Search

Mesoproterozoic (ca. 1.3 Ga) magmatism in the North China Craton (NCC) was dominated by mafic intrusions (dolerite sills) with lesser amounts of granitic magmatism, but our lack of knowledge of this magmatism hinders our understanding of the evolution of the NCC during this period. This study investigated porphyritic granites from the Huade–Kangbao area on the northern margin of the NCC. Zircon dating indicates the porphyritic granites were intruded during the Mesoproterozoic between 1285.4 ± 2.6 and 1278.6 ± 6.1 Ma. The granites have high silica contents (SiO2 = 63.10–73.73 wt.%), exhibit alkali enrichment (total alkalis = 7.71–8.79 wt.%), are peraluminous, and can be classified as weakly peraluminous A2-type granites. The granites have negative Eu anomalies (δEu = 0.14–0.44), enrichments in large-ion lithophile elements (LILEs; e.g., K, Rb, Th, and U), and depletions in high-field-strength elements (HFSEs; e.g., Nb, Ta, and Ti). εHf(t) values range from –6.43 to +2.41, with tDM2 ages of 1905–2462 Ma, suggesting the magmas were derived by partial melting of ancient crustal material. The geochronological and geochemical data, and regional geological features, indicate the Mesoproterozoic porphyritic granites from the northern margin of the NCC formed in an intraplate tectonic setting during continental extension and rifting, which represents the response of the NCC to the break-up of the Columbia supercontinent. [ABSTRACT FROM AUTHOR]

Seafloor spreading is an important cornerstone of the theory of plate tectonics. Asymmetric seafloor spreading and oceanic ridge jumps are common phenomena in this process and play important roles in controlling oceanic crust accretion, regional tectonics and geological geometric boundaries. As the largest marginal sea in the western Pacific, the South China Sea is an ideal laboratory for dissecting the Wilson cycle of small marginal sea-type ocean basins restricted by surrounding blocks and exploring the deep dynamic processes of confined small ocean basins. In recent years, a lot of research has been conducted on the spreading history of the South China Sea and has achieved fruitful results. However, the detailed dynamic mechanisms of asymmetric seafloor spreading and ridge jumps are still unclear. Therefore, this paper summarizes the basic understanding about the dynamic mechanisms of global asymmetric seafloor spreading and ridge jumps and reviews the related research results of asymmetric seafloor spreading and ridge jumps in the South China Sea. Previous studies have basically confirmed that seafloor spreading in the South China Sea started between ~32 and 34 Ma in the east sub-basin and ended at ~15 Ma in the northwest sub-basin, with at least once oceanic ridge jump in the east sub-basin. The current research mainly focuses on the age of the seafloor spreading in the South China Sea and the location, time and stage of the ridge jumps, but there are relatively few studies on high-resolution lithospheric structure across these ridges and the dynamic mechanism of oceanic ridge jumps. Based on the current research progress, we propose that further studies should focus on the lithosphere–asthenosphere scale in the future, suggesting that marine magnetotelluric and Ocean Bottom Seismometer (OBS) surveys should be conducted across the residual oceanic ridges to perform a detailed analysis of the tectonics magmatism in the east sub-basin to gain insights into the dynamic mechanisms of oceanic ridge jumps and asymmetric seafloor spreading, which can promote understanding of the tectonic evolution of the South China Sea and improve the classical plate tectonics theory that was constructed based on the open ocean basins. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing incorporates multiple indicators, and there exists a mutually constraining relationship among these indicators. Different application fields have varying requirements for indicators. Therefore, indicator testing and comprehensive evaluations are crucial for engineering applications. In this paper, we conducted a theoretical analysis of key indicators, including frequency response, sensitivity, spatial resolution, sensing distance, multi-point perturbation, and temperature influence. The indicator test scheme was developed, and a test system was constructed. The test data were analyzed and compared in the time-frequency domain. A performance evaluation method for distributed acoustic sensing, based on the analytic hierarchy process, is proposed, and a comprehensive evaluation example focused on high-frequency applications is presented. The results show that the test scheme and method presented in this paper can accurately measure the upper limits of each indicator of distributed acoustic sensing. The proposed comprehensive evaluation method enables the assessment of sensor performance and applicability based on engineering practices. It addresses the challenge of evaluating distributed acoustic sensing with multiple indicators and offers an efficient approach for equipment development and engineering applications. [ABSTRACT FROM AUTHOR]

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range–time–Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m. [ABSTRACT FROM AUTHOR]

In order to clarify the trapping efficiency of different types of sex pheromone traps on populations of Spodoptera litura in soybean fields, three kinds of conventional traps, called YL-VT, YL-HEMT and YL-NMT, and types of traps equipped with a automatic catch monitoring mechanism, called AIM and AIM-lite-A, have been tested. These last two allow monitoring the diurnal and seasonal rhythms of the catches. From 5 August to 5 October 2020, each YL-VT trap caught an average of 84.4 heads of S. litura moths per day, which was significantly higher than the YL-HEMT (11.8 heads), YL-NMT (16.5 heads) and AIM (9.7 heads), which did not show significant differences between them. The half-life of pheromone attraction effect in YL-VT trap is about 17–20 days. Considering the number of trapping and the duration of pheromone attraction effect, YL-VT trap is the best. For efficient monitoring, the validity period is approximately 15 days. Among the causes that could explain its higher capture rate and efficiency, the YL-VT trap has the smallest and largest number of inlet devices, and in it, the lure core is located inside of the insect inlet device. Under different occurrence numbers of S. litura, the accuracy of the automatic count of AIM-lite-A is 90% greater than in the AIM trap. The diurnal rhythms of S. litura observed by AIM-lite-A show that S. litura have two active periods that are from 18:00 to 21:00 and from 23:00 to 4:00 at night and that the first active period may be affected by sunshine and temperature. The annual activity rhythm of S. litura monitored by AIM showed that the activity of S. litura was related to temperature, there were approximately 6 generations of S. litura in a year and that the activity peak was from August to September in Dangtu, China. AIM and AIM-lite-A can be used for the annual monitoring of S. litura population in soybean fields because of their automatic counting function. AIM can simultaneously record a variety of meteorological data, AIM-lite-A has higher accuracy and a lower cost, which can be selected according to different situations. In conventional traps, the trap YL-VT can be used for the population monitoring of S. litura in soybean fields and pest control in the field, especially in greenhouses and net rooms where organic soybeans are grown, due to its low cost and efficient trapping ability. [ABSTRACT FROM AUTHOR]

Legionella pneumophila RpoS (LpRpoS), an alternative sigma factor of RNA polymerase (RNAP), is essential for virulence and stress resistance. To investigate the mechanism of RpoS in the intracellular pathogen L. pneumophila, we determined the high-resolution crystal structure of the LpRpoS95-195 containing a partial region 1.2 and region 2. The structure of LpRpoS95-195 reveals that the conserved residues are critical for promoter melting, DNA and core RNAP binding. The differences in regulatory factor binding site between Escherichia coli RpoS and LpRpoS suggest that LpRpoS may employ a distinct mechanism to recruit alternative regulatory factors controlling transcription initiation. [ABSTRACT FROM AUTHOR]

In this paper, a new solar-assisted heat pump drying system with waste heat recovery and double water tanks (SCAHP) was established and the system optimized by TRNSYS and variable air volume experiment. The annual cumulative efficiency of the SCAHP (COPac), buffer tank heating efficiency (ηBT) and hot water storage tank heating efficiency (ηST) are all considered as optimization objectives, and this paper discusses the relationship between the three optimization objectives and studies the influence of hot air volume (qDR), area of solar collector (ASC), inclination angle of solar collector (ISC) and volume of heat storage water tank (VST) on system efficiency in the drying process. In order to explore the general rule of system optimization, numerical analysis and optimization were carried out in five typical cities in five climatic regions of China. The results show that the mixed variable air volume mode can increase the drying rate with less energy loss, and shorten the drying period by 28.6–33.3%. When the system surface body ratio (SBR) in Nanjing is between 3.1 and 4.1, the COPac, ηBT, and ηST can reach the maximum value simultaneously. It is estimated that the cost can be recovered in 5 years when the system configuration parameters are optimized. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment. [ABSTRACT FROM AUTHOR]