Ensemble learning method for hidden markov models.

For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data

An adaptive Cahn-Hilliard equation for enhanced edges in binary image inpainting

We consider the Cahn-Hilliard equation for solving the binary image inpainting problem with emphasis on the recovery of low-order sets (edges, corners) and enhanced edges. The model consists in solving a modified Cahn-Hilliard equation by weighting the diffusion operator with a function which will be selected locally and adaptively. The diffusivity selection is dynamically adopted at the discrete level using the residual error indicator. We combine the adaptive approach with a standard mesh adaptation technique in order to well approximate and recover the singular set of the solution. We give some numerical examples and comparisons with the classical Cahn-Hillard equation for different scenarios. The numerical results illustrate the effectiveness of the proposed model. </jats:p

Computer-aided growth medium design for optimal growth of Chinese hamster ovary cells

Systems biology and metabolic engineering tools hold a tremendous promise in improving biomanufacturing attributes since they represent one of the auspicious modern biomanufacturing optimization approaches. The emergence of omics tools and bioinformatics enables the development of new strategies to optimize expression platforms in general and Chinese hamster ovary (CHO) cell lines in particular, which are the most commonly used cell lines for the production of recombinant proteins. Computational modelling combined with CHO cell omics data can help optimizing growth parameters, as well as improving the final product yield. Here we use a genome-scale metabolic model (GSMM) of CHO to study the growth and metabolic behavior of CHO cells in response to environmental stimuli, such as changing amino acids levels. To study this influence, GSMM combined with an in-house developed algorithm was employed to determine the minimal medium formulation to sustain optimal growth for non-recombinant as well as for recombinant CHO cells lines. Optflux tool was used to predict metabolic behavior of the cells in response to the environmental constraints tested. Based on in silico predictions, growth yield value was improved for non-recombinant and recombinant CHO cells lines comparing to previously reported data. Furthermore, toxic by-products such as ammonium were decreased to their lowest levels. In silico-based approaches for medium optimization are powerful tools for predicting the metabolic interconnexion in the cell and for selecting potential experimental conditions for further validation in bioreactor systems.info:eu-repo/semantics/publishedVersio

Towards metabolic optimization of CHO cells: in silico improvement of culture medium

The emergence of omics tools and bioinformatics potentiated the development of new strategies to optimize several expression platforms, in particular mammalian cell lines, being CHO cells one of the most commonly used cell line for the production of recombinant proteins. Foremost, computational modelling combined with CHO cell omics data can help optimizing growth parameters, as well as improving the final product yield. In this context, CHO genome scale metabolic model (GSSM) was used in order to study the metabolic behavior of the cells in response to variations in environmental constraints, such as amino acids levels, targeting the development of a novel chemically defined culture medium formulation for CHO cells. To study this influence, GSSM combined with an in-house developed algorithm was employed to determine the minimal medium formulation to sustain growth for non-recombinant as well as for recombinant CHO cells lines. Optflux tool was used to predict metabolic behavior of the cells in response to the environmental constraints tested. Based on in silico predictions, growth yield value was improved 2.8 times and 1.8 times, respectively, for non-recombinant and recombinant CHO cells lines comparing to previously reported data. Furthermore, toxic by-products such as ammonium were decreased to their lowest levels. In silico-based approaches for medium optimization are powerful tools for predicting the metabolic interconnexion in the cell and for selecting potential experimental conditions for further validation in bioreactor systems.info:eu-repo/semantics/publishedVersio

Thermal performance study of a vacuum integrated solar storage collector (ISSC) with compound parabolic concentrator (CPC)

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A nonstandard higher-order variational model to speckle noise removal and thin-structure detection

In this work, we propose a multiscale approach for a nonstandard higher-order PDE based on the $p(\cdot)$-Kirchhoff energy. First, we consider a topological gradient approach for a semilinear case in order to detect important object of image. Then, we consider a fully nonlinear $p(\cdot)$-Kirchhoff equation with variables exponent functions that are chosen adaptively based on the map furnished by the topological gradient in order to preserve important features of the image. Then, we consider the split Bregman method for the numerical implementation of our proposed model. We compare our model with other classical variational approaches such that the TVL and biharmonic restoration models. Finally, we present some numerical results to illustrate the effectiveness of our approach

Ramadan Observance Is Associated with Impaired Kung-Fu-Specific Decision-Making Skills

The aim of the present study is to evaluate the effect of Ramadan observance (RAM) on decision-making in Kung-Fu athletes. Fourteen male Kung-Fu athletes (mean age = 19 ± 3 years) completed two test sessions: before Ramadan (BR) and at the end of Ramadan (ER). In the afternoon of each session (between 16:00 h and 18:00 h), participants completed: Epworth Sleepiness Scale (ESS), Profile of Mood States (POMS), and Pittsburg Sleep Quality Index (PSQI). Subjects also reported subjective fatigue, alertness, and concentration. Additionally, all participants performed video-based decision-making tasks (i.e., reaction time and decision-making). Results indicated that reaction time decreased by 30% at ER vs. BR (p p p p p < 0.05). In conclusion, Ramadan observance was associated with an adverse effect on sleep and decision making, as well as feelings of fatigue, alertness, and concentration

Training during the COVID-19 lockdown : knowledge, beliefs, and practices of 12,526 athletes from 142 countries and six continents

OBJECTIVE Our objective was to explore the training-related knowledge, beliefs, and practices of athletes and the influence of lockdowns in response to the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS Athletes (n = 12,526, comprising 13% world class, 21% international, 36% national, 24% state, and 6% recreational) completed an online survey that was available from 17 May to 5 July 2020 and explored their training behaviors (training knowledge, beliefs/attitudes, and practices), including specific questions on their training intensity, frequency, and session duration before and during lockdown (March–June 2020). RESULTS Overall, 85% of athletes wanted to “maintain training,” and 79% disagreed with the statement that it is “okay to not train during lockdown,” with a greater prevalence for both in higher-level athletes. In total, 60% of athletes considered “coaching by correspondence (remote coaching)” to be sufficient (highest amongst world-class athletes). During lockdown, < 40% were able to maintain sport-specific training (e.g., long endurance [39%], interval training [35%], weightlifting [33%], most (83%) training for “general fitness and health maintenance” during lockdown. Athletes trained alone (80%) and focused on bodyweight (65%) and cardiovascular (59%) exercise/training during lockdown. Compared with before lockdown, most athletes reported reduced training frequency (from between five and seven sessions per week to four or fewer), shorter training sessions (from ≥ 60 to < 60 min), and lower sport-specific intensity (~ 38% reduction), irrespective of athlete classification. CONCLUSIONS COVID-19-related lockdowns saw marked reductions in athletic training specificity, intensity, frequency, and duration, with notable within-sample differences (by athlete classification). Higher classification athletes had the strongest desire to “maintain” training and the greatest opposition to “not training” during lockdowns. These higher classification athletes retained training specificity to a greater degree than others, probably because of preferential access to limited training resources. More higher classification athletes considered “coaching by correspondence” as sufficient than did lower classification athletes. These lockdown-mediated changes in training were not conducive to maintenance or progression of athletes’ physical capacities and were also likely detrimental to athletes’ mental health. These data can be used by policy makers, athletes, and their multidisciplinary teams to modulate their practice, with a degree of individualization, in the current and continued pandemic-related scenario. Furthermore, the data may drive training-related educational resources for athletes and their multidisciplinary teams. Such upskilling would provide athletes with evidence to inform their training modifications in response to germane situations (e.g., COVID related, injury, and illness).A specific funding was provided by the National Sports Institute of Malaysia for this study.The National Sports Institute of Malaysia.https://www.springer.com/journal/40279am2023Sports Medicin

COVID-19 lockdown : a global study investigating athletes’ sport classification and sex on training practices

PURPOSE : To investigate differences in athletes’ knowledge, beliefs, and training practices during COVID-19 lockdowns with reference to sport classification and sex. This work extends an initial descriptive evaluation focusing on athlete classification. METHODS : Athletes (12,526; 66% male; 142 countries) completed an online survey (May–July 2020) assessing knowledge, beliefs, and practices toward training. Sports were classified as team sports (45%), endurance (20%), power/technical (10%), combat (9%), aquatic (6%), recreational (4%), racquet (3%), precision (2%), parasports (1%), and others (1%). Further analysis by sex was performed. RESULTS : During lockdown, athletes practiced body-weight-based exercises routinely (67% females and 64% males), ranging from 50% (precision) to 78% (parasports). More sport-specific technical skills were performed in combat, parasports, and precision (∼50%) than other sports (∼35%). Most athletes (range: 50% [parasports] to 75% [endurance]) performed cardiorespiratory training (trivial sex differences). Compared to prelockdown, perceived training intensity was reduced by 29% to 41%, depending on sport (largest decline: ∼38% in team sports, unaffected by sex). Some athletes (range: 7%–49%) maintained their training intensity for strength, endurance, speed, plyometric, change-of-direction, and technical training. Athletes who previously trained ≥5 sessions per week reduced their volume (range: 18%–28%) during lockdown. The proportion of athletes (81%) training ≥60 min/session reduced by 31% to 43% during lockdown. Males and females had comparable moderate levels of training knowledge (56% vs 58%) and beliefs/attitudes (54% vs 56%). CONCLUSIONS : Changes in athletes’ training practices were sport-specific, with few or no sex differences. Team-based sports were generally more susceptible to changes than individual sports. Policy makers should provide athletes with specific training arrangements and educational resources to facilitate remote and/or home-based training during lockdown-type events.https://journals.humankinetics.com/view/journals/ijspp/ijspp-overview.xmlhj2023Sports Medicin