Interface-Engineered Core-Shell Quantum Dots Enable Carrier Confinement in Polymer Nanodielectrics for High-Voltage Direct Current Applications.

Reliable polymer dielectrics are critical for high-voltage direct current (HVDC) power transmission, but their performance is fundamentally limited by detrimental charge carrier migration, particularly under the extreme electric fields and elevated temperatures encountered in service. This study addresses these challenges by engineering novel polymer nanodielectrics based on polyethylene (PE) modified with CdSe@ZnS core-shell quantum dots (QDs). Nanocomposites are fabricated via a solvent-assisted blending method, with homogeneous QD dispersion confirmed through microstructural analyses.

Column-free purification of functional HIV-1 capsid protein and its application in assembly and inhibitor assays.

The HIV-1 capsid protein (CA) is essential for viral replication and serves as a validated antiviral drug target. Traditional purification of CA relies on multi-step chromatographic protocols, which are time-consuming and labor-intensive. In this study, we established a rapid, column-free purification strategy using a cleavable self-aggregating tag (cSAT) to produce functional wild-type CA protein from E.

Multi-task detection of pesticide residues in Allium tuberosum using a colorimetric sensor array based on anthocyanin dyes combined with a pattern recognition algorithm.

Rapid and reliable detection of pesticide residues in Allium tuberosum is essential to ensure food safety and sustainable agriculture. In this study, a novel colorimetric sensor array (CSA) was developed using nine environmentally friendly anthocyanins as dyes and silica gel plates as carriers. And the color change of the CSA in this study was verified to relate to the potential-of-Hydrogen of volatile organic compounds in the sample by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry.

RFID-Based Real-Time Salt Concentration Monitoring with Adaptive EKF.

Salt concentration monitoring is crucial for industrial process control and wastewater management, yet existing methods often lack real-time capability or require invasive sampling. This paper presents a novel RFID wireless sensing system for noninvasive solution concentration monitoring, combining physical modeling with advanced estimation algorithms. By combining the Cole-Cole model and the slit cylindrical capacitor (SCC) model, the system establishes physics-based state-space models to characterize concentration-dependent RFID signal variations.

ICAFormer: An Image Dehazing Transformer Based on Interactive Channel Attention.

Single image dehazing is a fundamental task in computer vision, aiming to recover a clear scene from a hazy input image. To address the limitations of traditional dehazing algorithms-particularly in global feature association and local detail preservation-this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. The proposed architecture adopts a U-shaped encoder-decoder framework, incorporating key components such as a feature extraction module and a feature fusion module based on interactive attention.

Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation.

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher-student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation () framework for industrial anomaly detection.

A Data-Driven Loose Contact Diagnosis Method for Smart Meters.

In smart meters, loose contact at screw terminals can lead to prolonged overheating and arcing, posing significant fire hazards. To mitigate these risks through early fault detection, this study proposes a data-driven framework integrating the Local Outlier Factor (LOF) and Multiple Linear Regression (MLR) algorithms. Voltage differentials, extracted from operational data collected via a simulated multi-meter metering enclosure, are leveraged to diagnose terminal contact degradation.

SGF-SLAM: Semantic Gaussian Filtering SLAM for Urban Road Environments.

With the growing deployment of autonomous driving and unmanned systems in road environments, efficiently and accurately performing environmental perception and map construction has become a significant challenge for SLAM systems. In this paper, we propose an innovative SLAM framework comprising a frontend tracking network called SGF-net and a backend filtering mechanism, namely Semantic Gaussian Filter. This framework effectively suppresses dynamic objects by integrating feature point detection and semantic segmentation networks, filtering out Gaussian point clouds that degrade mapping quality, thus enhancing system performance in complex outdoor scenarios.

Hippocampal LFP Responses during Pigeon Homing Flight in Outdoors.

The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages.

Insulator Defect Detection in Complex Environments Based on Improved YOLOv8.

Insulator defect detection is important in ensuring power systems' safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder-modulator-attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model's loss function was used to accelerate model convergence.

Transmitting Status Updates on Infinite Capacity Systems with Eavesdropper: Freshness Advantage of Legitimate Receiver.

We consider the scenario in which the source sends status updates, or packets, to the receiver through an infinite capacity transmitter, where the transmitted packets are subject to potential illegal eavesdropping. Time is discretized into identical time slots. In recent years, the age of information (AoI) metric, which was defined as the time has elapsed since the generation instant of the latest received packet, has been widely applied to characterize the freshness of obtained packets.

Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.

Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed.

Dual-mode colorimetric-SPR immunosensor based on gold nanoparticles for sCD146 detection in plasma.

Soluble cluster of differentiation 146 (sCD146) is expressed in various immune cells and plays crucial roles in cellular processes linked to tumor cell metastasis, as well as in cell signalling, migration, proliferation, and inflammation. Detecting sCD146 in plasma has become a promising approach for early cancer screening. A rapid and highly specific dual-mode immunosensor that integrates colorimetry and surface plasmon resonance (SPR) techniques was successfully developed in this study.

Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.

The prediction of epileptic seizures heavily depends on the precise embedding and classification of complex, multi-dimensional electroencephalogram (EEG) signals. Due to individual variability and the dynamic non-linear nature of EEG signals, extracting highly discriminative spatiotemporal features is a core challenge in this field. In this study, to address this issue, we proposed a novel architecture based on the Epilepsy Prediction using Multi-Scale Hybrid Neural Network (EPM-HNN), which integrates adaptive channel weighting, multi-scale spatial feature extraction, and bidirectional temporal dependency modeling.

3D Magnetic resonance image denoising using nonlocal and nonconvex tensor train regularization.

Magnetic resonance images (MRI) denoising aims to obtain clean image for further treatment by doctors. Recently, low-rank tensor methods have achieved amazing results in MRI denoising. Nevertheless, imbalanced matricization from Tucker decomposition and nuclear norm penalty mechanism are incapable of fully characterizing the internal structure information of 3D MR image.

The multikinetic fusion feature of PPG was combined with MCNN_vision_transformer for diabetes detection.

Background: Diabetes is a chronic condition that significantly impacts the cardiovascular system and various other organs. Photoplethysmogram (PPG) signals have been shown to correlate with variations in vascular blood flow and the presence of atherosclerosis. To effectively explore the complex nonlinear relationship between PPG signals and diabetes, we propose an automatic detection model based on the fusion of PPG features.

Bayesian Semi-Supervised Learning (BSSL) for spectral variable selection.

Variable selection are increasingly developed and deployed in spectra analysis, ensuring their reliability has become crucial. However, external disturbances during spectrum measurement, such as noise and distribution shifts, can introduce uncertainties that challenge the reliability of variable selection. To address these challenges, a Bayesian Semi-Supervised Learning (BSSL) framework for variable selection and model updating was developed.

Optimisation of electronic nose performance with multi-attention and domain adaptation for fire forensics.

The identification of ignitable liquids at fire scenes is a crucial component of forensic investigations. However, conventional analytical methods such as gas chromatography-mass spectrometry, often require costly equipment, specialised expertise, and extended analysis times, which limits their effectiveness for rapid on-site detection. As such, electronic nose (e-nose) technology presents a cost-effective and portable alternative.

BalancedDiff: Balanced Diffusion Network for High-Quality Molecule Generation.

Traditional drug discovery and development are time-consuming and expensive. Deep learning-based molecule generation techniques can reduce costs and improve efficiency, helping to generate high-quality molecules with desirable properties. However, existing deep learning-based methods focus on designing complex network structures to extract key features, which ignore the impact of sample bias and rarely take biochemical principles into account.

Nonlinear self-triggered MPC without terminal conditions for trajectory tracking.

In this article, a trajectory tracking problem for a class of nonlinear discrete-time systems is investigated by a model predictive control (MPC) strategy. Compared with the standard MPC strategy, the proposed MPC strategy removes terminal conditions, including terminal penalty terms and terminal state constraints. This novel design requires fewer parameters to be determined, which leads to high practicability.

Speech-to-Noise Ratio and Voice-to-Noise Ratio of Voice Databases With Implications for Acoustic Voice Analysis.

Objectives: This study aimed to examine the speech-to-noise ratio (SNR) and voice-to-noise ratio (VNR) in five freely available databases and one university voice database to clarify whether they meet the signal quality requirements for use in acoustic voice analysis.

Methods: This was a retrospective study that extracted the prolonged vowel /ɑ/, short phrases, and connected speech in 977 nondisordered and voice-disordered speakers from 6 preexisting voice and speech databases, including Advanced Voice Function Assessment Databases (AVFAD), Massachusetts Eye and Ear Infirmary (MEEI), Perceptual Voice Qualities Database (PVQD), Saarbruecken Voice Database (SVD), Uncommon Voice, and University of Sydney Voice Assessment Clinic (USVAC). These vocal tasks were extracted from randomly selected study identitys and were used to measure SNR and VNR using a Praat script.

Empirical mode decomposition in clinical signal analysis: A systematic review.

This systematic review examines the transformative applications of empirical mode decomposition (EMD) in healthcare, focusing on its ability to analyse diverse physiological signals. By a thorough exploration of key databases and stringent study selection, the effectiveness of EMD has been highlighted across various medical fields. In cardiology, EMD has significantly improved electrocardiographic analysis, surpassing conventional techniques such as Fourier and wavelet transforms, with accuracy rates reaching up to 98 % for detecting subtle cardiac abnormalities.

LUFormer : A luminance-informed localized transformer with frequency augmentation for nighttime flare removal.

Flare caused by unintended light scattering or reflection in night scenes significantly degrades image quality. Existing methods explore frequency factors and semantic priors but fail to comprehensively integrate all relevant information. To address this, we propose LUFormer, a luminance-informed Transformer network with localized frequency augmentation.

Applications and Prospects of Artificial Intelligence in Proteomics Via Mass Spectrometry: A Review.

Proteomics holds immense significance in fundamental and applied research in various fields, including life sciences, medicinal sciences, and pharmaceutical sciences. The rapid development of mass spectrometry (MS) technologies has facilitated MS-based proteomics research, which has emerged as one of the primary methods for determining the composition, structures, and functions of proteins. The necessity of processing these complex datasets has increased significantly owing to the growing volume and diversity of MS data pertaining to proteins.

Interaction Between Starch Expansion and Gluten Network Formation Enhances the Texture of Noodles: The Cooperation of Synergy During Processing and Antagonism During Cooking.

The evolution of gluten network formation and starch swelling during noodle-making and its impact on the texture of cooked noodles were explored. Numerical analysis of whole and local cooked noodle microstructure was applied in this study. The findings indicated that the expansion of starch and the development of gluten network exhibited synergistic effects during the processing of dried noodles, while the interaction during cooking was antagonistic.