Stress Monitoring Research Articles

Evaluation of a transgenic tg(Hspa8b: GFP) Nile tilapia for monitoring of stress and anti-stress effects of the influence of temperature and vibration

Evaluation of a transgenic tg(Hspa8b: GFP) Nile tilapia for monitoring of stress and anti-stress effects of the influence of temperature and vibration

Multi-Objective Optimization for Controlling Conflicts in Roadway Surrounding Rock Induced by Floor Stress-Relief Groove

This paper studies the effectiveness of the stress-relief groove on the floor of deep coal roadway and determines the influence of the stress-relief groove parameters on the surrounding rock through qualitative analysis. Based on the displacement conflict problem, evaluation indicators were established, and the optimal solution set was obtained. The innovations of this research include: 1. For geotechnical numerical simulations, novel stress monitoring and plastic zone monitoring techniques have been introduced to accurately reflect the condition of the surrounding rock; 2. The effects of floor relief grooves in deep roadway on surrounding rock have been analyzed, and the advantages and utilities of central and corner relief grooves have been determined; 3. The usability of small datasets has been enhanced by applying SEGA to optimize machine learning models with data augmentation techniques; 4. Multi-objective optimization algorithms have been applied to geotechnical engineering, providing valuable references for decision making. The results demonstrate that multi-objective optimization can significantly enhance the effectiveness of surrounding rock control, resolve conflicts, and achieve more reasonable construction plans. This research provides new theoretical foundations and practical guidance for deep mine roadway-surrounding rock control.

Structural Health Monitoring and Failure Analysis of Large-Scale Hydro-Steel Structures, Based on Multi-Sensor Information Fusion

Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system’s capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure.

Self-Reporting Multiple Microscopic Stresses Through Tunable Microcapsule Arrays.

Self-reporting materials have emerged as a promising tool for real-time monitoring of stress and damage in structural materials. When critical stress is applied to these materials, an optical response is triggered - for example by dye release, or molecular cleavage. A key challenge is to extend these systems to respond to multiple different stress levels. To achieve this, a novel microcapsule-based assembly strategy is presented. Microfluidic synthesis is used to create microcapsules that release dye at a precise level of applied force. Subsequently, capillary assembly is used to combine microcapsules with different stress-responsiveness and different fluorescent dyes into chains, which are uniformly patterned into regular arrays, and embed these into the self-reporting materials. Through indentation experiments, it is shown that these materials can distinguish and record spatially resolved local stresses based on the fluorescence emitted upon microcapsule rupture. Crucially, the technique's accuracy is significantly improved when microcapsules are spatially organized within the material. This versatile technique can be applied to a range of different materials, via the use of thin coatings containing the regularly patterned microcapsule chains.

Development of flexible glucose biosensor for fish stress monitoring

Development of flexible glucose biosensor for fish stress monitoring

Determination of drought resistance of miniature rose varieties

ABSTRACT The researchers aimed to determine the effects of different irrigation levels on plant water consumption (ET), physiological, morphological and biomass traits of four different miniature rose varieties (Rosa Spp, cv. Mandarin, Orange Juwel, Zwergen Fee 09 and Zwergkönig 78). Four different irrigation levels (100% (control), 75%, 50%, 25%) were created in the experiment. The research was carried out at Çanakkale Onsekiz Mart University, Crop Stress Monitoring and Thermography Laboratory in 2020–2021. Water stress negatively affected the physiological and morphological traits measured in all four varieties. The six-month ET values of Mandarin, Orange Juwel, Zwergen Fee 09 and Zwerkönig 78 miniature rose varieties were 255.7–642.3 mm, 293.6–796.3 mm, 277.3–736.2 mm and 261.8–675.9 mm, respectively. Mandarin is more resistant to drought because its ET values are lower than other varieties.

LSTM‐based real‐time stress detection using PPG signals on raspberry Pi

AbstractStress, widely recognised for its profound adverse effects on both physical and mental health, necessitates the development of innovative real‐time detection methods. In this context, the escalating prevalence of wearable embedded systems, integrated with artificial intelligence (AI) for the continuous monitoring of critical physiological indicators like heart rate and blood pressure, accentuates their growing relevance in the efficient detection of stress. This article presents an innovative methodology employing deep learning algorithms on the Raspberry Pi 3, a platform distinguished by its cost‐effectiveness and limited resources. The authors have developed an advanced AI algorithm that achieves high accuracy in real‐time stress detection using photoplethysmography (PPG) sensors while significantly reducing computational demands. The authors’ method utilises an artificial neural network with long short‐term memory (LSTM) layers, proving highly effective in time‐series data analysis. In this study, the authors implement key TensorFlow toolkit optimisation techniques including quantisation aware training (QAT), Pruning and prune‐preserving quantisation aware training. These techniques are applied to refine the authors’ model, decreasing size and latency without sacrificing accuracy. The results highlight the LSTM‐based model's proficiency in accurately detecting stress using raw PPG sensor data on the Raspberry Pi 3, a comparatively affordable platform. The model attains an accuracy of 89.32% and an F1 score of 89.55% on the diverse wearable stress and affect detection stress‐level dataset. Additionally, the authors’ optimised model exhibits substantial reductions in both size and latency while maintaining high accuracy. This approach shows great potential for various applications, such as stress monitoring in healthcare, sports, and workplace settings. The use of the Raspberry Pi 3 makes the system portable, cost‐effective, and energy‐efficient, enhancing its potential impact and accessibility.

Research and application of hydraulic fracturing axial roof cutting technology for gently inclined hard roof based on abrasive jet

In order to explore a new method and mode of gently inclined hard roof treatment, the hydraulic fracturing axial roof cutting technology based on abrasive jet is introduced, and the key technical parameters of abrasive jet axial cutting and fracturing are determined based on indoor experiments and field industrial experiments. Taking the mining of working face under the condition of hard roof in Kuangou Coal Mine as the background, the implementation process and the effect of mining process are tested by means of water pressure gauge, drilling peep and observation well water level observation, mi-croseismic monitoring, support fracture monitoring and coal stress monitoring. The research results show that the key technical parameters of slotting and fracturing are mastered in the axial cutting test of abrasive jet. Wherein the kerf depth is 200 m, the kerf length is 300 m, the kerf pressure is 40–50 mpa, the fracturing pressure is 50–55 mpa, and the fracturing time is 20–30 min. After grooving fracturing, the cracks in the roof strata are effectively generated and expanded, which destroys the integrity of the roof, and the fracturing radius is 10–20 m. During the mining period, compared with the tradi-tional blasting technology, the concentrated area of microseismic events was shifted from 80 m in front of the working face to 130 m after the combined treatment of abrasive jet axial roof cutting and blasting, and the microseismic energy release was mainly small energy events. After the application of abrasive jet axial roof cutting and scour prevention technology in hard roof, the periodic weighting step is obviously reduced, the influence range of mining stress and stress concentration coefficient are obviously reduced, the activity intensity and dynamic load effect of surrounding rock are obviously weakened. The research results provide a basis for effective prevention and control of rockburst disasters under the condition of hard roof.

Study on the Dynamic Evolution of Mining-Induced Stress and Displacement in the Floor Coal-Rock Induced by Protective Layer Mining

Protective layer mining is the most effective means to prevent and control coal and gas outbursts. In order to deeply understand the dynamic evolution law of mining stress and displacement of the bottom plate coal rock body in the process of protective layer mining, the effects of upper protective layer mining on stress variation and displacement deformation in the underlying coal seam were studied using the similar experiment and FLAC3D simulations. The results reveal that mining in the 82# coal seam notably alleviates pressure in the 9# coal seam below, with an average relief rate of 86.2%, demonstrated by the maximal strike expansion deformation rate of 11.3‰ in the 9# coal seam post-mining. Stress monitoring data indicates a stress concentration zone within 32 m ahead of the working face, and a pressure relief zone within 51 m behind it. The research provides a scientific foundation for pressure-relief gas extraction techniques, affirming the substantial impact of upper protective layer mining on alleviating pressure in underlying coal seams, enhancing safety, and optimizing mining efficiency.

Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches

Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants (Carthamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop.

SH guided wave excitation in rails for defect and stress monitoring

Monitoring rail defects and thermal stress is imperative in ensuring the safety of railway transportation. Guided-wave-based inspection techniques emerge as a promising solution for concurrently detecting both the defects and thermal stress in rails. However, the substantial multimodal and dispersive nature of guided waves in the rail poses limitations on the application and advancement of this technology. This study systematically investigates the propagation characteristics of the SH0-like wave (the lowest-order shear horizontal-like wave) in the rail and its effectiveness in defect and axial stress monitoring through finite element simulations and experiments. A method based on dual opposite polarization active elements, and the corresponding piezoelectric transducers are proposed to excite SH0-like wave in the rail web and rail foot. Results show that SH0-like wave can travel stably across a broad frequency range in the rail web and rail foot. Additionally, both the simulations and experiments confirm that the SH0-like wave in the rail web is suitable for monitoring axial stress, as its velocity changes linearly with axial stress variations. Furthermore, the ability of the SH0-like wave to detect cracks and hole defects in the rail web is experimentally demonstrated. Given that the SH0-like wave is quasi-nondispersive in a broad frequency range and an SH0-like wave with high SRN can be effectively generated using the proposed method, this research aims to offer a practical technical solution to overcome the challenges of multimodal and dispersive effects in the guided-wave-based rail inspection techniques. It also sets the stage for developing a comprehensive system capable of simultaneously monitoring the defects and thermal stress in rails.

Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate.

Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R2 scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations.

Retraction Note: An efficient deep learning based stress monitoring model through wearable devices for health care applications

Retraction Note: An efficient deep learning based stress monitoring model through wearable devices for health care applications

Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.

Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.

A New Era in Stress Monitoring: A Review of Embedded Devices and Tools for Detecting Stress in the Workplace

Detection of stress and the development of innovative platforms for stress monitoring have attracted significant attention in recent years due to the growing awareness of the harmful effects of stress on mental and physical health. Stress is a widespread issue affecting individuals and often goes unnoticed as a health concern. It can lead to various negative physiological conditions, including anxiety, depression, cardiovascular diseases and cognitive impairments. The aim of this paper is to provide an overview of studies focusing on embedded devices for non-invasive stress detection, primarily in the form of a modified computer mouse or keyboard. This study not only fills a critical gap in the literature but also provides valuable insights into the design and implementation of hardware-based stress-detection methods. By focusing on embedded devices, specifically computer peripherals, this research highlights the potential for integrating stress monitoring into everyday workplace tools, thereby offering practical solutions for improving occupational health and well-being.

Simultaneous In Vivo Imaging of Neutrophil Elastase and Oxidative Stress in Atherosclerotic Plaques Using a Unimolecular Photoacoustic Probe.

Atherosclerosis, a major global health concern with high morbidity and mortality rates, involves complex interactions of chronic inflammation, oxidative stress, and proteolytic enzymes. Conventional imaging methods struggle to capture the dynamic biochemical processes in atherosclerotic plaques. Here, we introduce a novel unimolecular photoacoustic probe (UMAPP) designed with specific binding sites for neutrophil elastase (NE) and the redox pair O2⋅-/GSH, enabling real-time monitoring of oxidative stress and activated neutrophils in plaques. UMAPP, comprising a boron-dipyrromethene (BODIPY) core linked to a hydrophilic NE-cleavable tetrapeptide and dual oxidative stress-responsive catechol moieties, facilitates NE-mediated modulation of photoinduced electron transfer impacting photoacoustic intensity at 685 nm (PA685). Furthermore, oxidation and reduction of catechol groups by O2⋅- and GSH induce reversible, ratiometric changes in the photoacoustic spectrum (PA745/PA685 ratio). Initial UMAPP applications successfully distinguished atherosclerotic and healthy mice, evaluated pneumonia's effect on plaque composition and verified the probe's effectiveness in drug-treatment studies by detecting molecular alterations before visible histopathological changes. The integrated molecular imaging capabilities of UMAPP offer promising advancements in atherosclerosis diagnosis and management, enabling early and accurate identification of vulnerable plaques.

Simultaneous In Vivo Imaging of Neutrophil Elastase and Oxidative Stress in Atherosclerotic Plaques Using a Unimolecular Photoacoustic Probe

AbstractAtherosclerosis, a major global health concern with high morbidity and mortality rates, involves complex interactions of chronic inflammation, oxidative stress, and proteolytic enzymes. Conventional imaging methods struggle to capture the dynamic biochemical processes in atherosclerotic plaques. Here, we introduce a novel unimolecular photoacoustic probe (UMAPP) designed with specific binding sites for neutrophil elastase (NE) and the redox pair O2⋅−/GSH, enabling real‐time monitoring of oxidative stress and activated neutrophils in plaques. UMAPP, comprising a boron‐dipyrromethene (BODIPY) core linked to a hydrophilic NE‐cleavable tetrapeptide and dual oxidative stress‐responsive catechol moieties, facilitates NE‐mediated modulation of photoinduced electron transfer impacting photoacoustic intensity at 685 nm (PA685). Furthermore, oxidation and reduction of catechol groups by O2⋅− and GSH induce reversible, ratiometric changes in the photoacoustic spectrum (PA745/PA685 ratio). Initial UMAPP applications successfully distinguished atherosclerotic and healthy mice, evaluated pneumonia‘s effect on plaque composition and verified the probe‘s effectiveness in drug‐treatment studies by detecting molecular alterations before visible histopathological changes. The integrated molecular imaging capabilities of UMAPP offer promising advancements in atherosclerosis diagnosis and management, enabling early and accurate identification of vulnerable plaques.

Impact of Work Climate, Workload, and Stress on Fatigue for Improving Health and Work Outcomes

Background: Fatigue is a prevalent health issue affecting physical and mental well-being, particularly in industrial workers. Prolonged fatigue can reduce cognitive function, increase susceptibility to illness, and lower work productivity. Factors such as poor work climate, excessive workload, and stress exacerbate fatigue and negatively impact productivity. Methods: This observational cross-sectional study was conducted at PT. Makassar Tene, a sugar refinery in Makassar, Indonesia, with 118 employees. Data on work climate, workload, stress, fatigue, and productivity were collected using a Heat Stress Monitor, reaction timers, and structured interviews. Path analysis was employed to examine the direct and indirect effects of these variables on productivity. Results: The study revealed a significant direct effect of work climate on fatigue (p < 0.05) and productivity (p = 0.008). Similarly, workload directly influenced fatigue (p = 0.001) and productivity (p = 0.044). Stress was found to impact productivity indirectly through fatigue (p = 0.018). Fatigue emerged as a critical mediator, significantly reducing productivity. Conclusion: Poor work climate and excessive workload contribute to fatigue, which in turn diminishes productivity. Addressing environmental and workload factors is essential to improving employee well-being and optimizing productivity in industrial settings. Implementing effective interventions to manage work stress and fatigue is crucial for enhancing both health and work outcomes.

Effects of recirculation dredging on density, strength, settling and oxygen concentration of fluid mud in the port of Emden

Abstract Purpose Recirculation dredging is a port maintenance concept developed in the Port of Emden, Germany to create a navigable fluid mud layer. This study investigates the effects of recirculation on key sediment properties, including density, yield stress, and oxygen concentration. Methods Six field monitoring surveys were carried out at two locations at different times of the year to assess changes before and after recirculation. Bathymetry, bulk density, yield stress, and oxygen concentration profiles were measured in situ. The settling properties and oxygen concentration levels on collected fluid mud samples were analyzed in the laboratory. Results The investigation reveals minimal changes in the density of recirculated fluid mud. However, the post-recirculation measurements showed a decrease in yield stress, ranging from 18 to 51% at Große Seeschleuse (GS) and 36% to 52% at Industriehafen (IH). The yield stress and density vary depending on the frequency of dredging. After structural density (1166 kg m−3 in GS and 1173 kg m−3 in IH), the yield stress of fluid mud increased exponentially. Therefore, monitoring of the yield stress is important for recirculation. A slight increase in oxygen concentration was observed post-recirculation, especially during winter. Yet, the rapid decline in oxygen levels post-mixing in the laboratory showed that sustaining long-term elevated oxygenation levels is not feasible by recirculation dredging alone. Conclusions The findings highlight the effectiveness of the recirculation on the yield stress, density, and oxygen concentration of fluid mud and illustrate the importance of considering both density and yield stress in sediment management practices. Future research should address the temporal evolution of density, yield stress, and oxygen levels following a dredging intervention and the influence of extracellular polymeric substances (EPS) and organic matter decay on sediment behavior.

Based on Flac3D Research and Analysis of the Mining Pressure Development Law in the Top Coal Section of the Roadway Support of the Working Face Transport

Top coal supporting roadway is widely used in the transportation roadway of thick coal seam face, which can not only increase the coal extraction rate but also reduce the driving cost. Based on the engineering geological conditions of the transportation lane of the working face of Zhongma Cun mine, the law of mine pressure development in the roadway of the top coal section is studied and analyzed based on Flac3D and field monitoring methods. The numerical simulation analysis shows that when the working face is pushed to 55m for the first time, the displacement of the area affected by the leading abutment pressure and the surrounding rock on the mining side of the roadway reaches the peak value, and the step distance of the first time is 55m. According to the analysis of stress monitoring data of single pillar and roof anchor cable on site, the first pressure occurs when the working face advances to 51m, which is consistent with the numerical simulation conclusion.