Greigite formed in early Pleistocene lacustrine sediments from the Heqing Basin, southwest China, and its paleoenvironmental implications

The ferrimagnetic iron sulfide greigite (Fe3S4) occurs widely in sulfidic lacustrine and marine sedimentary environments. Knowledge of its formation and persistence is important for both magnetostratigraphic and paleoenvironmental studies. Although the formation mechanism of greigite has been widely demonstrated, the sedimentary environments associated with greigite formation in lakes, especially on relatively long timescales, are poorly understood. A long and continuous sequence of Pleistocene lacustrine sediments was recovered in the Heqing drill core from southwestern China, which provides an outstanding record of continental climate and environment. Integrated magnetic, geochemical, and paleoclimatic analysis of the lacustrine sequence provides an opportunity to improve our understanding of the environmental controls on greigite formation. Rock magnetic and scanning electron microscope analyses of selected samples from the core reveal that greigite is present in the lower part of the core (part 1, 665.8-372.5 m). Greigite occurs throughout this interval and is the dominant magnetic mineral, irrespective of the climatic state. The magnetic susceptibility (chi) record, which is mainly controlled by the concentration of greigite, matches well with variations in the Indian Summer Monsoon (ISM) index and total organic carbon (TOC) content, with no significant time lag. This indicates that the greigite formed during early diagenesis. In greigite-bearing intervals, with the chi increase, B-c value increase and tends to be stable at about 50 mT. Therefore, we suggest that chi values could estimate the variation of greigite concentration approximately in the Heqing core. Greigite favored more abundant in terrigenous-rich and organic poor layers associated with weak summer monsoon which are characterized by high chi values, high Fe content, high Rb/Sr ratio and low TOC content. Greigite enhancement can be explained by variations in terrigenous inputs. Our studies demonstrate that, not only the greigite formation, but also its concentration changes could be useful for studying climatic and environmental variability in sulfidic environments

Late Miocene magnetostratigraphy of Jianzha Basin in the northeastern margin of the Tibetan Plateau and changes in the East Asian summer monsoon

Jianzha Basin is located in the northeastern Tibetan Plateau (NETP) and contains a thick sequence of Cenozoic sediments that are an archive of information about the growth of the Tibetan Plateau and the evolution of the arid environment of the interior of Asia. Here, we present magnetostratigraphic and palaeoenvironmental records from a 361-m-thick sequence of Late Cenozoic eolian Red Clay and intercalated fluviolacustrine deposits in the Jianzha Basin. The magnetostratigraphic results show that the sediments have recorded a continuous geomagnetic polarity sequence from C5r.3r to C3r, spanning the interval from 11.8 to 5.8Ma in the Late Miocene. There are two intervals of rapidly fluctuating sedimentation rates between similar to 10 and similar to 6Ma, which we interpret as a response to a series of uplifts and expansions to the north and to the east in the NETP. The fluctuations in Rb/Sr ratio and magnetic susceptibility before similar to 8.57Ma reflect intensified East Asian summer monsoon (EASM) precipitation which resulted from the growth of the NETP. From similar to 8.57 to similar to 7.21Ma, the EASM was impacted by global cooling and ice build-up in the Northern Hemisphere in addition to the uplift of the Tibetan Plateau (TP) in the Late Miocene. From similar to 8.57 to similar to 7.21Ma, there is a lack of coherency between the fluctuations in MS and Rb/Sr ratio; however, subsequently, there is significant coherency between the Rb/Sr ratio and the deep-sea oxygen isotope record present. This suggests that from similar to 8.57Ma, the eolian Red Clay sediments in the Jianzha Basin were significantly affected by the addition of dust derived from the deforming and uplifting areas of the TP

A Sarsa( λ

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control

Unregistered Biological Words Recognition by Q-Learning with Transfer Learning

Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, and F score results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning

The Sihailongwan Maar Lake, northeastern China as a candidate Global Boundary Stratotype Section and Point for the Anthropocene Series

Sihailongwan Maar Lake, located in Northeast China, is a candidate Global boundary Stratotype Section and Point (GSSP) for demarcation of the Anthropocene. The lake’s varved sediments are formed by alternating allogenic atmospheric inputs and authigenic lake processes and store a record of environmental and human impacts at a continental-global scale. Varve counting and radiometric dating provided a precise annual-resolution sediment chronology for the site. Time series records of radioactive (239,240Pu, 129I and soot 14C), chemical (spheroidal carbonaceous particles, polycyclic aromatic hydrocarbons, soot, heavy metals, δ13C, etc), physical (magnetic susceptibility and grayscale) and biological (environmental DNA) indicators all show rapid changes in the mid-20th century, coincident with clear lithological changes of the sediments. Statistical analyses of these proxies show a tipping point in 1954 CE. 239,240Pu activities follow a typical unimodal globally-distributed profile, and are proposed as the primary marker for the Anthropocene. A rapid increase in 239,240Pu activities at 88 mm depth in core SHLW21-Fr-13 (1953 CE) is synchronous with rapid changes of other anthropogenic proxies and the Great Acceleration, marking the onset of the Anthropocene. The results indicate that Sihailongwan Maar Lake is an ideal site for the Anthropocene GSSP

The Unsteady Numerical Simulation and Fluid-Structure Interaction Analysis of TLB600-700 Desulphurization Pump

Based on the design theory of liquid-solid two-phase flow centrifugal pump, a new type TLB600-700 desulphurization pump was designed with huge distortions blades design method and impeller inlet super long extension blades design method. Three-dimensional model of internal flow field in TLB600-700 desulphurization pump was built by software PROE5.0, and the three-dimensional unsteady numerical simulation of the internal flow field was calculated, which revealed that the rotor-stator interaction between rotating impeller and volute is the reason why unstable flow generated. Statics analysis was carried out on the impeller in the stationary flow state with the method of fluid-structure interaction, and results indicated that the impeller strength and stiffness meet the design requirements. External characteristic test results of TLB600-700 desulphurization pump showed that all parameters of desulphurization pump designed by innovative method meet design requirements; especially the pump efficiency was increased by 4.15% higher than Chinese national standard

Greigite formed in early Pleistocene lacustrine sediments from the HeqingBasin, southwest China, and its paleoenvironmental implications

The ferrimagnetic iron sulfide greigite (Fe3S4) occurs widely in sulfidic lacustrine and marine sedimentary environments. Knowledge of its formation and persistence is important for both magnetostratigraphic and paleoenvironmental studies. Although the formation mechanism of greigite has been widely demonstrated, the sedimentary environments associated with greigite formation in lakes, especially on relatively long timescales, are poorly understood. A long and continuous sequence of Pleistocene lacustrine sediments was recovered in the Heqing drill core from southwestern China, which provides an outstanding record of continental climate and environment. Integrated magnetic, geochemical, and paleoclimatic analysis of the lacustrine sequence provides an opportunity to improve our understanding of the environmental controls on greigite formation. Rock magnetic and scanning electron microscope analyses of selected samples from the core reveal that greigite is present in the lower part of the core (part 1, 665.8–372.5 m). Greigite occurs throughout this interval and is the dominant magnetic mineral, irrespective of the climatic state. The magnetic susceptibility (χ) record, which is mainly controlled by the concentration of greigite, matches well with variations in the Indian Summer Monsoon (ISM) index and total organic carbon (TOC) content, with no significant time lag. This indicates that the greigite formed during early diagenesis. In greigite-bearing intervals, with the χ increase, Bc value increase and tends to be stable at about 50 mT. Therefore, we suggest that χ values could estimate the variation of greigite concentration approximately in the Heqing core. Greigite favored more abundant in terrigenous-rich and organic-poor layers associated with weak summer monsoon which are characterized by high χ values, high Fe content, high Rb/Sr ratio and low TOC content. Greigite enhancement can be explained by variations in terrigenous inputs. Our studies demonstrate that, not only the greigite formation, but also its concentration changes could be useful for studying climatic and environmental variability in sulfidic environments

Magnetic mineral dissolution recorded in a lacustrine sequence from the Heqing Basin, SW China, and its relationship with changes in the Indian monsoon

The dissolution of magnetic oxides is an important process in many lake environments. Understanding this phenomenon is crucial for the paleoenvironmental interpretation of the magnetic properties of lake sediments. In order to reveal its effects on sedimentary magnetic properties, and to assess the possible associated paleoenvironmental implications, we carried out detailed rock magnetic analyses of the selected samples from a 920-kyr lacustrine sequence from the Heqing Basin, SW China. The results indicate that the sedimentary magnetic properties are controlled by the concentration and grain size of magnetite and maghemite. High magnetic susceptibility (chi) intervals contain more fine-grained magnetite and maghemite, while low chi intervals contain only minor amounts of residual magnetite. The decreased content of fine-grained maghemite from high chi to low chi intervals reflects the dissolution of magnetic oxides during deposition. Intervals affected by strong magnetic dissolution have a high TOC content and correspond to times of high Antarctic temperatures, suggesting that magnetic mineral dissolution intensity was associated with variations in the strength of the Indian Summer Monsoon (ISM). Notably, the ISM is sensitive to Southern Hemisphere warming. Weak magnetic dissolution indicates a dry climate occurred since similar to 320 kyr in the Heqing Basin. This dry/cool event was widespread across the Eastern Bay of Bengal, Equatorial Indian Ocean and Northern Australia, and was linked to a strengthened Indian Ocean Dipole (IOD). Since the moisture source of the Heqing Basin was mainly from the above regions, we infer that the influence of the IOD extended northwards to SW China

Edge-Nodes Representation Neural Machine for Link Prediction

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics