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At present, people’s living standard and consumption level are increasing day by day, the demand for edible oil crops is increasing. As the main source of oil crops, rape needs to use more accurate management and cultivation techniques, change backward technology, increase overall yield, and ensure the supply of oil crops. By means of artificial intelligence, the spectral segments of plants were analyzed to determine the species of light and the nutrients produced by plants, the monitoring of development and the prediction of crop yield. According to the spectral information of plant absorption and reflection, the chlorophyll level contained in plants can provide data support for rape planting and management, and monitor the real-time status of its growth and development. Mastering the nutrition and demand of rape in the whole process of growth and reproduction is very beneficial to the management and yield improvement of rape. And can adjust the cultivation method in time, play the advantage of hyperspectral technology.

IVS-II-5 GC (The clinical phenotypes associated with compound heterozygosity for the IVS-II-5 GC mutation and other β-thalassemia mutations, together with the genetic modifiers' potential effect of the genetic modifiers α-thalassemia, were studied in 13 patients. In addition, analyses of red cell indices, hemoglobin component, iron status, and α-globin genes were carried out in 19 heterozygotes.Next-generation sequencing of 24 undiagnosed patients with transfusion-dependent thalassemia (TDT) or non-transfusion-dependent thalassemia (NTDT) identified 13 carriers of the IVS-II-5 GC mutation. There was a wide spectrum of phenotypic severity in compound heterozygotes and 6 (46.2%) of 13 were transfusion dependent. Analysis of 19 heterozygotes indicated that most were hematologically normal without appreciable microcytosis or hypochromia, and approximately half had normal hemoglobin ACompound heterozygotes for IVS-II-5 GC and other severe β-thalassemia mutations are phenotypically severe enough to necessitate appropriate therapy and counselling. Co-inheritance of this nucleotide substitution with other β-thalassemia mutations may account for a considerable portion of the incidence of undiagnosed patients with NTDT and TDT in Guangxi. Therefore, the IVS-II-5 GC mutation can pose serious difficulties in screening and counselling.

In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR574, SPI-NDSI(R575, R576)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content.

In the past few years, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time.

In large-scale heterogeneous cluster computing systems, processor and network failures are inevitable and can have an adverse effect on applications executing on such systems. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing scheduling algorithms for precedence constrained tasks in heterogeneous systems only consider scheduling length, and not efficiently satisfy the reliability requirements of task. In recognition of this problem, we build an application reliability analysis model based on Weibull distribution, which can dynamically measure the reliability of task executing on heterogeneous cluster with arbitrary networks architectures. Then, we propose a reliability-driven earliest finish time with duplication scheduling algorithm (REFTD) which incorporates task reliability overhead into scheduling. Furthermore, to improve system reliability, it duplicates task as if task hazard rate is more than threshold $$\theta $$ ? . The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm can shorten schedule length and improve system reliability significantly.

This paper addresses the problems in scheduling a precedence constrained tasks of parallel application with random tasks processing time and edges communication time on Grid computing systems so as to minimize the makespan in stochastic environment. This is a difficult problem and few efforts have been reported on its solution in the literature. The problem is first formulated in a form of stochastic scheduling model on Grid systems. Then, a stochastic heterogeneous earliest finish time (SHEFT) scheduling algorithm is developed that incorporates the expected value and variance of stochastic processing time into scheduling. Our rigorous performance evaluation study, based on randomly generated stochastic parallel application DAG graphs, shows that our proposed SHEFT scheduling algorithm performs much better than the existing scheduling algorithms in terms of makespan, speedup, and makespan standard deviation.

AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.

Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.

The plumpness is an important index of crop seed. However, traditional measurements are time-consuming and labor intensive. The computer vision technology, which may offer more efficient and non-destructive methods for measurement, has recently appeared. But it is very difficult to accurately estimate the plumpness of single seed by the ratio between area and perimeter because of the diversity of rapeseed seed’s size. This paper focused on rapeseed seed plumpness recognition and quantification, based on computer vision. A new method, the coefficient of variation of radius (CVR), was used to estimate seed plumpness. The recognition and quantification model for plumpness in single seed were established by using the fuzzy C-means (FCM) clustering and fuzzy math method. The plumpness of the seed is full if plumpness is greater than or equal to 0.6. Some correlative index are calculated and analyzed to verify the validity of this method. The tests show that there is no correlation between plumpness or plumpness ratio, and 1000-seed weight or equivalence diameter. But there are significantly partial correlation between plumpness or plumpness ratio, 1000-seed weight and equivalence diameter. Finally, plumpness ratio index is significantly different among the 12 varieties rapeseed was determined. With the mean value of plumpness ratio of rapeseed variety, the plumpness degree was plotted 10 grades. The results show that the application of computer vision technology is significantly valid for quantitative determination of plumpness in rapeseed seed.

In recent years, many government and private organization efforts have been devoted to bring e-business technologies to agricultural domain. It is quite possible in agriculture marketing scenario that for a complete business process, an agricultural marketing activity can make the use of various existing business applications with loosely coupled and open computing environment. However, there is a need to develop a proper underlying infrastructure that links legacy business applications and provides an environment for a competitive business. Web service is an internet-based software component that can shield all sorts of heterogeneous business applications on basis of Service-oriented Architecture standards. In this paper, a Web service composition framework (WSCF) is presented to offer the effective business integration for agriculture marketing. It models various business processes as Web services and the process-based Web services composition can be carried out a complete business process spanned across various existing business applications. We are following WSCF to achieve our objective in ongoing project e-agricultural business platform.

SOA has become a new software basic architecture which offers an intelligent infrastructure to reduce the complexity of service reuse and then integrates all kinds of technologies, protocols and applications. Current products of SOA-based enterprise service bus (ESBsoa) can mainly offer Web services container but not unified adapter mechanism to integrate some kinds of enterprise applications. It restricts the availability and scalability of ESBsoa. In this paper, it first presents a new SOA-based enterprise application integration environment which clarifies the status and function of ESBsoa in constructing Web services-based application supporting platform. Then, it puts forth a new implementation architecture for ESBsoa, and then analyzes some key technologies including transport adaptation, service adaptation and common services etc. At last, ESBsoa is proved to be feasible and effective by simulation experiments.

The implementation of new methods for fast classification of seeds is of major technical importance in the large-scale investigation of seeds identification. A few indices with biological significance were used to identify rapeseed type and variety. The plumpness and the plumpness ratio of rapeseed were extracted by using the variation coefficient of radius for fourteen rapeseed varieties at Chinese five locations. The equivalence diameter was extracted by using reference calibration method. The major color ratio and color saturation were extracted by using nine color HSV model and major color method. ANN-BP models were established for rapeseed varieties classification. The equivalent diameter and plumpness ratio were used to identify two classes' rapeseed, B.compestris and B. napus L. with accuracy of 100%. The equivalent diameter, plumpness ratio and color saturation were used to identify six rapeseed varieties, with accuracy of 92.06% to 92.37%,, with average of 92.15%. By machine vision, it is feasible to identify rapeseed type and variety with a few biological indices.

In this paper, we first introduced the development of the international E-learning standards investigation, and then we made a brief comparison among the comprehensive ones. Afterwards, we choose the SCORM standards as the representation to make deeper study because of its rapid and leading development.

To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M) where 40 is the table tennis ball size (40mm) and 191 as image pixels table tennis (M); measurement results revealed that between the algorithm results and the manual measurements, the absolute error was