A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns

Mapping Forest Health Using Spectral And Textural Information Extracted From Spot-5 Satellite Images

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj \u3e 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images

Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in-situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model-simulated reservoir inflow with conceptual reservoir operation schemes within a land surface-hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography

Research progress on the gel properties of fermented sausage

Fermented sausage is a fermented meat product favored by a large number of of consumers. In fermented sausages, the formation of gel is one of the key steps, and its composition mainly includes meat protein and fat, among which myofibrillar protein is essential for the gel properties of fermented sausages. During the processing of fermented sausages, the myofibrillar protein and myosin in the meat are deformed, losing their original solubility and becoming a gel substance, while the metabolites and enzymes of microorganisms in the fermented sausage can interact with the protein and fat in the meat to promote the formation of gel, thereby helping to improve the texture, water retention and taste of the fermented sausage. In this paper, the formation mechanism of gel properties of fermented sausage was comprehensively discussed, the effects of lactic acid bacteria, temperature, and other factors on the gel properties of fermented sausage were analyzed, and the research methods of gel properties were introduced. The aim was to provide a basis for the control of process parameters in the processing of fermented sausage and to provide a reference for further improving the gel quality of fermented sausage

Toward improved parameterizations of reservoir operation in ungauged basins: a synergistic framework coupling satellite remote sensing, hydrologic modeling, and conceptual operation schemes

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in-situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model-simulated reservoir inflow with conceptual reservoir operation schemes within a land surface-hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography

The glycogen synthase kinase MoGsk1, regulated by Mps1 MAP kinase, is required for fungal development and pathogenicity in Magnaporthe oryzae

Magnaporthe oryzae, the causal agent of blast disease, is one of the most destructive plant pathogens, causing significant yield losses on staple crops such as rice and wheat. The fungus infects plants with a specialized cell called an appressorium, whose development is tightly regulated by MAPK signaling pathways following the activation of upstream sensors in response to environmental stimuli. Here, we show the expression of the Glycogen synthase kinase 3 (GSK3) MoGSK1 in M. oryzae is regulated by Mps1 MAP kinase, particularly under the stressed conditions. Thus, MoGSK1 is functionally characterized in this study. MoGsk1 is functionally homologues to the Saccharomyces cerevisiae GSK3 homolog MCK1. Gene replacement of MoGSK1 caused significant delay in mycelial growth, complete loss of conidiation and inability to penetrate the host surface by mycelia-formed appressorium-like structures, consequently resulting in loss of pathogenicity. However, the developmental and pathogenic defects of Delta mogsk1 are recovered via the heterologous expression of Fusarium graminearum GSK3 homolog gene FGK3, whose coding products also shows the similar cytoplasmic localization as MoGsk1 does in M. oryzae. By contrast, overexpression of MoGSK1 produced deformed appressoria in M. oryzae. In summary, our results suggest that MoGsk1, as a highly conservative signal modulator, dictates growth, conidiation and pathogenicity of M. oryzae

A calcium ion-imprinted porous film prepared from a cellulose-alginate composite

An ion-imprinted (IIP) film has been successfully prepared in this work. Firstly, mixture solution of cellulose and alginate was obtained by dissolving those polymers in a NaOH/urea aqueous solution. Then the mixture solution was cast onto glass plate and coagulated in CaCl2 aqueous solution bath to prepare a composite film. The matrix of the film was further fixed by cross-linking. Finally, the chelated Ca2+ in the matrix was removed to obtain the IIP film. The IIP film was characterized to show satisfactory mechanical properties, and to exhibit porous mesh network microstructure. The equilibrium swelling ratio of the IIP film was determined to be 700?%. The IIP film was immersed into Ca2+, Ca2+/Cu2+, Ca2+/Zn2+ and Ca2+/Mg2+ solutions to check the adsorption behavior, respectively. The results indicate that the IIP film displayed highly selective Ca2+ recognition, and the presence of additional cations had little effect on the Ca2+ recognition. Thus prepared Ca2+ imprinted film have potential applications in fields such as hard water softening, and Ca2+ enrichment or recognition