A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Change detection and deformation monitoring is an active area of research within the field of engineering surveying as well as overlapping areas such as structural and civil engineering. The application of Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete structures has increased over the years as illustrated in the past studies. This paper presents a review of literature on TLS application in the monitoring of structures and discusses registration and georeferencing of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS research work has shown some trends in addressing issues such as accurate registration and georeferencing of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud processing techniques for deformation analysis, scanner calibration issues and assessing the potential of TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to investigation as far as TLS is concerned in change detection and deformation monitoring studies such as rigorous and efficient workflow methodology of point cloud processing for change detection and deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise structures, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. Even though some studies have attempted to address these issues, some gaps exist as information is still limited. Some methods reviewed in the case studies have been applied in landslide monitoring and they seem promising to be applied in engineering surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are being developed necessitating more research for precise measurements in the monitoring of structures

Natural History, Phenotypic Spectrum, and Discriminative Features of Multisystemic RFC1-disease

OBJECTIVE: To delineate the full phenotypic spectrum, discriminative features, piloting longitudinal progression data, and sample size calculations of RFC1-repeat expansions, recently identified as causing cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS). METHODS: Multimodal RFC1 repeat screening (PCR, southern blot, whole-exome/genome (WES/WGS)-based approaches) combined with cross-sectional and longitudinal deep-phenotyping in (i) cross-European cohort A (70 families) with ≥2 features of CANVAS and/or ataxia-with-chronic-cough (ACC); and (ii) Turkish cohort B (105 families) with unselected late-onset ataxia. RESULTS: Prevalence of RFC1-disease was 67% in cohort A, 14% in unselected cohort B, 68% in clinical CANVAS, and 100% in ACC. RFC1-disease was also identified in Western and Eastern Asians, and even by WES. Visual compensation, sensory symptoms, and cough were strong positive discriminative predictors (>90%) against RFC1-negative patients. The phenotype across 70 RFC1-positive patients was mostly multisystemic (69%), including dysautonomia (62%) and bradykinesia (28%) (=overlap with cerebellar-type multiple system atrophy [MSA-C]), postural instability (49%), slow vertical saccades (17%), and chorea and/or dystonia (11%). Ataxia progression was ∼1.3 SARA points/year (32 cross-sectional, 17 longitudinal assessments, follow-up ≤9 years [mean 3.1]), but also included early falls, variable non-linear phases of MSA-C-like progression (SARA 2.5-5.5/year), and premature death. Treatment trials require 330 (1-year-trial) and 132 (2-year-trial) patients in total to detect 50% reduced progression. CONCLUSIONS: RFC1-disease is frequent and occurs across continents, with CANVAS and ACC as highly diagnostic phenotypes, yet as variable, overlapping clusters along a continuous multisystemic disease spectrum, including MSA-C-overlap. Our natural history data help to inform future RFC1-treatment trials. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that RFC1-repeat expansions are associated with CANVAS and ACC

Natural history, phenotypic spectrum, and discriminative features of multisystemic RFC1 disease

Objective To delineate the full phenotypic spectrum, discriminative features, piloting longitudinal progression data, and sample size calculations of replication factor complex subunit 1 (RFC1) repeat expansions, recently identified as causing cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS). Methods Multimodal RFC1 repeat screening (PCR, Southern blot, whole-exome/genome sequencing?based approaches) combined with cross-sectional and longitudinal deep phenotyping in (1) cross-European cohort A (70 families) with ?2 features of CANVAS or ataxia with chronic cough (ACC) and (2) Turkish cohort B (105 families) with unselected late-onset ataxia. Results Prevalence of RFC1 disease was 67% in cohort A, 14% in unselected cohort B, 68% in clinical CANVAS, and 100% in ACC. RFC1 disease was also identified in Western and Eastern Asian individuals and even by whole-exome sequencing. Visual compensation, sensory symptoms, and cough were strong positive discriminative predictors (>90%) against RFC1-negative patients. The phenotype across 70 RFC1-positive patients was mostly multisystemic (69%), including dysautonomia (62%) and bradykinesia (28%) (overlap with cerebellar-type multiple system atrophy [MSA-C]), postural instability (49%), slow vertical saccades (17%), and chorea or dystonia (11%). Ataxia progression was ?1.3 Scale for the Assessment and Rating of Ataxia points per year (32 cross-sectional, 17 longitudinal assessments, follow-up ?9 years [mean 3.1 years]) but also included early falls, variable nonlinear phases of MSA-C?like progression (SARA points 2.5?5.5 per year), and premature death. Treatment trials require 330 (1-year trial) and 132 (2-year trial) patients in total to detect 50% reduced progression. Conclusions RFC1 disease is frequent and occurs across continents, with CANVAS and ACC as highly diagnostic phenotypes yet as variable, overlapping clusters along a continuous multisystemic disease spectrum, including MSA-C-overlap. Our natural history data help to inform future RFC1 treatment trials. Classification of Evidence This study provides Class II evidence that RFC1 repeat expansions are associated with CANVAS and ACC.FUNDING: Study Funding This work was supported via the European Union’s Horizon 2020 research and innovation program by the BMBF under the frame of the E-Rare-3 network PREPARE (01GM1607; to M. Synofzik,M.A., H.P., B.P.v.d.W.), by the DFG under the frame of EJP-RD network PROSPAX (No. 441409627; M. Synofzik, B.P.v.d.W., A.N.B.), and grant 779257 “Solve-RD” (toM. Synofzik, B.P.v.d.W.). B.P.v.d.W. receives additional research support from ZonMW, Hersenstichting, Gossweiler Foundation, uniQure, and Radboud University Medical Centre. T.B.H. was supported by the DFG (No 418081722). A.T. receives funding from the University of T¨ubingen, medical faculty, for the Clinician Scientist Program grant 439-0-0. A.C. thanks Medical Research Council, MR/T001712/1) and Fondazione CARIPLO (2019-1836) for grant support. L.S., T.K., B.P.v.d.W., and M. Synofzik are members of the European Reference Network for Rare Neurological Diseases, project 739510. A.N.B. is supported by the Suna and Inan Kirac Foundation and Koç University School of Medicine

Load Balancing Performance of Dynamic Scheduling on NUMA Multiprocessors

Self-scheduling is a method for task scheduling in parallel programs, in which each processor acquires a new block of tasks for execution whenever it becomes idle. To get the best performance, the block size must be chosen to balance the scheduling overhead against the load imbalance. To determine the best block size, a better understanding of the role of load imbalance in self-scheduling performance is needed. In this paper we study the effect of memory contention on task duration distributions and, hence, load balancing in self-scheduling on a Non Uniform Memory Access (NUMA) machine. Experimental studies on a BBN TC2000 are used to reveal the strengths and weaknesses of analytical performance models to predict running time and optimal block size. The models are shown to be very accurate for small block sizes. However, the models fail when the block size is large due to a previously unrecognized source of load imbalance. We extend the analytical models to address this failure. The implications for the construction of compilers and runtime systems are discussed

FACETS : A CLOUDCOMPARE PLUGIN TO EXTRACT GEOLOGICAL PLANES FROM UNSTRUCTURED 3D POINT CLOUDS

Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/ ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects

Algorithms for Data Locality Optimization

this report we first present an introduction to data locality, and the problems involved. Then we introduce the notion of reference window which is the main concept we use for studying data locality. This concept capture the data locality properties by considering only data subject to reuse, and so allow to improve the benefit expected from locality by devoting fast memory only to data subject to reuse. We show then, how the reference window can be computed. Then we consider an algorithm based on the window concept for optimizing locality in programs. We show then how spatial locality can be taken into account in our framework. The next sections show how to exploit reference windows, and present a prototype implementation of the algorithm. Section 11 considers unimodular transformations. Then we show how our work relates to other's results. 2 Data Locality Notio

A systematic study of topographical memory and posterior cerebral artery infarctions

International audienc