A Compact RFID Reader Antenna for UHF Near-Field and Far-Field Operations

A compact loop antenna is presented for mobile ultrahigh frequency (UHF) radio frequency identification (RFID) application. This antenna, printed on a 0.8 mm thick FR4 substrate with a small size of 31 mm × 31 mm, achieves good impedance bandwidth from 897 to 928 MHz, which covers USA RFID Band (902–928 MHz). The proposed loop configuration, with a split-ring resonator (SRR) coupled inside it, demonstrates strong and uniform magnetic field distribution in the near-field antenna region. Its linearly polarized radiation pattern provides available far-field gain. Finally, the reading capabilities of antenna are up to 56 mm for near-field and 1.05 m for far-field UHF RFID operations, respectively

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic and instance segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%, 4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%, 2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.Comment: 46 pages, 10 figure

An unsymmetric 8-node hexahedral element with high distortion tolerance

Among all 3D 8-node hexahedral solid elements in current finite element library, the ‘best’ one can produce good results for bending problems using coarse regular meshes. However, once the mesh is distorted, the accuracy will drop dramatically. And how to solve this problem is still a challenge that remains outstanding. This paper develops an 8-node, 24-DOF (three conventional DOFs per node) hexahedral element based on the virtual work principle, in which two different sets of displacement fields are employed simultaneously to formulate an unsymmetric element stiffness matrix. The first set simply utilizes the formulations of the traditional 8-node trilinear isoparametric element, while the second set mainly employs the analytical trial functions in terms of 3D oblique coordinates (R, S, T). The resulting element, denoted by US-ATFH8, contains no adjustable factor and can be used for both isotropic and anisotropic cases. Numerical examples show it can strictly pass both the first-order (constant stress/strain) patch test and the second-order patch test for pure bending, remove the volume locking, and provide the invariance for coordinate rotation. Especially, it is insensitive to various severe mesh distortions

Identification of the ortho-Benzoquinone Intermediate of 5-O- Caffeoylquinic Acid In Vitro and In Vivo: Comparison of Bioactivation under Normal and Pathological Situations □ S

ABSTRACT: 5-O-Caffeoylquinic acid (5-CQA) is one of the major bioactive ingredients in some Chinese herbal injections. Occasional anaphylaxis has been reported for these injections during their clinical use, possibly caused by reactive metabolites of 5-CQA. This study aimed at characterizing the bioactivation pathway(s) of 5-CQA and the metabolic enzyme(s) involved. After incubating 5-CQA with GSH and NADPH-supplemented human liver microsomes, two types of GSH conjugates were characterized: one was M1-1 from the 1,4-addition of GSH to ortho-benzoquinone intermediate; the other was M2-1 and M2-2 from the 1,4-addition of GSH directly to the ␣,␤-unsaturated carbonyl group of the parent. The formation of M1-1 was cytochrome P450 (P450)-mediated, with 3A4 and 2E1 as the principal catalyzing enzymes, whereas the formation of M2-1 and M2-2 was independent of NADPH and could be accelerated by cytosolic glutathione transferase. In the presence of cumene hydroperoxide, M1-1 formation increased 6-fold, indicating that 5-CQA can also be bioactivated by P450 peroxidase under oxidizing conditions. Furthermore, M1-1 could be formed by myeloperoxidase in activated human leukocytes, implying that 5-CQA bioactivation is more likely to occur under inflammatory conditions. This finding was supported by experiments on lipopolysaccharideinduced inflammatory rats, where a greater amount of M1-1 was detected. In S-adenosyl methionine-and GSH-supplemented human S9 incubations, M1-1 formation decreased by 80% but increased after tolcapone-inhibited catechol-O-methyltransferase (COMT) activity. In summary, the high reactivities of the orthobenzoquinone metabolite and ␣,␤-unsaturated carbonyl group of 5-CQA to nucleophiles have been demonstrated. Different pathological situations and COMT activities in patients may alter the bioactivation extent of 5-CQA

Segment Anything in 3D with NeRFs

The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any object/part in various 2D images, yet its ability for 3D has not been fully explored. The real world is composed of numerous 3D scenes and objects. Due to the scarcity of accessible 3D data and high cost of its acquisition and annotation, lifting SAM to 3D is a challenging but valuable research avenue. With this in mind, we propose a novel framework to Segment Anything in 3D, named SA3D. Given a neural radiance field (NeRF) model, SA3D allows users to obtain the 3D segmentation result of any target object via only one-shot manual prompting in a single rendered view. With input prompts, SAM cuts out the target object from the according view. The obtained 2D segmentation mask is projected onto 3D mask grids via density-guided inverse rendering. 2D masks from other views are then rendered, which are mostly uncompleted but used as cross-view self-prompts to be fed into SAM again. Complete masks can be obtained and projected onto mask grids. This procedure is executed via an iterative manner while accurate 3D masks can be finally learned. SA3D can adapt to various radiance fields effectively without any additional redesigning. The entire segmentation process can be completed in approximately two minutes without any engineering optimization. Our experiments demonstrate the effectiveness of SA3D in different scenes, highlighting the potential of SAM in 3D scene perception. The project page is at https://jumpat.github.io/SA3D/.Comment: Work in progress. Project page: https://jumpat.github.io/SA3D

A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.Comment: Accepted to IEEE TPAM

Understanding Large Language Model Based Fuzz Driver Generation

Fuzz drivers are a necessary component of API fuzzing. However, automatically generating correct and robust fuzz drivers is a difficult task. Compared to existing approaches, LLM-based (Large Language Model) generation is a promising direction due to its ability to operate with low requirements on consumer programs, leverage multiple dimensions of API usage information, and generate human-friendly output code. Nonetheless, the challenges and effectiveness of LLM-based fuzz driver generation remain unclear. To address this, we conducted a study on the effects, challenges, and techniques of LLM-based fuzz driver generation. Our study involved building a quiz with 86 fuzz driver generation questions from 30 popular C projects, constructing precise effectiveness validation criteria for each question, and developing a framework for semi-automated evaluation. We designed five query strategies, evaluated 36,506 generated fuzz drivers. Furthermore, the drivers were compared with manually written ones to obtain practical insights. Our evaluation revealed that: while the overall performance was promising (passing 91% of questions), there were still practical challenges in filtering out the ineffective fuzz drivers for large scale application; basic strategies achieved a decent correctness rate (53%), but struggled with complex API-specific usage questions. In such cases, example code snippets and iterative queries proved helpful; while LLM-generated drivers showed competent fuzzing outcomes compared to manually written ones, there was still significant room for improvement, such as incorporating semantic oracles for logical bugs detection.Comment: 17 pages, 14 figure

Metabolism, Fibrosis, and Apoptosis: The Effect of Lipids and Their Derivatives on Keloid Formation

Keloids, pathological scars resulting from skin trauma, have traditionally posed significant clinical management challenges due to their persistence and high recurrence rates. Our research elucidates the pivotal roles of lipids and their derivatives in keloid development, driven by underlying mechanisms of abnormal cell proliferation, apoptosis, and extracellular matrix deposition. Key findings suggest that abnormalities in arachidonic acid (AA) synthesis and non-essential fatty acid synthesis are integral to keloid formation. Further, a complex interplay exists between lipid derivatives, notably butyric acid (BA), prostaglandin E2 (PGE2), prostaglandin D2 (PGD2), and the regulation of hyperfibrosis. Additionally, combinations of docosahexaenoic acid (DHA) with BA and 15-deoxy-Δ12,14-Prostaglandin J2 have exhibited pronounced cytotoxic effects. Among sphingolipids, ceramide (Cer) displayed limited pro-apoptotic effects in keloid fibroblasts (KFBs), whereas sphingosine 1-phosphate (S1P) was found to promote keloid hyperfibrosis, with its analogue, FTY720, demonstrating contrasting benefits. Both Vitamin D and hexadecylphosphorylcholine (HePC) showed potential antifibrotic and antiproliferative properties, suggesting their utility in keloid management. While keloids remain a prevalent concern in clinical practice, this study underscores the promising potential of targeting specific lipid molecules for the advancement of keloid therapeutic strategies

A Clinical Analysis of Risk Factors for Interstitial Lung Disease in Patients with Idiopathic Inflammatory Myopathy

Interstitial lung disease (ILD) is a common and severe complication of idiopathic inflammatory myopathies (IIM). The aim of our study was to identify risk factors for ILD by evaluating both clinical and biochemical features in IIM patients with or without ILD. From January 2008 to December 2011, medical records of 134 IIM patients in our rheumatology unit were reviewed. The patients were divided into ILD group (83 patients) and non-ILD group (51 patients). The clinical features and laboratory findings were compared. The univariable analyses indicated that arthritis/arthralgia (54.2% versus 17.6%, P<0.05), Mechanic’s hand (16.9% versus 2.0%, P<0.05), Raynaud’s phenomenon (36.1% versus 2.0%, P<0.05), heliotrope rash (44.6% versus 19.6%, P<0.05), fever (43.4% versus 21.6%, P<0.05), elevated ESR (60.2% versus 35.3%, P<0.05), elevated CRP (55.4% versus 31.4%, P<0.05), or anti-Jo-1 antibody (20.5% versus 5.9%, P<0.05) were risk factors for developing ILD in IIM. Multivariable unconditional logistic regression analysis that showed arthritis/arthralgia (OR 7.1, 95% CI 2.8–18.1), Raynaud’s phenomenon (OR 29.1, 95% CI 3.6–233.7), and amyopathic dermatomyositis (ADM) (OR 20.2, 95% CI 2.4–171.2) were the independent risk factors for developing ILD in IIM