The Zaghawa and the Midob of North Darfur – A comparison of migration behaviour

Two ethnic groups of North Darfur, the Zaghawa and the Midob, are being compared as regards their migration behaviour and their identification in their destination areas. Both groups have severely suffered as a result of the drought phase of the last three decades, during which more than half of the Zaghawa and Midob have migrated southwards and to the towns. Despite the similarity of the two ethnic groups, being non-Arab agropastoralists who are socially and politically marginalised both within Darfur and within the Sudan, they show marked differences in their migration behaviour and ethnic solidarity. The Zaghawa migrants are considerably more economically successful than the Midob migrants. The main reasons are: – The Zaghawa started their outmigration earlier than the Midob. Thus, they could establish ethnically-based networks in Libya and the Gulf countries, to which Zaghawa migrants can resort to make a good start in their destination areas. By the time the Midob started their migration in the 1980s, the conditions of employment in the Arab oil countries had worsened. – The Zaghawa show a strong clan solidarity. They readily lend money to young members of their clan to cover the high costs of travelling to the Arab oil countries for work, where many of them have already established themselves. – The Midob, both in the Sudan and abroad, have much less resources available. The only country open for them outside the Sudan is Libya. Lacking capital, the Midob go there on camels, mostly illegally. Having no connections in Libya, except those to other poor Midob there, they usually try to work in herding sheep and goats, which is not a lucrative profession. This revised version was published online in July 2006 with corrections to the Cover Date.     

Lead removal from wastewater using faujasite tuff

The Jordanian chabazite-phillipsite tuff and faujasite-phillipsite tuff have suitable mineralogical and technical properties that enable them to be used for ion-exchange processes. These include suitable grain size and total cation exchange capacity, acceptable zeolite content, good attrition resistance and high packed-bed density. The chabazite-phillipsite tuff (ZE1 and ZE2) has an excellent efficiency to remove Pb and an acceptable efficiency to remove Fe, Cu, Zn and Ni from effluent wastewater of a battery factory and other industries. The faujasite-phillipsite tuff (ZE3) is 6.97 times more efficient than the ZE1 and ZE2. A bed of ZE3 (1,000 kg) loaded in a 1.17-m³ column is capable of cleaning about 2,456 m³ of the effluent from the factory at a cost of US $200/ton. The wastewater is contaminated with 20 ppm Pb in the presence of competing ions including Ca (210 ppm), Na (1,950 ppm) and Fe (169 ppm). This quantity of wastewater is equivalent to 120 working days of effluent discharge from the factory at a flow rate of 20 m³/day.     

Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces

Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure.The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.     

Modeling the stochastic dependence of air pollution index data

The air pollution index (API) is a common tool, which is often used for determining the quality of air in the environment. In this study, a discrete-time Markov chain model is applied for describing the stochastic behaviour of API data. The study reported in this paper is conducted based on the data collected from Klang city in Malaysia for a period of 3 years (2012–2014). Based on the API data, we considered a five-state Markov chain for depicting the five different states of the air pollution. We identified the Markov chain is an ergodic Markov chain and determined the limiting distribution for each state of the air pollution. In addition, we have identified the mean first passage time from one state to another. Based on the limiting distribution and the mean return time, we found that the risk of occurrences for unhealthy events is small. However, the risk remains notably troubling. Therefore, the standard of air quality in Klang falls within a margin that is considered healthy for human beings.     

Dynamic behavior of a smart device on a surface subjected to earthquake motion

This paper leverages concepts from an existing model to simulate the planar response of a smart device subjected to friction forces induced by an underlying moving plane. An interpolation technique is used to enhance detection of transition points (between sticking and sliding states), which must be accurately identified because of the frequency of their occurrence during seismic motion. The behavior of a smart device on an unconstrained table or desk, which is itself on a moving floor, is introduced and discussed. After validation of the results using experimental data, the revised model is used to study the sliding potential of smart devices on a surface during strong seismic events. Sliding spectra associated with selected ground motions are presented and extended to incorporate the effect of vertical accelerations with the purpose of assessing their influence. It is shown that vertical accelerations have a minimal effect on the sliding behavior of smart devices and that a “probability of exceeding the slip limit” curve can be developed to relate the probability of sticking to a demand parameter that represents the ground motion.     

Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.     

Simulation of airflow around rain gauges: Comparison of LES with RANS models

Wind is responsible for systematic errors that affect rain gauge measurements. The authors investigate the use of computational fluid dynamics (CFD) to calculate airflow around rain gauges by applying a high-resolution large eddy simulation (LES) model to determine the flow fields around a measuring system of two rain gauges. The simulated air flow field is characterized by the presence of massive separation which induces the formation and shedding of highly unsteady eddies in the detached shear layers and wakes. Parts of these detached structures occur over the orifice of the rain gauges and may substantially affect the dynamics of the raindrops in this critical region. Non-dissipative LES methods used with fine enough meshes can successfully predict these eddies and their associated fluctuations. The authors compare statistics from LES with steady-state Reynolds averaged Navier–Stokes (RANS) simulations using the k–ε and shear stress transport k–ω turbulence models. They find that both RANS and LES models predict similar mean velocity distributions around the rain gauges. However, they determine the distribution of the resolved turbulent kinetic energy (TKE) to be strongly dependent on the RANS model used. Neither RANS model predictions of TKE are close to those of LES. The authors conclude that the failure of RANS to predict TKE is an important limitation, as TKE is needed to scale the local velocity fluctuations in stochastic models used to calculate the motion of raindrops in the flow field.     

Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks

The chemical oxygen demand (COD) parameter of a wastewater treatment plant is predicted based on wavelet decomposition, entropy, and neural networks (NN) for rapid COD analysis. This paper also describes the usage of wavelet and NNs for parameter prediction. Data from a wastewater treatment plant in Malatya, Turkey, were used. This dataset consists of daily values of influents and effluents for a year. To reduce the dimension of input parameters and to decrease the NN training time, wavelet decomposition and entropy were used. Test results were presented graphically. The test results of the trained model were found to be closer to the measured COD values.     

Petrology of Cr-rich Ophiolitic Chromitites of Bulqiza, Eastern Ophiolitic Belt, Albania

<正>The ultramafic massif of Bulqiza,which belongs to the eastern ophiolitic belt of Albania,is the most important area for metallurgical chromitite ores.The massif consists of a thick(4 km)rock sequence,with a generalized profile from the bottom to the top as follows.The tectonite     

An Overview of the Albanian Ophiolite and Related Ore Minerals

<正>1 A Short Introduction on the Albanian Ophiolite Albanian ophiolite(or Mirdita ophiolite)represents a remnant of the Tethyan oceanic lithosphere in the territory of Albania,as an elongated belt from south to the north,about 250 km long and 30-50 km wide.It covers an area of about 4300 km2 or 1/7 of the territory of Albania and is