadily from 41% (55 of 134) to 57% (254 of 447) from the first epidemic to the 2016–17 epidemic. Proportions of cases in semi-urban and rural residents in the 2015–16 and 2016–17 epidemics (63% 72 of 114 and 61% 274 of 447, respectively) were higher than those in the first three epidemics (39% 52 of 134, 55% 169 of 306, and 56% 122 of 219, respectively). The clinical severity of individuals admitted to hospital in the 2016–17 epidemic was similar to that in the previous epidemics. Interpretation Age distribution and case sources have changed gradually across epidemics since 2013, while clinical severity has not changed substantially. Continued vigilance and sustained intensive control efforts are needed to minimise the risk of human infection with A H7N9 virus. Funding The National Science Fund for Distinguished Young Scholars.
Two dimensional lamellar membranes are attractive for anomalous water and ion transfer, but performance is hindered by swelling. Here, the authors stabilize a MXene membrane laminar architecture with ...fixed nanochannels, achieving highly selective acid recovery from iron-based wastewater.
Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to ...determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.
MXene nanosheets with attractive electrical conductivity and tunable work function have been adopted as multifunctional interfacial modifier between InGaN nanorods and Si for photoelectrochemical ...water oxidation for the first time. Compared to bare InGaN/Si systems, MXene interfacial layers give rise to an ultralow onset potential of 75 mV versus reversible hydrogen electrode (RHE), which is the highest ever reported for InGaN‐ or Si‐based photoanodes by interfacial modification. Furthermore, the modified photoanode exhibits a significantly enhanced photocurrent density (7.27 mA cm−2) at 1.23 V versus RHE, which is about 10 times higher than that achieved with the InGaN/Si photoanode. The detailed mechanism demonstrates that the formed type‐II band alignment in InGaN/MXene heterojunction and an Ohmic junction at the MXene/Si interface make MXene an ideal electron‐migration channel to enhance charge separation and transfer process. This synergetic effect of MXene can significantly decrease the charge resistance at semiconductor/Si and semiconductor/electrolyte hetero‐interfaces, eventually resulting in the fast hole injection efficiency of 82% and superior stability against photocorrosion. This work not only provides valuable guidance for designing high‐efficiency photoelectrodes through the integration of multiscale and multifunctional materials, but also presents a novel strategy for achieving high‐performance artificial photosynthesis by introducing interfacial modifier.
An InGaN/MXene hetero‐structured photoanode with highly conductive MXene (≈117.06 mΩ) is developed as a multifunctional interfacial modifier for InGaN nanorods and Si substrates using a molecular beam epitaxy system. This approach presents a valuable avenue for achieving high‐efficiency artificial photosynthesis through the integration of multiscale and multifunctional materials.
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