Continuous improvement through knowledge-guided analysis in experience feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.     

Laser-induced growth of nanocrystals embedded in porous materials

Space localization of the linear and nonlinear optical properties in a transparent medium at the submicron scale is still a challenge to yield the future generation of photonic devices. Laser irradiation techniques have always been thought to structure the matter at the nanometer scale, but combining them with doping methods made it possible to generate local growth of several types of nanocrystals in different kinds of silicate matrices. This paper summarizes the most recent works developed in our group, where the investigated nanoparticles are either made of metal (gold) or chalcogenide semiconductors (CdS, PbS), grown in precursor-impregnated porous xerogels under different laser irradiations. This review is associated to new results on silver nanocrystals in the same kind of matrices. It is shown that, depending on the employed laser, the particles can be formed near the sample surface or deep inside the silica matrix. Photothermal and/or photochemical mechanisms may be invoked to explain the nanoparticle growth, depending on the laser, precursor, and matrix. One striking result is that metal salt reduction, necessary to the production of the corresponding nanoparticles, can efficiently occur due to the thermal wrenching of electrons from the matrix itself or due to multiphoton absorption of the laser light by a reducer additive in femtosecond regime. Very localized semiconductor quantum dots could also be generated using ultrashort pulses, but while PbS nanoparticles grow faster than CdS particles due to one-photon absorption, this better efficiency is counterbalanced by a sensitivity to oxidation. In most cases where the reaction efficiency is high, particles larger than the pores have been obtained, showing that a fast diffusion of the species through the interconnected porosity can modify the matrix itself. Based on our experience in these techniques, we compare several examples of laser-induced nanocrystal growth in porous silica xerogels, which allows extracting the best experimental conditions to obtain an efficient particle production and to avoid stability or oxidation problems.     

Setting adaptive spike detection threshold for smoothed TEO based on robust statistics theory

We propose a novel approach aimed at adaptively setting the threshold of the smoothed Teager energy operator (STEO) detector to be used in extracellular recording of neural signals. In this proposed approach, to set the adaptive threshold of the STEO detector, we derive the relationship between the low-order statistics of its input signal and the ones of its output signal. This relationship is determined with only the background noise component assumed to be present at the input. Robust statistics theory techniques were used to achieve an unbiased estimation of these low-order statistics of the background noise component directly from the neural input signal. In this paper, the emphasis is made on extracellular neural recordings. However, the proposed method can be used in the analysis of different biomedical signals where spikes are important for diagnostic (e.g., ECG, EEG, etc.). We validated the efficacy of the proposed method using synthetic neural signals constructed from real neural recordings signals. Four different sets of extracellular recordings from four distinct neural sources have been exploited to that purpose. The first dataset is recorded from an adult male monkey using the Utath 10×10 microelectrode array implemented in the prefrontal cortex, the second one was obtained from the visual cortex of a rat using a stainless-steel-tipped microelectrode, the third dataset came from recording in a human medial lobe using intracranial electrode, and finally, the fourth one was extracted from recordings in a macaque parietal cortex using a single tetrode. Simulation results show that our approach is effective and robust, and outperforms state-of-the-art adaptive detection methods in its category (i.e., efficient and simple, and do not require a priori knowledge about neural spike waveforms shapes).     

First extraction of polyphenol oxidase from edible desert truffle (Terfezia leonis Tul.) and its thermal behavior

Polyphenol oxidase (PPO) is a common enzyme with large applications in food processing and analysis, especially based on their monophenolase activity. In this context, extraction and surfactant-mediated activation of PPO from desert truffle Terfezia leonis Tul. were successfully achieved. In the presence of l-tyrosine, the cresolase activity was optimal in the pH 5–6 domain and in the 35–45 °C temperature range. In the presence of pyrocatechol, the catecholase activity was optimal at neutral pH and 30 °C. Kinetics studies revealed higher affinity of PPO for l-tyrosine than for pyrocatechol. Both enzyme–substrate complexes were structurally robust, and their thermosensitivity was mainly related to entropy changes. These properties may reflect the adaptation to desert conditions where T. leonis grows and should be useful for the development of enzymatic catalysts and sensors.     

Intraocular lens materials and styles: a review

OBJECTIVE: To compare criteria for coronary revascularization developed by the expert panel process and by decision analysis. METHOD: We reviewed the medical records of 3080 chronic stable angina patients who either underwent coronary artery bypass graft surgery (CABG) or percutaneous transluminal coronary angioplasty (PTCA) and determined the agreement between appropriateness ratings made by two expert physician panels, one from the United States and the second from The Netherlands. We also evaluated the agreement between these panels' appropriateness ratings and a decision analytic model's effectiveness categories. RESULTS: There was poor agreement between U.S. and Dutch panel appropriateness ratings for PTCA (kappa = 0.03) and slight agreement for bypass surgery (kappa = 0.18). Dutch ratings had substantial agreement with the decision analytic models effectiveness categories for both PTCA and CABG (kappa = 0.83 and 0.79, respectively) whereas there was no systematic agreement between U.S. ratings and the decision analytic model for PTCA and poor agreement for CABG (kappa = 0.00 and 0.18, respectively). CONCLUSIONS: Although the level of agreement between expert panels and decision analysis on when a procedure is appropriate or effective may vary by procedure and the strength of the scientific evidence, we found that Dutch physicians agree much more strongly with decision analysis than U.S. physicians.     

Special issue on computational intelligence-based modeling,control and estimation in modern mechatronic systems

Neural Computing and Applications -     

A novel multiclass priority algorithm for task scheduling in cloud computing

The Journal of Supercomputing - Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the...