The reliable detection of cortisol in sweat presents a promising, noninvasive approach for stress monitoring. However, in demanding scenarios such as firefighting and mining, rapid sweat evaporation concentrates salts to high levels that destabilize hydration layers and compromise electrochemical biosensors based on conventional zwitterionic peptides. In this work, we developed a salt-tolerant antifouling layer using the polypeptide CPPPP(HE)6, which features alternating histidine and glutamic acid residues, and integrated it with cortisol aptamers to construct a wearable electrochemical biosensor. Compared with conventional zwitterionic peptides of CPPPP(EK)6, the CPPPP(HE)6 peptide was designed by replacing the charge-localized ammonium cation (e.g., lysine-derived NH3+) with a charge-delocalized imidazolium cation from histidine. Combined computational and experimental studies reveal that both systems undergo electron redistribution with Cl- (Cl- is the most abundant anion in sweat). In contrast to the single-site polarization in NH3+, imidazolium+ features multicenter rearrangement coupled with proton migration, which reinforces the hydrogen-bonding network and fosters an exceptionally stable hydration layer under high salinity. As a result, the CPPPP(HE)6 coating exhibits superior fouling resistance compared to CPPPP(EK)6. The resulting antifouling electrochemical biosensor, functionalized with CPPPP(HE)6 and cortisol aptamers, enabled accurate and reliable detection of cortisol in human sweat and high-salinity conditions, with results validated against the standard ELISA method. This work provides a molecular-level design strategy for creating robust antifouling materials, advancing the development of high-fidelity sensors for complex biofluids.

Seamless monitoring of stress levels leveraging a foundational model for time sequences.

From resistance to detection: The role of nanomaterials in plant stress responses.

Copper contamination is a major challenge in brackishwater aquaculture, yet the molecular timeline of sublethal metal stress in penaeid prawns remains poorly understood. To address this, we applied time-resolved differential proteomics and in silico analyses to identify copper-responsive proteins in post-larval Penaeus indicus. Larvae were exposed to 0.1641mgL-3 copper (1/5 LC₅₀) for 24h, 48h, 96h, 10 days, 20 days, and 30 days. Two-dimensional gel electrophoresis and MALDI-TOF/TOF revealed thirteen proteins across forty-one differential spots. Physicochemical and secondary structure characteristics were predicted using ExPASy ProtParam and the Self-Optimized Prediction Method with Alignment (SOPMA). Expression dynamics indicated three temporal phases: (i) early oxidative stress regulators (24-48h), including thioredoxin and nuclear autoantigenic sperm protein, which were small, hydrophilic (GRAVY≈-0.28), and coil-rich; (ii) mid-phase proteins such as ferritin (48-96h), with a high α-helical content (61%) and moderate instability index (37.3), supporting metal sequestration and antioxidant buffering; and (iii) late-phase proteins (20-30 days), including variant transformer-2 and ubiquitin-activating enzyme, associated with proteostasis and cellular remodeling. Together, these findings suggest a three-tier biomarker model: thioredoxin (early), ferritin (mid), and variant transformer-2 (late). This timeline-based framework may improve the precision of copper stress monitoring and supports future investigations integrating proteomic data with transcriptomic and functional validation in crustaceans.

This study demonstrates the remote sensing detection and monitoring of vegetation stress, enabling the assessment of potential metal-induced contamination across a spatiotemporal range. Specifically, the vegetation surrounding coal ash impoundments and landfills at the Belews Creek Steam Station, a coal-fired power plant in North Carolina, U.S., was monitored using multispectral imagery from the Sentinel-2 satellite between 2019 and 2023 and correlations were investigated with the groundwater monitoring well data from 2011-2019. The effectiveness of six vegetation indices (VIs) and three biophysical parameter indices (BPIs) derived from Sentinel-2 imagery were investigated in detecting vegetation stress and their correlation with leached metal concentrations near coal ash impoundments. Among BPIs, Leaf Area Index (LAI) exhibited the strongest correlation with VIs, while Canopy Chlorophyll Content (CCC) detected the highest stressed vegetation. The Chlorophyll Index Red Edge (CIRE) demonstrated the highest sensitivity to BPIs and detected the highest stressed vegetation among VIs. When stressed vegetation maps were further compared with metal concentrations, Leaf Chlorophyll Content (LCC) and the Normalized Difference Vegetation Index (NDVI) showed the strongest correlations among BPIs and VIs, respectively. Moderate positive correlations were observed for several metals, including arsenic, barium, cadmium, cobalt, lithium, radium and thallium, suggesting their contribution to vegetation stress, while molybdenum exhibited moderate negative correlation indicating its potential role in reducing stress. Additionally, higher vegetation stress levels were detected around the unlined active ash basin, suggesting increased metal leaching from this impoundment which is contributing to the observed stress on the surrounding vegetation. The proposed methodology and tools in this study can contribute to the growing body of knowledge on satellite-based remote sensing for vegetation stress detection, with potential applications in environmental monitoring and management.

Agentic AI System for Stress Monitoring: A Multi-agent Healthcare Crew

Introduction and purpose: Overtraining syndrome (OTS) might occur in athletes experiencing extreme physical and mental stress over a longer period of time without adequate recovery (Meeusen et al., 2013). A decrease in sports performance and chronic fatigue are the most frequent symptoms (Carrard et al., 2021; Meeusen et al., 2013). Reliable diagnostic and monitoring tools are lacking but are strongly needed due to the high prevalence of OTS of 5 to 64 % (depending on definition and sample) and its potential reducing risk of injury (Meeusen et al., 2013). We aimed to develop novel sex-specific, non-invasive and multiparametric recovery monitoring models. Methods: Seventy-three youth and young adult elite athletes (51 females, age 19.7 ± 4.0 years) from mainly team and speed/power-oriented sports, e.g., handball and athletics, participated. Weekly measurements were conducted over 16 weeks to assess the athletes’ recovery state, resulting in 663 measurement timepoints. Forty parameters – including sleep, training load, occupational load, social load, menstrual cycle, heart rate and heart rate variability (HRV), core body temperature, grip strength, and single and double leg jump performance – served as predictors of the athletes’ subjective rating of recovery and stress (Short Recovery and Stress Scale, SRSS, Kellmann & Kölling, 2020). Lasso, Ridge, and Elastic Net regularized regression was applied for automated parameter selection, training, and cross-validation of the binomial prediction models. Results: For the female athletes’ model AUC = 0.819 was calculated (sensitivity = 79.8%, specificity = 72.9%). Thereby, the parameters social load, single and double leg jump performance, sleep quality, training load, grip strength, and occupational load were ranked within the top ten highest predictive parameters (Figure 1). The male athletes’ model demonstrated similar predictive performance with AUC = 0.797 (sensitivity = 74.3%, specificity = 71.4%). Thereby, grip strength, HRV, single leg jump performance, and social load were among the top ten most predictive parameters. Discussion: A broad and novel combination of non-invasive parameters was analysed to capture a holistic picture of the athletes’ recovery and stress state. The resulting sex-specific models showed good predictive performance. The development of sex-specific recovery monitoring prediction models seemed crucial due to the observed differences in parameter importance. Conclusion: This study provides a deeper understanding of the relevance of specific parameters for recovery and stress monitoring in female and male youth and young adult elite athletes.

High-risk working conditions in construction, such as working at height, may elicit elevated mental stress in workers and pose significant safety challenges. This study aims to physiologically assess construction workers' mental stress under high-risk working conditions using heart rate variability (HRV) features derived from electrocardiograph (ECG) signals. An experimental study in the field was conducted, where inexperienced scaffolding workers' (n = 20) ECG signals were collected when working at three different heights corresponding to low, medium, and high levels of mental stress. Supervised machine learning algorithms, including Support Vector Machine (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF), were applied for model development. The results show that the HRV features obtained good prediction accuracy. The classification accuracy is up to 85.00% between low and medium stress levels, 92.50% for differentiating low and high stress levels, and 87.50% for classifying medium and high stress levels. These findings demonstrate the potential of ECG-derived HRV features for differentiating the mental stress responses of construction workers under high-risk working conditions and provide empirical evidence supporting the feasibility of physiological monitoring of workers' mental stress in real construction environments.

Abstract The special-shaped single-tower cable-stayed bridge (STCSB) combines the merits of a conventional STCSB with its own innovative design. During the construction phase, the load will change with the change of construction conditions, and the structural response caused by different construction methods is also different. Precise monitoring throughout the construction process is essential for acquiring verifiable structural response data, which is vital for ensuring the structural safety. However, during the monitoring process, the absolute strain of the structure is not accurate enough. Therefore, a real-time comparison method of response increment based on stress was adopted to solve the above problems. Based on an actual spatial special-shaped STCSB in China, four construction methods were proposed and simulated, and the optimal construction method was analysed. A novel monitoring method of the combination of stress and cable frequency based on response increment comparison was proposed for the construction monitoring of the special-shaped STCSB. The feasibility of using the response increment method for construction monitoring was validated through stress monitoring and numerical simulation comparisons. Dynamic stiffness theory and optimized Wittrick-Williams (W-W) algorithm were used to compute the first-order natural frequencies of cable under various conditions. By comparing the measured frequency variations with the theoretical values, the validity of the response increment method for construction monitoring was further verified. Finally, the efficiency of construction monitoring and the safety of the structure were guaranteed.

High spatiotemporal resolution monitoring of crop water stress across the contiguous United States using Harmonized Landsat and Sentinel-2 data

In situ stress monitoring and calibration of fiber Bragg Gratings embedded inside aluminum samples at high temperatures

New steel coupons hosting different residual stress amplitudes are proposed in this paper, that are able to calibrate magnetic sensors for residual stress monitoring. The new stress coupons can be developed by local Joule heating of the steel grade up to 400 °C at one end of it, monitoring the temperature elevation at the other end. As soon as the temperature at the other end increases 1 ° to 10 °, a consequent quenching in cold or iced water, oil and liquid nitrogen, thus transforming the temperature gradient to stress gradient. The 400 ° temperature limit was experimentally selected to avoid any type of phase transformation and geometrical deformation of the steel grade sample. This method of developing stress coupons is much better than the current method based on welded samples, allowing for low uncertainty in inter-laboratory tests, that permitting the preparation of a corresponding standard.

Real-time estimation of core body temperature for heat stress monitoring in hot environments using wearable heart rate sensors

Towards lightweight stress monitoring on biometric data for IoMT environments.

Stress is a universal experience impacting mental and physical health. However, no precise, objective wearable tool exists for continuous, long-term stress monitoring, which is essential for understanding stress-related health outcomes. To address this gap, we introduce SQC-SAS, a multimodal wearable device that simultaneously and continuously measures multiple physiological and molecularstress biomarkers for quantitative stress assessment and sub-classification. This device features exceptional environmental stability, reusability, and fully wireless data and power operation. Machine learning enables data-driven stress assessment and classification across multiple stress states, allowing biomarker profiles to be correlated with each state. Its wristband-like design enables continuous stress monitoring and real-time visualization. We envision our wearable will greatly advance precise, objective stress assessment and monitoring, offering unprecedented capabilities and laying the foundation for personalized interventions and a deeper understanding of stress-related outcomes.

HighlightsWhat are the main findings?A novel in situ rock stress monitoring device was developed through structural optimization and material selection for deep and complex stress environments.Laboratory tests demonstrate a high consistency between monitored stress and applied stress, with peak error controlled within 5% and a fitting coefficient of R2 > 0.98.What are the implications of the main findings?The proposed device provides a reliable method for real-time and accurate in situ stress monitoring in deep underground engineering.The findings offer technical support for stress evaluation and disaster prevention in deep mining and geotechnical engineering applications.Under deep mining conditions, coal and rock masses are subjected to high in situ stress and strong mining-induced disturbances, leading to intensified stress unloading, concentration, and redistribution processes. The stability of surrounding rock is therefore closely related to mine safety. Direct, real-time, and continuous monitoring of in situ stress magnitude, orientation, and evolution is a critical requirement for deep underground engineering. To overcome the limitations of conventional stress monitoring methods under high-stress and strong-disturbance conditions, a novel in situ stress monitoring device was developed, and its performance was systematically verified through laboratory experiments. Typical unloading–reloading and biaxial unequal stress paths of deep surrounding rock were adopted. Tests were conducted on intact specimens and specimens with initial damage levels of 30%, 50%, and 70% to evaluate monitoring performance under different degradation conditions. The results show that the device can stably acquire strain signals throughout the entire loading–unloading process. The inverted monitoring stress exhibits high consistency with the loading system in terms of evolution trends and peak stress positions, with peak stress errors below 5% and correlation coefficients (R2) exceeding 0.95. Although more serious initial damage increases high-frequency fluctuations in the monitoring curves, the overall evolution pattern and unloading response remain stable. Combined acoustic emission results further confirm the reliability of the monitoring outcomes. These findings demonstrate that the proposed device enables accurate and dynamic in situ stress monitoring under deep mining conditions, providing a practical technical approach for surrounding rock stability analysis and disaster prevention.

Purpose This paper explores how Agile methods, combined with human factors engineering and adaptive leadership, can improve healthcare delivery by enhancing system flexibility, workforce resilience and patient-centered care. As healthcare environments become more complex and rapidly changing, traditional rigid systems often fail to meet evolving needs. The paper identifies significant barriers to Agile adoption, including rigid organizational structures, limited resources and cultural resistance. It introduces an integrated solution, a Biometric Stress Monitoring Framework to reduce burnout and improve performance. This strategy supports the development of a flexible, responsive and sustainable healthcare system focused on quality outcomes. Design/methodology/approach This paper applies a conceptual and theoretical analysis of leadership models, systems engineering and human factors. The framework presents a solution that align with clinical safety standards and organizational needs, offering practical guidance for implementing Agile principles in dynamic healthcare settings. Findings Agile adoption strengthens healthcare system responsiveness, collaboration and adaptability. Key challenges include weak leadership capacity, fragmented infrastructure and resistance to cultural change. The proposed solutions support proactive stress management and leadership growth, improving staff well-being, better decision-making and higher quality in patient care delivery. Originality/value This paper offers a novel perspective by connecting Agile principles with human-centered design and leadership development. The proposed approaches provide practical tools to support employee well-being and real-time responsiveness in healthcare. They contribute to addressing long-standing organizational challenges while advancing a more resilient and effective model of care.

The integration of bioplastics with Triboelectric Nanogenerators (TENGs) enables environmentally friendly energy harvesting, but their combustibility and inefficiency restrict their application in high‐temperature environments. Herein, inspired by lotus leaves, a robust, heat‐resistant, chitosan‐based bioplastic is developed via an ethanol‐induced surface association (EISA) effect for superinsulated multifunctional TENGs. This approach created a surface topological structure of the bioplastic with a high charge density and large contact area on the upper surface, complemented by a smooth lower surface. The unique structure and intrinsic nitrogen‐phosphorus synergy endowed the bioplastic with enhanced energy harvesting ability, superior mechanical strength and exceptional non‐combustibility (limiting oxygen index exceeds 70%, UL94 V‐0 rating). With these attributes, a noncombustible bioplastic‐based TENG with ultra heat‐resistance (above 200 °C), excellent stress‐ and temperature‐sensitivity, along with high energy harvesting capacity is designed. It enables stable high electrical output even when exposed to external fire sources and facilitates temperature and stress monitoring in fire scenarios. Overall, this study presents a promising approach to designing super flame‐retardant bioplastic‐based TENG with temperature and stress sensitivity, suitable for intelligent firefighting applications such as early fire detection, fire scene monitoring, and firefighter hazard alerts.

Traditional ultrasonic bolt stress measurement is hindered by high power consumption. Lowering excitation voltage reduces power but degrades signal-to-noise ratio (SNR), compromising accuracy. This paper proposes a synergistic algorithm combining Empirical Mode Decomposition (EMD) with Adaptive Threshold Wavelet Denoising (ATWD). The method preserves transient features by reconstructing high-frequency components via EMD, then suppresses noise by precisely processing low-frequency components using ATWD. Finally, cross-correlation estimates ultrasonic delay. Evaluated at excitation voltages from 12 V to 0.5 V, the EMD-ATWD method maintains measurement errors below 10% even at 0.5 V, improving accuracy by over 48% compared to conventional Finite Impulse Response (FIR) and Threshold Wavelet Denoising (WTD) methods, while enhancing key echo waveform fidelity by over 35%. This method provides a reliable low-power bolt stress monitoring idea for engineering applications.

Machine Learning-Based Stress Monitoring and Personalized Management