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"Zhang, Tao"
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An intelligent referee selection approach in martial arts using CoCoSo MCDM algorithm
Martial arts tournament regulation requires strict adherence to principles which include fairness and both disciplined behavior and accurate decision-making processes. Competition martial arts need referees to uphold integrity by carrying out fair rule enforcement together with exact decision-making responsibilities. The selection process for referees becomes problematic due to unclear evaluation methods, which include decision unpredictability, together with subjective assessment, and qualifications that differ from one another. The research utilizes the CoCoSo multi-criteria decision-making (MCDM) algorithm and interval-valued t-spherical fuzzy sets (IVTSFS) to process referee candidate evaluations with greater accuracy and methodically under uncertain situations. Three expert evaluators conduct assessments of different referee candidates through vital selection parameter analysis, which includes assessment of experience levels, along with decision accuracy rules, understanding physical fitness capabilities, stress management competencies, consistency performance, and communication abilities. Both the CoCoSo algorithm and IVTSFS system enable successful integration of expert judgments and handle ambiguous referee decision-making approaches respectively. The research presents an optimized process for athletic referee selection which guarantees fair competition and selects referees who demonstrate the best qualities during martial arts competitions.
Journal Article
Data driven discovery of cyber physical systems
Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.
Discovery of hybrid dynamical models for real-world cyber-physical systems remains a challenge. This paper proposes a general framework for automating mechanistic modeling of hybrid dynamical systems from observed data with low computational complexity and noise resilience.
Journal Article
How to characterize figures of merit of two-dimensional photodetectors
Photodetectors based on two-dimensional (2D) materials have been the focus of intensive research and development over the past decade. However, a gap has long persisted between fundamental research and mature applications. One of the main reasons behind this gap has been the lack of a practical and unified approach for the characterization of their figures of merit, which should be compatible with the traditional performance evaluation system of photodetectors. This is essential to determine the degree of compatibility of laboratory prototypes with industrial technologies. Here we propose general guidelines for the characterization of the figures of merit of 2D photodetectors and analyze common situations when the specific detectivity, responsivity, dark current, and speed can be misestimated. Our guidelines should help improve the standardization and industrial compatibility of 2D photodetectors.
The lack of a standardized approach for the characterization of the performance of 2D photodetectors represents an important obstacle towards their industrialization. Here, the authors propose practical guidelines to characterize their figures of merit and analyse common situations where their performance can be misestimated.
Journal Article
Oridonin: A Review of Its Pharmacology, Pharmacokinetics and Toxicity
Oridonin, as a natural terpenoids found in traditional Chinese herbal medicine Isodon rubescens (Hemsl.) H.Hara, is widely present in numerous Chinese medicine preparations. The purpose of this review focuses on providing the latest and comprehensive information on the pharmacology, pharmacokinetics and toxicity of oridonin, to excavate the therapeutic potential and explore promising ways to balance toxicity and efficacy of this natural compound. Information concerning oridonin was systematically collected from the authoritative internet database of PubMed, Elsevier, Web of Science, Wiley Online Library and Europe PMC applying a combination of keywords involving “pharmacology,” “pharmacokinetics,” and “toxicology”. New evidence shows that oridonin possesses a wide range of pharmacological properties, including anticancer, anti-inflammatory, hepatorenal activities as well as cardioprotective protective activities and so on. Although significant advancement has been witnessed in this field, some basic and intricate issues still exist such as the specific mechanism of oridonin against related diseases not being clear. Moreover, several lines of evidence indicated that oridonin may exhibit adverse effects, even toxicity under specific circumstances, which sparked intense debate and concern about security of oridonin. Based on the current progress, future research directions should emphasize on 1) investigating the interrelationship between concentration and pharmacological effects as well as toxicity, 2) reducing pharmacological toxicity, and 3) modifying the structure of oridonin—one of the pivotal approaches to strengthen pharmacological activity and bioavailability. We hope that this review can provide some inspiration for the research of oridonin in the future.
Journal Article
NF‐κB signaling in inflammation and cancer
Since nuclear factor of κ‐light chain of enhancer‐activated B cells (NF‐κB) was discovered in 1986, extraordinary efforts have been made to understand the function and regulating mechanism of NF‐κB for 35 years, which lead to significant progress. Meanwhile, the molecular mechanisms regulating NF‐κB activation have also been illuminated, the cascades of signaling events leading to NF‐κB activity and key components of the NF‐κB pathway are also identified. It has been suggested NF‐κB plays an important role in human diseases, especially inflammation‐related diseases. These studies make the NF‐κB an attractive target for disease treatment. This review aims to summarize the knowledge of the family members of NF‐κB, as well as the basic mechanisms of NF‐κB signaling pathway activation. We will also review the effects of dysregulated NF‐κB on inflammation, tumorigenesis, and tumor microenvironment. The progression of the translational study and drug development targeting NF‐κB for inflammatory diseases and cancer treatment and the potential obstacles will be discussed. Further investigations on the precise functions of NF‐κB in the physiological and pathological settings and underlying mechanisms are in the urgent need to develop drugs targeting NF‐κB for inflammatory diseases and cancer treatment, with minimal side effects. This year (2021) marks the 35th anniversary of the discovery of NF‐κB. With so many years of in‐depth research on NF‐κB, people have realized that the NF‐κB signaling pathway plays an important role in inflammation, immunity, cell survival and proliferation. This review summarizes the relevant knowledge of the NF‐κB signaling pathway, inflammation, and cancer. The progression of the translational study and drug development targeting NF‐κB for inflammatory diseases and cancer treatment and the potential obstacles will also be discussed.
Journal Article
Deep Learning Method Based on Physics Informed Neural Network with Resnet Block for Solving Fluid Flow Problems
Solving fluid dynamics problems mainly rely on experimental methods and numerical simulation. However, in experimental methods it is difficult to simulate the physical problems in reality, and there is also a high-cost to the economy while numerical simulation methods are sensitive about meshing a complicated structure. It is also time-consuming due to the billion degrees of freedom in relevant spatial-temporal flow fields. Therefore, constructing a cost-effective model to settle fluid dynamics problems is of significant meaning. Deep learning (DL) has great abilities to handle strong nonlinearity and high dimensionality that attracts much attention for solving fluid problems. Unfortunately, the proposed surrogate models in DL are almost black-box models and lack interpretation. In this paper, the Physical Informed Neural Network (PINN) combined with Resnet blocks is proposed to solve fluid flows depending on the partial differential equations (i.e., Navier-Stokes equation) which are embedded into the loss function of the deep neural network to drive the model. In addition, the initial conditions and boundary conditions are also considered in the loss function. To validate the performance of the PINN with Resnet blocks, Burger’s equation with a discontinuous solution and Navier-Stokes (N-S) equation with continuous solution are selected. The results show that the PINN with Resnet blocks (Res-PINN) has stronger predictive ability than traditional deep learning methods. In addition, the Res-PINN can predict the whole velocity fields and pressure fields in spatial-temporal fluid flows, the magnitude of the mean square error of the fluid flow reaches to 10−5. The inverse problems of the fluid flows are also well conducted. The errors of the inverse parameters are 0.98% and 3.1% in clean data and 0.99% and 3.1% in noisy data.
Journal Article
A reversible long-life lithium–air battery in ambient air
Electrolyte degradation, Li dendrite formation and parasitic reactions with H
2
O and CO
2
are all directly correlated to reversibility and cycleability of Li–air batteries when operated in ambient air. Here we replace easily decomposable liquid electrolytes with a solid Li-ion conductor, which acts as both a catholyte and a Li protector. Meanwhile, the conventional solid air cathodes are replaced with a gel cathode, which contacts directly with the solid catholyte to form a closed and sustainable gel/solid interface. The proposed Li–air cell has sustained repeated cycling in ambient air for 100 cycles (~78 days), with discharge capacity of 2,000 mAh g
−1
. The recharging is based largely on the reversible reactions of Li
2
CO
3
product, originating from the initial discharge product of Li
2
O
2
instead of electrolyte degradation. Our results demonstrate that a reversible long-life Li–air battery is attainable by coordinated approaches towards the focal issues of electrolytes and Li metal.
Lithium air batteries have among the highest energy storage capacities, but their effective lifetime is short when using liquid electrolytes. Zhang
et al
. realize a lithium air battery with much improved cycling stability in ambient air by combining a solid electrolyte and a gel cathode.
Journal Article
Unsupervised learning to detect loops using deep neural networks for visual SLAM system
This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems. We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously learns an compressed representation from the raw input data in an unsupervised way. Different with the traditional bag-of-words based methods, the deep network has the ability to learn the complex inner structures in image data, while no longer needs to manually design the visual features. Our approach employs the characteristics of the SDA to solve the loop detection problem. The workflow of training the network, utilizing the features and computing the similarity score is presented. The performance of SDA is evaluated by a comparison study with Fab-map 2.0 using data from open datasets and physical robots. The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems.
Journal Article
Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)
Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82–90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46–62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
Journal Article