Individual and ensemble functional link neural networks for data classification

This study investigated the Functional Link Neural Network (FLNN) for solving data classification problems. FLNN based models were developed using evolutionary methods as well as ensemble methods. The outcomes of the experiments covering benchmark classification problems, positively demonstrated the efficacy of the proposed models for undertaking data classification problems

A study and a directory of energy consumption data sets of buildings

Energy consumption data are required to perform analysis, modelling, evaluation, and optimisation of energy usage in buildings. While a variety of energy consumption data sets have been examined and reported in the literature, there is a lack of a comprehensive categorisation and analysis of the available data sets. In this study, an overview of energy consumption data of buildings is provided. Three common strategies for generating energy consumption data, i.e., measurement, survey, and simulation, are described. A number of important characteristics pertaining to each strategy and the resulting data sets are discussed. In addition, a directory of energy consumption data sets of buildings is developed. The data sets are collected from either published papers or energy related organisations. The main contributions of this study include establishing a resource pertaining to energy consumption data sets and providing information related to the characteristics and availability of the respective data sets; therefore facilitating and promoting research activities in energy consumption data analysis

Effects of thermal, non-thermal and emulsification processes on the gastrointestinal digestibility of egg white proteins

International audienceBackground: Egg white proteins (EWPs) are an excellent source of essential amino acids for human nutrition. Also, egg white is commonly used in food processing because of its numerous technological functionalities. Processing can influence the EWPs digestibility and thereby amino acid bioavailability. A better understanding of processing effects on EWPs digestibility enables improving the nutritional value of EWP products. Scope and approach: This review elucidates the impact of diverse processing methods on EWPs at molecular level (individual molecules), supramolecular level (aggregated state), and macroscopic level (gelled state). Key findings and conclusions: Heat, high pressure, ultrasound, pulsed electric fields, and adsorption at interface can unfold native EWPs, causing exposure of hydrolysis sites and improvement of protein digestibility at molecular level. However, the Maillard glycation of EWPs may restrict the access of digestive enzymes to the proteins and reduce their digestibility. The Maillard reaction can also lower the IgE-binding capacity of ovalbumin, which could potentially reduce allergenicity. At supramolecular level, protein-protein interactions between the unfolded EWPs lead to the formation of aggregates with different morphologies, depending on the pH and ionic strength. The accessibility of digestive enzymes to the cleavage sites of heat-induced spherical aggregates is lower compared to the linear counterparts. However, gels formed from the linear aggregates show high resistance to digestion owing to the dense network of these gels. A combination of processes can increase the impact of digestibility. For instance, quick production of specific bioactive peptides can be achieved by applying enzymatic treatment to EWPs under high pressure

Predicting performance in 4 x 200-m freestyle swimming relay events

AIM: The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. DATA AND METHODS: Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010–2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. RESULTS: Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [−0.94, −0.30]) and American swimmers (−0.48 s [−0.89, −0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. DISCUSSION: Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer’s season’s best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success