Fuzzy logic algorithm to extract specific interaction forces from atomic force microscopy data

The atomic force microscope is not only a very convenient tool for studying the topography of different samples, but it can also be used to measure specific binding forces between molecules. For this purpose, one type of molecule is attached to the tip and the other one to the substrate. Approaching the tip to the substrate allows the molecules to bind together. Retracting the tip breaks the newly formed bond. The rupture of a specific bond appears in the force-distance curves as a spike from which the binding force can be deduced. In this article we present an algorithm to automatically process force-distance curves in order to obtain bond strength histograms. The algorithm is based on a fuzzy logic approach that permits an evaluation of "quality" for every event and makes the detection procedure much faster compared to a manual selection. In this article, the software has been applied to measure the binding strength between tubuline and microtubuline associated proteins.

Debris flow hazard modelling on medium scale: Valtellina di Tirano, Italy

Debris flow hazard modelling at medium (regional) scale has been subject of various studies in recent years. In this study, hazard zonation was carried out, incorporating information about debris flow initiation probability (spatial and temporal), and the delimitation of the potential runout areas. Debris flow hazard zonation was carried out in the area of the Consortium of Mountain Municipalities of Valtellina di Tirano (Central Alps, Italy). The complexity of the phenomenon, the scale of the study, the variability of local conditioning factors, and the lacking data limited the use of process-based models for the runout zone delimitation. Firstly, a map of hazard initiation probabilities was prepared for the study area, based on the available susceptibility zoning information, and the analysis of two sets of aerial photographs for the temporal probability estimation. Afterwards, the hazard initiation map was used as one of the inputs for an empirical GIS-based model (Flow-R), developed at the University of Lausanne (Switzerland). An estimation of the debris flow magnitude was neglected as the main aim of the analysis was to prepare a debris flow hazard map at medium scale. A digital elevation model, with a 10 m resolution, was used together with landuse, geology and debris flow hazard initiation maps as inputs of the Flow-R model to restrict potential areas within each hazard initiation probability class to locations where debris flows are most likely to initiate. Afterwards, runout areas were calculated using multiple flow direction and energy based algorithms. Maximum probable runout zones were calibrated using documented past events and aerial photographs. Finally, two debris flow hazard maps were prepared. The first simply delimits five hazard zones, while the second incorporates the information about debris flow spreading direction probabilities, showing areas more likely to be affected by future debris flows. Limitations of the modelling arise mainly from the models applied and analysis scale, which are neglecting local controlling factors of debris flow hazard. The presented approach of debris flow hazard analysis, associating automatic detection of the source areas and a simple assessment of the debris flow spreading, provided results for consequent hazard and risk studies. However, for the validation and transferability of the parameters and results to other study areas, more testing is needed.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Assessment of debris-flow susceptibility at medium-scale in the Barcelonnette Basin, France

Debris flows are among the most dangerous processes in mountainous areas due to their rapid rate of movement and long runout zone. Sudden and rather unexpected impacts produce not only damages to buildings and infrastructure but also threaten human lives. Medium- to regional-scale susceptibility analyses allow the identification of the most endangered areas and suggest where further detailed studies have to be carried out. Since data availability for larger regions is mostly the key limiting factor, empirical models with low data requirements are suitable for first overviews. In this study a susceptibility analysis was carried out for the Barcelonnette Basin, situated in the southern French Alps. By means of a methodology based on empirical rules for source identification and the empirical angle of reach concept for the 2-D runout computation, a worst-case scenario was first modelled. In a second step, scenarios for high, medium and low frequency events were developed. A comparison with the footprints of a few mapped events indicates reasonable results but suggests a high dependency on the quality of the digital elevation model. This fact emphasises the need for a careful interpretation of the results while remaining conscious of the inherent assumptions of the model used and quality of the input data.

Memory-based scheduling of scientific computing clusters

This study looks at how increased memory utilisation affects throughput and energy consumption in scientific computing, especially in high-energy physics. Our aim is to minimise energy consumed by a set of jobs without increasing the processing time. The earlier tests indicated that, especially in data analysis, throughput can increase over 100% and energy consumption decrease 50% by processing multiple jobs in parallel per CPU core. Since jobs are heterogeneous, it is not possible to find an optimum value for the number of parallel jobs. A better solution is based on memory utilisation, but finding an optimum memory threshold is not straightforward. Therefore, a fuzzy logic-based algorithm was developed that can dynamically adapt the memory threshold based on the overall load. In this way, it is possible to keep memory consumption stable with different workloads while achieving significantly higher throughput and energy-efficiency than using a traditional fixed number of jobs or fixed memory threshold approaches.

Use of LIDAR in landslide investigations: A review

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDARderived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

Characterization and management of rockslide hazard at Turtle Mountain, Alberta, Canada

In 1903, more than 30 million m3 of rock fell from the east slopes of Turtle Mountain in Alberta, Canada, causing a rock avalanche that killed about 70 people in the town of Frank. The Alberta Government, in response to continuing instabilities at the crest of the mountain, established a sophisticated field laboratory where state-of-the-art monitoring techniques have been installed and tested as part of an early-warning system. In this chapter, we provide an overview of the causes, trigger, and extreme mobility of the landslide. We then present new data relevant to the characterization and detection of the present-day instabilities on Turtle Mountain. Fourteen potential instabilities have been identified through field mapping and remote sensing. Lastly, we provide a detailed review of the different in-situ and remote monitoring systems that have been installed on the mountain. The implications of the new data for the future stability of Turtle Mountain and related landslide runout, and for monitoring strategies and risk management, are discussed.

From Regional Landslide Detection to Site-Specific Slope Deformation Monitoring and Modelling Based on Active Remote Sensors

Landslide processes can have direct and indirect consequences affecting human lives and activities. In order to improve landslide risk management procedures, this PhD thesis aims to investigate capabilities of active LiDAR and RaDAR sensors for landslides detection and characterization at regional scales, spatial risk assessment over large areas and slope instabilities monitoring and modelling at site-specific scales. At regional scales, we first demonstrated recent boat-based mobile LiDAR capabilities to model topography of the Normand coastal cliffs. By comparing annual acquisitions, we validated as well our approach to detect surface changes and thus map rock collapses, landslides and toe erosions affecting the shoreline at a county scale. Then, we applied a spaceborne InSAR approach to detect large slope instabilities in Argentina. Based on both phase and amplitude RaDAR signals, we extracted decisive information to detect, characterize and monitor two unknown extremely slow landslides, and to quantify water level variations of an involved close dam reservoir. Finally, advanced investigations on fragmental rockfall risk assessment were conducted along roads of the Val de Bagnes, by improving approaches of the Slope Angle Distribution and the FlowR software. Therefore, both rock-mass-failure susceptibilities and relative frequencies of block propagations were assessed and rockfall hazard and risk maps could be established at the valley scale. At slope-specific scales, in the Swiss Alps, we first integrated ground-based InSAR and terrestrial LiDAR acquisitions to map, monitor and model the Perraire rock slope deformation. By interpreting both methods individually and originally integrated as well, we therefore delimited the rockslide borders, computed volumes and highlighted non-uniform translational displacements along a wedge failure surface. Finally, we studied specific requirements and practical issues experimented on early warning systems of some of the most studied landslides worldwide. As a result, we highlighted valuable key recommendations to design new reliable systems; in addition, we also underlined conceptual issues that must be solved to improve current procedures. To sum up, the diversity of experimented situations brought an extensive experience that revealed the potential and limitations of both methods and highlighted as well the necessity of their complementary and integrated uses.

Use of terrestrial laser scanning for the characterization of retrogressive landslides in sensitive clay and rotational landslides in river banks

Depuis plus de 10 ans les modèles numériques d'altitude (MNA) produits par technologie de « light detection and ranging » (« LIDAR ») ont fourni de nouveaux outils très utiles pour des études géomorphologiques, particulièrement dans le cas des glissements de terrain. Le balayage laser terrestre (« TLS ») permet une utilisation très souple. Le TLS peut être employé pour la surveillance ou dans des situations d'urgence qui nécessitent une acquisition rapide d'un MNA afin d'évaluer l'aléa. Au travers de trois exemples, nous démontrons l'utilité du TLS pour la quantification de volumes de glissements de terrain, la création de profils et l'analyse de séries temporelles. Ces études de cas sont des glissements de terrain situés dans les argiles sensibles de l'est du Canada (Québec, Canada) ou de petits glissements rotationnels dans les berges d'une rivière (Suisse).

Rockfall hazard and risk assessments along roads at a regional scale: Example in Swiss Alps

Unlike fragmental rockfall runout assessments, there are only few robust methods to quantify rock-mass-failure susceptibilities at regional scale. A detailed slope angle analysis of recent Digital Elevation Models (DEM) can be used to detect potential rockfall source areas, thanks to the Slope Angle Distribution procedure. However, this method does not provide any information on block-release frequencies inside identified areas. The present paper adds to the Slope Angle Distribution of cliffs unit its normalized cumulative distribution function. This improvement is assimilated to a quantitative weighting of slope angles, introducing rock-mass-failure susceptibilities inside rockfall source areas previously detected. Then rockfall runout assessment is performed using the GIS- and process-based software Flow-R, providing relative frequencies for runout. Thus, taking into consideration both susceptibility results, this approach can be used to establish, after calibration, hazard and risk maps at regional scale. As an example, a risk analysis of vehicle traffic exposed to rockfalls is performed along the main roads of the Swiss alpine valley of Bagnes.

Stability analysis of the 2007 Chehalis lake landslide based on long-range terrestrial photogrammetry and airborne LIDAR data

On December 4th 2007, a 3-Mm3 landslide occurred along the northwestern shore of Chehalis Lake. The initiation zone is located at the intersection of the main valley slope and the northern sidewall of a prominent gully. The slope failure caused a displacement wave that ran up to 38 m on the opposite shore of the lake. The landslide is temporally associated with a rain-on-snow meteorological event which is thought to have triggered it. This paper describes the Chehalis Lake landslide and presents a comparison of discontinuity orientation datasets obtained using three techniques: field measurements, terrestrial photogrammetric 3D models and an airborne LiDAR digital elevation model to describe the orientation and characteristics of the five discontinuity sets present. The discontinuity orientation data are used to perform kinematic, surface wedge limit equilibrium and three-dimensional distinct element analyses. The kinematic and surface wedge analyses suggest that the location of the slope failure (intersection of the valley slope and a gully wall) has facilitated the development of the unstable rock mass which initiated as a planar sliding failure. Results from the three-dimensional distinct element analyses suggest that the presence, orientation and high persistence of a discontinuity set dipping obliquely to the slope were critical to the development of the landslide and led to a failure mechanism dominated by planar sliding. The three-dimensional distinct element modelling also suggests that the presence of a steeply dipping discontinuity set striking perpendicular to the slope and associated with a fault exerted a significant control on the volume and extent of the failed rock mass but not on the overall stability of the slope.