Author(s): Alicia Estefania Antonio Figueroa

This work implements deep learning models to capture non-linear and complex data behavior in aluminum price data. Deep learning models include the long short-term memory (LSTM) and deep feedforward neural networks (FFNN). The support vector regression (SVR) is employed as a base model for comparison. Each predictive model is tuned by using two different o ...

Article GUID: 41751647

Author(s): Yu S; Jin Y; Guo T; Li H; Liu W; Chen Z; Wang X; Guo J;

Biological denitrification is limited by slow nitrate (NO3-) reduction due to low electron transfer efficiency, unsatisfactory community functional efficiency and insufficient metabolic activity of microbial communities. To overcome these challenges, Ni2+ and Fe2+ were incorporated with gallic acid (GA) to form bimetallic polyphenol networks (NiFeGA BPNs) ...

Article GUID: 41707775

Author(s): Lahmiri S; Tadj C; Gargour C;

Voice analysis and classification for biomedical diagnosis purpose is receiving a growing attention to assist physicians in the decision-making process in clinical milieu. In this study, we develop and test deep feedforward neural networks (DFFNN) to distinguish between healthy and unhealthy newborns. The DFFNN are trained with acoustic features measured ...

Article GUID: 41294952

Author(s): Rattray GF; Jourde HR; Baillet S; Coffey EBJ;

Subcortical brain regions like the thalamus are integral to numerous sensory and cognitive functions. Magnetoencephalography (MEG) enables the study of widespread brain networks with high temporal resolution, but the degree to which deep sources like the thalamus can be resolved remains unclear. Functional connectivity methods may enhance differentiation, ...

Article GUID: 41002111

Author(s): Cheng M; He S; Pan Y; Lin M; Zhu WP;

The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption rema ...

Article GUID: 40942666

Author(s): Moore C; Donhauser PW; Klein D; Byers-Heinlein K;

The remarkable human capacity for bilingual and multilingual acquisition raises fundamental questions about how the brain develops efficient systems for processing multiple languages. In this study, we used neural network models trained on natural speech input to examine how these efficient representations emerge. Our models show that multiple phonologica ...

Article GUID: 40828024

Author(s): Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G;

Personalized closed-loop brain stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications. Precise stimulation of sleep spindles-transient patterns of brain activity that occu ...

Article GUID: 40609549

Author(s): Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A;

The transition to sustainable energy has become imperative due to the depletion of fossil fuels. Solar energy presents a viable alternative owing to its abundance and environmental benefits. However, the intermittent nature of solar energy requires accurate forecasting of solar irradiance (SI) for reliable operation of photovoltaics (PVs) integrated syste ...

Article GUID: 40185800

Author(s): Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D;

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from cli ...

Article GUID: 40147442

Author(s): Rehan S; Phillips NA;

Psychosocial function is associated with cognitive performance cross-sectionally and cognitive decline over time. Using data from the COMPASS-ND study, we examined associations between psychosocial and cognitive function in 126 individuals with mild cognitive impairment, an at-risk group for Alzheimer's disease (AD). Psychosocial function was measured ...

Article GUID: 39773214

Author(s): Gallinger C; Popovic L;

We consider a general class of autocatalytic reactions, which has been shown to display stochastic switching behaviour (discreteness-induced transitions (DITs)) in some parameter regimes. This behaviour was shown to occur either when the overall species count is low or when the rate of inflow and outflow of species is relatively much smaller than the rate ...

Article GUID: 39679357