A protocol for evaluation of similarity measures for rigid registration

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.     

Structure and enhanced antimicrobial activity of mechanically activated nano TiO2

Titanium dioxide is a photocatalyst, known not only for its ability to oxidize organic contaminants, but also for its antimicrobial properties. In this article, significant enhancement of the antimicrobial activity of TiO₂ (up to 32 times) was demonstrated after its activation by ball milling. The antimicrobial activity was analyzed for one fungal and 13 bacterial ATCC strains using the microdilution method and recording the minimum inhibitory concentration (MIC) values. In order to further investigate the correlation between the mechanical activation of TiO₂ and its antimicrobial activity, the structure, morphology and phase composition of the material were studied by means of Electron Microscopy, X-ray diffraction and nitrogen adsorption-desorption measurements. UV-Vis diffuse reflectance spectra were recorded and the Kubelka-Munk function was applied to convert reflectance into the equivalent band gap energy (E_g) and, consequently, to investigate changes in the E_g value. X-ray photoelectron spectroscopy was used to analyze the influence of mechanical activation on the Ti 2p and O 1s spectra. The presented results are expected to enable the development of more sustainable and effective advanced TiO₂-based materials with antimicrobial properties that could be used in numerous green technology applications.     

Automatic domain modeling for human–robot interaction

Intelligent Service Robotics - This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically...     

Modeling the impacts of climate change on the thermal and oxygen dynamics of Lake Volta

Climatic changes influence the thermal and oxygen dynamics of a lake and thus its ecological functioning. The impacts of climatic changes on tropical lakes are so far poorly studied and the extent of the effects is therefore uncertain, most investigations describing only potential effects. In this study, we applied the one-dimensional lake ecosystem model GOTM-ERGOM to quantify the effects of climate change on thermal stratification, oxygen dynamics, and primary production in meso-oligotrophic Lake Volta. GOTM-ERGOM was calibrated and validated using two years of observed data. The validated model was used to evaluate a series of future climate change scenarios. The model simulations showed good agreement with observed water temperature, dissolved oxygen and chlorophyll-a and indicated intensified stratification and reduced oxygen levels in the productive water layers of the lake. However, the longer-lasting stratification (prolonged stability) did not translate into permanent stratification. A relatively small (1?m) upward shift of thermocline depth resulted in an 8%–12% volume loss of the oxygen-rich upper mixed layer, which may be significant for the fisheries of the lake as it diminishes the size of suitable fish habitats. Light limitation of primary production renders the lake somewhat resilient to intensive algae blooms, as traceable in both the present and in the future climate scenarios. In the long term, the ongoing climate change may affect riparian communities that depend on the lake's fisheries for their livelihood. In consequence, future lake management strategies for implementation need to account for the impacts of future climate change.     

The linear assignment method for multicriteria group decision making based on interval‐valued Pythagorean fuzzy Bonferroni mean

The interval‐valued Pythagorean fuzzy sets can easily handle uncertain information more flexibly in the process of decision making. Considering the interrelationship among the input arguments, we extend the Bonferroni mean and the geometric Bonferroni mean to the interval‐valued Pythagorean fuzzy environment and solve its practical application problems. First, we develop the interval‐valued Pythagorean fuzzy Bonferroni mean and the weighted interval‐valued Pythagorean fuzzy Bonferroni mean (WIVPFBM) operators. The properties of these aggregation operators are investigated. Then, we also develop the interval‐valued Pythagorean fuzzy geometric Bonferroni mean and the weighted interval‐valued Pythagorean fuzzy geometric Bonferroni mean (WIVPFGBM) operators and analyze their properties. Third, we utilize the WIVPFBM and WIVPFGBM operators to fuse the information in the interval‐valued Pythagorean fuzzy multicriteria group decision making (IVPFMCGDM) problem, which can obtain much more information in the process of group decision making. With the aid of the linear assignment method, we present its extension and further design a new algorithm for the application of IVPFMCGDM. Finally, an example is given to elaborate our proposed algorithm and validate its excellent performance.     

Training a molecular automaton to play a game

Research at the interface between chemistry and cybernetics has led to reports of 'programmable molecules', but what does it mean to say 'we programmed a set of solution-phase molecules to do X'? A survey of recently implemented solution-phase circuitry indicates that this statement could be replaced with 'we pre-mixed a set of molecules to do X and functional subsets of X'. These hard-wired mixtures are then exposed to a set of molecular inputs, which can be interpreted as being keyed to human moves in a game, or as assertions of logical propositions. In nucleic acids-based systems, stemming from DNA computation, these inputs can be seen as generic oligonucleotides. Here, we report using reconfigurable nucleic acid catalyst-based units to build a multipurpose reprogrammable molecular automaton that goes beyond single-purpose 'hard-wired' molecular automata. The automaton covers all possible responses to two consecutive sets of four inputs (such as four first and four second moves for a generic set of trivial two-player two-move games). This is a model system for more general molecular field programmable gate array (FPGA)-like devices that can be programmed by example, which means that the operator need not have any knowledge of molecular computing methods.     

Feedforward-feedback control of a solid oxide fuel cell power system

One of the main challenges for wide-spread utilization of the solid oxide fuel cell (SOFC) power systems is how to achieve high electrical efficiency without increasing the degradation rate of the fuel cells. To run the SOFC power system at high efficiency over a long period of time, properly designed controllers are indispensable.Although a number of various approaches to control SOFC have been proposed so far, it seems that the design of control system, along with simple tuning procedure, has not been treated in a consistent manner. This issue is addressed in the present paper resulting in a feedforward-feedback control structure. The feedforward part is based on the stoichiometry of electro-oxidation, reforming and combustion reactions, which allow immediate response to variable current demand. The feedback part performs additional fine adjustment of fuel and air supply in order to minimize the undesired system temperatures variations. The selection of pairings of manipulated and controlled variables for control is based on physical knowledge of the system. Input/output pairing for single-loop feedback control is assessed by the relative gain analysis. An efficient procedure for tuning the parameters of the feedback controllers is suggested, relying on simple open-loop step responses of the system.The proposed low-level control is assessed on a detailed physical model of a 2.5 kW SOFC power system by simulating two nonstationary load regimes. Simulations show that the control provides a robust operation under large load variations while meeting the operating constraints. Due to its simplicity, the control is feasible for implementation on a real SOFC system.