Search Results (8,645)

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses under biochar-mediated alleviation of Cd stress remain insufficient. Spectral modeling methods can enable rapid and nondestructive monitoring of crop physiological status. In this preliminary experiment, Zhengdan 958 maize seedlings grown in Cd-contaminated soil were subjected to five biochar application rates: 0, 10, 30, 50, and 70 g/pot, designated as CK, A1, A3, A5, and A7, respectively. The study established a non-destructive spectral detection model for relative chlorophyll content expressed as SPAD values of maize leaves to achieve spectral inversion of leaf physiological information. The alleviating effect of biochar on Cd stress was evaluated by analyzing SPAD values and Cd accumulation in roots, stems, and leaves. The original spectral data underwent preprocessing steps including multivariate scattering correction, standard normal variable transformation, normalization, trend removal, first-order derivative transformation, and second-order derivative transformation. The effectiveness of different preprocessing methods was compared using partial least squares regression. Feature bands were identified via Pearson correlation analysis, and support vector regression models were established based on genetic algorithm (GA), particle swarm optimization (PSO), and grid search optimization. The results demonstrated that biochar application significantly increased the SPAD values of corn leaves (r = 0.879) and reduced the proportion of bioavailable Cd in soil, with the A7 treatment showing the most substantial decrease (30%). This indicates that biochar effectively mitigates Cd’s inhibitory effect on chlorophyll synthesis, with the alleviation effect enhancing as biochar application rates increased. Validation of the partial least squares regression model revealed that detrended spectra achieved optimal predictive performance (R²c = 0.94, RMSEC = 0.82, R²p = 0.88, RMSEP = 1.15), leading to the development of three optimized support vector regression models: GA-SVR, PSO-SVR, and GS-SVR. The GA-SVR model with a sigmoid kernel demonstrated the best internal validation performance for predicting SPAD values in maize leaves (R²c = 0.95, RMSEC = 0.24; R²p = 0.75, RMSEP = 1.63). This study provides preliminary theoretical support and technical reference for rapid spectral detection of the physiological status of maize under biochar-mediated mitigation of cadmium stress. Full article

Decoding mental states from non-invasive neural recordings is central to brain-computer interface research. Multimodal acquisition that combines electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) couples the high temporal resolution of EEG with the spatial specificity of fNIRS, compensating for the individual limitations of each modality. While such hybrid systems achieve strong intra-subject performance, cross-subject generalization remains constrained by inter-individual variability in neural responses. This study introduces the Eigenvalue-Guided Positional Encoding Classification Transformer (EPECT), an architecture that integrates eigenvalue-aware multi-head self-attention with sinusoidal positional encoding to capture both the spectral structure of the learned feature representations and the temporal ordering of multimodal sequences. Stacked one-dimensional convolutions extract local patterns prior to transformer encoding, and global average pooling aggregates the final representation for classification. EPECT was evaluated on two publicly available EEG-fNIRS datasets covering motor imagery (MI), n-back, discrimination/selection response (DSR), and word generation (WG) paradigms under a cross-subject protocol. The model achieved classification accuracies of 97.3%, 96.3%, 98.1%, and 97.9% on the MI, n-back, DSR, and WG tasks, respectively. Ablation studies quantified the contribution of each architectural component, and integrated gradients analysis revealed structured modality-specific attribution patterns aligned with task-relevant cortical regions. Additional experiments with synthetic cortical perturbations demonstrate the sensitivity of EPECT to subtle activity changes, indicating potential utility for tracking neurorehabilitation outcomes in future clinical applications. Full article

Background: Heller myotomy (HM) is the standard surgical treatment for achalasia. Complete muscular division while preserving mucosal integrity is essential for optimal outcomes. Indocyanine green fluorescence (ICG) imaging has recently emerged as a potential intraoperative adjunct during minimally invasive HM, although evidence remains limited and heterogeneous. Methods: A scoping review was conducted according to PRISMA-ScR recommendations. PubMed, Scopus, and Web of Science were systematically searched to identify clinical studies reporting intraoperative ICG use during laparoscopic or robotic HM. Data regarding surgical approach, fluorescence technique, intraoperative applications, and outcomes were extracted and descriptively synthesized. Results: Four clinical studies published between 2022 and 2024 were included, involving 58 patients overall, of whom 41 underwent minimally invasive HM with intraoperative ICG fluorescence assessment. Two main fluorescence strategies were identified. Intravenous ICG administration was exclusively evaluated during robotic Heller myotomy, whereas all laparoscopic studies employed intraluminal ICG instillation through a nasogastric tube. Fluorescence imaging was used to assess myotomy completeness, identify residual muscle fibers, and detect mucosal perforation. Intraluminal ICG enabled direct visualization of the mucosal tube and facilitated leak detection, whereas intravenous administration enhanced tissue contrast and identification of residual muscular bundles. No ICG-related adverse events were reported. However, the available evidence was limited to small observational series with heterogeneous protocols and inconsistent outcome reporting. Conclusions: ICG fluorescence appears technically feasible during minimally invasive HM and may support intraoperative assessment of myotomy completeness and mucosal integrity. Although early clinical experience is encouraging, the available evidence remains insufficient to support routine implementation of ICG-guided assessment during Heller myotomy, highlighting the need for standardized prospective comparative studies. Full article

Spatial updating—the ability to continuously revise spatial representations during locomotion—is fundamental to adaptive navigation and depends on flexible reference frames. Although previous research has established independent effects of field cognitive style and scene layout on spatial performance, their interaction and underlying neural substrates remain poorly understood. The present study examined how field dependence–independence and environment geometry jointly modulate spatial updating by combing the judgment of relative direction (JRD) paradigm with functional near-infrared spectroscopy (fNIRS). Forty participants were recruited and assigned to two groups (20 field-independent [FI] and 20 field-dependent [FD]) based on Embedded Figures Test scores. They completed directional pointing tasks in two virtual environments: a geometrically structured rectangular spaces affording explicit orthogonal reference axes and an ambiguous oval environments devoid of stable global geometric anchors. Behaviorally, FI individuals exhibited shorter response time in rectangular layouts yet superior accuracy in oval layouts relative to FD individuals. Neurally, the middle frontal gyrus (MFG) emerged as a critical locus exhibiting a significant interaction effect between cognitive style and environmental layout. Significant main effects of field cognitive style were observed in the precentral gyrus, superior parietal lobule, and paracentral lobule, with FI individuals showing greater oxyhemoglobin (HbO) elevation than FD participants. Collectively, these findings may tentatively suggest an interpretation that FI individuals flexibly alternate between internal egocentric and external allocentric reference frames during spatial information processing, whereas FD individuals predominantly rely on inherent structural cues embedded in the external environment. These findings may reflect cortical hemodynamic correlates of field cognitive style differences during spatial processing, and may offer empirical references for relevant cognitive neuroscience research and subsequent exploratory applications. Full article

The ability to detect individual photons with exquisite temporal precision has transformed single-photon avalanche diodes (SPADs) into indispensable tools across photonics, with quantum communication emerging as one of their most demanding frontiers. In recent years, device engineering breakthroughs, including refined junction geometries, advanced avalanche quenching schemes and scalable array integration, have redefined the limits of SPAD performance. Parallel advances in fabrication precision and CMOS-compatible architectures have not only expanded spectral sensitivity from the visible to the near-infrared but also enabled systematic suppression of dark count rates, afterpulsing, and timing jitter. These developments have directly impacted the feasibility and robustness of quantum key distribution and time-correlated single-photon counting, where detection efficiency and noise suppression determine system fidelity. This review unifies recent progress in SPAD technology, weaving together innovations in device design, fabrication strategies, and parameter optimization to reveal their collective influence on next-generation quantum-enabled photonic systems. Looking forward, the convergence of hybrid material platforms, on-chip photonic–electronic co-integration, and intelligent quenching control is poised to elevate SPAD performance to meet, and potentially exceed, the stringent requirements of future quantum communication infrastructures. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article

Accurate measurement of coal calorific value is critical for efficient power generation. To address the limitations of conventional models on large-scale, heterogeneous datasets, this study proposes a novel deep learning framework, the Multi-View Transformer (MVFormer), utilizing fused Near-Infrared Spectroscopy (NIRS) and X-ray Fluorescence (XRF) data. The architecture employs a dual-pathway Transformer with a Masked Autoencoder pre-training strategy to enhance feature representation from over 20,000 coal samples. Furthermore, a multi-view fusion mechanism integrates diverse pre-processing perspectives to enhance generalization. Experimental results demonstrate that this approach significantly outperforms traditional Partial Least Squares (PLS) regression and Multilayer Perceptron (MLP) models. These findings validate the framework as a robust and precise solution for real-time industrial coal quality analysis, successfully achieving precise prediction of calorific value on large-scale coal datasets. Full article

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans using canopy hyperspectral reflectance in the visible to near-infrared (VNIR) range from 501 to 801 nm. The dataset included multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach). Model performance was assessed using Kennard–Stone validation and leave-one-year-out cross-validation. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS performed well for AGB under Kennard–Stone validation, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. Leave-one-year-out cross-validation showed that year-to-year transferability was more difficult for AGB than for LAI and CC. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and that selected VNIR bands can provide candidate spectral regions for simplified sensing of soybean canopy traits. Full article

Artificial intelligence-assisted food detection is increasingly moving from single-task classification toward integrated analytical systems capable of producing multiple quality-related outputs from one sensing workflow. However, most hyperspectral food detection studies still rely on a single spectral range or simple feature concatenation, which limits their ability to exploit complementary physicochemical information from heterogeneous sensors. In this study, an artificial intelligence-enabled visible–near-infrared and short-wave infrared (VNIR-SWIR) hyperspectral fusion framework is proposed for multi-task fruit detection, using origin authentication and bruise localization as representative tasks. The proposed method first constructs an observation-consistent fused representation from high-resolution VNIR images and low-resolution SWIR images. Collaborative spectral unmixing is used to couple cross-modal material distributions, while abundance-consistency and downsampled observation-consistency constraints are introduced to estimate SWIR-informed features on the VNIR spatial grid without assuming measured high-resolution SWIR ground truth. The fused representation is then processed by a shared spectral–spatial deep encoder with two task-specific heads: a fruit-level classification head for origin authentication and a pixel-level segmentation head for bruise detection. Experiments on apple and kiwifruit datasets show that the proposed framework outperforms VNIR-only, SWIR-only, bicubic-fusion, CNMF-style fusion, and TV-regularized fusion baselines under five fruit-level stratified random splits. For origin-category authentication, the proposed method achieved an accuracy of almost 93.85 for apples and almost 94.35 for kiwifruit. For bruise localization, the proposed method achieved higher overall accuracy, average accuracy, and Cohen’s kappa across the evaluated fruit categories. Full article

Lithological mapping with multispectral remote sensing remains challenging when diagnostic spectral information is limited and reliable labeled samples are scarce. This problem is particularly relevant when convolutional neural networks (CNNs) are applied to lithological classification, because limited spectral dimensionality and scarce training samples may hinder the learning of discriminative spatial–spectral features. In this study, we developed a limited-sample lithological mapping framework for the Shibaocheng area of Subei County, Gansu Province, China, using band-integrated ASTER and Sentinel-2A multispectral imagery. ASTER shortwave infrared (SWIR) bands were co-registered and resampled to Sentinel-2A imagery, and then integrated with Sentinel-2A visible and near-infrared (VNIR) and red-edge bands to construct a complementary multispectral dataset. A compact spectrally enhanced multi-scale CNN was designed, incorporating a residual spectral feature enhancement module for inter-band representation learning and a parallel multi-scale hybrid convolution module for capturing spatial–spectral features. Eight lithological units were classified under limited-label conditions using 8158 training samples and 3497 spatially independent validation samples. Experimental results show that the band-integrated ASTER–Sentinel-2A dataset improved classification performance compared with single-sensor inputs. Using the proposed model, the band-integrated dataset achieved an overall accuracy (OA) of 94.12%, average accuracy (AA) of 94.04%, and Kappa coefficient of 0.932, compared with OA values of 93.14% and 92.40% obtained using ASTER and Sentinel-2A alone, respectively. The positive effect of band-level integration was also observed for spectral angle mapper (SAM), support vector machine (SVM), and 3D-CNN, whose OA values increased to 54.33%, 86.12%, and 92.29%, respectively. The proposed CNN achieved the highest OA among the evaluated methods, outperforming SAM, SVM, and the conventional 3D-CNN. In addition, t-SNE visualization indicated that incorporating spatial texture features produced more compact and better-separated lithological clusters than using spectral features alone. Ablation experiments further demonstrated that the proposed spectral feature enhancement and multi-scale hybrid convolution modules each contributed to improving lithological classification performance. These results demonstrate that integrating freely available multispectral data with a lightweight spectral–spatial CNN provides a practical and cost-effective solution for lithological mapping in bedrock-exposed arid to semi-arid regions, especially where hyperspectral imagery and dense field samples are unavailable. Full article

Purpose: This study aimed to develop and validate a comprehensive multi-sensor integrated platform for objective assessment of skeletal muscle recovery kinetics following exercise-induced muscle damage (EIMD), combining biomechanical, thermal, and biochemical monitoring modalities. Methods: Forty elite male athletes were randomized to microwave diathermy (MWD, n = 20, 2.45 GHz, 160 W, 45 min/session) or control (n = 20) groups. Time-synchronized multi-sensor assessments at baseline, 24 h, 48 h, and 72 h post-EIMD included: biomechanical sensors (knee flexion range of motion via goniometry and isokinetic peak torque), thermal sensor (skin surface temperature via infrared thermometry), and biochemical sensor array (serum CK, IL-6, and CRP via high-sensitivity ELISA). Two-way repeated-measures ANOVA with Bonferroni correction examined group × time interactions across all sensor channels. Results: Pre-study validation confirmed high reliability across all sensor modalities. Cross-modality concordance analysis revealed significant correlations between biomechanical and biochemical recovery trajectories (isokinetic torque vs. IL-6: r = −0.73, p < 0.001; pain vs. IL-6: r = 0.68, p < 0.001). MWD intervention demonstrated accelerated recovery across all sensor channels: complete ROM recovery by 48 h (MWDG post-2 vs. baseline, p > 0.05; CG post-3 43% below baseline, p < 0.001), complete isokinetic torque restoration by 72 h (MWDG post-3 vs. baseline, p > 0.05; CG 44% below baseline, p < 0.001), and near-complete pain resolution (VAS 1.70 ± 2.50 mm, p < 0.05). Biomarker sensors demonstrated differential recovery kinetics: IL-6 normalized by 48 h (1.52 ± 0.14 pg/mL, p > 0.05 vs. baseline), CRP approached baseline by 72 h (0.73 ± 0.24 mg/L, p > 0.05), while CK remained elevated at post-3 (169.70 ± 22.58 U/L, 30% above baseline, p < 0.001), indicating incomplete myofiber membrane integrity recovery despite resolution of systemic inflammatory markers. The control group exhibited persistent deficits across all sensor channels with no clinically meaningful recovery. Conclusions: This study validated an integrated multi-sensor platform for recovery assessment. Microwave diathermy demonstrated efficacy by 72 h with complete functional recovery and inflammatory normalization (though CK remained elevated). Cross-modality concordance (r = −0.73 to 0.68) confirmed superior assessment compared to single-modality approaches. This laboratory-based methodology provides a framework for future portable sensor systems in athletic surveillance. Full article

Photothermal-assisted electrocatalysis is an effective approach to enhance the efficiency of the oxygen evolution reaction (OER), but the synergistic mechanism between the photothermal effect and the regulation of catalyst electronic structure remains unclear. This work reports the construction of NiFeS/Cs_0.32WO₃ heterostructures, which integrate interfacial electron transfer and localized surface plasmon resonance (LSPR)-induced photothermal effects to enhance OER performance. The Cs_0.32WO₃ component with hexagonal tungsten bronze structure exhibits strong absorption in the near-infrared region, attributed to LSPR (1100 nm to 2500 nm) and small polaron transition (780 nm to 1100 nm), endowing the NiFeS/Cs_0.32WO₃ composite with excellent photothermal conversion capability. Under 808 nm laser irradiation, the steady-state surface temperature of the heterostructure reaches 65.1 °C. X-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy analyses reveal that spontaneous electron transfer from NiFeS to Cs_0.32WO₃ occurs at the heterostructure interface, thereby optimizing the electronic structure of active sites. Electrochemical measurements demonstrate that at a current density of 50 mA cm⁻², the NiFeS/Cs_0.32WO₃ composite exhibits an overpotential of 301 mV under near-infrared irradiation, representing a reduction of 53 mV compared to NiFeS under dark conditions. At a current density of 50 mA cm⁻², the photothermal enhancement effect of the NiFeS/Cs_0.32WO₃ composite is identified as the predominant contributor to the overall performance improvement. Nevertheless, the intrinsic interfacial effect associated with the heterojunction also plays a crucial role and makes a non-negligible contribution to the enhanced electrocatalytic activity. The Tafel slope decreases from 57.8 mV dec⁻¹ to 44.5 mV dec⁻¹ under near-infrared illumination, indicating accelerated OER kinetics. This work elucidates the mechanism of synergistic enhancement between heterostructure construction and photothermal effects, providing insights for the design of advanced photothermal electrocatalysts. Full article

To investigate the influence of decision-making styles on college students’ sports and health consumption preferences and the underlying cognitive neural mechanisms, this study recruited 39 college students as participants, adopted a one-factor within-subjects design, and combined behavioral experiments with functional near-infrared spectroscopy (fNIRS). It examined consumption preferences and brain activation characteristics in maximizers and satisficers under three conditions: no promotion, discount promotion, and public welfare promotion. In behavioral terms, college students demonstrated the highest inclination towards public welfare promotions, with discounts being the second most favored, while the no-promotion condition received the lowest preference. Maximizers preferred discount promotion, while satisficers prioritized public welfare promotion. In neural terms, public welfare promotion widely activated the left dorsolateral prefrontal cortex, whereas discount promotion only activated a local region of this cortex. Maximizers showed the strongest activation in the corresponding region under discount promotion, and satisficers exhibited more significant activation in the corresponding region under public welfare promotion. Decision-making styles shaped consumption preferences through depth of information processing and brain activation patterns: maximizers focused on rational calculation and benefit maximization, while satisficers relied on intuitive experience and value perception. These findings provide behavioral and neuroscientific evidence for precision marketing in the sport and health consumption market and the implementation of the national fitness program. Full article

Low-dimensional materials (LDMs) are often favored by researchers in the ultrafast photonics field for their low optical loss, ultrafast carrier response, broadband nonlinear absorption, and easy integration with optoelectronic systems. High-performance broadband saturable absorbers (SAs) fabricated from LDMs have become core components for achieving compact and miniaturized ultrafast laser. This paper systematically reviews the laser applications of LDM SAs in the near/mid-infrared spectral region, focusing on the pulse modulation mechanisms, material systems, and device integration approaches. It analyzes current research progress and challenges while outlining future development trends for LDM SAs in ultrafast pulsed lasers and optoelectronic devices. Full article