An efficient deadlock prevention approach for service oriented transaction processing

Transaction processing can guarantee the reliability of business applications. Locking resources is widely used in distributed transaction management (e.g., two phase commit, 2PC) to keep the system consistent. The locking mechanism, however, potentially results in various deadlocks. In service oriented architecture (SOA), the deadlock problem becomes even worse because multiple (sub)transactions try to lock shared resources in the unexpectable way due to the more randomicity of transaction requests, which has not been solved by existing research results. In this paper, we investigate how to prevent local deadlocks, caused by the resource competition among multiple sub-transactions of a global transaction, and global deadlocks from the competition among different global transactions. We propose a replication based approach to avoid the local deadlocks, and a timestamp based approach to significantly mitigate the global deadlocks. A general algorithm is designed for both local and global deadlock prevention. The experimental results demonstrate the effectiveness and efficiency of our deadlock prevention approach. Further, it is also proved that our approach provides higher system performance than traditional resource allocation schemes. © 2011 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

Forest change mapping in northeast China using SAR and INSAR data

One of the objectives of Forest Dragon 2 project is the generation of a forest cover change map between 1990s and 2000s for Northeast China based on ERS-1/2 tandem data and ENVISAT ASAR data. For the 1990s’ map, an automatic and seasonal-adaptive retrieval of forest biomass method was used to produce a forest biomass map based on the ERS tandem interferometric coherence. The biomass map was integrated into two categories (forest and nonforest). For the 2000s’ map, an object-based classification method was developed for ASAR HH/HV images acquired on a single date. Post-classification comparison method for change detection was adopted based on the two forest/nonforest maps

Forest Dragon 2: Final results of the European partners

The European contribution to the Forest DRAGON 2 focused on the evaluation of multi-temporal, multi-sensor and multi-scale Earth Observation images and data products within the vegetation ecosystem of Northeast China. The forest growing stock volume (GSV) map produced with ERS-1/2 coherence images for 1995-1998 and two GSV maps produced from Envisat ASAR ScanSAR data for 2005 and 2010 were inter-compared with respect to several datasets (in situ, EO images and EO data products) to assess the plausibility of the GSV estimates, the contribution to land cover mapping and the dynamics over time. For this purpose, a multi-source database was set up including in situ data and EO data products. Land use / land cover (LULC) datasets identified mis-classification of GSV in the ERS dataset primarily for cropland. An a posteriori correction of the GSV resulted in an increase of overall accuracy up to 7%. LULC products can also support the fine tuning of the algorithm to estimate GSV from ASAR data particularly in transition regions between forest and shrubland. The ASAR-based GSV estimates were consistent and highlighted areas of change. From the two ASAR maps, slight loss of volume from 2005 to 2010 was estimated

Inhibitory Effect of Methyleugenol on IgE-Mediated Allergic Inflammation in RBL-2H3 Cells

Allergic diseases, such as asthma and allergic rhinitis, are common. Therefore, the discovery of therapeutic drugs for these conditions is essential. Methyleugenol (ME) is a natural compound with antiallergic, antianaphylactic, antinociceptive, and anti-inflammatory effects. This study examined the antiallergic effect of ME on IgE-mediated inflammatory responses and its antiallergy mechanism in the mast cell line, RBL-2H3. We found that ME significantly inhibited the release of β-hexosaminidase, tumor necrosis factor- (TNF-) α, and interleukin- (IL-) 4, and was not cytotoxic at the tested concentrations (0–100 μM). Additionally, ME markedly reduced the production of the proinflammatory lipid mediators prostaglandin E2 (PGE2), prostaglandin D2 (PGD2), leukotriene B4 (LTB4), and leukotriene C4 (LTC4). We further evaluated the effect of ME on the early stages of the FcεRI cascade. ME significantly inhibited Syk phosphorylation and expression but had no effect on Lyn. Furthermore, it suppressed ERK1/2, p38, and JNK phosphorylation, which is implicated in proinflammatory cytokine expression. ME also decreased cytosolic phospholipase A2 (cPLA2) and 5-lipoxygenase (5-LO) phosphorylation and cyclooxygenase-2 (COX-2) expression. These results suggest that ME inhibits allergic response by suppressing the activation of Syk, ERK1/2, p38, JNK, cPLA2, and 5-LO. Furthermore, the strong inhibition of COX-2 expression may also contribute to the antiallergic action of ME. Our study provides further information about the biological functions of ME

Comparison of crop classification capabilities of spaceborne multi-parameter SAR data

With the arisen spaceborne multi-parameter Synthetic Aperture Radar (SAR) systems, such as Envisat ASAR, TerraSAR-X, ALOS PALSAR, and RADARSAT-2, the interest of crop mapping has been increasing. The present study compares the capabilities of the multi-parameter SAR in discriminating the main crop types by object-based classification in Haian county of Jiangsu province, South China. Two kinds of information, SAR intensity based and SAR statistical properties based are used for Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) respectively. The results show that, the L-band SAR can uniquely identify mulberry from dryland crops, such as maize and vegetable and C-band SAR has some advantages in mapping rice. Specifically, the polarimetric RADARASAT-2 data can identify the rice with accuracy about 75% ∼ 80% which is similar as the result from X-band TerraSAR-X Spotlight data but higher than that from C-band dual-polarization Envisat ASAR data. Nevertheless, both of X- and C-band can hardly separate the mulberry from the other dry-land crops

Regional forest and non-forest mapping using Envisat ASAR data

Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Land- sat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classif ication method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images of ASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the pro- ducer’s accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is prom- ising for operational forest mapping at regional scale

Dynamic analysis and modeling of Forest above-ground biomass

Estimating forest above-ground biomass (AGB) and monitoring its variation are relevant for sustainable forest management, monitoring global change, carbon accounting, particularly for the Qilian Mountains (QMs), a water resource protection zone. In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. An optimized k-Nearest Neighbor (k-NN) method was determined by varying both the mathematical formulation of the algorithm and remote sensing data input which resulted in 3,000 different model configurations. Following the sun-canopy-sensor plus C (SCS+C) topographic correction, performance of the optimized k-NN method was satisfied (R2=0.59, RMSE=24.92 ton/ha) which indicated that the optimized k-NN is capable of operational applications of forest AGB estimates in regions where only a few inventory data are available. Afterwards, the calibrated BIOME-BGC was applied to simulate the carbon fluxes over QMs forests with satisfactory accuracy. Finally, the dynamic analysis and modeling of forest AGB was conducted based on the remotely sensed estimation of forest AGB and the annual forest AGB increment from the ecological process model

Prediction of BOD in water body of Chaohu Lake based on GA-RandomForest

Aiming at the problems existing in the process of river water quality detection, this paper proposes a BOD prediction model based on GA-RandomForest optimization, and evaluates the model through MAE and MAPE, and achieves good results. And the forecast experiment is carried out through the water quality data of Chaohu Lake for 3 months. The experimental results show that the BOD prediction model based on GA-RandomForest is effective, and the model is based on higher accuracy