Poznań University of Technology

Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

Electrical energy storage (EES) is one of the most critical areas of technological research around the world. Storing and efficiently using electricity generated by intermittent sources and the transition of our transportation fleet to electric drive depend fundamentally on the development of EES systems with high energy and power densities. Supercapacitors are promising devices for highly efficient energy storage and power management, yet they still suffer from moderate energy densities compared to batteries. To establish a detailed understanding of the science and technology of carbon/carbon supercapacitors, this review discusses the basic principles of the electrical double-layer (EDL), especially regarding the correlation between ion size/ion solvation and the pore size of porous carbon electrodes. We summarize the key aspects of various carbon materials synthesized for use in supercapacitors. With the objective of improving the energy density, the last two sections are dedicated to strategies to increase the capacitance by either introducing pseudocapacitive materials or by using novel electrolytes that allow to increasing the cell voltage. In particular, advances in ionic liquids, but also in the field of organic electrolytes, are discussed and electrode mass balancing is expanded because of its importance to create higher performance asymmetric electrochemical capacitors.

Zinc oxide can be called a multifunctional material thanks to its unique physical and chemical properties. The first part of this paper presents the most important methods of preparation of ZnO divided into metallurgical and chemical methods. The mechanochemical process, controlled precipitation, sol-gel method, solvothermal and hydrothermal method, method using emulsion and microemulsion enviroment and other methods of obtaining zinc oxide were classified as chemical methods. In the next part of this review, the modification methods of ZnO were characterized. The modification with organic (carboxylic acid, silanes) and inroganic (metal oxides) compounds, and polymer matrices were mainly described. Finally, we present possible applications in various branches of industry: rubber, pharmaceutical, cosmetics, textile, electronic and electrotechnology, photocatalysis were introduced. This review provides useful information for specialist dealings with zinc oxide.

The most commonly used electrode materials for electrochemical capacitors are activated carbons, because they are commercially available and cheap, and they can be produced with large specific surface area. However, only the electrochemically available surface area is useful for charging the electrical double layer (EDL). The EDL formation is especially efficient in carbon pores of size below 1 nm because of the lack of space charge and a good attraction of ions along the pore walls. The pore size should ideally match the size of the ions. However, for good dynamic charge propagation, some small mesopores are useful. An asymmetric configuration, where the positive and negative electrodes are constructed from different materials, e.g., activated carbon, transition metal oxide or conducting polymer, is of great interest because of an important extension of the operating voltage. In such a case, the energy as well as power is greatly increased. It appears that nanotubes are a perfect conducting additive and/or support for materials with pseudocapacitance properties, e.g. MnO(2), conducting polymers. Substitutional heteroatoms in the carbon network (nitrogen, oxygen) are a promising way to enhance the capacitance. Carbons obtained by one-step pyrolysis of organic precursors rich in heteroatoms (nitrogen and/or oxygen) are very interesting, because they are denser than activated carbons. The application of a novel type of electrolyte with a broad voltage window (ionic liquids) is considered, but the stability of this new generation of electrolyte during long term cycling of capacitors is not yet confirmed.

A hierarchy of low-dimensional Galerkin models is proposed for the viscous, incompressible flow around a circular cylinder building on the pioneering works of Stuart (1958), Deane et al . (1991), and Ma & Karniadakis (2002). The empirical Galerkin model is based on an eight-dimensional Karhunen–Loève decomposition of a numerical simulation and incorporates a new ‘shift-mode’ representing the mean-field correction. The inclusion of the shift-mode significantly improves the resolution of the transient dynamics from the onset of vortex shedding to the periodic von Kármán vortex street. In addition, the Reynolds-number dependence of the flow can be described with good accuracy. The inclusion of stability eigenmodes further enhances the accuracy of fluctuation dynamics. Mathematical and physical system reduction approaches lead to invariant-manifold and to mean-field models, respectively. The corresponding two-dimensional dynamical systems are further reduced to the Landau amplitude equation.

This paper proposes new definitions of lower and upper approximations, which are basic concepts of the rough set theory. These definitions follow naturally from the concept of ambiguity introduced in this paper. The new definitions are compared to the classical definitions and are shown to be more general, in the sense that they are the only ones which can be used for any type of indiscernibility or similarity relation.

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400 \thinspace000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87), and fares well on pathological cases.

Endowed with unparalleled high catalytic activity and selectivity, enzymes offer enormous potential as catalysts in practical applications. These applications, however, are seriously hampered by enzymes’ low thermal and chemical stabilities. One way to improve these stabilities is the enzyme immobilization. Among various tested methods of this process that make use of different enzyme-carrier interactions, immobilization by adsorption on solid carriers has appeared most common. According to these findings, in this review we present a comparative analysis of the literature reports on the recent trends in the immobilization of the enzymes by adsorption. This thorough study was prepared in order to provide a deeper understanding of the process. Both carriers, carrier modifiers and procedures developed for effective adsorption of the enzymes are discussed. The review may thus be helpful in choosing the right adsorption scheme for a given enzyme to achieve the improvement of its stability and activity for a specific application.

In recent years, enzyme immobilization has been presented as a powerful tool for the improvement of enzyme properties such as stability and reusability. However, the type of support material used plays a crucial role in the immobilization process due to the strong effect of these materials on the properties of the produced catalytic system. A large variety of inorganic and organic as well as hybrid and composite materials may be used as stable and efficient supports for biocatalysts. This review provides a general overview of the characteristics and properties of the materials applied for enzyme immobilization. For the purposes of this literature study, support materials are divided into two main groups, called Classic and New materials. The review will be useful in selection of appropriate support materials with tailored properties for the production of highly effective biocatalytic systems for use in various processes.

A modular, ionic liquid (IL)-based strategy allows compartmentalized molecular level design of a wide range of new materials with tunable biological, as well as the well known physical and chemical, properties of ILs, which thus deserve consideration as ‘tunable’ active pharmaceutical ingredients (APIs) with novel performance enhancement and delivery options. IL strategies can take advantage of the dual nature (discrete ions) of ILs to realize enhancements which may include controlled solubility (e.g., both hydrophilic and hydrophobic ILs are possible), bioavailability or bioactivity, stability, elimination of polymorphism, new delivery options (e.g., slow release or the IL-API as ‘solvent’), or even customized pharmaceutical cocktails. Here we exemplify this approach with, among others, lidocaine docusate (LD), a hydrophobic room temperature IL which, when compared to lidocaine hydrochloride, exhibits modified solubility, increased thermal stability, and a significant enhancement in the efficacy of topical analgesia in two different models of mouse antinociception. Studies of the suppression of nerve growth factor mediated neuronal differentiation in rat pheochromocytoma (PC12) cells suggests potential differences between LD and lidocaine hydrochloride at the cellular level indicating an entirely different mechanism of action. Taken together these results suggest that the unique physiochemical properties of ILs in general, may confer a novel effect for the bioactivity of an API due to (at least) slow-release properties in addition to novel delivery mechanisms.

This paper is focused on neutral aqueous medium, i.e.lithium, sodium and potassium sulfate solutions in a wide range of concentrations (0.1–2.5 mol L−1) as promising electrolytes for electrochemical capacitors because they are cheap, non-corrosive and allow applying diverse current collectors. These properties make the capacitor assembling process much easier and cheaper. Additionally, such electrolytes are electrochemically stable and environmentally friendly. Electrochemical investigations carried out especially for 1 mol L−1Li2SO4 aqueous solution confirmed the possibility of efficient capacitor work in a wider voltage range, i.e. even at 2.2 V without any significant capacitance fade during 15 000 cycles. The physicochemical properties of ions (i.e. solvation, diffusion or mobility) and their influence on the capacitor electrochemical behaviour are considered.

Understanding the numerous functions that RNAs play in living cells depends critically on knowledge of their three-dimensional structure. Due to the difficulties in experimentally assessing structures of large RNAs, there is currently great demand for new high-resolution structure prediction methods. We present the novel method for the fully automated prediction of RNA 3D structures from a user-defined secondary structure. The concept is founded on the machine translation system. The translation engine operates on the RNA FRABASE database tailored to the dictionary relating the RNA secondary structure and tertiary structure elements. The translation algorithm is very fast. Initial 3D structure is composed in a range of seconds on a single processor. The method assures the prediction of large RNA 3D structures of high quality. Our approach needs neither structural templates nor RNA sequence alignment, required for comparative methods. This enables the building of unresolved yet native and artificial RNA structures. The method is implemented in a publicly available, user-friendly server RNAComposer. It works in an interactive mode and a batch mode. The batch mode is designed for large-scale modelling and accepts atomic distance restraints. Presently, the server is set to build RNA structures of up to 500 residues.

This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating solutions. Each solution explores its neighbourhood in a way similar to that of classical simulated annealing. Weights of the objectives, used for their local aggregation, are tuned in each iteration in order to assure a tendency for approaching the efficient solutions set while maintaining a uniform distribution of the generating solutions over this set. A computational experiment shows that the method is a better tool for approximating the efficient set than some previous proposals. © 1998 John Wiley & Sons, Ltd.

Electrochemical characteristics of supercapacitors built from multiwalled carbon nanotubes electrodes have been investigated and correlated with microtexture and elemental composition of the materials. Capacitance has been estimated by cyclovoltammetry at different scan rates from 1 to 10 mV/s, galvanostatic discharge, and impedance spectroscopy in the frequency range from 100 kHz to 1 mHz. The presence of mesopores due to the central canal and/or entanglement is at the origin of an easy accessibility of the ions to the electrode/electrolyte interface for charging the electrical double layer. Pure electrostatic attraction of ions as well as quick pseudofaradaic reactions have been detected upon varying surface functionality. The values of specific capacitance varied from 4 to 135 F/g, depending on the type of nanotubes or/and their posttreatments. Even with moderate specific surface area (below 470 m2/g), due to their accessible mesopores, multiwalled carbon nanotubes represent attractive materials for supercapacitors as compared to the best activated carbons.

Carbon nanotubes act as a good percolator, perfect support for fuel cell catalyst or component of supercapacitor electrode material.

Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes? Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise. We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity. Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.

This paper presents a novel test-data volume-compression methodology called the embedded deterministic test (EDT), which reduces manufacturing test cost by providing one to two orders of magnitude reduction in scan test data volume and scan test time. The presented scheme is widely applicable and easy to deploy because it is based on the standard scan/ATPG methodology and adopts a very simple flow. It is nonintrusive as it does not require any modifications to the core logic such as the insertion of test points or logic bounding unknown states. The EDT scheme consists of logic embedded on a chip and a new deterministic test-pattern generation technique. The main contributions of the paper are test-stimuli compression schemes that allow us to deliver test data to the on-chip continuous-flow decompressor. In particular, it can be done by repeating certain patterns at the rates, which are adjusted to the requirements of the test cubes. Experimental results show that for industrial circuits with test cubes with very low fill rates, ranging from 3% to 0.2%, these schemes result in compression ratios of 30 to 500 times. A comprehensive analysis of the encoding efficiency of the proposed compression schemes is also provided.

Renewable and cheap materials in electrodes could meet the need for low-cost, intermittent electrical energy storage in a renewable energy system if sufficient charge density is obtained. Brown liquor, the waste product from paper processing, contains lignin derivatives. Polymer cathodes can be prepared by electrochemical oxidation of pyrrole to polypyrrole in solutions of lignin derivatives. The quinone group in lignin is used for electron and proton storage and exchange during redox cycling, thus combining charge storage in lignin and polypyrrole in an interpenetrating polypyrrole/lignin composite.

Ionic liquids (ILs) are shown to display anti-microbial activity with the activities being greatly affected by alkyl chain lengths. Shorter substituents on the cation result in a lack of activity against cocci, rods and fungi. ILs containing 10, 11, 12 and 14 carbon atoms in an alkoxy group show very high anti-microbial activities. The use of microorganisms in the IL require consideration of their minimum inhibitory concentration (MIC) values.

Lean Production is widely recognized and accepted in the industrial setting. It concerns the strict integration of humans in the manufacturing process, a continuous improvement and focus on value-adding activities by avoiding waste. However, a new paradigm called Industry 4.0 or the fourth industrial revolution has recently emerged in the manufacturing sector. It allows creating a smart network of machines, products, components, properties, individuals and ICT systems in the entire value chain to have an intelligent factory. So, now a question arises if, and how these two approaches can coexist and support each other.