UV-activated acetylene sensor based on WO/NiO-modified ZnO heterostructures with good stability in transformer oil.

Dissolved gas analysis (DGA) is an effective method for diagnosing potential faults in oil-immersed power transformers. Metal oxide semiconductor (MOS) gas sensors exhibit excellent performance. However, high operating temperatures can accelerate device aging, thereby reducing the reliability of online monitoring.

Revolutionizing Dual-Band Modulation and Superior Cycling Stability in GDQDs-Doped WO Electrochromic Films for Advanced Smart Window Applications.

Dual-band tungsten oxide (WO) electrochromic films are extensively investigated, yet challenges persist regarding complex fabrication processes and limited cyclic stability. In this paper, a novel approach to prepare graphdiyne quantum dots (GDQDs) doped WO films with a hexagonal crystal structure, is presented. Structural characterization reveals that the GDQDs/WO possesses a coral-like, loose structure with high crystallinity due to the synergistic modulation of morphology and crystallinity.

Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach.

Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces.

An Improved Global and Local Fusion Path-Planning Algorithm for Mobile Robots.

Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm.

Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics.

The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously.

Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.

Inferring causal networks from noisy observations is of vital importance in various fields. Due to the complexity of system modeling, the way in which universal and feasible inference algorithms are studied is a key challenge for network reconstruction. In this study, without any assumptions, we develop a novel model-free framework to uncover only the direct relationships in networked systems from observations of their nonlinear dynamics.

Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.

Background/objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.

Tumor detection on bronchoscopic images by unsupervised learning.

The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians with insufficient experience may lead to misdiagnosis or misjudgment of tumors. To address this issue, a datasets for intratracheal tumor detection has been constructed to simulate the diagnostic level of experienced specialists, and a Knowledge Distillation-based Memory Feature Unsupervised Anomaly Detection (KD-MFAD) model was proposed to learn from this simulated experience.

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet).

Wheat disease recognition method based on the SC-ConvNeXt network model.

When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model's recognition performance is often disrupted by complex factors in natural environments.

Optimizing dual energy X-ray image enhancement using a novel hybrid fusion method.

Background: Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.

Efficient Output and Stability Triboelectric Materials Enabled by High Deep Trap Density.

With the increasing global focus on sustainable materials, paper is favored for its biodegradability and low cost. Their integration with triboelectric nanogenerators (TENGs) establishes broad prospects for self-powered, paper-based triboelectric materials. However, these materials inherently lack efficient charge storage structures, leading to rapid charge dissipation.

Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.

The integration and interaction of cross-modal senses in brain neural networks can facilitate high-level cognitive functionalities. In this work, we proposed a bioinspired multisensory integration neural network (MINN) that integrates visual and audio senses for recognizing multimodal information across different sensory modalities. This deep learning-based model incorporates a cascading framework of parallel convolutional neural networks (CNNs) for extracting intrinsic features from visual and audio inputs, and a recurrent neural network (RNN) for multimodal information integration and interaction.

Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.

Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods.

Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.

Background: With the increasing burden of dental diseases and the limited availability of healthcare resources, traditional triage methods are inadequate in efficiently utilizing healthcare resources and meeting patient needs. The aim of this study is to develop an advanced triage system that combines oral radiomics and biological multi-omics data, which enables accurate departmental referral of patients by automatically interpreting biological information in oral X-ray images.

Methods: Using a multi-label learning algorithm, we analyzed multi-omics data from 3,942 patients with oral diseases from three cohorts between July 1, 2023 and August 18, 2023, and continuously monitored classification accuracy (ACC) metrics.

Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning.

Background: Aiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.

Methods: This paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains.

Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals.

Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.

Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation.

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets.

Effects of dendritic Ca spike on the modulation of spike timing with transcranial direct current stimulation in cortical pyramidal neurons.

Transcranial direct current stimulation (tDCS) generates a weak electric field (EF) within the brain, which induces opposite polarization in the soma and distal dendrite of cortical pyramidal neurons. The somatic polarization directly affects the spike timing, and dendritic polarization modulates the synaptically evoked dendritic activities. Ca spike, the most dramatic dendritic activity, is crucial for synaptic integration and top-down signal transmission, thereby indirectly influencing the output spikes of pyramidal cells.

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning.

Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment. It is an important approach to solving sequential decision-making problems. By combining it with deep learning, deep reinforcement learning possesses powerful perception and decision-making capabilities and has been widely applied to various domains to tackle complex decision problems.

CISepsis: a causal inference framework for early sepsis detection.

Introduction: The early prediction of sepsis based on machine learning or deep learning has achieved good results.Most of the methods use structured data stored in electronic medical records, but the pathological characteristics of sepsis involve complex interactions between multiple physiological systems and signaling pathways, resulting in mixed structured data. Some researchers will introduce unstructured data when also introduce confounders.