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.     

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...     

Multi-factor impact analysis of agricultural production in Bangladesh with climate change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.