Context-aware computing-based reducing cost of service method in resource discovery and interaction

Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.

Local events-based fast RTM surface-offset gathers via dip-guided interpolation

Reverse Time Migration(RTM)Surface Ofset Gathers(SOGs)are demonstrated to deliver more superior residual dip information than ray-based approaches.It appears more powerful in complex geological settings,such as salt areas.Still,the computational cost of constructing RTM SOGs is a big challenge in applying it to 3D feld data.To tackle this challenge,we propose a novel method using dips of local events as a guide for RTM gather interpolation.The residual-dip information of the SOGs is created by connecting local events from depth-domain to time-domain via ray tracing.The proposed method is validated by a synthetic experiment and a feld example.It mitigates the computational cost by an order of magnitude while producing comparable results as fully computed RTM SOGs.

The forced activation of asexual conidiation in Aspergillus niger simplifies bioproduction

Aspergillus niger is an efficient cell factory for organic acids production,particularly l-malic acid,through genetic manipulation.However,the traditional method of collecting A.niger spores for inoculation is labor-intensive and resource-consuming.In our study,we used the CRISPR-Cas9 system to replace the promoter of brlA,a key gene in Aspergillus conidiation,with a xylose-inducible promoter xylP in l-malic acid-producing A.niger strain RG0095,generating strain brlAxylP.When induced with xylose in submerged liquid culture,brlAxylP exhibited significant upregulation of conidiation-related genes.This induction allowed us to easily collect an abundance of brlAxylP spores(>7.1×106/mL)in liquid xylose medium.Significantly,the submerged conidiation approach preserves the substantial potential of A.niger as a foundational cellular platform for the biosynthesis of organic acids,including but not limited to l-malic acid.In summary,our study offers a simplified submerged conidiation strategy to streamline the preparation stage and reduce labor and material costs for industrial organic acid production using Aspergillus species.

Active Anomaly Detection Technology Based on Ensemble Learning

Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.