Science Progress最新文献

Understanding aerodynamic entrainment, a critical process in wind-blown sand dynamics, remains challenging due to the difficulty of isolating it from other mechanisms, such as impact entrainment. Aerodynamic entrainment initiates the movement of surface particles, influencing large-scale processes like sediment transport and dune formation. Previous studies focused on average aerodynamic shear stress to estimate entrainment, but the role of impulse events, which cause significant shear stress fluctuations, remains under-explored. We used 12 hot-film shear sensors to measure the spatiotemporal distribution of aerodynamic shear stress during wind-blown sand flow development. We identified impulse events exceeding the entrainment threshold and analyzed their intensity, classifying particle movement as rocking, rolling, or saltation. Results indicate that after a 2-m fetch, sediment mass flux stabilizes, with aerodynamic shear stress decreasing to 78% of the entrainment threshold. We identified key trends, including the stabilization of rocking events beyond x = 4.5 m and a significant decrease in saltation frequency, indicating fully developed wind-blown sand flow. Impulse characteristics stabilize at a greater distance (4.5 m) than sediment transport (2 m) because turbulent airflow evolves more slowly. Our findings show that impulse events significantly influence aerodynamic entrainment. These insights enhance understanding of sediment transport dynamics and improve modeling of sand dune movement.

With the growing number of tram operation lines, tram-related traffic incidents, particularly train collisions, have become a major issue. Therefore, the ability to identify foreign objects on a track is critical to tram operational safety. Accurately identifying the rail edge is a critical technology for recognizing the track area and providing early warnings of potential threats. Therefore, this study proposes a self-adaptive rail-edge detection algorithm that uses mathematical morphology and computer vision technology to accurately extract rail edges. The performance of the proposed algorithm was compared to that of existing algorithms, including the Canny algorithm and two other methods described in publication. Three scenes in the level crossing area of trams were considered as the research objects, and the effects of two types of noise in the image were explored in terms of the investigated using mean square error (MSE), peak signal-to-noise ratio (PSNR), and computational time. The results showed that the proposed model exhibited strong robustness for different scenes, particularly in the presence of noise. This suggests that the proposed algorithm could be used in early warning systems of trams to identify rail edges.

With the increasing speed of high-speed train, it is more and more difficult to reduce the vibration and noise inside the train. The floor of the train, as a carriage component in direct contact with passengers, is of great significance to improve its vibration and sound isolation performance to ensure the comfort of passengers. In this article, a floor vibration isolator with quasi-zero stiffness is designed based on the dimensional parameters of the traditional floor vibration isolator, and the vibration isolation performance is analyzed by the finite element model of the floor vibration isolator from the static and dynamics aspects, respectively. The load-displacement curves of the floor vibration isolator are obtained through static simulation calculations, and the dynamic analysis of the floor vibration isolator is carried out by simulation methods, which verifies the low-frequency vibration isolation performance of the floor vibration isolator under different working conditions, and the vibration isolator has a good prospect for development.

Objectives: The most recent version of ClinVar was utilized to filter variants of the MC4R gene based on location, condition, and clinical significance with the goal of obtaining benign and disease-associated variants of the MC4R gene. MC4R gene variants can lead to dysregulation of energy expenditure and appetite control, which prompted this study to delineate the distinctive features of MC4R gene variants submitted to the ClinVar repository regarding their association with obesity and related phenotypes.

Method: A thorough search was conducted in the ClinVar repository for clinically significant MC4R variants through the utilization of the gene name MC4R[gene] and MeSH terms "MC4R[gene]" and "single gene"[properties]" in the search box. Leading to the identification of clinically significant genetic variants associated with obesity.

Results: Utilizing the ClinVar clinical significance ranking system, the MC4R variants were categorized into six groups based on ClinVar/ClinGen's ranking system: pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), benign (B), likely benign (LB), and conflicting classifications (CC). A total of 103 pathogenic variants were observed. These variants have different clinical significance that are associated with monogenic obesity, monogenic diabetes, and body mass index quantitative traits. It was observed that over 80% of the mutations were single nucleotide variants, with nearly half being missense mutations spread throughout the topological and transmembrane domains. Furthermore, TM7 had the highest number of single nucleotide missense mutations.

Conclusion: Further analysis of the relationships between monogenic obesity and diabetes requires additional investigation to discover the underlying causes of these conditions. The study findings imply that mutations in MC4R's topological and transmembrane regions may significantly influence receptor activation and signaling. As more MC4R variants are discovered and their correlation with obesity is established, there is potential to definitively establish a strong connection between MC4R pathogenic variants and the development of obesity.

Uterine adenosarcoma coexisting with endometrial carcinoma is a very rare disease. Herein, we reported the case of uterine adenosarcoma coexisting with endometrioid endometrial carcinoma. Transvaginal ultrasound, computed tomography, and magnetic resonance imaging examinations all indicated a space-occupying lesion in the uterine cavity, and initially was considered endometrial carcinoma. Subsequently, total hysterectomy combined with bilateral salpingo-oophorectomy, pelvic lymphadenectomy, and para-aortic lymphadenectomy were performed. The coexistence of uterine adenosarcoma and endometrioid endometrial carcinoma was histologically confirmed postoperatively. The patient recovered well after surgery and was discharged on postoperative day 7. At a follow-up examination 10 months after surgery, we found no evidence of discomforting symptoms and recurrence or metastasis. Since the coexistence of uterine adenosarcoma and endometrial carcinoma is rare, it is easy to be overlooked the presence of uterine adenosarcoma on imaging or morphology, and thus be misdiagnosed as a more common disease, namely endometrial carcinoma. Observing the cystic structure within the lesion on magnetic resonance imaging is helpful for the diagnosis of uterine adenosarcoma. This article summarizes the imaging characteristics, clinicopathological features, molecular correlation, treatment, and prognosis of the disease.

Silkworm breeding, as a pivotal economic activity across various regions of China, plays a crucial role in promoting rural revitalization. Notably, the early stage of silkworm development, during which the larvae are most vulnerable and environmentally sensitive, poses significant challenges due to their high pathogenicity and mortality rates. To enhance the efficiency of silkworm breeding, it is imperative to accurately and rapidly identify the physiological state of these small silkworms, ensuring timely feedback to farmers. By using the manually labeled data set, we trained a neural network model to identify the age of the small silkworm through the external characteristics and body length of different instars, and the model used the output center point coordinates to evaluate whether the silkworm entered the dormancy period. If the small silkworm enters the dormant period, the small silkworm will not move. By comparing the maximum difference of the coordinates of the center point of the small silkworm in the experimental group during the dormant period and the feeding period, a certain threshold is set. If the maximum difference of the coordinates of the center point is less than the threshold, the small silkworm is judged to enter the dormant period. To further enhance the model's performance, we introduced an improved target detection network model, building upon the established YOLOv5 architecture. This enhanced model integrates the C3-SE attention mechanism, enabling the network to focus more intently on the target of interest, thus improving detection accuracy. Additionally, we replaced the CIoU loss function in the original target detection network model with the Focal-EIoU loss function. This adjustment effectively mitigates the issue of imbalanced positive and negative samples, accelerating the convergence speed of the network and ultimately enhancing the model's accuracy and recall rate. To validate the accuracy of the proposed model, we randomly selected sample pictures from the curated small silkworm dataset, constituting the test and verification sets. This dataset comprised images and videos capturing different developmental stages of small silkworms. The test results demonstrate that the improved YOLOv5 model achieves an average accuracy of 92.2%, surpassing the preimproved network model by 2.29%. Specifically, the model exhibits a 0.3% increase in accuracy, a 3.4% improvement in recall rate, and a significant 7.7% enhancement in frames per second. These findings indicate that the enhanced YOLOv5 model is capable of accurately and efficiently identifying the physiological state of small silkworms.

The present study focuses on the analysis and design of a novel fuzzy adaptive PID control algorithm, aiming to enhance the speed control accuracy of valve-controlled hydraulic motors under load fluctuating conditions. The method of model identification was adopted to calculate the transfer function of valve-controlled hydraulic motor based on the established hydraulic technology scheme. The chassis of the valve-controlled hydraulic motor is subsequently investigated, and a model for E-grade pavement is constructed using the harmonic superposition method. Through dynamic simulation, the load fluctuation range under two common operating conditions is determined. The fuzzy adaptive PID algorithm was subsequently designed in detail, with the error and its rate of change being considered as input parameters, while the increments of the proportional coefficient, integral constant, and differential constant were regarded as output parameters. Co-simulation data indicate that, compared with the PID algorithm, the average error of the fuzzy adaptive PID algorithm can be reduced by more than 50% and the rise time is reduced by 0.04 seconds. To validate the theoretical analysis, a tracked hydraulic chassis was developed and tested. At an expected speed of 200 rev/min, the average error decreased by 1.68 rev/min, while at an expected speed of 1000 rev/min, the average error reduced by 2.68 rev/min. The designed intelligent control algorithm can effectively improve the control accuracy and stability under load fluctuating conditions.

Objectives: The objective of this study was to develop and validate an automated solid-phase extraction (SPE) coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the detection of sodium pentachlorophenolate (PCP-Na) residues on cutting boards. Given the potential hazards and environmental persistence of PCP-Na, a sensitive and reliable method is crucial for monitoring its residues in food contact materials to ensure consumer safety.

Methods: Wood shavings from cutting boards were extracted using 10% methanol in water, followed by purification using an automated SPE system. The eluent was concentrated, reconstituted, and analyzed by UPLC-MS/MS. An isotope-labeled internal standard was used to mitigate matrix effects, enhancing detection sensitivity. The method was validated by assessing linearity, limit of detection (LOD), limit of quantification (LOQ), recovery rates, and relative standard deviations (RSDs) across various concentration levels.

Results: The method demonstrated excellent linearity over a concentration range of 0 to 100 μg/L with a regression equation of Y = 1.035X-0.7771 and an R² of 0.9996. The LOD and LOQ were determined to be 0.4 and 1.0 μg/kg, respectively. Recovery rates ranged from 71.75% to 96.50% with RSDs between 5.19% and 16.66%. When applied to 30 market cutting board samples, PCP-Na residues were detected in 50% of the samples, with concentrations ranging from 0 to 83,990 µg/kg.

Conclusion: This study presents a robust UPLC-MS/MS method for the detection of PCP-Na on cutting boards, offering improved sensitivity and simplified sample preparation. The high detection rate in commercial samples underscores the need for stringent monitoring and regulatory measures to mitigate the exposure risk to consumers.

Trustworthy positioning is critical in the operational control and management of trains. For a train positioning system (TPS) based on a global navigation satellite system (GNSS), a spoofing attack significantly threatens the trustworthiness of positioning. However, the influence and recognition of GNSS spoofing attacks are not considered in the existing research on GNSS-enabled TPS. Spoofing attacks affect the performance of GNSS observations and the positioning results, allowing the development of data-driven spoofing recognition solutions. This study aims to achieve effective spoofing recognition for active security protection in TPS. Different features were designed to reflect the effects of a spoofing attack, including GNSS observation-related indicators and odometer-enabled parameters, and a novel Bayesian optimization-light gradient boosting machine (BO-LightGBM) solution was proposed. In particular, a Bayesian optimization technique was introduced into the LightGBM framework to improve the hyperparameter determination capability for recognition model training. Using a GNSS spoofing test platform with a specific GNSS signal generator and the SimSAFE spoofing test tool, different spoofing attack modes were tested to collect sample datasets for model training and evaluation. The results of model establishment and comparison of the model performance indicators illustrated the advantages of the proposed solution, its adaptability to different spoofing attack situations, and its superiority over state-of-the-art modeling strategies.

Breakthroughs in marine science and technology have profoundly expanded our knowledge of the ocean, driving unprecedented levels of marine resource utilization. Consequently, the immense value of marine genetic resources (MGRs) in areas beyond national jurisdiction (ABNJ) has catapulted them to the forefront of international competition. In response to the existing gap in international legal frameworks governing the conservation and sustainable use of biodiversity in ABNJ, the Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas Beyond National Jurisdiction (BBNJ Agreement) was adopted on 19 June 2023. This study aimed to explore China's participation in this new frontier of global ocean governance, focusing on the opportunities and challenges it faces. By employing document analysis and policy analysis, the study delved into international legal frameworks such as the United Nations Convention on the Law of the Sea (UNCLOS) and the BBNJ Agreement, as well as China's domestic policies related to marine resource development. It systematically examined the relevant legal frameworks and their historical contexts. Additionally, comparative analysis was used to contrast the policy-making and development practices of developed and developing countries, allowing for a comprehensive assessment of China's role and challenges in the sustainable use of MGRs in ABNJ. It is concluded that while China has made remarkable progress in MGR research, it still lags behind traditional maritime powers in patent accumulation and the commercialization of MGRs. China also lacks comprehensive domestic legislation to address issues such as benefit-sharing and effective regulation of MGRs in ABNJ. Therefore, China must enhance its scientific research and innovation capabilities, actively participate in the follow-up work of the BBNJ Agreement, and promote global marine biodiversity conservation guided by the concept of a Maritime Community with a Shared Future.