Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models.

Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.

Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.

Fault Diagnosis Method of Special Vehicle Bearing Based on Multi-Scale Feature Fusion and Transfer Adversarial Learning.

To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability.

Enhanced Hybrid Vision Transformer with Multi-Scale Feature Integration and Patch Dropping for Facial Expression Recognition.

Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in real-world environments remains highly challenging. At the same time, methods solely based on CNN heavily rely on local spatial features, lack global information, and struggle to balance the relationship between computational complexity and recognition accuracy.

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion.

Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion.

Correction to: High sensation seeking is associated with behavioral and neural insensitivity to increased negative outcomes during decision-making under uncertainty.

The original article unfortunately had "?" in place of "훥 "on few lines and those are corrected below.

High sensation seeking is associated with behavioral and neural insensitivity to increased negative outcomes during decision-making under uncertainty.

High-level sensation seeking (HSS) has been linked to a range of risky and unhealthy behavior; however, the neural mechanisms underlying such linkage remain unclear. In the present study, we used event-related potential (ERP) with a Balloon Analogue Risk Task (BART) to investigate how sensation seeking modulates brain responses to sequential decision-making with variable reward/loss outcome magnitudes. Behavior data showed that decision-making behavior was significantly affected by the large compared with the small magnitude of monetary outcome in the BART for individuals with low-level sensation seeking (LSS), but not for individuals with HSS.

Hypothetical Versus Real Monetary Reward Decrease the Behavioral and Affective Effects in the Balloon Analogue Risk Task.

A critical question is whether the same decision-making processes underlie task performance with hypothetical and real money as rewards. Across two studies, we administered the Balloon Analogue Risk Task to healthy young adults under these two reward conditions. We found that participants displayed greater risk aversion during trials immediately after the balloon exploded in the previous trial in case the reward was real money, than if the reward was hypothetical money and exhibited greater subjective ratings of regret following losing trials.

Modification of nanocrystalline TiO coatings with molecularly imprinted TiO for uric acid recognition.

Combining the surface modification and molecular imprinting technique, a novel piezoelectric sensing platform with excellent molecular recognition capability was established for the detection of uric acid (UA) based on the immobilization of TiO nanoparticles onto quartz crystal microbalance (QCM) electrode and modification of molecularly imprinted TiO (MIT) layer on TiO nanoparticles. The performance of the fabricated biosensor was evaluated, and the results indicated that the biosensor exhibited high sensitivity in UA detection, with a linear range from 0.04 to 45 μM and a limit of detection of 0.

[Broad excitation band alkaline-earth silicate luminescent materials activated by rare earth and its applications].

Series of novel broad excitation band phosphors M2 MgSis O7 : Eu, Dy(M = Ca, Sr) were prepared by a high temperature solid-state reaction method. The crystal structure of compound was characterized. And the effects of part substitution of alkaline-earth on crystal structure, photoluminescence spectra and luminescence properties were also investigated.