Search

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. The excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

Summary As an analog of graphene, carbon nitrides have drawn increasing attention, ranging from artificial photosynthesis to synthetic organic chemistry. Notably, the most studied polymeric, C3N4, and other stoichiometric ones, such as C3N5, C4N3, and C3N, are commonly employed repetitive units of six-membered rings. Building blocks of five-membered rings (FMRs) are also fascinating and prevalent from a fundamental point of view, but FMR-based carbon nitrides are scarcely reported. Here, we propose a stable FMR-based C3N2 and the kinetic-oriented synthesis method via a pre-stabilization strategy using abundant-available zeolitic imidazolate framework as a precursor. The FMR topological structure and the associated dangling bonds were disclosed in C3N2, leading to an interesting p-π conjugation and splitting molecular orbitals, which remarkably narrowed the optical bandgap unusually down to 0.81 eV. The as-obtained C3N2 was further successfully applied to real-time, dynamic, and quantitative photoelectrochemical biosensing for practical non-transparent biosamples under near-infrared light irradiation.

In a classical metasurface-based holographic display system, a clear holographic reconstruction image can be obtained only in the back-focal-plane of the lens. However, when the receiving plane deviates from the focal plane, the reconstructed holographic image suffers from degradations that limit the quality of the images. Hence, we propose a novel metasurface-based holographic display to realize clearly continuous imaging within the specified position range by superimposing the meta-axilens phase. It can effectively relieve the alignment requirements of the imaging system. Firstly, the parameters and properties of all-dielectric meta-atom is analyzed. Then we compare and demonstrate two sets of metasurface-based optical elements (meta-axicon, meta-lens, and meta-axilens), made by silicon meta-atoms working at 610 nm. Furthermore, we show that the metasurface hologram with the axilens phase can form a series of the relatively clear holographic images away from the focal plane. Our proposal suggests a method to reconstruct holographic wavefront in different planes simultaneously, and one can find their application domain, such as 3D biological imaging, spectroscopy, optical information storage, and encryption.

Background: Despite the better prognosis given by surgical resection and chemotherapy in low-grade glioma (LGG), progressive transformation is still a huge concern. In this case, the S100A gene family, being capable of regulating inflammatory responses, can promote tumor development. Methods: The analysis was carried out via ONCOMINE, GEPIA, cBioPortal, String, GeneMANIA, WebGestalt, LinkedOmics, TIMER, CGGA, R 4.0.2 and immunohistochemistry. Results: S100A2, S100A6, S100A10, S100A11, and S100A16 were up-regulated and S100A1 and S100A13 were down-regulated in LGG compared to normal tissues. S100A3, S100A4, S100A8, and S100A9 expression was up-regulated during the progression of glioma grade. In addition, genetic variation of the S100A family was high in LGG, and the S100A family genes mostly function through IL-17 signaling pathway, S100 binding protein, and inflammatory responses. The TIMER database also revealed a relationship between gene expression and immune cell infiltration. High expression of S100A2, S100A3, S100A4, S100A6, S100A8, S100A9, S100A10, S100A11, S100A13, and S100A16 was significantly associated with poor prognosis in LGG patients. S100A family genes S100A2, S100A3, S100A6, S100A10, and S100A11 may be prognosis-related genes in LGG, and were significantly associated with IDH mutation and 1p19q codeletion. The immunohistochemical staining results also confirmed that S100A2, S100A3, S100A6, S100A10, and S100A11 expression was upregulated in LGG. Conclusion: The S100A family plays a vital role in LGG pathogenesis, presumably facilitating LGG progression via modulating inflammatory state and immune cell infiltration.

We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accur...

Small-object detection is a basic and challenging problem in computer vision tasks. It is widely used in pedestrian detection, traffic sign detection, and other fields. This paper proposes a deep learning small-object detection method based on image super-resolution to improve the speed and accuracy of small-object detection. First, we add a feature texture transfer (FTT) module at the input end to improve the image resolution at this end as well as to remove the noise in the image. Then, in the backbone network, using the Darknet53 framework, we use dense blocks to replace residual blocks to reduce the number of network structure parameters to avoid unnecessary calculations. Then, to make full use of the features of small targets in the image, the neck uses a combination of SPPnet and PANnet to complete this part of the multi-scale feature fusion work. Finally, the problem of image background and foreground imbalance is solved by adding the foreground and background balance loss function to the YOLOv4 loss function part. The results of the experiment conducted using our self-built dataset show that the proposed method has higher accuracy and speed compared with the currently available small-target detection methods.

Z-scheme photocatalytic systems are an ideal band alignment structure for photocatalysis because of the high separation efficiency of photo-induced carriers while simultaneously preserving the strong reduction activity of electrons and oxidation activity of holes. However, the design and construction of Z-scheme photocatalysts is challenging because of the need for appropriate energy band alignment and built-in electric field. Here, we propose a novel approach to a Z-scheme photocatalytic system using density functional theory calculations with the HSE06 hybrid functional. The undesirable type-I g-C3 N4 /MoSe2 heterojunction is transformed into a direct Z-scheme system through boron doping of g-C3 N4 (B-doped C3 N4 /MoSe2 ). Detailed analysis of the total and partial density of states, work functions and differential charge density distribution of the B-doped C3 N4 /MoSe2 heterojunction shows the proper band alignment and existence of a built-in electric field at the interface, with the direction from g-C3 N4 to MoSe2 , demonstrating a direct Z-scheme heterojunction. Further investigation on the absorption spectra reveals a large enhancement of the light absorption efficiency after boron doping. The results consistently confirm that electronic structures and photocatalytic performance can be effectively manipulated by a facile boron doping. Modulating the band alignment of heterojunctions in this way provides valuable insights for the rational design of highly efficient heterojunction-based photocatalytic systems.

Emerging distributed applications, such as microservices, machine learning, big data analysis, consist of both compute and network tasks. DAG-based abstraction primarily targets compute tasks and has no explicit network-level scheduling. In contrast, Coflow abstraction collectively schedules network flows among compute tasks but lacks the end-to-end view of the application DAG. Because of the dependencies and interactions between these two types of tasks, it is sub-optimal to only consider one of them. We argue that co-scheduling of both compute and network tasks can help applications towards the globally optimal end-to-end performance. However, none of the existing abstractions can provide fine-grained information for co-scheduling. We propose MXDAG, an abstraction to treat both compute and network tasks explicitly. It can capture the dependencies and interactions of both compute and network tasks leading to improved application performance.

Multiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic reward algorithm, which introduces an intrinsic reward encoder to generate an individual intrinsic reward for each agent and utilizes the hypernetworks as the decoder to help to estimate the individual action values of the decomposition methods based on the generated individual intrinsic reward. Experimental results in the StarCraft II micromanagement benchmark prove that the proposed algorithm can increase learning efficiency and improve policy performance.

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

BackgroundGlioma is one of the most common malignancies in the central nervous system and has limited effective therapeutic options. Therefore, we sought to identify a suitable target for immunotherapy.Materials and MethodsWe screened prognostic genes for glioma in the CGGA database and GSE43378 dataset using survival analysis, receiver operating characteristic (ROC) curves, independent prognostic analysis, and clinical correlation analysis. The results were intersected with immune genes from the ImmPort database through Venn diagrams to obtain likely target genes. The target genes were validated as prognostically relevant immune genes for glioma using survival, ROC curve, independent prognostic, and clinical correlation analyses in samples from the CGGA database and GSE43378 dataset, respectively. We also constructed a nomogram using statistically significant glioma prognostic factors in the CGGA samples and verified their sensitivity and specificity with ROC curves. The functions, pathways, and co-expression-related genes for the glioma target genes were assessed using PPI networks, enrichment analysis, and correlation analysis. The correlation between target gene expression and immune cell infiltration in glioma and the relationship with the survival of glioma patients were investigated using the TIMER database. Finally, target gene expression in normal brain, low-grade glioma, and high-grade glioma tissues was detected using immunohistochemical staining.ResultsWe identified TNFRSF12A as the target gene. Satisfactory results from survival, ROC curve, independent prognosis, and clinical correlation analyses in the CGGA and GSE43378 samples verified that TNFRSF12A was significantly associated with the prognosis of glioma patients. A nomogram was constructed using glioma prognostic correlates, including TNFRSF12A expression, primary-recurrent-secondary (PRS) type, grade, age, chemotherapy, IDH mutation, and 1p19q co-deletion in CGGA samples with an AUC value of 0.860, which illustrated the accuracy of the prognosis prediction. The results of the TIMER analysis validated the significant correlation of TNFRSF12A with immune cell infiltration and glioma survival. The immunohistochemical staining results verified the progressive up-regulation of TNFRSF12A expression in normal brain, low-grade glioma, and high-grade glioma tissues.ConclusionWe concluded that TNFRSF12A was a viable prognostic biomarker and a potential immunotherapeutic target for glioma.

Van der Waals (vdW) heterostructures, which are produced by the precise assemblies of varieties of two-dimensional (2D) materials, have demonstrated many novel properties and functionalities. Here we report a nano-plasmonic study of vdW heterostructures that were produced by depositing ordered molecular layers of pentacene on top of graphene. We find through nano-infrared (IR) imaging that surface plasmons formed due to the collective oscillations of Dirac fermions in graphene are highly sensitive to the adjacent pentacene layers. In particular, the plasmon wavelength declines systematically but nonlinearly with increasing pentacene thickness. Further analysis and density functional theory (DFT) calculations indicate that the observed peculiar thickness dependence is mainly due to the tunneling-type electron transfer from pentacene to graphene. Our work unveils a new method for tailoring graphene plasmons and deepens our understanding of the intriguing nano-optical phenomena due to interlayer couplings in novel vdW heterostructures., Comment: 21 pages

Unremitting efforts have been made to develop high-performance photoelectrochemical (PEC) water-splitting system to produce clean hydrogen fuel using sunlight. In this work, a novel way, combining highly-ordered nanowires (NWs) structure and piezotronic effect of p-type ZnO has been demonstrated to dramatically enhance PEC hydrogen evolution performance. Systematic characterizations indicate that the Sb atoms uniformly dope into ZnO NWs and substitute Zn sites with the introduction of two zinc vacancies to form the shallow acceptor SbZn–2VZn complex. Detailed synchrotron-based X-ray absorption near-edge structure (XANES) experiments in O K-edge and Zn L-edge further confirm the formation of the complex, and theoretical calculation verifies the Sb5+ state dominating the complex. The optimal photocurrent density of the 0.2Sb/ZnO-anneal NWs can reach −0.85 mA/cm2 (0 VRHE) which is 17.2 times larger than that of the n-ZnO NWs under sunlight illumination (100 mW/cm2). Furthermore, the piezotronic effect can be introduced to regulate the charge separation and transfer in the ZnO NWs through modulating the band structure near the interface. The photocurrent density can further increase to −1.08 mA/cm2 (0 VRHE) under a 0.6% tensile strain, which is 27.4% enhancement with respect to the ZnO sample without strain. These results provide an efficient way to design and develop high-performance photoelectrodes toward PEC hydrogen evolution.

Metformin has been widely used as an oral drug for the treatment of diabetes mellitus. However, its consumption can be influenced by many economic and social factors. In this study, we investigated the spatial consumption pattern of metformin in China through wastewater-based epidemiology (WBE) approach. Influent wastewater samples were collected from 21 wastewater treatment plants (WWTPs) in 19 cities of the northeast China. A method using solid-phase extraction combined with N-Methyl-bis (trifluoroacetamide) derivatization for GC-MS detection was applied for metformin analysis. In 21 days, metformin showed high stability in wastewater at 24 °C and −20 °C. The mean concentrations of metformin in all WWTPs ranged from 2.42 μg L−1 to 53.6 μg L−1. The consumption of metformin was 0.66–15.6 mg d−1 capita−1 with the mean value of 5.54 ± 4.28 mg d−1 capita−1. The prevalence of metformin ranged from 0.09% to 2.10% with an average of 0.74%. Both the consumption and prevalence of metformin displays significant spatial variations in northeast China. A statistical correlation analysis indicated that the consumption of metformin increases with the decrease of per capita disposable income of urban residents. To further predict the use of metformin in China, we developed a regress model and depicted a consumption map. The annual consumption of urban residents in Chinese provinces range from 1085-63,828 kg yr−1 with mean value of 25,347 kg yr−1, which would provide a certain reference value for public health care and diabetes control.

The vector nature of dissipative-soliton-resonance (DSR) pulses is numerically investigated in an all-normal-dispersion Yb-doped fiber laser mode-locked by the nonlinear polarization rotation technique. We calculate the polarization ellipses of the DSR pulse throughout the cavity, which shows that the polarization state varies during the propagation. The features of pulse polarization are also displayed on the Poincaré sphere. The impact of nonlinear birefringence on the polarization evolution is analyzed. Furthermore, we find that the polarization state is not uniform across a DSR pulse at a fixed intracavity position. The flat-top part of the pulse has a nearly fixed polarization, while its leading and trailing edges feature polarization states that vary with time. The peak power ratio of the two orthogonal polarization components in the pulse is limited in the range from 4 to 31. Our simulation results offer insight into the polarization properties of DSR pulses in mode-locked fiber lasers containing polarization discrimination devices.

Bi2GeTe4 is a promising near room-temperature thermoelectric candidate with a low lattice thermal conductivity. Carrier concentration of intrinsic Bi2GeTe4 changes dramatically with tiny Ge content adjustment, leading to a challenge in carrier concentration optimization. To overcome this challenge, we firstly introduce excessive Ge into Bi2GeTe4 to shift the Fermi level deep into the conduction band and transfer Bi2GeTe4 into a highly degenerate n-type semiconductor. Secondly, the embedded p-type Bi2Ge2Te5 secondary phase induces further optimization of the Fermi level and carrier concentration. Finally, the power factor of the as-synthesized Bi2GeTe4-based material is significantly increased from ∼0.08 μW cm-1 K-2 to ∼4.2 μW cm-1 K-2 at 423 K when increasing the nominal Ge content (x) of Bi2GexTe4 from 1 to 1.45. Correspondingly, a high figure-of-merit of ∼0.22 at 423 K is achieved in Bi2GeTe4-based thermoelectric materials. This result indicates our viable stepwise strategy can be used to optimize carrier concentration and achieve high thermoelectric performance of the n-type Bi2GeTe4.

Manipulating the electronic properties of graphene has been a subject of great interest since it can aid material design to extend the applications of graphene to many different areas. In this paper, we systematically investigate the effect of lead (Pb) intercalation on the structural and electronic properties of epitaxial graphene on the SiC(0001) substrate. We show that the band structure of Pb-intercalated few-layer graphene can be effectively tuned through changing intercalation conditions, such as coverage, location of Pb, and the initial number of graphene layers. Lead intercalation at the interface between the buffer layer (BL) and the SiC substrate decouples the BL from the substrate and transforms the BL into a $p$-doped graphene layer. We also show that Pb atoms tend to donate electrons to neighboring layers, leading to an $n$-doping graphene layer and a small gap in the Dirac cone under a sufficiently high Pb coverage. This paper provides useful guidance for manipulating the electronic properties of graphene layers on the SiC substrate.

The Supernumerary Robotic Limbs (SRL) is an emerging kind of wearable robot to help reconstruct and enhance human movement functionality by adding extra limbs, such as arms, legs, or fingers. For the control, EEG can transmit human intentions to control external devices independently, hence it has the particular advantages to control the SRL naturally especially when human limbs are occupied. However, the classification accuracy of EEG is very limited. In this paper, we proposed a novel EEG-EMG strategy to improve the classification accuracy. In addition, a novel supernumerary robotic finger (SRF) system is built with a modular design consideration. The whole system contained seven modules: EEG acquisition, EEG control, EMG acquisition, SRF control, SRF finger, TENS feedback and status information Module. To the best of our knowledge, this is the first wearable brain control SRL system. Meanwhile, as the SRF is a soft finger, the system is compliance, affordable, wearability, modularity, and lightweight, named CAWML-SRF. The finger weight is less than 195 g, and the whole system weight is less than 1 kg. The experiments show that the CAWML-SRF can enhance the grasp functionality for helping the single hand to accomplish the bimanual grasp task (such as opening the bottle and grasping the larger objects), and assist the remaining functionality to accomplish the grasp task. A 4-week EEG triggering training experiment is conducted to further evaluate the rehabilitation application potentials: the high control accuracy rate (94.53% ± 0.044) and fine learning performance of human brain to the system are verified.

Sb-Based phase-change materials have exhibited tremendous advantages in both data storage and reconfigurable photonic devices. Despite the intensive studies on their structures and properties in the crystalline state, the widely used amorphous phase remains elusive. Here, we investigate amorphous Sb2Te3, Sb2Se3, and Sb2S3 through ab initio calculations to link their unique properties to the local structure and bonding nature. We discover that Sb forms shorter and stronger bonds with Se and S than Te, and the average bonding angles of Se (92.0°) and S (94.1°) show larger distortion than that of Te (91.5°). This leads to larger Peierls-like distortion in Sb2Se3 and Sb2S3. On the other hand, more charge transfer and void fraction are presented, opening band gaps and leading to different electronic and optical properties. In contrast, Sb2Te3, due to its semiconducting behavior and low thermal stability, enables its application in phase-change memory. Our results reveal the physics of vastly different electronic and optical properties induced by S, Se, and Te alloying, providing an effective strategy for materials design.

Radar imaging has been one of the basic functions for modern radar as well as searching, detection and tracking, especially for those ground-based space surveillance phased array radars. Due to the periodic revisiting characteristics of the in-orbit objects, multi-pass observations for space targets (e.g. satellites) within small pitching angle variation can be accumulated, which lead to the 3D inverse synthetic aperture radar (ISAR) image formation. Consequently, the strategy of allocating the limited radar observation resource for a reliable 3D ISAR imaging result is becoming a fundamental but critical issue. In this letter, a sparse aperture based radar observation resource allocating method is proposed to facilitate the space target 3D ISAR imaging. According to the sparsity estimation of the target along the azimuth dimension, the required observation resource is calculated and assigned in accordance with the imaging task priority. Furthermore, the experimental boundary of the imaging performance with different signal-to-noise ratios (SNR) and pulse extraction rates are discussed in detail, which gives an intuitive guidance for the radar observation allocation under different application scenarios. The experimental results performed on simulated data validate the feasibility and effectiveness of the proposed method.

Sentiment analysis (SA) is to predict the sentiment polarity towards a given document. Our work focuses on sentiment analysis towards comments of legal cases, which reflects people’s concern about public safety and the impartiality of judgment result. The existing methods treat this problem as a coarse document-level classification and ignore topics information, which cannot obtain an accurate sentiment polarity towards a sentence. With topics information attended, we present a novel segment-level model, to boost the performance. Firstly, we divide a sentence into several segments delimited by some important conjunctions. We find that opinion in a segment is usually explicit. Secondly, we apply two-step attention to obtain accurate representation. To enhance the interactions between comments and topics, we apply attention mechanisms twice, once between topics and context in the segment level, and the other between segments. Thirdly, we concatenate the segment and context representation to receive final representation. It unifies two types of information into a classifier. Experimental results on real-world datasets demonstrate that our model outperforms existing methods.

In a one-on-one air combat game, the opponent’s maneuver strategy is usually not deterministic, which leads us to consider a variety of opponent’s strategies when designing our maneuver strategy. In this paper, an alternate freeze game framework based on deep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. The maneuver strategy agents for aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario. Middleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning environment for agent training. A reward shaping approach is used, by which the training speed is increased, and the performance of the generated trajectory is improved. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal with nonstationarity. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive strategies. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical angle fight tactics can be learnt by the deep reinforcement learning agents. For the training of an opponent with the adaptive strategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. In a competition with all opponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing is about 75%.

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

The bacteria LPS is one of the leading endotoxins responsible for sepsis; its sensing pathway–induced pyroptosis plays an important role in innate immunity. However, excessive pyroptosis might cause immunological diseases, even multiple organ failure and death by undefined mechanisms. Given that the development of acute kidney injury (AKI) in patients with sepsis causes significant morbidity and mortality, the mechanism of pyroptosis in regulating septic AKI remains unknown. In this study, we establish a zebrafish crispant in vivo analysis model and reveal that both caspy2 and gasdermin Eb (GSDMEb) contribute to lethal LPS-induced septic shock. Meanwhile, the in vitro analysis reveals that caspy2 activation can specifically cleave GSDMEb to release its N terminus to mediate pyroptosis, which functions as GSDMD in mammals. Interestingly, we establish an in vivo propidium iodide–staining method and reveal that the caspy2–GSDMEb signaling cascade is essential for enhancing renal tubular damage during lethal LPS-induced septic shock, whereas administration of the zebrafish-specific GSDMEb-derived peptide inhibitor Ac-FEID-CMK can attenuate mortality and septic AKI in vivo. Moreover, we confirm that either caspase-11 or GSDMD deficiency decreases both inflammatory cytokines and kidney dysfunction enzyme release and prolongs survival in a murine model of septic shock. Taken together, these findings demonstrate an evolutionary executor for pyroptosis in zebrafish and reveal that the pyroptosis of renal tubular cells is a major cause of septic AKI, and also provide an ideal in vivo screening model for potential antisepsis therapeutic strategies.

Recently, the Supernumerary Robotic Limbs (SRL) have emerged in the field of robotics for enhancing the user's ability without replacing natural limbs. Lots of researches have contributed to the mechanical design and control of SRL. However, building an artificial sensory nerve pathway of SRL has been largely ignored. In this paper, we built a multi-modal sensory feedback pathway for SRL. Three kinds of sensory pathway contained 1) fingertip pressure, 2) slippage and 3) proprioception information are built under the consideration of SRL platform through electrical stimulation. According to the experiment results of event-related potential (ERP) amplitude, establish the relationship between the physical parameters of the stimulus and the sensation. In addition, enhancement potentials of human sensation based on the brain-computer interface (BCI) is found in two aspects contained: 1) improvement of subjective perception resolution; 2) Shorten the time of sensory recognition. This paper can be used to objectively benchmark the human subjective and individualized sensation, and guide researchers to build the SRL sensory feedback pathways.

Interatomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. Here, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in $\mathrm{P}{\mathrm{d}}_{82}\mathrm{S}{\mathrm{i}}_{18}$ and $\mathrm{P}{\mathrm{d}}_{75}\mathrm{S}{\mathrm{i}}_{25}$ liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at $\mathrm{P}{\mathrm{d}}_{3}\mathrm{Si}$ and $\mathrm{P}{\mathrm{d}}_{9}\mathrm{S}{\mathrm{i}}_{2}$ composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.

Temporary shotcrete lining plays an important role in stabilizing tunnel structures. However, demolition of the temporary lining has negative impacts on the preliminary lining. Based on a case study of using pre-tensioned anchor cables to minimize the risk caused by removing temporary linings, a comprehensive study has been carried out on the displacement caused by removing temporary linings as well as countermeasures. A calculation model was established based on a bedded-beam model recommended by the International Tunneling Association (ITA). Using this model, the influences of three factors, the axial force in the temporary lining, the thickness of the preliminary lining and the deformation modulus of the ground, were studied in detail. Countermeasures were proposed corresponding to each of the influence factors and a countermeasure grading system was constructed. The research results presented in this paper can be valuable references for design and construction of tunnels involving use of temporary linings.

It is important to ensure the radiation safety of workers in nuclear facilities. The path-planning technology is one of the effective ways to reduce the radiation exposure to the workers in the radioactive environment of nuclear facilities. This work studies the path-planning technology in radioactive environment of nuclear facilities, and proposes an improved particle swarm optimization (PSO) algorithm to solve the path-planning problems. To be specific, particle swarm optimization algorithm is associated with chaos optimization algorithm, and new mathematical dose calculation models for the path-planning problem are built. Then, experimental simulation for 3 cases is carried out and the results are compared to those from traditional PSO method. In the first two cases, the average effective doses from the improved PSO method are similar to those from traditional PSO method, but its possibility that the effective dose value is too large becomes smaller. And this trend is more obvious in Case 3. Specifically, the effective dose value above 800 μSv is cut by 25% for the improved PSO method. Besides, the average effective dose decreases about 29 μSv in Case 3. Meanwhile, the overall convergence rate isn’t affected. Hence, the proposed path-planning method in the radioactive environment of nuclear facilities is demonstrated to be effective through the experiments and analysis.

The losses of Superconducting Magnetic Energy Storage (SMES) magnet are not neglectable during the power exchange process with the grid. In order to prevent the thermal runaway of a SMES magnet, quantitative analysis of its thermal status is inevitable. In this paper, the loss characteristics of a self-developed 150 kJ SMES magnet are analyzed by means of experiments and simulations, including the loss of the joint resistance, the eddy current loss of the metal cooling structures and the AC loss of the superconducting magnet. A thermal model of the magnet was built to theoretically analyze the relationship between the current and magnet temperature variation. Three kinds of power compensation experiments were carried out to study the thermal characteristics of the SMES magnet. The temperature variation of the magnet under these three operating conditions was simulated. The simulation results and experimental results are compared and analyzed.

Novel heterostructured titanium dioxide (TiO2) nanotree arrays (NTAs) were synthesized through a secondary hydrothermal process on a FTO substrate, and silver quantum dots (QDs) were loaded by a magnetron sputtering method for enhancing the photoelectrochemical (PEC) properties. The hierarchical TiO2 NTAs composed of rutile phase nanorods (NRs) as trunks and anatase nanosheets as branches possess an enlarged specific surface area, which allows for enhanced QDs attaching. The hierarchical structure of the rutile/anatase junctions allows for a lower recombination rate of electron-hole (e-h) pairs and an accelerated electron transfer compared to TiO2 NRs. After the decoration of Ag QDs, the absorption of light expanded to visible light range due to the surface plasmon resonance (SPR) of Ag QDs. The morphology, Raman spectral, optical properties, and PEC performance were investigated. The morphology is dendriform. The absorption edges of the samples show a slight red-shift and a SPR in the UV–vis absorption spectra. The SPR peaks range from 524 to 600 nm. The analysis of performance including linear voltammetry and transient photocurrent reveal that the photocurrent of Ag(15s)/TiO2 NTAs is up to 140 times the photocurrent of pure TiO2 NTAs, reaching 0.52 mV/cm2. All these results indicate the potential application of the novel TiO2 NTAs in solar cells.

In order to comprehensively evaluate the performance of dilution type cold patch asphalt mixture (CPAM), basic performances and deformation behaviors of dilution type CPAM under different stress modes were tested. The compaction performance and stability increasing property of CPAM were tested by rotation compaction test and Marshall stability test. The deformation behaviors of CPAM were tested by rolling test, small beam bending test and penetration shear test. Results show that when the using temperature is below 25°C, the compaction ability of CPAM is affected by temperature. The evaporation rate of organic solvents is slow, and affects the strength growing and rolling deformation behavior. It needs about 10d for the stability of CPAM rising to 3kN. The residue of organic solvents may lead the deformation of CPAM to constantly instability under rolling load. The stretching deformation and penetration shear deformation are affected by temperature. The low temperature and high temperature performance of CPAM is below standard requirements of hot asphalt mixture (HMA).

A simple and efficient low-temperature thermal treatment process has been demonstrated to dramatically improve the photoelectrochemical (PEC) performance of ZnO nanorod/CdS quantum dots heterostructure. The ZnO/CdS heterojunction was sequentially fabricated by atomic layer deposition, hydrothermal method and successive ionic layer adsorption-reaction method. In contrast to the traditional annealing usually conducted in muffle furnace at high temperature, a two-step low-temperature thermal treatment has been first carried out just on a hot plate at 150 °C for 10 min and then 250 °C for another 10 min, which significantly enhanced the PEC performance of the ZnO/CdS photoanodes. The optimal photocurrent density and the corresponding photoconversion efficiency can reach 9.16 mA cm−2 (0.4 VSCE) and 4.03% under a standard simulated illumination condition (AM 1.5 G, 100 mW cm−2), which improved 75% and 44% as much as those of the unheated sample. The incident photon-to-current efficiency (IPCE) is raised up to 95% at 350 nm. These are one of the best results ever reported on the similar ZnO/CdS photoanodes. Systematic PEC experiments attribute the enhancement to the optimized interface between ZnO and CdS by the thermal treatment which can promote charge carrier separation and transportation. Further theoretical calculation confirms the importance of the interface modification on improving the photoelectric properties. The two-step low-temperature thermal treatment thus presents a facile method for the design and optimization of high-performance PEC photoelectrodes.

The structures and electronic properties of partial C, Si and Ge decorated graphene were investigated by first-principles calculations. The calculations show that the interaction between graphene and the decoration patches is weak and the semiconductor patches act as agents for weak electron doping without much disturbing graphene electronic π-bands. Redistribution of electrons due to the partial decoration causes the electrostatic potential lower in the decorated graphene areas, thus induced an electric field across the boundary between the decorated and non-decorated domains. Such an alternating electric field can change normal stochastic adatom diffusion to biased diffusion, leading to selective mass transport.

The state-of-the-art phase-change memory is usually composed of ovonic threshold switching (OTS) material and ovonic memory switching (OMS) material for selective and data storage, respectively. OMS materials have been intensely studied, while the knowledge of the OTS mechanism is still inadequate. In this article, we have explored the local structure and electronic property of a simple OTS material, the amorphous (a-) SiTe, by first-principles calculations. The results reveal that most of the atoms in a-SiTe obey the “8-N” rule in contrast to a-GeTe, a well-studied OMS material. 76.5% of Si-centered configurations are in the form of randomly distributed tetrahedral clusters, while Te-centered configurations are relatively disordered without notable conformation. Furthermore, a large number of fivefold rings are found in a-SiTe. All of these structural characteristics lead to the high stability of a-SiTe, prohibiting its crystallization. In addition, the p state of Te also contributes much to the mid-gap states, which may be relevant for OTS behavior. Our findings provide an in-depth understanding of the structural signature and electronic properties of a-SiTe, having important implications for the design and applications of OTS materials.

Effects of alkaline earth metal atoms doping in boron hydrides at high pressure are investigated by first-principles calculations. The calculated results showed that doping with Mg, Ca, Ba, and Sr in B8H16 at 50 GPa is thermodynamically favorable and dynamically stable. The doping changes the B8H16 from a semiconductor to a metal with substantial electronic density-of-state around the Fermi level. The superconductivity of the alkaline earth metal doped B8H16 is studied based on electron-phonon coupling mechanism. The calculated critical superconducting transition temperatures (Tc) range from 10 to 25 K at 50 GPa upon doping. These results suggest that doping metal atoms in boron hydrides is an efficient way in designing superconducting materials.

Dissipative solitons (DSs) are self-organized localized structures in non-conservative systems, which require a continuous energy exchange with external sources. In addition to parameter-invariant stationary DSs, there exists a variety of dynamical ones manifesting breathing behaviors. Such intriguing phenomena, termed as soliton pulsations, have been widely studied in recent years under the impetus of advances in real-time spectroscopy. Here, we experimentally investigate various pulsating period-doubled solitons (PDSs) in a fiber laser mode-locked by single-wall carbon nanotubes. Both single- and double-periodic PDS pulsations are found in the cavity. Thanks to the emerging dispersive Fourier transform technique, the polarization-resolved transient spectra of these pulsating PDSs are measured. It is shown that their polarization ellipses rotate with a period of two cavity roundtrips. Moreover, the intensity-modulation behaviors of the two orthogonal polarization components in the odd (even) roundtrips are always asynchronous, which confirms additional slower polarization modulations. Especially, we demonstrate that three combined intensity-modulation periods are involved in the double-periodic PDS pulsation process for the first time, to the best of our knowledge. Our results would stimulate further research on the vector features of multiple-period pulsating solitons in mode-locked fiber lasers.

We report on a novel curvilinear optical vortex beam named twin curvilinear vortex beams (TCVBs) with intensity and phase distribution along a pair of two- or three-dimensional curves, both of which share the same shape and the same topological charge. The TCVBs also possess the character of perfect optical vortex, namely having a size independent of topological charge. We theoretically demonstrate that a TCVB rather than a single-curve vortex beam can be created by the Fourier transform of a cylindrically polarized beam. The behavior of TCVBs generated through our method is investigated by simulation and experiment, including interference experiments for identifying the vortex property of the TCVBs. The TCVBs may find applications in optical tweezers, such as trapping low refractive index particles in the dark region between two curves and driving them moving along the curvilinear trajectory.

Sewage epidemiology is a real-time tool used to monitor tobacco consumption. In this study, we investigated the tobacco consumption in eight cities in Jilin province using sewage epidemiology. We collected influent wastewater samples from ten wastewater treatment plants (WWTPs) that serve nearly four million people. Mean nicotine (NIC) loads ranged from 1.42 to 14.2mg/d/capita, whereas mean cotinine (COT) loads showed lower levels with 0.33 to 2.15mg/d/capita. Population size was estimated to provide an accurate and real-time population based on ammonia nitrogen (NH4-N) concentration in influent. To verify the NH4-N equivalent population, we compared these results with the corresponding population estimated based on the expert knowledge of the local WWTPs operators. Daily consumption of NIC was estimated to be approximately 2.39±1.47mg/d/capita. Monte Carlo simulation was used to analyze uncertainty and variability in the number of cigarettes consumed by smokers in the range of 9.8 to 31.4 per day with a median of 16.9. The data of tobacco consumption in this study coordinated strongly with a traditional survey on the consumption of tobacco in China, indicating sewage epidemiology with NH4-N equivalent population estimation may provide a suitable and useful tool for tobacco use monitoring.

A sensor for a Moore fractal antenna was designed to detect partial discharge (PD) in gas-insulated substations. This worked at an ultrahigh frequency. First, the multifrequency characteristics of this antenna were analyzed and ANSYS HFSS was used to simulate the Moore fractal antenna and the Hilbert fractal antenna for comparison. The results show that the Moore fractal antenna had good directivity and omnidirectional radiation features. We then developed physical models for them according to the simulation results. The two antennas were tested using a vector network analyzer. The results show that the Moore fractal antenna had more frequency bands than the Hilbert fractal antenna. The PD was tested in the laboratory and detected by the Moore fractal antenna and the Hilbert fractal antenna. A comparison of the measured results showed that the proposed antenna was promising for online PD detection applications.

Research into novel one-dimensional (1D) materials and associated structural transitions is of significant scientific interest. It is widely accepted that a 1D system with a short-range interaction cannot have 1D phase transition at finite temperature. Herein, we propose a series of new stable carbon nanotubes by rolling up penta-graphene sheets, which exhibit fascinating well-defined 1D phase transitions triggered by axial strain. Our first-principles calculations show that such penta-graphene nanotubes (PGNTs) are dynamically stable by phonon calculations, but transform from a tri-layer structure to a highly defective single-walled nanotube at low temperature in molecular dynamics simulations. We show that moderate compressive strains can drive structural transitions of (4,4), (5,5), and (6,6) PGNTs, during which the distances of neighboring carbon dimers in the inner shell have a sudden drop, corresponding to dimer-dimer nonbonding to bonding transitions. After such transition, the tubes become much more thermally stable and undergo semiconductor-metal transitions under increasing strain. The band gaps of PGNTs are not sensitive to chirality whereas they can be tuned effectively from visible to short-wavelength infrared by appropriate strain, making them appealing materials for flexible nano-optoelectronics. These findings provide useful insight into unusual phase transitions in low-dimensional systems.

Rapidly and correctly identifying eye irritants or corrosive chemicals is an important issue in health hazard assessment. The purpose of this study is to describe the development of in silico methods for the classification of chemicals into irritants/corrosives or non-irritants/non-corrosives. A total of 5220 chemicals for a serious eye irritation (EI) dataset and 2299 chemicals as an eye corrosion (EC) dataset were collected from available databases and literature. Structure–activity relationship (SAR) models were developed to separately predict serious EI or EC via machine learning methods. According to the overall prediction accuracy, the Pub-SVM model gave the best results for both serious EI (overall classification accuracy CA = 0.946) and EC (CA = 0.959). The sensitivity and specificity of serious EI were 97.3% and 86.7% for the training set, and 96.9% and 82.7% for the external validation set, respectively. Similarly, the sensitivity and specificity of EC were 95.5% and 96.2% for the training set, and 94.9% and 96.2% for the external validation set, respectively. The high specificity and sensitivity indicated that our models were reliable and robust, which can be used to predict the potential seriousness of EI/EC of compounds. Moreover, several structural alerts for characterizing serious EI/EC were identified using the combination of information gain and substructure frequency analysis.

Background In recent years, methamphetamine (METH) has increased dramatically in China. However, little is known regarding the prevalence of METH use among the general population in China. Methods We provided a method to estimate the consumption and prevalence of METH in a city based on wastewater analysis. Monte Carlo simulations were employed to assess the uncertainty and variability of the consumption and prevalence rate in Beijing. Results METH consumption in Beijing based on Monte Carlo simulation varied between 1.3 and 5.2kg per day with a median of 2.6kg per day. In 2013, the 12-month prevalence of METH use was 0.58% (95% confidential interval 0.08–3.16) among the general population aged 15–64 years. A population-weight consumption map displayed the geographical differences in Beijing. Conclusions The proposed method dramatically improved the accuracy and reliability and can be used as a useful complementary tool to official methodologies for drug use monitoring. This report describes the first comprehensive study concerning the prevalence of METH among the general population in mainland China.

The dynamic behavior of the keyhole and molten pool is associated with the quality of weld seam. In this study, an on-line visual monitoring system is devised to photograph the keyhole and molten pool during external magnetic field assisted laser welding on the AISI 2205 duplex stainless steel plates. Seven features are defined to describe the morphology of the keyhole and molten pool. Then, the principal component analysis (PCA) algorithm is applied to reduce the dimensions of these features to obtain different number of principal components (PCs). Three different machine learning algorithms, i.e. the back propagation neural network (BPNN), the radial based function neural network (RBFNN) and the support vector regression (SVR), are utilized to fit the relationship between the chosen PCs and the weld width. Finally, the global and local prediction accuracy of these three machine learning algorithms are compared under different number of PCs. Results illustrated that data dimensionality reduction is helpful to improve the modeling efficiency. Machine learning algorithms can be exploited to predict the weld quality during laser welding with high accuracy. Among them, the BPNN model performs best and SVR model performs better than RBFNN model in this research. This work aims to model the relation between the features in weld zone and the weld quality with different machine learning algorithms, and provides a guideline of model selection for laser welding on-line monitoring and a necessary foundation for realizing intelligent welding with advanced algorithm.

Aquatic toxicity is a crucial endpoint for evaluating chemically adverse effects on ecosystems. Therefore, we developed in silico methods for the prediction of chemical aquatic toxicity in marine environment. At first, a diverse data set including different crustacean species was constructed. We then built local binary models using Mysidae data and global binary models using Mysidae, Palaemonidae, and Penaeidae data. Molecular fingerprints and descriptors were employed to represent chemical structures separately. All the models were built by six machine learning methods. The AUC (area under the receiver operating characteristic curve) values of the better local and global models were around 0.8 and 0.9 for the test sets, respectively. We also identified several chemicals with selective toxicity on different species. The analysis of selective toxicity would promote to design greener chemicals in a specific environment. Finally, to understand and interpret the models, we explored the relationships between chemical aquatic toxicity and the molecular descriptors. Our study would be helpful in gaining further insights into marine organisms, prediction of chemical aquatic toxicity and prioritization of environmental hazard assessment.

According to International Diabetes Federation estimates, China has the highest rate of diabetes in the world. To monitor the prevalence of diabetes mellitus (DM) in near real-time, a first-line medication for the treatment of type 2 diabetes, metformin, was used. Wastewater-based epidemiology (WBE) was applied to estimate the consumption of metformin in Dalian from 2015 to 2018. Quantification of metformin was undertaken using solid-phase extraction (SPE) and N-methyl-bis (trifluoroacetamide) derivatization prior to GC–MS analysis. The concentrations of metformin in eleven wastewater treatment plants (WWTPs) ranged from 1.7 μg/L to 239.0 μg/L, with an average value of 68.3 μg/L. For metformin consumption, there was a gradual increase from 12.1 mg/d/capita in 2015 to 28.4 mg/d/capita in 2018. Meanwhile, the prevalence of metformin in the Dalian population ranged from 1.6% in 2015 to 3.8% in 2018. Similarly, the prevalence of DM showed an increasing trend from 12.2% in 2015 to 21.6% in 2018, which is consistent with the data predicted by traditional surveys (15.2–19.8%). Additionally, the prevalence of DM in 2015 estimated based on WBE was 12.2%, which agreed with the results from the traditional survey (12.3%). These results indicated that the proposed method provided a feasible way to reveal the prevalence of DM through metformin monitoring by the WBE approach.