The chromite deposits associated with ophiolite complexes, Southeastern Desert, Egypt： Petrological and geochemical characteristics and mineralization

1 Introduction The association of massive Fe-Ni-Cu sulfides andchromite is a very unusual feature of podiformchromitites occurring in mantle tectonites of ophioliticcomplexes. It has only been described in theSoutheastern Desert, Egypt, where sulfides a…     

Water availability for the greening of Kuwait

Water resources for agricultural development and landscape enhancement in Kuwait are very scarce; the quality is saline (brackish), and the soil texture is sandy, incapable of holding nutrients and moisture. Most of the rainfall in Kuwait either evaporates due to the high temperatures or percolates through the soil. The rainfall is not sufficient to recharge the underground water supply. A variety of alternative water sources have been studied, including seawater desalination and wastewater treatment. Wastewater treatment proved to be convenient due to the relatively low salinity detected in the treated water. Furthermore, the amount of wastewater is expected to increase as more residential areas are connected to the sewage system as the population increases.

The development of a greenery plan for Kuwait requires intensive water management. This goal can be achieved through manpower training in landscape development, selection of plants tolerant to arid environments, usage of appropriate irrigation and drainage systems, promotion of greenery programs within public and the governmental agencies for their direct participation in urban area beautification, and extension of wastewater treatment techniques.     

浙江花岗岩中斜长石钾交代产生的条纹长石

对研究区内花岗岩类的60个薄片进行了岩相学研究.研究的目的是确定区内岩石中普遍存在的矿物组成及结构类型.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

Air pollution forecasting from sky images with shallow and deep classifiers

Air pollution is one of the most important problems in the new era. Detecting the level of air pollution from an image taken by a camera can be informative for the people who are not aware of exact air pollution level be declared daily by some organizations like municipalities. In this paper, we propose a method to predict the level of the air pollution of a location by taking an image by a camera of a smart phone then processing it. We collected an image dataset from city of Tehran. Afterward, we proposed two methods for estimation of level of air pollution. In the first method, the images are preprocessed and then Gabor transform is used to extract features from the images. At the end, two shallow classification methods are employed to model and predict the level of air pollution. In the second proposed method, a Convolutional Neural Network(CNN) is designed to receive a sky image as an input and result a level of air pollution. Some experiments have been done to evaluate the proposed method. The results show that the proposed 9 method has an acceptable accuracy in detection of the air pollution level. Our deep classifier achieved accuracy about 59.38% which is 10 about 6% higher than traditional combination of feature extraction and classification methods.     

Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques

In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes.     

Ranking of productivity improvement strategies in Iran mineral sector based on integrated SWOT-FAHP-FTOPSIS analysis

The Iran’s mineral sector, as a major supplier of mineral industries, plays a central role on Iran economy. Promoting the productivity in this area leads to the enhancement of the business environment. Also, it can influence the production chain and minerals value added. In this study, a comprehensive study was conducted on the major barriers/drivers factors affecting the efficiency in the mining sector. Furthermore, 16 key factors were identified using the expert views. These parameters are divided into four main categories: strengths, weaknesses, opportunities, and threats. In addition, a hierarchy model was designed in which the mentioned four efficient strategic parameters were implemented and the SWOT analysis was applied, as well as the nine macro strategies were determined. In order to prioritize these strategies, multiple steps were carried out. First, the subfactors were weighted by implementing the fuzzy multi attribute decision-making technique by combining the experts’ views and triangular fuzzy numbers. Subsequently, the strategies were prioritized by employing the group Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The sensitive analysis of the results presents some minor changes in score of the alternatives, but no alteration was affected in the prioritized strategies. The results revealed the importance of resource allocation strategies related to the existing investments as well as supporting and facilitating the new technology in the mining sector.