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Gao Y.-H., Miao W., Guo R.-J., Li S.-D. (2015): Real time PCR quantification of Sclerotium rolfsii in chilli tissue and soil. Plant Protect. Sci., 51: 61–66. Root rot disease caused by Sclerotium rolfsii is one of the important factors limiting chilli production in southern China. Rapid methods of detection and identification of S. rolfsii are required for disease prediction and management. Herein, we adapted a four-step real time quantitative PCR method to detect S. rolfsii using new designed primer pair S301S/S301A. By using this assay, S. rolfsii was detected in ten soil samples and eight plant samples at concentrations as low as 10 –7 g mycelium/g soil or plant, both with and without typical symptoms, while tissue-culturing method could only detect the fungus in two samples. This rapid and quantitative method is efficient for detection and determination of S. rolfsii population in soil and plants.

Tumorigenic Agrobacterium-induced crown gall disease significantly limits the production of peach, cherry, and other fruit trees in China. Thus, the rapid detection of pathogens is necessary for disease prediction and management. In this study, we designed the primer pair ipt 3F/ipt 3R on the basis of the ipt gene. This primer pair effectively amplified a 247 bp DNA fragment from the transferred DNA (T-DNA) sequences of tumorigenic Agrobacterium. By using this primer pair, we developed a sensitive real-time PCR method for quantifying tumorigenic Agrobacterium in soil at a level as low as 102 cfu/g. We used this assay to detect tumorigenic Agrobacterium in 19 soil samples. Levels were ≤102 cfu/g in maize or uncultivated soil and 103 to106 cfu/g in soil planting with peach or cherry. The relationship between the disease severity and the tumorigenic Agrobacterium density in soil was verified by artificial inoculation test. The disease incidence was 30 to 50 %, and the disease index was 20 to 30 in soil inoculated with A. tumefaciens at 103 cfu/g. The disease incidence increased to >80 % when the inoculation density of A. tumefaciens was increased to >105 cfu/g soil. These results indicated that tumorigenic Agrobacterium density in soil severely affected the incidence of crown gall formation on peach seedlings, and 103 bacterial cells in 1 g of soil were inappropriate for peach planting. Determination of pathogen density for gall formation can improve the prediction of crown gall disease outbreaks as well as aid in the management of this disease.

First we show that tight nonorthogonal fusion frames a relatively easy to com by. In order to do this we need to establish a classification of how to to wire a self adjoint operator as a product of (nonorthogonal) projection operators. We also discuss the link between nonorthogonal fusion frames and positive operator valued measures, we define and study a nonorthogonal fusion frame potential, and we introduce the idea of random nonorthogonal fusion frames.

In order to ensure the quality of service of the communication link, the user equipment(UE) can connect with one or more micro base stations(MBSs) in the user-centric millimeter-wave distributed cell. How to select the optimal MBSs to communicate with UE is a problem according to the channel environment. Based on the limited feedback beamforming technique of random vector quantization (RVQ) codebook this paper derives the expression that maximizes the average effective signal-to-noise ratio (ESNR), proposing an adaptive micro base station selection algorithm (AMBSS) and verifying its validity. The algorithm can adaptively select MBSs to communicate with UE based on the user's location, maximizing ESNR. The simulation results show that the performance of the algorithm is better than that of the fixed number of MBSs with obtaining more than 2dB in gain, reaching the optimal performance in the case of a fixed number of feedback bits.

Thresholding based iterative algorithms have the trade-off between effectiveness and optimality. Some are effective but involving sub-matrix inversions in every step of iterations. For systems of large sizes, such algorithms can be computationally expensive and/or prohibitive. The null space tuning algorithm with hard thresholding and feedbacks (NST+HT+FB) has a mean to expedite its procedure by a suboptimal feedback, in which sub-matrix inversion is replaced by an eigenvalue-based approximation. The resulting suboptimal feedback scheme becomes exceedingly effective for large system recovery problems. An adaptive algorithm based on thresholding, suboptimal feedback and null space tuning (AdptNST+HT+subOptFB) without a prior knowledge of the sparsity level is also proposed and analyzed. Convergence analysis is the focus of this article. Numerical simulations are also carried out to demonstrate the superior efficiency of the algorithm compared with state-of-the-art iterative thresholding algorithms at the same level of recovery accuracy, particularly for large systems.

Iterative algorithms based on thresholding, feedback and null space tuning (NST+HT+FB) for sparse signal recovery are exceedingly effective and fast, particularly for large scale problems. The core algorithm is shown to converge in finitely many steps under a (preconditioned) restricted isometry condition. In this paper, we present a new perspective to analyze the algorithm, which turns out that the efficiency of the algorithm can be further elaborated by an estimate of the number of iterations for the guaranteed convergence. The convergence condition of NST+HT+FB is also improved. Moreover, an adaptive scheme (AdptNST+HT+FB) without the knowledge of the sparsity level is proposed with its convergence guarantee. The number of iterations for the finite step of convergence of the AdptNST+HT+FB scheme is also derived. It is further shown that the number of iterations can be significantly reduced by exploiting the structure of the specific sparse signal or the random measurement matrix.

The depth-shallow resistivity ratio method for the same measuring point is an important method to identify oil and water reservoirs, But its application effect in conventional logging data is not good. By studying the invasion characteristics of resistivity curves of reservoirs, the ratio of deep resistivity and flush zone resistivity at the same measuring point is used to evaluate oil and water layers with a good effect. Further research shows that the ratio of deep resistivity to flushing zone resistivity can reflect the oil-bearing property of reservoir. The fact that the ratio of deep resistivity and flush zone resistivity of water layers in the same well is basically a constant supports this view, which can be deduced from Archie's formula. That is, the ratio of deep resistivity to flushing zone resistivity is Rw/Rmf, which is the ratio of formation water resistivity to mud filtrate resistivity. On this basis, a method of evaluating oil and water reservoir by using the ratio of deep resistivity and flush zone resistivity is proposed. One is logging crossplot evaluation method, the other is apparent water saturation calculation and evaluation method. These two methods are not affected by the changes of lithology and resistivity of the reservoir, and do not need data such as porosity, rock-electro parameters and formation water resistivity, which can quickly identify oil and water layers. As the ratio between the deep resistivity and the resistivity of the flush zone is not related to the resistivity of the reservoir, it can be used to evaluate the low resistivity oil reservoir. The results show that it is effective to identify low resistivity reservoirs and it is a reliable method to evaluate low resistivity reservoirs.

Solving compressed sensing problems relies on the properties of sparse signals. It is commonly assumed that the sparsity s needs to be less than one half of the spark of the sensing matrix A, and then the unique sparsest solution exists, and recoverable by $\ell_1$-minimization or related procedures. We discover, however, a measure theoretical uniqueness exists for nearly spark-level sparsity from compressed measurements Ax = b. Specifically, suppose A is of full spark with m rows, and suppose $\frac{m}{2}$ < s < m. Then the solution to Ax = b is unique for x with $\|x\|_0 \leq s$ up to a set of measure 0 in every s-sparse plane. This phenomenon is observed and confirmed by an $\ell_1$-tail minimization procedure, which recovers sparse signals uniquely with s > $\frac{m}{2}$ in thousands and thousands of random tests. We further show instead that the mere $\ell_1$-minimization would actually fail if s > $\frac{m}{2}$ even from the same measure theoretical point of view., 12 pages, 2 figures

The problem of recovering signals of high complexity from low quality sensing devices is analyzed via a combination of tools from signal processing and harmonic analysis. By using the rich structure offered by the recent development in fusion frames, we introduce a compressed sensing framework in which we split the dense information into sub-channel or local pieces and then fuse the local estimations. Each piece of information is measured by potentially low quality sensors, modeled by linear matrices and recovered via compressed sensing -- when necessary. Finally, by a fusion process within the fusion frames, we are able to recover accurately the original signal. Using our new method, we show, and illustrate on simple numerical examples, that it is possible, and sometimes necessary, to split a signal via local projections and / or filtering for accurate, stable, and robust estimation. In particular, we show that by increasing the size of the fusion frame, a certain robustness to noise can also be achieved. While the computational complexity remains relatively low, we achieve stronger recovery performance compared to usual single-device compressed sensing systems., Comment: 17 figures, 42 pages

According to the requirements of the conversion of farmland to forests project (CFFP), we investigated the vegetation factors and environmental factors from more than 6,105 sub-compartments in Liangcheng County, Inner Mongolia by using the Matlab, analytic hierarchy process (AHP), and the hierarchical cluster method (HCM). The site conditions were classified quantitatively. The results show that CFFP at this site comprises five site-type groups and 19 site types. A quantitative site classification system method has been established in this paper.

Thermal delivery has become an even tougher a challenge with the increasing levels of integration, which drives the demand for low bond line thicknesses of the thermal interface materials (TIM) in electronic packages. The low elongation property of thin bond line thermal interface in turn leads to significant complications for reliable electronic packaging. Package encapsulation needs to be carefully designed to handle the thermal expansion mismatch driven stress engendered during the bond and assembly (BA) process and in field operation. In this paper, special attention is paid to the material characterization of the thin bond line thermal interface. As the thickness of TIM is comparable to its filler particle size, the mechanical behavior of the TIM cannot be described by the material properties determined with traditional testing techniques using bulk specimens. To fill this gap, testing coupons are built with the dimensions of field application. A testing technique developed for characterizing the TIM will be discussed. The material properties obtained will be implemented into the commercial finite element codes ABAQUS through its cohesive zone model. Thermo-mechanical modeling to predict the propagation of TIM delamination and model verification will be presented. The impact of TIM tearing on other risks associated with electronics encapsulation will be discussed.

Signals with sparse frame representations comprise a much more realistic model of nature than that with orthonomal bases. Studies about the signal recovery associated with such sparsity models have been one of major focuses in compressed sensing. In such settings, one important and widely used signal recovery approach is known as $\ell_1$-synthesis (or Basis Pursuit). We present in this article a more effective performance analysis (than what are available) of this approach in which the dictionary $\Dbf$ may be highly, and even perfectly correlated. Under suitable conditions on the sensing matrix $\Phibf$, an error bound of the recovered signal $\hat{\fbf}$ (by the $\ell_1$-synthesis method) is established. Such an error bound is governed by the decaying property of $\tilde{\Dbf}_{\text{o}}^*\fbf$, where $\fbf$ is the true signal and $\tilde{\Dbf}_{\text{o}}$ denotes the optimal dual frame of $\Dbf$ in the sense that $\|\tilde{\Dbf}_{\text{o}}^*\hat{\fbf}\|_1$ produces the smallest $\|\tilde{\Dbf}^*\tilde{\fbf}\|_1$ in value among all dual frames $\tilde{\Dbf}$ of $\Dbf$ and all feasible signals $\tilde{\fbf}$. This new performance analysis departs from the usual description of the combo $\Phibf\Dbf$, and places the description on $\Phibf$. Examples are demonstrated to show that when the usual analysis fails to explain the working performance of the synthesis approach, the newly established results do., Comment: 19 pages, 7 figures

Taking the teaching of electronic technology course for vocational students major in HVAC (Heating Ventilation and Air-Conditioning) as an example, this paper discussed the existing situation and its defect in macro instructional design, and illustrated a new macro instructional strategy which restructured the syllabus and pedagogy to solve the conflict between the students’ limited leaning ability and the electronic skills required as a qualified HVAC professional.

A number of researchers put on attention on nanoparticles suspension (nanofluids) as part of nanotechnology application in enhanced oil recovery (EOR) nowadays. This experimental study is preliminary stage of nanofluids for EOR project at NTNU. This paper presents the investigation of interfacial tension reduction, nanoparticles retention and permeability impairment in porous media by injecting nanoparticles suspension into glass micromodel. The deposition and pore-blockage of nanoparticles in glass micromodel were investigated and microscopically visualized by taking sequential images. A hydrophilic nanoparticles and synthetic seawater (brine, NaCl 3 wt. %) as base fluid were chosen in this study. The nanofluids were made with various concentration from 0.1 to 1.0 wt. %. The sonicator as liquid homogenization tool was used just before injecting the nanofluids into glass micromodel to avoid agglomeration. A dynamic interfacial tension (IFT) phenomenon has observed during this experiment. Introducing dispersed nanoparticles in brine has reduced dynamic IFT. It will decrease when increasing nanoparticles concentration. Theoretically, it makes oil easier to move out since the friction force between water-phase and oil-phase will also decrease (Afrapoli, 2010). Based on microscopic visualization from glass micromodel, it observed nanoparticle has deposited and adsorbed at surface pore network. In permeability measurement, it reduced 41–72% after injected with nanoparticles. Dynamic light scattering analysis is also performed for nanoparticles entrapment analysis. Another nanoparticles which has bigger average size and lower specific surface area, showed similar behavior with previous nanoparticles suspension. However the permeability reduction is less around 17–21% at similar nanofluids concentration. In conclusion, this phenomenon of nanoparticles transport process possibly occurs due to its deposition on pore surface and blockage in pore throat of glass micromodel. This study provides essential knowledge for us of nanoparticles behavior in pore media before going further experiment stage to as EOR method.