Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600 000 of lncRNAs. However, only a tiny fraction (<1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https://www.sdklab-biophysics-dzu.net/EVlncRNA-Dpred/index.html, and the data and source code can be freely available along with the web server.

Su-Field analysis, as one of TRIZ analytical tool for solving inventive problems, can be used to improve the performance of the technical system effectively. Generally, choosing an appropriate inventive standard is critical to solving inventive problems efficiently and accurately. However, these standards are summarized and categorized based on the enormous amount of patents in different domains, and they are built in the high level of abstraction, independently of the specific application field, making their use require much more technical knowledge than other TRIZ tools. In order to facilitate the use of inventive standards, especially for capturing the uncertainty or imprecision depicted in the standards, a rule-based heuristic methodology is proposed in this paper. Firstly, Su-Field analysis ontology and fuzzy analysis ontology are built to represent the precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) inference and fuzzy inference are performed for generating heuristic concept solution. Finally, a prototype is developed and the resolution of the case of the “Auguste Piccard’s Stratostat” in prototype is elaborated in detail.

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Traditional Chinese medicine has been using Tripterygium wilfordii Hook f. (TWHF) against systemic lupus erythematosus (SLE) over 200 years. Triptolide is an active compound of TWHF with therapeutic effects for auto-immune disease SLE. However, till now, few associated studies were reported and little is known about the mechanism of triptolide against SLE which blocks the new drug discovery. In this study, focused on the proteins participated in the process of immune respond, an integrated bioinformatics analysis covering targeted proteins, SLE OMIM genes, biological process enrichment, and protein-protein interactions (PPI) was deployed. As a result, the candidate therapeutic network against SLE with negative regulation of immune response was proposed. It contains a PPI network of 7 targeted proteins and 7 SLE OMIM genes further regulated by triptolide. Primary validation of this network indicating that processes of apoptosis and pro-inflammatory processes were involved.

Extracted from Chinese herbal medicine Tripterygium wilfordii Hook f., triptolide is an active compound with positive therapeutic effects on immune-regulated disease of rheumatoid arthritis. However, the male reproductive toxicity blocks its wider clinical applications. With the mechanisms of male toxicity is still obscure, it is difficult to tackle its toxicity. In this study, triptolide's male reproductive toxicity mechanisms were carried out through integrated bioinformatics analysis which covers target proteins, further regulated testis proteins, biological process enrichment, and protein-protein interactions. As a result, (1) MYC was identified as the target protein; (2) testis proteins of CCNB1, EZH2, PEG10, and THBS1 were found participated in spermatogenesis; and (3) 3 new candidate testis proteins of CHKA, GAS1, and RCC1 were proposed which might be connected to the male reproductive toxicity.

MOTIVATION: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ. RESULTS: This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction. AVAILABILITY AND IMPLEMENTATION: The method is available at http://sparks-lab.org CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, the spatial variation of winter wheat growth and soil nutrient were studied as the objects. Winter wheat growth spatial distribution is not homogeneity, and the Vari-700 index is the best vegetation index to represent winter wheat spatial distribution in heading stage. Winter wheat growth spatial heterogeneity is mainly affected by altitude and soil nitrate nitrogen content in the returning-green stage. Winter wheat grows well in high soil nitrogen content area, grows poor in low soil nitrogen content region. Crop growth is good in high terrain region, bad in low terrain. In addition, the growth of some crop is poor in high soil nitrogen content region because of the low terrain.

Estimation of canopy biophysical and biochemical parameters using remote sensing data is important for regional crop-growth condition monitoring and yield assessment. The inversion of the radiative transfer model PROSAIL based on the look-up table (LUT) approach is widely used for this purpose, taking remotely sensed reflectance as input. For the LUT-based parameter mapping, the main part is searching for the optimal solution from a large LUT. Due to the computational complexity of the sorting algorithm and size of the LUT for the solution search, a substantial amount of time is normally needed for estimation. In order to speed up the mapping of parameters using remote sensing observations, a faster method is developed for searching the LUT by introducing a binary search algorithm. The results of the experiments based on SPOT-5 imagery show that the proposed method can increase the mapping speed by about 70 times compared to the sorting algorithm.

Recently, raising yield per unit of available arable land becomes necessary to combat the challenge due to increasing population with decreasing arable land. Therefore, it is imperative to evaluate the yield potential for the cropland. The objective of this study was to evaluate the winter wheat yield potential for cropland of Beijing suburb. Three years' continuous remote sensing yield monitoring experiments were carried out from 2007 to 2009. Multitemporal remote sensing images of Landsat5 TM and BJ-1 were collected at the winter wheat growth season. The winter wheat yield monitoring models were established based on the remote sensing vegetation index and the actual yield data for each year. Then, relationship between three years winter wheat monitoring yield and habitat factors, including climatic factors, soil factors and terrain factors, were analyzed. Principal Component Analysis (PCA) and Multi-linear regression (MLR) analysis method are combined to construct the wheat yield potential assessment model, to comprehensively evaluate the wheat-growing areas and classify medium- and low-yield fields in Beijing suburb. The information from this study allow us to systematically understand the wheat medium- and low-yield fields of Beijing area and their spatial distribution features, identify key potential barrier factors, and establish reference for medium- and low-yield farmland in transformation, crop distribution management and rational fertilization in Beijing area.

This paper presents a multi-agent cooperative reinforcement learning approach in cooperative design system. For effectively speed up the learning process, this approach adopts dynamic niche technology grouping design agents, and selects the optimal design agent in every groups. The selected agents make reinforcement learning via interaction with designers and carry on cooperative learning each other, and then spread the learned knowledge in respective groups. The radius of the niches and selected design agents are dynamically adjusted during cooperative reinforcement learning process.

Sudden cooling during the crop growing phase seriously impacts crop growth. Crop vulnerability to low temperature stress is affected both by crop growing condition and the lowest environmental temperature. In this study taking winter wheat in Huang-Huai-Hai Plain of China as an example, vulnerability index to low temperature stress was assessed based on time series satellite data in nearly ten years. Growth sensitivity of wheat-frost was quantified by vegetation index change between tillering stage before winter and shooting stage in the next year. Exposure index of wheat-frost was defined as lowest land surface temperature during critical growth period. Cluster analysis of crop mean vulnerability index showed that high vulnerability of wheat-frost located in central and northern of Hebei Province in nearly 10 years. The result will help to prevent frost damage of winter wheat.

Leaf area index (LAI) of crop canopies is important for crop growth monitoring and yield estimation. Considering the practical need of achieving distribution properties of LAI at a special spatial scale, and the difficult acquisition of corresponding observations at the same scale, a method integrating multi-resolution data at larger scales based on continuous wavelet theory is proposed to provide a more effective LAI dataset. For this method, firstly multi-scale wavelet theory is selected for multi-resolution data decomposition, and then decomposed signals and statistics of observations are coupled for wavelet reconstruction. Finally, the new constructed data is used for LAI estimation through multiple linear regression method. Barley is selected as experimental object. The performance of this method is quantitatively analyzed by testing indicators, i.e. Number of effective bands, R2, and MRA. Theory analysis and numerical practices fully confirm the feasibility and validity of the proposed method in crop LAI estimation.

In traditional, the research of wheat stripe rust spreading mode was focus on the large scale meteorological propagation model. In this paper, we present a diagnosis and propagation model of wheat stripe rust in field scales based on Bayesian network, which can provides technical support for short-term accurate prediction and precision pesticide of stripe rust in small scale. Through the wheat stripe rust induction experiment in different scales field grids, we get the spatio-temporal occurring and spreading law of wheat stripe rust in different scales. Then we can choice the accurate scale of model, structure learning method and parameters learning method of the spatio-temporal spreading model of wheat stripe rust. After constructing the Bayesian network model, we can predict and analyze incidence of stripe rust in a certain fields and can verify the accuracy of model by contrast the real disease in field with the predict result.

Based on the theory of radiation transfer model, this paper modified the Simultaneous Heat and Water model to calculate FPAR vertical distribution in maize canopy and analyzed the relationships between FPAR and some parameters like maize canopy structure, solar zenith, soil reflectance, etc. The validation results using field measurements prove the model to be accurate.

Land surface temperature (LST) is a key parameter in numerous environmental studies. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. The verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product(R2= 0.709 and RMSE = 2.702 K).

This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of -means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by -means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data.

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively.

In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent cooperative system are put forward. The aim is to use socio-cultural perspectives and niche technology for supporting design reuse and share in a cooperative design system.

BACKGROUND: The studies on protein folding/unfolding indicate that the native state topology is an important determinant of protein folding mechanism. The folding/unfolding behaviors of proteins which have similar topologies have been studied under Cartesian space and the results indicate that some proteins share the similar folding/unfolding characters. RESULTS: We construct physical property space with twelve different physical properties. By studying the unfolding process of the protein G and protein L under the property space, we find that the two proteins have the similar unfolding pathways that can be divided into three types and the one which with the umbrella-shape represents the preferred pathway. Moreover, the unfolding simulation time of the two proteins is different and protein L unfolding faster than protein G. Additionally, the distributing area of unfolded state ensemble of protein L is larger than that of protein G. CONCLUSION: Under the physical property space, the protein G and protein L have the similar folding/unfolding behaviors, which agree with the previous results obtained from the studies under Cartesian coordinate space. At the same time, some different unfolding properties can be detected easily, which can not be analyzed under Cartesian coordinate space.

This paper focuses on the methodology of estimating biophysical and biochemical parameters and yield of winter wheat based on Landsat TM images. In order to develop the method of retrieving wheat parameters from remote sensing data, five Landsat TM images were acquired respectively at erecting stage, jointing stage, heading stage, early grain-filling stage and grain-filling stage of winter wheat. Experiment sites' wheat biophysical and biochemical parameters such as biomass, LAI, chlorophyll, nitrogen content and yield were measured. Based on the TM images, vegetation indices such as NDVI, OSAVI, MSAVI EVI, SIPI and NDWI were calculated. Then the correlation coefficients between wheat parameters and spectral indices of the experiment sites were computed. According to the correlation coefficients, the optimal spectral indices for estimating wheat parameters were determined. The best-fitting method was employed to build the relationship models between wheat parameters and the optimal spectral indices. Finally, the models were used to estimate wheat biophysical and biochemical parameters and yield based on Landsat TM data. Research results show wheat parameter estimation models possessed more R2values than 0.615.

The effect of crop leaf angle on the canopy-reflected spectrum cannot be ignored in the inversion of leaf area index (LAI) and the monitoring of the crop-growth condition using remote-sensing technology. In this paper, experiments on winter wheat (Triticum aestivumL.) were conducted to identify the crop leaf angle distribution (LAD) by two-temporal (erecting and elongation stages) and bidirectionalin situreflected spectrum and the Airborne Multiangle Thermal Infrared (TIR) Visible Near-Infrared (VNIR) Imaging System (AMTIS) images. The distribution characters of the leaf angle for different LAD varieties were expressed using the beta-distribution function and the SAILTH radiative transfer models. The proportion of the leaf angle in 5deg angle classes (from 5deg to 90deg) for erectophile, planophile, and horizontal varieties was dominated by 75deg, 55deg, and 35deg. The different LAD varieties had a similar canopy reflectance in 680 nm (red) and 800 nm (near-infrared band) at the erecting stage, while they had significant differences at the elongation stage. The ratio of the canopy reflectance of 800 nm at the erecting stage [R800(B)] to the canopy reflectance of 800 nm at the elongation stage [R800(A)] was used to identify the different LAD varieties through the selected two-temporal canopy reflectance. A method based on the semiempirical model of the bidirectional reflectance distribution function (BRDF) was also introduced in this paper. The structural parameter-sensitive index (SPEI) was used in this paper for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index and the normalized difference f-index. We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying two-temporal and bidirectional canopy-reflected spectrum

A novel instrument to measure photochemical reflectance index (PRI) and nitrogen reflectance index (NRI) is presented. It can measure the incident and reflected radiance of vegetation at 531 nm, 570 nm and 670 nm bands by the 6 specially designed interference filters, then PRI and NRI is derived. The PRI index can be used for the diagnosis of crop stress, such as waterlogging and drought. The NRI can be used for the diagnosis of crop nitrogen nutrition deficiency. The instruments can diagnose the plant growth status by the acquired spectral response. This optical instrument includes photoelectric detector module, signal process and A/D convert module, the data storing and transmission module and human-machine interface module. The detector is the core of the instrument which measures the spectrums at special bands. The microprocessor calculates the PRI and NRI value based on the A/D value. And the value can be displayed on the instrument's LCD, stored in the flash memory of instrument and can also be uploaded to PC through the PC's RS232 serial interface. The prototype was tested in the crop field at different view directions. The PRI and NRI data acquired by the novel instrument are compared and calibrated by an ASD FieldSpec spectrometer, which shows that this instrument is successfully developed and reliable. The winter wheat's leaf area index, water content, chlorophyll density and nitrogen content are successfully detected by the PRI and NRI instrument, this novel instrument is portable and could be fixed on the agricultural machine traveling in the field, which demonstrates the promising application of the novel portable instrument.

It is difficult to quantify the amount of chlorophyll fluorescence emitted by a leaf or canopy under natural sunlight because the reflected light obscures the fluorescence signal. In this study, two diurnal experiments were conducted on winter wheat (Triticum aestivum L.) and Japan Creeper (Parthenocissus tricuspidata) to detect the solar-induced chlorophyll fluorescence from field radiance spectra. In the separation of the fluorescence emissive signal from canopy radiance spectrum based on Fraunhofer lines, two Fraunhofer lines of the terrestrial oxygen absorption at 688 and 760 nm were observed in the radiance spectra by an Analytical Spectral Devices FieldSpec Pro NIR spectrometer, which largely overlaps the chlorophyll fluorescence emission spectrum of leaves. Therefore, Fraunhofer lines at 688 and 760 nm were selected to detect the emissive fluorescence. The diurnal changes of chlorophyll fluorescence in the two experiments were primarily affected by the diurnal changes of photosynthetically available radiation (PAR). The correlation coefficients (R/sup 2/) were greater than 0.9 for all the relationships between PAR and the solar-induced fluorescence of winter wheat and Japan Creeper at 688 and 760 nm based on Fraunhofer line-depth (FLD), suggesting that the solar-induced fluorescence could closely track the changes of PAR and chlorophyll fluorescence. The relative solar-induced fluorescence based on FLD was negatively related to Fv/Fm measured by an OS1-FL modulated chlorophyll fluorometer. The correlation coefficients (R/sup 2/) were 0.97 at 688 nm and 0.99 at 760 nm for winter wheat, and 0.79 at 688 nm and 0.78 at 760 nm for Japan Creeper. These results demonstrate that the solar-induced fluorescence from plant canopies can be detected from field radiance spectra based on the Fraunhofer line principle.

In the first part of this article, traditional methods for deriving city vegetation information through remotely sensed images, the study of the area's characteristics and the research flow are introduced. In this article, two main issues are studied, one is city shadow correction, the other is the classifying method of city vegetation through remote sensing images. The biggest problem of high-resolution images is the shadow of high buildings. City shadows decrease the classification accuracy, so a shadow adjustment method must be studied. In this article, we analyze the radiation correction model, and acquire the shadow adjustment model. The model's parameters can be calculated through the image pixel values. The result of shadow correction shows that the model can correct for city shadow. After shadow correction, the vegetation's pixel value in the shadow is similar to the pixel value of vegetation in other areas. Shadow correction increases classification accuracy. In the second part of this article, three methods are studied to derive city vegetation information, including the vegetation index method, a back propagation neural network method, and a texture method. Finally, the three methods' classification accuracies are calculated and appraised. A conclusion is drawn, which is that the texture classification method is a good classification method. The accuracy of texture classification method can reach 91.53%.

Recently, there were many strong sandstorms occurred in North China, which became a severely economical, social and environmental problem, especially for Beijing City. The bare soil is an important source of sandstorm, and the croplands naked in winter in 55 counties around Beijing City became the main source of the sandstorm. It is very important to monitor the seasonal bare soil areas using remote sensing data. The multitemporal TM images of Beijing area in May 16, 1997, April 7, 2003, May 25, 2003, October 24, 2003 and January 28, 2004 were selected. Firstly, the smoothing filter-based intensity modulation (SFIM) completed the image fusion. Secondly, the multistage classification approach was adopted. The multisource data, such as DEM, NDVI, bare soil index (BI), shadow index (SI), were integrated and applied in classification, and the seasonal bare soil areas, such as wasteland, cropland, were successfully extracted. Thirdly, the seasonal and inter-annual dynamic changes of the bare soil areas were also analyzed.

This work focused on developing the appropriate spectral index to monitor yellow rust disease, which is relatively insensitive to species, canopy structures (such as leaf area index and leaf distribution angles), foliar inner structures, and soil condition variations. Firstly, canopy spectra and canopy chlorophyll concentration were acquired during growth duration, and the relationship between SPAD value and measured chlorophyll concentration was established. It indicated that the logarithmic relationship exists between TCARI/OSAVI and SPAD value, with a coefficient of determination R/sup 2/ = 0.7795 (n = 320). Therefore, the combined index (CCII) can be used to minimize the effects of LAI and no photosynthetic materials on the retrieval of chlorophyll concentration at canopy level. Secondly, The photochemical reflectance index (PRI) was chosen for disease index inversion by minimizing internal leaf structure effect, has and the linear negative relationship exists between the normalized photochemical reflectance index (NPRI) and disease index (DI), with a coefficient of determination R/sup 2/ = 0.8477 (n = 63), so the normalized photochemical reflectance index (NPRI) can be used to monitor the disease index of yellow rust.

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The traditional method for the winter wheat yellow rust field survey is time consuming. It was discussed of the selection method of characteristic spectral bands and the establishing of inversion model to monitor winter wheat yellow rust using hyperspectral data in this study. The correlation coefficients between selected vegetation index and disease incidence (DI) at infected stages. Inversion models between DI and vegetation index such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) were used to monitor yellow rust. The multi-temporal hyperspectral airborne images were acquired from winter booting stage to milking stage, and the yellow rust disease of winter wheat was analyzed using hyperspectral images. Compared with healthy wheat, spectral reflectance of disease wheat was higher in 560-670 nm bands but lower in near infrared bands and the absorption depth of chlorophyll in red band and reflectance peak in green band are relatively reduced. A novel spectral index for yellow rust indices was presented, and the degree and area of yellow rust disease were successfully remotely sensed from the multi-temporal hyperspectral data based on spectral index.

Remote sensing offers the potential to determine rapidly the physiological condition of crop over large areas. Canopy reflectance data selected at key growth stages of winter wheat were analyzed. It indicated that winter wheat growth phonological changes can be evaluated by red edge variables (REV) characteristics. Regression equations between chlorophyll concentration, nitrogen concentration, soluble sugar were established, prediction of foliar soluble sugar and chlorophyll concentration by red edge position, foliar nitrogen concentration by the amplitude of red edge, starch concentration by the near infrared plateau, leaf area index by the area of red edge peak are successful and feasible. For this purpose red edge variables can play a vital role in providing time-specific and time-critical information for precision farming, due to their capabilities in measuring canopy spectrum variability.

NDVI is the most widely applied vegetation index, it can indicate the information of growth status, LAI, et al. However, NDVI is also affected by the atmosphere conditions, soil background, view conditions and so on. In this paper, we analyses the effects of chlorophyll, surface soil moisture, the ratio of sky light, view zenith and Sun zenith on NDVI under different LAI using the leaf radiative transfer model PROSPECT and canopy reflectance model SAIL, and find the sensitive factors and ranges.

In recent years, as the availability of remote sensing imageries of varying resolution has increased, merging images of differing spatial and spectral resolutions has become a significant operation in the field of remote sensing. With the development of quantitative remote sensing, not only improving spatial details but also preserving the spectral information of multispectral bands were required. The principle, algorithm and methods of two fusion algorithms, SFIM (Smoothing Filter-based Intensity Modulation) and Gram-Schmidt (Gram-Schmidt transform), were described, also advantage and disadvantage of them were discussed. In a case of Ikonos image in city, visual judgment, quantitative statistical parameters and graph method were used to qualitatively and quantitatively assess these two algorithms, also compared to the traditional fusion methods of IHS transform and PC (principal component) transform. The results showed that there was no distinct difference in spatial details improved. However in terms of spectral information fidelity, both IHS and PC method were the worst, Gram-Schmidt method was better, while SFIM method was the best.

The ground cover of vegetation indicates light interceptor of plant, and plant productivity. The red and near-infrared vegetation indices have been employed to predict the ground coverage of crop, however these vegetation indices were much affected by uncertain factors such as leaf color, crop cultivar and others. This work focused on the feasibility of predicting ground cover by near infrared and mid-infrared spectral indices, which were little sensitive to cultivar, fertilization, irrigation and leaf color. The digital photographs of wheat canopy were taken vertically 1.5 m aboveground and then spectra were measured by an ASD Fieldspec FR2500 spectrometer. The ground cover was automatically extracted by a novel image processing procedure. 8 diagnostic bands in infrared and short-infrared region were selected to calculate 56 ratio indices and 28 normalized difference indices. Also the popular 7 red and near-infrared vegetation indices were calculated. The general linear model (GLM) was applied to assess the relationship between ground coverage and spectral indices, and to evaluate whether they were sensitive to cultivar, fertilization, irrigation and leaf color. Results showed that red and near infrared vegetation indices to predict ground coverage were affected by at least one of the four factors mentioned above, however it is promising that some infrared indices, such as R1690/R1450, R1450/R1690, R1450:R1690, predict ground coverage well and not sensitive to cultivar, fertilization, irrigation and leaf color.

The objective in this study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises and interested in measuring and assessing soil and crop status in order to apply adequate fertilizers quantities to crop growth. This work focused on studying the relationship between SPAD readings and crop chlorophyll concentration and/or nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The recommended rate of conventional uniform rate fertilizer management was higher than that of variable management. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured between CK and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and 1000-grain weight, but it didn't increased crop yield and grain protein content significantly, compared to traditional nitrogen application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology (VRT) based on SPAD readings could be used to prevent the water pollution and environmental deterioration.

The seedtime plays an important role on the growth, yield and quality of winter wheat. Firstly, the seedtime was successfully remotely sensed by NDVI derived from a LandSat TM image captured in elongation stage. Secondly, an optimized yield estimation model was designed based on NDVI and seedtime, and the optimized model was successfully tested by 3 LandSat TM images captured in heading, grain filling and milking stages.

A method for estimating grain quality of winter wheat using Landsat Thematic Mapper data and GIS data is presented, and it gave the development and application of the Geographical Information Application system for estimating wheat grain quality. The system, supported by COMGIS, JModel-Base Management System and other advanced Spatial Information Technologies, can deal with the fusion analysis of remotely sensed data, GIS data and other multisources data, flexible management of models and knowledge

PHI (pushbroom hyperspectral imager) is a hyperspectral imaging sensor which was developed by Chinese Academy of Science. From April to May of 2002, three airborne PHI images were obtained over a winter wheat field near Beijing. In this paper, wheat yield prediction models are established using PHI spectrum information and after-harvest grain yields data. Within-field yield variability was mapped for entire wheat field based on the models. A yield map was produced from the combine harvest yield data to evaluate those models. The analysis results indicates that (1) hyperspectral remote sensing data could be used to estimate yields in winter wheat yield, (2) new and more effective indicators are needed to capture more of the factor's that affect crop yield, and such parameters should be extracted from remote sensing data

There is considerable interest in assessing leaf area index (LAI) to evaluate crop growth and production. In this article, an operational approach was proposed to evaluating LAI of summer maize for different cultivars under different nitrogen treatments and developmental stages by selecting ten familiar remote sensing vegetation indices (VIs). The result indicated that VIs had the potential for faithfully estimating LAI, and the estimation power of VIs for assessing LAI was best from bell stage to silking stage, which primarily depended on the LAI dynamic variation during the process of the growth. When the estimation power of VIs was systematically verified with the other independent data set, the VIs could accurately evaluate LAI. The ratio spectral index of R/sub 810//R/sub 560/ was the best index to estimate LAI, which was wondrously sensitive to LAI dynamic variation almost without the influence of cultivars, growth stages and nitrogen treatments. The exponential regression model for LAI based on R/sub 810//R/sub 560/ was also established with a mean determination of coefficient (R/sup 2/) of 0.9573 (P value=0.01) and a mean root mean square error (RAISE) of 0.0365. Therefore, the spectral index of R/sub 810//R/sub 560/ could be considered as a sensitive indicator as LAI of summer maize.

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The four tested winter wheat varieties were under four different irrigation treatment levels at two different growth stages, one at booting stage and another at grain filling stage, the winter wheat canopy and bare soil reflected spectrum, foliar and soil moisture condition were tested in the field at an interval of 7 days from erecting stage to ripening stage. The combined spectral indices of photochemical reflectance index (PRI) and normalized difference water index (NDWI) were chosen in this study. Regression equations between the ration of NPRI to NDWI and soil water content (SWC) were found to be most appropriate for assessing soil water condition under vegetation covering, and relationship between the ration of PRI to NDWI and soil irrigation demanding conditions were also studied. It indicated that soil irrigation demanding condition under vegetation covering conditions could be evaluated by a pragmatic approach for canopy level estimation with proper combined vegetation index

Vegetation chlorophyll is a key indicator in ecosystem. Operational chlorophyll content estimation models by remote sensing are needed. In this paper, some modifications were made to an existing semi-empirical chlorophyll content estimation model, a new estimation model was given and validation results are promising

In this study, the selection method of characteristic spectral band and the establishing of inversion model to monitor winter wheat stripe rust using hyperspectral data is discussed. The correlation coefficients between the DI (disease incidence) at different stages of infection and the initial canopy reflectance spectral and the derivative of the reflectance spectrum were compared, respectively. The results showed that the derivative of the reflectance spectra has reached higher significant level with the DI than the initial reflectance spectral data. The initial reflectance in the visible light 680 nm wavelength and the near infrared 976 nm, 1010 nm wavelength were selected to do regression with the DI of winter wheat stripe rust. And some inversion models between the DI and the hyperspectral data or its conversion patterns like NDVI (Normalized difference vegetation index), RVI (Ratio vegetation index), TVI (Transformed vegetation index) and its differential values of the canopy spectral reflectance data to monitor winter wheat stripe rust were established. Meanwhile, those correlation coefficients were compared respectively, of which we found the pattern of vegetation index has more efficient commonly than initial canopy spectral reflectance data by aggression analysis with the DI. The paper also suggested that the possibility of developing a special visible/near-infrared sensor for the detection of the DI of winter wheat stripe rust theoretically. Else, the SRSI (stripe rust stress index) mechanism model was presented for the first time in This work.

The wheat grain quality and its influence were introduced, and the mechanism and methods to forecast grain quality were studied. Firstly, the leaf nitrogen content at anthesis stage was proved to be an indicator of grain protein content, and the spectral indices significant correlated to leaf nitrogen content at anthesis stage were the potential indictors for grain protein content. The vegetation index. VIgreen, derived from the spectral reflectance at green and red bands, was significant correlated to the leaf nitrogen content at anthesis stage, and also high significant to the final grain protein content. Secondly, the environment stress, such as irrigation, fertilization, temperature, had important influence on grain quality. The irrigation stress can increase grain protein content. The leaf water content depends on irrigation levels, therefore, the spectral indices correlated to leaf water content were also potential indictors for grain quality. The spectral reflectance at SWIR band and other water indices at seeding filling stage were proved to be high significant correlate to grain protein. Finally, the predicting models of grain protein content were built based on the transfer principle of leaf nitrogen content and the effect of irrigation stress

This paper mainly proposed using multi-temporal spectral data with given weight to estimate yield using contribution ratios of different stages to improve yield estimation, then use stepwise regression to build models for yield estimation. The contribution ratio is calculated by principal component analysis respectively. Result show that this methodology reflects the crop growth status, physiological characters at different stages, and improves the accuracy obviously.

Remote sensing of vegetation liquid water has important application in agriculture and forestry. The spectral index or features of water absorption in NIR and SWIR have been found useful for the detection of plant water content (PWC). It is unfortunately that the foliar liquid water absorption is superposed by the atmospheric vapor absorption, and it is very difficult to distinguish the contribution of foliar liquid water and atmospheric vapor on the water absorption spectral features. In this study, the novel methods using red edge width, which located outside of water absorption bands, were proposed for deriving winter wheat PWC from field canopy spectra. The significant positive correlation coefficients were observed between PWC and spectral reflectances in 740-900nm region for all the 6 growth stages. And this mechanism also affects the spectral red edge in 680- 740nm region. The correlation coefficients between PWC and red edge width derived from inverted gaussian model is significant at 0.999 confident level, and we established the statistical models for PWC by red edge width in all the 6 growth stages.

At XiaoTangShan Precision Agriculture Experiment Base, suburban of Beijing city, we obtained the hyperspectral data of wheat canopy by field measurement and synchronal relative foliage water content (RFWC) at lab, according to the different growth stage of wheat, in the spring of 2002. In this paper, we extracted spectral feature parameters firstly. Then, the correlation analysis between the spectral features parameters and RFWC was made. two spectral feature parameters, which have good relativity to foliage water content, to make linear regression models between RFWC and spectral feature parameter according to the growth stages of wheat.

Spectral data are strongly related to canopy parameters, which are related to final yield at a critical stage of the crop growth, so it is possible to estimate wheat yield from spectral data. In this paper, firstly, the yield data were related to the field hyperspectral reflectance data in 8 different growth stages from regreening stage to milking stage. The statistical results show that yield is positive correlative with spectral reflectance in NIR bands and negative correlative in visible and SWIR bands. Secondly, the normalized difference spectral indices defined by [860 nm, 1200 nm], [890 nm, 980 nm], [820 nm, 1650 nm], [820 nm, 1650 nm] and [820 nm, 2200 nm] are designed and related to yields in all the 8 growth stages. Compared with NDVI, these spectral indices can predict yield earlier and more reliably. Finally, the remote sensing models in different growth stages for yield were built by [820 nm, 1650 nm] weak water absorption index.

Overhaul and repair services are an important segment of the remanufacturing industry, where an asset is disassembled, and component parts are repaired and then assembled to restore the asset to an "as new" condition. A key characteristic of such services is the wide use of rotable parts, i.e., using repaired parts from other assets for assembly, as opposed to waiting for the completion of part repairs from the original assets. However, it is difficult to evaluate the performance of such system. In this paper, and overhaul and repair system characterized by a fork/join structure with rotable inventory is studied. The effects of rotable inventory to system performance are analyzed, and an approximation method is then developed for evaluating key performance measures. By modelling the rotable inventory as a "negative" queue, a system with one overhaul center and one rotable repair shop is first reduced to a tandem queue. Numerical results demonstrate that by this model reduction, TAT can be efficiently estimated within ten percent loss of accuracy as compared with results obtained from hours of simulation. A system with one overhaul center and N rotable repair shops is then reduced to a fork/join system with multiple branches of tandem queues. With appropriate methods to analyze fork/join tandem systems, approximated performance measures can be evaluated.

Most current video retrieval systems use shot as the basis for information organization and access. In cinematography, scene is the basic story unit that the directors use to convey their ideas. This paper proposes a framework based on the concept of continuity to analyze video contents and extract scene boundaries. Starting from a set of shots, the framework successively applies the concept of visual, position, cameral focal distance, motion, audio and semantic continuity to group shots that exhibit some form of continuity into scenes. The idea is tested using the first three levels of continuity to extract the scenes defined using most common cinematic rules. The method has been found to be effective.

A machining center is an advanced NC (numerical control) machine that has the capability to perform a variety of operations on a part by automatically changing the cutting tools. Because of the versatile processing capabilities, a machining center is often a production bottleneck, and effective scheduling can result in significant improvement of system performance. The problem, however, is very difficult since many factors such as machine setups, pallets, tool magazine, and the possible tool overlapping among different part types, have to be considered. This paper presents an optimization-based approach for the scheduling of a machining center with two pallets. A novel "separable" problem formulation that considers the above mentioned factors is presented. Lagrangian relaxation is applied to decompose the problem into simple subproblems, which are efficiently solved without encountering complexity difficulties. The subgradient method is then used to update the multipliers. Preliminary testing results indicate that the approach is effective, and the algorithm provides a valuable tool for solving standalone machining center scheduling problems.