Search Results (709)

Inkjet printing technology shows significant potential for producing high-performance conductive circuits in printed electronics. However, conventional silver nanoparticle (Ag NP) inks often face challenges such as nozzle clogging, poor stability, and low conductivity after low-temperature sintering. While most existing studies focus solely on dispersant selection or individual process optimization, few have systematically explored the synergistic effects of particle size distribution, dispersion methods, and dispersant dosage. This study proposes a sequential optimization approach involving centrifugal classification to identify an optimal Ag NPs source and size distribution, followed by comparison and optimization of different dispersion methods. Furthermore, the effects of dispersant (a PEO-PPO-PEO triblock copolymer) concentration and application strategy (individual or combined use) on the rheological properties and conductivity of the ink were systematically investigated. The optimized Ag NP ink demonstrated excellent jetting stability with no nozzle clogging, exhibiting a surface tension of 19.60 mN/m and a viscosity of 6.83 mPa·s. After low-temperature sintering at 260 °C on glass or polyimide (PI) substrates, the printed patterns achieved a high electrical conductivity of 1.506 × 10⁷ S/m. Printing on polyethylene terephthalate (PET) at 150 °C confirmed compatibility with heat-sensitive flexible substrates. This work offers a comprehensive and practical strategy for developing highly reliable and conductive Ag NP inks, facilitating their application in next-generation printed electronics. Full article

The emergence of radio frequency machine learning has significantly propelled the application of deep learning (DL) methods in automatic modulation classification (AMC). However, under non-cooperative scenarios, the performance of DL-based AMC suffers severe performance degradation due to scarce labeled samples and noise interference. To enhance noise robustness in few-shot AMC, this paper proposes a complex-domain autoencoder-based method where a complex-valued noise reduction network (CNRN) is embedded into the AMC framework, jointly extracting complex-valued and temporal features from noisy signals to achieve signal–noise separation. Our framework executes four sequential operations: high-signal-to-noise-ratio (high-SNR) samples are first isolated from limited raw data via unsupervised classification; rotation and cyclic time-shifting operations then augment the sample space; the CNRN is subsequently trained on augmented data; and final AMC classification is implemented through DL-based classifiers. Experimental validation on RML 2016.10a dataset demonstrates: (1) for −20 dB signals, denoising achieves 20.18 dB SNR improvement with 87.74% mean squared error reduction; (2) across the −20 dB to 18 dB range, denoised signals exhibit accuracy improvements of 21.57% under DL-based classifiers. Physical validation further confirms that the proposed method exhibits enhanced noise robustness, demonstrating its practical utility in real-world scenarios. Full article

Background/Objectives: Otitis media (OM), including acute otitis media (AOM) and chronic otitis media (COM), is a common middle ear disease that can lead to significant morbidity if not accurately diagnosed. Otoscopic interpretation remains subjective and operator-dependent, underscoring the need for objective and reproducible diagnostic support. Recent advances in artificial intelligence (AI) offer promising solutions for automated otoscopic image analysis. Methods: We developed an AI-based diagnostic framework consisting of three sequential steps: (1) semi-supervised learning for automatic recognition and semantic segmentation of tympanic membrane structures, (2) region-based feature extraction, and (3) disease classification. A total of 607 clinical otoscopic images were retrospectively collected, including normal ears (n = 220), AOM (n = 157), and COM with tympanic membrane perforation (n = 230). Among these, 485 images were used for training and 122 for independent testing. Semantic segmentation of five anatomically relevant regions was performed using multiple convolutional neural network architectures, including U-Net, PSPNet, HRNet, and DeepLabV3+. Following segmentation, color and texture features were extracted from each region and used to train a neural network-based classifier to differentiate disease states. Results: Among the evaluated segmentation models, U-Net demonstrated superior performance, achieving an overall pixel accuracy of 96.76% and a mean Dice similarity coefficient of 71.68%. The segmented regions enabled reliable extraction of discriminative chromatic and texture features. In the final classification stage, the proposed framework achieved diagnostic accuracies of 100% for normal ears, 100% for AOM, and 91.3% for COM on the independent test set, with an overall accuracy of 96.72%. Conclusions: This study demonstrates that a semi-supervised, segmentation-driven AI pipeline integrating feature extraction and classification can achieve high diagnostic accuracy for otitis media. The proposed framework offers a clinically interpretable and fully automated approach that may enhance diagnostic consistency, support clinical decision-making, and facilitate scalable otoscopic assessment in diverse healthcare screening settings for disease prevention and health education. Full article

Background: Both borderline personality disorder (BPD) and schizoaffective disorder (SAD), as well as their potential connection, remain controversial diagnoses. To explore whether BPD may be part of the spectrum of SAD, we conducted a longitudinal study of a large clinical cohort of patients with BPD. Methods: We assessed the diagnostic trajectories of 402 patients with BPD in a 20-year retrospective study based on electronic clinical records from a psychiatric hospital using ICD-9 diagnoses. Data were descriptively examined on concurrent and sequential diagnoses in patients with BPD. For the classification of SAD, a proxy diagnosis was used. Results: The study population showed a high prevalence of affective disorders and a high frequency of concurrent diagnoses of affective–BPD. Together, stable BPD, stable affective disorder sequences and transitions from affective disorders to BPD represented 79% of all longitudinal trajectories. Conclusion: These findings should be considered exploratory and do not allow confirmation or refutation of the hypothesis that BPD serves as a precursor, prodrome, or component within the spectrum of SAD. Full article

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features but struggle to capture long-range dependencies and global contextual information. To address this limitation, we propose a Triple-path Heterogeneous Feature Collaboration Network (TPHFC-Net), which synergistically integrates three distinct temporal modeling pathways: a multi-scale Temporal Convolutional Network (TCN) to capture fine-grained local dynamics, a Transformer branch to model global dependencies via multi-head self-attention, and a Long Short-Term Memory (LSTM) network to track sequential state evolution. These heterogeneous features are subsequently fused adaptively by a dynamic gating mechanism. In addition, the model’s robustness and discriminative power are further augmented by a lightweight front-end denoising diffusion model for enhanced noisy feature representation and a back-end prototype attention mechanism to bolster the inter-class separability of non-stationary EEG features. Extensive experiments on the BCI Competition IV-2a and IV-2b datasets validate the superiority of the proposed model, achieving mean classification accuracies of 82.45% and 89.49%, respectively, on the subject-dependent MI task and significantly outperforming existing mainstream baselines. Full article

Cattle behavior is closely related to their health status, and monitoring cattle behavior using intelligent devices can assist herders in achieving precise and scientific livestock management. Current behavior recognition algorithms are typically executed on server platforms, resulting in increased power consumption due to data transmission from edge devices and hindering real-time computation. An edge-based cattle behavior recognition method via Graph Neural Network (GNN) compression is proposed in this paper. Firstly, this paper proposes a wearable device that integrates data acquisition and model inference. This device achieves low-power edge inference function through a high-performance embedded microcontroller. Secondly, a sequential residual model tailored for single-frame data based on Inertial Measurement Unit (IMU) and displacement information is proposed. The model incrementally extracts deep features through two Residual Blocks (Resblocks), enabling effective cattle behavior classification. Finally, a compression method based on GNNs is introduced to adapt edge devices’ limited storage and computational resources. The method adopts GNNs as the backbone of the Actor–Critic model to autonomously search for an optimal pruning strategy under Floating-Point Operations (FLOPs) constraints. The experimental results demonstrate the effectiveness of the proposed method in cattle behavior classification. Moreover, enabling real-time inference on edge devices significantly reduces computational latency and power consumption, thereby highlighting the proposed method’s advantages for low-power, long-term operation. Full article

Human activity recognition in privacy-sensitive indoor environments requires sensing modalities that remain robust under illumination variation and background clutter while preserving user anonymity. To this end, this study proposes a lightweight radar–camera fusion deep learning model that integrates motion signatures from FMCW radar with coarse spatial cues from ultra-low-resolution camera frames. The radar stream is processed as a Range–Doppler–Time cube, where each frame is flattened and sequentially encoded using a Transformer-based temporal model to capture fine-grained micro-Doppler patterns. The visual stream employs a privacy-preserving $4\times 5$-pixel camera input, from which a temporal sequence of difference frames is extracted and modeled with a dedicated camera Transformer encoder. The two modality-specific feature vectors—each representing the temporal dynamics of motion—are concatenated and passed through a lightweight fully connected classifier to predict human activity categories. A multimodal dataset of synchronized radar cubes and ultra-low-resolution camera sequences across 15 activity classes was constructed for evaluation. Experimental results show that the proposed fusion model achieves 98.74% classification accuracy, significantly outperforming single-modality baselines (single-radar and single-camera). Despite its performance, the entire model requires only 11 million floating-point operations (11 MFLOPs), making it highly efficient for deployment on embedded or edge devices. Full article

Background: Dental disorders, such as cavities, periodontal disease, and periapical infections, remain major global health issues, often resulting in pain, tooth loss, and systemic complications if not identified early. Traditional diagnostic methods rely heavily on visual inspection and manual interpretation of panoramic X-ray images by dental professionals, making them time-consuming, subjective, and less accessible in resource-limited settings. Objectives: Accurate and timely diagnosis is vital for effective treatment and prevention of disease progression, reducing healthcare costs and patient discomfort. Recent advances in deep learning (DL) have demonstrated remarkable potential to automate and improve the precision of dental diagnostics by objectively analyzing panoramic, periapical, and bitewing X-rays. Methods: In this research, a hybrid feature-fusion framework is proposed. It integrates handcrafted Histogram of Oriented Gradients (HOG) features with deep representations from DenseNet-201 and the Shifted Window (Swin) Transformer models. Sequential dependencies among the fused features were learned utilizing the Long Short-Term Memory (LSTM) classifier. The framework was evaluated on the Dental Radiography Analysis and Diagnosis (DRAD) dataset following preprocessing steps, including resizing, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, and image cropping. Results: The proposed LSTM-based hybrid model achieved 96.47% accuracy, 91.76% specificity, 94.92% precision, 91.76% recall, and 93.14% F1-score. Conclusions: The proposed framework offers flexibility, interpretability, and strong empirical performance, making it suitable for various image-based recognition applications and serving as a reproducible framework for future research on hybrid feature fusion and sequence-based classification. Full article

This study aims to develop effective screening tools for cognitive impairment by integrating optimised speech classification features with various machine learning models. A total of 65 patients diagnosed with early-stage Mild Cognitive Impairment (MCI) and 55 healthy controls (HCs) were included. Audio data were collected through a picture description task and processed using the Python-based Librosa library for speech feature extraction. Three machine learning models were constructed: the Random Forest (RF) and Support Vector Machine (SVM) models utilised speech classification features optimised via the Sequential Forward Selection (SFS) algorithm, while the Extreme Gradient Boosting (XGBoost) model was trained on preprocessed speech data. After parameter tuning, the Librosa library successfully extracted 41 speech classification features from all participants. The application of the SFS optimisation strategy and the use of preprocessed data significantly improved identification accuracy. The SVM model achieved an accuracy of 0.825 (AUC: 0.91), the RF model reached 0.88 (AUC: 0.86), and the XGBoost model attained 0.92 (AUC: 0.91). These results suggest that speech-based machine learning models markedly improve the accuracy of distinguishing MCI patients from healthy older adults, providing reliable support for early cognitive deficit identification. Full article

The rapid development of autonomous driving systems has increased the demand for scalable frameworks capable of modeling vehicle motion patterns in complex traffic environments. This paper proposes a big data spatio-temporal modeling architecture that integrates Apache Spark version 4.0.1 (SparkML) with Long Short-Term Memory (LSTM) networks to analyze and classify vehicle trajectory patterns. The proposed SparkML–LSTM framework exploits Spark’s distributed processing capabilities and LSTM’s strength in sequential learning to handle large-scale traffic trajectory data efficiently. Experiments were conducted using the DETRAC dataset, which is a large-scale benchmark for vehicle detection and multi-object tracking consisting of more than 10 h of video captured at 24 different locations. The videos were recorded at 25 frames per second with a resolution of 960 × 540 pixels and annotated across more than 140,000 frames, covering 8.250 vehicles and approximately 1.21 million bounding box annotations. The dataset provides detailed annotations, including vehicle categories (Car, Bus, Van, Others), weather conditions (Sunny, Cloudy, Rainy, Night), occlusion ratio, truncation ratio, and vehicle scale. Based on the extracted trajectory features, vehicle motion patterns were categorized into predefined movement classes derived from trajectory dynamics. The experimental results demonstrate strong classification performance. These findings suggest that the proposed SparkML–LSTM architecture is effective for large-scale spatio-temporal trajectory modeling and traffic behavior analysis, and can serve as a foundation for higher-level decision-making modules in intelligent transportation system. Full article

Background and Objectives: Early recognition of life-threatening organ dysfunction is central to modern sepsis frameworks. We systematically reviewed the prognostic performance and clinical utility of the Neonatal Sequential Organ Failure Assessment (nSOFA) for mortality and major morbidity in NICU populations. The search identified 939 records across databases; after screening and full-text assessment, 16 studies met the inclusion criteria. Methods: Following PRISMA guidance, we searched major databases (2019–2025) for observational or interventional studies reporting discrimination or risk stratification using nSOFA in neonates. Populations included suspected/proven infection and condition-specific cohorts. Heterogeneity in timing, thresholds, and outcomes precluded meta-analysis. Results: A cumulative sample exceeding 25,000 neonates was identified across late- and early-onset infection, all-NICU admissions, necrotizing enterocolitis, respiratory distress, and very preterm screening cohorts. Across settings and timepoints, nSOFA demonstrated consistent, good-to-excellent mortality discrimination, with reported AUROCs ≥ 0.80 and upper ranges near 0.90–0.92; serial scoring within the first 6–12 h generally improved risk classification. Disease-specific applications (NEC, early-onset infection) showed similar discrimination for death or composite adverse outcomes. Conclusions: Evidence from diverse NICU contexts indicates that nSOFA is a pragmatic, EHR-ready organ dysfunction score with robust discrimination for mortality and serious morbidity, supporting routine, serial use for risk stratification and standardized endpoints in neonatal sepsis pathways, aligned with contemporary organ dysfunction–based pediatric criteria. Full article

Wrapped unsupervised band selection (WUBS) is a powerful means of reducing the dimensions of hyperspectral images (HSIs) and has drawn much focus recently. Nevertheless, numerous WUBS approaches struggle to strike a balance between computational complexity and performance and typically disregard high-level information between bands. This paper presents a new reconstruction error-based algorithm called distance density (DD) and Wasserstein generative adversarial network (WGAN)-driven WUBS (DW-WUBS), which is intended to overcome these problems. Minutely, DW-WUBS employs DD to weigh the spectral fluctuation in different band groups and thus determine the detailed expression of the importance of each group. At the same time, it uses a sequential search method on the important band group instead of the original HSIs, thereby reducing the computational complexity of band retrieval. Afterwards, DW-WUBS trains a WGAN and applies its critical network to test the representativeness of the searched bands by considering their contribution to HSI reconstruction. This automatically derives underlying and higher-level structure information of the spectrum. The superiority of DW-WUBS is certified by comprehensive experiments on three benchmark data sets. For instance, on the Pavia Center scene, the peaked mean accuracy (MA) using the twelve bands chosen via DW-WUBS with the CART classifier exceeds the baseline (i.e., all bands) by 0.91% in the classification task. Full article

Maintenance and repair play a crucial role in industry. Smart systems for technical diagnostics can help to save money and to prevent the breakdown of machines and plants. These systems and its classifiers benefit from plausible features because they tend toward robust classification. Although concepts for knowledge discovery are well-known in various scientific fields, they are not established in the field of rotating machines. Knowledge discovery from experimental data is a framework that combines valid methods for knowledge discovery with expert knowledge and automated experiments. For the central data mining step, feature selection algorithms based on heuristic or meta-heuristic search are established. The objective is to identify plausible pattern with a limited number of features and the best combination of these features. The results in this work show which strategies align the best with the requirements of knowledge discovery using experimental data to find plausible features. For this study, well-configured search strategies, namely, sequential forward selection and ant colony optimization, were applied on real data. The data represent several fault severity levels for parallel misalignment and cavitation. The plausible feature vectors and features exhibited good behavior when applied to new targets. It is expected that the obtained knowledge will be transferable to new classification tasks with only minimal optimization of the reference data or the classifier. Full article

The integration of emojis into Early Childhood Education (ECE) presents a promising avenue for enhancing student engagement, emotional expression, and comprehension. While prior studies suggest the benefit of visual aids in learning, systematic frameworks for pedagogically aligned emoji recommendation remain underdeveloped. This paper presents EduEmoji-ECE, a pedagogically annotated dataset of early-childhood learning text segments. Specifically, the proposed model incorporates Bidirectional Encoder Representations from Transformers (BERTs) for contextual embedding extraction, Gated Recurrent Units (GRUs) for sequential pattern recognition, Deep Neural Networks (DNNs) for classification and emoji recommendation, and DECOC for improving emoji class prediction robustness. This hybrid BERT-GRU-DNN-DECOC architecture effectively captures textual semantics, emotional tone, and pedagogical intent, ensuring the alignment of emoji class recommendation with learning objectives. The experimental results show that the system is effective, with an accuracy of 95.3%, a precision of 93%, a recall of 91.8%, and an F1-score of 92.3%, outperforming baseline models in terms of contextual understanding and overall accuracy. This work helps fill a gap in AI-based education by combining learning with visual support for young children. The results suggest an association between emoji-enhanced materials and improved engagement/comprehension indicators in our exploratory classroom setting; however, causal attribution to the AI placement mechanism is not supported by the current study design. Full article

Procypris rabaudi, a member of the Cyprinidae family and genus Procypris, has been designated as a national second-class protected wildlife species in China due to a significant decline in its wild populations. Understanding its genomic characteristics and mitochondrial genome structure is crucial for germplasm conservation and systematic classification. In this study, we utilized high-throughput sequencing to investigate the genome of P. rabaudi. The genome size was 1.5 Gb, with a heterozygosity rate of 0.44% and 61.47% of repetitive sequences. We identified 1,151,980 simple sequence repeats (SSRs), with mononucleotide repeats being the most abundant at 55.34%. The complete mitochondrial genome was assembled with 16,595 bp length. A phylogenetic tree constructed from 13 mitochondrial protein-coding genes indicated that genus Procypris was most closely related to genus Luciocyprinus and formed a monophyletic group with Cyprinus, Carassioides, and Carassius. Pairwise Sequentially Markovian Coalescent (PSMC) analysis revealed a rapid population expansion prior to the Last Interglacial Period, followed by a decline after reaching its peak during Last Glacial Period. Notably, P. rabaudi exhibited a two-peak demographic pattern during both the Last Glacial Period. These genomic data provide valuable resources for the conservation of P. rabaudi germplasm and for future studies on cyprinid classification and evolution. Full article