2,665 research outputs found
Sustained selective attention in adolescence: Cognitive development and predictors of distractibility at school
Despite much research into the development of attention in adolescence, mixed results and between-task differences have precluded clear conclusions regarding the relative early or late maturation of attention abilities. Moreover, although adolescents constantly face the need to pay attention at school, it remains unclear whether laboratory measures of attention can predict their ability to sustain attention focus during lessons. Therefore, here we devised a task that was sensitive to measure both sustained and selective attention and tested whether task measures could predict adolescents’ levels of inattention during lessons. In total, 166 adolescents (aged 12–17 years) and 50 adults performed a sustained selective attention task, searching for letter targets while ignoring salient yet entirely irrelevant distractor faces, under different levels of perceptual load—an established determinant of attention in adults. Inattention levels during a just preceding classroom lesson were measured using a novel self-report classroom distractibility checklist. The results established that sustained attention (measured with response variability) continued to develop throughout adolescence across perceptual load levels. In contrast, there was an earlier maturation of the effect of perceptual load on selective attention; load modulation of distractor interference was larger in the early adolescence period compared with later periods. Both distractor interference and response variability were significant unique predictors of distractibility in the classroom, including when controlling for interest in the lesson and cognitive aptitude. Overall, the results demonstrate divergence of development of sustained and selective attention in adolescence and establish both as significant predictors of attention in the important educational setting of school lessons
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
Forecasting weekly emergency department demand in a Portuguese private hospital
The overcrowding phenomenon is a worldwide problem that has been negatively affecting both
public and private hospitals. A suitable and efficient planning of ED resources may diminish the
effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory
models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality
of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a
big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients
weekly ED arrivals for the same unit
Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies
Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
On top of machine learning models, uncertainty quantification (UQ) functions
as an essential layer of safety assurance that could lead to more principled
decision making by enabling sound risk assessment and management. The safety
and reliability improvement of ML models empowered by UQ has the potential to
significantly facilitate the broad adoption of ML solutions in high-stakes
decision settings, such as healthcare, manufacturing, and aviation, to name a
few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods
for ML models with a particular focus on neural networks and the applications
of these UQ methods in tackling engineering design as well as prognostics and
health management problems. Toward this goal, we start with a comprehensive
classification of uncertainty types, sources, and causes pertaining to UQ of ML
models. Next, we provide a tutorial-style description of several
state-of-the-art UQ methods: Gaussian process regression, Bayesian neural
network, neural network ensemble, and deterministic UQ methods focusing on
spectral-normalized neural Gaussian process. Established upon the mathematical
formulations, we subsequently examine the soundness of these UQ methods
quantitatively and qualitatively (by a toy regression example) to examine their
strengths and shortcomings from different dimensions. Then, we review
quantitative metrics commonly used to assess the quality of predictive
uncertainty in classification and regression problems. Afterward, we discuss
the increasingly important role of UQ of ML models in solving challenging
problems in engineering design and health prognostics. Two case studies with
source codes available on GitHub are used to demonstrate these UQ methods and
compare their performance in the life prediction of lithium-ion batteries at
the early stage and the remaining useful life prediction of turbofan engines
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