Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Application of Fuzzy c-means algorithm for the estimation of Asphaltene precipitation

One of problematic topics in petroleum engineering is Asphaltene precipitation issue which causes problems such as tubing plugging and formation damage due to temperature, pressure and composition changes so the notability of this issue increases. In the present investigation a novel Fuzzy c-means (FCM) algorithm was developed to predict precipitated asphaltene as function of dilution ratio, carbon number of precipitants and temperature for solving the problem. The results showed that this novel approach has great ability to predict precipitated asphaltene in terms of aforementioned parameters. The coefficients of determination (R²) for training and testing steps are calculated as 0.9828 and 0.9387 respectively. This great degree of accuracy expresses that the predicting algorithm has potential to be utilized as software for prediction of asphaltene behavior.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Modeling and analysis of hydrogen production in steam methane reforming (SMR) process

Hydrogen is one a gas that demands continue to grow across many industries. Due to the growth for this gas the means of producing it and the ability to supply this demand is of great importance. As a result of this, steam methane reforming is a process of high significance as it is one of the most economically and popular means of producing hydrogen. The value of this process is tremendous as it is able to provide up to 48% of global demands, with this only predicted to increase. Therefore, the understanding of what occurs during this process and the steps that it experiences must be understood to ensure that an efficient system is created.

Steam methane reforming operates by converting the hydrocarbons located in methane into hydrogen and CO_x. This process will generally occur over two different stages, a reformer stage, before going into a water-gas shift reactor. After these main processes occur the product produced may undergo purification to remove any containments and ensuring that the hydrogen is at the industry standard. To help investigate this process and how various stages affect others it can be modeled through software such as Unisim which allows modifications to be made and analyzed the effect this had on the system, allowing a potential more efficient system to be designed which will help meet the growing demand.     

Scopolamine impairs human recognition memory: Data and modeling.

Eight subjects studied a set of complex visual images after administration of 0.4 mg scopolamine. Another 8 subjects performed the same task without drug administration. On a subsequent item recognition test, subjects rated, on a 5-point scale, their confidence that the studied pictures and an equal number of unstudied lures were actually presented. Results showed that scopolamine affected responses to studied items, but not unstudied lures, demonstrating an unambiguous effect of scopolamine on recognition memory. To describe the scopolamine-injected subjects' data, the authors constructed a new model of 2-process recognition that includes the A. P. Yonelinas (1994) model as a limiting case. The model analysis suggests that scopolamine affected both familiarity and recollection. In particular, scopolamine did not affect the frequency with which recollection took place, but rather, affected the amount of recollected information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Application of a modified VIKOR method for decision-making problems in lean tool selection

Group decision making with multiple criteria is the most popular method for ranking a set of alternatives. In this regard, all alternatives are compared based on a common criteria set. Meanwhile, decision makers sometimes encounter special situations, for example, having to select from among a set of alternatives without a set of criteria or with a set of criteria that are grouped/related to various alternatives. Thus, it may be impossible to select from among a set of alternatives using typical methods. Hence, in this study, a new, modified VIKOR method is proposed to address the lean tool selection problems in manufacturing systems. In this study, a model was developed to help practitioners improve their ability to solve problems when the possible solutions have their own individual criteria. In fact the modified VIKOR method can be applied to rank alternatives in threefold: alternatives with common criteria, without common criteria, with integrated common, and uncommon criteria. This paper offers numerical examples of the model, using a case study to illustrate an application of the proposed model and properly assess the validity of this new method. The results demonstrate the usefulness and effectiveness of the modified new method. The model covers the lack existing in the current literature to assess effectiveness of applying lean tools.     

AZ31-O镁合金和6061-T6铝合金搅拌摩擦焊接头的显微组织和力学性能(英文)

对3 mm厚的AZ31-O镁合金板和6061-T6铝合金板进行异种材料搅拌摩擦焊。实验中搅拌针的旋转速度为600~1400 r/min,前进速度为20~60 mm/min。结果表明,在旋转速度1000 r/min、前进速度40 mm/min的工艺条件下,可以获得无缺陷的焊接接头。组织观察发现搅拌区的晶粒尺寸要比基材区的明显小很多。在搅拌区形成了复杂的金属流动。显微硬度测试表明搅拌区的硬度分别是不均匀的。拉伸实验结果表明,接头的拉伸强度约为基材AZ31镁合金的76%,或6061铝合金的60%。接头拉伸断口SEM形貌观察表明为脆性断裂。     

Dual-Function Semaphorin 4D Released by Platelets: Suppression of Osteoblastogenesis and Promotion of Osteoclastogenesis

Effects of the antiosteoblastogenesis factor Semaphorin 4D (Sema4D), expressed by thrombin-activated platelets (TPs), on osteoblastogenesis, as well as osteoclastogenesis, were investigated in vitro. Intact platelets released both Sema4D and IGF-1. However, in response to stimulation with thrombin, platelets upregulated the release of Sema4D, but not IGF-1. Anti-Sema4D-neutralizing monoclonal antibody (mAb) upregulated TP-mediated osteoblastogenesis in MC3T3-E1 osteoblast precursors. MC3T3-E1 cells exposed to TPs induced phosphorylation of Akt and ERK further upregulated by the addition of anti-sema4D-mAb, suggesting the suppressive effects of TP-expressing Sema4D on osteoblastogenesis. On the other hand, TPs promoted RANKL-mediated osteoclastogenesis in the primary culture of bone-marrow-derived mononuclear cells (BMMCs). Among the known three receptors of Sema4D, including Plexin B1, Plexin B2 and CD72, little Plexin B2 was detected, and no Plexin B1 was detected, but a high level of CD72 mRNA was detected in RANKL-stimulated BMMCs by qPCR. Both anti-Sema4D-mAb and anti-CD72-mAb suppressed RANKL-induced osteoclast formation and bone resorptive activity, suggesting that Sema4D released by TPs promotes osteoclastogenesis via ligation to a CD72 receptor. This study demonstrated that Sema4D released by TPs suppresses osteogenic activity and promotes osteoclastogenesis, suggesting the novel property of platelets in bone-remodeling processes.     

Quantum dots: synthesis, bioapplications, and toxicity

Ferrite nanoparticles of basic composition Ni_0.7-x Zn _x Cu_0.3Fe₂O₄ (0.0 ?? x ?? 0.2, x = 0.05) were synthesized through auto-combustion method and were characterized for structural properties using X-ray diffraction [XRD], scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy [FT-IR]. XRD analysis of the powder samples sintered at 600°C for 4 h showed the cubic spinel structure for ferrites with a narrow size distribution from 28 to 32 nm. FT-IR showed two absorption bands (v ₁ and v ₂) that are attributed to the stretching vibration of tetrahedral and octahedral sites. The effect of Zn doping on the electrical properties was studied using dielectric and impedance spectroscopy at room temperature. The dielectric parameters (??', ????, tan??, and ?? _ac) show their maximum value for 10% Zn doping. The dielectric constant and loss tangent decrease with increasing frequency of the applied field. The results are explained in the light of dielectric polarization which is similar to the conduction phenomenon. The complex impedance shows that the conduction process in grown nanoparticles takes place predominantly through grain boundary volume. PACS: 75.50.Gg; 78.20; 77.22.Gm.     

Substrate Temperature Dependent Surface Morphology and Photoluminescence of Germanium Quantum Dots Grown by Radio Frequency Magnetron Sputtering

The visible luminescence from Ge nanoparticles and nanocrystallites has generated interest due to the feasibility of tuning band gap by controlling the sizes. Germanium (Ge) quantum dots (QDs) with average diameter ~16 to 8 nm are synthesized by radio frequency magnetron sputtering under different growth conditions. These QDs with narrow size distribution and high density, characterized using atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM) are obtained under the optimal growth conditions of 400 °C substrate temperature, 100 W radio frequency powers and 10 Sccm Argon flow. The possibility of surface passivation and configuration of these dots are confirmed by elemental energy dispersive X-ray (EDX) analysis. The room temperature strong visible photoluminescence (PL) from such QDs suggests their potential application in optoelectronics. The sample grown at 400 °C in particular, shows three PL peaks at around ~2.95 eV, 3.34 eV and 4.36 eV attributed to the interaction between Ge, GeO_x manifesting the possibility of the formation of core-shell structures. A red shift of ~0.11 eV in the PL peak is observed with decreasing substrate temperature. We assert that our easy and economic method is suitable for the large-scale production of Ge QDs useful in optoelectronic devices.