The relationship between chronic PTSD,cortical volumetry and white matter microstructure among Australian combat veterans

Background:Posttraumatic stress disorder(PTSD)has been associated with volumetric and white matter microstructural changes among general and veteran populations.However,regions implicated have greatly varied and often conflict between studies,potentially due to confounding comorbidities within samples.This study compared grey matter volume and white matter microstructure among Australian combat veterans with and without a lifetime diagnosis of PTSD,in a homogenous sample assessed for known confounding comorbidities.Methods:Sixty-eight male trauma-exposed veterans(16 PTSD-diagnosed;mean age 69 years)completed a battery of psychometric assessments and underwent magnetic resonance and diffusion tensor imaging.Analyses included tractbased spatial statistics,voxel-wise analyses,diffusion connectome-based group-wise analysis,and volumetric analysis.Results:Significantly smaller grey matter volumes were observed in the left prefrontal cortex(P=0.026),bilateral middle frontal gyrus(P=0.021),and left anterior insula(P=0.048)in the PTSD group compared to controls.Significant negative correlations were found between PTSD symptom severity and fractional anisotropy values in the left corticospinal tract(R^(2)=0.34,P=0.024)and left inferior cerebellar peduncle(R^(2)=0.62,P=0.016).No connectome-based differences in white matter properties were observed.Conclusions:Findings from this study reinforce reports of white matter alterations,as indicated by reduced fractional anisotropy values,in relation to PTSD symptom severity,as well as patterns of reduced volume in the prefrontal cortex.These results contribute to the developing profile of neuroanatomical differences uniquely attributable to veterans who suffer from chronic PTSD.

An Improved Memory Cache Management Study Based on Spark

Spark is a fast unified analysis engine for big data and machine learning,in which the memory is a crucial resource.Resilient Distribution Datasets(RDDs)are parallel data structures that allow users explicitly persist intermediate results in memory or on disk,and each one can be divided into several partitions.During task execution,Spark automatically monitors cache usage on each node.And when there is a RDD that needs to be stored in the cache where the space is insufficient,the system would drop out old data partitions in a least recently used(LRU)fashion to release more space.However,there is no mechanism specifically for caching RDD in Spark,and the dependency of RDDs and the need for future stages are not been taken into consideration with LRU.In this paper,we propose the optimization approach for RDDs cache and LRU based on the features of partitions,which includes three parts:the prediction mechanism for persistence,the weight model by using the entropy method,and the update mechanism of weight and memory based on RDDs partition feature.Finally,through the verification on the spark platform,the experiment results show that our strategy can effectively reduce the time in performing and improve the memory usage.

Natural Language Semantic Construction Based on Cloud Database

Natural language semantic construction improves natural language comprehension ability and analytical skills of the machine.It is the basis for realizing the information exchange in the intelligent cloud-computing environment.This paper proposes a natural language semantic construction method based on cloud database,mainly including two parts:natural language cloud database construction and natural language semantic construction.Natural Language cloud database is established on the CloudStack cloud-computing environment,which is composed by corpus,thesaurus,word vector library and ontology knowledge base.In this section,we concentrate on the pretreatment of corpus and the presentation of background knowledge ontology,and then put forward a TF-IDF and word vector distance based algorithm for duplicated webpages(TWDW).It raises the recognition efficiency of repeated web pages.The part of natural language semantic construction mainly introduces the dynamic process of semantic construction and proposes a mapping algorithm based on semantic similarity(MBSS),which is a bridge between Predicate-Argument(PA)structure and background knowledge ontology.Experiments show that compared with the relevant algorithms,the precision and recall of both algorithms we propose have been significantly improved.The work in this paper improves the understanding of natural language semantics,and provides effective data support for the natural language interaction function of the cloud service.

Flexible Task Scheduling Based on Edge Computing and Cloud Collaboration

With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficiencies werefound during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved atpresent. In 5 g environments, edge computing can better meet the needs of lowdelay and wide connection applications, and support the fast request of terminalusers. However, edge computing only has the edge layer computing advantage,and it is difficult to achieve global resource scheduling and configuration, whichmay lead to the problems of low resource utilization rate, long task processingdelay and unbalanced system load, so as to lead to affect the service quality ofusers. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes agenetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve taskscheduling and resource allocation, and designs a series of experiments to verifythe effectiveness of the GSA-EDGE algorithm. The experimental results showthat the proposed method can reduce the time delay of task processing comparedwith the local task processing method and the task average allocation method.