A living cell is a complex out-of-equilibrium system, in which a great variety of biochemical and physical processes have to be coordinated to ensure viability. We investigate properties of intracellular transport in single cells of the amoeba Dictyostelium discoideum, a relevant model organism due to its cytoskeleton simplicity. In the cells, vesicles undergo two types of motion: directed transport, driven by molecular motors on filaments, or thermal diffusion in a crowded active medium. We present results obtained with our recently developed TRAnSpORT algorithm, which performs a high-resolution temporal analysis of the track of endosomal superparamagnetic particles and splits intracellular transport into different motion states. It results in a two-state model, distinguishing active and passive transport phenomena. We can extract the precise effect of cellular micro- and nanoarchitecture on endosomal transport by disturbing the cytoskeleton through the use of depolymerizing drugs (Benomyl for microtubules, and Latrunculin A for F-actin). Further, we investigate how cytoskeleton filaments act together in order to maintain cell integrity, by applying external mechanical force on the magnetic particle and influencing its motion.

This paper concerns damage identification of a bridge located in Luxembourg. Vibration responses were captured from measurable and adjustable harmonic swept sine excitation and hammer impact. Different analysis methods were applied to the data measured from the structure showing interesting results. However, some difficulties arise, especially due to environmental influences (temperature and soil-behaviour variations) which overlay the structural changes caused by damage. These environmental effects are investigated in detail in this work. First, the modal parameters are identified from the response data. In the next step, they are statistically collected and processed through Principal Component Analysis (PCA) and Kernel PCA. Damage indexes are based on outlier analysis.

The aim of this paper is to apply both time- and frequency-domain-based approaches on real-life civil engineering structures and to assess their capability for damage detection. The methodology is based on Principal Component Analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions. Damage detection is performed using the concept of subspace angles between a current (possibly damaged state) and a reference (undamaged) state. The first structure is the Champangshiehl Bridge located in Luxembourg. Several damage levels were intentionally created by cutting a growing number of prestressed tendons and vibration data were acquired by the University of Luxembourg for each damaged state. The second example consists in reinforced and prestressed concrete panels. Successive damages were introduced in the panels by loading heavy weights and by cutting steel wires. The illustrations show different consequences in damage identification by the considered techniques.

The paper focuses on damage detection of civil engineering structures and especially on concrete bridges. A method for structural health monitoring based on vibrational measurements is presented and discussed. Experimentally identified modal parameters (eigenfrequencies, mode shapes and modal masses) of bridge structures are used to calculate the inverse stiffness matrix, the so-called flexibility matrix. By monitoring of the stiffness matrix, damage can easily be detected, quantified and localized by tracking changes of its individual elements. However, based on dynamic field measurements, the acquisition of the flexibility matrix instead of the stiffness matrix is often the only choice and hence more relevant for practice. But the flexibility-based quantification and localisation of damage are often possible but more difficult, as it depends on the type of support and the location of the damage. These issues are discussed and synthetized, that is an originality of this paper and is believed useful for engineers in the damage detection of different bridge structures. First the theoretical background is briefly repeated prior to the illustration of the differences between stiffness and flexibility matrix on analytical and numerical examples. Then the flexibility-based detection is demonstrated on two true bridges with real-time measurement data and the results are promising.