Search Results (3,918)

Rapid and non-destructive monitoring of soil total nitrogen (STN) is important for precision nutrient management in ecologically fragile agricultural systems. This study established a controlled spectral resampling experiment to simulate the multispectral responses of Sentinel-2, WorldView-3, GF-6, and Landsat-9 from laboratory ASD hyperspectral measurements of Hemerocallis citrina fields. Two-dimensional (DI, RI, and NDI) and three-dimensional (TBI1–TBI5) spectral indices were constructed and evaluated using eight machine learning algorithms across five phenological stages. The results demonstrate that: (1) Three-dimensional spectral indices exhibited substantially higher sensitivity to STN than conventional two-dimensional indices, with TBI3 showing the strongest overall correlation; among the simulated sensor configurations, GF-6 delivered the best mean performance due to its dual red-edge bands. (2) Genetic algorithm-optimized backpropagation neural network (GA-BPNN) effectively addressed the local-minima limitation of standard backpropagation neural networks (BPNNs) and displayed robust generalization under multi-sensor and multi-phenological scenarios. (3) Phenology-specific modeling reduced spectral heterogeneity caused by pooling across growth stages, increasing R² by 20.28% and decreasing RMSE by 18.35%, with leaf expansion and bolting identified as optimal estimation windows. These findings elucidate the spectral response potential explanations of STN across phenological stages and provide a reference for applying multi-source simulated remote sensing data to nutrient monitoring in specialty agricultural systems. Full article

Objective feedback is important for improving welding training and reducing dependence on experienced instructors. This study proposes an image-based sensing framework for extracting motion and geometric features from videos acquired using a simple visualization system mounted inside a welding helmet during semi-automatic gas metal arc welding (GMAW) and manual gas tungsten arc welding (GTAW). The welding videos were acquired in real time, whereas the subsequent image-processing and quantitative feature-extraction procedures were performed offline. This visualization system consists of an instructor-side unit and a welder-side unit equipped with a prototype compact camera. Welding images were trimmed to 288 × 288 pixels and classified into eight regions for GMAW and six regions for GTAW using a U-Net-based semantic segmentation model. The segmented regions in GMAW included the arc, molten pool, groove, torch, wire, overlap regions, and background, whereas those in GTAW included the arc, bead, groove, electrode, filler wire, and background. Groove edges were detected using Canny edge detection and the Hough transform. The wire-tip position in GMAW and the electrode-tip position in GTAW were estimated using the arc centroid as an image-based positional proxy and normalized by the detected groove width to obtain normalized wire-tip and electrode-tip motion, respectively. In addition, the molten-pool width in GMAW and the bead width in GTAW were extracted and normalized using the same procedure to obtain groove-normalized geometric features. For the GMAW dataset, the segmentation model achieved a validation accuracy of 95.2% and a validation mean intersection over union (mIoU) of approximately 67.1%. For the GTAW dataset, the corresponding validation accuracy and validation mIoU were 96.6% and approximately 85.9%, respectively. The results showed that the proposed framework can successfully extract normalized wire-tip motion and molten-pool width in GMAW, as well as normalized electrode-tip motion and bead width in GTAW, from welding videos acquired from different welders. These findings demonstrate the potential of the proposed framework to extract quantitative motion and geometric features from welding videos, providing useful data for future welding training support and welding behavior analysis. Full article

Underground coal mining operations remain significantly threatened by the accumulation of methane (CH₄) and carbon monoxide (CO): Methane poses an acute explosion risk, and carbon monoxide serves as a critical biomarker for spontaneous coal combustion. Consequently, rigorous real-time monitoring to ensure environmental safety is necessitated, which is based on superior gas sensor devices. Although various detection modalities exist, conventional methods are frequently constrained by environmental sensitivity and limitations regarding long-term sensor stability. This review provides a comprehensive analysis of recent advancements in chemiresistive gas sensors based on metal oxide (MO) semiconductor materials with low cost, high stability, high sensitivity, and easy preparation, which are engineered for the detection of methane and carbon monoxide in coal mining environments. This study examines the redox-sensing mechanisms of both n-type and p-type MO semiconductors, for which special attention is directed toward optimization strategies designed to overcome the high activation energy of methane and improve carbon monoxide response kinetics. Importantly, novel approaches to lower high operating temperatures and improve the selectivity of MO sensors under complex mine environments have been comprehensively discussed. Full article

Volatile fluorinated compounds (VFCs) are indispensable to modern industry, yet their potent greenhouse effects pose critical environmental challenges. Functional porous materials, ranging from zeolites and semiconductor oxides to metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and other advanced porous materials, have emerged as versatile platforms for VFC sensing and capture, leveraging their structural tunability, ultrahigh surface areas, and designable pore chemistry. This review provides a systematic summary of recent advances in porous materials for VFC management. For sensing, we examine transduction mechanisms (chemiresistive, optical, and gravimetric approaches) with emphasis on structure–signal relationships. For capture, we evaluate adsorptive performance across VFC subclasses, highlighting design principles that govern selectivity and capacity. Based on recent achievements, we assess persistent gaps between laboratory-scale achievements and practical deployment. Possible pathways toward integrated sense-and-capture systems are also explored. By bridging fundamental materials science, this review highlights cross-cutting design strategies that may accelerate the development of next-generation VFC management platforms. Full article

Accurate historical estimates of forest carbon stocks are essential for greenhouse gas inventories and REDD+ Measurement, Reporting and Verification (MRV) systems, particularly in remote and persistently cloudy regions where field inventories and optical remote sensing are limited. This study presents a retrospective mapping framework with potential relevance for Tier-3 forest carbon estimation of forest volume and carbon stocks in the temperate forests of southern Chile using historical ALOS PALSAR L-band SAR data integrated with Chile’s Continuous National Forest Inventory (CNFI). Three pilot zones in Los Lagos, Aysén, and Magallanes were analysed, covering approximately 42,000 km² of native forests dominated by Lenga, Coihue de Magallanes, Siempreverde, Roble–Raulí–Coihue, Coihue–Raulí–Tepa, and Alerce forest types. Annual 25 m ALOS PALSAR mosaics were processed to derive HH and HV backscatter, HH/HV ratio, and Radar Forest Degradation Index (RFDI) layers, which were used as predictors in k-nearest neighbours (k-NN) models calibrated with inventory plots projected to the 2010 reference year. Model performance varied substantially among forest types and pilot zones, with test r² values ranging from 0.12 to 0.90 and RMSE values between approximately 100 and 300 m³·ha⁻¹; the highest r² values were associated with forest types represented by relatively small samples and should therefore be interpreted cautiously. m³·ha⁻¹ Stratification by altitude and restriction to moderate volume ranges improved predictive performance in several cases, highlighting the influence of ecological gradients and SAR signal saturation at high levels of biomass. Despite substantial pixel-level uncertainty, the methodology reproduced broad regional patterns of forest structure and carbon distribution. Results demonstrate the potential of combining historical ALOS PALSAR archives with national forest inventories to support spatially explicit historical carbon estimation in data-limited forest regions. Full article

Neurotropic viruses have evolved sophisticated mechanisms to evade host immune responses within the central nervous system (CNS), enabling viral replication, persistence, latency, and neuropathogenesis while minimizing irreversible neuronal damage. Unlike peripheral tissues, the CNS requires tightly regulated antiviral immunity to balance effective pathogen control with the preservation of neural function. This review examines the diverse yet convergent immune evasion strategies employed by major neurotropic RNA and DNA viruses, including herpes simplex virus (HSV), varicella-zoster virus (VZV), cytomegalovirus (CMV), rabies virus (RABV), flaviviruses, alphaviruses, enteroviruses, and JC virus (JCV). We discuss viral interference with innate immune sensing pathways, including RIG-I-like receptors (RLRs) and cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) signaling, inhibition of type I interferon induction and Janus kinase–signal transducer and activator of transcription (JAK–STAT) signaling, modulation of interferon-stimulated effector mechanisms, and disruption of antigen presentation and adaptive immune surveillance. The review further highlights the distinct roles of viral latency, long-term persistence, neuronal–glial interactions, and metabolic reprogramming in facilitating prolonged infection within the CNS. Emerging evidence indicates that successful neurotropic viruses rarely achieve immune evasion through complete suppression of host defenses; instead, they fine-tune antiviral responses to preserve host cell viability while preventing viral clearance. Finally, we discuss current knowledge gaps and emphasize the need for advanced human-relevant models, single-cell and spatial multi-omics, and systems-level approaches to better define virus–host interactions within the CNS. A deeper understanding of these integrated immune evasion networks may reveal novel therapeutic strategies that enhance antiviral immunity while limiting neuroinflammation and preserving neurological function. Full article

Significant research efforts have recently focused on nanomaterial processing for gas sensors and related sensing applications. However, the major challenges in the field involve the choice of material for the sensing layer of the sensor device element, together with the right structure, assembly, and morphology through which the full sensing properties of the material can be realised. Herein, we critically review the hierarchical nanostructures of V₂O₅ nanomaterial for application in gas sensing technology. Beyond the sheet structure, which serves as the fundamental building block of the V₂O₅’smolecular arrangement, nanostructures ranging from nanobelts to nanowires, nanorods, nanoribbons, nanofibres, nanotubes, and thin films were discovered as preferred configurations and thermodynamically favourable structures, according to many synthesis processes. Ethanol (C₂H₅OH) and Nitrogen dioxide (NO₂) gases were identified as preferred molecules commonly detected by various V₂O₅ morphologies, with the nanotube structure showing preferential sensitivity and selectivity to C₂H₅OH. We also discuss perspectives from density functional theory (DFT) studies of V₂O₅ nanostructures and other (2D) materials structures for gas sensing applications. The studies highlight enhanced adsorption energy, increase conductivity, and band gap variation as a result of an upper shift in the Fermi level, all as a consequence of surface interaction between semiconductor crystal orientation and chemical molecules. Finally, our calculations of the optimised parameters for α-V₂O₅ orthorhombic structure showed good agreement with experimental and other theoretical data in the literature. The adsorption energy profile for NO₂ molecules revealed that the Ag-doped surface exhibits the most negative adsorption energy compared with the clean surface and other doped surfaces. Full article

Surface plasmon resonance (SPR) sensors translate refractive-index (RI) changes near an interface into measurable angular shifts, but a large shift is useful only when the resonance remains sufficiently narrow and deep. Conventional single-metal SPR structures typically force a trade-off in which sensitivity gains come at the cost of broader resonances or shallower reflectance dips. Here, a BAK1/Cu/Al/BaTiO₃/FASnI₃/BP multilayer SPR refractive-index sensor is proposed and optimized using the transfer matrix method (TMM) coupled with a genetic algorithm (GA). The Cu/Al bimetallic region provides a plasmonic metal core, BaTiO₃ and FASnI₃ progressively enhance the evanescent field, and black phosphorus (BP) forms the analyte-facing sensing interface. To avoid sensitivity-only optimization, the GA uses a composite sensitivity figure (CSF) that integrates angular sensitivity, resonance dip depth, and full width at half maximum as the fitness function. At an analyte refractive index (RI) of 1.355, the sensor reaches a maximum sensitivity of 510.11°/RIU and a CSF of 76.39 RIU⁻¹. These results establish the GA-CSF framework as a generalizable route to the balanced design of multilayer SPR refractive-index sensors and provide a computationally guided starting point for experimental implementation. Full article

Circular economy is today a key driver of every transformation process aimed at reducing and optimizing the use of energy and materials. The production of solid biofuel from waste is a typical route to lower the potential impact of greenhouse-gas emissions. In this context, olive pomace is a relevant feedstock, as 4 million tonnes are generated worldwide each year alongside olive oil production. However, only a small fraction of olive pomace is currently valorized. Fresh olive pomace must first be quickly dried to a low, controlled moisture. This step is performed poorly and at a high energy cost. This paper presents an automation-based approach to enhance biomass production from olive pomace, thereby advancing circular-economy practices in olive oil production. The work is focused on four aspects. In the first part, a review of the state of automation in agricultural engineering with a focus on biomass and olive pomace is proposed. Then, the design and construction of an innovative drying system that integrates an infrared solution directly into the transporting screw conveyor is described, integrating real-time online microwave moisture sensing and PLC control. After that, a cloud-based service is presented for remote monitoring, data analysis, and optimization. The innovative and automated drying system was validated during a preliminary field campaign at an olive mill. After about sixteen hours of continuous, cloud-monitored operation, the resulting olive pomace moisture fell below the 5% threshold across a wide range of inlet-moisture conditions, with a stable electrical power demand of approximately 1.85 kW. Finally, an environmental analysis is provided to evaluate the environmental aspects related to the proposed system. The preliminary analysis confirms a significant avoided-carbon potential if the resulting olive pomace is reused as biomass for energy production. The impact associated with 1 kWh_-eq produced from olive pomace is in the range of 0.006–0.033 kg CO_2-eq. Full article

Benzene, toluene, and xylene (BTX) gases pose severe threats to public health on account of their toxic, carcinogenic properties and environmental recalcitrance. In addition, their similar molecular structures and chemical inertness complicate the practical quantification of BTX, severely hindering their high-selectivity detection. In this work, a chemiresistive sensor based on F-doped In₂O₃ nanofibers was constructed. This strategy successfully enhanced the sensor response values toward benzene, toluene, and xylene by approximately 4.3, 4.5, and 4.2 times, respectively. Meanwhile, the limits of detection were reduced from 1, 0.5, and 0.25 ppm to 0.5, 0.25 and 0.05 ppm, respectively. The theoretical lower detection limits for benzene, toluene, and xylene are as low as 9.9 ppb, 5.7 ppb, and 2.5 ppb, respectively. Machine learning methods were further employed for the identification of BTX gases and their binary mixtures. By capturing the distinctive and reproducible patterns of feature vectors from various analytes, the optimal algorithm established decision boundaries and achieved a classification accuracy of 92.1%. Meanwhile, the reasons for the improved gas sensing performance are discussed. Furthermore, a portable wireless gas sensing module based on F-doped In₂O₃ nanofibers was developed, which can be applied for detecting BTX. Full article

Metal oxide semiconductor (MOS) gas sensors are widely used for low-cost chemical detection, but their practical performance is still limited by high operating temperature, insufficient selectivity, signal drift, and device-to-device variation. Recent advances in microelectromechanical systems (MEMS), dynamic sensing protocols, and artificial intelligence (AI) provide new opportunities to improve MOS gas sensing from both hardware and data-processing perspectives. MEMS micro-hotplates enable miniaturized devices, low-power heating, rapid thermal control, temperature-modulated operation, and compatibility with integrated readout and interface circuits, while AI methods extract multivariate, nonlinear, and temporal information from cross-sensitive sensor responses. This review summarizes the fundamentals of MOS sensing materials, MEMS micro-hotplate platforms, material–device integration strategies, and AI-assisted data-processing methods ranging from classical statistical analysis to deep learning. Representative strategies are discussed, including single-sensor feature extraction, sensor-array recognition, temperature-modulated sensing, drift compensation, and AI-guided material design. Application studies in food quality assessment, agriculture, medical diagnostics, environmental monitoring, and public safety are further reviewed to show how sensing tasks evolve from odor-fingerprint discrimination to nonlinear feature interpretation, dynamic response analysis, domain adaptation, and deployable intelligent monitoring. Particular attention is given to the role of high-consistency integration of MOS sensing layers on MEMS platforms, since reproducible material loading, morphology, electrode coverage, and thermal coupling are essential for reliable datasets and transferable AI models. Finally, key challenges are discussed, including dataset heterogeneity, long-term drift, edge deployment, and material–device reproducibility. This review highlights that future AI-assisted MOS/MEMS gas sensors require coordinated design of sensing materials, device platforms, fabrication processes, operating protocols, and data-driven models. Full article

Cancer cells survive, proliferate, and metastasize in part because the immune system fails to detect and eliminate them. Moreover, the tumor microenvironment (TME) that surrounds the tumor supports cancer cell survival and resistance to chemo- and immunotherapies by inhibiting antitumor immune responses and thereby reducing the efficacy of immunotherapeutic interventions. cGAS-STING signaling senses cytoplasmic DNA and coordinates innate immune responses that shape tumor-intrinsic outcomes and the TME. Emerging evidence reveals a context-dependent, dualistic role for cGAS-STING in metastatic progression and cancer dormancy. Acute, robust activation in antigen-presenting cells promotes type I interferon responses, leading to suppression of tumor growth. By contrast, chronic, low-level cancer-intrinsic STING signaling can engage inflammatory programs that foster immune suppression and therapy resistance. Dormant disseminated tumor cells exploit niche cues to downregulate STING signaling and evade immune detection, whereas reactivation of dormant cells often involves restoration of STING activity that can promote immune elimination. In this article, we review mechanisms linking genome instability and cytoplasmic DNA to STING activation, summarize evidence for tumor-suppressive versus tumor-promoting functions across metastatic niches, and discuss how STING agonists and combination strategies may be optimized to maximize antitumor immunity while avoiding protumorigenic effects. Full article

Unmanned aerial vehicles (UAVs) have been increasingly used as flexible sensing platforms for remote sensing applications due to their rapid deployment and efficient data acquisition capabilities. Cooperative vehicle–UAV systems have shown great potential for large-scale remote sensing monitoring and field verification. However, existing task planning methods often overlook the characteristics of remote sensing verification missions, including fragmented target parcels and spatiotemporal uncertainties caused by complex terrain, which limits scheduling efficiency and robustness. To address these challenges, this paper proposes a spatiotemporal uncertainty-aware task planning framework for vehicle–UAV cooperative remote sensing verification. The framework integrates UAV capability-constrained task region generation, terrain-driven spatial uncertainty risk classification, a dual-channel genetic algorithm (DC-GA), and an uncertainty-aware two-stage scheduling framework (UATSF). Experiments in two real-world study areas validate the effectiveness of the proposed framework. The region-merging strategy reduces total travel distance and travel time while improving UAV utilization, and DC-GA consistently reduces the system makespan across different vehicle configurations. Moreover, the two-stage strategy, which combines deterministic optimization with Monte Carlo robustness assessment, reduces planned completion time by 6.80–11.09% compared with worst-case scheduling while achieving 86.20–98.40% reliability under the modeled uncertainty and assumed simulation settings. The results demonstrate that the proposed framework improves task planning efficiency and robustness for vehicle–UAV cooperative operations in complex terrain environments. Full article

Photon-efficient LiDAR provides an active remote sensing pathway for long-range, low-light three-dimensional observation, but photon-limited reconstruction remains difficult when background events dominate sparse returns. In dynamic scenes, line-of-sight motion further disperses weak-target photons across range-time bins and broadens fixed-bin accumulation. Existing methods mainly exploit histogram statistics, spatial priors, or learned representations, whereas explicit range-time migration constraints for interframe photon alignment remain insufficiently explored. We present a range-time trajectory-guided photon accumulation method for a 1064 nm InGaAs/InP $64\times 64$ GM-APD array. The method combines range-dependent background correction, multi-scale photon statistics, bounded trajectory parameter optimization using PSO, trajectory-aligned accumulation, and confidence-weighted spatial-range fusion. At -12.34 dB in simulation, SSIM, RMSE (range bins), PSNR, IoU, and FPR were 0.61, 223.22, 6.36 dB, 0.74, and 0.10%, respectively. Across 19 experimentally generated low-SBR evaluation cases derived from measured GM-APD LiDAR sequences, the corresponding mean values were $0.541\pm 0.165$, $332.93\pm 79.60$, $8.60\pm 2.49$ dB, $0.702\pm 0.061$, and $0.33\pm 0.08$%. Under the tested conditions, range-time guidance preserved spatial structure and pixel-wise range information while suppressing background responses, supporting photon-limited reconstruction in active remote sensing. Full article

Distributed acoustic sensing (DAS) converts fiber cables into dense strain-rate sensor arrays, capturing direct, reflected and guided seismic phases with ultra-fine spatial sampling. However, DAS interrogators suffer far stronger random noise than traditional geophones, limiting its microseismic imaging capacity. This work adopts curvelet-transform denoising to suppress noise. Curvelets partition the frequency–wavenumber plane into multiscale directional sectors; coherent wave energy concentrates in limited angular wedges, while stochastic noise disperses evenly across all transform coefficients, enabling noise-signal separation via wedge-wise thresholding. We test four default threshold schemes on synthetic downhole DAS microseismic data. Parameter tuning proves all methods deliver comparable performance, so we compare their out-of-box reliability for shale reservoir monitoring. Three noise-statistic-based strategies perform stably: median-absolute-deviation (MAD), quiet-window and empirical-cumulative-distribution-Function (ECDF percentile) thresholding. By contrast, the default knee-point algorithm from mainstream DAS toolboxes fails, as its preset threshold falls within noise components and barely removes interference. We propose MAD as a robust default for the tested downhole DAS microseismic setting for it estimates thresholds directly from noisy traces without blank reference windows and offers superior operational stability. Applied to field DAS records from a southwest China shale-gas horizontal monitor well, the MAD curvelet workflow greatly enhances microseismic arrivals with negligible spurious events. Benchmarks against standard 2D Daubechies-4 wavelet and adaptive Goldstein FK filtering verify curvelet denoising as a physically interpretable, efficient tool for DAS wavefields. Full article