Theoretical and experimental investigation of pulsed laser grooving process

A theoretical model has been developed for simulating the laser grooving process. It takes into account the interaction among subsequent pulses, the required time for the melting temperature to be reached and the subsequent removal of a finite volume of material during each laser pulse. The model predicts the maximum groove depth that can be achieved for a specified set of process parameters, such as laser power, pulsing frequency, and scanning velocity. The theoretical predictions have been experimentally tested with a medium-power laser beam.     

The global k-means clustering algorithm

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts.     

A new method for the control of the river Nile

This paper introduces a new stochastic control method, Extended Linear Quadratic Gaussian control, that appears to be particularly promising for the management of complex reservoir configurations such as the River Nile system. The method is introduced by means of a short case study related to the regulation of the Equatorial Lakes in the Nile basin. If the necessary data are available, the method can be easily adapted to the entire Nile system and used to investigate a variety of management issues.     

Fibro/chondrogenic differentiation of dental stem cells into chitosan/alginate scaffolds towards temporomandibular joint disc regeneration

Tissue engineering (TE) may provide effective alternative treatment for challenging temporomandibular joint (TMJ) pathologies associated with disc malpositioning or degeneration and leading to severe masticatory dysfunction. Aim of this study was to evaluate the potential of chitosan/alginate (Ch/Alg) scaffolds to promote fibro/chondrogenic differentiation of dental pulp stem cells (DPSCs) and production of fibrocartilage tissue, serving as a replacement of the natural TMJ disc. Ch/Alg scaffolds were fabricated by crosslinking with CaCl2 combined or not with glutaraldehyde, resulting in two scaffold types that were physicochemically characterized, seeded with DPSCs or human nucleus pulposus cells (hNPCs) used as control and evaluated for cell attachment, viability, and proliferation. The DPSCs/scaffold constructs were incubated for up to 8 weeks and assessed for extracellular matrix production by means of histology, immunofluorescence, and thermomechanical analysis. Both Ch/Alg scaffold types with a mass ratio of 1:1 presented a gel-like structure with interconnected pores. Scaffolds supported cell adhesion and long-term viability/proliferation of DPSCs and hNPCs. DPSCs cultured into Ch/Alg scaffolds demonstrated a significant increase of gene expression of fibrocartilaginous markers (COLI, COL X, SOX9, COM, ACAN) after up to 3 weeks in culture. Dynamic thermomechanical analysis revealed that scaffolds loaded with DPSCs significantly increased storage modulus and elastic response compared to cell-free scaffolds, obtaining values similar to those of native TMJ disc. Histological data and immunochemical staining for aggrecan after 4 to 8 weeks indicated that the scaffolds support abundant fibrocartilaginous tissue formation, thus providing a promising strategy for TMJ disc TE-based replacement.

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Bayesian feature and model selection for Gaussian mixture models

We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of mixture components. The proposed learning algorithm follows the variational framework and can simultaneously optimize over the number of components, the saliency of the features, and the parameters of the mixture model. Experimental results using high-dimensional artificial and real data illustrate the effectiveness of the method.     

Reinforcement Learning Using the Stochastic Fuzzy Min–Max Neural Network

The fuzzy min–max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. An extension to this network has been proposed recently, that is based on the notion of random hyperboxes and is suitable for reinforcement learning problems with discrete action space. In this work, we elaborate further on the random hyperbox idea and propose the stochastic fuzzy min–max neural network, where each hyperbox is associated with a stochastic learning automaton. Experimental results using the pole balancing problem indicate that the employment of this model as an action selection network in reinforcement learning schemes leads to superior learning performance compared with the traditional approach where the multilayer perceptron is employed.     

A class-adaptive spatially variant mixture model for image segmentation.

We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.     

Bayesian kernel methods for analysis of functional neuroimages

We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.     

Lactose uptake rate measurements by C-labelled lactose reveals promotional activity of porous cellulose in whey fermentation by kefir yeast

Lactose uptake rate by kefir yeast, immobilized on tubular cellulose and gluten pellets during fermentation of lactose and whey, was monitored using ¹⁴C-labelled lactose. Results illustrated that, in all cases, lactose uptake rate was strongly correlated with fermentation rate and the fermentation’s kinetic parameters were improved by kefir yeast entrapped in tubular cellulose. As a result, twofold faster fermentations were achieved in comparison with kefir yeast immobilized on gluten. This is probably due to cluster and hydrogen bonds formation between cellulose and inhibitors, such as Ca⁺⁺ and generated lactic acid, by which they leave the liquid medium. The findings, regarding the promotional effect of cellulose, seem promising for application in industrial whey fermentations.