Planning versus youth: Stamping out spatial unruliness in Harare

The paper examines the illegal occupation and use of urban spaces by Harare’s youth and the ensuing tussles with the repressive machinery of the local authority and the nation-state. It analyses efforts by planning to contain rampant spatial unruliness. The paper maps the patterns of official hostile responses to the illegal activities of the youth whose daily routines inevitably entail the disregard of the spatial planning framework as reflected in existing legal and regulatory controls. The analysis reveals the reliance by the urban planning and management system on the use of force and violence, a feat made possible by the mobilisation of the repressive state apparatus. The discussion argues that what comes out in the relationship between the order-imposing planning system and the regulation-flouting youth is a situation that can largely be comprehended by turning to the non-progressive side of planning.     

Parameter estimation of copula functions using an optimization-based method

Application of the copulas can be useful for the accurate multivariate frequency analysis of hydrological phenomena. There are many copula functions and some methods were proposed for estimating the copula parameters. Since the copula functions are mathematically complicated, estimating of the copula parameter is an effortful work. In the present study, an optimization-based method (OBM) is proposed to obtain the parameters of copulas. The usefulness of the proposed method is illustrated on drought events. For this purpose, three commonly used copulas of Archimedean family, namely, Clayton, Frank, and Gumbel copulas are used to construct the joint probability distribution of drought characteristics of 60 gauging sites located in East-Azarbaijan province, Iran. The performance of OBM was compared with two conventional methods, namely, method of moments and inference function for margins. The results illustrate the supremacy of the OBM to estimate the copula parameters compared to the other considered methods.     

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.     

Multi-temporal forest cover dynamics in Kashmir Himalayan region for assessing deforestation and forest degradation in the context of REDD+ policy

The role of forests is being actively considered under the agenda of REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus) aimed at reducing emissions related to changes in forest cover and forest quality. Forests in general have undergone negative changes in the past in the form of deforestation and degradation, while in some countries positive changes are reported in the form of conservation, sustainable management of forests and enhancement of carbon stock. The present study in the Kashmir Himalayan forests is an effort to assess historical forest cover changes that took place from 1980 to 2009 and to predict the same for 2030 on the basis of past trend using geospatial modeling approach. Landsat data (Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+)) was used for the years 1980, 1990 and (2001, 2009) respectively and change detection analysis between the dates was performed. The maps generated were validated through ground truthing. The study area (3375.62 km²) from 1980-2009 has uffered deforestation and forest degradation of about 126 km² and 139.02 km² respectively which can be claimed under negative options of REDD+, while as the area that experienced no change (1514 km²) can be claimed under conservation. A small area (23.31 km2) observed as positive change can be claimed under positive options. The projected estimates of forest cover for 2030 showed increased deforestation and forest degradation on the basis of trend analysis using Cellular Automata (CA) Markov modeling. Despite the fact that country as a whole has registered a net positive change in the past few decades, but there are regions like Kashmir region of western Himalaya which have constantly undergoing deforestation as well as degradation in the past few decades.     

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Global land cover (LC) maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection, and environmental planning. Due to the importance of LC, there is a pressing need to increase the temporal and spatial resolution of global LC maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, that is, CORINE, Urban Atlas (UA), OpenStreetMap, and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing LC in Germany, and paves the way for further regional and national validation efforts.     

Gas-Phase Chemistry of the Star Forming Region W3 IRS4

We have followed the chemistry of W3 IRS4, as one of the most famous regions with a chemistry similar to that found in quiescent molecular clouds. Here the time scale, since star formation, has probably been long enough for the gas-phase ion-molecule chemistry to become dominant again. We have constructed a reaction system containing the chemical families of H, C, O, N, S and metals (Me:Fe, Mg, Na and Si). A total of 282 species have been included and a network of 1270 reactions has been used. The chemical kinetic equations were integrated as a function of pseudo-time-dependent approach. The evolution of the different chemical species are followed in models of density n equal to 10⁶ cm^-3 and temperature T equal to 55 K. Different initial abundances have been assumed. Our results are in reasonable agreement with the observation, especially in Model 6. The resulting molecular abundances are compared with those obtained in the intermediate mass star NGC 2264 IRS1, as well as the low mass IRAS 16293-2422. This revised version was published online in July 2006 with corrections to the Cover Date.     

Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Natural Resources Research - This study developed a new perspective of artificial neural networks using dimensional analysis to be applicable to certain prediction problems. To this end,...     

Bioleaching potential of bacterial communities in historic mine waste areas

Microbial activity has the potential to alter all cultural heritage in mining and metallurgy, due to metal mobilization by leaching. This communication shows the consequences of the bioleaching ability of two natural enrichments on copper slag samples from a historic ore smelting site in Sangerhausen (Mansfeld, Südharz, Saxony-Anhalt, Germany). Enrichment cultures gained from mine drainage were dominated by either the iron and sulfur-oxidizing Acidithiobacillus ferrivorans, or by the iron-oxidizing Leptospirillum. During 35 days of bioleaching in media containing copper slag pulp, inoculated with these enrichments, the change in pH and solubilized metal concentrations of the systems were monitored. Both bacterial strains were completely different from each other in their pattern of pH variation and rates of metal solubilization. The maximum removal of Cu (1725 mg/l) and Zn (715 mg/l) from copper slag substrate was achieved with enrichment culture of A. ferrivorans SCUT-1. However, maximum Mn (207 mg/l), Pb (86 mg/l), and Ni (75 mg/l) removal was observed with enrichment culture of Leptospirillum strain YQP-1. Implications for metal mobilization along with alteration of artifacts from not only historic mining areas but also aspects of decontamination and remediation are discussed.     

Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria

This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons.