A noninvasive intelligent brain-computer interface (BCI) is integrated with a reconfigurable metasurface to establish a direct neural-to-electromagnetic transduction pathway for real-time, multimodal wave control. By combining convolutional-neural-network-based decoding of motor-imagery electroencephalography (EEG) signals with threshold-based recognition of electrooculography (EOG) commands, the proposed system converts neural and ocular activities into high-speed control commands, which are subsequently implemented through an FPGA-driven dual-polarized reconfigurable metasurface for real-time electromagnetic-wave manipulation. This architecture concurrently enables beam steering, polarization conversion, and radar cross-section (RCS) reduction within a unified platform. Three distinct operational modes are demonstrated: polarization-invariant beam deflection, joint steering and polarization conversion, and broadband RCS reduction via random phase coding. Experimental results reveal a peak gain of 18.54-19.29 dBi with a 3 dB beamwidth of 12°-15° at a +20° steering angle, and an RCS reduction exceeding 10 dB across 3.7-3.85 GHz. The proposed intelligent BCI-metasurface paradigm validates the feasibility of brain-controlled electromagnetic manipulation, opening new avenues for adaptive wireless communications, immersive virtual environments, and next-generation assistive technologies.

Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiological mechanisms by which it reshapes neural dynamics remain incompletely understood. A hallmark of psychedelic states is widespread cortical desynchronization, typically inferred from reductions in spectral power, but whether such effects reflect genuine weakening of neural oscillations or are confounded by shifts in oscillatory peak frequencies remains unresolved. We combine source-resolved magnetoencephalography (MEG), spectral parameterization, temporal complexity metrics, and interpretable machine learning in an LSD versus placebo design, with and without music. We show that LSD induces robust, spatially structured increases in alpha and beta peak frequencies alongside genuine attenuation of oscillatory power, with partly dissociable cortical patterns. Beyond rhythmic activity, LSD is associated with flattening of the aperiodic 1/f spectral slope and increased neural signal fractality and complexity, preferentially affecting sensory, language, emotion, and imagery-related networks while sparing motor cortex. Machine-learning analyses identify peak-frequency shifts, aperiodic parameters, and complexity measures as key discriminators of the psychedelic state. Music does not robustly amplify these neural signatures and instead shows a trend toward attenuation. These findings provide an electrophysiological account of how LSD reorganizes large-scale human brain dynamics and highlight features that may differentiate its neural signature from other psychedelics.

The mirror neuron network is activated during both execution and observation of the same action, enabling visual-to-motor transformation. Two main hypotheses account for this process: the direct matching hypothesis proposes a direct link between visual cues and the observer's motor representations, whereas the predictive hypothesis suggests that motor predictions are generated and compared with visual information. We tested these hypotheses during a locomotion task. Motor prediction was assessed by comparing actual and imagined walking (forward or backward) over a 9 m path. Action observation (AO) was examined through a time-estimation inference task in which participants observed an agent walking 3 m before occlusion and estimated when the agent would reach the end of the 9 m path. We manipulated these tasks by mechanically vibrating the leg muscles, creating sensory illusions. We found that vibration-induced sensory illusions altered motor imagery timing, affecting sensorimotor prediction. Opposite effects on AO and numerical simulations supported the predictive hypothesis.

Growing anthropogenic pressure on aquatic environments has intensified processes of water quality degradation; compromising ecosystems, the health and wellness of populations that depend on natural resources, and associated economic activities. This study aims to assess the water quality of Lagoa da Conceição, in Florianópolis, SC, Brazil, through two distinct and complementary approaches; the evaluation of potentially toxic chemical elements in nanoparticles (NPs) and ultrafine particles present in aquatic sediments collected between 2024 and 2025, and the analysis of the variables chlorophyll-a (CHL), total suspended matter (TSM), and absorption of dissolved organic matter at 443 nm (ADG443), obtained from Sentinel-3A/B OLCI satellite imagery between 2019 and 2025. Methodologically, aquatic sediments were collected at 18 sites across Lagoa da Conceição for the analysis of NPs and ultrafine particles. In parallel, the CHL, TSM, and ADG443 variables were retrieved from Sentinel-3A/B satellite imagery at 153 points processed in the Sentinel Application Platform (SNAP), with the resulting data subjected to K-means clustering and robust ANOVA to identify geospatial patterns. As a result, sediment analyses identified iron (Fe) nanoparticles containing up to 60% carbon, frequently associated with chromium (Cr), nickel (Ni), vanadium (V), zinc (Zn), and zirconium (Zr), in addition to the presence of plastic nanoparticles. Concurrently, the bio-optical variables of the water column exhibited elevated values of CHL (7.187 mg/m[3]), TSM (17.213 g/m[3]), and ADG443 (5.555 m[-][1]) over the 2019-2025 period. The geospatial patterns obtained through K-means clustering revealed moderate-to-high concentrations of these indicators within the interior of Lagoa da Conceição and significantly lower values in the adjacent coastal areas. Taken together, these two distinct and complementary approaches provided evidence of water quality degradation in the study area.

Reliable brain-computer interfaces (BCIs) require electroencephalography (EEG) sensors that maintain conformal, low-impedance contact through hair, motion, and repeated use. Here, we present MindStretcH, a stretchable and hair-compatible EEG platform integrating a kirigami-patterned elastomeric mesh, deformable eutectic gallium-indium (EGaIn) interconnects, and detachable hydrogel-based conical electrodes. The system adapts to variations in head geometry, preserves electrical performance under substantial mechanical deformation, and maintains stable electrode-scalp contact over prolonged use. Longitudinal motor-imagery recordings and repeated online BCI experiments showed consistent task-related EEG activity without measurable deterioration in recording quality. These results establish MindStretcH as a reusable platform for long-term EEG monitoring and BCI applications in laboratory, clinical, and home settings.

Global climate change is intensifying pressure on marine ecosystems, particularly coastal bays affected by natural variability and anthropogenic disturbances. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and eutrophication status. This study investigated the spatiotemporal variability and potential drivers of Chl-a in Haizhou Bay during 2016-2025 by integrating Sentinel-3 Ocean and Land Colour Instrument (OLCI) imagery, in situ measurements, and multi-source ancillary data. Among the evaluated models, random forest showed the best overall performance and stability, outperforming support vector regression, extreme gradient boosting, and European Space Agency operational algorithms, with a coefficient of determination of 0.72 and a root mean square error of 0.66 in the logarithmic domain. The retrieved Chl-a fields were reconstructed into a spatiotemporally continuous 2016-2025 dataset for analyzing long-term dynamics and potential drivers. The principal contribution is the construction of this continuous dataset and its integration with environmental records to assess associations with mariculture activities, nearshore nutrient pressure, and episodic green-tide disturbances. Annual mean Chl-a initially increased, declined after 2019, and rebounded slightly in 2025, while remaining consistently higher nearshore than offshore. Chl-a peaked in May-June, and seasonal variability was associated with Pyropia cultivation, nutrient dynamics, and episodic green tides. Annual mean Chl-a was correlated with Pyropia cultivation area (r = 0.81, p < 0.01), whereas the areal proportion of high-Chl-a waters showed a positive but non-significant correlation with the eutrophication index (r = 0.63, p > 0.05). These findings provide a long-term evidence base for eutrophication control, mariculture management, and refined coastal management.

Hyperspectral image (HSI) and multispectral image (MSI) fusion is a critical technique for generating imagery with high spatial and spectral resolution. However, developing robust fusion models is fundamentally challenged by the severe scarcity of large-scale, precisely paired HSI-MSI datasets, which are prohibitively difficult to acquire. This data limitation constrains the performance and generalization of existing data-driven methods. Furthermore, effectively leveraging the complementary strengths - the rich spectral prior of HSIs and the detailed spatial prior of MSIs - remains a complex problem. To overcome this limitation, we propose DSES-Diff, a foundation model for HSI-MSI fusion that eliminates the dependence on large-scale paired data. Our approach separately trains a dynamic-spectral network and an efficient-spatial network using extensive unpaired high-resolution spectral and spatial datasets to extract strong modality-specific priors. Specifically, we introduce AdaSpecNet, a spectral network built upon the Diffusion Transformer (DiT) architecture, which treats each individual spectral band as a sequence token. By leveraging positional padding and attention masks, AdaSpec-Net can flexibly accommodate scalable spectral inputs with band numbers in a unified manner. Furthermore, we propose MoESpatNet, a spatial network that incorporates a Mixture-of-Experts (MoE) module to enhance the network's ability to model diverse spatial distributions. The MoE design enables sparse expert activation during inference, improving fusion quality and computational efficiency. Extensive experiments across various datasets demonstrate that our method achieves superior fusion performance under diverse conditions, showcasing its strong potential to serve as a versatile foundation for scalable HSI and MSI fusion.

Hemispheric strokes impair motor control in contralateral body parts, necessitating effective rehabilitation strategies. Motor imagery-based brain-computer interfaces (MI-BCIs) promote neuroplasticity, aiding the recovery of motor functions. While deep learning has shown promise in decoding MI actions for stroke rehabilitation, existing studies largely focus on bilateral MI actions and are limited to offline evaluations. Decoding directional information from unilateral MI, however, offers a more natural control interface with greater degrees of freedom but remains challenging due to spatially overlapping neural activity. This work proposes a novel deep learning framework for online decoding of binary directional MI signals from the dominant hand of 20 healthy subjects. The proposed method employs EEGNet-based convolutional filters to extract temporal and spatial features. The EEGNet model is enhanced by squeeze-and-excitation (SE) layers that rank the electrode importance and feature maps. A subject-independent model is initially trained using calibration data from multiple subjects and fine-tuned for subject-specific adaptation. The performance of the proposed method is evaluated using subject-specific online session data. The proposed method achieved an average right vs. left binary direction-decoding accuracy of 58.7±8% for unilateral MI tasks, outperforming the existing deep learning models. Additionally, the SE-layer ranking offers insights into electrode contribution, enabling potential subject-specific BCI optimization. The findings highlight the efficacy of the proposed method in advancing MI-BCI applications for a more natural and effective control of BCI systems.

The Jiangsu coastal zone has experienced substantial land use/land cover (LULC) changes under intensified anthropogenic activities, while spectral confusion caused by land-sea interactions increases the difficulty of accurate LULC classification. This study investigated LULC dynamics in the Jiangsu coastal zone in 2000, 2008, 2016, and 2024 using Landsat imagery and the Google Earth Engine platform. A total of 33 features, including spectral bands, spectral indices, texture, topographic variables, and nighttime light data, were extracted. To improve classification accuracy and model generalization, a Pearson-SHAP-RFE feature selection framework was developed by integrating Pearson correlation analysis, SHapley Additive exPlanations, and recursive feature elimination. Four classification algorithms, namely Random Forest, Extreme Gradient Boosting, Classification and Regression Trees, and Support Vector Machine, were compared. Feature selection improved classification accuracy across the four algorithms, with improvements ranging from 4.05% to 5.15% compared with the spectral feature baseline. Random Forest achieved the highest overall accuracy of 92.28%, with a Kappa coefficient of 0.9019. The long-term classification results showed that LULC changes mainly occurred among forest-grassland, construction land, and cultivated land. Water bodies showed the smallest centroid shift, whereas cultivated land exhibited the greatest spatial displacement.

Object detection in Wide Area Motion Imagery (WAMI) is crucial for large-scale intelligent surveillance and monitoring systems. However, detecting extremely small moving targets in low-frame-rate grayscale WAMI remains highly challenging. In particular, three key problems limit the performance of existing methods: weak motion responses caused by low target contrast can lead to missed detections; densely distributed targets often produce merged responses that are difficult to separate; and registration artifacts, parallax, and dynamic background clutter can generate numerous false alarms. To address these problems, we propose MDF-Det, a coarse-to-fine spatiotemporal framework for WAMI small moving target detection. The framework consists of three complementary components. First, a Motion and Appearance Feature Fusion (MAFF) strategy integrates dense optical-flow-derived motion saliency with grayscale frame differencing to enhance weak target responses and improve candidate preservation. Second, a Spatial Attention-Guided Target Decoupling (SA-TD) module employs fine-grained heatmap decoding and step-threshold degradation to separate merged responses in densely populated scenes. Finally, a Scene-Prior Guided Filtering (SPGF) mechanism learns complementary vehicle-accessibility and motion-activity priors from scene context to suppress contextually implausible false alarms caused by complex background interference. Extensive experiments on six evaluation AOIs of the WPAFB 2009 dataset demonstrate that MDF-Det achieves an average F1 score of 0.878, corresponding to a relative improvement of 5.5% over the strongest baseline.