Synthesis, characterization and photodynamic therapy properties of an octa-4-tert-butylphenoxy-substituted phosphorus (V) triazatetrabenzcorrole

A novel octa-4-tert-butylphenoxy-substituted phosphorus(V) triazatetrabenzcorrole (PVTBC), has been synthesized and characterized by MALDI-TOF MS and NMR, FT-IR and MCD spectroscopy. The fluorescence emission spectrum was used to determine the fluorescence quantum yield and the quantum yield for singlet oxygen generation was calculated by using 1,3-diphenylisobenzofuran as a scavenger. The photocytoxicity against U87MG cells was measured. The results indicated that PVTBC is potentially useful as an NIR region photosensitizer for photodynamic therapy (PDT)

A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential

Searching for the nano-Hertz stochastic gravitational wave background with the Chinese Pulsar Timing Array Data Release I

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude \log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8} for spectral index in the range of $\alpha\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$\sigma$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results.Comment: 18 pages, 6 figures, submitted to "Research in astronomy and astrophysics" 22nd March 202

clustering with feature order preferences

Vietnamese Acad Sci & Technol, Minist Sci & Technol Vietnam, Hanoi Univ Technol, Vietnam Natl UnivAir Force Off Sci Res, Asian Off Aerosp Res & DevWe propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature 8 is more important than feature t. Our clustering formulation aims to incorporate feature order preferences into prototyp

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application

Sensitivity of EPA of Ground Motion to Soil Slope Dynamic Response

To study the influence law of effective peak acceleration (EPA) on the seismic response of soil slope, the finite element method was used to simulate the slope response under earthquake action with 100 actual seismic records were selected, the influence law of the EPA under four different definitions commonly used in domestic and foreign codes on the soil slope seismic response was discussed, and which was compared with the influence law of the peak acceleration (PGA). The results showed that the deformation and the maximum principal stress of soil slope both increased with the EPA and PGA, which had an obvious linear relationship, but the correlation degree were different with the parameters of PGA and EPA by the different definitions. EPA1 by the first definition has the highest correlation with the soil slope seismic response, followed by PGA, which was close to EPA1. Other parameters in order of correlation coefficient were EPA2, EPA3 and EPA4. In this example, EPA1 and PGA could better describe the response degree of soil slope in earthquake. The results are expected to provide a basis for the selection of seismic parameters in soil slope seismic stability evaluation

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Summary: Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

Abstract Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies