Theoretical study on self-synchronization of two homodromy rotors coupled with a pendulum rod in a far-resonant vibrating system

The objective of this paper is to investigate the self-synchronization of two homodromy rotors coupled with a pendulum rod in a far-resonant vibrating system. Using the average method and revisionary small parameters, we derive the dimensionless equation of the self-synchronization criterion and synchronous stability of the vibrating system. Meanwhile, to prove the correctness of the theoretical analysis, the diversity feature of the vibrating system is simulated numerically. Both results of theoretical analysis and numerical simulation show that increasing the length of the pendulum rod or decreasing the mass of the rotor connected with pendulum rod can ensure the self-synchronization and synchronous stability of two homodromy rotors in the vibrating system

Formation and evolution of soil salinization based on multivariate statistical methods in Ningxia Plain, China

The Ningxia Plain, situated in the arid zone of northwest China, is a typical dryland plain that faces significant challenges to sustainable agricultural development due to soil salinization. In this study, we employed multivariate analysis and geostatistical methods to investigate the degree and distribution types of soil salinization and the hydrochemical characteristics of shallow groundwater. We also examined the relationship between soil salinization and hydrogeochemical characteristics by analyzing the sources of groundwater ions. This study developed a hydrogeochemical model to describe the soil salinization process in the Ningxia Plain. The results indicate that the majority of surface soils are alkaline type soils, followed by chloride-sulfate type soils. The groundwater is mainly fresh water and brackish water, with a hydrochemical type of SO4·HCO3–Ca·Mg or SO4·Cl–Ca·Mg. Saline water and salt water are represented by Cl–Na·Mg or Cl·SO4–Na·Mg. We also observed spatial trends in groundwater depth and total dissolved solids (TDS) concentrations that were opposite to soil salinity, which suggests a certain degree of second-order trend effect. Furthermore, the degree of soil salinization increased and then decreased from the pre-mountain alluvial plain to the Yellow River alluvial plain, while the groundwater chemistry ranged from simple to complex. The most severe area of soil salinization was found to be concentrated between Hongguang and Yaofu, which is also the area where shallow groundwater salinity accumulation is mainly influenced by continental salinization. In summary, this study provides valuable insights into the hydrogeochemical characteristics of the Ningxia Plain, which can inform strategies for mitigating soil salinization and promoting sustainable agriculture development in arid regions

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without exten-sive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without exten-sive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Segmental Membranous Glomerulopathy in Adults

Introduction: The clinicopathological features of segmental membranous glomerulopathy (SMGN) have not been well characterized. The aim of this study was to investigate the prevalence and clinicopathological features of SMGN in adults. Methods: Adult patients with biopsy-confirmed SMGN in the native kidney at our center between January 2017 to September 2020 were identified. The clinicopathological features of SMGN were collected. The glomerular deposition of IgG subclasses, M-type phospholipase A2 receptor 1 (PLA2R), thrombospondin type 1 domain-containing 7A (THSD7A) and neural epidermal growth factor-like 1 protein (NELL1) were tested. Clinical and pathologic features were comparable between NELL1-positive and NELL1-negative SMGN. Results: A total of 167 patients with biopsy-proven SMGN were enrolled. During the same period, 32,640 (33.0%) out of 98,939 renal biopsies were diagnosed with membranous nephropathy (MN) in adults. SMGN accounted for 0.17% of total kidney biopsies and 0.51% of MN in adults. One hundred and fifty (89.8%) cases were isolated SMGN and 17 (10.2%) cases were complicated with other kidney disease. Clinically, the median age of isolated SMGN patients was 41.5 years, with female (74%) predominance, and 33.1% had full nephrotic syndrome. Pathologically, IgG1 was the dominant subclass (92.5%), followed by IgG4 (45.0%). PLA2R and THSD7A staining were done in 142 and 136 isolated SMGN cases, respectively. In which, all the cases showed negative. NELL1 staining was done in 135 isolated SMGN cases, 58 cases (43.0%) showed positive. Fifty-eight patients (41.1%) had diffuse (≥90%) foot process effacement, 119 patients (83.8%) had either stage I (38.0%) or stage II (45.8%) membranous alterations in patients with SMGN. Most patients with NELL1-positive SMGN were female. Patients with NELL1-positive SMGN were more likely with lower prevalence of full nephrotic syndrome than NELL1-negative SMGN. Conclusions: SMGN is a relatively rare pathological type. Majority of patients with isolated SMGN were female, with a median age of 41.5 years, 33.1% had full nephrotic syndrome, absence of PLA2R and THSD7A, 43.0% with NELL1-positive, and mainly stage I or II MN (83.8%). NELL1 is the major target antigen of SMGN in adults

Effect of Vaccination on Bordetella pertussis Strains, China

Strains in China may differ from those in countries that have long histories of high vaccination coverage

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV