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The effect of heat acclimation on critical environmental limits and rate of rectal temperature change

Journal of applied Physiology 2025年第5期138卷 1150-1160页

作者： Bogaard, Timo van den Klous, Lisa Cottle, Rachel M. Erp, Jan Van Daanen, Hein A. M. Department of Human Movement Sciences Faculty of Behavioural and Movement Sciences Amsterdam Movement Sciences Vrije Universiteit van Amsterdam Amsterdam Netherlands Netherlands Organization for Applied Scientific Research (TNO) Department of Human Performance Unit Defence Safety and Security Soesterberg Netherlands Department of Kinesiology Pennsylvania State University Pennsylvania United States Department Human Media Interaction Faculty Electrical Engineering University of Twente Mathematics and Computer Science Enschede Netherlands

Quantifying the effect of heat acclimation (HA) on critical wet-bulb globe temperature (WBGTcrit) and rate of rectal temperature change (vTre) is relevant for developing guidelines with regards to occupational safety while working in warm environments. This study quantified the effect of HA and the period following cessation of the HA protocol on WBGTcrit and vTre. Twenty-eight non-acclimatized participants were divided into a HA (n = 15) and control (CON;n = 13) group. The HA group underwent a warm-humid (35_C, 65% relative humidity) controlled hyperthermia HA protocol (5-9 days of achieving Tre _38.5_C for 60 min) and four progressive heat stress tests (HSTs) to identify WBGTcrit and examine vTre: pre-, after 5 and 9 days of HA, and 4 to 8 days of no heat exposure following HA. CON performed two HSTs on average 13 days apart without heat exposure in between. HA increased WBGTcrit after nine (28.5 ±2.7_C vs. 30.5 ± 2.0_C;P = 0.016) but not 5 days (28.5 ± 2.4;P > 0.05). No effect of HA on vTre was observed (P > 0.05). Four-to-eight days post-HA, WBGTcrit and vTre did not differ compared with 9 days of HA (P > 0.05). However, a reduction in vTre (_0.4 ± 0.3 _C/h) was observed when comparing 4 to 8 days post-HA to pre-HA. In conclusion, our results demonstrate that more than 5 days of HA are required to increase WBGTcrit and indicate that 9 days of HA proceeded by adequate recovery reduced vTre during exercise in the heat. Copyright © 2025 The Authors.

关键词： compensable heat stress critical environmental limits heat acclimation rate of rectal temperature increase wet-bulb globe temperature

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External-Wrench Estimation for Aerial Robots Exploiting a Learned Model

External-Wrench Estimation for Aerial Robots Exploiting a Le...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ayham Alharbat Gabriele Ruscelli Roberto Diversi Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a firstprinciples model and a neural network. This framework addresses one of the limitations of the state-of-the-art modelbased wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind); in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a firstprinciples model.

关键词： Uncertain systems Analytical models Statistical analysis Atmospheric modeling Dynamics Neural networks Observers Autonomous aerial vehicles Numerical simulation Numerical models

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External-Wrench Estimation for Aerial Robots Exploiting a Learned Model

arXiv

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arXiv 2025年

作者： Alharbat, Ayham Ruscelli, Gabriele Diversi, Roberto Mersha, Abeje Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a first-principles model and a neural network. This framework addresses one of the limitations of the state-of-the-art model-based wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind);in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a first-principles model. © 2025, CC BY-NC-ND.

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A Gaussian Reputation-based Hybrid BFT Consensus with a Formal Security Framework

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Ningbin Tang, Chunming Deng, Zhihong He, Debiao Guangzhou University School of Mathematics and Information Science Guangzhou510006 China Guangzhou University Guangzhou Center for Applied Mathematics Guangzhou510006 China Guangzhou University Key Laboratory of Information Security Technology Jointly by Guangdong Province and Guangzhou City Guangzhou510006 China Wuhan University School of Cyber Science and Engineering Wuhan China Shandong Computer Science Center Jinan250014 China

Blockchain systems have evolved over decades, addressing the inefficiencies and high costs associated with centralized architectures. Among various consensus mechanisms, committee-based hybrid Byzantine Fault Tolerant (BFT) protocols are a fundamental approach to blockchain consensus. However, designing a hybrid BFT consensus protocol that ensures fairness, responsiveness, and formal security remains challenging. In this paper, we propose GRBFT: a Gaussian reputation-based hybrid BFT blockchain consensus protocol with a formal security framework. Our proposed protocol integrates a multilateral Gaussian reputation evaluation to incentivize trusted nodes' participation in the consensus. We use threshold signatures and verifiable random functions (VRFs) to randomly select committee members and leaders, ensuring fair reconfiguration and unbiased sortition. A formal security framework is utilized to design and analyze the blockchain consensus system. Additionally, we design a speculative GRBFT (S-GRBFT) protocol to circumvent the traditional O(n2) leader sortition complexity and reduce the communication to O(n) within a single round. Moreover, we present a secure candidate committee reconfiguration method that efficiently updates members based on their reputation and a Proof-of-Stake (PoS) mechanism. The proposed GRBFT protocol is proven to achieve consistency and liveness under the corruption and liveness parameters. © 2004-2012 IEEE.

关键词： Gaussian distribution

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data

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Procedia computer science 2025年 256卷 1358-1366页

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems.

关键词： Activities of daily living low-resolution infrared sensors deep residual networks lightweight architecture healthcare monitoring

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors

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Procedia computer science 2025年 256卷 1350-1357页

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. This research presents a novel deep multi-task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-art methods. This demonstrates the effectiveness of the multi-task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks.

关键词： human activity recognition user identification multi-task learning deep learning smartphone sensors

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Fundamental Limit of Angular Resolution in Partly Calibrated Arrays with Position Errors

arXiv

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arXiv 2025年

作者： Zhang, Guangbin Wang, Yan Huang, Tianyao Eldar, Yonina C. Yangtze Delta Region Academy Beijing Institute of Technology Zhejiang Jiaxing China Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel

We consider high angular resolution detection using distributed mobile platforms implemented with so-called partly calibrated arrays, where position errors between subarrays exist and the counterparts within each subarray are ideally calibrated. Since position errors between antenna arrays affect the coherent processing of measurements from these arrays, it is commonly believed that its angular resolution is influenced. A key question is whether and how much the angular resolution of partly calibrated arrays is affected by the position errors, in comparison with ideally calibrated arrays. To address this fundamental problem, we theoretically illustrate that partly calibrated arrays approximately achieve high angular resolution. Our analysis uses a special characteristic of Cramér-Rao lower bound (CRB) w.r.t. the source separation: When the source separation increases, the CRB first declines rapidly, then plateaus out, and the turning point is close to the angular resolution limit. This means that the turning point of CRB can be used to indicate angular resolution. We then theoretically analyze the declining and plateau phases of CRB, and explain that the turning point of CRB in partly calibrated arrays is close to the angular resolution limit of distributed arrays without errors, demonstrating high resolution ability. This work thus provides a theoretical guarantee for the high-resolution performance of distributed antenna arrays in mobile platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Source separation

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Forecasting day-ahead electric power prices with functional data analysis

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Frontiers in Energy Research 2025年 13卷

作者： Jan, Faheem Iftikhar, Hasnain Tahir, Mehwish Khan, Mehak Department of Mathematics and Statistics Bacha Khan University Charsadda Pakistan Department of Statistics Quaid-i-Azam University Islamabad Pakistan Department of Mathematics Shaheed Benazir Bhutto Women University Peshawar Pakistan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Day-ahead electricity prices in today’s competitive electric power markets have complex features such as high frequency, high volatility, non-linearity, non-stationarity, mean reversion, multiple periodicities, and calendar effects. These complicated features make price forecasting difficult. To address this, this research examines the application of functional data analysis to forecasting day-ahead electric power prices. Compared to classical time series forecasting approaches, functional data analysis is more appealing since it anticipates the daily profile, allowing for short-term projections. This technique uses a functional autoregressive ((Formula presented.) AR) and a functional autoregressive with exogenous predictors ((Formula presented.) AR (Formula presented.)) model to predict the next-day electric power prices. In addition, standard time-series forecasting models, including autoregressive (AR) AR (Formula presented.), autoregressive integrated moving average (ARIMA), and ARIMA (Formula presented.) are also utilized for comparison. The model’s prediction performance was evaluated using data on electricity prices from the British electricity market, considering forecast error indicators and the same forecast statistical test. The results show that the proposed functional models ((Formula presented.) AR and (Formula presented.) AR (Formula presented.)) outperform standard time series models. In comparison to the benchmark models (AR, AR (Formula presented.), ARIMA, ARIMA (Formula presented.), and the proposed (Formula presented.) AR model), the (Formula presented.) AR (Formula presented.) model reduces: the day-ahead forecasting average MAPE by ranges of 5.02%–45.77%, 4.07%–40.63%, 3.80%–38.99%, 1.90%–24.22%, and 0.95%–13.78%;MAE by ranges of 9.43%–69.32%, 5.17%–65.48%, 6.04%–59.16%, 3.02%–42.01%, and 1.51%–26.59%;RMSE by ranges of 8.98%–40.97%, 6.68%–34.03%, 4.22%–24.58%, 3.91%–23.20%, and 2.30%–15.11%. Furthermore, compared with the literature-proposed b

关键词： classical time series models day-ahead electricity price forecasting electric power market functional data analysis functional time series models

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Evaluating Gaussianity of heterogeneous fractional Brownian motion

arXiv

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arXiv 2025年

作者： Balcerek, Michal Pacheco-Pozo, Adrian Wylomańska, Agnieszka Krapf, Diego Department of Electrical and Computer Engineering School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw50-370 Poland

Heterogeneous diffusion processes are prevalent in various fields, including the motion of proteins in living cells, the migratory movement of birds and mammals, and finance. These processes are often characterized by time-varying dynamics, where interactions with the environment evolve, and the system undergoes fluctuations in diffusivity. Moreover, in many complex systems anomalous diffusion is observed, where the mean square displacement (MSD) exhibits non-linear scaling with time. Among the models used to describe this phenomenon, fractional Brownian motion (FBM) is a widely applied stochastic process, particularly for systems exhibiting long-range temporal correlations. Although FBM is characterized by Gaussian increments, heterogeneous processes with FBM-like characteristics may deviate from Gaussianity. In this article, we study the non-Gaussian behavior of switching fractional Brownian motion (SFBM), a model in which the diffusivity of the FBM process varies while temporal correlations are maintained. To characterize non-Gaussianity, we evaluate the kurtosis, a common tool used to quantify deviations from the normal distribution. We derive exact expressions for the kurtosis of the considered heterogeneous anomalous diffusion process and investigate how it can identify non-Gaussian behavior. We also compare the kurtosis results with those obtained using the Hellinger distance, a classical measure of divergence between probability density functions. Through both analytical and numerical methods, we demonstrate the potential of kurtosis as a metric for detecting non-Gaussianity in heterogeneous anomalous diffusion processes. © 2025, CC BY.

关键词： Brownian movement

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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