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In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments.

Daytime passive radiative cooling is a promising electricity-free pathway for cooling terrestrial buildings. Current research interest in this cooling strategy mainly lies in tailoring the optical spectra of materials for strong thermal emission and high solar reflection. However, environmental heat gain poses a crucial challenge to building cooling at subambient temperatures. Herein, we devise a scalable thermal insulating cooler (TIC) consisting of hierarchically hollow microfibers as the building envelope that simultaneously achieves passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The TIC demonstrates efficient solar reflection (94%) and long-wave infrared emission (94%), yielding a temperature drop of about 9 °C under sunlight of 900 W/m2. Notably, the thermal conductivity of the TIC is lower than that of air, thus preventing heat flow from external environments to indoor space in the summer, an additional benefit that does not sacrifice the radiative cooling performance. A building energy simulation shows that 48.5% of cooling energy could be saved if the TIC is widely deployed in China.

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance.

Developing cost-efficient electrocatalysts that are highly efficient and stable for pH-universal hydrogen evolution reaction (HER) is of great significance for scale-up electricity-to-fuel conversion. Herein, we report a free-standing cathode that the Pd NPs are embedded in a porous carbonized wood membrane (Pd@CWM) for efficient pH-universal HER. The 3D CWM not only serves as an effective porous matrix for hosting Pd nanoparticles with high dispersion but also acts as a conductive current collector. More importantly, the aligned open microchannels inside the CWM provide unimpeded pathways for electrolyte transport and gas release. Thanks to these structural benefits, the as-fabricated Pd@CWM electrodes display superior electrocatalytic activity for the HER, and the most efficient Pd@CWM-2 electrode only needs 154 and 302 mV overpotentials to deliver a HER current density of 10 mA cm−2 in 0.5 M H2SO4 and 1.0 M KOH, respectively. More impressively, Pd@CWM-2 electrode also exhibits excellent stability at a high current density (> 50 mA cm−2) for the HER with 100% faradaic efficiencies in both electrolyte solutions. This work provides a promising strategy for designing free-standing carbon membrane–based pH-universal electrode for large-scale clean fuel production.

Microbial infection and the limitation of tissue regeneration are main obstacles to chronic wound healing. Herein, a biofunctional hydrogel is prepared to simultaneously kill bacteria efficiently and promote would healing. First, a rose bengal/polypyrrole hybrid poly(vinyl alcohol) hydrogel (RB/PPy PVA HD) is synthesized and its antibacterial property is investigated under coirradiation of 550 nm visible light and 808 near-infrared light. The hydrogel exhibits excellent antibacterial activity within 10 min below 45 °C in vitro due to the synergistic effect of photothermal and photodynamic antibacterial therapy. Next, the recombined human epidermal growth factor (rhEGF) is physically absorbed on the surface of the porous hydrogel to form a RB/PPy/rhEGF hybrid PVA HD (rhEGF/RB/PPy PVA HD). The introduction of rhEGF enables the hydrogel to promote fibroblast proliferation and collagen secretion. Furthermore, the in vivo results indicate that the rhEGF/RB/PPy PVA HD can control infection effectively and promote wound healing significantly.

Artemisinin (ART) is well known as an antimalarial drug, and it can also be used to treat inflammation as well as cancer. Although many researchers have reported the antitumor activity of ART, most of these studies were investigated in vitro. In addition, ART is sparingly soluble in water, limiting its clinical relevance in drug development. Based on the data from our preliminary study, ART is not cytotoxic at low micromolar concentrations. Thus, we hypothesized that smart nanocarriers are beneficial for not only increasing the solubility of ART but also elevating the concentration of the drug at the target, thereby inducing the ideal antitumor effect. In this article, a reversibly activatable cell-penetrating peptide ((HE)10-G5-R6 or HE-R6) was introduced to modify artemisinin (ART)-loaded liposomes (ART-Lip-HE-R6) against tumors, and in vitro and in vivo performance were investigated. ART-Lip-HE-R6 exhibited sustained release under different pH conditions. The internalization and cytotoxicity of liposomes were enhanced at low pH, i.e., 6.5, after modification with HE-R6 versus nonmodified liposomes. Moreover, a longer retention time in tumors could be observed in the ART-Lip-HE-R6 group, followed by higher efficiency of tumor suppression. In conclusion, Lip-HE-R6 might be a promising delivery system for ART in cancer therapy.

Lithium sulfur batteries with high energy density are thought to be the most potential energy storage technology that can be commercialized. However, the shuttle effect of polysulfides deteriorates its electrochemical performance. Herein, a novel Co9S8 nanostructure derived from metal organic framework material (MOF) was explored by simple liquid phase reaction and heat vulcanization of 2-methylimidazole and Co(NO3)2·6H2O on the surface of the original PP separator. The Co9S8 nano-flower cluster array wall was vertically and closely arranged with the thickness of 200 nm, and the polysulfide can be adsorbed by its physical and chemical action to slow down the “shuttle effect”. It is found that the cell with the modified separator can achieve an ideal discharge capacity of about 600 mAh/g at 1 C. The specific capacity is maintained at 500 mAh/g after 200 cycles, with only 0.11 % of capacity decay per cycle. It provides a new way for the utilization of MOF material derivatives to modify the separator in order to improve the electrochemical performance of lithium-sulfur batteries.

Exploring electrode materials with a high volumetric energy density and high rate capability remains of a great challenge for nanosized-Li4Ti5O12 (LTO) batteries. Here, hierarchical porous Ti3+-C-N-Br co-doped LTO (LTOCPB-CC) is synthesized using carboxyl-grafted nanocarbon (CC) and cetylpyridinium bromide (CPB) as combined structure-directing agents. Ti4+-O-CPB/Li+-CC is designed as a new molecular chelate, in which CPB and CC promote the uniform mixing of Li+ and Ti4+ and control the morphology of TiO2 and the final product. The defects (oxygen vacancies and ion dopants) formed during the annealing process increase the electron/hole concentration and reduce the band gap, both of which enhance the n-type electron modification of LTO. As-prepared LTOCPB-CC has a large specific surface area and high tap density, as well as a high electronic conductivity (2.84 × 10−4 S cm−1) and ionic conductivity (3.82 × 10−12 cm2 s−1), which are responsible for its excellent rate capability (157.7 mA h g−1 at 20 C) and stable long-term cycling performance (0.008% fade per cycle after 1000 cycles at 20 C).

Taking the VII oil group of N21 reservoir in Gasikule Oilfield as an example, the sedimentary time unit was divided into the barrier layer, interbed, and sand bed by analyzing the sedimentary system and sand body development model in this paper. The single sand body was analyzed for the sand bed time unit. The residual muddy intercalation period division theory was proposed for the first time to divide the sedimentary periods of the sedimentary time unit of the sand bed. Finally, a new “sandwich” layer point coincidence modeling technology was proposed to characterize the sedimentary time units of a barrier layer accurately and interbed and finely describe the sedimentary time unit of the multiperiod sand bed.

A series of well-defined di- and triblock acrylic copolymers consisting of hard poly(dicyclopentanyl acrylate) (PdcPA) and soft poly(alkyl acrylate) (PAA) blocks are synthesized by the N-(trimethylsilyl)bis(trifluoromethanesulfonyl)imide (Me3SiNTf2) catalyzed group transfer polymerization (GTP) of dicyclopentanyl acrylate (dcPA) and alkyl acrylate (AA) including n-butyl acrylate (nBA), n-octylacrylate (nOA), 2-ethylhexyl acrylate (EHA), and n-dodecyl acrylate (nDA). The distribution of the monomeric units of the copolymers is controlled through the addition sequence of the comonomers and by the used initiator. In greater detail, the mixed addition of AA and dcPA using the monofunctional 1-methoxy-1-triisopropylsiloxy-2-methylpropene (MTSiPr) as the initiator produces the statistical poly[(alkyl acrylate)-stat-(dicyclopentanyl acrylate)] (Poly(AA-stat-dcPA)), the sequential addition of AA and then dcPA using MTSiPr as an initiator produces the AB-type hard–soft diblock poly(alkyl acrylate)-block-poly(dicyclopentanyl acrylate) (PAA-b-PdcPA), the sequential addition of AA and then dcPA using the bifunctional 1,2-bis[2-methyl-1-(triisopropylsiloxy)prop-1-enyloxy]ethane (MTSiPr2) as the initiator affords the ABA-type hard–soft–hard triblock poly(dicyclopentanyl acrylate)-block-poly(alkyl acrylate)-block-poly(dicyclopentanyl acrylate) (PdcPA-b-PAA-b-PdcPA), and the reverse addition of the two monomers using MTSiPr2 affords the soft–hard–soft BAB-type triblock poly(alkyl acrylate)-block-poly(dicyclopentanyl acrylate)-block-poly(alkyl acrylate) (PAA-b-PdcPA-b-PAA). The formation of the statistical and block copolymers has been confirmed by 1H NMR spectroscopy and size exclusion chromatography (SEC) measurements. Afterwards, the glass transition behavior of the resulting copolymers is analyzed by both differential scanning calorimetry (DSC) and rheometry. A detailed discussion of this part is presented in the following sections.

Metal halide perovskites are emerging semiconducting materials with a wide range of applications, including photovoltaics, thin-film transistors, and light-emitting diodes. A key advantage of perovskites over more established semiconductor technologies is solution processability, which reduces processing costs and enables rapid production by roll-to-roll printing. However, metal halide perovskites suffer from poor thermomechanical and chemical stability that must be overcome for successful commercialization of perovskite-based technologies. These instabilities have been shown to stem from defect states present at perovskite interfaces, and particularly grain boundaries, that result in destructive trap states. This review focuses on one successful strategy—organic additive engineering—that has been demonstrated to inhibit the formation of defect sites in perovskite films. This review begins by discussing defects present at grain boundaries—e.g., undercoordinated bonds, vacancies, and antisites—that develop during crystallization, and how these defects lead to reduced device performance via non-radiative recombination and hysteresis, as well as poor chemical and thermal stability. The bulk of this review is dedicated to additive engineering strategies to enhance performance and stability in 3D, quasi-2D, and 2D perovskites. Lewis acid–base interactions between the additives and perovskite constituents are highlighted, with additives ranging from small molecules and solvents to polymers. Special attention is paid to Lewis base additives containing amino and/or carbonyl functional groups, and a short discussion on semiconducting additives for improved charge transport is also included. This review concludes by identifying several areas of further discovery in organic additives for metal halide perovskites.

Most cluster identification studies regarding consumer electricity load is faced with problems of erroneous clustering method similarity, low clustering quality and poor identification accuracy. To solve these problems, this paper utilizes the elbow method, k- Means ++, entropy weight method and a graph convolutional neural network to provide a means for cluster identification based on electrical appliance power data collected via smart sockets. In this article, elbow and entropy weight methods were used to achieve the adaptive clustering algorithm. To obtain the electrical appliance load curves, Euclidean and dynamic time warping (DTW) distances were integrated and the similarity measurement method was used to improve the k-means ++ algorithm, which was then applied to data collected via smart socket clustering. Next, clustering results were input into the graph convolutional neural network (GCN) for identification purposes and appliance type information was obtained. Finally, experiments were conducted using electrical load data from 20 commercial users. The method used was a combination of the k-means algorithm and long short-term memory network (LSTM). The results show that under optimal K value conditions (as determined by the elbow score), the methods used in this paper have improved clustering quality and recognition accuracy, when compared to LSTM.

In this paper, the finite-time position tracking control for the fractional-order permanent magnet synchronous motor with input saturation, load disturbance and parameter uncertainties is considered. Based on the command filtered backstepping method, a novel adaptive neural network finite-time controller is presented. First, the neural network is introduced to approximate the uncertain function. Then, by using a command filter at the output side of the virtual signal, the issue of “explosion of complexity” is avoided. In addition, an adaptive law is applied to eliminate the approximation error and filtering error. Meanwhile, by utilizing the terminal sliding mode control technique, the finite-time signal tracking is achieved. Furthermore, an auxiliary design system is constructed to cope with the input saturation constraint. The proposed scheme not only possesses the superiorities of the classical command filtered backstepping, but also holds quick finite-time convergence property. Numerical simulations and experiment results are included to reveal the validity of the proposed design.

The tumor microenvironment (TME) significantly influences cancer evolution and therapeutic efficacy. Targeting biofunctional molecules to the TME has long been appreciated as a means of raising local drug concentrations and reducing systemic toxicities. The booming nanotechnology field has realized the importance of cathepsin B to derive a variety of intelligent enzyme-responsive nanosized drug delivery systems (nanoDDS) to improve treatment responses and clinical outcomes. In this tutorial review, after introducing the molecular structure and physiological/pathological functions of cathepsin B, the outstanding achievements of cathepsin B-responsive nanoplatforms in the precise diagnosis, targeted therapy, and synergistic theranostics of malignant tumors are systematically described. Finally, the challenges of enzyme-substrate incompatibility, low diagnostic sensitivity, mass production and biocompatibility of multifunctional nanoDDS are considered in order to successfully promote them to clinical applications

In this work, a CoNxC active sites-rich three-dimensional porous carbon nanofibers network derived from bacterial cellulose and bimetal-ZIFs is prepared via a nucleation growth strategy and a pyrolysis process. The material displays excellent electrocatalytic activity for the oxygen reduction reaction, reaching a high limiting diffusion current density of −7.8 mA cm−2, outperforming metal–organic frameworks derived multifunctional electrocatalysts, and oxygen evolution reaction and hydrogen evolution reaction with low overpotentials of 380 and 107 mV, respectively. When the electrochemical properties are further evaluated, the electrocatalyst as an air cathode for Zn-air batteries exhibits a high cycling stability for 63 h as well as a maximum power density of 308 mW cm−2, which is better than those for most Zn-air batteries reported to date. In addition, a power density of 152 mW cm−2 is provided by the solid-state Zn-air batteries, and the cycling stability is outstanding for 24 h. The remarkable electrocatalytic properties are attributed to the synergistic effect of the 3D porous carbon nanofibers network and abundant inserted CoNxC active sites, which enable the fast transmission of ions and mass and simultaneously provide a large contact area for the electrode/electrolyte.

In this contribution, a series of ionic liquids (ILs) with tri-n-butylethyl-phosphonium cation ([P4442]) and functional anions are designed and prepared, including pyridinolate anion ([PyO]), pyrid...

Daytime radiative cooling is a passive strategy to cool down a terrestrial object under direct sunlight without the need of electricity input. It functions by simultaneously reflecting solar irradiance and sending heat as infrared (IR) thermal radiation through the atmospheric transparent window into the cold outer space. In spite of extensive studies on daytime radiative cooling, most of previous works were conducted in dry regions mainly in North America. Here, we explore the feasibility of achieving efficient radiative cooling in humid subtropical areas such as Hong Kong, where abundant atmosphere water vapor exists. In this case, the atmospheric transparent window is almost closed since water is highly absorptive of IR radiation. We report a simple approach to achieve efficient daytime radiative cooling in Hong Kong. Our design comprises an expanded polytetrafluoroethylene (ePTFE) film and a Ag layer deposited on a transparent glass substrate. We show that the combination of highly diffusely reflective ePTFE and all-band reflective Ag results in a reflectivity of 98% in the solar spectrum, allowing for a temperature drop up to 2.7 °C under a solar intensity of 1000 W/m2 on a humid day in Hong Kong.

Multiple pollutants including pathogenic microorganism contaminations and emerging organic contaminations (EOCs) have shown a growing threat to the environment, especially the natural waters. However, the control and removal of pathogenic microorganism contaminations and EOCs have been greatly limited since limited knowledge of their environmental behaviors. Thus, a novel and efficient photocatalyst Ag2O/BiOBr heterojunction was synthesized and used for removal of multiple pollutants including Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), tetracycline and acetaminophen under visible light. The results showed that there were valid electron transfer pathways between BiOBr and Ag2O, the main electron transfer direction was the BiOBr to Ag2O. Photo-generated electrons were stored in Ag2O and thus separation efficiency between holes and photo-generated electrons was obviously enhanced. Active oxygen species were highly produced and eventually end up with the high efficiency of removal of multiple pollutants. For Ag2O/BiOBr with Ag2O content at 3% (the best performance) under visible light, log decrease of E. coli was 7.16 (removal efficiency was 100%) in 120 min, log decrease of S. aureus was 7.23 (removal efficiency was 100%) in 160 min, C/C0 of tetracycline was 0.06 in 180 min, C/C0 of acetaminophen was 0.17 in 180 min. This work could provide a promising candidate in the actual contaminated natural waters for cleaning multiple pollutants.

Development of composite adsorbents having high removal efficiency as well as convenient separation and regeneration properties is important for water treatment. This study synthesized magnetic activated carbon (MAC) from activated carbon (AC) by impregnation of iron oxides in the porous structure. Systematic characterization results demonstrated that MAC contained Fe3O4 and had size of ∼0.5 mm, saturated magnetization of 8.99 emu/g, specific surface area of 473 m2/g, and pore volume of 0.54 cm3/g. Batch adsorption experiments were conducted using 4-chlorophenol (4-CP) as the probing pollutant and both MAC and AC as the adsorbents. Results showed that the adsorption rate followed intra-particle diffusion model and pseudo-second-order rate model, and that the adsorption equilibrium can fit to Freundlich isotherm equation. After adsorption, H2O2 was added to initiate a heterogeneous Fenton reaction catalyzed by impregnated Fe3O4 on MAC. More than 90% of aqueous 4-CP was degraded within 1 h, with optimal conditions of pH 3.0, 20 mM H2O2, and Fe2+/Fe3+ ratio of 3:1. After 5 cycles of adsorption-regeneration, the MAC composite remained >80% removal efficiency for 4-CP, indicating that it could be reused as an effective and retrievable adsorbent in water treatment of organic pollutants.

Introduction Oxaliplatin (L-OHP) is a well-known third-generation platinum anticancer drug with severe systemic- and neuro-toxicity. The main objective of the current research was to develop a targeted long-circulating thermosensitive smart-release liposome (LCTL) system for better therapeutic efficacy and less toxicity. Methods The reverse-phase evaporation method (REV) was used to prepare L-OHP loaded LCTL (L-OHP/LCTL). The physical characteristics were evaluated including encapsulation efficiency (EE), size, zeta potential and stability. The release behavior, cytotoxicity and in vivo evaluation were also carried out. Results EE of LCTL was around 25% with a uniform size distribution, and LCTL achieved almost complete release at 42°C while it was only 10% at 37°C. Moreover, the LCTL showed significantly higher cytotoxicity at 42°C than that at 37°C. The in vivo results indicated LCTL could target tumors and enhance retention for more than 24 h, thereby enhancing anti-tumor efficacy on 4T1-bearing mice. Discussion These results indicated that LCTL not only possessed a prolonged circulation time but it also enhanced accumulation and achieved selective release at the tumor sites. Conclusively, LCTL could serve as a promising carrier for oxaliplatin delivery to treat solid tumors.

Powderous transition metal-based electrocatalysts have been widely studied for hydrogen evolution reaction (HER), but their practical application still remains challenging due to the tedious slurry-based electrode assembly process and unavoidable stability decay at high current density. Herein, a self-supported H2 evolution cathode based on pomelo peel (PP)-derived porous carbon (PPDC) membrane with embedded Co nanoparticles (Co@PPDC) is developed by direct carbonization of Co-adsorbed PP (Co2+-PP). Benefiting from the large surface areas, the abundant open and interconnected pores, the highly graphitic PPDC membrane, and the highly dispersed Co nanoparticles, the self-supported Co@PPDC electrode demonstrates superior electrocatalytic performance for HER in 1.0 M KOH solution, with overpotentials of 154 and 264 mV at current densities of 10 and 100 mA cm−2, respectively. In addition, owing to the good structural integrity, the Co@PPDC electrode exhibits an excellent cycling stability for 2000 cycles and a stable current density of ∼100 mA cm−2 at a constant overpotential of 265 mV over 12 h with a nearly 100% Faradaic efficiency (FE) and H2 production rate of 1.56 mmol h−1. Therefore, this work provides a versatile and effective strategy for development of high-performance self-supported electrodes at low cost for large-scale H2 production from electrochemical water splitting.

In this paper, a CNN-based learning scheme is proposed to enable a quadrotor unmanned aerial vehicle (UAV) to avoid obstacles automatically in unknown and unstructured environments. In order to reduce the decision delay and to improve the robustness for the UAV, a two-stage end-to-end obstacle avoidance architecture is designed, where a forward-facing monocular camera is used only. In the first stage, a convolutional neural network (CNN)-based model is adopted as the prediction mechanism. Utilizing three effective operations, namely depthwise convolution, group convolution and channel split, the model predicts the steering angle and the collision probability simultaneously. In the second stage, the control mechanism maps the steering angle to an instruction that changes the yaw angle of the UAV. Consequently, when the UAV encounters an obstacle, it can avoid collision by steering automatically. Meanwhile, the collision probability is mapped as a forward speed to maintain the flight or stop going forward. The presented automatic obstacle avoidance scheme of quadrotor UAV is verified by several indoor/outdoor tests, where the feasibility and efficacy have been demonstrated clearly. The novelties of the method lie in its low sensor requirement, light-weight network structure, strong learning ability and environmental adaptability.

This study investigated the distribution of 209 polychlorinated biphenyl (PCB) congeners in sediment samples collected from seven sites in the Raritan River estuary (NJ, USA) using gas chromatography/tandem quadrupole mass spectrometry (GC/MS/MS). Results showed that concentrations of 209 PCB congeners ranged up to 20 ng/g, that total PCB concentrations by homolog groups varied from 9.0 to 327 ng/g, and that total PCB mass ranged from 101.0 to 1,476.3 ng with the order of sites 6 > 7 > 3 > 4 > 2 > 1 > 5. Furthermore, higher PCB concentrations were detected for homolog groups including hexa- and hepta-PCBs. More PCBs were associated with sediments at downstream sites possibly due to accumulation and deposition in the estuary, as supported by strong correlations of PCB concentrations between different sampling sites (R2 ≤ 0.96). Comparisons with prior reports showed that all total PCB concentrations were below those reported by the Contamination Assessment and Reduction Project (CARP) and Delaware River Basin Commission (DRBC), except for sites 6 and 7. Sites 3 and 4 had PCB concentrations exceeding the 22.7 ng/g limit of the Sediment Quality Guidelines (SQGs), whereas sites 6 and 7 exceeded the 180 ng/g limit, indicating potential health hazards from PCBs.

A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration.

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This article presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories.

In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot's learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods.

Mobile robots can complete a task in cooperation with a human partner. In this letter, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and electromyography (EMG) signals sensor to control the mobile robot. A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform. This shared control method enables the human partner to tele-control the mobile robot's motion and achieve obstacles avoidance synchronously. Compared with conventional shared control methods, this proposed one provides a force feedback based on muscle activation and drives the human partners to update their control intention with predictability. Experimental results demonstrate the enhanced performance of the mobile robots in comparison with the methods in the literature.

We develop a rapid and ulsensitive colorimetric method for alkaline phosphatase (ALP) activity based on dark-field microscope. In the presence of ALP, the ALP will hydrolyze L-ascorbic acid 2-phosp...

A dual-function fluorescent probe (Probe 1) was developed for discriminative detection of Hg2+ and N2 H4 . Probe 1 could discriminatively detect Hg2+ and N2 H4 through two different reaction sites, with the mechanism for Probe 1 for Hg2+ depending on a desulfurization reaction and for N2 H4 depending on the Schiff-base reaction. N2 H4 had minimal effect on Hg2+ detection in dimethyl sulfoxide (DMSO)/H2 O solution, but Hg2+ could interfere with N2 H4 detection in DMSO/buffer solution. Different concentrations of Hg2+ and N2 H4 resulted in different blue shades of Probe 1 test strips, and the shade of blue was different with the same concentration of Hg2+ or N2 H4 , as observed under ultraviolet light at 365 nm wavelength.

In order to machine complex box and shell structures, the cutter with large ratio of length to diameter is usually used, whereas the low rigidity of the cutter hinders the increase of the stable machining parameters. This article develops one kind of damping cutter to improve the stable processing condition in this milling operation. The influences of modal characteristic of the cutter on chatter stability are investigated, where it is proven that increasing the dynamic stiffness can efficiently extend the stable zone. On this basis, two damping cutters are designed by inlaying the strips and damping core into the toolholder body. The influences of geometrical parameters and material property of the damping cutters on modal characteristics are theoretically analyzed, and the optimal geometrical dimensions are determined using the finite element simulation. Then, the two damping cutters are fabricated. After modal tests and milling machining experiments, the measured results validate the effectiveness of the developed damping cutters in terms of improvement of the stability frontier.

This paper presents a thorough evaluation of twenty-one state-of-the-art widely-used crop segmentation algorithms, motived by their significance in vision tasks for further analysis. An ideal crop segmentation algorithm can effectively extract crop information, thus providing an important precondition for the application of intelligent agriculture analytics. In order to enable researchers in this field to fully understand various crop segmentation methods, this paper proposes a new classification strategy of object segmentation by dividing the algorithms into pixel-based and region-based approaches at first, and then systematically evaluating various crop segmentation methods with a unified data benchmark and four common metrics. A new dataset which incorporates crop variety, environment condition and observation distance into consideration is constructed for demonstrating the experiments and comparisons. The effectiveness and robustness of these algorithms were evaluated by three sets of comparative experiments. Based on the quantitative results, we summarize the advantages and disadvantages of the evaluated algorithms from the segmentation performances with four metric indicators. Furthermore, the discussion and evaluation results will provide great support for precision agriculture analysis.

Benefitting from the versatility and biocompatibility of food sourced materials, the construction of hybrid structures via their molecular interplay generates novel platforms with unexpected properties. In this work, two hydrophobic biomolecules were co-assembled into water-soluble amphiphiles at pH 7 by a facile pH-cycle approach. Wheat gluten proteins (WPs) and shellac were dissolved together at pH 12 followed by a one-step adjustment to pH 7, yielding nanospheres with a protein recovery over 90%. Structural characterization evidenced that shellac stiffened the protein backbones that were resistant against thermodynamically-favored folding. The reactions were proven to be initiated between the protein secondary structures and shellac, forming a relatively unfolded three-dimensional conformation during the acid-induced co-assembly. Silybin was employed as a hydrophobic bioactive model and was entrapped following the same procedure as the hybrid assembly, with a maximum loading of 102 mg g−1 hybrid. The bioavailability of silybin loaded in shellac–WP co-assemblies was improved as assessed by cell proliferation assays, due to the improved dispersity and cell internalization of the co-assemblies. The preparation method based on a simple pH manipulation may be applied to encapsulate various hydrophobic bioactive compounds, apart from the silybin explored in this study.

In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

The paper investigates the robustness of pullback attractors and pullback exponential attractors of the non-autonomous Kirchhoff wave models with strong nonlinear damping: $$u_{tt}- (1+\epsilon \Vert \nabla u\Vert ^2)\Delta u-\sigma (\Vert \nabla u\Vert ^2) \Delta u_t+f(u)=g(x,t)$$ , where $$\epsilon \in [0,1]$$ is an extensibility parameter. It shows that when the growth exponent p of the nonlinearity f(u) is up to the supercritical range: $$1\le p

Poly (N-isopropylacrylamide) (PNIPAM) is an intelligent hydrogel which exhibits volume transition in response to temperature change. This work reports the PNIPAM/AM-QDs hybrid hydrogels, in which N-isopropylacrylamide, acrylamide as monomers and CdTe/CdS quantum dots (QDs) as filler, are prepared by glow discharge electrolysis plasma (GDEP). The hydrogels are investigated by transmission electron microscopy (TEM), X-ray diffraction (XRD), FTIR spectra, differential scanning calorimetry (DSC) and PL spectra, as well as the swelling rate, temperature sensitivity and fluorescence reversibility. The results show that the PNIPAM hydrogels and QDs are cross-linked by coordination bond, and the QDs are well dispersed and combined firmly in the networks of the hydrogels. The phase transition temperature of PNIPAM/AM-QDs hybrid hydrogels is 32.5°C. The fluorescence intensity of hydrogels is 1490 a.u. and 20 a.u. at 20 and 40°C, respectively. Furthermore, the luminescence intensity of hydrogels is almost not weakened after five heating-cooling cycles, which should be used as a temperature-controlled light switch.

In this work, we design a three-dimensional (3D) strongly-coupled polyaniline/crosslinked carbon nanosheets (PANI/CCNs) composite using a facile in-situ chemical polymerization method. The PANI is uniformly and conformally covered on the thin graphene-like biomass-derived CCNs. The interconnected CCNs with high surface area and electrical conductivity can expose more active sites of PANI and enhance the interfacial electron transfer, which contributes to the improvement of specific capacitance and rate performance. The sheets-like PANI can introduce pseudocapacitance through the surface redox reactions on the surface. As a result, the PANI/CCNs composite electrode can reach high specific capacitance of 444 and 340 F g−1 at current densities of 0.1 and 5 A g−1, respectively. The assembled PANI/CCNs//PANI/CCNs symmetric supercapacitor can reach a high specific capacitance of 72.0 F g−1 at a current density of 0.1 A g−1. Moreover, the PANI/CCNs//PANI/CCNs symmetric cell exhibits a high energy density of 28.9 Wh kg−1 at a power density of 85.1 W kg−1, which is competitive in the aqueous PANI-based symmetric supercapacitors.

With the phasing out of traditional polybrominated diphenyl ethers (PBDEs), significant volumes of alternative brominated flame retardants (aBFRs) are being used and released into the environment compartment, especially in coastal regions. The levels and distribution of PBDEs, aBFRs, and dechlorane plus (DPs) were investigated in the surface sediments of the Yellow Sea (YS) and East China Sea (ECS) to examine the distribution and sources of these hydrophobic contaminants. The level and distribution of pollutants in the sediments of YS and ECS show obvious regional differences. As a major replacement for decabromodiphenyl ether (BDE 209), decabromodiphenyl ethane (DBDPE) was the dominant compound observed in the surface sediments, with a concentration one order of magnitude higher than that of BDE 209. High concentrations were found in the depositional zones of the YS, indicating that these contaminants may originate from land-based pollution sources (likely from the Laizhou Bay manufacturing base) near the Bohai Sea. The pollutants can be carried by the coastal current together with the sediment from the Yellow River, transported through the Bohai Strait and deposited in the mud zone of Northern and Southern YS. Low levels of halogenated flame retardants (HFRs) were found in the estuary of the Yangtze River and ECS, indicating that Yangtze River contributes less HFRs to the region. Riverine discharge, atmospheric deposition, surface runoff, ocean current system, and mud area deposition effects may be significant factors influencing the distributions of HFRs.

This study aims to investigate how multiple parameters affect the two-phase flow in compressed gas diffusion layer (GDL). A stochastic model is adopted to reconstruct the GDL microstructures. Solid mechanics simulations on the reconstructed GDL microstructures are performed, based on the finite element method (FEM). Various pore morphologies and distributions of compressed GDLs are observed. Two-phase flow in GDL is simulated using a volume of fluid (VOF) model. Corner droplet (on the GDL surface) and water flow (emerging from GDL bottom) are considered. It is found that two-phase flow in the GDL is highly influenced by compression, fiber diameter, porosity, and GDL thickness. The results indicate that a larger fiber diameter or higher porosity contributes to the water transport due to larger average pore size. Furthermore, water removal from a thicker GDL is more difficult, whereas water transport in the lower part of a compressed thick GDL is easy.

Research on the anomalous Hall effect (AHE) has been lasting for a century to make clear the underlying physical mechanism. Generally, the AHE appears in magnetic materials, in which the extrinsic process related to scattering effects and intrinsic contribution connected with Berry curvature are crucial. Recently, AHE has been counterintuitively observed in nonmagnetic topological materials and attributed to the existence of Weyl points. However, the Weyl point scenario would lead to unsaturated AHE even in large magnetic fields and contradicts the saturation of AHE in several tesla (T) in experiments. In this work, we investigate the Hall effect of $\mathrm{Zr}{\mathrm{Te}}_{5}$ and $\mathrm{Hf}{\mathrm{Te}}_{5}$ thin flakes in static ultrahigh magnetic fields up to 33 T. We find the AHE saturates to $55\phantom{\rule{0.16em}{0ex}}(70)\phantom{\rule{0.16em}{0ex}}{\mathrm{\ensuremath{\Omega}}}^{\text{}1}\phantom{\rule{0.16em}{0ex}}\mathrm{c}{\mathrm{m}}^{\text{}1}$ for $\mathrm{Zr}{\mathrm{Te}}_{5}$ ($\mathrm{Hf}{\mathrm{Te}}_{5}$) thin flakes above $\ensuremath{\sim}10\phantom{\rule{0.16em}{0ex}}\mathrm{T}$. Combining detailed magnetotransport experiments and Berry curvature calculations, we clarify that the splitting of massive Dirac bands without Weyl points can be responsible for AHE in nonmagnetic topological materials $\mathrm{Zr}{\mathrm{Te}}_{5}$ and $\mathrm{Hf}{\mathrm{Te}}_{5}$ thin flakes. This model can identify our thin flake samples to be weak topological insulators and serve as a tool to probe the band structure topology in topological materials.

In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.

Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from cooperation to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. In this article, we demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours.

Catechins are major antioxidant constituents of green tea, which have attracted much attention as human health promoters. To identify catechins, we built a chrono-colorimetric sensor array for iden...

The weight losses of cement-based material samples were often used to characterize the content of their hydration products and explain the strength changes of cement-based materials. However, the quantitative relationship has not been studied between the weight loss of hydration products and strength of cement-based materials. This paper studied the relationship between the strengths of cemented tailings backfills (CTBs) and the weight losses of its hydration products. The CTB samples have been done, especially on different binders, cement-tailing ratios, mass concentrations, and different curing ages. The uniaxial compressive strength (UCS) experiments were used to test the mechanical strength of samples; X-ray diffraction (XRD) experiments were used to test the crystalline phases of hydration products by samples; thermal analyses (thermogravimetric and differential thermogravimetric (TG/DTG)) experiments were used to test the weight losses of hydration products. By means of regression analysis, the relationship model was established between the UCS of CTB and the weight losses of hydration products at different concentrations. The results show that there is a strong linear correlation between the UCS and the weight loss of the hydration product calcium silicate hydrate (C-S-H) for the CTB made of glue powder, while the UCS is related to the weight losses of C-S-H and Ca(OH)2 for the CTB made of ordinary Portland cement. The results acquired by this paper provide a scientific basis for studying hydration products by thermal analyses and explaining the strength changes of cement-based materials.

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named \textit{prior differential privacy (PDP)}, is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

Nanosized Li4Ti5O12 with both a Li2TiO3 coating and C-N-Br co-doping (CLLTO) was successfully synthesized via a facile reverse microemulsion method in one step using hexadecyl trimethyl ammonium bromide as a surface control agent and as a carbon, nitrogen, and bromine source. A uniform Li2TiO3 layer was formed on the surface and strongly adhered to the host material Li4Ti5O12 (LTO), which played an important role in improving the cyclic stability of CLLTO. The thin and stable Li2TiO3 layer has the same cubic structure as LTO, which provides many three-dimensional channels for ion transport. C, N, and Br co-doping in CLLTO promoted the transition of Ti4+ to Ti3+ in Li4Ti5O12, which could improve the capacity and facilitate the Li+ ion and electron transfer at the interface. The conductive behavior induced by co-doping was estimated by UV-vis diffuse reflectance spectra and further supported by theoretical calculations. The electrical conductivity of both p-type and n-type LTO can be well improved by co-doping C, N, and Br. This improvement may be due to the band gap reduction and the increased n-type electronic modification of the entire LTO. Owing to the synergistic effect of coating, co-doping, and nanosizing at one time, the CLLTO exhibits a high discharge capacity of 177.3-153.9 mA h g-1 at the working rate of 0.1C-20C, with a capacity retention of 86%. The stable cycling of CLLTO is also obtained after 500 cycles at 20C, with a capacity retention of 95.5% (approximately 8 times higher than that of pure LTO) and almost 100% Coulombic efficiency. With high capacity, excellent rate performance, and good cycling stability, CLLTO can be applied in high-power lithium-ion batteries.

In this paper, we studied the femtosecond laser induced periodic surface structures (fs-LIPSS) formation on LiNbO3:Fe crystal with the assistance of elevated temperature. Uniform deep-subwavelength fs-LIPSS with the period of 190 ± 10 nm was successfully fabricated on crystal surface as the sample temperature was at 1000 °C. And the measurement of Transmission Electron Microscopy showed that fs-laser treated samples remained crystalline. We believed that thermal excitation plays a critical role in the formation of fs-LIPSS. The initial carrier density of LiNbO3:Fe are effectively increased during this process, and high concentration surface carriers are effectively excited by femtosecond laser irradiation, which make great contribution to formation of fs-LIPSS. Moreover, we also found that the doped Fe2+ ions are conducive to the fs-LIPSS formation. The iron doped LiNbO3 crystal with uniform surface structure has great application prospects in related fields, such as on-chip photonic-integrated platforms, nanophotonics, and nanostructures for micro-fluidics.

Iron oxide (Fe3O4)-based theranostic agents show great promise toward advancing personalized nanomedicine due to their extraordinary physicochemical and biological properties. This original review aims to highlight and summarize the most recent progress of Fe3O4, starting with the synthesis and surface modification of superparamagnetic iron oxide nanoparticles (NPs). Desirable features of Fe3O4 are the initial focus, followed by a review of their theranostic applications including sensitive MRI, multimodal imaging and MRI-guided cancer therapy. Finally, potential nanotoxicity, regulatory and clinical translation barriers are addressed to outline future perspectives on Fe3O4 NP-based multifunctional theranostic platforms. It is strongly believed that in the near future, Fe3O4 NPs will open new routes with regard to cancer management.

Bio-foaming during the anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW) is a major cause of unstable reactor operations, especially under high organic loading rates (OLRs). Here, we used two 40-liter anaerobic reactors to deal with the actual organic fraction of municipal solid waste at OLRs of 5 and 7.5 kgVS/m3/d and tried to inhibit bio-foam generation by dosing with Fe0. One of reactors had 400 g of Fe0 added before operation and additional 200 g of Fe0 was added when the operating conditions changed; the other one was the control reactor with no Fe0 dosing. The results show that bio-foam was generated in the control reactor at an organic shock loading of 10 kgVS/m3/d and re-startup at 5 kgVS/m3/d, while the reactor containing Fe0 operated normally. Due to the surfactant attributes of volatile fatty acids (VFAs), the probability of bio-foam generation was positively correlated with the effluent’s degree of surface activity (SA) induced by VFAs, which is a parameter representing the foaming potential. Because VFA biodegradation was enhanced in the reactor with added Fe0, at the position of Fe0 accumulation, the SA was decreased by 49.7–59.2%. Furthermore, the effect of Fe0 dosing on inhibiting bio-foam was evaluated by comparison with traditional commercial anti-foam agents. The results suggest possible reasons for bio-foam generation in OFMSW AD and suggest the reasonable usage of Fe0 to inhibit bio-foam formation.

An L-stable method over time intervals for differential-algebraic equations (DAEs) of multibody system dynamics is presented in this paper. The solution format is established based on equidistant nodes and nonequidistant nodes such as Chebyshev nodes and Legendre nodes. Based on Ehle’s theorem and conjecture, the unknown matrix and vector in the L-stable solution formula are obtained by comparison with Pade approximation. Newton iteration method is used during the solution process. Taking the planar two-link manipulator system as an example, the results of L-stable method presented are compared for different number of nodes in the time interval and the step size in the simulation, and also compared with the classic Runge-Kutta method, A-stable method, Radau IA, Radau IIA, and Lobatto IIIC methods. The results show that the method has the advantages of good stability and high precision and is suitable for multibody system dynamics simulation under long-term conditions.