Estimation of the current stress field and fault reactivation analysis in the Asmari reservoir, SW Iran

Knowing the current condition of the faults and fractures in a reservoir is crucial for production and injection activities.A good estimation of the fault reactivation potential in the current stress field is a useful tool for locating the appropriate spot to drill injection wells and to calculate the maximum sustainable pore pressure in enhanced oil recovery and geosequestration projects.In this study,after specifying the current stress state in the Gachsaran oilfield based on Anderson's faulting theory,the reactivation tendency of four faults(F1,F2,F3,and F4)in the Asmari reservoir is analyzed using 3D Mohr diagrams and slip tendency factors.Results showed that all the faults are stable in the current stress state,and F2 has the potential to undergo the highest pore pressure build-up in the field.On the other hand,F3 has the proper conditions(i.e.,strike and dip referring to σHmax orientation)for reactivation.Stress polygons were also applied to show the effect of the pore pressure increase on fault stability,in a graphical manner.According to the results,the best location for drilling a new injection well in this part of the field is the NW side of F2,due to the lower risk of reactivation.It was found that both methods of 3D Mohr diagrams and slip tendency factors predict similar results,and with the lack of image logs for stress orientation determination,the slip tendency method can be applied.The results of such studies can also be used for locating safe injection points and determining the injection pressure prior to numerical modeling in further geomechanical studies.     

Thermal Properties of the Binary‐Filler Hybrid Composites with Graphene and Copper Nanoparticles

The thermal properties of epoxy‐based binary composites comprised of graphene and copper nanoparticles are reported. It is found that the “synergistic” filler effect, revealed as a strong enhancement of the thermal conductivity of composites with the size‐dissimilar fillers, has a well‐defined filler loading threshold. The thermal conductivity of composites with a moderate graphene concentration of f_g = 15 wt% exhibits an abrupt increase as the loading of copper nanoparticles approaches f_Cu ≈ 40 wt%, followed by saturation. The effect is attributed to intercalation of spherical copper nanoparticles between the large graphene flakes, resulting in formation of the highly thermally conductive percolation network. In contrast, in composites with a high graphene concentration, f_g = 40 wt%, the thermal conductivity increases linearly with addition of copper nanoparticles. A thermal conductivity of 13.5 ± 1.6 Wm^?1K^?1 is achieved in composites with binary fillers of f_g = 40 wt% and f_Cu = 35 wt%. It has also been demonstrated that the thermal percolation can occur prior to electrical percolation even in composites with electrically conductive fillers. The obtained results shed light on the interaction between graphene fillers and copper nanoparticles in the composites and demonstrate potential of such hybrid epoxy composites for practical applications in thermal interface materials and adhesives.     

Facile and clean electrochemical synthesis of new acetaminophen derivatives through electrochemical oxidation of acetaminophen in the presence of thiouracil derivatives

The electrochemical oxidation of acetaminophen (1a) is carried out in the presence of thiouracil derivatives (3a–c), as nucleophiles, in an acetate buffer solution (0.15?M, pH 5) mixed with Dimethylformamide (DMF) using cyclic voltammetry and coulometry under a constant potential. The results obtained indicate that N-acetyl-p-benzoquinone-imine derived from acetaminophen participates in a 1,4-Michael-type addition reaction with thiouracils to form the corresponding acetaminophen derivatives (4a–c) in good yields and with high purities using a facile, catalyst-free, and one-pot electrochemical method using three carbon electrodes in an undivided cell under mild conditions. The products obtained were characterized after purification by IR, ¹H NMR, and ¹³C NMR spectroscopies, and by the elemental analysis method.     

Effects of Flow Depth on Water Flow and Solute Transport in Furrow Irrigation: Field Data Analysis

Because of field-scale heterogeneity in soil hydraulic and solute transport properties, relatively large-scale experiments are now increasingly believed to be critical to better understand and predict the movement of water and dissolved solutes under field conditions. In this study, five field experiments were conducted on short blocked-end furrows to assess the effects of irrigation water level on water flow and solute transport in furrows. Three experiments were carried out, each of the same duration but with different amounts of water and solutes resulting from 6, 10, and 14 cm furrow water depths, designated as low, moderate, and high water levels, respectively. Two more experiments were performed with the same amounts of applied water and solute and, consequently, different durations, on furrows with depths of 6 and 10 cm of water. Results show that both the water level and the duration play an important role in transporting and distributing water and solutes in the soil profile. A positive correlation was found between water level and infiltrated amount of water or solute. Irrigation/solute application amounts increased with decreasing water level. Water and solutes were both distributed almost vertically (one-dimensionally) for the low water level and short application treatments, while they moved much more two-dimensionally with low and moderate water depths but longer application times. Irrigation with the 14 cm water level and short application time improved the distribution of water and solutes within the soil profiles, while also causing relatively less deep percolation of water and solutes as compared to low and moderate water levels and relatively long duration times.     

Oxidized vitrinites — their aromaticity,optical properties and possible detection

The behaviour of the dispersion curves of refractive and absorptive index of a vitrinite of high-volatile rank oxidized for different periods of time up to 128 days at 105 °C supports the suggestion that the aromaticity and/or condensation of the vitrinite rise when it is oxidized. Equivocal optical data previously obtained for oxidized vitrinites can probably be explained by the extensive use in earlier microscopical studies of wavelengths in the green region of the visible spectrum. Reflectivity-dispersion data indicate that there is greater possibility of distinguishing oxidized and weathered coals from fresh samples with the microscope by comparing their optical properties in the blue region of the visible spectrum or in the ultra-violet.     

Mixed-model assembly line balancing in the make-to-order and stochastic environment using multi-objective evolutionary algorithms

The present study introduces a multi-objective genetic algorithm (MOGA) to solve a mixed-model assembly line problem (MMALBP), considering cycle time (CT) and the number of stations simultaneously. A mixed-model assembly line is one capable of producing different types of products to respond to different market demands, while minimizing on capital costs of designing multiple assembly lines. In this research, according to the stochastic environment of production systems, a mixed-model assembly line has been put forth in a make-to-order (MTO) environment. Furthermore, a MOGA approach is presented to solve the corresponding balancing problem and the decision maker is provided with the subsequent answers to pick one based on the specific situation. Finally, a comparison is carried out between six multi-objective evolutionary algorithms (MOEA) so as to determine the best method to solve this specific problem.     

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Sleep apnea is a relatively prevalent breathing disorder characterized by temporary interruptions in airflow during sleep. There are 2 major types of sleep apnea. Obstructive sleep apnea occurs when air cannot flow through the upper airway despite efforts to breathe. Central sleep apnea occurs when the brain fails to signal to the muscles to maintain breathing. The standard diagnostic test is polysomnography, which is expensive and time consuming. The aim of this study was to design an automatic diagnostic and classifying algorithm for sleep apneas employing thoracic respiratory effort and oximetric signals. This algorithm was trained and tested applying a database of 54 subjects who had undergone polysomnography. A feature extraction stage was conducted to compute features. An optimal genetic algorithm was applied to select optimal features of these 2 kinds of signals. The classification technique was based on the support vector machine classifier to classify the selected features in 3 classes as healthy, obstructive, and central sleep apnea events. The results show that our automated classification algorithm can diagnose sleep apnea and its types with an average accuracy level of 90.2% (87.5‐95.8) in the test set and 90.9% in the validation set with high acceptable accuracy.     

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.