Magnitude of industrialization in Jordan

In spite of Jordan's insignificant natural resources and geopolitical problems, industrial development is being emphasized in Jordan since 1972. Industries are now the second largest sector of economy. Industries contributed 10% of the GDP in 1972, 19.5% in 1989 and 17.4% in 1993. This is remarkable as the national investment in industries is declining. The investments for industrial development was 30% of the total national investment in five year development plans (1976–1980), 23% in the next plan (1981–1985), and 13% in the following plan (1986–1990).The number of; industries has increased from 2500 in 1959 to 4684 in 1974, to 8533 in 1984, and 18600 in 1992. The employment or labor force also increased from 10,000 in 1959 to 70,000 in 1988, and to 110,000 in 1992. It represents about 16.4% of the total labor force in Jordan, the index number of industrial production also increased from (100) in 1979 which was the same base year, to (151) in 1983 to (205) in 1987 and to (215) in 1993. The study was based on three hypotheses: (i) the magnitude varies from one industry to another at the national (Jordan) and local (governorate levels), (ii) temporal change in the magnitude of industries has taken place during the period 1979–1992, (iii) the temporal change in the magnitude of industries was positive in some industries and negative in others.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

The impact of land use and land cover changes on soil erosion in western Iran

Natural Hazards - Analysis of long-term land use and land cover (LULC) changes requires up-to-date remotely sensed data to assess their effects on erosion. This is a particularly important...     

Generating non-stationary random fields of auto-correlated,normally distributed CPT profile by matrix decomposition method

This paper emphasises the true realisation of Cone Penetration Test (CPT) profiles considering non-stationary nature of the data. Formulation of stationary random field theory has been modified and adapted to non-stationary state in order to take into account the mean and variance variability for soil properties. Multi-variance correlation matrix along with the Cholesky decomposition technique was employed to produce realisations of non-homogenous and non-stationary random fields of CPT profiles. A piecewise and segmental data realisation according to the lithology and site class specifications acquired directly from CPT data is adopted in this study so as to render an accurate data simulation. For validation of proposed method 8 CPT test profiles collected from Urmia Lake site have been introduced and simulated by the stationary and non-stationary algorithms. The mean correlation coefficient between the actual CPT data profiles and related realisations along with some other important statistical parameters and their coefficients of variation strongly demonstrate that non-stationary random field generation technique gives quite better accuracy, by comparison to the conventional stationary random field generation scheme.     

Automatic Selection of Landmarks for Navigation Guidance

Although current navigation services provide significant benefits to people's mobility, the turn‐by‐turn instructions they provide are sometimes ineffective. These instructions require people to maintain a high level of attention and cognitive workload while performing distance or angle measurements on their own mental map. To overcome this problem, landmarks have been identified as playing a major role in turn‐by‐turn instructions. This requires the availability of landmarks in navigation databases. Landmarks are commonly selected manually, which involves time‐consuming and tedious tasks. Automatic selection of landmarks has recently gained the attention of researchers but currently there are only a few techniques that can select appropriate landmarks. In this article, we present a technique based on a neural network model, where both static and dynamic features are used for selecting landmarks automatically. To train and test this model, two labeling approaches, manual labeling and rule‐based labeling, are also discussed. Experiments on the developed technique were conducted and the results show that rule‐based labeling has a precision of approximately 90%, which makes the technique suitable and reliable for automatic selection of landmarks.     

Adaptation to climate change in desertified lands of the marginal regions in Egypt through sustainable crop and livestock diversification systems

Environmental degradation resulting from current climate changes, including prolonged drought, land degradation, desertification, and loss of biodiversity, is presenting enormous challenges to achieve ...     

Land desertification and restoration in Middle East and North Africa (MENA) region

The Middle East and North Africa (MENA) region is characterized by high population growth, degraded and fragile nat-ural ecosystems, and a limited amount of arable lands. It is one of the most water-sc...