Electromagnetic (EM) wave absorbing materials play an increasingly important role in modern society for their multi‐functional in military stealth and incoming 5G smart era. Dielectric loss EM wave ...absorbers and underlying loss mechanism investigation are of great significance to unveil EM wave attenuation behaviors of materials and guide novel dielectric loss materials design. However, current researches focus more on materials synthesis rather than in‐depth mechanism study. Herein, comprehensive views toward dielectric loss mechanisms including interfacial polarization, dipolar polarization, conductive loss, and defect‐induced polarization are provided. Particularly, some misunderstandings and ambiguous concepts for each mechanism are highlighted. Besides, in‐depth dielectric loss study and novel dielectric loss mechanisms are emphasized. Moreover, new dielectric loss mechanism regulation strategies instead of regular components compositing are summarized to provide inspiring thoughts toward simple and effective EM wave attenuation behavior modulation.
Electromagnetic wave loss mechanism investigation for dielectric absorbing materials is of great importance to guide the preparation of novel high‐performance absorbers. This review provides comprehensive insights into dielectric loss mechanisms and clarifies some misunderstandings and ambiguous concepts for each mechanism. In‐depth dielectric loss study and novel dielectric loss mechanisms regulation strategies are also highlighted.
Defect engineering is an effective approach to manipulate electromagnetic (EM) parameters and enhance absorption ability, but defect induced dielectric loss dominant mechanism has not been completely ...clarified. Here the defect induced dielectric loss dominant mechanism in virtue of multi‐shelled spinel hollow sphere for the first time is demonstrated. The unique but identical morphology design as well as suitable composition modulation for serial spinels can exclude the disturbance of EM wave dissipation from dipolar/interfacial polarization and conduction loss. In temperature‐regulated defect in NiCo2O4 serial materials, two kinds of defects, defect in spinel structure and oxygen vacancy are detected. Defect in spinel structure played more profound role on determining materials’ EM wave dissipation than that of oxygen vacancy. When evaluated serial Co‐based materials as absorbers, defect induced polarization loss is responsible for the superior absorption performance of NiCo2O4‐based material due to its more defect sites in spinel structure. It is discovered that electron spin resonance test may be adopted as a novel approach to directly probe EM wave absorption capacities of materials. This work not only provides a strategy to prepare lightweight, efficient EM wave absorber but also illustrates the importance of defect engineering on regulation of materials’ dielectric loss capacity.
The importance of defect induced polarization loss in electromagnetic wave absorption is revealed by engineering defect in serial Co‐based multi‐shelled spinel hollow spheres. For the first time, defect in spinel structure and its induced loss mechanism is found to play dominate role over other loss mechanisms. This unique hollow structure also affords lightweight spinel electromagnetic wave absorbing materials.
Aiming at the problems of excessive instantiation complexity, poor interpretability and low generalization ability of knowledge reasoning techniques, this paper unites inductive logic programming ILP ...with HET neural network to construct a hybrid logic rule and neural network knowledge graph reasoning model - HETIL model. The ILP is utilized to quantify the first-order logic rules, and the multi-layer rule space is constructed by arranging and combining the rules through logic symbols. The rules are instantiated and fed into the HET network, and the attention coefficients aggregate the node features to complete the end-to-end training and generate the rule learning model. Finally, the validity of the model is verified by machine translation experiments, and the results show that the accuracy of the HETIL model in syntactic structure types is more than 0.8 overall. The accuracy in terms of phrase structure reaches 0.879. The average BLEU value of the HETIL model can reach 29.24, which is 1.51 BLEU points higher than the benchmark model. Therefore, the effect of English translation by applying a knowledge graph is better than traditional machine translation.
The Yangtze River is one of the most important rivers in China due to its large basin size, the large population along the river, and the numerous large dams and reservoirs on the river. The Jinsha ...River, the upper reach of the Yangtze River, was dammed twice recently at Baige, Tibet, one on 10 October 2018 and the other on 3 November 2018 (UTC + 8). Accordingly, two large landslide dams, 61 m and 96 m in height to the lowest dam crest, were formed in a 3-week interval. Due to the large inflow rates at the time of damming, the barrier lake level rose rapidly, posing huge risks to the downstream residents and properties. In managing the landslide dam risk, one of the important tasks is to predict the dam breaching flood beforehand. This paper focuses on rapid prediction of the dam breaching hydrograph and breach geometric parameters of the two landslide dams. The predictions were made timely before the breaching of the two landslide dams using both erosion-based empirical equations and numerical simulation and were refined based on detailed field investigation at the site after breaching. Comprehensive field investigations were conducted to determine the geological structures of the landslide dams, characterize the erodibility of dam materials, and measure the final beach dimensions. The simulated dam breaching processes, outflow hydrographs, lake water level changes, and final breach dimensions were validated by field observations. Compared with the hypothetical scenario without a diversion channel on the second landslide dam, a diversion channel 15 m in depth successfully lowered the peak flood discharge by about one third and helped to mitigate the flood risk significantly. The analysis outcome serves as basis for warning and evacuation of the downstream residents and making appropriate engineering risk mitigation plans.
Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from ...small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.
Plain Language Summary
Strong earthquakes in mountainous regions trigger chains of events that modify mountain landscapes over days, years, and millennia. Earthquake shaking can cause many tens of thousands of landslides on steep mountain slopes. Some of these sudden slope failures can block rivers and form temporary lakes that can later collapse and cause huge floods. Other landslides move more slowly, in some cases in a stop‐start fashion during heavy rains or earthquake aftershocks. Debris from these landslides can clog channels, and during heavy rainfall, the debris can be transported downstream for many kilometers with catastrophic consequences. New landslides tend to happen more frequently than usual for months to years following an earthquake because the strong ground shaking has fractured and weakened the slopes. Other effects of large earthquakes can last, in various forms, over geologic time scales. Over the past two decades, our understanding of these issues has advanced because of the detailed study of the 1999 Chi‐Chi earthquake in Taiwan and the 2008 Wenchuan earthquake in China. We compile and discuss the results of research on these and other earthquakes and explain what we have learned, what we still need to know, and where we should direct future studies.
Key Points
Coupled surface processes initiated by strong seismic shaking are important hazards in mountain landscapes
Earthquake‐induced landslides pose challenges to hazard and risk assessment, management, and mitigation
Multidisciplinary approaches further the understanding of the earthquake hazard cascade, yet challenges remain
Hypochlorite (ClO–) and glutathione (GSH) have been reported to closely correlate with oxidative stress and related diseases; however, a clear mechanism is still unknown, mainly owing to a lack of ...accurate analytical methods for live cells. Herein we create a novel surface-enhanced Raman scattering (SERS) nanoprobe, 4-mercaptophenol (4-MP)-functionalized gold flowers (AuF/MP), for imaging and biosensing of ClO– and GSH in RAW 264.7 macrophage cells upon oxidative stress. The SERS spectra of AuF/MP change with the reaction between ClO– and 4-MP on AuFs within 1 min and then recover after reaction with GSH, resulting in the ratiometric detection of ClO– and GSH with high accuracy. The single SERS probe also shows high selectivity for ClO– and GSH detection against other reactive oxygen species and amino acids which may exist in biological systems, as well as remarkable sensitivity ascribed to a larger amount of hot spots on AuFs. The significant analytical performance of the developed nanoprobe, together with good biocompatibility and high cell-permeability, enables the present SERS probe imaging and real-time detection of ClO– and GSH in live cells upon oxidative stress.
Landslide identification using machine learning Wang, Haojie; Zhang, Limin; Yin, Kesheng ...
Di xue qian yuan.,
January 2021, 2021-01-00, 2021, 2021-01-01, Letnik:
12, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Landslide identification is critical for risk assessment and mitigation. This paper proposes a novel machine-learning and deep-learning method to identify natural-terrain landslides using integrated ...geodatabases. First, landslide-related data are compiled, including topographic data, geological data and rainfall-related data. Then, three integrated geodatabases are established; namely, Recent Landslide Database (RecLD), Relict Landslide Database (RelLD) and Joint Landslide Database (JLD). After that, five machine learning and deep learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), boosting methods and convolutional neural network (CNN), are utilized and evaluated on each database. A case study in Lantau, Hong Kong, is conducted to demonstrate the application of the proposed method. From the results of the case study, CNN achieves an identification accuracy of 92.5% on RecLD, and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing. Boosting methods come second in terms of accuracy, followed by RF, LR and SVM. By using machine learning and deep learning techniques, the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.
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•Landslide identification is critical for risk assessment and mitigation.•A novel integrated machine-learning and deep-learning method is proposed to identify natural-terrain landslides.•Multiple machine learning and deep learning models are trained and evaluated on three landslide databases.•A case study in Lantau, Hong Kong, is worked out, achieving an identification accuracy of 92.5%.•The deep convolutional neural network model is found to be the most promising for landslide identification.
A biosensor was created for the simultaneous monitoring of endogenous H2Sn and H2S in mouse brains and exploring their roles in activation of the TRPA1 channel under two types of brain disease ...models: ischemia and Alzheimer's disease (AD). Based on DFT calculations and electrochemical measurements, two probes, 3,4‐bis((2‐fluoro‐5‐nitrobenzoyl)oxy)‐benzoic acid (MPS‐1) and N‐(4‐(2,5‐dinitrophenoxy) phenyl)‐5‐(1, 2‐dithiolan‐3‐yl)pentanamide (MHS‐1), were synthesized for specific recognition of H2Sn and H2S. Through co‐assembly of the two probes at the mesoporous gold film with good anti‐biofouling ability and electrocatalytic activity, this microsensor showed high selectivity for H2Sn and H2S against potential biological interferences. The biosensor can simultaneously determine the concentration of H2Sn from 0.2 to 50 μm, as well as that of H2S from 0.2 to 40 μm. The expression of TRPA1 protein positively correlated with levels of H2Sn under both ischemia and AD.
A single electrochemical sensor was created for simultaneous detection of H2Sn and H2S in the brain. The expression of TRPA1 channel was found to be positively correlated with the levels of H2Sn in two brain disease models under ischemia and Alzheimer's diseases, and H2Sn is more active for expression of TRPA1 protein than that of H2S.
Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial ...points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed.
•A novel artificial intelligence and object-based landslide susceptibility method is proposed and validated.•The first set of territory-wide landslide susceptibility maps for Hong Kong is produced based on AI techniques.•CNN-LSTM and BiLSTM-RNN are successfully applied to landslide susceptibility analysis for the first time.•The proposed AI-powered object-based method outperforms the cell-based method significantly.
A catastrophic debris flow occurred in Zhouqu County, Gansu Province, Northwestern China, at midnight on 7 August 2010, resulting in damage to >390 buildings. Assessment of the vulnerability of ...buildings to debris flow hazards is essential to quantify the risk of debris flows. This paper presents a new family of physical vulnerability curves to assess the damage to buildings by debris flows based on extensive observed data of building damage and simulated debris flow intensity. A database of damaged buildings in the affected area of the Zhouqu debris flow was reconstructed. Numerical simulations using FLO-2D were conducted to determine the intensity of debris flows. A series of physical vulnerability empirical models for assessing the building damage has been proposed using various hazard intensity measures. Since buildings of different structural types have different resistances to debris flows, the vulnerability functions were developed for two typical types of structures, i.e. brick-concrete structures and reinforced concrete-frame structures. The proposed vulnerability functions are comparable with those for debris flows in Switzerland, Italy and Austria. The buildings may be completely destroyed when the debris flow depth is more than one half of the building height for both types of building structures.
•A family of new physical vulnerability models has been developed to assess building damage by debris flows.•A database of damaged buildings by the Zhouqu debris flow was constructed.•The intensity of the debris flow was reproduced by numerical simulations.•The vulnerability models relate the degree of building damage to hazard intensity indices.
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