Accurate and interpretable assessment of cognitive workload from EEG remains a central challenge in neuroergonomics and real-time human–machine interaction. To address the limitations of existing Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) approaches, particularly their instability, limited neuroscientific grounding, and sensitivity to amplitude fluctuations, this paper introduces Scale-Stabilized Relative Intrinsic Mode Energy (SS-RIME), a theoretically motivated and physiologically informed feature extraction framework. SS-RIME integrates instantaneous frequency stabilization to enforce a consistent oscillatory hierarchy across subjects, delta (1–4 Hz) and theta (4–7.5 Hz) spectral weighting based on established frontal-midline activity, and cross-IMF energy normalization to reduce amplitude-driven variability. Applied to 64-channel EEG recorded during N-back tasks, the proposed framework achieved high performance, outperforming both classical machine-learning baselines and deep learning models such as EEGNet, DeepConvNet, and ShallowConvNet. SS-RIME yielded accuracies of $99.12\pm 0.41\%$ (0 vs. 2-back), $97.84\pm 0.63\%$ (0 vs. 3-back), and $92.31\pm 1.12\%$ (2 vs. 3-back), demonstrating strong cross-subject generalization. Theta-dominant IMFs over frontal midline regions emerged as the most discriminative components, supporting the neuroscientific validity of the stabilized and spectrally weighted Hilbert–Huang representation. With an inference time below 20 ms per epoch, SS-RIME is computationally efficient and suitable for real-time neuroergonomics applications, providing a robust, explainable, and physiologically grounded solution for EEG-based cognitive workload decoding while addressing key methodological gaps in prior EMD/CEEMDAN and deep learning approaches.

Traceability, counterfeit data, and trust in food supply chains remain problems in agricultural food supply chains. Existing systems rely on isolated record-keeping and centralized databases, which are susceptible to manipulation, slow data updates, and a lack of transparency across the entire supply chain. Past research has developed blockchain technology to address these problems, but it still faces challenges in scalability, permission control, and end-consumer verification. This research proposes a permissioned blockchain-based traceability system to improve the agricultural food supply chain and trustworthiness. The framework uses Hyperledger Fabric, smart contracts, and Quick Response (QR) code consumer verification to record events securely, to perform automatic verification, and to provide details of batch history. The approach involves data extraction from real-world demand datasets, data preprocessing, creation of simulated traceability events, development of blockchain ledger structure, development of smart contract rules, and QR-based verification. Results from experiments show enhanced traceability, verification speed, throughput, and consumer verification rate over the existing system. The system is built based on Hyperledger Fabric, Docker, Node.js, and Python for modularity and scalability.

This study introduces C-EMDNet, a nonlinear speech denoising approach that combines the adaptive decomposition capabilities of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep convolutional architecture operating directly in the time-intrinsic mode function (IMF) domain. Unlike conventional enhancement methods that rely on fixed time–frequency representations, such as the short-time Fourier transform (STFT), the proposed approach interprets CEEMDAN IMFs as a morphological latent space that captures the multi-scale structure of speech. A U-Net-like network was trained to estimate mode-wise masks, enabling selective noise suppression while preserving the harmonic and formant structures. Experiments on standard noisy speech datasets show that C-EMDNet outperforms classical denoising algorithms and competitive deep learning baselines. These results highlight the promise of nonlinear morphological representations for an alternative framework speech enhancement.

Epileptic seizure detection from electroencephalography (EEG) remains challenging due to the non-stationary, multi-channel, and patient-specific nature of ictal activity. Handcrafted spectral features and fixed-basis wavelet transforms fail to fully capture the irregular, multi-scale oscillatory patterns that characterize seizure onset, while wavelet scattering transforms, although stable to time-warping deformations, are typically applied directly to the raw multi-channel signal, where seizure-related activity remains spectrally entangled with background brain activity and inter-channel redundancy is not explicitly modeled. This paper introduces an attention-guided Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)–wavelet scattering framework that first decomposes each EEG channel into intrinsic mode functions (IMFs), applies a wavelet scattering network independently to each retained mode, and aggregates the resulting representations through a dual-level learned attention mechanism operating jointly over intrinsic mode functions and EEG channels. Our approach achieves an overall accuracy of 99.68% and an F1-score of 98.91% on the CHB-MIT dataset, with a cross-dataset generalization accuracy of 99.10% when training on CHB-MIT and testing on the Bonn University dataset. Robustness experiments further show that performance remains above 96% accuracy under a 50% channel reduction and above 95% accuracy under moderate additive noise.