基于数据质量评价的目标估计

针对目标估计过程需要大量人工参与、自动化程度低的问题，提出了基于数据质量评价的目标估计方法。利用目标数据质量评价方法，对不同传感器得到的目标数据质量进行科学、有效的测度和评价，并根据质量得分动态调整各数据源在目标估计过程中所占的权重，从而减少人工干预，提高目标估计效能。仿真试验结果证明了该方法的有效性。     

Facile synthesis of Ti3C2 MXene-modified Bi2.15WO6 nanosheets with enhanced reactivity for photocatalytic reduction of Cr(VI)

Through a facile hydrothermal method, we have successfully prepared Ti₃C₂/Bi_2.15WO₆ (TC/BWO) composite, and systematically investigated their reactivity for the photocatalytic reduction of Cr(VI) under visible light. X-ray diffraction and Raman analysis confirm the formation of heterostructure between Bi_2.15WO₆ and Ti₃C₂. The resultant 7TC/BWO composite exhibits enhanced photoactivity toward Cr(VI) reduction. After 120 min irradiation, the conversion of Cr(VI) reaches 92.5% with the quasi-first-order kinetic constant of k = 0.0145 min^?1, which is higher than that of pure BWO (30% and k = 0.0005 min^?1). The electrochemical and photoluminescent characterization confirm that the introduction of Ti₃C₂ is conducive to the separation of carriers, thus significantly improves the photocatalytic performance of TC/BWO. Furthermore, the radical capture experiments verify that the electrons are important for enhancing reduction of Cr(VI) to Cr(III). As a result, this research provides a comprehensive understanding of the reduction of Cr(VI) by TC/BWO composite under visible light.     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.     

Enhanced oxygen evolution reaction kinetics through biochar-based nickel-iron phosphides nanocages in water electrolysis for hydrogen production

Highly-efficient and stable non-noble metal electrocatalysts for overcoming the sluggish kinetics of oxygen evolution reaction (OER) is urgent for water electrolysis. Biomass-derived biochar has been considered as promising carbon material because of its advantages such as low-cost, renewable, simple preparation, rich structure, and easy to obtain heteroatom by in-situ doping. Herein, Ni₂P–Fe₂P bimetallic phosphide spherical nanocages encapsulated in N/P-doped pine needles biochar is prepared via a simple two-step pyrolysis method. Benefiting from the maximum synergistic effects of bimetallic phosphide and biochar, high conductivity of biochar encapsulation, highly exposed active sites of Ni₂P–Fe₂P spherical nanocages, rapid mass transfer in porous channels with large specific surface area, and the promotion in adsorption of reaction intermediates by high-level heteroatom doping, the (Ni_0.75Fe_0.25)₂P@NP/C demonstrates excellent OER activity with an overpotential of 250 mV and a Tafel slope of 48 mV/dec at 10 mA/cm² in 1 M KOH. Also it exhibits a long-term durability in 10 h electrolysis and its activity even improves during the electrocatalytic process. The present work provides a favorable strategy for the inexpensive synthesis of biochar-based transition metal electrocatalysts toward OER, and improves the water electrolysis for hydrogen production.     

A triptycene derived hypercrosslinked polymer for gas capture and separation applications

This work describes facile synthesis of a porous polymeric material ( T-HCP ) using readily available reagents. Specifically, T-HCP is a thermally stable and hypercrosslinked polymer (HCP) that is essentially microporous with a high BET specific surface area (940 m² g^?1). Triptycene based polymers are known to feature internal free volume. Thus, the incorporation of triptycene units and extensive crosslinking by an external cross-linker in T-HCP makes it a promising adsorbent for small gas capture applications. Experimental results show that T-HCP demonstrated good CO₂ capture capacity of 132 mg g^?1 (273 K, 1 bar). Molecular hydrogen storage capacity of T-HCP is estimated to be 17.7 mg g^?1 (77 K, 1 bar). T-HCP revealed high CO₂/N₂ selectivity (up to 63) as well as promising CO₂/CH₄ (up to 9.1) selectivity suggesting its potential applicability for CO₂ separation from flue and natural gases.     

高效液相色谱法测定化妆品中依克多因的含量

建立高效液相色谱法测定化妆品中依克多因的分析方法,采用Agilent Poroshell 120 EC-C₁₈色谱柱（100 mm×3.0 mm,2.7μm）分离,以甲醇和p H为3.0的40 mmol/L磷酸二氢钠-10 mmol/L 1-庚烷磺酸钠缓冲溶液梯度洗脱,流速0.8 m L/min,柱温30℃,检测波长210 nm。采用外标法定量测定化妆品中的依克多因含量。结果表明,依克多因在5～800 mg/L的质量浓度范围内呈现良好线性关系,相关系数为0.999 8,方法的检出限和定量限分别为0.3和1.0 mg/L。该方法具有分离效率高、分析时间短、节省溶剂等优点,解决了依克多因在C₁₈色谱柱上保留弱的问题。     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.