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1. Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

   https://doi.org/10.2196/46347  

   King, Zachary D ; Yu, Han ; Vaessen, Thomas ; Myin-Germeys, Inez ; Sano, Akane ( January 2024 , JMIR mHealth and uHealth)

   As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods.

   This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs.

   We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity.

   Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions.

   Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

    

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2. Tissue‐Adaptive Materials with Independently Regulated Modulus and Transition Temperature

   https://doi.org/10.1002/adma.202005314  

   Zhang, Daixuan ; Dashtimoghadam, Erfan ; Fahimipour, Farahnaz ; Hu, Xiaobo ; Li, Qiaoxi ; Bersenev, Egor A. ; Ivanov, Dimitri A. ; Vatankhah‐Varnoosfaderani, Mohammad ; Sheiko, Sergei S. ( November 2020 , Advanced Materials)

   The ability of living species to transition between rigid and flexible shapes represents one of their survival mechanisms, which has been adopted by various human technologies. Such transition is especially desired in medical devices as rigidity facilitates the implantation process, while flexibility and softness favor biocompatibility with surrounding tissue. Traditional thermoplastics cannot match soft tissue mechanics, while gels leach into the body and alter their properties over time. Here, a single‐component system with an unprecedented drop of Young's modulus by up to six orders of magnitude from the GPa to kPa level at a controlled temperature within 28–43 °C is demonstrated. This approach is based on brush‐like polymer networks with crystallizable side chains, e.g., poly(valerolactone), affording independent control of melting temperature and Young's modulus by concurrently altering side chain length and crosslink density. Softening down to the tissue level at the physiological temperature allows the design of tissue‐adaptive implants that can be inserted as rigid devices followed by matching the surrounding tissue mechanics at body temperature. This transition also enables thermally triggered release of embedded drugs for anti‐inflammatory treatment.

    

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3. Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study

   https://doi.org/10.3390/signals2040051  

   Agarwal, Ankita ; Graft, Josephine ; Schroeder, Noah ; Romine, William ( December 2021 , Signals)

   Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device. 

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4. Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

   https://doi.org/10.1088/1361-665X/acb2a1  

   Emu, Md Esharuzzaman ( January 2023 , Smart Materials and Structures)

   Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. Under variable stress and a constant bias field, MSMAs can be used in power harvesting or sensing devices, e.g. in structural health monitoring applications. This behavior is primarily a result of the approximately tetragonal unit cell whose magnetic easy axis is approximately aligned with the short axis of the unit cell within the Ni-Mn-Ga single crystals. Under an applied field, the magnetic easy axis tends to align with the external field. Similarly, under an applied compressive force, the short side of the unit cell tends to align with the direction of the force. This work introduced a new feature to the existing macro-scale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D’silvaet al(2020Shape Mem. Superelasticity667–88). This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and field-controlled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels, and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberleet al(2019Smart Mater. Struct.28025022). Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was trained using the experimental data (stress, strain, magnetic field, & emf) so that it can make a reasonably better prediction.

    

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5. Estimate of the Mass and Radial Profile of the Orphan–Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home

   https://doi.org/10.3847/1538-4357/ac498a  

   Mendelsohn, Eric J. ; Newberg, Heidi Jo ; Shelton, Siddhartha ; Widrow, Lawrence M. ; Thompson, Jeffery M. ; Grillmair, Carl J. ( February 2022 , The Astrophysical Journal)

   We fit the mass and radial profile of the Orphan–Chenab Stream’s (OCS) dwarf-galaxy progenitor by using turnoff stars in the Sloan Digital Sky Survey and the Dark Energy Camera to constrainN-body simulations of the OCS progenitor falling into the Milky Way on the 1.5 PetaFLOPS MilkyWay@home distributed supercomputer. We infer the internal structure of the OCS’s progenitor under the assumption that it was a spherically symmetric dwarf galaxy composed of a stellar system embedded in an extended dark matter halo. We optimize the evolution time, the baryonic and dark matter scale radii, and the baryonic and dark matter masses of the progenitor using a differential evolution algorithm. The likelihood score for each set of parameters is determined by comparing the simulated tidal stream to the angular distribution of OCS stars observed in the sky. We fit the total mass of the OCS’s progenitor to (2.0 ± 0.3) × 10⁷M_⊙with a mass-to-light ratio ofγ= 73.5 ± 10.6 and (1.1 ± 0.2) × 10⁶M_⊙within 300 pc of its center. Within the progenitor’s half-light radius, we estimate a total mass of (4.0 ± 1.0) × 10⁵M_⊙. We also fit the current sky position of the progenitor’s remnant to be (α,δ) = ((166.0 ± 0.9)°, (−11.1 ± 2.5)°) and show that it is gravitationally unbound at the present time. The measured progenitor mass is on the low end of previous measurements and, if confirmed, lowers the mass range of ultrafaint dwarf galaxies. Our optimization assumes a fixed Milky Way potential, OCS orbit, and radial profile for the progenitor, ignoring the impact of the Large Magellanic Cloud.

    

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