基于美度测量的APP界面评价方法研究

界面作为人机交互的媒介,其设计严重影响着使用者的体验。跳出传统的工效学研究,从界面美度出发,设计界面美度量化评价体系,通过测量界面的美度指标值,结合灰色关联分析法求解界面的美度综合值完成对界面美度的排序。之后通过环比法验证评价方法是否符合用户的内心感性认知,结果表明该评价方法是具有一定可信度和客观性的,建立了理性评价与感性认知间的关联性。

理性思维帮助形成化学基本概念——以苏教版“盐类的水解反应”教学为例

化学概念不应该是教师讲出来的,而应该是学生通过理性分析建构出来的。本文以苏教版"盐类的水解反应"教学为例,对"盐类水解"概念的教学进行尝试与实践,收到了较满意的效果。

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

Hydrological Modeling in a Semi-Arid Catchment Using SWAT Model

In the field of the water resources, hydrologic models have been used to assess water quality performance of complex watersheds and river basins. Hydrologic models can provide essential information for making decisions on sustainable management system of water resources within watersheds. The main objective of this study was to validate the performance of the Soil and Water Assessment Tool （SWAT） and the feasibility of using this model as a simulator of runoff at a catchment scale in semi-arid area in Northwestern Tunisia. Calibration and validation of the model output were performed by comparing predicted runoff with corresponding measurements from the Sarrath outlet for the periods 1990-1995 for calibration and 2000-2005 for validation. The time series for the years 1996-1999 showed discrepancies between the measured rainfall and the observed runoff indicating errors due to either the observations or to a dysfunction in the equipments. Sensitivity analysis shows that sensitive parameters for the simulation of discharge include curve number, soil evaporation compensation factor, depth of water in shallow aquifer and slope of subbasin. Statistical comparisons between monthly simulated results and observed data for the calibration period gave a reasonable agreement with a coefficient of determination （R2） greater than 0.75 and Nash-Sutcliffe Coefficient （NSE） equal to 0.72. These values were respectively 0.70 and 0.64 for validation period. Overall, the SWAT model has the capability to predict runoff within a complex semi-arid catchment.

Node cooperation based location secure verification algorithm in wireless sensor networks localization

For the application of wireless sensor networks in the military field, one of the main challenges is security. To solve the problem of verifying the location claim for a node, a new location verifica- tion algorithm called node cooperation based location secure verification （NCBLSV） algorithm is proposed. NCBLSV could verify malicious nodes by contrasting neighbor nodes and nodes under beam width angle using an adaptive array antenna at a base point. Simulation experiments are con- ducted to evaluate the performance of this algorithm by varying the communication range and the an- tenna beam width angle. Results show that NCBLSV algorithm has high probability of successful ma- licious nodes detection and low probability of false nodes detection. Thus, it is proved that the NCBLSV algorithm is useful and necessary in the wireless sensor networks security.

PIR-based data integrity verification method in sensor network

Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.

Service-Oriented Middleware for Heterogeneous Environment in Intemet of Things

A great number of sensor technologies are applied in the Intemet of Things （loT） currently and more are emerging, which rmkes the loT a heterogeneous network. This paper discusses the convergence and integration problem in IoT. A Service-Oriented Middleware for Heterogeneous Environment （SOMHE） in IoT is proposed. The purpose of the middleware is to shield the differ- ence between protocols in the sensor layers such as Radio Frequency Identification （RFID） and Zig-Bee by defining the data conversion and mapping model. A Web service interface is supplied by this middleware, thus the complexity of high level appli-cation development can be reduced greatly. The feasibility and reliability of this middleware is veri-fied by a demonstration systelTL

Suitability mapping of global wetland areas and validation with remotely sensed data

With increasing urbanization and agricultural expansion, large tracts of wetlands have been either disturbed or converted to other uses. To protect wetlands, accurate distribution maps are needed. However, because of the dramatic diversity of wetlands and difficulties in field work, wetland mapping on a large spatial scale is very difficult to do. Until recently there were only a few high resolution global wetland distribution datasets developed for wetland protection and restoration. In this paper, we used hydrologic and climatic variables in combination with Compound Topographic Index （CTI） data in modeling the average annual water table depth at 30 arc-second grids over the continental areas of the world except for Antarctica. The water table depth data were modeled without considering influences of anthropogenic activities. We adopted a relationship between poten- tial wetland distribution and water table depth to develop the global wetland suitability distribution dataset. The modeling re- suits showed that the total area of global wetland reached 3.316× 10^7 km^2. Remote-sensing-based validation based on a compi- lation of wetland areas from multiple sources indicates that the overall accuracy of our product is 83.7%. This result can be used as the basis for mapping the actual global wetland distribution. Because the modeling process did not account for the im- pact of anthropogenic water management such as irrigation and reservoir construction over suitable wetland areas, our result represents the upper bound of wetland areas when compared with some other global wetland datasets. Our method requires relatively fewer datasets and has a higher accuracy than a recently developed global wetland dataset.

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression(LR), Spatial Autoregression(SAR), Geographical Weighted Regression(GWR), and Support Vector Regression(SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic(ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic(SROC) curve and the spatial success rate(SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve(AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest sus-ceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.