Science Progress最新文献

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.

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

The complexity of test and fault information within electronic devices makes their integrated diagnosis a challenging problem when designing equipment reliability. Current integrated diagnosis is analyzed for test optimization and test resource optimization. However, this neglects the connection between them. This paper proposes a design strategy for integrated diagnosis optimization based on the spatial mapping principle to quantitatively describe the constraint relationship between them. The integrated diagnosis optimization model is established by constructing the logical mapping relationship between test space, resource space, and fault space, and the optimal test configuration and test resource configuration are sought based on the grey wolf optimization algorithm. Seven high-dimensional benchmark functions and an integrated diagnosis model of electronic equipment are used to verify the efficiency of the algorithm proposed in this paper. The proposed algorithm is compared with the other four in terms of the algorithm's optimization speed and accuracy. The results indicate that the electronic equipment after integrated diagnosis optimization has critical fault detection, fault detection, fault isolation, and false alarm rates of 100%, 99.99%, 98.99%, and 0.2993%, respectively. After the integrated diagnosis optimization, the number of tests of the equipment is reduced by 88.9%, and the test cost is saved by 89%. Compared with the other algorithms, grey wolf optimization achieves the best optimization results, reduces the number of tests by 42%-55%, and decreases the test cost by 77.63%-83.91%. This strategy not only considers the test optimization of equipment and test resources optimization but also dramatically reduces the test cost while improving the test efficiency.