Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding classification of Alzheimer's Disease using MRI: "Do Vision Transformer-based models perform better than CNN-based models?" and "Is it possible to use a shallow 3D CNN-based model to obtain satisfying results?" To achieve these goals, we propose two models that can take in and process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT) architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans

RS5M: A Large Scale Vision-Language Dataset for Remote Sensing Vision-Language Foundation Model

Pre-trained Vision-Language Foundation Models utilizing extensive image-text paired data have demonstrated unprecedented image-text association capabilities, achieving remarkable results across various downstream tasks. A critical challenge is how to make use of existing large-scale pre-trained VLMs, which are trained on common objects, to perform the domain-specific transfer for accomplishing domain-related downstream tasks. In this paper, we propose a new framework that includes the Domain Foundation Model (DFM), bridging the gap between the General Foundation Model (GFM) and domain-specific downstream tasks. Moreover, we present an image-text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions. The dataset is obtained from filtering publicly available image-text paired datasets and captioning label-only RS datasets with pre-trained VLM. These constitute the first large-scale RS image-text paired dataset. Additionally, we tried several Parameter-Efficient Fine-Tuning methods on RS5M to implement the DFM. Experimental results show that our proposed dataset are highly effective for various tasks, improving upon the baseline by $8 \% \sim 16 \%$ in zero-shot classification tasks, and obtaining good results in both Vision-Language Retrieval and Semantic Localization tasks. \url{https://github.com/om-ai-lab/RS5M}Comment: RS5M dataset v

Injecting Image Details into CLIP's Feature Space

Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224), subtle details are lost in the feature representation if we input high-resolution images (e.g., 2240). In this work, we introduce an efficient framework that can produce a single feature representation for a high-resolution image that injects image details and shares the same semantic space as the original CLIP. In the framework, we train a feature fusing model based on CLIP features extracted from a carefully designed image patch method that can cover objects of any scale, weakly supervised by image-agnostic class prompted queries. We validate our framework by retrieving images from class prompted queries on the real world and synthetic datasets, showing significant performance improvement on these tasks. Furthermore, to fully demonstrate our framework's detail retrieval ability, we construct a CLEVR-like synthetic dataset called CLVER-DS, which is fully annotated and has a controllable object scale

in KKAy mice

and mechanisms of resveratrol on the amelioration of oxidative stress and hepatic steatosi

Adaptive beamforming for optical wireless communication via fiber modal control

High-speed optical wireless communication can address the exponential growth in data traffic. Adaptive beamforming customized for the target location is crucial, but existing solutions such as liquidcrystal spatial light modulators and microelectromechanical systems require costly micro/nano manufacturing, delicate alignment, and a high degree of mechanical stability. These challenges reflect the fragility of integrating a fiber network with micro/nano mechanical or photonic systems. Here, we realize low-cost, low-loss, and fiber-compatible beamforming and continuous beam steering through controlled bending of a multi-mode fiber that modifies its modal coupling, and use it to enable flexible optical wireless communication at 10 Gb/s. By using the fiber modal coupling as degrees of freedom rather than an impediment, this approach offers a promising solution for flexible and cost-effective optical wireless communication networks.Comment: 17 pages, 7 figure

A novel online monitoring method of ground fault in tree form distribution networks based on power line carrier devices

The distribution network can continue to operate for a short time once a single-phase grounding fault arises, but if the defect is not monitored and corrected right away, it might cause serious harm. However, the bulk of fault monitoring algorithms struggle to identify faults when applied to complex distribution networks with a tree structure. In this article, a fault online monitoring method based on power line communication equipment is proposed. The method identifies the fault by monitoring the change of channel frequency response (CFR) of the distribution network in real-time, locates the fault branch by comparing the change amplitude of CFR of different receiving devices, calculates the impedance amplitude of the fault branch, and combines the relation between impedance and distance to further realize the accurate location of the fault point. A large number of simulations show that the method is accurate and stable, and can effectively monitor both high and low-resistance ground faults. Under the simulation conditions without considering signal interference, the fault branch detection accuracy reaches 100%, and the average error of fault distance localization is 19.38 m (1.98%). Compared with other methods, this method is highly applicable, with good real-time performance and high positioning accuracy

Experimental study on particle deposition of Fe3O4 in supercritical heat exchange tube

Particle deposition poses a significant challenge to the economics and safety of supercritical boilers. Under-standing the deposition behavior of particles on the steam-water side wall is essential. A supercritical particle deposition system was designed and build according to the actual conditions. The effects of fluid thermal state, flow rate and exposure time on particle morphology, deposition layer morphology and deposition distribution were investigated. The results showed that FeCl2 was oxidized to micron sized Fe3O4 particle, and the salt crystals had a redissolution behavior in supercritical water. The particle deposition layer was a three-layer structure, possibly related to the gas-like fluid clusters and turbulence. The deposition distribution was related to the flow state, and there was a forward shift of the peak point with exposure time. Our results help to un-derstand the particle deposition behavior in supercritical heat exchangers

Optimization analysis of dust field under the comprehensive control of air flow control device and jet air curtain at fully mechanized excavation face

To solve the problem of serious pollution and dust accumulation under the traditional mixed ventilation of the fully mechanized excavation face, the idea of using the air flow control device of the air outlet and the jet air curtain of the exhaust outlet to optimize the dust field is proposed. Taking the fully mechanized excavation face of a mine in northern Shaanxi as the research object, a finite element model of the airflow-dust gas-solid coupling field was established, and the influence of the diameter and right angle of the air flow control device and the width, speed and opening angle of the air curtain outlet on the dust field was analyzed, and orthogonal experiments were designed to determine the best comprehensive control scheme. The results show that under the ventilation system of the fully mechanized excavation face, when the diameter of the control device is 1.2 m, the right deviation angle of the control device is 9°, the width of the air curtain outlet is 0.14 m, the speed of the air curtain outlet is 7 m/s, and the angle of the air curtain outlet is 60°, the dust mass concentration at the driver’s position and the average dust mass concentration at the height of the pedestrian breathing belt are reduced by 91.7% and 74.9%, respectively, and the test error is less than 10%, effectively improving the ventilation environment

Progress in the Preclinical and Clinical Study of Resveratrol for Vascular Metabolic Disease

Vascular metabolic dysfunction presents in various diseases, such as atherosclerosis, hypertension, and diabetes mellitus. Due to the high prevalence of these diseases, it is important to explore treatment strategies to protect vascular function. Resveratrol (RSV), a natural polyphenolic phytochemical, is regarded as an agent to regulate metabolic pathways. Many studies have proven that RSV has beneficial effects on improving metabolism in endothelial cells (ECs) and vascular smooth muscle cells (VSMCs), which provide new directions to treat vascular metabolic diseases. Herein, we overviewed that RSV could regulate cell metabolism activity by inhibiting glucose uptake, suppressing glycolysis, preventing cells from fatty acid-related damages, reducing lipogenesis, increasing fatty acid oxidation, enhancing lipolysis, elevating uptake and synthesis of glutamine, and increasing NO release. Furthermore, in clinical trials, although the results from different studies remain controversial, we proposed that RSV had better therapeutic effects at high concentrations and for patients with metabolic disorders