ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.
Shell-boring species Polydora brevipalpa Zachs, 1933 is redescribed based on morphological observations and molecular approach for future unambiguous identification. Genetic distance analyses showed that the interspecific polydorid variation(16.7%–25.6%) was at least 15 times higher than the intraspecific one(0.2%–0.9%) based on the cytochrome c oxidase subunit I(CO1) gene sequences of polydorids. However, 18 S rDNA variation pattern demonstrated a rather narrow barcoding gap, with the interspecific polydorid variation(0.5%–5.6%) being very close to the intraspecific one(0.0%–0.4%). As such, the CO1 gene exhibited better DNA barcode for identification of polydorids than the 18 S rDNA gene because of the su ciently large barcoding gaps. Analysis of molecular variance results based on CO1 gene sequences showed that most variations in sequences(97.79%) lay within groups of adult worms and egg capsules rather than between them. This indicated that egg capsules from Crassostrea gigas(Thunberg,1793) in Ningbo and Nantong were related to the adult worms from Patinopecten yessoensis(Jay, 1857) in Dalian, and both of them belonged to P. brevipalpa. This result was further supported by parsimony network analysis, which showed that egg capsules collected from dif ferent localities and adult worms shared a single haplotype. This study was the first to report both P. brevipalpa infestation on C. gigas and to utilise the known CO1 sequences of the adult polydorids to validate morphologically unidentified egg capsules or early larvae. P. brevipalpa was most possibly brought to Chinese waters through transportation of Pa. yessoensis brood stock from Japan. 相似文献
