High-velocity impact experiment of aluminum foam sample using powder gun

Porous materials such as aluminum foam have been investigated for possible use as impact shock absorbers in transportation aeronautic applications. However, the response of aluminum foam during impacts at high velocities of more than 100 m/s is not yet fully understood. A high-velocity impact experiment was therefore carried out to clarify impact shock absorption properties of aluminum foam. A one-stage powder gun was used to accelerate an aluminum foam sample to impact a rigid wall. Velocity and deformation of the aluminum foam sample during impact was studied using a digital high-speed video camera, while the pressure wave in the aluminum foam sample was measured using a PVDF gauge. The experimental observations revealed uneven collapse of the aluminum foam sample structure during high speed impact with a general stress plateau effect, typical for cellular material structures when subjected to quasi-static loading.     

Persistent excitation condition within the dual control framework

Model Predictive Control framework is currently used in many different fields of expertise. The inherent part and very often also the main bottleneck is the model of a process used for the computation of predictions.Due to many reasons e.g. ageing, from time to time there exists a need to adjust/re-identify (if there was already some kind of a model-based controller) or to construct a brand new model (in other cases). Frequently, the process generating the data is under some kind of control, imposing thus problems when classical open loop identification methods are considered. The need for models identified from the data gathered in a closed-loop fashion and a request for possible re-identification of the model parameters lead to the emerge of dual control where the problems of control and system identification are addressed simultaneously.In this paper, we present a new algorithm based on the persistent excitation condition when the minimal eigenvalue of the information matrix is maximized in order to have sufficiently exciting optimal control signal satisfying the control requirements.     

A chamber for biomechanical,electrochemical and electrophysiological measurements in functional segments of peripheral nerves

The aim of the work was to develop the chamber to be used in biomechanical, electrochemical and electrophysiological measurements in functional segments of peripheral nerves, when electrical stimulating pulses are selectively applied to preselected locations along the nerve and neural responses are measured.     

Controllable Broadband Absorption in the Mixed Phase of Metamagnets

Materials with broad absorption bands are highly desirable for electromagnetic filtering and processing applications, especially if the absorption can be externally controlled. Here, a new class of broadband‐absorption materials is introduced. Namely, layered metamagnets exhibit an electromagnetic excitation continuum in the magnetic‐field‐induced mixed ferro‐ and anti­ferromagnetic phase. Employing a series of complementary experimental techniques involving neutron scattering, muon spin relaxation, specific heat, ac and dc magnetization measurements, and electron magnetic resonance, a detailed magnetic phase diagram of Cu₃Bi(SeO₃)₂O₂Br is determined and it is found that the excitations in the mixed phase extend over at least ten decades of frequency. The results, which reveal a new dynamical aspect of the mixed phase in metamagnets, open up a novel approach to controllable microwave filtering.     

Autonomous evolutionary algorithm in medical data analysis

An autonomous evolutionary algorithm for constructing decision trees is presented. The algorithm requires no or minimal human interaction and shows some interesting properties when used on different medical datasets. The algorithm uses a non-standard implicit fitness evaluation in the selection phase of a co-evolving environment. Together with self-adaptation of evolution parameters and with some other improvements it can monitor and adjust its own behavior. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some dataset is just difficult or impossible to analyze. The autonomous algorithm on average produces very general solutions or gives no solution if the dataset is prone to the overfitting problem.     

A comparative study on the electrical and mechanical behaviour of multi‐walled carbon nanotube composites prepared by diluting a masterbatch with various types of polypropylenes

Polypropylene (PP) nanocomposites with multi‐walled carbon nanotubes (CNT) were produced by a small‐scale masterbatch melt dilution technique using five PP differing in melt flow index (MFI) and degree of maleination. PP used in a masterbatch has MFI = 12 (PP₁₂), the others used PP which have MFI = 2 or MFI = 8. The state of CNT dispersion as assessed by melt rheological and morphological investigations indicated a better dispersion when using unmodified PP with MFI = 8 (PP₈) and the masterbatch's PP₁₂. Electrical conductivity results showed nanotube percolation at contents between 1.1 and 2.0 vol %, whereas lower values were obtained for the matrices with the best dispersion, i.e., PP₈ and PP₁₂. The dependencies of the relative Young's modulus on the CNT content showed that the maleinization improved the interfacial interactions between the components, especially in the case of maleated PP with MFI = 8 (PP‐MA₈), but the better dispersion was prevented by the incompatibility between polar groups of PP‐MA and the nonpolar origin masterbatch PP₁₂. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Mechanochemical synthesis of ternary chalcogenide chalcostibite CuSbS2 and its characterization

Journal of Materials Science: Materials in Electronics - In this work, the very rapid one-step mechanochemical synthesis of nanocrystalline ternary chalcogenide chalcostibite CuSbS2 prepared from...     

The Tumor Proteolytic Landscape: A Challenging Frontier in Cancer Diagnosis and Therapy

In recent decades, dysregulation of proteases and atypical proteolysis have become increasingly recognized as important hallmarks of cancer, driving community-wide efforts to explore the proteolytic landscape of oncologic disease. With more than 100 proteases currently associated with different aspects of cancer development and progression, there is a clear impetus to harness their potential in the context of oncology. Advances in the protease field have yielded technologies enabling sensitive protease detection in various settings, paving the way towards diagnostic profiling of disease-related protease activity patterns. Methods including activity-based probes and substrates, antibodies, and various nanosystems that generate reporter signals, i.e., for PET or MRI, after interaction with the target protease have shown potential for clinical translation. Nevertheless, these technologies are costly, not easily multiplexed, and require advanced imaging technologies. While the current clinical applications of protease-responsive technologies in oncologic settings are still limited, emerging technologies and protease sensors are poised to enable comprehensive exploration of the tumor proteolytic landscape as a diagnostic and therapeutic frontier. This review aims to give an overview of the most relevant classes of proteases as indicators for tumor diagnosis, current approaches to detect and monitor their activity in vivo, and associated therapeutic applications.     

A model-based approach to the evaluation of flame-protective garments

High-quality protective garments are essential in many occupational assignments, e.g., in the petrochemical industry and for firefighting. The problem addressed in this paper is how to objectively asses the performance of protective garments designed to resist fire. We present a system based on a flame mannequin equipped with an array of temperature sensors that provide information about the temperature on the mannequin’s surface during exposure to fire. Particular attention is devoted to reconstructing the heat flux reaching the mannequin’s surface on the basis of the available temperature records (the inverse heat-conduction problem). The estimated heat flux is used in a skin-simulation model to predict the level of injury that would occur in human exposed to the same heat. The paper includes a novel computational procedure based on a detailed model of the temperature sensor and a numerical solution of the accompanying heat equation. The effectiveness of the solution is demonstrated with experimental data.