Role of market agents in mitigating the climate change effects on food economy

Natural Hazards - Agriculture’s ability to adapt to the impacts of climate change is critical for agricultural households as well as the general public and policymakers. Economic agents can...     

塔克拉玛干沙漠腹地地表辐射与能量平衡

利用2013年塔克拉玛干沙漠塔中流动沙面地表辐射、土壤热通量、土壤温湿度和湍流通量观测资料,分析了沙漠腹地地表辐射和能量收支特征及闭合状况。结果表明：除潜热通量外,其余地表辐射各分量和能量平衡分量的月平均日变化结果整体均表现为标准的单峰型日循环形态,其中R_s↓与R_s↑变化同步,R_l↑、R_l↓滞后R_s↓0.5 ~ 1 h。各分量均表现出夏季高、春秋季次之、冬季低的季节波动性。干旱和极低的植被覆盖造成沙漠腹地全年潜热通量始终较为微弱,约占净辐射的2.8%,感热通量成为能量的主要消耗形式,约占净辐射的49%。偶尔的降水会刺激潜热通量突然增加。地表反照率相对较高且稳定,日变化呈早晚大、正午小的“U”型趋势,并具有明显的冬季高、夏季低的季节波动性,年均值0.28,月均值0.25~0.32。能量残差各月的日变化也均呈单峰曲线,日出后和日落前能量闭合程度最佳,并出现过闭合现象,全年夏季小,春秋季次之,冬季较大,月平均日峰值5.1~99.9 W·m^-2。土壤表层热储存是影响该地区能量平衡的重要因子之一,考虑表层土壤热存储后,地表能量闭合率达75.3%,能量闭合率夏季 > 春季 > 秋季 > 冬季,白天相比夜间有大幅提升。     

Structural control of El Sela granites and associated uranium deposits, Southern Eastern Desert, Egypt

The El Sela area is a part of the basement complex of the Eastern Desert of Egypt and the Pan-African Shield. The area comprises outcrops of dismembered ophiolites thrust over arc volcano-sedimentary sequence and intruded by different syn- to post-tectonic granitoids. Structural analysis of the area enabled the separation and definition of four structural episodes: (E1) folding–thrusting episode associated with the cratonization of the arc/inter-arc rock association and the intrusion of the syntectonic (Older) granites. (E2) Upright folding episode associated with the compression and shortening to the ENE–WSW direction which is different from the NNW–SSE shortening direction during E1; at the end of E2, late tectonic granites were intruded. (E3) Post-tectonic granitic intrusion episode: two mica granite and granitic dikes were intruded during this episode. (E4) Fracturing, faulting, and post-granitic dike extrusion episodes caused different faults that took place after cratonization until the present. There are three generations of folds during ductile deformation (E1 and E2). The F2 folds are nearly coaxial (along ENE–WSW trend) with the F1 folds. The F3 folding is displayed by folds generally trending NNW–SSE. Therefore, the ENE–WSW and NNW–SSE trends can considered as preexisting discontinuities and mechanical anisotropy of the crust in the following structure episodes. Brittle deformation (E3 and E4) reveals the importance of those trends which control the multi-injections and many alteration features in the study area. During reactivation, a simple shear parallel to the inherited ductile fabrics was responsible for the development of mineralized structures along the ENE–WSW and NNW–SSE trends. So they can be considered as paleochannel trends for deep-seated structures and can act as a good trap for uranium and/or other mineral resources. Most of the uranium anomalies are delineated along ENE–WSW and NNW–SSE shear zones where quartz-bearing veins bounded the lamprophyre dike and microgranites and dissected them in relation to the successive fracturation and brecciation corresponding to the repeated rejuvenation of the structures. Therefore, the structural controls of the uranium mineralizations in the El Sela area appear to be related to the interaction between inherited ductile fabrics and overprinting brittle structures.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Measured performance of the application of EGNOS in the road traffic sector

European Geostationary Navigation Overlay Service (EGNOS) is a satellite-based system developed to improve the performance of GPS in the European region. It not only enhances the estimated positioning accuracy but also provides an integrity solution. We assess the EGNOS performance through analysis of real measurements, performed in different road user environments. We investigate the use of the integrity concept in future Global Navigation Satellite System (GNSS)-based applications in the road environment, considering both suburban and dense urban areas. The analysis is motivated by the fact that there is an increasing number of road applications, such as billing system, transportation of dangerous goods, remote tracking of trucks and snow plows, fleet management and “pay as you go” car insurances, where the system liability has to be guaranteed. The scope of the analysis is to investigate to which extent the use of the integrity concept can be used for such purpose; the assessment is based on real measurement campaigns in representative environments, providing statistical results in terms of accuracy and integrity, and in a wider sense of the feasibility of the use of EGNOS for augmenting the reliability of GNSS positioning for road applications.     

Coherent integration time limit of a mobile receiver for indoor GNSS applications

There is an emerging requirement for processing global navigation satellite system (GNSS) signals indoor where the signal is very weak and subjected to spatial fading. Typically, longer coherent integration intervals provide the additional processing gain required for the detection and processing of such weak signals. However, the arbitrary physical motion of the handset imputed by the user limits the effectiveness of longer coherent integration intervals due to the spatial decorrelation of the multipath-faded GNSS signal. In this paper, limits of coherent integration due to spatial decorrelation are derived and corroborated with experimental verification. A general result is that the processing gain resulting from direct coherent integration saturates after the antenna has moved through a certain distance, which for typical indoor propagation, is about half a carrier wavelength. However, a refined Doppler search coupled with a prolonged coherent integration interval extends this limit, which is effectively a manifestation of selective diversity.     

An alternative method for non-negative estimation of variance components

A typical problem of estimation principles of variance and covariance components is that they do not produce positive variances in general. This caveat is due, in particular, to a variety of reasons: (1) a badly chosen set of initial variance components, namely initial value problem (IVP), (2) low redundancy in functional model, (3) an improper stochastic model, and (4) data’s possibility of containing outliers. Accordingly, a lot of effort has been made in order to design non-negative estimates of variance components. However, the desires on non-negative and unbiased estimation can seldom be met simultaneously. Likewise, in order to search for a practical non-negative estimator, one has to give up the condition on unbiasedness, which implies that the estimator will be biased. On the other hand, unlike the variance components, the covariance components can be negative, so the methods for obtaining non-negative estimates of variance components are not applicable. This study presents an alternative method to non-negative estimation of variance components such that non-negativity of the variance components is automatically supported. The idea is based upon the use of the functions whose range is the set of all positive real numbers, namely positive-valued functions (PVFs), for unknown variance components in stochastic model instead of using variance components themselves. Using the PVF could eliminate the effect of IVP on the estimation process. This concept is reparameterized on the restricted maximum likelihood with no effect on the unbiasedness of the scheme. The numerical results show the successful estimation of non-negativity estimation of variance components (as positive values) as well as covariance components (as negative or positive values).     

Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m³s^?1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.