Source-dependent absorption Ångström exponent in the Los Angeles Basin: Multi-time resolution factor analyses of ambient PM and aerosol optical absorption.

Advanced receptor models can leverage the information derived from optical and chemical variables as input by a variety of instruments at different time resolutions to extract the source specific absorption Ångström exponent (AAE) from aerosol absorption. The multilinear engine (ME-2), a Positive Matrix Factorization (PMF) solver, serves as a proficient tool for performing such analyses, thereby overcoming the constraints imposed by the assumptions in current optical source apportionment methods such as the Aethalometer approach since the use of a-priori AAE values introduces additional uncertainty into the results of optical methods. Comprehensive PM chemical speciation datasets, and aerosol absorption coefficients (b, λ) at seven wavelengths measured by an Aethalometer (AE33), were used in multi-time source apportionment (MT-PMF).

A European aerosol phenomenology - 9: Light absorption properties of carbonaceous aerosol particles across surface Europe.

Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (b) and BrC (b) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)).

Source apportionment of ultrafine particles in urban Europe.

There is a body of evidence that ultrafine particles (UFP, those with diameters ≤ 100 nm) might have significant impacts on health. Accordingly, identifying sources of UFP is essential to develop abatement policies. This study focuses on urban Europe, and aims at identifying sources and quantifying their contributions to particle number size distribution (PNSD) using receptor modelling (Positive Matrix Factorization, PMF), and evaluating long-term trends of these source contributions using the non-parametric Theil-Sen's method.

Inter-annual trends of ultrafine particles in urban Europe.

Ultrafine particles (UFP, those with diameters ≤ 100 nm), have been reported to potentially penetrate deeply into the respiratory system, translocate through the alveoli, and affect various organs, potentially correlating with increased mortality. The aim of this study is to assess long-term trends (5-11 years) in mostly urban UFP concentrations based on measurements of particle number size distributions (PNSD). Additionally, concentrations of other pollutants and meteorological variables were evaluated to support the interpretations.

Constructing transferable and interpretable machine learning models for black carbon concentrations.

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe.

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated.

The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe.

This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 and 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe.

Deep Neural Networks for Human's Fall-risk Prediction using Force-Plate Time Series Signal.

Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks' capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series.

Multidirectional Overground Robotic Training Leads to Improvements in Balance in Older Adults.

For the rapidly growing aging demographic worldwide, robotic training methods could be impactful towards improving balance critical for everyday life. Here, we investigated the hypothesis that non-bodyweight supportive (nBWS) overground robotic balance training would lead to improvements in balance performance and balance confidence in older adults. Sixteen healthy older participants (69.

Easy axis anisotropy creating high contrast magnetic zones on magnetic tunnel junctions based molecular spintronics devices (MTJMSD).

Magnetic tunnel junction-based molecular spintronics device (MTJMSD) may enable novel magnetic metamaterials by chemically bonding magnetic molecules and ferromagnets (FM) with a vast range of magnetic anisotropy. MTJMSD have experimentally shown intriguing microscopic phenomenon such as the development of highly contrasting magnetic phases on a ferromagnetic electrode at room temperature. This paper focuses on Monte Carlo Simulations (MCS) on MTJMSD to understand the potential mechanism and explore fundamental knowledge about the impact of magnetic anisotropy.

Investigating Relationships between Balance Confidence and Balance Ability in Older Adults.

Increasing balance confidence in older individuals is important towards improving their quality of life and reducing activity avoidance. Here, we investigated if balance confidence (perceived ability) and balance performance (ability) in older adults were related to one another and would improve after balance training. The relationship of balance confidence in conjunction with balance performance for varied conditions (such as limiting vision, modifying somatosensory cues, and also base of support) was explored.

Sensory integration training improves balance in older individuals.

With the massive growth of the aging population worldwide, of utmost importance is reducing falls. Critical to reducing fall risk is one's ability to weight incoming sensory information towards maintaining balance. The purpose of this research was to investigate if simple, targeted sensory training on aging individuals (50 - 80 years old), including twelve healthy and eight individuals with chronic stroke, could improve their balance.

A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal.

This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG).