The dielectric relaxation dynamics in polymer composites critically determine their functional performance in advanced electrical systems. This study systematically investigates hybrid epoxy composites comprising neat epoxy resin (EP) and paper-reinforced systems (EIP), modified with 10–50 wt% polypropylene glycol diglycidyl ether (PEGDGE) plasticizer. Through synergistic application of differential scanning calorimetry (DSC) and broadband dielectric spectroscopy (10⁻¹–10⁶ Hz), the quantitative relationships between plasticizer content, glass transition temperature (Tg), and dielectric relaxation processes were established. DSC analysis reveals a linear Tg dependence with increasing PEGDGE content, attributed to enhanced molecular mobility. Dielectric characterization demonstrates three distinct relaxation regimes: α-relaxation below Tg, interfacial polarization at epoxy/PEGDGE boundaries, and paper/epoxy interfacial effects in EIP systems. A quantitative dielectric relaxation model was developed based on complex modulus formalism, coupled with Vogel–Fulcher–Tammann (VFT) analysis of DC conductivity. Activation energy mapping through Arrhenius decomposition reveals three characteristic values: (1) 82.01–87.80 kJ/mol for α-relaxation, (2) 55.96–64.64 kJ/mol for epoxy/PEGDGE interfaces, and (3) 30.88–44.38 kJ/mol for epoxy/paper interfaces. Crucially, the plasticizer content modulates these activation energies, demonstrating its role in tailoring interfacial dynamics.

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

Partial discharge evaluation is a principal method for assessing insulation conditions in power transformers. Traditional singular value decomposition (SVD) approaches, however, face issues like high residual noise and loss of signal details in white noise suppression. This article introduces an advanced denoising algorithm integrating SVD, variational mode decomposition (VMD), and wavelet thresholding to effectively address mixed noise in on-site power transformer assessments. The algorithm initially employs SVD to suppress mixed noise, specifically targeting narrowband interference by decomposing the noisy signal and nullifying the corresponding singular values. Post-SVD, the signal is further processed through VMD, with its modal components refined via wavelet thresholding. The final reconstruction of these denoised components effectively eliminates white noise. Applied to an input signal with a signal-to-noise ratio of -27.593 dB, the proposed method achieves a postdenoising ratio of 13.654 dB. Comparative analysis indicates its superiority over existing algorithms in mitigating white noise and narrowband interference and more accurately restoring the partial discharge signal.