Transformer-based Multimodal Change Detection with Multitask Consistency Constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging digital surface model (DSM) data and aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship, which involves obtaining pseudo change through height change thresholding and minimizing the difference between semantic and pseudo change within their overlapping regions. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements

Study on the Effects of Vane Parameters on Separation Performance in an Axial Flow Cyclone Separator

The oil-gas cyclone separator is a key component to an oil injection compressor system for its advantages of small volume, simple structure, high separation efficiency and low pressure loss. This paper presents the investigation on new type axial flow cyclone separator performance under different structural parameters, including the angle of vanes, the number of vanes, the rotation angle of single vane, by numerical simulation and verification experiments. A numerical model of two-phase flow in the cyclone separator was established and the separation efficiency and pressure loss of cyclone separators were simulated. A test rig was built and the diameter distributions of droplets at the inlet and outlet of separator were measured by a Malvern laser particle size analyzer to verify the simulation model. The results showed that the separation efficiency and pressure loss can be improved with the increase of the rotation angle of vanes. With the decrease of the outlet angle of the first stage vane and the increase of the number of vanes, the critical separated droplet diameter of separator can be lowered effectively. The results showed that the optimum outlet angle of vanes is 22°~25° considering the separation efficiency and the pressure loss of separators

Ink-printed metal/graphene aerogel for glucose electro-oxidation

Three-dimensional (3D) printing has become one of the promising technologies for the development of bulk-sized nanomaterial composites for electrocatalysis. However, traditional methods such as field deposition modeling and stereolithography are not suitable for the development of functionalized materials for practical use. A large number of studies have focused on the development of the direct ink writing (DIW) printing technique for the fabrication of graphene aerogel (GA)-based electrodes with binders for electrocatalysis. Only a few studies have focused on the synthesis of GA materials from binder-free graphene oxide (GO) using the DIW 3D printing method. Here, we describe the preparation of GA-based electrodes (without size contraction) with different Pd–Pt loadings using the DIW printing method with a commercial 3D food printer. The electron microscopy results showed that a Pd–Pt/GA monolith with a high Pd–Pt loading (59.43 wt%) could be obtained. The DIW-printed Pd–Pt/GA-2 electrode showed good electrochemical performance in glucose electrooxidation (GOR), with a high output current density of 0.94 A g−1 in 0.3 M glucose/1 M NaOH solution at the 3000th cycle operation (60 h). This study shows the potential of DIW-printed binder-free Pd–Pt/GA electrodes for use in fuel cell applications

Artemisia pollen allergy in China : Component-resolved diagnosis reveals allergic asthma patients have significant multiple allergen sensitization

Background: Artemisia pollen allergy is a major cause of asthma in Northern China. Possible associations between IgE responses to Artemisia allergen components and clinical phenotypes have not yet been evaluated. This study was to establish sensitization patterns of four Artemisia allergens and possible associations with demographic characteristics and clinical phenotypes in three areas of China. Methods: Two hundred and forty patients allergic to Artemisia pollen were examined, 178 from Shanxi and 30 from Shandong Provinces in Northern China, and 32 from Yunnan Province in Southwestern China. Allergic asthma, rhinitis, conjunctivitis, and eczema symptoms were diagnosed. All patients sera were tested by ImmunoCAP with mugwort pollen extract and the natural components nArt v 1, nArt ar 2, nArt v 3, and nArt an 7. Results: The frequency of sensitization and the IgE levels of the four components in Artemisia allergic patients from Southwestern China were significantly lower than in those from the North. Art v 1 and Art an 7 were the most frequently recognized allergens (84% and 87%, respectively), followed by Art v 3 (66%) and Art ar 2 (48%). Patients from Northern China were more likely to have allergic asthma (50%) than patients from Southwestern China (3%), and being sensitized to more than two allergens increased the risk of allergic asthma, in which cosensitization to three major allergens Art v 1, Art v 3, and Art an 7 is prominent. Conclusions: Componentresolved diagnosis of Chinese Artemisia pollenallergic patients helps assess the potential risk of mugwortassociated allergic asthma.(VLID)329956

A Secure Storage and Sharing Scheme of Stroke Electronic Medical Records Based on Consortium Blockchain

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination

Enhancing YOLO for occluded vehicle detection with grouped orthogonal attention and dense object repulsion

Abstract In real-life complex traffic environments, vehicles are often occluded by extraneous background objects and other vehicles, leading to severe degradation of object detector performance. To address this issue, we propose a method named YOLO-OVD (YOLO for occluded vehicle detection) and a dataset for effectively handling vehicle occlusion in various scenarios. To highlight the model attention in unobstructed region of vehicles, we design a novel grouped orthogonal attention (GOA) module to achieve maximum information extraction between channels. We utilize grouping and channel shuffling to address the initialization and computational issues of original orthogonal filters, followed by spatial attention for enhancing spatial features in vehicle-visible regions. We introduce a CIoU-based repulsion term into the loss function to augment the network’s localization accuracy in scenarios involving densely packed vehicles. Moreover, we explore the effect of the knowledge-based Laplacian Pyramid on the OVD performance, which contributes to fast convergence in training and ensures more detailed and comprehensive feature retention. We conduct extensive experiments on the established occluded vehicle detection dataset, which demonstrates that the proposed YOLO-OVD model significantly outperforms 14 representative object detectors. Notably, it achieves improvements of 4.7% in Precision, 3.6% in [email protected], and 1.9% in [email protected]:0.95 compared to the YOLOv5 baseline

Comparative evaluation in treating qi-yin deficiency and phlegm stasis syndrome of type 2 diabetes mellitus in a rat model

Objective: To compare the efficacy of traditional Chinese medicine (TCM), western medicine and integrative medicine in treating type 2 diabetes mellitus (T2DM) in a rat model. Methods: The T2DM rat model was established with a high-fat diet (HFD) for 35 days and a single injection of streptozotocin (STZ, 30 mg/kg). The T2DM-induced rats were divided into three groups, and treated with Yiqi Yangyin Huatan (YQYYHT) granules (3.84 g/kg per day), pioglitazone (1.35 mg/kg per day) or YQYYHT granules + pioglitazone (3.84 g/kg per day+1.35 mg/kg per day) respectively for 14 days. Clinical features and behavioral changes, as well as T2DM indicators, were recorded to evaluate therapeutics effects in each treatment group. Results: The T2DM rat model expressed insulin resistance (IR), with features similar to qi-yin deficiency and phlegm stasis syndrome, including decreased cyclic adenosine monophosphate/cyclic guanosine monophosphate (cAMP/cGMP) ratio, decreased levels of Na+-K+-ATPase, superoxide dismutase (SOD) and high density lipoprotein-cholesterol (HDL-C), and increased levels of serum triglyceride (TG), total cholesterol (TC) and low density lipoprotein (LDL-C). All three treatment groups showed significant decreases in fasting blood glucose (FBG) and fasting insulin (Fins), and improvement of TCM syndrome to different degrees. Importantly, YQYYHT improved the most of the indicators of T2DM, followed by integrative medicine and pioglitazone alone. Conclusion: Compared with western medicine or integrative medicine, prescription of TCM based on syndrome differentiation may offer more advantages in the prevention and treatment of T2DM. Keywords: Type 2 diabetes mellitus, Qi-yin deficiency, Phlegm stasis syndrome, Integrative medicine, Yiqi Yangyin Huatan prescriptio

Display Line Defect Detection Method Based on Color Feature Fusion

Display color line defect detection is an important step in the production quality inspection process. In order to improve the detection accuracy of low contrast line defects, we propose a display line defect detection method based on color feature fusion. The color saliency channels in the RG|GR and BY|YB channels were obtained using the relative entropy maximum criterion. Then, RG|GR were combined with the a channel and BY|YB with the b channel to calculate the red-green and the blue-yellow color fusion maps. The fusion color saliency map of the red-green and the blue-yellow color fusion maps was obtained by color feature fusion. Finally, the segmentation threshold was calculated according to the mean and standard deviation of the fusion color saliency map. The fused color saliency map was binarized and segmented to obtain a binary map of color line defects. The experimental results show that for the detection of multi-background offline defects, the detection accuracy of the algorithm in this paper is better than 90%, while other mainstreams fail to detect. Compared with state-of-the-art saliency detection algorithms, our method is capable of real-time low-contrast line defect detection

A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution

Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other applications, is one of the most difficult challenges. This paper proposes an encoding-decoding network based on Deeplabv3+ to semantically segment SAR imagery. A new potential energy loss function based on the Gibbs distribution is proposed here to establish the semantic dependence among different categories through the relationship among different cliques in the neighborhood system. This paper introduces an improved channel and spatial attention module to the Mobilenetv2 backbone to improve the recognition accuracy of small object categories in SAR imagery. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method