In Operando Monitoring the Stress Evolution of Silicon Anode Electrodes during Battery Operation via Optical Fiber Sensors.

Silicon (Si) anode has attracted broad attention because of its high theoretical specific capacity and low working potential. However, the severe volumetric changes of Si particles during the lithiation process cause expansion and contraction of the electrodes, which induces a repeatedly repair of solid electrolyte interphase, resulting in an excessive consuming of electrolyte and rapid capacity decay. Clearly known the deformation and stress changing at µÎµ resolution in the Si-based electrode during battery operation provides invaluable information for the battery research and development. Here, an in operando approach is developed to monitor the stress evolution of Si anode electrodes via optical fiber Bragg grating (FBG) sensors. By implanting FBG sensor at specific locations in the pouch cells with different Si anodes, the stress evolution of Si electrodes has been systematically investigated, and Δσ/areal capacity is proposed for stress assessment. The results indicate that the differences in stress evolution are nested in the morphological changes of Si particles and the evolution characteristics of electrode structures. The proposed technique provides a brand-new view for understanding the electrochemical mechanics of Si electrodes during battery operation.

Ultrasensitive electrochemical sensor for detection of salivary cortisol in stress conditions.

A natural stress response induces elevated cortisol levels in biological fluids, such as saliva. While current sensor technologies can detect cortisol in real time, their sensitivity and reliability for human subjects have not been assured. This is due to relatively low concentrations of salivary cortisol, which fluctuate throughout the day and vary significantly between individuals. To address these challenges, we present an improved electrochemical biosensor leveraging graphene's exceptional conductivity and physicochemical properties. A 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE-NHS)-modified commercial graphene foam (GF) electrode is presented to realize an ultra-sensitive biosensor for cortisol detection directly in human saliva. The biosensor fabrication process entails the attachment of anti-cortisol monoclonal antibodies (mAb-cort) onto a PBASE-NHS/GF electrode through noncovalent immobilization on the vertically stratified graphene foam electrode surface. This unique immobilization strategy preserves graphene's structural integrity and electrical conductivity while facilitating antibody immobilization. The binding of cortisol to immobilized mAb-cort is read out via differential pulse voltammetry using ferri/ferro redox reactions. The immunosensor demonstrates an exceptional dynamic range of 1.0 fg mL-1 to 10,000 pg mL-1 (R2 = 0.9914) with a detection limit of 0.24 fg mL-1 (n = 3) for cortisol. Furthermore, we have established the reliability of cortisol sensors in monitoring human saliva. We have also performed multiple modes of validation, one against the established enzyme-linked immunosorbent assay (ELISA) and a second by a third-party service Salimetric on 16 student volunteers exposed to different stress levels, showing excellent correlation (r = 0.9961). These findings suggest the potential for using mAb-cort/PBASE-NHS/GF-based cortisol electrodes for monitoring salivary cortisol in the general population.

Near-Infrared Fluorescent Carbon Nanotube Sensors for the Plant Hormone Family Gibberellins.

Gibberellins (GAs) are a class of phytohormones, important for plant growth, and very difficult to distinguish because of their similarity in chemical structures. Herein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA3 and GA4, triggering near-infrared (NIR) fluorescence intensity changes. Using a new coupled Raman/NIR fluorimeter that enables self-referencing of nanosensor NIR fluorescence with its Raman G-band, we demonstrated detection of cellular GA in Arabidopsis, lettuce, and basil roots. The nanosensors reported increased endogenous GA levels in transgenic Arabidopsis mutants that overexpress GA and in emerging lateral roots. Our approach allows rapid spatiotemporal detection of GA across species. The reversible sensor captured the decreasing GA levels in salt-treated lettuce roots, which correlated remarkably with fresh weight changes. This work demonstrates the potential for nanosensors to solve longstanding problems in plant biotechnology.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data.

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Measuring stress: a review of the current cortisol and dehydroepiandrosterone (DHEA) measurement techniques and considerations for the future of mental health monitoring.

Psychological stress and its inevitable trajectory toward mental health deteriorations such as clinical and major depression has become an unprecedented global burden. The diagnostic procedures involved in the characterization of mental illnesses commonly follow qualitative and subjective measures of stress, often leading to greater socioeconomic burdens due to misdiagnosis and poor understanding of the severity of such illnesses, further fueled by the stigmatization surrounding mental health. In recent years, the application of cortisol and stress hormone measurements has given rise to an alternative, quantifiable approach for the psychological evaluation of stress and depression. This review comprehensively evaluates the current state-of-the-art technology for measuring cortisol and dehydroepiandrosterone (DHEA) and their applications within stress monitoring in humans. Recent advancements in these fields have shown the importance of measuring stress hormones for the characterization of stress manifestation within the human body, and its relevance in mental health decline. Preliminary results from studies considering multimodal approaches toward stress monitoring have showcased promising developments, emphasizing the need for further technological advancement in this field, which consider both neurochemical and physiological biomarkers of stress, for global benefit.

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models.

Stress in surgical educational environments: a systematic review.

BACKGROUND: The effects of stress on surgical residents and how stress management training can prepare residents to effectively manage stressful situations is a relevant topic. This systematic review aimed to analyze the literature regarding (1) the current stress monitoring tools and their use in surgical environments, (2) the current methods in surgical stress management training, and (3) how stress affects surgical performance. METHODS: A search strategy was implemented to retrieve relevant articles from Web of Science, Scopus, and PubMed. The 787 initially retrieved articles were reviewed for further evaluation according to the inclusion/exclusion criteria (Prospero registration number CRD42021252682). RESULTS: Sixty-one articles were included in the review. The stress monitoring methods found in the articles showed heart rate analysis as the most used monitoring tool for physiological parameters while the STAI-6 scale was preferred for psychological parameters. The stress management methods found in the articles were mental-, simulation- and feedback-based training, with the mental-based training showing clear positive effects on participants. The studies analyzing the effects of stress on surgical performance showed both negative and positive effects on technical and non-technical performance. CONCLUSIONS: The impact of stress responses presents an important factor in surgical environments, affecting residents' training and performance. This study identified the main methods used for monitoring stress parameters in surgical educational environments. The applied surgical stress management training methods were diverse and demonstrated positive effects on surgeons' stress levels and performance. There were negative and positive effects of stress on surgical performance, although a collective pattern on their effects was not clear.

Prototype of Instrumented Rock Bolt for Continuous Monitoring of Roof Fall Hazard in Deep Underground Mines.

Roof falls are currently one of the most dangerous threats associated with underground mining at great depth. Every occurrence of such an event poses a significant risk to the mining crew and disturbs the continuity of the mining process, which clearly affects the economy of the exploitation process. The development of a reliable monitoring system may significantly reduce the impact of eventual roof failure and will have a positive effect on the sustainability of the extraction process. Within this research study, a prototype of an instrumented rock bolt developed for continuous stress measurement is presented. The procedure of a 4-groove multilevel instrumented rock bolt is described and the calibration process is shown. Then, preliminary results of long-term in situ monitoring are presented. Based on the continuous monitoring of stress distribution within immediate roof strata, it was concluded that the developed instrumented rock bolt provides reliable results and is a very useful device, ensuring the possibility of early warning for miners about increasing roof fall risk.

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC).

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data.

Stress Monitoring of Concrete via Uniaxial Piezoelectric Sensor.

The uniaxial piezoelectric sensor was developed to overcome the problem of reflecting the output charge of the piezoelectric element as a combination of vectors in the three stress directions. The work performance of the uniaxial piezoelectric sensor under varying load patterns and load rates was investigated. The sensor was embedded in concrete to monitor stress, and its elastic modulus was used as the intermediate bridge to establish the correlation between the embedded sensor and the external sensor. Furthermore, a correction factor for the charge transformation strain was suggested to overcome the mismatching of the sensor's medium and the concrete. Considering related circumstances, a new stress monitoring method based on a uniaxial piezoelectric sensor was proposed, which can achieve stress whole-process monitoring in concrete and confining stress monitoring in the reinforced concrete column. The results reveal that through the proposed method, the output charge curve of the sensor has a substantial overlap with the stress waveform and high fitting linearity. The work performance of the sensor was stable, and its sensitivity was not affected by loading rate and load pattern. The sensor was embedded in concrete and can coordinate with the concrete deformation. The correction factor of strain obtained by the sensor embedded in concrete was 1.07. The relationship between the charge produced by the embedded sensor and its external calibration sensitivity may be used to implement the whole process of stress monitoring in concrete.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.

Impact of Tohoku-Oki 3.11 M9.0 Earthquake on the Fault Slip Potential of the Active Quaternary Faults in Beijing City: New Insights from In Situ Stress Monitoring Data.

In order to ascertain the impact of the Tohoku-Oki 3.11 M9.0 earthquake on the stability of the faults in the Beijing Plain, we investigated the adjustment of the in situ stress field of the Beijing Plain after this earthquake based on in situ stress monitoring data. Then, we analyzed the stability of the five main faults in each adjustment stage of the in situ stress field based on the Mohr−Coulomb failure criteria and Byerlee's law. Finally, we studied the fault slip potential (FSP) of the main faults under the current in situ stress field. The research results show that (1) after the Tohoku-Oki 3.11 M9.0 earthquake, the tectonic environment of the Beijing Plain area changed rapidly from nearly EW extrusion to nearly EW extension, and this state was maintained until June 2012. After this, it began to gradually adjust to the state present before the earthquake. As of September 2019, the tectonic environment has not recovered to the state present before the earthquake. (2) The ratios of shear stress to normal stress on the fault plane of the fault subsections in the three time periods before the Tohoku-Oki 3.11 M9.0 earthquake, 6 June 2012 and 8 September 2019 were 0.1−0.34, 0.28−0.52, and 0.06−0.29, respectively. It shows that the stress accumulation level of faults in the Beijing Plain area increased in a short time after the earthquake and then gradually decreased. (3) Under the current in situ stress field, most of the subsections of the five main faults have a low FSP (<5%). The areas with high FSP are mainly concentrated in the central and southeastern parts of the Beijing Plain, including the Nankou-Sunhe fault, the northern section of the Xiadian fault, and the areas where the five faults intersect.

High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image.

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Hair cortisol as a reliable indicator of stress physiology in the snowshoe hare: Influence of body region, sex, season, and predator-prey population dynamics.

Hair cortisol concentration is increasingly used as a convenient, non-invasive, and integrative measure of physiology and health in natural populations of mammals. However, the use of this index is potentially confounded by individual variation in body region-specific differences in cortisol deposition rates. Here we examine correlations in cortisol concentrations in hair collected from the ear, shoulder, and thigh of wild snowshoe hares, Lepus americanus, as well as the influence of sex on cortisol measurements. We further evaluated this technique's ability to capture seasonal and cyclical patterns of snowshoe hare glucocorticoid secretion from 2013 to 2015 in the southwestern Yukon (Canada). We found positive correlations (R2 = 0.20-0.32) in all pairwise comparisons among body regions, and differences among individuals accounted for the greatest proportion of variance (47.3%) in measurements. From 2013 to 2015 the hares' primary predator - Canada lynx - approximately doubled in population abundance. We found a significant increase in hare hair cortisol concentrations across this time period. Cortisol indices were higher in summer than winter pelage, reflecting predicted physiological responses to seasonal variation in food availability and individual risk. Variation in hair cortisol concentrations was more similar to long-term (weeks-months) integrative indices of adrenal capacity than point samples of fecal glucocorticoid metabolite concentrations. Overall, we find that hair cortisol analysis is a simple and useful tool for estimations of population-level stress physiology in wild mammals, and sampling of core body regions with consistent moulting patterns produced the most robust results in this species.

Hybrid Temperature and Stress Monitoring of Woven Fabric Thermoplastic Composite Using Fiber Bragg Grating Based Sensing Technique.

Process monitoring of woven fabric thermoplastic composite is crucial to enhance the quality of composite products. In this work, a new fiber Bragg grating based technique was proposed to achieve hybrid temperature and stress monitoring according to the changes of wavelength and reflectivity, respectively. The sensor head consisting of a pre-annealed fiber Bragg grating and a steel capillary was properly designed to overcome the challenge of high forming temperatures up to 332 °C, complex woven structure, and high forming pressure of 2 MPa, which hinder the use of the conventional fiber Bragg grating sensor during the forming process. The forming temperature changes of thermoplastic composite in the heating, dwelling, and cooling phases can be precisely measured by the proposed sensor head after using a curve-reconstruction algorithm based on cubic polynomial fitting. The measured difference from the reference thermocouple is 2.92 °C, averaged from three sets of repeated experiments. Meanwhile, the change of the residual stresses of the composite can be illustrated by using the micro-bending-caused optical power loss of the fiber pigtail commencing at the glass-transition temperature in the cooling phase. The decrease of grating reflectivity that was equivalent to the optical loss was discussed by comparing to strain change detected by strain gauges and a calculated theoretical curve. These results are beneficial for developing an advanced in situ monitoring technique and understanding the forming process of the woven fabric thermoplastic composite.

Quantitative evaluation of stress in Japanese anesthesiology residents based on heart rate variability and psychological testing.

Clinical anesthesiologists, particularly residents, work in stressful environments. However, evidence-based physiological and psychological tests to evaluate stress are still lacking. In this single-center study of 33 residents, we investigated the relationship between heart rate variability (HRV), which had the potential to screen residents' stress levels using Holter electrocardiography (ECG) and psychological mood as assessed by the Profile of Mood States (POMS) questionnaire. HRV analysis revealed 2 findings. Firstly, standard deviation of the average of 5-min normal-to-normal R-R intervals (SDANN) was significant lower than that of same-aged healthy volunteers (69.3 ± 27.9 vs. 137.0 ± 43.0 ms, P < 0.05), which indicated suppression of autonomic nervous system activity throughout their work. Secondly, at induction of anesthesia, significant higher low frequency/high frequency ratio (LF/HF ratio: 1.326 vs. 0.846; P < 0.05) and lower HF (3326 vs. 5967 ms2; P < 0.05) and lower standard deviation of normal-to-normal R-R intervals (SDNN: 50.5 vs. 79.4 ms; nervous system was suppressed at the induction of anesthesia: expected to be the most stressful period of their work. On the other hand, deviation scores of POMS questionnaire elucidated that all the residents were within normal range of psychological mood, and without any significant diurnal changes with respect to total mood disturbance deviation (TMD) scores (48 vs. 47; P = 0.368). HRV elucidated physiological stress among anesthesiology residents quantitatively by evaluating autonomic nervous activities, especially at induction of anesthesia. These changes in HRV could be observed regardless of psychological mood.

Technical note: Development of a noninvasive respiration rate sensor for cattle.

The measurement of the respiration rate (RR) in cattle is a valuable tool for monitoring health status. Thus, an RR sensor can be essential for stress detection, especially heat stress. Heat stress leads to a deviation of the normal RR and results in a decrease of milk production and fertility. Therefore, continuous monitoring of the RR can help early detection of heat stress and, thus, initiate timely counteractive actions to minimize physical stress. The most common method to measure the RR in cattle is to count the flank movement visually; however, this method is time-consuming and labor-intensive. In addition, the continuous measurement of the RR is difficult to implement and can be physically strenuous. Therefore, a device based on a differential pressure sensor that can record RR automatically has been developed to make continuous long-term studies possible. The aim of this study was to validate the data measured by the device with the help of a reference method. The reference method used was counting the flank movements of a total of 6 cows (Holstein-Friesian). The rear flank movements of each cow were recorded by a camera and counted independently of the device by an observer. Eight videos of 1 min each were recorded per cow. The data analysis was done with cows in 3 different body positions: dozing, lying, and standing. A total of 48 RR measurements of the device were compared with the counted RR frequencies of the video recording. The results were highly correlated during dozing [correlation coefficient (r) = 0.92, n = 13], lying (r = 0.98, n = 15), and standing (r = 0.99, n = 20). The evaluation showed that the device is suitable for automated RR counting. Further development of a marketable device is planned.

A Two-Dimensional Wireless and Passive Sensor for Stress Monitoring.

A new two-dimensional wireless and passive stress sensor using the inverse magnetostrictive effect is proposed, designed, analyzed, fabricated, and tested in this work. Three pieces of magnetostrictive material are bonded on the surface of a smart elastomer structure base to form the sensor. Using the external load, an amplitude change in the higher-order harmonic signal of the magnetic material is detected (as a result of the passive variation of the magnetic permeability wirelessly). The finite element method (FEM) is used to accomplish the design and analysis process. The strain-sensitive regions of the tension and torque are distributed at different locations, following the FEM analysis. After the fabrication of a sensor prototype, the mechanical output performance is measured. The effective measurement range is 0⁻40 N and 0⁻4 N·M under tension and torque, respectively. Finally, the error of the sensor after calibration and decoupling for Fx is 3.4% and for Tx is 4.2% under a compound test load (35 N and 3.5 N·M). The proposed sensor exhibits the merits of being passive and wireless, and has an ingenious structure. This passive and wireless sensor is useful for the long-term detection of mechanical loading within a moving object, and can even potentially be used for tracing dangerous overloads and for preventing implant failures by monitoring the deformation of implants in the human body.

Worker heat stress prevention and work metabolism estimation: comparing two assessment methods of the heart rate thermal component.

The heart rate thermal component ( ΔHRT ) can increase with body heat accumulation and lead to work metabolism (WM) overestimation. We used two methods (VOGT and KAMP) to assess ΔHRT of 35 forest workers throughout their work shifts, then compared ΔHRT at work and at rest using limits of agreement (LoA). Next, for a subsample of 20 forest workers, we produced corrected WM estimates from ΔHRT and compared them to measured WM. Although both methods produced significantly different ΔHRT time-related profiles, they yielded comparable average thermal cardiac reactivity (VOGT: 24.8 bpm °C-1; KAMP: 24.5 bpm °C-1), average ΔHRT (LoA: 0.7 ± 11.2 bpm) and average WM estimates (LoA: 0.2 ± 3.4 ml O2 kg-1min-1 for VOGT, and 0.0 ± 5.4 ml O2 kg-1min-1 for KAMP). Both methods are suitable to assess heat stress through ΔHRT and improve WM estimation. Practitioner summary: We compared two methods for assessing the heart rate thermal component ( ΔHRT ), which is needed to produce a corrected HR profile for estimating work metabolism (WM). Both methods yielded similar ΔHRT estimates that allowed accurate estimations of heat stress and WM at the group level, but they were imprecise at the individual level. Abbreviations: AIC: akaike information criterion; bpm: beats per minute; CI: confidence intervals; CV: coefficient of variation in %; CV drift: cardiovascular drift; ΔHRT: the heart rate thermal component in bpm; ΔHRT: the heart rate thermal component in bpm; ΔΔHRT: variation in the heart rate thermal component in bpm; ΔTC: variation in core body temperature in °C; HR: heart rate in bpm; HRmax: maximal heart rate in bpm; Icl: cloting insulation in clo; KAMP: Kampmann et al. (2001) method to determe ΔHRT; LoA: Limits of Agreement; PMV-PPD: the Predicted Mean Vote and Predicted Percentage Dissatisfied; PHS: Predicted Heat Strain model; RCM: random coefficients model; SD: standard deviation; TC: core body temperature in °C; TCR: thermal cardiac reactivity in bpm °C-1; τΔHRT: rate of change in the heart rate thermal component in bpm min-1; τTC: rate of change in core body temperature in °C min-1; tα,n-1: Student's t statistic with level of confidence alpha and n-1 degrees of freedom; TWL: Thermal Work Limit model; V̇O2 : oxygen consumption in ml O2 kg-1 min-1; V̇O2 max: maximal oxygen consumption in ml O2 kg-1 min-1; VOGT: Vogt et al. (1973) method to determine ΔHRT; WBGT: Wet-Bulb Globe Temperature in °C; WM: work metabolism.

Validation of Finite Element Model by Smart Aggregate-Based Stress Monitoring.

Concrete compressive strength is an important parameter of material properties for assessing seismic performance of reinforced concrete (RC) structures, which has a certain level of uncertainty due to its inherent variability. In this paper, the method of concrete strength validation of finite element model using smart aggregate (SA)-based stress monitoring is proposed. The FE model was established using Open System for Earthquake Engineering Simulation (OpenSEES) platform. The concrete strengths obtained from the material test, peak stress of SA, and estimated concrete strength based on SA stress were employed in FE models. The lateral displacement monitored by Liner variable differential transformer and vertical axial load monitored by load cell in the experiment are applied in the model. By comparing the global response (i.e., lateral reaction force and hysteretic loop), local response (i.e., concrete stress, rebar strain, and cross-section moment) and corresponding root-mean-square error obtained from experiment and numerical analysis, the capabilities of validation of FE model using SA-based stress monitoring method were demonstrated.