Bayesian inference for quantifying Listeria monocytogenes prevalence and concentration in minced pork meat from presence/absence microbiological testing

The purpose of this work was to estimate the prevalence and concentration of Listeria monocytogenes in minced pork meat by the application of a Bayesian modeling approach. Samples (n?=?100) collected from local markets were tested for L.?monocytogenes using in parallel the PALCAM, ALOA and RAPID'L.mono selective media. Presence of the pathogen was confirmed through biochemical and molecular tests. Independent experiments (n?=?10) for validation purposes were performed. No L.?monocytogenes was enumerated by direct-plating (<10?CFU/g), though the pathogen was detected in 22% of the samples. Sensitivity and specificity varied depending on the culture method. L.?monocytogenes concentration was estimated at 14-17?CFU/kg. Validation showed good agreement between observed and predicted prevalence (error?=?-2.17%). The use of at least two culture media in parallel enhanced the efficiency of L.?monocytogenes detection. Bayesian modeling may reduce the time needed to draw conclusions regarding L.?monocytogenes presence and the uncertainty of the results obtained.     

Learning Part Boundaries from 3D Point Clouds

We present a method that detects boundaries of parts in 3D shapes represented as point clouds. Our method is based on a graph convolutional network architecture that outputs a probability for a point to lie in an area that separates two or more parts in a 3D shape. Our boundary detector is quite generic: it can be trained to localize boundaries of semantic parts or geometric primitives commonly used in 3D modeling. Our experiments demonstrate that our method can extract more accurate boundaries that are closer to ground-truth ones compared to alternatives. We also demonstrate an application of our network to fine-grained semantic shape segmentation, where we also show improvements in terms of part labeling performance.     

Assessing stationarity in web analytics: A study of bounce rates

Evidence-based methods for evaluating marketing interventions such as A/B testing have become standard practice. However, the pitfalls associated with the misuse of this decision-making instrument are not well understood by managers and analytics professionals. In this study, we assess the impact of stationarity on the validity of samples from conditioned time series, which are abundant in web metrics. Such a prominent metric is the bounce rate, which is prevalent in assessing engagement with web content as well as the performance of marketing touchpoints. In this study, we show how to control for stationarity using an algorithmic transformation to calculate the optimum sampling period. This distance is based on a novel stationary ergodic process that considers that a stationary series presents reversible symmetric features and is calculated using a dynamic time warping algorithm in a self-correlation procedure. This study contributes to the expert and intelligent systems literature by demonstrating a robust method for sub-sampling time-series data, which are critical in decision making.     

Wearable data analysis,visualisation and recommendations on the go using android middleware

Wearable technology comes with the promise of improving one’s lifestyles thru data mining of their physiological condition. The potential to generate a change in daily or routine habits thru these devices leaves little doubt. Whilst the hardware capabilities of wearables have evolved rapidly, software apps that interpret and present the physiological data and make recommendations in a simple, clear and meaningful way have not followed a similar pattern of evolution. Existing fitness apps provide routinely some information to the wearer by mining personal data but the subsequent analysis is limited to supporting ad hoc personal goals. The information and recommendations presented are often either not entirely relevant or incomplete and often not easy to interpret by the wearer. The primary motivation behind this research is to address this wearable technology software challenge by developing a middleware mobile app that is supported by data analytics and machine learning to assist with interpretation of wearer data and with making of personal lifestyle improvement recommendations on the go which may then be used to feedback to the wearer’s daily goals and activities. The secondary motivation is to correlate and compare with trends in the wearer’s peer community.     

An offset-free neural controller based on a non-extrapolating scheme for approximating the inverse process dynamics

This work presents a novel control scheme based on approximating the inverse process dynamics with a radial basis function (RBF) neural network model, trained with the fuzzy means algorithm. The produced RBF network constitutes an inverse model of the process, which can be applied as an explicit control law. In order to avoid extrapolation in the RBF model predictions, a concept borrowed from chemometrics, namely the applicability domain, is incorporated to the proposed framework. Moreover, an error correction term is added, allowing the inverse neural controller to account for modeling errors and process uncertainty and eliminate offset. The proposed approach is applied to the control of a nonlinear Continuous Stirred Tank Reactor (CSTR) exhibiting multiple equilibrium points, including an unstable one. A comparison with other control schemes on various tests, including set-point tracking, unmeasured disturbance rejection and process uncertainty highlights the advantages of the proposed controller.     

Study on the kinetics of hazardous pollutants adsorption and desorption by biomass: Mechanistic considerations

The kinetics of biosorption and desorption of lindane, diazinon and 2-chlorobiphenyl by inactive activated sludge and Rhizopus arrhizus biomass was studied. The effects of solution initial concentration, temperature and type of biomass on the observed biosorption and desorption rates were also examined. The results of the present work show that the biosorption rates are reasonably rapid, with equilibrium attained within 4 h by activated sludge and 1 h by R. arrhizus. The observed biosorption kinetics consist of a rapid initial stage followed by a slower second stage. The observed rate of organic pollutants biosorption by activated sludge during the first stage can be described by second order kinetics. The biosorption of lindane by both biomass types is reversible and the desorption process is rapid and could be described by zero order kinetics when activated sludge is used as adsorbent. The type of biomass can have a significant effect on the observed biosorption and desorption rates.