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Many color-tunable LiLaSiO4: αTm3+, βDy3+ phosphors were prepared using the traditional high-temperature solid-phase method. The phase composition, surface morphology, fluorescence spectrum, fluorescence lifetime, energy transfer mechanism, and color coordinates of the samples were analyzed using XRD, SEM, TEM and fluorescence spectrometer. The results show that LiLaSiO4: αTm3+ phosphor emits high intensity blue light at 460 nm and the concentration quenching point of Tm3+, α = 0.015 mol. In LiLaSiO4: αTm3+, βDy3+ phosphors, as the doped Dy3+ doping increases, the luminescence intensity of Tm3+ gradually decreases, the luminescence intensity of Dy3+ first increases and then decreases. The concentration quenching point of Dy3+, β = 0.015 mol. Adjustable light-emitting color can be obtained by changing the doping molar mass of Dy3+ or the wavelength of the excitation light. There is an effective energy transfer between Tm3+→Dy3+. The energy transfer mechanism is the electric dipole-electric dipole interaction, and the energy transfer efficiency reaches 90.11% when β = 0.06 mol. The quantum yield of LiLaSiO4:0.015 Tm3+,0.015Dy3+ was 39.0%. The single-matrix white light LiLaSiO4: αTm3+, βDy3+ phosphors with excellent performance is a prospective material for of white LEDs.

As one of the most promising semi-active control devices, magnetorheological (MR) damper has received significant attention in the past few decades because it can offer the adaptability of active control devices in the absence of the associated large power sources. In this study, a new single-rod MR damper with combine volume compensator are proposed, and the design method of combined compensator with independent functions of volume compensation and compensation force is proposed. Then the dynamic tests of the single-rod MR damper are carried out to demonstrate its working performance. The identified sigmoid model is adopted as forward model to characterize the performance of the MR damper. And the force tracking method is put forward by performing inverse of the forward model. Finally, the force tracking simulation of the optimal LQR control force for the stay cable is given to show its feasibility.

The TiO2 nanotube arrays were prepared on Ti plate by anodizing technology, and then Co (OH)2 nanoparticles in-situ grew into TiO2 nanotubes with "Tube-particle bonding" nanocomposited structure. Co (OH)2 nanoparticles with a particle size of 40.5±8 nm were infiltrated in the TiO2 nanotube arrays, and the Co (OH)2 outside the tube presented a nanosheet structure. The specific capacitance of the Co (OH)2/TiO2 nanotube array composite reached 260 F/g at the current density~1 A/g. The capacity retention rate was 82.5 % after 2,000 cycles at the current density~5 A/g. The high-rate performance of the Co (OH)2/TiO2 nanotube array composite reached 210 F/g at the current density~10 A/g.

In this article, a systematic assessment of cyber–physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.

The active magnetic bearing system exhibits mass imbalance and sensor runout which cause the system to generate harmonic vibration force and moment. Repetitive control is an effective method to eliminate such harmonic vibration. Traditional repetitive control will eliminate all of the harmonic frequency components. However, in a practical system, the odd harmonic components usually dominate. Meanwhile, the existing method only suppresses the vibration force in the magnetic bearing system, and there is little research on the suppression of moment. Aiming at these problems, the harmonic vibration moment of the active magnetic bearing system is taken as the control object. This study investigates a hybrid control method that combines a second-order odd harmonic repetitive control with finite-dimensional repetitive control. And the virtual variable sampling is applied to construct any virtual sampling period in the proposed method, which effectively solves the problem of non-integer delay of digital repetitive control. The stability of the active magnetic bearing system is analyzed. The experimental results show that this method has faster response speed and better robustness when the frequency fluctuates.

The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.

Particular tasks performed in narrow and confined spaces require a capability of passing through a limited pathway. Consequently, previous research developed a snake-like robot that generates a 1-D rectilinear gait with an appropriate mechanical design. In contrast, a rigorous mathematical model and its theoretical analysis framework is still lacking, which are inevitable for locomotion control and performance optimization. Under such background, this letter introduces a modified snake-like robot model with additional oscillation elements. Efficient and stable rectilinear gait is generated via positively utilizing the resonance effect. Moreover, linear components associate with oscillation properties are extracted from the nonlinear dynamical system. Accordingly, theoretical analysis based on dynamic morphological computation demonstrates that resonance curves of the linear oscillator can indeed elucidate the locomotion performance quite well. This enables estimation of the optimal parameters for locomotion control by conveniently calculating the resonance curve instead of solving the nonlinear equation of motion. Our methods provide a novel approach for theoretical analysis and performance optimization of locomotion robots with periodical gaits.

Over the past decade, N-heterocyclic carbenes (NHCs) have attracted remarkable attention as metal-coordinating ligands because of their ability to strongly interact with transition metal complexes ...

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

Distributed energy storage systems (DESSs) play an important role in maintaining voltage stability in DC microgrids. In order to improve the inertia of DC microgrid and balance the charge/discharge power and the state-of-charge (SOC) of each energy storage unit (ESU), an SOC-based virtual DC machine (VDCM) control strategy for DESSs in DC microgrid is proposed in this paper. By adding SOC to the armature resistance, the improved virtual machine algorithm can dynamically balance power and SOC simultaneously. In this article, the transient stability of DC bus voltage is enhanced using the output inertia of VDCM, and the SOC differences between ESUs are rapidly reduced by introducing the average SOC of ESUs. Small-signal models of both each ESU-interfacing converter and DESS-inclusive DC microgrid with proposed control method are established. The impact of VDCM parameters on voltage stability is analyzed. Finally, a test DC microgrid is built to verify feasibility and effectiveness of the VDCM control strategy.

An effective and easy to implement ligand exchange strategy is paramount to the design of stable and multifunctional gold and other inorganic nanocolloids. This is also crucial for their use in bio...

Purpose This paper aims to present the design and experimental tests of an active circulating cooling system for the Experimental Advanced Superconducting Tokamak in-vessel inspection manipulator, which will help the current manipulator prototype to achieve a full-scale in-vessel high temperature environment compatibility. Design/methodology/approach The high-temperature effects and heat transfer conditions of the manipulator under in-vessel environment were analyzed. An active circulating cooling system was designed and implemented on the manipulator prototype. A simulative in-vessel inspection task in a high temperature environment of 100°C was carried out to evaluate the performance of the active circulating cooling system. Findings The proposed active circulating cooling system was proved effective in helping the manipulator prototype to achieve its basic in-vessel inspection capability in a high temperature environment. The active circulating cooling system performance can be further improved considering the cooling structure coefficient differences in different manipulator parts. Originality/value For the first time, the active circulating cooling system was implemented and tested on a full-scale of the in-vessel inspection manipulator. The experimental data of the temperature distribution inside the manipulator and the operating status of the circulating system were helpful to evaluate the current active circulating cooling system design and provided effective guidance for improving the overall system performance.

Thin sand layers are widely developed in the Cretaceous Baxigai Formation in the Yudong area, western Tabei Uplift, and series of lithologic traps controlled by the structures are formed. The thickness of the target layer is 3–5 m, which is beyond the resolution of conventional seismic data; this causes the prediction of the reservoir to be difficult. Starting from the forward simulation, a forward model was designed based on the well logging data and characteristics of the strata in the well area to analyze the impact of different thin reservoir development modes on the seismic waveform. The experimental results show that the amplitude of thick sandstone is strong, while that of the thin sandstone is weaker. When mudstone barriers develop in sandstones, the amplitude decreases and the trough widens. Based on the results above, the seismic facies plan is formed by clustering the two attributes of wave trough width and amplitude, and the qualitative prediction of the reservoir can be realized on the plane by referring to the drilled rock information. Based on this, high-resolution seismic waveform inversion was carried out to achieve vertical reservoir prediction. The inversion results on the profile are higher than those of well logging. Furthermore, geological knowledge is achieved on the plane and good application results are obtained.

Nickel-based composite coatings reinforced by spherical tungsten carbide were deposited on 42CrMo alloy steel using plasma transfer arc welding (PTAW) process. Their electrochemical corrosion properties in NaCl solution under atmospheric and high pressure were studied by polarization curve, electrochemical impedance spectroscopy. The corrosion and erosion resistance of the coatings were also investigated by salt spray corrosion and erosion corrosion tests. The results show that the self-corrosion potential of the composite coatings increased with the increase of tungsten carbide content, and the Cr element in Ni60 sample formed a stable and compact passivation film. Compared with corrosion at atmospheric pressure, the adsorption and penetration of Cl- on the coating surface enhanced due to the increase of Cl- activity under pressure, thereby to weaken the corrosion resistance. The Samples that passivated in salt spray environment, cannot completely hinder the corrosion of the coating, just only to slow down the corrosion. This study can provide theoretical basis for deep-sea oil drilling and production engineering equipment.

We detail the preparation of highly fluorescent quantum dots (QDs), surface-engineered with multifunctional polymer ligands that are compact and readily compatible with strain-promoted click conjug...

This article presents a noncooperative game-theoretic framework to model the social welfare optimization (SWO) problem with load aggregators participating in integrated economic dispatch and demand response. In the proposed framework, distribution system operators interact with generation units and load aggregators to maximize the overall social welfare. The proposed SWO problem addresses practical system constraints and falls into the scope of mixed integer nonlinear programs, which cannot be well handled by existing distributed algorithms. The proposed SWO problem is formulated by a special noncooperative strategic game, known as the potential game, and solved by a revised version of the spatial adaptive play under network anomaly. It is shown that the proposed framework has guaranteed convergence to a Nash equilibrium that is also a global optimizer. Simulations on a 15-generator benchmark distribution network have been conducted to validate the proposed framework.

In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of co-clustering based dual learning mechanism for more interpretable and compact data reconstruction. We then adopt the local sensitive loss function, which emphasizes more on most similar pairs with small errors to better characterize the local structure of data. In this way, our method can select features with better clustering results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. We fully investigate the performance improvement by the newly developed terms, individually and simultaneously. Extensive experiments on benchmark datasets further show that LSDCL outperforms many state-of-the-art unsupervised feature selection algorithms.

Concept Factorization (CF) improves Nonnegative matrix factorization (NMF), which can be only performed in the original data space, by conducting factorization within proper kernel space where the structure of data become much clear than the original data space. CF-based methods have been widely applied and yielded impressive results in optimal data representation and clustering tasks. However, CF methods still face with the problem of proper kernel function design or selection in practice. Most existing Multiple Kernel Clustering (MKC) algorithms do not sufficiently consider the intrinsic neighborhood structure of base kernels. In this paper, we propose a novel Discriminative Multiple Kernel Concept Factorization method for data representation and clustering. We first extend the original kernel concept factorization with the integration of multiple kernel clustering framework to alleviate the problem of kernel selection. For each base kernel, we extract the local discriminant structure of data via the local discriminant models with global integration. Moreover, we further linearly combine all these kernel-level local discriminant models to obtain an integrated consensus characterization of the intrinsic structure across base kernels. In this way, it is expected that our method can achieve better results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. Extensive experiments on benchmark datasets further show that the proposed method outperforms many state-of-the-art algorithms.

Summary Cancer cell stemness (CCS) plays critical roles in both malignancy maintenance and metastasis, yet the underlying molecular mechanisms are far from complete. Although the importance of SOX2 in cancer development and CCS are well recognized, the role of MTA3 in these processes is unknown. In this study, we used esophageal squamous cell carcinoma (ESCC) as a model system to demonstrate that MTA3 can repress both CCS and metastasis in vitro and in vivo. Mechanistically, by forming a repressive complex with GATA3, MTA3 downregulates SOX2OT, subsequently suppresses the SOX2OT/SOX2 axis, and ultimately represses CCS and metastasis. More importantly, MTA3low/SOX2high is associated with poor prognosis and could serve as an independent prognostic factor. These findings altogether indicate that MTA3/SOX2OT/SOX2 axis plays an indispensable role in CCS. Therefore, this axis could be potentially used in cancer stratification and serves as a therapeutic target., Graphical Abstract, Highlights • MTA3 is recruited by GATA3 to repress SOX2OT transcription • The MTA3/GATA3 complex dampens SOX2 signaling by inhibiting SOX2OT transcription • MTA3 can repress both cancer stemness and metastasis of squamous esophageal cancer • Low MTA3 and high SOX2 expression predicts poor prognosis in esophageal cancer, Biological Sciences; Molecular Biology; Cell Biology; Stem Cells Research; Cancer

PurposeThis paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.Design/methodology/approachBecause of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.FindingsThe results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.Practical implicationsThe novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.Originality/valueThe proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

The research and development (RD (2) Under the framework of super-conflict equilibrium of GPTs, an incentive mechanism is designed to promote the R&D of GPTs from the perspective of cost sharing, and its incentive effect is stable and sustainable. In addition, taking “Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO) of Jiangsu in China” as a case, this paper provides some interesting management suggestions through numerical simulation analysis, which are more suitable for developing countries with similar R&D situation in China's GPTs.

Objective: The objective of the current study was to identify the 100 top-cited studies in cancer immunotherapy. Materials and methods: A retrospective bibliometric analysis was performed in March 2019. Studies were searched on the Web of Science to identify the 100 top-cited studies in cancer immunotherapy. Studies were identified and analysed for authorship, journal, study type, year of publication and institution. Results: The 100 top-cited papers were cited from 591 to 5332 times and were published between 1986 and 2016. They were published in 27 journals, and New England Journal of Medicine published most of the studies (n = 14), followed by Nature (n = 11) and Journal of Clinical Oncology (n = 10). They were published from 10 countries, and the USA published most of the studies (n = 82), followed by France (n = 5) and Netherlands (n = 3). National Cancer Institute in USA was the leading institution and Rosenberg, SA was the most productive author. Conclusions: This study provides insights into development and most important papers in cancer immunotherapy and will provide evidence for future research in cancer immunotherapy.

Multi-view learning has attracted increasing attention in recent years, and the existing multi-view learning methods learn a consensus result by collecting all views. These methods have two obvious limitations. First, it is not scalable; with limited computational resources it would be difficult, if not impossible, to collect and process a large collection of views together. Second, in many applications views of data are available over time; it is infeasible to apply the existing multi-view learning methods to such streaming views. To address the two limitations, in this paper we propose a novel incremental multi-view spectral clustering (IMSC) method. In IMSC, instead of ensembling the collection of all views simultaneously, we integrate them one by one in an incremental way. We first learn an initial model from a small number of views; next when a new view is available, we need only use it to update the model and apply the updated model to learn a consensus result. This method is scalable and applicable to streaming views. To further reduce the time and space complexity, we apply low rank approximation by means of the well-known random Fourier features to construct the base kernels and do low rank SVD decompositions accordingly. The theoretical analysis and experimental results on benchmark data sets show that our incremental multi-view spectral clustering method is significantly faster in efficiency than the existing state-of-the-art non-incremental ones and is comparable or even better in clustering quality.

This paper focuses on compliance error calibration. Because kinematic parameter error is the main error of a robot, we should first compensate for it. And because the compliance errors of some joints are too small, not all joints should be compensated for he compliance errors. We rotate the single joint along its axis locked other joints to obtain the statistical properties of all joints. The compliance errors are induced by gravity and elastostatic. This paper presents a mapping from the compliance error onto the joint variable vector; on the other hand, it utilized a method to transform the robot compliance error from the laser tracker system frame to the robot frame. The main attention is paid to analyze each joint compliance error using a single axis rotating by laser tracking system. To compensate the compliance error, we divided this problem into three sequential subtasks: identifying the robot compliance matrix, computing the compliance error of gravity without external loading, compensating compliance errors of elastostatic error on external loading.

Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high-performance adaptive dual fractional-order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional-order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn- Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional-order differential masks. The domain of each region is determined adaptively. The order and size of the fractional-order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional-order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional-order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms on low-light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.

In complex group decision-making, decision makers and decision attributes are the core of the relevant activities. Targeting the problem of scheme ranking and behavioural characteristics that exist in group decision-making, from the perspective of group negotiation and decision-making system coordination, by exploiting the grey target and grey relation analysis, this paper establishes a novel grey group decision-making approach. We define a group measure matrix of scheme, consensus ideal scheme, and decision-making resource coefficient. Then, by borrowing Nash’s bargaining idea, and maximizing group negotiation satisfaction and minimizing system coordination deviation, we construct a two-step optimization model to solve for the group consensus ideal scheme and its measure value matrix. In addition, we take decision-making schemes as research objects; and from the two dimensions of decision maker and attribute, we characterize and measure the closeness degree of decision maker information and attribute information by using the distance between the group measure matrices of scheme and consensus ideal scheme, so that we are able to construct a novel grey scheme matrix similar incidence analysis model. Lastly, we take the group decision-making problem of selecting the location of a garbage disposal station as a case analysis, and explore the economic significance and theoretical value of the model.

Reacting poly(maleic anhydride)-based polymers with H2N–R nucleophiles is a flexible and highly effective approach for preparing a variety of multifunctional, multicoordinating, and multireactive polymers. The exact transformation of the anhydride ring during this addition reaction is still an open question. In this report, we characterize the transformation of a representative block copolymer, poly(isobutylene-alt-maleic anhydride), with a few H2N–R nucleophiles. In particular, we test the effects of varying a few reaction parameters/conditions (e.g., temperature, solvent, reaction time, and addition of thionyl chloride) on the nature of the anhydride transformation and bond formed between the polymer and the lateral R groups. The resulting polymers are characterized using a combination of analytical techniques including FT-IR, one- and two-dimensional NMR, and gel electrophoresis. We find that the ring opening transformation occurs under mild conditions. Conversely, cyclic imide transformation can take ...

Facing with the absence of supervised information to guide the search of relevant features and the grid-search of model/hyper-parameters, it is more preferred to develop parameter-free methods and avoid additional hyper-parameters tuning. In this paper, we propose a new simple and effective parameter-free unsupervised feature selection algorithm by minimizing the linear reconstruction weight between the nearest neighbor graphs constructed from all candidate features and each single feature. The obtained global optimal reconstruction weights actually select those features with highest relevance and lowest redundancy simultaneously. The experimental results on many benchmark data sets demonstrate that the proposed method outperforms many of the state-of-the-art unsupervised feature selection methods.

Kernel-based clustering methods can capture the non-linear structure and identify arbitrarily shaped clusters, so they have been widely used in machine learning tasks. Since the performance of kernel methods critically depends on the choices of kernels, multiple kernel learning methods are proposed to alleviate the effort for kernel designing. The conventional multiple kernel learning methods learn a consensus kernel by linearly combining all candidate kernels, whereas ignoring the influence of the noises. To improve the robustness of multiple kernel learning methods, in this paper, we analyze the local and global noises and design regularized terms to characterize them respectively. Then, we propose a novel local and global de-noising multiple kernel learning method which can explicitly extract the local and global noises to recover the clean kernels for multiple kernel learning. After that, a block coordinate descent algorithm is presented to solve the optimization problem. Finally, the extensive experiments on benchmark data sets will demonstrate the effectiveness of the proposed algorithm.

Despite the tremendous progress made in learning-based depth prediction, most methods rely heavily on large amounts of dense ground-truth depth data for training. To solve the tradeoff between the labeling cost and precision, we propose a novel weakly supervised approach, namely, the Guided-Net, by incorporating robust ground control points for guidance. By exploiting the guidance from ground control points, disparity edge gradients, and image appearance constraints, our improved network with deformable convolutional layers is empowered to learn in a more efficient way. The experiments on the KITTI, Cityscapes, and Make3D datasets demonstrate that the proposed method yields a performance superior to that of the existing weakly supervised approaches and achieves results comparable to those of the semisupervised and supervised frameworks.

In part because of their high drug loading, nanocrystals (NCs) have seen extensive use in drug delivery, particularly for insoluble or poorly soluble drugs. It remains a challenge, however, to prepare stable nanocrystals with tumor-targeting capability. Here, we designed a novel preparation of stable paclitaxel (PTX) nanocrystals with efficient active tumor-targeting properties. PTX NC was prepared using a bottom-up method and modified with both poly(ethylene glycol) (PEG) and folic acid (FA) derivatives using film hydration. The resulting PTX NC@lipid-PEG-FA had a rodlike shape, with hydrodynamic diameters and drug loading values of 201.90 ± 2.92 nm and 31.07 ± 3.41%, respectively. The size of the PTX NC@lipid-PEG-FA was unchanged after 168 h in the presence of plasma, whereas nonmodified paclitaxel nanocrystals (PTX NC) exceeded 600 nm within 12 h under the same conditions. Cellular uptake and cellular growth inhibition experiments in 4T1 breast cancer cells showed the superiority of PTX NC@lipid-PEG-FA over PTX NC or PEGylated paclitaxel nanocrystals without FA modification (PTX NC@lipid-PEG). A pharmacokinetic evaluation in rats revealed that PTX NC@lipid-PEG-FA significantly prolonged the circulation of PTX in the bloodstream, in comparison with PTX NC or Taxol. Tissue distribution and in vivo antitumor studies in 4T1 orthotopic breast cancer-bearing nude mice showed that PTX NC@lipid-PEG-FA significantly increased the intratumor accumulation of PTX and efficiently inhibited tumor growth, in comparison with PTX NC@lipid-PEG, PTX NC, or Taxol. In summary, PTX NC@lipid-PEG-FA showed good potential for breast cancer-targeted delivery for insoluble therapeutics.

We live in an era of big data, in which feature selection technology is getting more and more attention. Feature selection technology is one of the important methods to reduce the dimension of data. It can select some useful features for learning tasks. The traditional feature selection methods mainly select the useful features by the scores of the features under a certain standard. However, the performance of these methods are less satisfactory in many cases because they ignore the correlation between features. For this article, we present a new unsupervised method by minimizing the Kullback-Leibler(KL) divergence based on graph matching. Firstly, we extract manifold structures from all features of the original data space by using non-negative Local Linear Embedding(NNLLE). Then, we extract manifold structure of each feature by using non-negative local linear embedding (NNLLE). We assess the importance of every feature by minimizing the KL-divergence between the graphs using all features and weighted linear combination of base graphs on each individual feature. At the same time, a global optimization algorithm based on proximal gradient descent framework is proposed. Experiments show that the proposed method is better than many existing unsupervised methods.

Given multiple clustering algorithms with different candidate hyper-parameter configurations, we will generate multiple weak partition results for the same data set. By integrating a group of such base clustering results, clustering ensemble methods try to find the final consensus clustering result where the stability and even the performance of base clustering results could be further improved. Although the exact one-to-one mapping across clusters from different base clustering may not exist, we still have chance to directly find such weak correspondence between the final consensus clusters and the input base clusters. In this paper, we propose a novel method which maps all the clusters from base clustering to new permuted clusters and finds the consensus clustering by maximizing the alignment from permuted bases. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.

Creative design of grippers on their configurations, mechatronics control system, and multi-component collaborative algorithms is often utilized to realize complex operations in industrial applications, due to the environmental constraints or specific task requirements. Firstly, this paper introduces the background problems. As the main automatic equipment -- the shield machine -- in the field of tunnel boring construction, needs frequent tool (cutter) replacement during underground process, but has no practical automatic method yet, due to heavy payload, complex environment and work procedure. Thus, an integrated solution was proposed by developing a specific gripper and a snake-like manipulator to accomplish tool replacement in a cooperative way. Through simple and unique design of relative components, the solution realizes a fully automatic and precise approach including heavy load tool grasping and regrasping, posture adjustment, unlocking and disassembly, and installation and locking. Finally, this paper also describes the experimental process of tool replacement by the prototype under a real working condition, and discusses the feasibility of putting the scheme into practical application through comparison.

For having a stable and reliable smart grid system, an accurate short-term solar irradiance forecasting is necessary. The challenges arise while solar energy has variable and fluctuating nature due to complex weather conditions, temperature, and meteorological factors. Machine learning and deep learning techniques besides traditional time-series forecasting methods are considered to be effective tools to have precise short-term solar forecasting system, while they are analyzing the time-series solar data at single resolution and unable to capture their sudden and short variations. In this paper, we propose a novel deep architecture consisting multi-branch residual network (ResNet) to model the solar irradiance data at different resolutions and extract hierarchical features to improve the forecasting accuracy. We evaluate the performance of the proposed model relative to other deep learning models, ResNet, and long short-term memory (LSTM), using seventeen years of data from twelve different sites in Philadelphia. Numerical results show the state-of-the-art performance on half-day-ahead forecasting.

This paper proposes an optimal design of closed-loop-controlled grid-tied inverters based on harmonic state space (HSS) modeling. A comprehensive HSS model of the entire closed-loop system is derived, which includes HSS modeling of each component, i.e., abc/dq (dq/abc) transformation, sinusoidal pulse width modulation (SPWM), proportional-integral (PI) controllers, and LCL-filters in a synchronous d-q frame. Based on the HSS model, a high-order of the closed-loop system model (up to switching frequency) is obtained and then used towards an optimal design. With the proposed design framework, system parameters including both circuit and controller parameters are optimized. The obtained parameters can not only minimize the total harmonic distortion (THD) in inverter grid-connected current, but also guarantee that the harmonic spectrum meets the requirements of IEEE standards. Both the holistic HSS model and optimal design framework are implemented, and the simulation results verify the derived model and the proposed optimal design.

This paper presents an optimal design framework for photovoltaic (PV) power generation systems. With the objectives of minimizing levelized cost of energy (LCOE) and maximizing power density (PD) of PV inverters, all design components of PV inverters including inverter topologies, semiconductor (SC) devices, DC bus capacitors, switching frequency, AC output filter, and cooling system are designed in a holistic framework. Particularly, the design framework is formulated as a constrained optimization problem to determine the optimal combination of key components of PV inverters. With real solar profiles, three 100-kW PV systems are designed featuring an optimal LCOE with different weather conditions, and a high PD and energy conversion efficiency can be guaranteed using the proposed framework. The testing results using field data demonstrate the effectiveness of the proposed optimal design framework.

Concept factorization approximates the original data matrix with concepts and coefficients by minimizing the squared loss and has been widely used for data representation and clustering. The graph regularization has also been adopted on both sides of the factorization. In this paper, we propose the novel robust dual concept factorization method to improve the robustness against outliers and noises. In particular, we take the dual concept factorization model and use the corr-entropy induced metric to guide the data reconstruction and the corresponding dual graph regularization. As a result, the affect of the outliers can be alleviated from both data reconstruction and dual regularization. Performance also validates the method proposed to group data on reference data sets.

3D printed metal crowns can be used for dental restorations. The main quality control challenge of these dental metal is the method of quality inspection. Electronic quality is a process by which the quality of the process and the parts produced can be checked online, thereby improving the process and reducing the time it takes for the entire process. Here, we propose a combination of 3D scanning and 3D measurement for 3D inspection of metal crowns. The data extracted from the 3D printed metal crowns were used as case studies to prove the proposed methodology. The obtained results confirm that the new method has very high classification accuracy compared with the traditional inspection methods, and thus yields excellent results. Moreover, the proposed approach is capable to archive 3D models of the parts and achieve rapid quality control. This paper forms the basis for solving many other similar problems that occur in 3D printing related industries.

Accurate forecasting of solar irradiance plays an important role in the reliable grid integration of renewable energy. Forecasting of photovoltaic (PV) generation of solar sites is routinely studied in academia and industries, however spatio-temporal weather dependencies amongst regional plants are often ignored. It is intuitively expected that PV generation of neighboring solar plants are correlated and may show similar time-varying patterns. In this paper, we are investigating spatiotemporal correlation between solar sites which are at different geographical locations. We propose a hybrid deep learning model which is a combination of a residual network (ResNet) to learn different representations of data and long short-term memory (LSTM) to capture long temporal dependencies. We measure the performance of the proposed model relative to other deep learning models, such as a convolutional neural network (CNN) and long short-term memory (LSTM), ResNet, and ResNet/multilayer perceptron (MLP) using seventeen years of meteorological data from twelve different sites in Philadelphia. Our experiments show that the proposed architecture integrating special and temporal contexts provides superior performance in solar irradiance forecasting.

This paper aims at incorporating the supercapacitor charge redistribution effect into the cooperative control of networked hybrid AC/DC microgrids that consisting of electric buses charging infrastructures and PV generations. Hybrid energy storage systems, which consist of supercapacitors and Li-ion battery units, are controlled to integrate the highly pulsed charging demands caused by electrified bus fleets. Each major transit hub is modeled with a hybrid AC/DC microgrid and needs to handle charging needs of electric bus fleets as well as maximizing penetration of PV energy. Energy storage resources are shared among the neighboring microgrids in a coordinated manner based on the bus schedules. This paper includes a more accurate and advanced model of supercapacitors to integrate the supercapacitor redistribution effect to the control strategy. The supercapacitor redistribution is the automatic recovery of supercapacitor terminal voltage to a certain percentage depending its discharge pattern. For this application, the supercapacitor will be discharged quickly due to the high current of the electric bus load, thus the redistribution effect could recover 10-15% of supercapacitor voltage by delaying recharging the supercapacitor. This provides an opportunity for energy savings and improvement on the previous control strategy.

With the emergence of the smart inverter, EV charging stations have a tremendous potential to provide ancillary services to power distribution grids such as increasing the system capacity, reducing the system losses, and enhancing the system voltage profile. These inverters can be allowed to inject reactive power into the grid for voltage regulation, i.e., smoothing voltage fluctuations due to the variation of demands and also renewable generations. To our best knowledge, few works are conducted to analyze the effect of the EV charging station inverters on voltage regulation. In this paper, how EV charging station inverters can participate in voltage regulation formulated as an optimization problem. A game-theoretic machine learning framework is proposed to design a decentralized Volt-Var control strategy. The proposed framework and algorithm are validated on the Southern California Edison 47-bus system.

Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion and inherent variance of point density with the distance to sensors, appearance of a same object varies a lot in point cloud data. Designing robust feature representation against such appearance changes is hence the key issue in a 3D object detection method. In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation. More specifically, we bridge the gap between the perceptual domain where the feature comes from a real scene and the conceptual domain where the feature is extracted from an augmented scene consisting of non-occlusion point cloud rich of detailed information. This domain adaptation approach mimics the functionality of the human brain when proceeding object perception. Extensive experiments demonstrate that our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results., Comment: 8 pages, 5 figures, CVPR 2020

In order to cope with the operational instability and scheduling complexity brought by wind power access to the grid, this paper aims at multi-domain optimal dispatching of wind power systems based on voltage stability constraints. The wind power system is divided into different grid operation domains, and the system is divided into normal domains, abnormal domains, and emergency domains according to the regulation capacity of conventional units and their changing trends. The optimal scheduling model considering voltage stability constraints in different operation regions is established. Through the simulation calculation of a typical example of IEEE-30 node, it is verified that the optimized scheduling method can improve the economy, security and wind power capacity of the system operation.

The Coronavirus Disease 2019 (COVID-19) has swept the whole world with high mortality. Since droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure. However, the virus has spread quite quickly, causing severe mask shortage. Finding alternative materials for homemade masks while ensuring the significant performance indicators will help alleviate the shortage of masks. Referring to the national standard for the “Surgical Mask” of China, 17 materials to be selected for homemade masks were tested in four key indicators: pressure difference, particle filtration efficiency, bacterial filtration efficiency and resistance to surface wetting. Eleven single-layer materials met the standard of pressure difference (≤49 Pa), of which 3 met the standard of resistance to surface wetting (≥3), 1 met the standard of particle filtration efficiency (≥30%), but none met the standard of bacterial filtration efficiency (≥95%). Based on the testing results of single-layer materials, fifteen combinations of paired materials were tested. The results showed that three double-layer materials including double-layer medical non-woven fabric, medical non-woven fabric plus non-woven shopping bag, and medical non-woven fabric plus granular tea towel could meet all the standards of pressure difference, particle filtration efficiency, and resistance to surface wetting, and were close to the standard of the bacterial filtration efficiency. In conclusion, if resources are severely lacking and medical masks cannot be obtained, homemade masks using available materials, based on the results of this study, can minimize the chance of infection to the maximum extent.

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost., Comment: icra 2020

Since its outbreak in December 2019, a series of clinical trials on coronavirus disease 2019 (COVID-19) have been registered or carried out. However, the significant heterogeneity and less critical outcomes of such trials may be leading to a waste of research resources. This study aimed to develop a core outcome set (COS) for clinical trials on COVID-19 in order to tackle the outcome issues. The study was conducted according to the Core Outcome Measures in Effectiveness Trials (COMET) Handbook: Version 1.0, a guideline for COS development. A research group was set up that included experts in respiratory and critical medicine, traditional Chinese medicine (TCM), evidence-based medicine, clinical pharmacology, and statistics, in addition to medical journal editors. Clinical trial registry websites (www.chictr.org.cn and clinicaltrials.gov) were searched to retrieve clinical trial protocols and outcomes in order to form an outcome pool. A total of 78 clinical trial protocols on COVID-19 were included and 259 outcomes were collected. After standardization, 132 outcomes were identified within seven different categories, of which 58 were selected to develop a preliminary outcome list for further consensus. After two rounds of Delphi survey and one consensus meeting, the most important outcomes for the different clinical classifications of COVID-19 were identified and determined to constitute the COS for clinical trials on COVID-19 (COS-COVID). The COS-COVID includes one outcome for the mild type (time to 2019 novel coronavirus (2019-nCoV) reverse transcription-polymerase chain reaction (RT-PCR) negativity), four outcomes for the ordinary type (length of hospital stay, composite events, score of clinical symptoms, and time to 2019-nCoV RT-PCR negativity), five outcomes for the severe type (composite events, length of hospital stay, arterial oxygen partial pressure (PaO2)/fraction of inspired oxygen (FiO2), duration of mechanical ventilation, and time to 2019-nCoV RT-PCR negativity), one outcome for critical type (all-cause mortality), and one outcome for rehabilitation period (pulmonary function). The COS-COVID is currently the most valuable and practical clinical outcome set for the evaluation of intervention effect, and is useful for evidence assessment and decision-making. With a deepening understanding of COVID-19 and application feedback, the COS-COVID should be continuously updated.

Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches. In this paper, we propose a novel domain adaptation based monocular 3D object detection framework named DA-3Ddet, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting. In order to solve the overlooked problem of inconsistency between the foreground mask of pseudo and real LiDAR caused by inaccurately estimated depth, we also introduce a context-aware foreground segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin.

Zinc-ion hybrid supercapacitors are a promising energy storage device as they simultaneously combine the high capacity of batteries and the high power of supercapacitors. However, the practical application of Zinc-ion hybrid supercapacitors is hindered by insufficient energy density and poor rate performance. In this study, a symmetrical zinc-ion hybrid supercapacitor device was constructed with hollow mesoporous-carbon nanospheres as electrode materials, and aqueous ZnSO4 adopted as an electrolyte. Benefiting from the mesoporous structure and high specific area (800 m2/g) of the hollow carbon nanospheres, fast capacitor-type ion adsorption/de-adsorption on both the cathode and the anode can be achieved, as well as additional battery-type Zn/Zn2+ electroplating/stripping on the anode. This device thus demonstrates outstanding electrochemical performance, with high capacity (212.1 F/g at 0.2 A/g), a high energy density (75.4 Wh/kg at 0.16 kW/kg), a good rate performance (34.2 Wh/kg energy density maintained at a high power density of 16.0 kW/kg) and excellent cycling stability with 99.4% capacitance retention after 2,500 cycles at 2 A/g. The engineering of this new configuration provides an extremely safe, high-rate, and durable energy-storage device.

We detail the design of a new set of multicoordinating polymer ligands based on the phosphonate anchoring motif and apply them for the surface coating of luminescent quantum dots, gold nanoparticles, and iron oxide nanoparticles alike. The ligand is synthesized via a nucleophilic addition reaction between poly(isobutylene-alt-maleic anhydride) and amine-modified phosphonate derivatives and short polyethylene glycol hydrophilic blocks, which allows the flexibility to tune the architecture and stoichiometry of the final compound. We find that these phosphonate-based polymers exhibit a strong coordinating affinity for ZnS-overcoated quantum dots (QDs), Au nanoparticles, and iron oxide nanoparticles, yielding nanocrystal dispersions that exhibit good colloidal stability for all three systems. The affinity of these ligands is also preserved when additional coordinating groups are introduced, such as imidazoles. Furthermore, the resulting polymer-coated nanocrystals are easily functionalized with reactive group...