Search Results (6,854)

Carbon-based materials, including graphene, carbon nanotubes, laser-induced graphene, and pyrolyzed glassy carbon, are widely used in sensing applications due to their high conductivity, large surface area, and tunable surface chemistry. Meanwhile, microfluidic systems enable precise fluid handling, reduced sample consumption, and enhanced analytical performance through improved mass transport and device miniaturization. The integration of carbon-based materials with microfluidic platforms has enabled the development of compact, portable, and highly sensitive devices for water monitoring. This review summarizes recent advances in carbon-based microfluidic sensors for water monitoring applications. Key carbon materials and their sensing mechanisms, particularly electrochemical transduction, are discussed. Various microfluidic integration strategies, including paper-based devices, polymer-based devices, MEMS-based systems, and flexible platforms, are highlighted, with emphasis on mass transport enhancement and overall system performance. Representative recent advances in carbon-based microfluidic sensors for water monitoring, including the detection of heavy metal ions, nutrients, and emerging contaminants, are reviewed. Finally, challenges related to scalable manufacturing, long-term operational stability, biofouling/surface fouling, and reproducible system integration are discussed, together with future perspectives on intelligent carbon-based microfluidic platforms featuring AI-assisted analytics, sense-response functionality, and self-healing and dynamic antifouling capabilities for water monitoring. These advances are expected to enable real-time, low-cost, and field-deployable water monitoring systems for environmental protection and public health management. Overall, this review highlights the critical role of integrating carbon-based sensing materials with microfluidic engineering in advancing next-generation water monitoring technologies. Full article

Chronic wounds remain a major global health challenge despite substantial advances in biomaterials, regenerative medicine, and wound-care technologies. Current therapeutic strategies are largely based on the assumption that chronic wounds represent impaired or incomplete healing responses and therefore require augmentation of regenerative processes. This paradigm has driven the development of increasingly sophisticated wound dressings incorporating extracellular matrix analogs, growth factors, stem cells, extracellular vesicles, biosensors, and bioelectronic components. However, the clinical impact of these innovations has often fallen short of expectations. In this review, we propose a conceptual framework intended to generate experimentally testable hypotheses rather than provide a definitive mechanistic model. Persistent alterations in immune, stromal, vascular, extracellular matrix, metabolic, mechanical, and microbial networks create interconnected feedback systems that resist transition toward regeneration. From this perspective, successful therapy requires not only stimulation of repair mechanisms but also disruption of the processes that stabilize chronicity. We discuss how advances in systems biology, immunomodulatory biomaterials, bioelectronics, artificial intelligence, and precision medicine support the emergence of adaptive therapeutic interfaces capable of sensing, interpreting, and reprogramming pathological tissue behavior. Unlike previous reviews that primarily summarize emerging wound dressings or regenerative biomaterials, this Review proposes a systems-level conceptual framework in which chronic wounds are interpreted as stable pathological tissue states maintained by multiscale biological memory. This perspective integrates biomaterials, systems biology, artificial intelligence, and tissue-state dynamics into a unified translational model that has not previously been presented in the wound-healing literature. Previous reviews have predominantly focused on the design, biological activity, or clinical performance of individual biomaterials. In contrast, the present Review proposes a systems-level framework that integrates wound biology, biological memory, tissue-state dynamics, artificial intelligence, and adaptive biomaterials into a unified conceptual model for precision wound medicine. This state-based model reframes advanced wound dressings as tools for biological state engineering and provides a translational framework for the future of chronic wound management. Full article

Rapid urbanization has significantly altered urban landscape composition and configuration, making it a key driver exacerbating the urban heat island (UHI) effect. As a rapidly expanding inland city in Central China, Zhengzhou is highly sensitive to changes in landscape composition and spatial configuration. Therefore, clarifying the nonlinear relationship between landscape patterns and the urban thermal environment is of great significance for sustainable urban planning and thermal environment regulation. Taking the main urban area of Zhengzhou as the study area, this paper retrieves land surface temperature (LST) using the radiative transfer equation method based on Landsat 8 remote sensing images from August 2015 to August 2024, and constructs the surface urban heat island intensity (SUHII) index. By integrating multi-dimensional landscape pattern indices, the XGBoost machine learning model, and the SHAP interpretability method, this study systematically analyzes the nonlinear response mechanisms of landscape composition and configuration to SUHII, key regulatory thresholds, and their changes between 2015 and 2024. The results show that: (1) The SUHII in Zhengzhou was substantially higher in 2024 than in 2015. The area proportions of strong and extremely strong heat islands were higher in 2024 (26.16% and 2.34%) than in 2015 (2.22% and 0.12%), and the thermal environment differed between 2015 and 2024, shifting from a localized patch pattern to a more continuously expanding pattern. (2) Landscape area-related indices are the key factors. The areas of green space and water bodies, along with the landscape diversity index, show significant negative correlations, while built-up area and aggregation index show significant positive correlations. (3) SHAP feature importance indicates that water body area is the primary cooling factor, whereas built-up area is the primary warming factor, jointly dominating the spatial pattern of the thermal environment in Zhengzhou. (4) Landscape composition and configuration exhibit significant nonlinear responses to SUHII with region-specific thresholds, and these thresholds were higher/lower in 2024 than in 2015, suggesting a possible association with urban expansion. Specifically, stable cooling effects occurred when the water body area exceeded 3.5 km² in 2015, with the threshold rising to 4.2 km² in 2024. The warming threshold for built-up area decreased from 18.8 km² to 8.5 km², suggesting a higher sensitivity of the thermal environment to built-up area expansion in 2024 compared to 2015, characterized by a regulation pattern of “dominant scale effect and weakened configuration effect”. This study identifies thresholds specific to Zhengzhou’s main urban area at two time points (2015 and 2024), providing quantitative support and scientific basis for blue–green space optimization, precise heat island mitigation, and territorial spatial planning in Zhengzhou. These findings are based on a comparison of two time points (2015 and 2024) and do not directly capture continuous temporal dynamics. Full article

Flexible sensors are important for wearable health monitoring, strain detection, and temperature sensing because of their mechanical flexibility and functional versatility. Here, a femtosecond laser direct scanning method was used to fabricate porous laser-induced graphene (LIG) and further modify it with platinum nanoparticles (PtNPs), forming Pt/LIG. This mask-free and rapid process enables simultaneous patterning and functionalization of flexible sensors. The introduction of PtNPs improves the electron transport and surface adsorption properties of LIG. As a result, the sheet resistance of Pt/LIG is reduced to 2.41 Ω/sq, enhancing electrical conductivity and suitability for sensing applications. Based on this method, highly sensitive strain and temperature sensors were fabricated. The Pt/LIG strain sensor shows a ΔR/R₀ of 1141.8 at a bending angle of 90°, about 213% higher than that of pristine LIG, with fast response and recovery times of 36 and 56 ms, respectively. The temperature sensitivity also improved by about 650%, with a temperature coefficient of resistance of 0.240%/°C, compared with −0.032%/°C for pristine LIG. Overall, this work provides a fast and precise strategy for fabricating nanoparticle–graphene composites for flexible electronics, wearable health monitoring, and environmental sensing. Full article

Major depressive disorder (MDD) remains a leading cause of disability; however, monoaminergic models do not fully explain delayed treatment onset, incomplete remission, or rapid responses to glutamatergic interventions. In this study, we proposed a system-level ionic homeostasis framework for MDD. In this model, genetic susceptibility, chronic stress, metabolic burden, and neuroinflammation converge in neuronal and glial ion-channel systems, disrupting calcium, potassium, chloride, and purinergic homeostasis. These disturbances alter intrinsic excitability, synaptic integration, inhibitory tone, glial buffering, and neuron–glia signaling, thereby promoting excitation–inhibition imbalance, impaired plasticity, and corticolimbic network instability. We reviewed the evidence implicating the CACNA1C/Cav1.2, TREK-1, KCNQ, NKCC1/KCC2, HCN, transient receptor potential/acid-sensing ion channels, and glial mediators, including P2X7R, Kir4.1, and AQP4. We also discuss how ketamine-related mechanisms, chloride-restoring strategies, anti-inflammatory ion channel targeting, neuromodulation, EEG biomarkers, and AI/multiomics approaches support mechanism-informed precision therapeutics. MDD could be conceptualized as a distributed failure of ionic homeostasis that links neuroinflammation, E/I imbalance, network instability, and impaired adaptive plasticity. Full article

Copper oxide (CuO) has emerged as a promising p-type semiconductor for a wide range of applications, including gas sensing and photoelectric devices. This is due to its narrow band gap, high chemical stability and low cost. In recent years, increasing attention has been paid to the development of high-quality CuO thin films with precisely controlled structural and electronic properties. Among various fabrication techniques, atomic layer deposition (ALD) provides unique advantages like excellent thickness control, conformality and tunability of film composition at the atomic scale. This review provides a comprehensive overview of CuO thin films with a particular focus on ALD-based fabrication approaches. First, conventional deposition methods are briefly discussed. Next, the fundamentals of ALD processes for CuO growth are presented including precursor chemistry, reaction mechanisms and the influence of key process parameters. Special attention is given to the correlation between deposition conditions and the resulting structural, optical and electrical properties of the films. Subsequently, the impact of these properties on device performance is analyzed in the context of gas sensing and photoelectric applications. Finally, current challenges and future perspectives are outlined, emphasizing the need for improved control over phase composition, defect engineering, and integration with nanostructured systems. Full article

In optical remote sensing 3D reconstruction, high-resolution satellite stereo matching is a critical task, yet it is challenged by extreme imaging geometries, texture-less and repetitive patterns, occlusions, and scene variations caused by spatio-temporal heterogeneity. To address these issues, we propose IFMA-Stereo, an innovative binocular disparity estimation method that leverages a monocular depth foundation model. Our approach constructs a multi-scale spatial information pyramid to jointly integrate the foundation model with a disparity extraction network. At the feature level, an attention interaction mechanism captures multi-dimensional contextual dependencies and transforms general scene understanding priors into long-range associative features suitable for stereo cost volume construction. At the pixel level, a cyclic iterative refinement module embeds depth information from the foundation model throughout the iteration process and performs joint optimization, enhancing the model’s adaptability in geometrically complex regions. Experiments on the US3D and GaoFen-7 datasets demonstrate that IFMA-Stereo achieves superior performance in challenging areas (texture-less regions, disparity discontinuities, repetitive patterns) and effectively mitigates prediction errors caused by spatio-temporal heterogeneity, albeit at the cost of increased inference time compared to baseline methods. Quantitatively, the method achieves an end-point error (EPE) of 1.347 and a D1 error of 7.26% on the US3D dataset, and an EPE of 1.585 and a D1 error of 13.41% on the GaoFen-7 dataset. Notably, the method also yields precise predictions for unseen urban areas, indicating strong generalization. These results confirm that IFMA-Stereo achieves state-of-the-art accuracy in remote sensing disparity estimation. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Smart wastewater treatment plants are increasingly essential for improving the resilience of water infrastructure under climate-related disturbances and operational variability. Despite significant advances in sensing technologies and data analytics, full-scale automation remains limited. One of the primary barriers is that monitoring strategies remain primarily designed for regulatory compliance, prioritizing absolute analytical accuracy often at relatively high cost, thereby limiting their widespread use for real-time decision-making. This perspective proposes a shift from compliance-oriented monitoring to purpose-oriented monitoring, in which monitoring systems are designed according to the operational purpose they support. To support this shift, we introduce a framework recognizing that different operational applications require different levels of measurement performance. Accordingly, compliance assessment, early warning, process troubleshooting, real-time control, and asset management each require different combinations of accuracy, precision, temporal resolution, and tolerance to measurement error. Within this framework, high-frequency multi-source data streams from conventional, spectral, visual, and virtual sensors can provide complementary information on process dynamics, effectively supporting operational decisions, even when individual measurements do not achieve laboratory-level analytical performance. When combined with hybrid models that integrate mechanistic and data-driven approaches, this purpose-oriented monitoring framework can enable predictive process management and accelerate the transition toward autonomous wastewater treatment systems. Full article

Grasslands represent one of the most extensive terrestrial biomes globally, covering approximately one-third of the Earth’s land surface, yet they are increasingly threatened by land-use change and overgrazing, underscoring the need for reliable monitoring approaches. This study compares the performance of machine learning and deep learning algorithms for grassland mapping using multi-source remote sensing data derived from Sentinel-1, Sentinel-2, and terrain variables. The research was conducted in Mpumalanga Province, South Africa, a heterogeneous landscape comprising lowland savannas, high-altitude grasslands, escarpments, and riverine wetlands. Random Forest (RF) and Support Vector Machine (SVM) classifiers were implemented in Google Earth Engine using fused satellite and terrain datasets with field-collected samples for training and validation, while a One-Dimensional Convolutional Neural Network (1D-CNN) was developed in Python 3.13.5 using the same inputs. Results demonstrate that integrating multi-source data improves classification accuracy, with radar-based features contributing the most. RF achieved the highest performance, with an overall accuracy of 97.7% and grass-class precision, recall, and F1-score exceeding 0.97, closely followed by the 1D-CNN with 91% overall accuracy and complete grass detection. In contrast, SVM performed notably lower with an overall accuracy of 80,8%. These findings highlight the effectiveness of advanced learning approaches for grassland mapping and support their application in ecological restoration and environmental management. Full article

In social sensing environments, misinformation and coordinated manipulation often manifest through implicit semantic signals, complex behavioral dynamics, and highly coupled propagation structures. These factors pose significant challenges to artificial intelligence-driven sensing systems regarding data quality, multimodal fusion, and robustness. To address these issues, this study proposes an artificial intelligence-driven multi-granularity sensing framework. This framework integrates heterogeneous sensing signals from post-level semantic perception, user-level behavioral sensing, and group-level structural sensing into a unified representation space. Hierarchical consistency constraints enable cross-granularity sensing collaboration. This mechanism enhances stability and discriminative capability under complex and noisy data conditions. Methodologically, the framework jointly incorporates semantic sensing via text encoding, temporal sensing via behavioral sequence modeling, and structural sensing via graph neural network-based propagation. This integration effectively mitigates information bias induced by single-perspective sensing and improves the modeling of latent risk patterns. Experimental results on real-world datasets demonstrate that the proposed framework achieves significant improvements across multiple evaluation metrics. Specifically, it achieves a Precision of 0.847, a Recall of 0.812, an F1-score of 0.829, an Accuracy of 0.856, and an Area Under Curve of 0.913. It consistently outperforms traditional machine learning models, as well as mainstream deep learning and graph-based approaches. Furthermore, comparison experiments validate the complementarity among semantic, behavioral, and structural sensing signals. The full model achieves an improvement of more than 3 percentage points in the F1-score compared to single-granularity configurations. An ablation study further demonstrates that each sensing module contributes substantially to performance enhancement, with the semantic sensing and hierarchical consistency constraints playing particularly critical roles. Overall, the proposed method exhibits a strong capability to handle complex heterogeneous sensing data. It improves robustness and enhances cross-level information utilization, providing an effective solution to data-related challenges in artificial intelligence-driven sensing systems. Full article

The production of “winter wines” in south-eastern Brazil, enabled by the double pruning technique, requires precise assessment of grape technological maturity to ensure high-quality outputs. However, conventional monitoring approaches are destructive, labor-intensive, and limited in their ability to capture vineyard spatial variability. This study aimed to develop and validate a non-destructive predictive framework for Soluble Solids (°Brix) and Titratable Acidity (TA) by integrating spatial remote sensing data with temporal agrometeorological information. Multispectral imagery was acquired via an unmanned aerial vehicle in a vineyard cultivated with Sauvignon Blanc and Syrah, from which vegetation indices were derived and combined with Growing Degree-Days to train machine learning models, including Random Forest, Multilayer Perceptron, and XGBoost. The incorporation of agrometeorological data significantly improved predictive performance compared to models based solely on vegetation indices. Among the tested algorithms, XGBoost achieved the highest accuracy, with coefficients of determination of 0.89 for °Brix and 0.77 for TA, achieved by XGBoost on an independent hold-out test set. Model interpretability analysis indicated that Growing Degree-Days and cultivar were the primary drivers of maturation dynamics, while vegetation indices refined predictions by accounting for spatial variability in plant vigor. Overall, the proposed approach represents a promising proof-of-concept framework for non-destructive maturity monitoring in precision viticulture, supporting improved monitoring of grape maturation. However, multi-season validation across diverse vineyard conditions is required to confirm its generalizability and support its application as a routine decision-support tool. Full article

The rapid expansion of microwave and millimeter-wave telecommunication systems has intensified the need for precise dielectric material characterization at high frequencies. As operating frequencies increase, small uncertainties in permittivity and loss tangent significantly degrade resonance stability, bandwidth control, and quality factor, directly affecting RF system reliability and performance. However, the growing diversity of resonator architectures and extraction methodologies has led to fragmentation in the literature, making it difficult to identify optimal solutions for telecommunication-oriented applications. This review provides a structured and application-driven assessment of high-frequency resonator-based dielectric characterization techniques relevant to modern telecommunication systems. Resonator topologies—including cavity, planar, substrate-integrated, metamaterial-inspireds—are systematically classified and critically compared. Their sensing mechanisms and parameter-extraction approaches are analyzed in terms of frequency-shift sensitivity, Q-factor performance, scalability toward millimeter-wave bands, integration capability, and measurement robustness. By synthesizing performance trade-offs, practical limitations, and emerging research directions, this review establishes clear design guidelines and a forward-looking framework for advancing dielectric metrology in next-generation high-frequency telecommunication technologies. Full article

Subtropical mountain economic tree plantations are constrained by pronounced spatial heterogeneity in resource availability, yet the spatial scales at which soil properties, topography, and canopy structure regulate vegetation vigor remain poorly resolved. To address this gap, a spatially consistent multi-scale dataset combining 10 m high-density soil sampling, UAV-LiDAR, and multispectral remote sensing was used to quantify the scale-dependent drivers of the Leaf Chlorophyll Index (LCI) across 3–50 m within a Chinese hickory (Carya cathayensis Sarg.) plantation. The relative contributions of canopy, soil, and topography to LCI were decomposed across scales using an interpretable machine-learning framework (XGBoost–SHAP). At fine scales (3–10 m), vegetation vigor was primarily controlled by tree-level canopy structure, particularly tree height, reflecting localized resource acquisition. At intermediate scales (10–20 m), a distinct coupling window emerged, characterized by increased interaction complexity: LCI was predominantly driven by interactions between canopy structure and soil nutrient availability, whereas single-factor effects weakened. Notably, at 20 m this interaction pattern largely weakened and reverted to single-metric dominance. At broader scales (>30 m), complex interactions re-emerged, and dominant SHAP contributions shifted from nutrients and canopy structure toward topography and soil texture. These findings reconcile strong fine-scale drivers with weaker predictability at intermediate extents and demonstrate that soil–canopy relationships reorganize across spatial scales rather than remaining static. On the basis of these findings, a scale-hierarchical framework for precision forestry is proposed that aligns management interventions with the ecological scales at which dominant correlates operate across spatial supports. Full article

Nietzsche claimed, in The Birth of Tragedy, that the Platonic dialogues are the lifeboat on which Socrates rescues older poetry, only to finish off comedy. This article contests that claim. Distinguishing comedy (a mode organized around to geloion, whose object Plato himself defines in the Philebus as self-ignorance) from irony (a two-level device of meaning), it argues that Socratic irony in Plato is comic in a precise threefold sense: genealogically, as the inheritance of the eirōn-mask from the Aristophanic Clouds; structurally, wherever the dialogues frame it for staged laughter; and thematically, as the instrument of the laughing exposure of doxosophia. Drawing on Kierkegaard’s reading of the Socratic maieutic as ‘indirect communication’ and Vlastos’s concept of ‘complex irony,’ the article offers a close reading of Alcibiades’ speech in the Symposium within the generic frame Plato himself names at 222d: the satyr-play, ‘tragedy at play,’ in which tragic material is held inside a laughing form. The speech’s mythological imagery (Silenus, Marsyas, Corybants, Sirens) carries a coordinated dark sub-text that reverses its overt meaning—the satyric signature, not a departure from the comic. Against Vlastos, who locates pedagogical failure in Alcibiades’ self-deception, and Lane, who finds complex irony’s deliberate indeterminacy ethically indefensible, the article proposes a meta-ironic resolution: Plato stages both positions simultaneously, performing at the authorial level the very pharmakon-logic the speech describes—a seriocomic philosophical method grounded in the mythological ambivalence of the theatre of Dionysus. Full article