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Abstract-We present the ASKALON environment whose goal is to simplify the development and execution of workflow applications on the Grid. ASKALON is centered around a set of high-level services for transparent and effective Grid access, including a Scheduler for optimized mapping of workflows onto the Grid, an Enactment Engine for reliable application execution, a Resource Manager covering both computers and application components, and a Performance Prediction service based on training phase and statistical methods. A sophisticated XMLbased programming interface that shields the user from the Grid middleware details allows the high-level composition of workflow applications. ASKALON is used to develop and port scientific applications as workflows in the Austrian Grid project. We present experimental results using two real-world scientific applications to demonstrate the effectiveness of our approach.

Currently Grid application developers often configure available application components into a workflow of tasks that they can submitfor executing on the Grid. In this paper, we present an Abstract Grid Workflow Language (AGWL) for describing Grid workflow applications at a high level of abstraction. AGWL has been designed such that the user can concentrate on specifying Grid applications without dealing with either the complexity of the Grid or any specific implementation technology (e.g. Web service). AGWL is an XML-based language which allows a programmer to define a graph of activities that refer mostly to computational tasks. Activities are connected by control and data flow links. A rich set of constructs (compound activities) is provided to simplify the specification of Grid workflow applications which includes compound activities such as if, forEach and whi 1 e loops as well as advanced compound activities including parallel sections, parallel loops and collection iterators. Moreover, AGWL supports a generic high level access mechanism to data repositories. AGWL is the main interface to the ASKALON Grid application development environment and has been applied to numerous real world applications. We describe a material science workflow that has been successfully ported to a Grid infrastructure based on an AGWL specification. Only a dozen AGWL activities are needed to describe a workflow with several hundred activity instances.

Using a reflector insert, the original HM-3 lithotripter field at 20 kV was altered significantly with the peak positive pressure (p(+)) in the focal plane increased from 49 to 87 MPa while the -6 dB focal width decreased concomitantly from 11 to 4 mm. Using the original reflector, p(+) of 33 MPa with a -6 dB focal width of 18 mm were measured in a pre-focal plane 15-mm proximal to the lithotripter focus. However, the acoustic pulse energy delivered to a 28-mm diameter area around the lithotripter axis was comparable ( approximately 120 mJ). For all three exposure conditions, similar stone comminution ( approximately 70%) was produced in a mesh holder of 15 mm after 250 shocks. In contrast, stone comminution produced by the modified reflector either in a 15-mm finger cot (45%) or in a 30-mm membrane holder (14%) was significantly reduced from the corresponding values (56% and 26%) produced by the original reflector (no statistically significant differences were observed between the focal and pre-focal planes). These observations suggest that a low-pressure/broad focal width lithotripter field will produce better stone comminution than its counterpart with high-pressure/narrow focal width under clinically relevant in vitro comminution conditions.

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.