945 research outputs found
Evaluation of road pavement conditions by Deep Neural Networks (DNN): an experimental application
No full textMonitoring the actual conditions of transport infrastructures is a priority for asset owners and administrators to ensure structural stability, operational safety and to prevent damages and deterioration. Currently, most protocols for assessing roads pavement conditions are based on visual on-site inspection conducted by specialized operators and, rarely, the local application of ground-based technologies and sensors such as terrestrial laser scanners (TLSs). However, the high costs of maintenance operations and on-site surveys still limit the application of these advanced procedures at the network level. Accordingly, the definition of innovative methodologies and procedures for continuous monitoring operations, especially for road pavements monitoring purposes, is still an open challenge.
This research aims at investigating the viability of an automatic methodology for the detection and classification of pavement distresses based on Machine Learning (ML) models. More specifically, the methodology is based on the latest generation of Deep Neural Networks (DNN) algorithms, among which “YOLO v5”, and “Faster R-CNN”. To this purpose, an experimental evaluation is conducted by the acquisition and the processing of a publicly open-source dataset made available within the context of the “IEEE Global Road Damage Detection Challenge” (GRDDC2020). The implementation of the presented approach provides a technologically enhanced and reliable methodology for the provision of the identification and localization of roads damages to be more rapidly processed and conclusively actioned by asset owners and management agencies giving crucial information that could be implemented for the prioritisation of maintenance activities within Pavements Management Systems (PMSs).
The outcomes of this study demonstrate that ML approaches and DNN algorithms, can be applied to complement Non-Destructive Remote Sensing technologies (e.g., ground-penetrating radars, Laser Scanners, satellite radar interferometry), localizing automatically the pavement damages, thereby paving the way for integrated approaches in the smart monitoring of infrastructure assets
Psychische Sättigung: Eine neue experimentelle Untersuchung zu einem alten Konstrukt
No full textDie vorliegende Untersuchung knüpft an die Arbeiten von Karsten (1928) und Lewin (1928) zum Phänomen der psychischen Sättigung an. Psychische Sättigung bezeichnet einen Verlust intrinsischer Motivation bei der wiederholten Ausführung von Handlungen. Im Gegensatz zur weithin konstatierten hohen theoretischen und praktischen Relevanz des Phänomens sind die Bedingungen, die psychische Sättigung auslösen und/oder verstärken, kaum empirisch untersucht worden. Auf Grundlage des von Karsten und Lewin formulierten sowie von Schulz-Hardt, Rott, Meinken und Frey (in Druck) präzisierten Sättigungskonzepts wird postuliert, daß schwer peripherisierbare (d. h. als weitgehend unbewußte Nebenhandlung ausführbare) sowie persönlich relevante Tätigkeiten in höherem Ausmaß zu Sättigung führen sollten als leicht peripherisierbare sowie persönlich wenig relevante Tätigkeiten. Dies wurde in einem Experiment mit 66 Schülern überprüft, die verschiedene Variationen des Konzentrations-Leistungs-Tests (KLT) bearbeiteten. Die Ergebnisse bestätigen weitgehend unsere Vorhersage. Zudem finden sich Hinweise darauf, daß der Zusammenhang zwischen Sättigung und Leistung durch die Peripherisierbarkeit sowie die persönliche Relevanz der Aufgabe moderiert wird.The present experiment on the phenomenon of psychic satiation expands on the work of Karsten (1928) and Lewin (1928). Psychic satiation denotes a loss of intrinsic motivation when the same action is performed repeatedly. Although many studies have shown the high theoretical and practical relevance of this phenomenon, the conditions which lead to or reinforce psychic satiation have rarely been investigated empirically. Based on the concept of psychic satiation as formulated by Karsten and Lewin and refined by Schulz-Hardt, Rott, Meinken, and Frey (in press), we predicted that psychic satiation will increase if the task does not lend itself to being carried out “peripherally¾ (i.e., as an almost unconscious incidental action) and if it has high personal relevance. These predictions were investigated in an experiment with 66 high-school students who performed different versions of the “Konzentrations-Leistungs-Test¾ (concentration-performance-test, KLT). The results are largely in line with our predictions. In addition, the results indicate that the relation between satiation and performance is moderated by personal relevance and the induced task characteristic
Integration of terrestrial laser scanning and UAV-SFM technique to generate a detailed 3D textured model of a heritage building
No full textThe digital twin is among the Top 10 of the strategic technological trends for the period 2007-2019, and it represents a powerful tool for the conservation and enhancement of cultural heritage. It reproduces with "precision" a physical asset, thus allowing to investigate its structure and to analyze the deformations that occur over the years. Various techniques have been introduced to obtain high-resolution 3D models. Among these, the Terrestrial Laser Scanner (TLS) is widely recognized as the gold standard to generate accurate 3D metric reconstructions. TLS allows acquiring a lot of data (point cloud) in a fast way, being not in physical contact with the objects of investigation. By integrating the point cloud coming from the TLS with the one coming from the photogrammetric processes based on the Structure from Motion (SfM) and Multi View Stereo (MVS) techniques, it is possible to obtain a complete model of the object under investigation. The Unmanned Aerial Vehicles (UAV) photogrammetric technique allows to investigate possible elements not detectable by TLS. Both techniques, if well performed, can show comparable accuracies. Data fusion approach, based on multi-sensor and multi-scale integration, was proposed in the present work as the optimal solution to exploit the potential of the various techniques. A high-quality virtual twin from the All Saints' Monastery of Cuti (Puglia) was produced. The resulting detailed 3D textured model was generated by integrating digital photogrammetry with laser scanning data. A discussion on data acquisition procedures, modeling approaches and accuracy of results is provided
Predicting international roughness index by deep neural networks with Levenberg-Marquardt backpropagation learning algorithm
No full textNon-Destructive High-Performance Techniques (NDTs) are the basis of efficient Pavement Management Systems (PMSs) considering their high accuracy, reliability, speed of execution, high coverage, and non-invasiveness. Generally, each of these techniques is appropriate for evaluating a specific aspect of infrastructures, and several NDT-based surveys are required to obtain a comprehensive assessment of an asset condition. In order to identify potential non-linear correlation among different surveys, and with the aim of reducing the number of on-site inspections, this paper presents a novel methodology based on Deep Neural Networks (DNNs) for integrating products derived by three on-ground NDTs. Specifically, we aim to estimate appropriately the International Roughness Index (IRI) detected by Laser Profiler (LaP) of 93 road sections of 100 meters in length by integrating the outcomes of surveys performed by Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). Moreover, several environmental parameters concerning climate and rainfall information have been considered. Therefore, different architectures of DNNs optimized by the Levenberg-Marquardt backpropagation algorithm have been trained (by using 70% of samples), validated (15% of samples), tested (15% of samples), and their performance evaluated. Outcomes reveal that DNNs allow recognizing efficiently hidden patterns between NDT-based surveys and made the integration possible and reliable. In particular, a DNN architecture composed of two hidden layers containing 23 and 12 artificial neurons, respectively, shows a Correlation Coefficient (R) of 0.902 for the training phase, 0.872 for the validation phase, and 0.861 for the test phase. Supported by these findings, we have re-calibrated the DNN including all road sections, reaching an R parameter of 0.872 and a Mean Square Error (MSE) of 0.203 mm/km for its predictions. Road authorities could consider DNNs for integrating appropriately NDT-based surveys, optimizing their monitoring plans, and improving their inspection activities
Novel Oligomeric Biodegradable Crosslinkers For Hybrid Biomaterial Fabrication For Regenerative Purposes
Full text linkINTRODUCTION
Increasing age of population is a great success of numerous breakthroughs in life science and improved health care. For a child born in 2015, for example, an average global life expectancy of meanwhile 71.4 years is assumed which increased by around 8% in the last decade [1]. In accordance with enhanced life expectancy, however, age-related health problems continuously rise. In this regard, the gap between patients awaiting transplantation and appropriate donors consequently will get larger in the future [2]. To this end, there is a need for new strategies in regenerative medicine [3]. Biomaterial matrices were developed to foster tissue regeneration by mimicking the key characteristics of the extracellular matrix (ECM) [4]. Modern biomaterial research focuses on 3D scaffolds, which can be adequately adapted toward specific requirements of the target tissue [5]. In this regard, flexible material platforms are wanted, whose properties can be adjusted over a wide range and independently of each other [6].
In this context, the macromer-based material concept is promising due to the high flexibility of macromers in chemical design and processability [7]. Macromers are reactive oligo- or polymeric molecules which act as monomers and can therefore be polymerized/cross-linked into a polymeric network [8]. The key principle of this approach is the synthesis of chemically well-defined structures which allows for a more precise control over the resulting properties of the cross-linked polymeric network when compared to conventional polymers. For example, macromer chemistry can be adjusted in terms of chemical macromer composition, valence, content of cross-linkable functionalities and molecular weight. The versatility of macromer-derived materials greatly increases when different macromer types are combined which potentially enables precise material tunability on multiple levels. The design flexibility of macromer-based networks motivated the investigation of two different macromer-based material concepts with regard to macromer processability and material adjustability.
The following objectives were proposed:
1) To synthesize two sets of biodegradable, multi-valent macromers by using free-radical polymerization and ring-opening polymerization combined with established activation strategies. The synthesis setups will be tuned toward high macromer yields which will be required for processing into biomaterials with relevant sizes.
2) To physico-chemically characterize oligomeric macromers with regard to chemical composition, molecular weight and reactivity in order to yield well-defined macromer structures. NMR spectroscopy, gel permeation chromatography (GPC) and wet chemistry will be applied.
3) To characterize macromer processability into covalently cross-linked hybrid matrices. This work will focus on a soft macromer-cross-linked gelatin-derived hydrogel system for versatile biomedical applications as well as a rigid macromer/sol-gel glass hybrid material for hard tissue regeneration. Sets of different formulations will be investigated in order to characterize the range of macromer processability and to establish structure-property relationships.
4) To investigate strategies for the adjustment of material porosity. Besides the adaption via cross-linking density, porogen-leaching and 3D-printing approaches will be followed in order to introduce macroporosity and to enable a decoupling of porosity and chemical (nano)structure of the cross-linked network.
5) To determine key material properties relevant for regenerative applications, including mechanical properties by compression tests and oscillation rheology, in vitro matrix degradability, as well as material cytocompatibility in indirect and direct contact experiments.
6) To identify strategies for covalent functionalization of the hybrid materials. Post-fabrication functionalization via specifically introduced chemical functionalities is favored as it enables effective material decoration (almost) independent of the physico-chemical matrix properties.
SUMMARY OF DISSERTATION
The first material concept was based on anhydride-containing macromers which can be processed into hydrogel matrices by covalent cross-linking of amine-bearing macromolecules, such as gelatin [9–11]. The innovative aspect of this work was to decouple material functionalization from the physico-chemical properties of the cross-linked hydrogel network. To this end, a second chemical functionality was introduced which remained reactive in the hydrogel state and was therefore available for covalent post-fabrication functionalization strategies. Specifically, dual-functional macromers were synthesized by free-radical polymerization of maleic anhydride (MA) with diacetone acrylamide (DAAm) and pentaerythritol diacrylate monostearate (PEDAS) to yield oligo(PEDAS-co-DAAm-co-MA) (oPDMA) [12]. Amphiphilic oligomers (molecular weight (Mn) < 7.5 kDa) with anhydride contents of 7-20% were obtained. Fractions of chemically intact anhydrides of around 70% enables effective cross-linking with low molecular-weight gelatinous peptides (Collagel® type B, 11 kDa). Rigid two-component hydrogels (elastic modulus (E) = 4-13 kPa) with adjustable composition and physicochemical properties were formed. Reactivity of the incorporated methyl ketone functionality toward hydrazides and hydrazines was shown on the macromer level and in the cross-linked hydrogel by different strategies. Firstly, pre-fabricated hydrogels were successfully reinforced by secondary cross-linking with adipic acid dihydrazide (ADH). Secondly, pH-dependent immobilization of 2,4-dinitrophenylhydrazine (DNPH) to acid-soluble macromer derivatives as well as cross-linked oPDMA/COL matrices was demonstrated. Thirdly, reversible immobilization of a fluorescent hydrazide (AFH) was shown which was controlled by hydrogel ketone content, hydrazide ligand concentration and medium pH. This triple-tunability of hydrazide immobilization holds promise for adjustable and cost-effective hydrogel modification. Lastly, proof-of-concept experiments with hydrazido-functionalized hyaluronan (ATTO-hyHA) demonstrated the potential for covalent post-fabrication hydrogel decoration with ECM components. Hydrogel cytocompatibility was demonstrated and the introduction of DAAm into the hydrogel system resulted in superior cell material interactions when compared with previously established analogous ketone-free gels [13].
Limited ability of cells to migrate into deeper regions of these macromer-cross-linked gelatin-based gels further motivated the investigation of two different strategies to enhance hydrogel porosity [10,14]. On the one hand, the introduction of macropores was attempted by hydrogel fabrication in presence of poly(ethylene glycol) (Mn = 8000 Da, P8k). This polymer acted as porogen by phase separation during hydrogel formation. It was found that P8k was effectively extracted from the cross-linked matrix, while physico-chemical hydrogel properties remained unchanged. The second approach aimed at increasing mesh size of the cross-linked network by using hydrogel building blocks with increased molecular weights. In particular, high molecular-weight gelatin (160 Bloom, G160) was cross-linked by macromers with low MA content. Homogeneous and mechanically stable hydrogels were obtained and physico-chemical properties were determined. Successful optimization of hydrogel porosity was functionally shown by enhanced cell migration and improved release profile of incorporated nanoparticles [15].
In the second macromer-based material, hydrolytically degradable multi-armed macromers were covalently introduced into a tetraethoxysilane(TEOS)-derived silica sol in order to address the insufficient degradability of glass-based materials [16]. In detail, oligo(D,L-lactide) units were introduced into three- (TMPEO, Tx) and four-armed (PETEO, Px) ethoxylated alcohols by ring-opening polymerization, followed by activation with 3-isocyanatopropyltriethoxysilane (ICPTES) to yield TxLAy-Si and PxLAy-Si macromers [17,18]. A series of 18 oligomers (Mn: 1100-3200 Da) with different degrees of ethoxylation and varying length of oligoester units was synthesized. Applicability of a previously established indirect rapid prototyping method enabled fabrication of macromer/sol-gel-glass-derived class II hybrid scaffolds with controlled porosity [19]. Successful processability of a total of 85 different hybrid scaffold formulations allowed for identification of relevant structure-property relationships. In vitro degradation was analyzed over 12 months and a continuous linear weight loss (0.2-0.5 wt%/d) was detected which was controlled by oligo(lactide) content and matrix hydrophilicity. Compressive strength (2-30 MPa) and compressive modulus (44-716 MPa) were determined and total content, oligo(ethylene oxide) content, oligo(lactide) content and molecular weight of the oligomeric cross-linkers as well as material porosity were identified as the main factors determining hybrid mechanics by multiple linear regression. Cell migration into the entire scaffold pore network was indicated in cell culture experiments with human adipose tissue-derived stem cells (hASC) and continuous proliferation over 14 days was found.
Overall, two macromer-based material platforms were established in which material versatility was realized by three main principles: I) synthesis of macromers with different chemical composition, II) combination of macromers with a second oligomeric building block, and III) flexible processability of these dual-component hybrid formulations into porous scaffold materials. Precise adjustability of material properties as demonstrated in both concepts offers potential for application of these hybrid materials for a wide range of regenerative purposes.
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Integrated health monitoring of masonry arch bridges by Remote Sensing and Ground Penetrating Radar technologies
No full textAdvances in data processing and the availability of larger SAR datasets from high-resolution (X-Band) satellite missions have consolidated the use of the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique in the near-real-time assessment of bridges and the health monitoring of transport infrastructures. This research aims to investigate the viability of a novel non-destructive health-monitoring approach based on satellite remote sensing and ground based techniques for structural assessment of bridges and the prevention of damages related to structural subsidence. To this purpose, high-resolution X-Band SAR products were acquired and processed by MT-InSAR technique. Analyses were developed to identify and monitor the structural displacements of the historical "Ponte Sisto" masonry bridge located in Rome, Italy, crossing the Tiber River. To this extent, the historical time-series of deformations were processed by Persistent Scatterers (PSs) relevant to critical structural elements of the bridge (i.e., bridge piers and arcs). On the other hand, an on-site Ground Penetrating Radar investigation was directly conducted over the masonry bridge. More specifically, several frequencies were implemented for this purpose, with the aim to investigate the condition of the layers of the superstructures at different propagation lengths. A novel data interpretation approach is proposed based on the selection of several PS data-points with coherent deformation trends and location on the bridge, and the analysis of the B-Scan obtained by GPR investigations. The outcomes of this study demonstrate how multi-temporal InSAR remote sensing techniques can be applied to complement non-destructive ground-based analyses (e.g., ground-penetrating radars), paving the way for future integrated approaches for monitoring of infrastructure asset
Being Fed Up
No full textBeing fed up with something is a prevalent and fundamental human experience. Although the relevance of mental satiation, that is, the process of becoming fed up with an action, is highly acknowledged in organizational psychology, almost no empirical research has examined this concept. In this article, we take a social cognitive neuroscience approach to mental satiation. By building on and extending the classic work of Lewin and Karsten, we propose a new model of mental satiation that focuses on the cognitive, motivational, and neural processes underlying mental satiation. Our model starts with the assumption that repeated performance of an action undermines one's need for competence and hence leads to a loss of intrinsic motivation. We then distinguish between two phases of the satiation process: The first phase is characterized by a loss of intrinsic motivation to perform the action. The second phase starts when the intrinsic motivation has vanished and volitional control is required to continue the action. We predict that the loss of intrinsic motivation in the first phase of the satiation process is correlated with a decrease in activity in brain regions associated with positive hedonic experience, such as the nucleus accumbens, the ventral pallidum, and the medial orbitofrontal cortex. In contrast, the growing aversion toward the action during the second phase of the satiation process is predicted to be correlated with an increase in activity in brain regions associated with unpleasant affect and volitional control, such as the amygdala, the anterior insula, and the anterior cingulate cortex
Ret is essential to mediate GDNF’s neuroprotective and neuroregenerative effect in a Parkinson disease mouse model
Full text linkGlial cell line-derived neurotrophic factor (GDNF) is a potent survival and regeneration-promoting factor for dopaminergic neurons in cell and animal models of Parkinson disease (PD). GDNF is currently tested in clinical trials on PD patients with so far inconclusive results. The receptor tyrosine kinase Ret is the canonical GDNF receptor, but several alternative GDNF receptors have been proposed, raising the question of which signaling receptor mediates here the beneficial GDNF effects. To address this question we overexpressed GDNF in the striatum of mice deficient for Ret in dopaminergic neurons and subsequently challenged these mice with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Strikingly, in this established PD mouse model, the absence of Ret completely abolished GDNF’s neuroprotective and regenerative effect on the midbrain dopaminergic system. This establishes Ret signaling as absolutely required for GDNF’s effects to prevent and compensate dopaminergic system degeneration and suggests Ret activation as the primary target of GDNF therapy in PD
A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis
Full text linkMarine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2values obtained for both optical and radar images
Last millennial environmental reconstruction based on a multi-proxy record from Laguna Nassau, Western Pampas, Argentina
Full text linkWe present a multi-proxy record from Laguna Nassau (LN), a shallow lake which developed in a blowout depression in the semi-arid sandy lowlands of the Western Pampas of Argentina. This multi-proxy stacked record reveals the evolution of an incipient water body subjected to warm and dry conditions from ~900 to 770 cal yr BP, an interval that is coeval with the Medieval Climatic Anomaly (MCA) period. Vegetation was dominated locally by Typha, and regionally by the Espinal, represented by Celtis, along with psammophytic-halophytic communities in sand dunes and lowlands. After that, variable water-depths and unstable conditions occurred in the water body in relation to dry and humid climatic phases until ~200 cal yr BP, likely related to a transition from warm and dry conditions, corresponding to the MCA, to a colder and wet climatic setting during a period concurrent with the Little Ice Age (LIA). This water body evolved into a shallow lake since then, with a more stable and higher water level, and regional vegetation characterized by the Espinal and psammophytic-halophytic communities mantling and stabilizing the nearby sand dunes. This evolution suggests complex hydrological responses of LN likely associated with its sensitivity to climatic variability. Our results highlight important contrasts in hydrologic balance during the MCA and LIA along the western and northeastern Pampas, suggesting important shifts mainly in the South-American Summer Monsoon at centennial timescales over the last millennium.Fil: Vilanova, Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Schittek, Karsten. Universitat zu Köln; AlemaniaFil: Geilenkirchen, Mathias. Universitat zu Köln; AlemaniaFil: Schäbitz, Frank. Universitat zu Köln; AlemaniaFil: Schulz, Wilfried. Universitat zu Köln; Alemani
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