A model-by-model analysis for historical simulations was necessary for identifying reasonably performing models in the updated Coupled Model Intercomparison Project (CMIP6) over the Tibetan Plateau. ...To determine whether the capacity of the CMIP6 models in simulating temperature and precipitation over the Plateau has been enhanced, we compared the outputs of 23 CMIP6 models with an observational dataset (CN05.1) for the period 1961–2014. The results suggest the systematic model biases (cold bias and wet bias) in the Tibetan Plateau still exist in CMIP6. Most models in CMIP6 realistically simulated the surface temperature and spatial distribution of precipitation, with a pattern correlation exceeding 0.75. The bias in the mean surface temperature of the multi-model ensemble (MME) simulation was 1.08 °C lower than the observational data, which had been decreased compared with the cold bias of CMIP5 (1.52 °C). At the seasonal scale, most models exhibited a warm temperature bias in summer and a cold bias in winter. The CMIP6 MME displayed a higher reproducibility of the precipitation amplitude over dry regions compared with CMIP5 and a lower ability over wet regions.
Bi2Te3‐related alloys dominate the commercial thermoelectric market, but the layered crystal structure leads to the dissociation and intrinsic brittle fracture, especially for single crystals that ...may worsen the practical efficiency. In this work, point defect configuration by S/Te/I defects engineering is engaged to boost thermoelectric and mechanical properties of n‐type Bi2Te3 alloy, which, coupled with p‐type BiSbTe, shows a competitive conversion efficiency for the fabricated module. First, as S alloying suppresses the intrinsic BiTe, antisite defects and forms a donor‐like effect, electronic transport properties are optimized, associated with the decreased thermal conductivity due to the point defect scattering. The periodide compound TeI4 is afterward adopted to further tune carrier concentration for the realization of an optimal ZT. Finally, an advanced average ZT of 1.05 with ultra‐high compressive strength of 230 MPa is achieved for Bi2Te2.9S0.1(TeI4)0.0012. Based on this optimum composition, a fabricated 17‐pair module demonstrates a maximum conversion efficiency of 5.37% under the temperature difference of 250 K, rivaling the current state‐of‐the‐art Bi2Te3 modules. This work reveals the novel mechanism of point defect reconfiguration in synergistic enhancement of thermoelectric and mechanical properties for durably commercial application, which may be applicable to other thermoelectric systems.
Mediating point defects simultaneously modulates the carrier concentration and microstructure in n‐type Bi2Te3 materials further generating superior thermoelectric and mechanical performance. With high strength and conversion efficiency of over 5% in a wide temperature range, the power generation module fabricated with this n‐type Bi2Te3 has comprehensive advances compared with commercial modules and may be greatly capable of serving in a hostile environment.
Increased risk of colorectal cancer (CRC) is associated with altered intestinal microbiota as well as short‐chain fatty acids (SCFAs) reduction of output The energy source of colon cells relies ...mainly on three SCFAs, namely butyrate (BT), propionate, and acetate, while CRC transformed cells rely mainly on aerobic glycolysis to provide energy. This review summarizes recent research results for dysregulated glucose metabolism of SCFAs, which could be initiated by gut microbiome of CRC. Moreover, the relationship between SCFA transporters and glycolysis, which may correlate with the initiation and progression of CRC, are also discussed. Additionally, this review explores the linkage of BT to transport of SCFAs expressions between normal and cancerous colonocyte cell growth for tumorigenesis inhibition in CRC. Furthermore, the link between gut microbiota and SCFAs in the metabolism of CRC, in addition, the proteins and genes related to SCFAs‐mediated signaling pathways, coupled with their correlation with the initiation and progression of CRC are also discussed. Therefore, targeting the SCFA transporters to regulate lactate generation and export of BT, as well as applying SCFAs or gut microbiota and natural compounds for chemoprevention may be clinically useful for CRCs treatment. Future research should focus on the combination these therapeutic agents with metabolic inhibitors to effectively target the tumor SCFAs and regulate the bacterial ecology for activation of potent anticancer effect, which may provide more effective application prospect for CRC therapy.
Short‐chain fatty acids (SCFAs) produced in the human colon are the major products of bacterial fermentation of undigested dietary fiber and starch that escape absorption in the small intestine, and serve as a major source of energy for colonocytes. SCFAs are microbial‐derived metabolites, which are readily absorbed and used as an energy source by colonocytes. Several mechanisms have been proposed to underlie the anticancerous mechanisms of SCFAs. SCFAs reduce epithelial inflammation and trigger cancer cell apoptosis via p21 activity, providing an important defensive capacity against colorectal carcinogenesis.
The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable tool for the epileptic seizure detection. Recent deep learning models fail to fully consider both spectral ...and temporal domain representations simultaneously, which may lead to omitting the nonstationary or nonlinear property in epileptic EEGs and further produce a suboptimal recognition performance consequently. In this paper, an end-to-end EEG seizure detection framework is proposed by using a novel channel-embedding spectral-temporal squeeze-and-excitation network (CE-stSENet) with a maximum mean discrepancy-based information maximizing loss. Specifically, the CE-stSENet firstly integrates both multi-level spectral and multi-scale temporal analysis simultaneously. Hierarchical multi-domain representations are then captured in a unified manner with a variant of squeeze-and-excitation block. The classification net is finally implemented for epileptic EEG recognition based on features extracted in previous subnetworks. Particularly, to address the fact that the scarcity of seizure events results in finite data distribution and the severe overfitting problem in seizure detection, the CE-stSENet is coordinated with a maximum mean discrepancy-based information maximizing loss for mitigating the overfitting problem. Competitive experimental results on three EEG datasets against the state-of-the-art methods demonstrate the effectiveness of the proposed framework in recognizing epileptic EEGs, indicating its powerful capability in the automatic seizure detection.
The matter state inside neutron stars (NSs) is an exciting problem in astrophysics, nuclear physics, and particle physics. The equation of state (EOS) of NSs plays a crucial role in the present ...multimessenger astronomy, especially after the event of GW170817. We propose a new NS EOS, "QMF18," from the quark level, which describes robust observational constraints from a free-space nucleon, nuclear matter saturation, heavy pulsar measurements, and the tidal deformability of the very recent GW170817 observation. For this purpose, we employ the quark mean-field model, which allows us to tune the density dependence of the symmetry energy and effectively study its correlations with the Love number and the tidal deformability. We provide tabulated data for the new EOS and compare it with other recent EOSs from various many-body frameworks.
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We consider the sensitivity of the DUNE experiment to a heavy neutral lepton, HNL (also known as sterile neutrino) in the mass range from a few MeV to a few GeV, interacting with the ...Standard Model via a transition magnetic moment to the active neutrinos, the so-called dipole portal. The HNL is produced via the up-scattering of active neutrinos, and the subsequent decay inside the detector provides a single-photon signal. We show that the tau-neutrino dipole portal can be efficiently probed at the DUNE far detector, using the tau-neutrino flux generated by neutrino oscillations, while the near detector provides better sensitivity to the electron- and muon-neutrino dipole portal. DUNE will be able to explore large regions of currently unconstrained parameter space and has comparable sensitivity to other planned dedicated experiments, such as SHiP. We also comment briefly on the sensitivity to pure HNL mixing with the tau neutrino at the DUNE far detector.
As a special family of cyclopropanes, alkylidenecyclopropanes (ACPs), exhibit outstanding physical and chemical activities, which provide opportunities to participate in fascinating chemical ...transformations to access cyclopropane‐containing units without ring‐opening processes and other unavailable compounds through conventional routes with ring‐opening processes owing to their strain‐driven reactivity and synthetic accessibility. Nowadays, intramolecular reactions of methylenecyclopropanes (MCPs) or ACPs with adjacent functionalities have emerged as a powerful synthetic protocol for the construction of a variety of polycyclic and heterocyclic compounds with different sized skeletons through catalytic methods. Recently, we put forward the concept of functional alkylidenecyclopropanes (FACPs) and in this Minireview, we will summarize the reactions of FACPs after 2016 including several important early works from three aspects: 1) reactions with distal C−C bond cleavage, 2) reactions with proximal C−C bond cleavage (including ring‐expansion reactions), and 3) reactions without C−C bond cleavage.
Polycycles: During the past few years, functionalized alkylidenecyclopropanes (FACPs) have been widely used for molecular complexity. This Minireview mainly focuses on the advances of this area after 2016 classified by C−C bond cleavage (see scheme).
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external ...devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.
In this paper, a global task coordinate frame (GTCF)-based learning adaptive robust contouring controller is proposed for an industrial X-Y linear-motor-driven stage to achieve not only good ...parametric adaptation ability and disturbance robustness, but also excellent contouring accuracy even under high-speed large-curvature contouring tasks. Specifically, the contouring controller employs GTCF to guarantee the multiaxes motion coordination. After transforming the system dynamics of the X-Y linear-motor-driven stage into the GTCF, a learning adaptive robust control (LARC) scheme is developed to deal with the strongly coupled dynamics under parametric uncertainty and uncertain disturbances. During the LARC, adaptive model compensation term, robust feedback term, and iterative learning term are organically integrated in a serial structure. The controller design process with the stability analysis is presented, while the essence of the practical achievable performance is also introduced for the nature of the GTCF-LARC. Comparative experiments are carried out on an industrial linear-motor-driven stage with different cases. The results consistently verify that the proposed GTCF-LARC contouring controller can simultaneously meet the industrial requirements of excellent transient/steady-state contouring accuracy, parametric adaptation ability, external disturbance robustness, and large-curvature high-speed contouring tasks. The proposed GTCF-LARC scheme actually provides a practical high-performance-oriented contouring control framework, and could be extended to other multiaxes applications.
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