Geochemistry, mineral paragenesis and geothermal conditions of oreforming fluids from the Ain El Bey Cu–Fe deposit: potential occurrence of native gold and precious metal traces (North African orogenic belt, Northern Tunisia)

The Ain El Bey abandoned mine, in North-West Tunisia, fits into the geodynamic context of the European and African plate boundary. Ore deposit corresponds to veins and breccia of multiphase Cu–Fe-rich mineralization related to various hydrothermal fluid circulations. Petromineralogical studies indicate a rich mineral paragenesis with a minimum of seven mineralization phases and, at least, six pyrite generations. As is also the case for galena and native silver, native gold is observed for the first time as inclusion in quartz which opens up, thus, new perspectives for prospecting and evaluating the potential for noble metals associated with the mineralization. Scanning Electron Microscope--Energy Dispersive Spectroscopy and Transmission electron microscopy analyses show, in addition, a large incorporation of trace elements, including Ag and Au, in mineral structures such as fahlores(tetrahedrite-tennantite) and chalcopyrite ones. The mineral/mineral associations, used as geothermometers, gave estimated temperatures for the mineralizing fluids varying from 254 to 330 ℃ for phase Ⅲ, from 254 to 350 ℃ for phase Ⅳ, and from 200 to 300 ℃ for phases Ⅴ and Ⅵ. The seventh and last identified mineralization phase, marked by a deposit of native gold, reflects a drop in the mineralizing fluid’s temperature(< 200 ℃) compatible with boiling conditions. Such results open up perspectives for the development of precious metal research and the revaluation of the Cu–Fe ore deposit at the Ain El Bey abandoned mine, as well as at the surrounding areas fitting in the geodynamic framework of the Africa-Europe plate boundary.

Long-Term Energy Forecasting System Based on LSTM and Deep Extreme Machine Learning

Due to the development of diversified and flexible building energy resources,the balancing energy supply and demand especially in smart build-ings caused an increasing problem.Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption.Recently,their management has a great significance as resources become scarcer and their emissions increase.In this article,we propose an intelligent energy forecasting method based on hybrid deep learning,in which the data collected by the smart home through meters is put into the pre-evaluation step.Next,the refined data is the input of a Long Short-Term Memory(LSTM)network,which captures the spatio-temporal correlations from the sequence and generates the feature maps.The output feature map is passed into a Deep Extreme Machine Learning network(with seven hidden layers)for learning,which provides the final prediction.Compared to existing techniques,the LSTM-DELM model offers better prediction results.The simulation values demonstrate the superior performance of the proposed model.

Multimodal imaging in Purtscher-like retinopathy associated with sarcoidosis:a case report

Dear Editor,We report a case of Purtscher-like retinopathy(PLR)in the presence of acute pancreatitis secondary to sarcoidosis.To our knowledge,this is the first case report of a PLR in a patient with sarcoidosis.

Study of the Heat Transfer of a Two-Tubular Exchanger with a Hemispherical End Buried

In this paper, a numerical study of a buried hemispherical double-pipe heat exchanger with soil by using geothermal energy is presented. Since the local air-wall exchange coefficient throughout the heat exchanger is unknown, a study of mathematics based on the theory of Green’s functions in the unsteady state was developed. The complexity of the geometry has led us to develop a numerical study that allows us to obtain results that reflect the importance of heat exchange. The applications are numerous, especially in the storage of energy in the soil to optimize greenhouses according to the cycle of the seasons.

Microwave-Assisted Hydrolysis of Hemicellulose over Sulfonated Catalysts

Sulfonated catalysts based on zirconia (SO3H-ZrO2), silica (SO3H-SBA-15) and zeolite (SO3H-ZSM-5) were studied in the catalytic hydrolysis reaction of hemicellulose in a microwave reactor. The prepared catalysts were characterized by various techniques (XRD, N2 physisorption at 77 K, SEM, TEM and NH3-TPD). The obtained results reveal that despite the differences in their structural and textural properties, the ZrO2, Al-SBA-15 and H-ZSM-5 supports show similar conversions. Doping supports with sulfonate species created hydrogen bonds between SO3H groups and increased the amount of weak acid sites, which enhanced the hydrolysis of hemicellulose. SO3H- ZSM-5 showed the highest catalytic activity followed by SO3H-SBA-15 while SO3H-ZrO2 exhibited a poor conversion. Furthermore, the catalytic hydrolysis of the hemicellulose leads to several interesting products, such as formic acid, acetic acid, lactic acid and xylan. The correlation between the catalytic performances and the acidic properties of the different samples indicates that the best catalytic performances were obtained with the least acidic solids and especially when the density of strong acid sites decreases.