With the rapid development of wireless techniques,the bandpass filter(BPF)is required to cover microwave and millimeter-wave frequency bands simultaneously with good mid-band suppression.However,it is difficult to imp...With the rapid development of wireless techniques,the bandpass filter(BPF)is required to cover microwave and millimeter-wave frequency bands simultaneously with good mid-band suppression.However,it is difficult to implement such BPF due to the large frequency ratio and wideband rejection.This paper presents a superior method to realize a dual-band BPF with a large frequency ratio maintaining compact size and low design complexity.This is contributed by an ultra-wide stopband BPF with inherent discriminating excited degree at spurious frequencies.By properly arranging the feeding position and electrical length ratio of stepped impedance resonator(SIR),the excited degree at specific spurious frequencies can be flexibly adjusted to achieve desired suppression level without affecting characteristics at the fundamental passband.For validation,two BPFs were simulated,fabricated and measured,exhibiting suppression levels of 20.3 dB and 35 dB up to 18f0 and 10.53f0 respectively.Based on this,a dual-band BPF with a large frequency ratio can be easily constructed.For demonstration,a dual-band BPF operating at 3.55 GHz and 43.15 GHz is implemented.A frequency ratio up to 12.15 and mid-band suppression level better than 28 dB had been achieved.Advantages of compactness,simplicity and excellent performance of the proposed work can be observed.展开更多
Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments ...Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides(polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validationpurposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic(ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves(AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can furtherfacilitate comparisons and choices between different methods for landslide susceptibility assessment.展开更多
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 ...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.展开更多
In 2003, the Three Gorges Project （TGP, China）, currently the world＇s largest hydroelectric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributarie...In 2003, the Three Gorges Project （TGP, China）, currently the world＇s largest hydroelectric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributaries and also due to resettlement, extensive environmental impacts like land use change and increase of geohazards are associated with the TGP. Within the Yangtze Project, we investigate these effects for the Xiangxi （香溪） catchment which is part of the Three Gorges Reservoir. The aim of this study is to evaluate the susceptibility for mass movement within the Xiangxi River backwater area using geographic information system （GIS）. We used existing mass movements and the conditioning factors （geology, elevation, slope, curvature, land use, and land use change） for analyzing mass movement susceptibility. Mass movements and geology were mapped in the field to establish a mass movement inventory and a geological map. Land use and digital elevation model （DEM） were obtained from remote-sensing data. We determined the relation between mass movements and the conditioning factors by using the frequency ratio method and found strong relation between mass movements and both natural and human-influenced conditioning factors.展开更多
Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarp...Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.展开更多
Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and...Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.展开更多
BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication.Its mechanism is related to nervous system damage during seizures,but there is no effective diagnostic biomarker.Neuronal pentrax...BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication.Its mechanism is related to nervous system damage during seizures,but there is no effective diagnostic biomarker.Neuronal pentraxin 2(NPTX2)is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity.This study explored how serum NPTX2 and electroencephalogram(EEG)slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.AIM To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.METHODS The participants of this study,conducted from January 2020 to December 2021,comprised 74 epilepsy patients with normal cognitive function(normal group),37 epilepsy patients with cognitive dysfunction[epilepsy patients with cognitive dysfunction(ECD)group]and 30 healthy people(control group).The minimental state examination(MMSE)scale was used to evaluate cognitive function.We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording.Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.RESULTS The serum NPTX2 level in the control group,normal group and ECD group were 240.00±35.06 pg/mL,235.80±38.01 pg/mL and 193.80±42.72 pg/mL,respectively.The MMSE score was lowest in the ECD group among the three,while no significant difference was observed between the control and normal groups.In epilepsy patients with cognitive dysfunction,NPTX2 level had a positive correlation with the MMSE score(r=0.367,P=0.0253)and a negative correlation with epilepsy duration(r=−0.443,P=0.0061)and the EEG slow wave/fast wave frequency ratio value in the temporal region(r=−0.339,P=0.039).CONCLUSION Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy.It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.展开更多
Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property.Avalanche susceptibility maps assist decision-makers and planners to execute suitable measu...Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property.Avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk.In the present study,a probabilistic data-driven geospatial fuzzy–frequency ratio(fuzzy–FR)model is proposed and developed for avalanche susceptibility mapping,especially for the large undocumented region.The fuzzy–FR model for avalanche susceptibility mapping is initially developed and applied for Lahaul-Spiti region.The fuzzy–FR model utilized the six avalanche occurrence factors(i.e.slope,aspect,curvature,elevation,terrain roughness and vegetation cover)and one referent avalanche inventory map to generate the avalanche susceptibility map.Amongst 292 documented avalanche locations from the avalanche inventory map,233(80%)were used for training the model and remaining 59(20%)were used for validation of the map.The avalanche susceptibility map is validated by calculating the area under the receiver operating characteristic curve(ROC-AUC)technique.For validation of the results using ROC-AUC technique,the success rate and prediction rate were calculated.The values of success rate and prediction rate were 94.07%and 91.76%,respectively.The validation of results using ROC-AUC indicated the fuzzy–FR model is appropriate for avalanche susceptibility mapping.展开更多
With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ...With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.展开更多
Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing ...Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.展开更多
Connection methods are essential for integrating environmental factors with machine learning models for landslide susceptibility assessments.However,current research does not consider the different characteristics of ...Connection methods are essential for integrating environmental factors with machine learning models for landslide susceptibility assessments.However,current research does not consider the different characteristics of continuity and discreteness within environmental factors and therefore does not analyze the suitability of various connection methods for different factor types.Moreover,the applicability of connection methods remains unclear when slope units are used as the basic assessment units.This study employed slope units as mapping units.The original data of 15 environmental factors,including 12 continuous and three discrete factors,and two connection methods,i.e.,frequency ratio(FR)and modified FR(MFR),were separately used to construct the input datasets for landslide susceptibility modeling.The performance of four widely used machine learning models,random forest(RF),support vector machine(SVM),logistic regression(LR),and multilayer perceptron(MLP),was analyzed to evaluate the suitability of the connection methods for landslide susceptibility mapping.The results show that,in contrast to the decision tree-based RF model,the use of different connection methods for different factor types significantly influences the results of nontree models,including SVM,MLP,and LR.SVM model is the most sensitive to factor types and connection methods.When the MFR is used as the connection method,it improves the mapping results,especially for the SVM model.This shows that it is essential to consider the different characteristics of the data and select an appropriate environmental factor connection strategy to increase the effectiveness of landslide susceptibility evaluation.Furthermore,this study explored the role of connective methods from a sample distribution perspective,providing a theoretical foundation for the more rational and effective integration of environmental factors.展开更多
Landslides remain a significant environmental hazard in India’s hill regions,particularly in the Nilgiris district of Tamil Nadu,due to its steep terrain,fractured geology,and heavy seasonal rainfall.This study appli...Landslides remain a significant environmental hazard in India’s hill regions,particularly in the Nilgiris district of Tamil Nadu,due to its steep terrain,fractured geology,and heavy seasonal rainfall.This study applies the Frequency Ratio(FR)model within a GIS and remote sensing framework to map landslide susceptibility and identify key contributing factors to slope instability.Ten thematic layers were used,including land use/land cover(LULC),NDVI,slope gradient,soil type and depth,geomorphology,aspect,rainfall,lineament density,and lineament proximity—derived from geological databases,DEMs,and satellite imagery.A landslide inventory was analyzed statistically to evaluate each factor’s role in landslide occurrence.Results indicate that slope gradient(9.15%)and LULC(8.37%)are the most influential factors,followed by geomorphology(7.78%),soil type(7.48%),and lineament density(4.50%).A key innovation of this study is the integration of lineament buffer zones to assess the influence of structural discontinuities,often overlooked in regional models.The model’s predictive performance was validated using the Area Under the Curve(AUC)method,yielding a value of 0.879,indicating high accuracy.The resulting susceptibility map categorizes the landscape into low,moderate,and high-risk zones,providing a critical tool for regional planning,infrastructure development,and disaster management.This research supports climate-resilient development and sustainable land-use planning in vulnerable hill regions,emphasizing that both natural terrain characteristics and humaninduced land alterations significantly contribute to landslide risk.展开更多
Landslides pose a significant threat in Nepal,causing substantial losses of life and property every year.This risk is heightened by the region's rugged steep mountainous terrain,heavy rainfall and tectonic activit...Landslides pose a significant threat in Nepal,causing substantial losses of life and property every year.This risk is heightened by the region's rugged steep mountainous terrain,heavy rainfall and tectonic activity.Additionally,human activities such as constructing roads and railways in extremely sensitive regions with geological hazards are major contributors to landslides in Nepal.The China-Nepal railway passes through high-risk zones like Rasuwa,Nuwakot,and Kathmandu,with the Saprubesi-Bidur corridor being especially vulnerable.Accurate landslide assessment is crucial for planning such large-scale projects.This study evaluates landslide susceptibility within a 10-kilometer buffer zone surrounding the proposed China-Nepal Railway part of the Belt and Road Initiative(BRI).Landslide susceptibility assessment is performed using certainty factor(CF),frequency ratio(FR),statistical index(SI)and weights of evidence(WoE)models.Altogether 599 landslides were inventoried from the image series in Google Earth and validated in the field.Of these landslides,70%were used for model development and the remaining 30%were used for validation.Nineteen conditional factors including elevation,relative relief,slope,aspect,plan curvature,profile curvature,topographical position index(TPI),stream power index(SPI),drainage density,topographic wetness index(TWI),rainfall,normalized difference vegetation index(NDVI),land cover,distance from roads,distance from rivers,geology,distance from faults,LS factor and ruggedness index were used for the mapping of landslide susceptibility.We found that the classes of factors and landslide occurrences were consistent across the CF,FR,SI,and WoE models with descriptive statistics indicating that the CF model offered the most stable estimates.Correlation analysis reveals strong relationships among the methods,particularly between WoE and SI.Conversely,FR and SI exhibit the weakest correlation,despite some variability in their distributions.Likewise,the effectiveness of the models was evaluated using the area under the curve(AUC)which revealed that the CF,FR,SI,and WoE models achieved success rates of 83.6%,82.0%,83.1%and 82.9%respectively.With a spatial correlation of over 90%among landslide susceptibility maps created through selected methods,any of the selected models'results could be effectively applied to the management of landslides.Additionally,the susceptibility values from the different models(CF,FR,SI,and WoE)are especially important in the development of railway projects when located within 2-4 km from a planned station.The landslide susceptibility maps can be useful and affordable planning tools for designers and engineers working on the China-Nepal Railway and other similar construction projects.展开更多
This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of...This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate.A nonlinear finite element(FE)formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory(FSDT)in conjunction with von Karman’s nonlinear strain displacement relations.The governing equations of the PFGS plate are derived using the principle of virtual work.The direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical relations.The influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various parameters.The effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.展开更多
A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven sym...A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven symmetric inductor to the f SR of the single-ended driven inductor is firstly predicted and explained.Compared with a single-ended configuration,experimental data demonstrate that the differential inductor offers a 127% greater maximum quality factor and a broader range of operating frequencies.Two differential inductors with low parasitical capacitance are developed and validated.展开更多
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study pres...Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.展开更多
In some studies on landslide susceptibility mapping(LSM),landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate pol...In some studies on landslide susceptibility mapping(LSM),landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form.Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes(LSIs);moreover,the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM.To address this issue by accurately drawing polygonal boundaries based on LSM,the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes,such as landslide points and circles,are compared.Within the research area of Ruijin City in China,a total of 370 landslides with accurate boundary information are obtained,and 10 environmental factors,such as slope and lithology,are selected.Then,correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio(FR)method.Next,a support vector machine(SVM)and random forest(RF)based on landslide points,circles and accurate landslide polygons are constructed as point-,circle-and polygon-based SVM and RF models,respectively,to address LSM.Finally,the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis,and the uncertainties of the predicted LSIs under the above models are discussed.The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy,compared to those based on the points and circles.Moreover,a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables.Additionally,the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases.In addition,the results under different conditions show that the polygon-based models have a higher LSM accuracy,with lower mean values and larger standard deviations compared with the point-and circle-based models.Finally,the overall LSM accuracy of the RF is superior to that of the SVM,and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.展开更多
We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a speci...We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a specific value, we show that by using multi-color harmonic laser pulses as the first pump component, the lasers as the second pump component can be adjusted in a continuous frequency range. Moreover, these multi-color laser pulses can effectively modulate and enhance the terahertz radiation, and the terahertz yield increases with the increase of the wavelength of the uncommon pump com- ponent and is stable to the laser relative phase. Finally, we utilize the electron densities and velocities of ionization events to illustrate the physical mechanism of the intense terahertz generation.展开更多
In the paper,an experiment investigation was conducted for one-and two-degree of freedom vortex-induced vibration（VIV） of a horizontally-oriented cylinder with diameter of 11 cm and length of 120 cm.In the experimen...In the paper,an experiment investigation was conducted for one-and two-degree of freedom vortex-induced vibration（VIV） of a horizontally-oriented cylinder with diameter of 11 cm and length of 120 cm.In the experiment,the spring constants in the cross-flow and in-line flow directions were regulated to change the natural vibration frequency of the model system.It was found that,in the one-degree of freedom VIV experiment,a ＂double peak＂ phenomenon was observed in its amplitude within the range of the reduced velocities tested,moreover,a ＂2T＂ wake appeared in the vicinity of the second peak.In the two-degree of freedom VIV experiment,the trajectory of cylinder exhibited a reverse ＂C＂ shape,i.e.,a ＂new moon＂ shape.Through analysis of these data,it appears that,besides the non-dimensional in-line and cross-flow natural vibration frequency ratios,the absolute value of the natural vibration frequency of cylinder is also one of the important parameters affecting its VIV behavior.展开更多
Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequen...Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.展开更多
基金supported by the National Natural Science Foundation of China(No.61671485).
文摘With the rapid development of wireless techniques,the bandpass filter(BPF)is required to cover microwave and millimeter-wave frequency bands simultaneously with good mid-band suppression.However,it is difficult to implement such BPF due to the large frequency ratio and wideband rejection.This paper presents a superior method to realize a dual-band BPF with a large frequency ratio maintaining compact size and low design complexity.This is contributed by an ultra-wide stopband BPF with inherent discriminating excited degree at spurious frequencies.By properly arranging the feeding position and electrical length ratio of stepped impedance resonator(SIR),the excited degree at specific spurious frequencies can be flexibly adjusted to achieve desired suppression level without affecting characteristics at the fundamental passband.For validation,two BPFs were simulated,fabricated and measured,exhibiting suppression levels of 20.3 dB and 35 dB up to 18f0 and 10.53f0 respectively.Based on this,a dual-band BPF with a large frequency ratio can be easily constructed.For demonstration,a dual-band BPF operating at 3.55 GHz and 43.15 GHz is implemented.A frequency ratio up to 12.15 and mid-band suppression level better than 28 dB had been achieved.Advantages of compactness,simplicity and excellent performance of the proposed work can be observed.
基金funded by the National Natural Science Foundation of China(Grant NO.41525010,41807291,41421001,41790443 and 41701458)the Strategic Priority Research Program of Chinese Academy of Sciences(CAS)(Grant NO.XDA23090301 and XDA19040304)+1 种基金the Key Research Program of Frontier Sciences of Chinese Academy of Sciences(CAS)(Grant NO.QYZDY-SSW-DQC019)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0904)
文摘Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides(polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validationpurposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic(ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves(AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can furtherfacilitate comparisons and choices between different methods for landslide susceptibility assessment.
基金financially supported by the National Key Research and Development Program of China(Grant Nos.2016YFA0602302 and 2016YFB0502502)。
文摘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.
基金supported by the German Federal Ministry of Education and Research (BMBF)
文摘In 2003, the Three Gorges Project （TGP, China）, currently the world＇s largest hydroelectric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributaries and also due to resettlement, extensive environmental impacts like land use change and increase of geohazards are associated with the TGP. Within the Yangtze Project, we investigate these effects for the Xiangxi （香溪） catchment which is part of the Three Gorges Reservoir. The aim of this study is to evaluate the susceptibility for mass movement within the Xiangxi River backwater area using geographic information system （GIS）. We used existing mass movements and the conditioning factors （geology, elevation, slope, curvature, land use, and land use change） for analyzing mass movement susceptibility. Mass movements and geology were mapped in the field to establish a mass movement inventory and a geological map. Land use and digital elevation model （DEM） were obtained from remote-sensing data. We determined the relation between mass movements and the conditioning factors by using the frequency ratio method and found strong relation between mass movements and both natural and human-influenced conditioning factors.
文摘Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.
文摘Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.
基金Supported by 2022 Educational Research Program for Young and Middle-aged Teachers in Fujian Province(Science and Technology),No.JAT220107.
文摘BACKGROUND Cognitive dysfunction in epileptic patients is a high-incidence complication.Its mechanism is related to nervous system damage during seizures,but there is no effective diagnostic biomarker.Neuronal pentraxin 2(NPTX2)is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity.This study explored how serum NPTX2 and electroencephalogram(EEG)slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.AIM To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.METHODS The participants of this study,conducted from January 2020 to December 2021,comprised 74 epilepsy patients with normal cognitive function(normal group),37 epilepsy patients with cognitive dysfunction[epilepsy patients with cognitive dysfunction(ECD)group]and 30 healthy people(control group).The minimental state examination(MMSE)scale was used to evaluate cognitive function.We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording.Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.RESULTS The serum NPTX2 level in the control group,normal group and ECD group were 240.00±35.06 pg/mL,235.80±38.01 pg/mL and 193.80±42.72 pg/mL,respectively.The MMSE score was lowest in the ECD group among the three,while no significant difference was observed between the control and normal groups.In epilepsy patients with cognitive dysfunction,NPTX2 level had a positive correlation with the MMSE score(r=0.367,P=0.0253)and a negative correlation with epilepsy duration(r=−0.443,P=0.0061)and the EEG slow wave/fast wave frequency ratio value in the temporal region(r=−0.339,P=0.039).CONCLUSION Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy.It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.
基金Defence Research&Development Organisation(DRDO)Ministry of Defence,Government of India for providing funds under Project IABP/HimUdaan,No.IF-10/SAS-42,dated 30 September 2011 to carry out this research work.
文摘Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property.Avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk.In the present study,a probabilistic data-driven geospatial fuzzy–frequency ratio(fuzzy–FR)model is proposed and developed for avalanche susceptibility mapping,especially for the large undocumented region.The fuzzy–FR model for avalanche susceptibility mapping is initially developed and applied for Lahaul-Spiti region.The fuzzy–FR model utilized the six avalanche occurrence factors(i.e.slope,aspect,curvature,elevation,terrain roughness and vegetation cover)and one referent avalanche inventory map to generate the avalanche susceptibility map.Amongst 292 documented avalanche locations from the avalanche inventory map,233(80%)were used for training the model and remaining 59(20%)were used for validation of the map.The avalanche susceptibility map is validated by calculating the area under the receiver operating characteristic curve(ROC-AUC)technique.For validation of the results using ROC-AUC technique,the success rate and prediction rate were calculated.The values of success rate and prediction rate were 94.07%and 91.76%,respectively.The validation of results using ROC-AUC indicated the fuzzy–FR model is appropriate for avalanche susceptibility mapping.
基金jointed supported by the National Natural Science Foundation of China(Nos.41920104007,41731284)。
文摘With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.
文摘Compact antenna designs have become a critical component in the recent advancements of wireless communication technologies over the past few decades. This paper presents a self-multiplexing antenna based on diplexing and quadruplexing Substrate-Integrated Waveguide (SIW) cavities. The diplexing structure incorporates two V-shaped slots, while the quadruplexing structure advances this concept by combining the slots to form a cross-shaped configuration within the cavity. The widths and lengths of the slots are carefully tuned to achieve variations in the respective operating frequencies without affecting the others. The proposed diplexing antenna resonates at 8.48 and 9.2 GHz, with a frequency ratio of 1.08, while the quadruplexing antenna operates at 6.9, 7.1, 7.48, and 8.2GHz. Both designs exhibit isolation levels well below –20dB and achieve a simulated peak gain of 5.6 dBi at the highest frequency, with a compact cavity area of 0.56 λg^(2). The proposed antennas operate within the NR bands (n12, n18, n26), making them suitable for modern high-speed wireless communication systems. Moreover, the properties like multiband operation, compactness, high isolation, low loss, and low interference make the antenna favorable for the high-speed railway communication systems.
基金supported by the National Key Research and Development Program of China(No.2023YFC3007202)Joint Research Project on Meteorological Capacity Enhancement of the China Meteorological Administration(No.23NLTSZ009)Project of the Department of Science and Technology of Sichuan Province(No.2024YFHZ0098)。
文摘Connection methods are essential for integrating environmental factors with machine learning models for landslide susceptibility assessments.However,current research does not consider the different characteristics of continuity and discreteness within environmental factors and therefore does not analyze the suitability of various connection methods for different factor types.Moreover,the applicability of connection methods remains unclear when slope units are used as the basic assessment units.This study employed slope units as mapping units.The original data of 15 environmental factors,including 12 continuous and three discrete factors,and two connection methods,i.e.,frequency ratio(FR)and modified FR(MFR),were separately used to construct the input datasets for landslide susceptibility modeling.The performance of four widely used machine learning models,random forest(RF),support vector machine(SVM),logistic regression(LR),and multilayer perceptron(MLP),was analyzed to evaluate the suitability of the connection methods for landslide susceptibility mapping.The results show that,in contrast to the decision tree-based RF model,the use of different connection methods for different factor types significantly influences the results of nontree models,including SVM,MLP,and LR.SVM model is the most sensitive to factor types and connection methods.When the MFR is used as the connection method,it improves the mapping results,especially for the SVM model.This shows that it is essential to consider the different characteristics of the data and select an appropriate environmental factor connection strategy to increase the effectiveness of landslide susceptibility evaluation.Furthermore,this study explored the role of connective methods from a sample distribution perspective,providing a theoretical foundation for the more rational and effective integration of environmental factors.
文摘Landslides remain a significant environmental hazard in India’s hill regions,particularly in the Nilgiris district of Tamil Nadu,due to its steep terrain,fractured geology,and heavy seasonal rainfall.This study applies the Frequency Ratio(FR)model within a GIS and remote sensing framework to map landslide susceptibility and identify key contributing factors to slope instability.Ten thematic layers were used,including land use/land cover(LULC),NDVI,slope gradient,soil type and depth,geomorphology,aspect,rainfall,lineament density,and lineament proximity—derived from geological databases,DEMs,and satellite imagery.A landslide inventory was analyzed statistically to evaluate each factor’s role in landslide occurrence.Results indicate that slope gradient(9.15%)and LULC(8.37%)are the most influential factors,followed by geomorphology(7.78%),soil type(7.48%),and lineament density(4.50%).A key innovation of this study is the integration of lineament buffer zones to assess the influence of structural discontinuities,often overlooked in regional models.The model’s predictive performance was validated using the Area Under the Curve(AUC)method,yielding a value of 0.879,indicating high accuracy.The resulting susceptibility map categorizes the landscape into low,moderate,and high-risk zones,providing a critical tool for regional planning,infrastructure development,and disaster management.This research supports climate-resilient development and sustainable land-use planning in vulnerable hill regions,emphasizing that both natural terrain characteristics and humaninduced land alterations significantly contribute to landslide risk.
基金supported by The Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0308)the National Natural Science Foundation of China(32260291).
文摘Landslides pose a significant threat in Nepal,causing substantial losses of life and property every year.This risk is heightened by the region's rugged steep mountainous terrain,heavy rainfall and tectonic activity.Additionally,human activities such as constructing roads and railways in extremely sensitive regions with geological hazards are major contributors to landslides in Nepal.The China-Nepal railway passes through high-risk zones like Rasuwa,Nuwakot,and Kathmandu,with the Saprubesi-Bidur corridor being especially vulnerable.Accurate landslide assessment is crucial for planning such large-scale projects.This study evaluates landslide susceptibility within a 10-kilometer buffer zone surrounding the proposed China-Nepal Railway part of the Belt and Road Initiative(BRI).Landslide susceptibility assessment is performed using certainty factor(CF),frequency ratio(FR),statistical index(SI)and weights of evidence(WoE)models.Altogether 599 landslides were inventoried from the image series in Google Earth and validated in the field.Of these landslides,70%were used for model development and the remaining 30%were used for validation.Nineteen conditional factors including elevation,relative relief,slope,aspect,plan curvature,profile curvature,topographical position index(TPI),stream power index(SPI),drainage density,topographic wetness index(TWI),rainfall,normalized difference vegetation index(NDVI),land cover,distance from roads,distance from rivers,geology,distance from faults,LS factor and ruggedness index were used for the mapping of landslide susceptibility.We found that the classes of factors and landslide occurrences were consistent across the CF,FR,SI,and WoE models with descriptive statistics indicating that the CF model offered the most stable estimates.Correlation analysis reveals strong relationships among the methods,particularly between WoE and SI.Conversely,FR and SI exhibit the weakest correlation,despite some variability in their distributions.Likewise,the effectiveness of the models was evaluated using the area under the curve(AUC)which revealed that the CF,FR,SI,and WoE models achieved success rates of 83.6%,82.0%,83.1%and 82.9%respectively.With a spatial correlation of over 90%among landslide susceptibility maps created through selected methods,any of the selected models'results could be effectively applied to the management of landslides.Additionally,the susceptibility values from the different models(CF,FR,SI,and WoE)are especially important in the development of railway projects when located within 2-4 km from a planned station.The landslide susceptibility maps can be useful and affordable planning tools for designers and engineers working on the China-Nepal Railway and other similar construction projects.
文摘This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate.A nonlinear finite element(FE)formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory(FSDT)in conjunction with von Karman’s nonlinear strain displacement relations.The governing equations of the PFGS plate are derived using the principle of virtual work.The direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical relations.The influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various parameters.The effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.
文摘A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven symmetric inductor to the f SR of the single-ended driven inductor is firstly predicted and explained.Compared with a single-ended configuration,experimental data demonstrate that the differential inductor offers a 127% greater maximum quality factor and a broader range of operating frequencies.Two differential inductors with low parasitical capacitance are developed and validated.
基金This research is funded by the National Natural Science Foundation of China(Grant Nos.41807285 and 51679117)Key Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(SKLGP2019Z002)+3 种基金the National Science Foundation of Jiangxi Province,China(20192BAB216034)the China Postdoctoral Science Foundation(2019M652287 and 2020T130274)the Jiangxi Provincial Postdoctoral Science Foundation(2019KY08)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)。
文摘Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.
基金funded by the National Natural Science Foundation of China(Nos.41807285,41972280,51679117)the National Science Foundation of Jiangxi Province,China(No.20192BAB216034)+1 种基金the China Postdoctoral Science Foundation(Nos.2019M652287,2020T130274)the Jiangxi Provincial Postdoctoral Science Foundation(No.2019KY08)。
文摘In some studies on landslide susceptibility mapping(LSM),landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form.Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes(LSIs);moreover,the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM.To address this issue by accurately drawing polygonal boundaries based on LSM,the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes,such as landslide points and circles,are compared.Within the research area of Ruijin City in China,a total of 370 landslides with accurate boundary information are obtained,and 10 environmental factors,such as slope and lithology,are selected.Then,correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio(FR)method.Next,a support vector machine(SVM)and random forest(RF)based on landslide points,circles and accurate landslide polygons are constructed as point-,circle-and polygon-based SVM and RF models,respectively,to address LSM.Finally,the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis,and the uncertainties of the predicted LSIs under the above models are discussed.The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy,compared to those based on the points and circles.Moreover,a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables.Additionally,the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases.In addition,the results under different conditions show that the polygon-based models have a higher LSM accuracy,with lower mean values and larger standard deviations compared with the point-and circle-based models.Finally,the overall LSM accuracy of the RF is superior to that of the SVM,and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.
基金Project supported by the National Natural Science Foundation of China(Grant No.11604205)the Talent Program of Shanghai University of Engineering Science,China
文摘We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a specific value, we show that by using multi-color harmonic laser pulses as the first pump component, the lasers as the second pump component can be adjusted in a continuous frequency range. Moreover, these multi-color laser pulses can effectively modulate and enhance the terahertz radiation, and the terahertz yield increases with the increase of the wavelength of the uncommon pump com- ponent and is stable to the laser relative phase. Finally, we utilize the electron densities and velocities of ionization events to illustrate the physical mechanism of the intense terahertz generation.
基金supported by the National Natural Science Foundation of China (51009033)the Fundamental Research Funds for the Central Universities
文摘In the paper,an experiment investigation was conducted for one-and two-degree of freedom vortex-induced vibration（VIV） of a horizontally-oriented cylinder with diameter of 11 cm and length of 120 cm.In the experiment,the spring constants in the cross-flow and in-line flow directions were regulated to change the natural vibration frequency of the model system.It was found that,in the one-degree of freedom VIV experiment,a ＂double peak＂ phenomenon was observed in its amplitude within the range of the reduced velocities tested,moreover,a ＂2T＂ wake appeared in the vicinity of the second peak.In the two-degree of freedom VIV experiment,the trajectory of cylinder exhibited a reverse ＂C＂ shape,i.e.,a ＂new moon＂ shape.Through analysis of these data,it appears that,besides the non-dimensional in-line and cross-flow natural vibration frequency ratios,the absolute value of the natural vibration frequency of cylinder is also one of the important parameters affecting its VIV behavior.
基金The study was financially supported by the Fund for Support of Researchers and Technologists of Iran(97022330)Panhellenic Infrastructure for Atmospheric Composition and Climate Change(PANACEA,MIS 5021516)+1 种基金Competitiveness,Entrepreneurship and Innovation(NSRF 2014-2020)co-financed by Greece and the European Union(European Regional Development Fund).
文摘Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.
