Search Results (2,527)

Conducting structural monitoring of highway subgrades is crucial for investigating damage evolution mechanisms under dynamic load-temperature coupling effects. However, existing sensing technologies struggle to achieve distributed, long-term, and high-precision measurements of subgrade structures. Therefore, this study employs next-generation fiber-optic array sensing technology to construct a distributed monitoring system based on weak reflection grating arrays. A dual-parameter sensing network for strain and temperature was designed and installed during the expansion and renovation of a highway in Fujian Province, enabling high-precision monitoring of the entire continuous strain field and temperature field of the subgrade structure. Through a comprehensive analysis of dynamic loading test data and long-term monitoring records, the system revealed the dynamic response patterns of subgrade structures under the interaction of modulus differences, burial depth effects, temperature gradients, and load parameters. It elucidated the mechanical sensitivity of flexible base layers and the interlayer stress redistribution mechanism. The study validated that grating array sensors not only offer advantages such as easy installation, a high survival rate, and excellent durability but also enable high-capacity, long-distance, and high-precision measurements of subgrade structures. This provides a new technical approach for full lifecycle monitoring of expressways. Full article

In this study, we explore the neuroprotective and modulatory potential of graphenic materials (GMs) in terms of the maturation of dopaminergic neurons and their capacity to counteract the cellular stress induced by toxins such as MPP⁺ (1-methyl-4-phenylpyridinium) and Tunicamycin. We found that GMs promote significant morphological changes in neuronal cells after prolonged exposure, enhancing both differentiation and cellular adhesion. Through structural analysis, we unveiled a complex organization of GMs and a marked upregulation of tyrosine hydroxylase (TH), a key marker of mature dopaminergic neurons. Under oxidative stress induced by MPP⁺, GMs significantly reduced the release of lactate dehydrogenase (LDH), indicating protection against mitochondrial damage. Moreover, GMs substantially decreased the levels of α-synuclein (α-Syn), a protein closely associated with neurodegenerative disorders such as Parkinson’s disease. Notably, partially reduced graphene oxide (PRGO) and fully reduced graphene oxide (FRGO) films were particularly effective at reducing α-Syn-associated toxicity compared to positive controls. Under conditions of endoplasmic reticulum (ER) stress triggered by Tunicamycin, GMs—especially PRGO microflakes—modulated the unfolded protein response (UPR) pathway. This effect was evidenced by the increased expression of BIP/GRP78 and the decreased phosphorylation of stress sensors such as PERK and eIF2α; this suggests that a protective role is played against ER stress. Additionally, GMs enhanced the synthesis of Torsin 1A, a chaperone protein involved in correcting protein folding defects, with PRGO microflakes showing up to a fivefold increase relative to the controls. Through the cFos analysis, we further revealed a pre-adaptive cellular response in GM-treated cells exposed to MPP⁺, with PRGO microflakes inducing a significant twofold increase in cFos expression compared to the positive control, indicating partial protection against oxidative stress. In conclusion, these results underscore GMs’ capacity to modulate the critical cellular pathways involved in oxidative, mitochondrial, and ER stress responses, positioning them as promising candidates for future neuroprotective and therapeutic strategies. Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article

This study investigated the effect of the number of sensors (8, 12, 16, and 20) on the measurement results of stress wave velocity in two-dimensional acoustic tomography of Hoop pine (Araucaria cunninghamii Sweet) trees and evaluated the method’s accuracy and operational efficiency in tree health diagnostics. Tests were conducted on five sample trees, two of which were confirmed to have internal damage using the drilling resistance method. The results showed that increasing the number of sensors improved image resolution and information completeness. However, differences in the average stress wave velocities among sensor configurations were not statistically significant (p ≥ 0.05), indicating limited overall velocity variation. In healthy trees, stress wave velocities measured with different sensor quantities (e.g., eight vs. twenty) exhibited weak linear correlations (R² = 0.06–0.58), reflecting a relatively uniform internal structure. In contrast, damaged trees showed strong consistency in velocity results (R² = 0.82–0.91, p < 0.01), with both minimum and average velocities being significantly lower than those in healthy trees. These findings demonstrate that acoustic tomography can effectively identify internal tree defects. Notably, even with only eight sensors, decay and cavities can still be accurately detected, which significantly enhances field inspection efficiency and reduces costs, thereby showing strong potential for practical applications. Full article

This study investigates the feasibility of using wearable technology and machine learning algorithms to predict academic performance based on physiological signals. It also examines the correlation between stress levels, reflected in the collected physiological data, and academic outcomes. To this aim, six key physiological signals, including skin conductance, heart rate, skin temperature, electrodermal activity, blood volume pulse, inter-beat interval, and accelerometer were recorded during three examination sessions using a wearable device. A novel pipeline, comprising data preprocessing and feature engineering, is proposed to prepare the collected data for training machine learning algorithms. We evaluated five machine learning models, including Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosted (CatBoost), and Gradient-Boosting Machine (GBM), to predict the exam outcomes. The Synthetic Minority Oversampling Technique (SMOTE), followed by hyperparameter tuning and dimensionality reduction, are implemented to optimise model performance and address issues like class imbalance and overfitting. The results obtained by our study demonstrate that physiological signals can effectively predict stress and its impact on academic performance, offering potential for real-time monitoring systems that support student well-being and academic success. Full article

Coal-rock dynamic disasters, especially rock bursts, require insight into the spatiotemporal evolution of strain and temperature to clarify failure mechanisms and improve early warning. This study aims to characterize the spatiotemporal evolution of the strain field during brittle rock instability by developing a multi-point Fiber Bragg Grating (FBG) strain–temperature monitoring and inversion method. Multi-directional, multi-location FBG deployment enables real-time reconstruction of strain tensors and temperature at each monitoring point, capturing both surface and internal responses under loading. The strain records resolve four stages—initial smoothing, linear growth, pre-peak nonlinearity, and failure fluctuation—with earlier sensitivity than Linear Variable Differential Transformers (LVDT), enabling finer localization of yielding and microcracking. The FBG sensors capture clear spatial heterogeneity and timing offsets during yielding, supporting instability warning. Temperature results show a slow rise followed by a surge from the end of the elastic stage into the plastic stage, reaching ~1.6 °C before declining; the thermal peak precedes the stress peak by ~0.38 s. Meanwhile, the temperature-field coefficient of variation jumps from <0.15 to >0.25, indicating a transition from diffuse heating to banded localization. Together, these strain–temperature precursors validate the FBG-based method as an effective and reliable approach for early warning of brittle rock instability. Full article

The use of vertically stacked architectures in three-dimensional integrated circuits (3DICs) offers a transformative path for advancing Moore’s Law by significantly boosting computational density. A key obstacle arises from the integration of heterogeneous materials, which introduces critical thermo-mechanical challenges, particularly due to the mismatch in the coefficients of thermal expansion (CTE) of silicon (Si) and copper (Cu). Such mismatches can compromise mechanical reliability and complicate the definition of the keep-out zone (KOZ) in dense systems. This paper provides a detailed analysis of the thermo-mechanical behavior of stacked 3DICs, exploring a range of device geometries and process conditions. The findings reveal that CTE-induced stress is the dominant factor influencing mechanical integrity, surpassing other mechanical forces. It is concluded that the KOZ must be no less than 1.5 times the feature diameter to adequately mitigate stress-related risks. Additionally, thermal stress interactions in configurations with adjacent structures can increase the KOZ requirement by up to 33.3% relative to isolated instances. Yet, multi-layered designs show enhanced thermal performance, a benefit attributed to the high thermal conductivity of copper. The knowledge gained from this study provides a valuable framework for optimizing the reliability and thermal management of 3DIC systems and is especially relevant for high-performance sensor devices where both mechanical stability and efficient heat dissipation are vital. Full article

Climate change significantly intensifies agroforestry workers’ exposure to atmospheric particulate matter (PM), raising occupational health concerns. This review, based on the analysis of 174 technical and scientific sources including articles, standards and guidelines published between 1974 and 2025, systematically analyses the main sources of PM in agricultural and forestry activities (including tillage, pesticide use, harvesting, sowing of treated seeds and mechanized wood processing) and focuses on the substantial contribution of agricultural and forestry machinery to PM emissions, both quantitatively and qualitatively. It highlights how changing climatic conditions, such as increased drought, wind and temperature, amplify PM generation and dispersion. The associated health risks, especially respiratory, dermatological and reproductive, are exacerbated by the presence of toxicants (such as heavy metals, volatile organic compounds and pesticide residues toxic for reproduction) in PM. Despite existing regulatory frameworks, significant gaps remain regarding PM exposure limits in the agroforestry sector. Emerging technologies, such as environmental sensors, AI-based predictive models and drone-assisted monitoring, are proposed for real-time risk detection and mitigation. A multidisciplinary and proactive approach integrating innovation, policies and occupational safety is essential to safeguard workers’ health in the context of increasing climate stress. Full article

Residual stresses and internal strains in 3D printing can lead to issues such as cracking, warping, and delamination—challenges that are amplified when using functional composite materials like magnetic PLA filaments. This study investigates the thermo-mechanical strain evolution during fused filament fabrication (FFF) of magnetite-filled PLA using an integrated methodology combining strain gauge sensors, high-resolution infrared thermal imaging, and synchrotron X-ray microtomography. Printing parameters, including nozzle temperature (190–220 °C), build platform temperature (30–100 °C), printing speed (30–60 mm/s), and cooling strategy (fan on/off) were systematically varied to evaluate their influence. Results reveal steep thermal gradients along the build direction (up to −1 °C/µm), residual strain magnitudes reaching 0.1 µε, and enhanced viscoelastic creep at elevated platform temperatures. The addition of magnetic particles modifies heat distribution and strain evolution, leading to strong sensitivity to process conditions. These findings provide valuable insight into the complex thermo-mechanical interactions governing the structural integrity of magnetically functionalized PLA composites in additive manufacturing. Full article

Operation time and variability in structural, thermal, and environmental loads are important factors affecting the operational safety of power plant structures. Although conventional testing techniques are usually used to assess the level of damage introduced to a structure due to prolonged service, most of them are destructive and time- and cost-intensive. Therefore, in this paper, a novel approach consisting of Rayleigh optic strain sensors for deformation monitoring under creep conditions is proposed. The suitability of this methodology was assessed during quasi-static loading tests at room temperature, as well as during a long-term creep test at 540 °C under constant stress of 130 MPa, which was performed on a specimen made of 13HMF power engineering steel. The sensor attached to the specimen’s surface was used to monitor strain evolution during 678 days of high-temperature exposure under creep conditions. It was confirmed that the methodology proposed could be successfully used to monitor strain changes under quasi-static and creep conditions, as an excellent agreement between the fiber optic strain sensors and conventional strain recorders was achieved. Full article

Soil moisture is one of the key hydrologic components indicating the performance of landfill final covers. Conventional compacted clay (CC) covers and evapotranspiration (ET) covers often suffer from moisture-induced stresses, such as desiccation cracking and irreversible hydraulic conductivity. Engineered turf (EnT) cover systems have been introduced recently as an alternative; however, their field-scale moisture distribution behavior remains unexplored. This study investigates and compares the soil moisture distribution characteristics of EnT, ET, and CC landfill covers at a shallow depth using one year of field-monitored data in a humid subtropical region. Three full-scale test Sections (3 m × 3 m × 1.2 m) were constructed side by side and instrumented with moisture sensors at a depth of 0.3 m. Distributional characteristics of moisture were evaluated with descriptive statistics, goodness-of-fit tests such as Shapiro–Wilk (SW) and Anderson–Darling (AD), Gaussian probability density functions, Q–Q plots, and standard-normal transformations. Results revealed that Shapiro–Wilk (W = 0.75–0.92, p < 0.001) and Anderson–Darling $(A^2=1.63\times {10}^3\mathrm{to}6.31\times {10}^3,p<0.001)$ tests rejected normality for every cover, while Levene’s test showed unequal variances between EnT and the other covers $(F>5.4\times {10}^4,$$p<0.001)$ but equivalence between CC and ET (F = 0.23, p = 0.628). EnT cover exhibited the narrowest moisture envelope $(95\%\mathrm{range}=0.156\mathrm{to}0.240{\mathrm{m}}^3/{\mathrm{m}}^3;\mathrm{CV}=10.6\%)$, whereas ET and CC covers showed markedly broader distributions (CV = 38.6 % and 33.3 %, respectively). These findings demonstrated that EnT cover maintains a more stable shallow soil moisture profile under dynamic weather conditions. Full article

Cold stress severely limits legume productivity, threatening global food security, particularly in climate-vulnerable regions. This review synthesizes advances in understanding and enhancing cold tolerance in key legumes (chickpea, soybean, lentil, and cowpea), addressing three core questions: (1) molecular/physiological foundations of cold tolerance; (2) how emerging technologies accelerate stress dissection and breeding; and (3) integration strategies and deployment challenges. Legume cold tolerance involves conserved pathways (e.g., ICE-CBF-COR, Inducer of CBF Expression, C-repeat Binding Factor, Cold-Responsive genes) and species-specific mechanisms like soybean’s GmTCF1a-mediated pathway. Multi-omics have identified critical genes (e.g., CaDREB1E in chickpea, NFR5 in pea) underlying adaptive traits (membrane stabilization, osmolyte accumulation) that reduce yield losses by 30–50% in tolerant genotypes. Technologically, AI and high-throughput phenotyping achieve >95% accuracy in early cold detection (3–7 days pre-symptoms) via hyperspectral/thermal imaging; deep learning (e.g., CNN-LSTM hybrids) improves trait prediction by 23% over linear models. Genomic selection cuts breeding cycles by 30–50% (to 3–5 years) using GEBVs (Genomic estimated breeding values) from hundreds of thousands of SNPs (Single-nucleotide polymorphisms). Advanced sensors (LIG-based, LoRaWAN) enable real-time monitoring (±0.1 °C precision, <30 s response), supporting precision irrigation that saves 15–40% water while maintaining yields. Key barriers include multi-omics data standardization and cost constraints in resource-limited regions. Integrating molecular insights with AI-driven phenomics and multi-omics is revolutionizing cold-tolerance breeding, accelerating climate-resilient variety development, and offering a blueprint for sustainable agricultural adaptation. Full article

The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern recognition receptor (PRR)-mediated immune surveillance. This review explores the concept that epigenetic marks may function as DAMPs or even mimic PAMPs. I highlight how unmethylated CpG motifs, which are typically suppressed using host methylation, are recognized as foreign via Toll-like receptor 9 (TLR9). I also examine how cytosolic DNA sensors, including cGAS, detect mislocalized or hypomethylated self-DNA resulting from genomic instability. In addition, I discuss how extracellular histones and nucleosomes released during cell death or stress can act as DAMPs that engage TLRs and activate inflammasomes. In the context of cancer, I review how epigenetic dysregulation can induce a “viral mimicry” state, where reactivation of endogenous retroelements produces double-stranded RNA sensed by RIG-I and MDA5, triggering type I interferon responses. Finally, I address open questions and future directions, including how immune recognition of epigenetic alterations might be leveraged for cancer immunotherapy or regulated to prevent autoimmunity. By integrating recent findings, this review underscores the emerging concept of the epigenome as a target of innate immune recognition, bridging the fields of immunology, epigenetics, and cancer biology. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

Flexible pressure sensors have shown promise applications in scenarios such as robotic tactile sensing due to their excellent sensitivity and linearity. However, the realization of flexible pressure sensors with both static and dynamic response capabilities still face significant challenges due to the properties of the sensing materials themselves. In this study, we propose a flexible pressure sensor that integrates piezoelectric and ionic capacitance mechanisms for full-domain response detection of dynamic and static forces: a “sandwich” sensing structure is constructed by printing a mixture of multi-walled carbon nanotubes (MWCNTs) onto the surface of the upper and lower electrodes, and sandwiching a polyvinylidene fluoride (PVDF) thin film between the electrodes. The device exhibits a sensitivity of 0.13 kPa⁻¹ in the pressure range of 0–150 kPa. The sensor has a rapid dynamic response (response time 19 ms/12 ms) with a sensitivity of 0.49 mV kPa⁻¹ based on the piezoelectric mechanism and a linearity of 0.9981 based on the ionic capacitance mechanism. The device maintains good response stability under the ball impact test, further validating its potential application in static/dynamic composite force monitoring scenarios. Full article