Search Results (2,430)

Background/Objectives: Dementia is a progressive neurodegenerative disorder for which accurate and timely diagnosis remains a major clinical challenge. Electroencephalography (EEG) offers a noninvasive and cost-effective means of capturing neurophysiological alterations, motivating the development of reliable EEG-based automated diagnostic frameworks. This study aims to systematically examine how different time–frequency representations (TFRs) affect dementia classification performance within a unified multi-channel EEG image fusion framework. Methods: Resting-state, eyes-closed EEG recordings from 88 subjects, including Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls, were preprocessed and segmented. Channel-wise signals were converted into two-dimensional time–frequency images using Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), Wigner–Ville Distribution (WVD), or Constant-Q Transform (CQT). Images from 19 EEG channels were fused into a structured grid and classified using pretrained convolutional neural networks, including MobileNetV2, ResNet-50, and InceptionV3. Results: Results indicate that classification performance is highly dependent on the chosen TFR. The STFT-based representation combined with InceptionV3 achieved the highest accuracy, reaching 98.8% with random splitting and 84.3% with subject-wise splitting, outperforming previous studies. CQT also showed competitive performance, whereas HHT and WVD were less effective. Gradient-weighted class activation mapping provided interpretable visualization of physiologically relevant EEG channel contributions. Conclusions: The proposed framework demonstrates the importance of structured multi-channel fusion and systematic TFR evaluation for robust and interpretable EEG-based dementia classification and serves as a foundation for future cross-dataset validation. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

Excessive tool wear can compromise machining precision and increase costs, rendering accurate tool remaining useful life (RUL) prediction imperative in intelligent manufacturing. Traditional methods exhibit intrinsic limitations in cross-modal modeling accuracy and capturing temporal dependencies, failing to meet practical requirements. To transcend these bottlenecks, this study proposes a robust tool RUL prediction framework that combines a multi-channel CNN and a Cross-Modal Transformer. The CNN performs convolution operations to extract local features from wear signals, while the Transformer adaptively synchronizes heterogeneous features (cutting force, vibration, and acoustic emission) to capture long-term degradation trends. Empirical evaluations conducted on the PHM2010 dataset demonstrate the model’s robustness and generalization capability: under the random shuffle–split protocol, the proposed method achieves an R² of up to 0.99, with the RMSE and MAE reaching 2.51 and 1.98, respectively. To further evaluate the framework’s extrapolation ability under domain shifts, a cross-cutter validation protocol was implemented. Under this condition, the experimental results yield an R² of 0.961, an RMSE of 6.92, and an MAE of 6.09. Additionally, the correlation between modality-specific attention weights and their corresponding physical interpretations is systematically investigated. These results confirm the model’s potential for cross-cutter life cycle management in smart manufacturing, providing stable and physically consistent wear estimation and remaining useful life prediction in noise-intensive environments. Full article

In the context of the transformation of welfare systems, and following the impact of the COVID-19 pandemic, communication and social marketing have taken on a strategic role in community social services. This study aims to analyse communication and social marketing campaigns developed from 2020 to the present, in order to identify the social issues addressed, the communication objectives and the strategies employed, as well as their link to Community Social Services. A qualitative, exploratory and descriptive design is adopted based on analysing the content of a corpus of 60 campaigns selected through intentional sampling from international public sources. The results show a clear intensification of campaigns in the post-COVID-19 period, with a predominance of themes such as gender violence, mental health, unwanted loneliness and social exclusion. The communication strategies are characterised by the use of emotional narratives, storytelling, audiovisual formats and multi-channel dissemination, combining awareness-raising, prevention and guidance towards social resources. It is concluded that community social marketing is consolidating its position as a transformative tool that reinforces access to rights, social cohesion and the role of Community Social Services as active agents of social change. Full article

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

More and more spaceborne infrared hyperspectral atmospheric observations are assimilated into data assimilation systems. The key to bias correction (BC) of these instruments depends on selecting predictors. However, it is difficult to find a set of predictors that are highly correlated with the O-B biases in all FY-3E/HIRAS-II channels, due to its multi-channel characteristics. A machine learning model XGBoost (EXtreme Gradient Boosting) BC scheme for FY-3E/HIRAS-II is established in this article. The selected predictors include model skin temperature, model total column water vapor, 1000–300 hPa thickness, 200–50 hPa thickness, scan position, observed brightness temperature (BT) and simulated BT. The method is also compared with the operational static BC and the variational BC, to validate its effect. The two-week data assimilation experiments show that the XGBoost BC is the most effective among the three BC schemes. The mean and standard deviation of O-B in all channels are the smallest after BC, and the effective observations through quality control are the largest, followed by the static BC. The static BC and variational BC are performed based on linear regression, which may lead to a small loss of valid observations in some channels that are weakly correlated with the predictor, whereas machine learning algorithms can search for the nonlinear correlation between biases and predictors. Compared with ERA5, both temperature- and humidity-analysis fields based on XGBoost BC are closest to ERA5 at all levels, and the root mean square errors do not change much over time. Full article

Particulate matter (PM) emissions are common in technological processes, and effective mitigation requires gas pre-treatment before high-efficiency filtration to reduce fine and ultrafine PM that are particularly dangerous to the human health. This study evaluates a multichannel electric field manipulator (agglomerator) as a flow pre-treatment stage and investigates the aerodynamic conditions that govern particle–gas flow distribution and variation in trajectories and dynamics at different flow rates. These factors provide meaningful assumptions about the possible behavior of particles in the flow, and they are critical for optimizing an agglomeration and its intensity. Such phenomena can have an impact on the probability of agglomeration in the manipulator channels, i.e., the adherence of small particles into larger ones, and this allows for improving the design and operating conditions of the apparatus. Gas flow velocities and pressure were analyzed experimentally at various cross-sectional points in the inlet and outlet ducts at inflow rates of 3.4 L/s and 50 L/s. The static inlet pressure of the manipulator ranged from 8 Pa to 178 Pa. This study provides new insights into flow pre-treatment using the electric field mechanism in a multichannel modular apparatus and provides a reasonable understanding of the necessary characteristics of gas flow distribution to support subsequent improvements targeting higher agglomeration. Full article

Addressing the challenges in predicting carbon–water fluxes in cold, temperate coniferous forests—specifically, the strong heterogeneity of driving factors, the significant non-linearity of processes, and the lack of consistency of ecological mechanisms in data-driven models—this paper constructs a Multi-Channel Fusion Attention BiLSTM (MCF-ABiLSTM) model. This model is designed for the joint prediction of Net Ecosystem Exchange (NEE) and Latent Heat Flux (LE). The model adopts a multi-channel structure to separately characterize meteorological, soil, and historical flux information, combining channel attention and temporal attention mechanisms to enhance the identification of key driving factors and critical temporal scales. On this basis, dynamic Water Use Efficiency (dWUE) and Sensitivity of Carbon–Water (SCW) indices are proposed to characterize the synergistic response features of carbon uptake and evapotranspiration under humidity and temperature gradients. The stable ecological relationships revealed by these indices are explicitly introduced into the model training process as ecological process consistency constraints, thereby guiding the model to adhere to known physiological mechanisms while improving prediction accuracy. Experimental results demonstrate that the MCF-ABiLSTM model outperforms various benchmark models in predicting both NEE and LE. Furthermore, flux contribution decomposition results indicate that the model’s response structure to environmental drivers is highly consistent with the known carbon–water coupling mechanisms of cold, temperate coniferous forests. This study achieves organic integration of high-precision carbon–water flux prediction, ecological process constraints, and mechanism analysis, providing a modeling framework that possesses both predictive capability and ecological interpretability for research on the carbon–water cycle in cold, temperate forest ecosystems. Full article

Automated landslide change detection using remote sensing imagery is critical for rapid disaster response. However, landslide change detection using bi-temporal optical imagery is frequently degraded by cross-region domain shifts and by the elongated, anisotropic morphology of landslide boundaries, leading to substantial pseudo-change alarms. To suppress pseudo-changes and improve cross-region robustness, we propose a DEM-assisted topography-conditioned and orientation-adaptive Siamese network (DEMO-Net) that injects topographic inductive bias through terrain-conditioned feature modulation and orientation-adaptive convolutions. Specifically, DEM-derived multi-channel priors are encoded to predict spatially varying FiLM parameters that recalibrate shallow optical features, suppressing spurious changes while preserving discriminative cues. In addition, we introduce an adaptive-oriented attention convolution that leverages a DEM-derived aspect to guide sparse multi-orientation aggregation via shared-kernel transformation, enabling direction-aware receptive-field alignment for elongated and direction-varying landslide structures without costly global attention. Experiments on the GVLM benchmark under a 5-fold site-wise cross-region protocol show that DEMO-Net achieves 85.17% F1 and 74.26% mIoU, outperforming the strongest CNN baseline FC-EF by 5.05% and 7.20%, respectively. These results demonstrate the effectiveness of jointly leveraging terrain-conditioned calibration and physically consistent orientation-aligned feature extraction for robust cross-region landslide change detection. Full article

Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA signals, which may limit their ability to capture complex spatiotemporal dependencies among operational variables. To address this limitation, this paper proposes a novel SCADA-driven power prediction framework that transforms selected SCADA variables into multi-channel grayscale images and leverages an optimized LeNet-5–LSTM hybrid neural network for active and reactive power prediction. First, the SCADA dataset is analyzed to identify the most influential variables affecting power output. Six key variables are then selected, segmented, and encoded as 2D grayscale images, enabling the model to learn richer feature representations compared to conventional raw SCADA data-based methods. The proposed network combines convolutional layers for spatial feature extraction from SCADA data-based grayscale images with LSTM layers to capture temporal dependencies. Model training incorporates a customized loss function that integrates both data-driven supervision and physics-based constraints. The model is trained using 70% of the image-based dataset, with five independent runs to ensure robustness and reproducibility, while the remaining 30% is used for testing. The proposed approach is validated using SCADA data from three real-world cases: (i) a 2 MW Siemens wind turbine in Poland, (ii) a Vestas V52 wind turbine in Ireland, and (iii) the La Haute Borne wind farm in France, consisting of four wind turbines. The results demonstrate that the SCADA-based image representation enables the proposed LeNet-5–LSTM model to effectively learn discriminative feature patterns and achieve accurate active and reactive power predictions across different turbine types and operating conditions. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of death and disability worldwide. Among its subtypes, coronary artery disease (CAD) is the most common and often develops silently, without noticeable symptoms. CAD-related murmurs typically fall below the human hearing threshold, limiting the effectiveness of traditional stethoscope-based auscultation. Currently, the gold standard for CAD diagnosis is coronary angiography, an invasive and expensive procedure usually reserved for symptomatic patients. This highlights the global need for a non-invasive, cost-effective pre-screening tool for asymptomatic CAD detection. Objectives: This study investigates the effectiveness of a wearable vest equipped with multiple digital stethoscopes to detect CAD. By applying signal processing and machine learning to multichannel phonocardiogram (PCG) data, we aim to evaluate the accuracy of CAD detection. We further assess the impact of incorporating patient metadata to enhance model performance. Methods: Data were collected from 40 CAD patients and 40 non-CAD individuals using a wearable vest with seven embedded PCG sensors. Subjects performed 10 s breath-hold recordings in a clinical setting. Linear-frequency cepstral coefficients were extracted from the PCG signals and classified using a support vector machine. Metadata, including body mass index, blood pressure, type 2 diabetes, and hypertension, were integrated to assess performance gains. Results: A combination of four channels achieved an accuracy of 80.44%, a 7% improvement over the best single-channel result. Incorporating metadata increased accuracy to 82.08%. Conclusions: The wearable vest demonstrated promising clinical potential, exceeding a 75% sensitivity-specificity average, and may support accessible, automated CAD screening in future validated settings. Full article

Accurate quantification of spatial distances between fluorescent signals in multi-channel 3D microscopy is essential for understanding genomic organization and gene regulation. However, chromatic aberration introduces systematic spatial offsets between channels that significantly bias distance measurements, particularly at short genomic distances. We present FISH-Dist, an automated computational pipeline for quantitative distance measurements in 3D fluorescence in situ hybridization (FISH) experiments acquired on standard confocal microscopes. Our method combines deep learning-based spot segmentation, 3D Gaussian fitting for sub-pixel localization, and two complementary chromatic aberration correction approaches: affine (ACC) and linear (LCC). We validated the pipeline by measuring the lengths of DNA origami nanorulers and systematically evaluated FISH probe design parameters, including probe spacing, density, and target sequence length. FISH-Dist achieves sub-pixel accuracy in signal detection and substantially reduces inter-channel distance measurement errors. This enables a reproducible quantification of spatial relationships in 3D FISH datasets. Unlike existing tools optimized for long-range chromosomal interactions or requiring super-resolution microscopy, FISH-Dist specifically addresses the technical challenges of standard confocal imaging at short genomic distances, where chromatic aberration has a proportionally greater impact on measurement accuracy. Full article

Cross-medium acoustic–radio collaborative networks enable integrated communication among underwater, surface, and aerial nodes for marine observation and detection. However, heterogeneous propagation characteristics of acoustic and radio channels significantly degrade the performance of conventional single-medium routing protocols, resulting in excessive control overhead, a low packet delivery ratio (PDR), and high latency. To address these challenges, this paper proposes an optimized AODV protocol for Cross-medium Acoustic–Radio Collaborative Networks (CACN-OAODV). The proposed protocol incorporates a medium-aware routing initiation mechanism to reduce unnecessary broadcasts, a link stability factor that jointly considers hop count and channel quality for reliable path selection, and a lightweight control optimization scheme to limit routing overhead in acoustic environments. Extensive simulations conducted in NS-3 with realistic multi-channel propagation models demonstrate that CACN-OAODV significantly outperforms the standard AODV protocol, achieving improved PDR, higher throughput, and reduced end-to-end delay. These results indicate that CACN-OAODV provides an effective routing solution for heterogeneous cross-medium marine communication networks. Full article

Background: The Sysmex XN series (XN-1000 and XN-9100, Sysmex Corporation, Kobe, Japan) represents a latest-generation automated hematology platform integrating fluorescence-based technologies and multi-channel analysis (WDF and WPC) to improve leukocyte characterization. This study aimed to evaluate the performance of the Sysmex XN series in detecting leukocyte abnormalities flagged during routine complete blood count analysis in a large cohort of healthy donors, using morphological assessment and flow cytometry as confirmatory methods. Methods: Approximately 8000 healthy blood donors from the AOU Meyer Transfusion Centre were evaluated between 2021 and 2024. All samples underwent CBC analysis using the XN-1000 and XN-9100 analyzers with the WDF channel. Samples showing WBC-related flags were subjected to reflex testing with the WPC channel, followed by digital blood smear review using the DI-60 system (CellaVision, Lund, Sweden) and flow cytometric immunophenotyping. Results: WDF flags for “blasts/abnormal lymphocytes” were identified in 23 samples. Two samples were negative on WPC analysis as well as on morphological and flow cytometric evaluation. Among the remaining cases, WPC analysis identified flags for abnormal lymphocytes, atypical lymphocytes, or blasts, which were variably associated with reactive changes, transient immune activation, or clonal lymphoproliferative conditions. In one donor, monoclonal B-cell lymphocytosis was diagnosed by flow cytometry. Overall, reactive morphological features confirmed by flow cytometry were observed in approximately 50% of flagged cases. Conclusions: WPC analysis provides relevant additional diagnostic information and demonstrates higher specificity compared with the WDF channel alone; however, it does not fully resolve all instrument-generated flags, confirming the essential role of morphological assessment. Interestingly, the frequent occurrence of inflammatory profiles in recently vaccinated donors suggests that transient immune activation may influence leukocyte flagging. Larger studies are warranted to further investigate this association and to optimize the diagnostic performance of the WPC channel in donor screening. Full article

To deal with the insufficient recognition accuracy of traditional signal modulation recognition methods, this paper proposes a new communication signal modulation recognition method with a deep neural network that integrates a multi-feature multi-channel ResNet and BiLSTM neural network (MF-MC ResNet-BiLSTM). By converting the original modulation data into three different vector formats, which are IQ format, AP format, and FFT format, we obtained the model inputs which contain various feature information. After inputting three types of vector signals into the multi-channel feature fusion module, the network converts these input signals into a high-dimensional feature space for feature fusion, and extracts features we need from different signal sources. Meanwhile, we designed a multi-channel model that integrates ResNet-BiLSTM to perform feature fusion, extracting key features of the modulation signal to avoid the degradation of orthogonality caused by parameter imbalance. To further enhance modulation recognition performance, an adaptive multi-head attention network was designed to extract features through weighted integration. Simulation results demonstrate that this method exhibits model generalization capabilities and good robustness. Experimental data validate that the method achieves a recognition rate of 95.67% and a recall rate of 94.56% in low signal-to-noise ratio (SNR) environments (−22 dB–2 dB), significantly outperforming existing networks like MMF(multimodal fusion), FGDNN(fusion GRU deep learning neural network), and LightMFFS(redlightweight multi-feature fusion structure). Full article