Experimentally supported 2D systems based iterative learning control law design for error convergence and performance

This paper considers iterative learning control law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of control laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of linear matrix inequalities (LMIs). It is also shown how this setting extends to allow the design of robust control laws in the presence of uncertainty in the dynamics produced along the trials. Results from the experimental application of these laws on a gantry robot performing a pick and place operation are also given.     

A small spiking neural network with LQR control applied to the acrobot

This paper presents the results of a computer simulation which, combined a small network of spiking neurons with linear quadratic regulator (LQR) control to solve the acrobot swing-up and balance task. To our knowledge, this task has not been previously solved with spiking neural networks. Input to the network was drawn from the state of the acrobot, and output was torque, either directly applied to the actuated joint, or via the switching of an LQR controller designed for balance. The neural network’s weights were tuned using a (μ + λ)-evolution strategy without recombination, and neurons’ parameters, were chosen to roughly approximate biological neurons.     

Substructure isolation and damage identification using free responses

Structural health monitoring (SHM) has become a hot and intensively researched field in civil engineering. Thereinto, damage identification play an important role in maintaining structural integrity and safety. Many effective methods have been proposed for damage identification. However, accurate global identification of large real-world structures is not easy due to their complex and often unknown boundary conditions, nonlinear components, insensitivity of global response to localized damages, etc. Furthermore, global identification often requires lots of sensors and involves large number of unknowns. This is costly, rarely feasible in practice, and usually yields severely ill-conditioned identification problems. Substructuring approach is a possible solution: substructuring methods can focus on local small substructures; they need only a few sensors placed on the substructure and yield smaller and numerically much more feasible identification problems. This paper proposed an improved substructure method using local free response for substructure damage identification. The virtual supports are constructed by Substructure Isolation Method (SIM) using the linear combination of the substructural responses. The influence of the global errors is isolated by adding the virtual supports on the main degree of freedoms (DOFs) of the substructure. Through the correlation analysis, the substructural modes are selected and used for damage identification of the substructure. A plain model of cable stayed bridge is used for the verification of the proposed method.     

Special Issue on the pervasive nature of HPC (PN-HPC)

This special issue on the Pervasive Nature of HPC (PN-HPC) collects an extension of the most valuable works presented at the sixth Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP-22), held in Gdansk (Poland) in September 2022, jointly with the 14th conference on Parallel Processing and Applied Mathematics (PPAM-22). New original papers related to the workshop themes are also included. The final aim is to provide a glimpse of the current state of knowledge related to the development of efficient methodologies and algorithms for HPC systems with multiple forms of parallelism.     

Electronic structure in thin film organic semiconductors studied using soft X-ray emission and resonant inelastic X-ray scattering

The electronic structure of thin films of the organic semiconductors copper and vanadyl (VO) phthalocyanine (Pc) has been measured using resonant soft X-ray emission spectroscopy and resonant inelastic X-ray scattering. For Cu-Pc we report the observation of two discrete states near E_F. This differs from published photoemission results, but is in excellent agreement with density functional calculations. For VO-Pc, the vanadyl species is shown to be highly localized. Both dipole forbidden V 3d to V 3d*, and O 2p to V 3d* charge transfer transitions are observed, and explained in a local molecular orbital model.     

Machine learning in the life sciences.

The articles in this special issue demonstrate how machine learning is used in the life sciences on a wide variety of medicinal problems and data.     

Multi‐scale Finite Element Cellular Automata Simulation of Multi‐step Cold Forging Operations

The application of new materials to produce forged connecting parts is presented in this work. Particular attention is put on modern bainitic steels due to their increased ductile and strength properties, which influence the behaviour of final products under further exploitation conditions. Bainitic steels do not require a series of thermo‐mechanical operations to obtain these elevated properties, which is one of the advantages of this material. Experimental analysis and numerical simulations of steel behaviour during multi‐step cold forging operations are described in this paper. Since it is one of the possible fracture initiation mechanisms, strain localization development during cold forging is investigated in detail. Conventional constitutive models used in finite element programs have limitations in modeling stochastic and discontinuous phenomena that are responsible for strain localization. The cellular automata model is used as constitutive law in this work to overcome these difficulties and investigate material flow during multi‐stage cold forging operations. Connection of the cellular automata (CA) and finite element (FE) methods creates a so‐called multi‐scale CAFE model. The main aspects of the model are described briefly in this paper. The experimental part of this work supports the numerical investigation. Comparison of the parameters measured and predicted by the CAFE model is presented and discussed as well.     

Biogenic amines in striatum of rats that had been treated with ethanol, and their brains later stored in different temperatures

The purpose of this study was to investigate how ethanol pretreatment and storage temperatures of brain striatum affect levels of biogenic amines in this tissue. Adult Wistar male rats were injected with 25% ethanol (5.0 g/kg i.p.) while the control rats were administered i.p. with the same volume of saline. Two hours later the rats were decapitated, their brains removed, and the striatum separated. Each striatum was divided into three parts: one part was immediately frozen on dry ice and kept at -70 degrees C; a second fragment was kept in a household refrigerator (+4 degrees C); and the third fragment was kept at +22 degrees C. Twenty-four hours later, levels of DA, DOPAC, HVA, 3-MT, 5-HT, and 5-HIAA in the striatum were assayed by HPLC/ED. Immediately after decapitation; ethanol levels were assayed in the serum of ethanol-pretreated and saline-pretreated rats using gas chromatography. Our results indicate that levels of striatal DA, DOPAC, and HVA in saline-pretreated rats decreased significantly when the storage temperature of the striatum was raised from -70 degrees C, through +4 degrees C, to +22 degrees C, while levels of striatal 5-HT and 5-HIAA remained constant within the temperature range tested and levels of 3-MT fluctuated. In ethanol-pretreated rats, striatal levels of DOPAC, HVA, and 5-HIAA were increased in all three storage temperatures, while levels of DA, 5-HT, and 3-MT were decreased in those temperatures. Those decreases were most profound in striatal samples kept at +22 degrees C. We conclude that concern about possible interactions between drugs and biogenic amines should be exercised.