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Abstract Traditional magnetic sub-Kelvin cooling relies on the nearly free local moments in hydrate paramagnetic salts, whose utility is hampered by the dilute magnetic ions and low thermal conductivity. Here we propose to use instead fractional excitations inherent to quantum spin liquids (QSLs) as an alternative, which are sensitive to external fields and can induce a very distinctive magnetocaloric effect. With state-of-the-art tensor-network approach, we compute low-temperature properties of Kitaev honeycomb model. For the ferromagnetic case, strong demagnetization cooling effect is observed due to the nearly free Z 2 vortices via spin fractionalization, described by a paramagnetic equation of state with a renormalized Curie constant. For the antiferromagnetic Kitaev case, we uncover an intermediate-field gapless QSL phase with very large spin entropy, possibly due to the emergence of spinon Fermi surface and gauge field. Potential realization of topological excitation magnetocalorics in Kitaev materials is also discussed, which may offer a promising pathway to circumvent existing limitations in the paramagnetic hydrates.

Abstract Phase transitions in condensed matter are a source of exotic emergent properties. We study the fully frustrated bilayer Heisenberg antiferromagnet to demonstrate that an applied magnetic field creates a previously unknown emergent criticality. The quantum phase diagram contains four states with distinctly different symmetries, all but one pair separated by first-order transitions. We show by quantum Monte Carlo simulations that the thermal phase diagram is dominated by a wall of discontinuities extending between the dimer-triplet phases and the singlet-containing phases. This wall is terminated at finite temperatures by a critical line, which becomes multicritical where the Berezinskii-Kosterlitz-Thouless (BKT) transition of the dimer-triplet antiferromagnet and the thermal Ising transition of the singlet-triplet crystal phase also terminate. The combination of merging symmetries leads to a 4-state Potts universality not contained in the microscopic Hamiltonian, which we interpret within the Ashkin-Teller model. Our results represent a systematic step in understanding emergent phenomena in quantum magnetic materials, including the “Shastry-Sutherland compound” SrCu2(BO3)2.

Abstract A rotary motor combined with fibrous string demonstrates excellent performance because it is powerful, lightweight, and prone to large strokes; however, the stiffness range and force-generating capability of twisted string transmission systems are limited. Here, we present a variable stiffness artificial muscle generated by impregnating shear stiffening gels (STGs) into a twisted string actuator (TSA). A high twisting speed produces a large impact force and causes shear stiffening of the STG, thereby improving the elasticity, stiffness, force capacity, and response time of the TSA. We show that at a twisting speed of 4186 rpm, the elasticity of an STG-TSA reached 30.92 N/mm, whereas at a low twisting speed of 200 rpm, it was only 10.51 N/mm. In addition, the STG-TSA exhibited a more prominent shear stiffening effect under a high stiffness load. Our work provides a promising approach for artificial muscles to coactivate with human muscles to effectively compensate for motion.

Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existing prediction models mainly focus on single-point time series displacement prediction and do not consider the spatial relationship between monitoring points. To fully take into account the temporal and spatial correlation of displacement monitoring data, this paper proposes two models based on the graph convolutional network (GCN) to perform spatiotemporal prediction of the displacement of the Tangjiao 1# landslide. Firstly, the landslide monitoring system is transformed into a fully connected graph (FCG) to depict the spatial relationship among monitoring points on the landslide. Secondly, a temporal graph convolutional network (T-GCN) model and an attention temporal graph convolutional network (A3T-GCN) model of landslide displacement based on the GCN and GRU models are established respectively. Thirdly, the two models are used to predict the displacement of the Tangjiao 1# landslide. The results show that the established spatiotemporal prediction models are effective in predicting the displacement of the Tangjiao 1# landslide, and the proposed A3T-GCN model achieves the highest prediction accuracy. Our conclusion validates the effectiveness of the attention mechanism in predicting landslide displacement.

The dynamic spreading mechanism of liquid on a specific surface is vital for understanding interface wetting and antifouling. Whereas, how to control the spreading process and accelerate the spreading speed is a major challenge. The rock‐climbing fish is characterized by its alepidote feature that lives in stream habitats dominated by strong currents. The mucus on its body surface plays a vital role in its adherence and maintenance of antifouling and antibacterial properties. However, the rapid, uniform, and efficient spreading mechanism of mucus on the fish body surface remains largely unknown. Herein, it is revealed that the surface of the rock‐climbing fish is overlaid fully by the microhexagonal texture structure. This hexagonal structure shows a superspreading effect on liquid diffusion, resulting from testing with bionic microfabrication inspired by the rock‐climbing fish. It is demonstrated that the microhexagonal‐textured surface can enhance liquid spreading quickly and evenly on the surface by regulating the moving contact line of the liquid. This kind of superspreading mechanism has great potential applications in the antifouling, electroencephalogram electrode interfaces, flexible skin sensors, and interfacial lubrication of underwater surfaces.

Small and medium-sized enterprises (SMEs) are the mainstay of China’s social and economic development, and the implementation of performance appraisal systems in SMEs is an important way for enterprises to promote the growth of their employees, and each enterprise has a performance appraisal system that matches the characteristics of its own corporate culture. However, at present, objective factors such as the scale and operation of SMEs in China lead to the fact that employee performance appraisal cannot play a good role, and the decision-making level of SMEs understand the implementation of the performance appraisal for subordinate employees in a formal way, and cannot develop a performance appraisal system that is in line with the enterprise itself by combining with its development status. A reasonable performance appraisal system can improve the corporate cohesion and industry competitiveness of the company’s employees. The imperfection of the current corporate employee performance mechanism is not addressed, and the planning of objective employees is not uniform and reasonable enough. This paper constructs a model by analyzing many factors influencing the implementation of performance appraisal systems in SMEs and combining them with incomplete information race theory. Ultimately, we explore the strategies and methods of the performance appraisal for SMEs, cultivate talents for SMEs, enhance the sense of belonging and creativity of enterprise employees, and provide a strong guarantee for enterprise development.

Abstract Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids.

BackgroundCT053PTSA is a novel tyrosine kinase inhibitor that targets MET, AXL, VEGFR2, FLT3 and MERTK. Here, we present preclinical data about CT053PTSA, and we conducted the first-in-human (FIH) study to evaluate the use of CT053PTSA in adult patients with pretreated advanced solid tumors.MethodsThe selectivity and antitumor activity of CT053PTSA were assessed in cell lines in vitro through kinase and cellular screening panels and in cell line-derived tumor xenograft (CDX) and patient-derived xenograft (PDX) models in vivo. The FIH, phase I, single-center, single-arm, dose escalation (3 + 3 design) study was conducted, patients received at least one dose of CT053PTSA (15 mg QD, 30 mg QD, 60 mg QD, 100 mg QD, and 150 mg QD). The primary objectives were to assess safety and tolerability, to determine the maximum tolerated dose (MTD), dose-limiting toxicity (DLT), and the recommended dose of CT053PTSA for further study. Secondary objectives included pharmacokinetics, antitumor activity.ResultsCT053 (free-base form of CT053PTSA) inhibited MET, AXL, VEGFR2, FLT3 and MERTK phosphorylation and suppressed tumor cell angiogenesis by blocking VEGF and HGF, respectively, in vitro. Moreover, cell lines with high MET expression exhibited strong sensitivity to CT053, and CT053 blocked the MET and AXL signaling pathways. In an in vivo study, CT053 significantly inhibited tumor growth in CDX and PDX models. Twenty eligible patients were enrolled in the FIH phase I trial. The most common treatment-related adverse events were transaminase elevation (65%), leukopenia (45%) and neutropenia (35%). DLTs occurred in 3 patients, 1/6 in the 100 mg group and 2/4 in the 150 mg group, so the MTD was set to 100 mg. CT053PTSA was rapidly absorbed after the oral administration of a single dose, and the Cmax and AUC increased proportionally as the dose increased. A total of 17 patients in this trial underwent tumor imaging evaluation, and 29.4% had stable disease.ConclusionsCT053PTSA has potent antitumor and antiangiogenic activity in preclinical models. In this FIH phase I trial, CT053PTSA was well tolerated and had a satisfactory safety profile. Further trials evaluating the clinical activity of CT053PTSA are ongoing.

The Pemo Highway is a critical transportation road to Medog County in the Tibet Plateau (TP). Since its completion in 2021, the Pemo Highway has been prone to frequent avalanches due to heavy rainfall and snowfall. Despite the lack of monitoring stations along the highway and limited research conducted in this area, remote sensing imagery provides valuable data for investigating avalanche hazards along the highway. In this paper, we first investigated the spatiotemporal characteristics of snow cover along the Pemo Highway over the past two years based on the GEE platform. Second, we integrated snow, topography, meteorology, and vegetation factors to assess avalanche susceptibility in January, February, and March 2023 along the highway using the AHP method. The results reveal that the exit of the Duoshungla Tunnel is particularly susceptible to avalanches during the winter months, specifically from January to March, with a significant risk observed in March. Approximately 3.7 km in the direction of the tunnel exit to Lager is prone to avalanche hazards during this period. The recent “1.17 avalanche” event along the Pemo Highway validates the accuracy of our analysis. The findings of this paper provide timely guidance for implementing effective avalanche prevention measures on the Pemo Highway.

Ruthenium (Ru)-based organometallic drugs have gained attention as chemotherapeutic and bioimaging agents due to their fewer side effects and excellent physical optical properties. Tuning the electronic structures of Ru complexes has been proven to increase the cytotoxicity of cancer cells and the luminescent efficiency of the analytical probes. However, the relationship between electronic structures and bioactivities is still unclear due to the potential enhancement of both electron donor and acceptor properties. Thus, we investigated the relationship between the electronic structures of Ru(II) complexes and cytotoxicity by optimizing the electron-withdrawing (complex 1), electron-neutral (complex 2), and electron-donating (complex 3) ligands through DFT calculations, bioactivities tests, and docking studies. Our results indicated that it was not sufficient to consider only either the effect of electron-withdrawing or electron-donating effects on biological activities instead of the total electronic effects. Furthermore, these complexes with electron-donating substituents (complex 3) featured unique “off-on” luminescent emission phenomena caused by the various “HOMO-LUMO” distributions when they interacted with DNA, while complex with electron-withdrawing substituent showed an “always-on” signature. These findings offer valuable insight into the development of bifunctional chemotherapeutic agents along with bioimaging ability.

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.

Biosyncretic robots, which are new nature-based robots in addition to bionic robots, that utilize biological materials to realize their core function, have been supposed to further promote the progress in robotics. Actuation as the main operation mechanism relates to the robotic overall performance. Therefore, biosyncretic robots actuated by living biological actuators have attracted increasing attention. However, innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic robots. In this work, a muscle tissue-based biosyncretic swimmer with a manta ray-inspired propelling mode has been developed. What is more, to improve the stable controllability of the biosyncretic swimmer, a dynamic control method based on circularly distributed multiple electrodes (CDME) has been proposed. In this method, the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the actuation tissue of the dynamic swimmer. Therefore, the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be eliminated. Finally, the biosyncretic robot has demonstrated stable, controllable, and effective swimming, by adjusting the electric stimulation pulse direction, amplitude, and frequency. This work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

A piezoelectric field promotes the photocatalytic activity of a photocatalyst by helping separating photo-generated charge carriers. Wurtzite phase ZnO is a typical photocatalyst with a piezoelectric property, thus self-assisted photocatalysis with ZnO based on the piezophototronic effect can be achieved. ZnO nanorods or nanowires with a clear c-axis have been well studied, while other morphologies have not been fully discussed. In this work, we prepared wurtzite phase ZnO with four different morphologies. By comparing their photocatalytic activity for degradation of Rhodamine B under the same mechanical energy source provided by ultrasound, the effect of morphology and exposed facets on photo-induced charge separation were highlighted. The ZnO nanowire photocatalyst delivered an impressive improvement in photocatalytic efficiency when ultrasound driven, suggesting that the morphology-related piezophototronic effect had a positive effect on separation of photo-generated charge carriers, and more exposed active facets benefitted the utilization of charge carriers.

Flavonoids from natural products are well-identified as potential antiviral agents in the treatment of SARS-CoV-2 (COVID-19) infection and related diseases. However, some major species of flavonoids from Chinese traditional folk medicine, such as of Artemisia argyi (A. argyi), have not been evaluated yet. Here, we choose five major flavonoids obtained from A. argyi, namely, Jaceosidin (1), Eupatilin (2), Apigenin (3), Eupafolin (4), and 5,6-Dihydroxy-7,3′,4′-trimethoxyflavone (5), compared to the well-studied Baicalein (6), as potential inhibitors analogs for COVID-19 by computational modeling strategies. The frontier molecular orbitals (FMOs), chemical reactivity descriptors, and electrostatic surface potential (ESP) were performed by density functional theory (DFT) calculations. Additionally, these flavonoids were docked on the main protease (PDB: 6LU7) of SARS-CoV-2 to evaluate the binding affinities. Computational analysis predicted that all of these compounds show a high affinity and might serve as potential inhibitors to SARS-CoV-2, among which compound (5) exhibits the least binding energy (−155.226 kcal/mol). The high binding affinity could be enhanced by increasing the electron repulsion due to the valence shell electron pair repulsion model (VSEPR). Consequently, the major flavonoids in Artemisia argyi have a significant ability to reduce the deterioration of COVID-19 in the terms of DFT calculations and molecular docking.

Bio-syncretic robots consisting of both living biological materials and non-living systems possess desirable attributes such as high energy efficiency, intrinsic safety, high sensitivity, and self-repairing capabilities. Compared with living biological materials or non-living traditional robots based on electromechanical systems, the combined system of a bio-syncretic robot holds many advantages. Therefore, developing bio-syncretic robots has been a topic of great interest, and significant progress has been achieved in this area over the past decade. This review systematically summarizes the development of bio-syncretic robots. First, potential trends in the development of bio-syncretic robots are discussed. Next, the current performance of bio-syncretic robots, including simple movement and controllability of velocity and direction, is reviewed. The living biological materials and non-living materials that are used in bio-syncretic robots, and the corresponding fabrication methods, are then discussed. In addition, recently developed control methods for bio-syncretic robots, including physical and chemical control methods, are described. Finally, challenges in the development of bio-syncretic robots are discussed from multiple viewpoints, including sensing and intelligence, living and non-living materials, control approaches, and information technology. Keywords: Bio-syncretic robot, Hybrid robot, Bio-actuator, Cardiomyocyte, Muscle cells

Unmanned aerial vehicle (UAV) plays a more and more important role in Internet of Things (IoT) for remote sensing and device interconnecting. Due to the limitation of computing capacity and energy, the UAV cannot handle complex tasks. Recently, computation offloading provides a promising way for the UAV to handle complex tasks by deep reinforcement learning (DRL)-based methods. However, existing DRL-based computation offloading methods merely protect usage pattern privacy and location privacy. In this paper, we consider a new privacy issue in UAV-assisted IoT, namely computation offloading preference leakage, which lacks through study. To cope with this issue, we propose a novel privacy-preserving online computation offloading method for UAV-assisted IoT. Our method integrates the differential privacy mechanism into deep reinforcement learning (DRL), which can protect UAV’s offloading preference. We provide the formal analysis on security and utility loss of our method. Extensive real-world experiments are conducted. Results demonstrate that, compared with baseline methods, our method can learn cost-efficient computation offloading policy without preference leakage and a priori knowledge of the wireless channel model.

The discrete element method (DEM) can be effectively used in investigations of the deformations and failures of jointed rock slopes. However, when to appropriately terminate the DEM iterative process is not clear. Recently, a displacement-based discrete element modeling method for jointed rock slopes was proposed to determine when the DEM iterative process is terminated, and it considers displacements that come from rock blocks located near the potential sliding surface that needs to be determined before the DEM modeling. In this paper, an energy-based discrete element modeling method combined with time-series analysis is proposed to investigate the deformations and failures of jointed rock slopes. The proposed method defines an energy-based criterion to determine when to terminate the DEM iterative process in analyzing the deformations and failures of jointed rock slopes. The novelty of the proposed energy-based method is that, it is more applicable than the displacement-based method because it does not need to determine the position of the potential sliding surface before DEM modeling. The proposed energy-based method is a generalized form of the displacement-based discrete element modeling method, and the proposed method considers not only the displacement of each block but also the weight of each block. Moreover, the computational cost of the proposed method is approximately the same as that of the displacement-based discrete element modeling method. To validate that the proposed energy-based method is effective, the proposed method is used to analyze a simple jointed rock slope; the result is compared to that achieved by using the displacement-based method, and the comparative results are basically consistent. The proposed energy-based method can be commonly used to analyze the deformations and failures of general rock slopes where it is difficult to determine the obvious potential sliding surface.

Mesh quality is a critical issue in numerical computing because it directly impacts both computational efficiency and accuracy. Tetrahedral meshes are widely used in various engineering and science applications. However, in large-scale and complicated application scenarios, there are a large number of tetrahedrons, and in this case, the improvement of mesh quality is computationally expensive. Laplacian mesh smoothing is a simple mesh optimization method that improves mesh quality by changing the locations of nodes. In this paper, by exploiting the parallelism features of the modern graphics processing unit (GPU), we specifically designed a parallel adaptive Laplacian smoothing algorithm for improving the quality of large-scale tetrahedral meshes. In the proposed adaptive algorithm, we defined the aspect ratio as a metric to judge the mesh quality after each iteration to ensure that every smoothing improves the mesh quality. The adaptive algorithm avoids the shortcoming of the ordinary Laplacian algorithm to create potential invalid elements in the concave area. We conducted 5 groups of comparative experimental tests to evaluate the performance of the proposed parallel algorithm. The results demonstrated that the proposed adaptive algorithm is up to 23 times faster than the serial algorithms; and the accuracy of the tetrahedral mesh is satisfactorily improved after adaptive Laplacian mesh smoothing. Compared with the ordinary Laplacian algorithm, the proposed adaptive Laplacian algorithm is more applicable, and can effectively deal with those tetrahedrons with extremely poor quality. This indicates that the proposed parallel algorithm can be applied to improve the mesh quality in large-scale and complicated application scenarios.

Trusted hardware sharing (THS) system can provide multiple trusted execution environments (TEE) via sharing the trusted hardware (e.g., sharing trusted platform module via virtualization) for stand-alone and isolation scenarios. However, the trusted function requests (TFRs) sent to the trusted hardware are emitted by multiple TEEs, which have to be processed by THS. Since different applications in different TEEs have different security requirements, the data in TFRs need to be protected from being leaked or modified in an unauthorized manner. To address this issue, we present a secure scheme for THS systems based on an information flow model that protects the sensitive data in TFRs. Each TFR is assigned a security level according to their owner, and processed in isolated environments with different security levels. We implement the prototype and conduct the experiments in both shared memory and isolated environments. The results indicate that the introduction of security mechanisms can lead to more time consumption on processing TFRs with the increase in the dimension of security levels. However, this degradation in performance is still acceptable and can be mitigated in the real world, because intensive TFR requests are not present as they are in the experimental environment.

Physical properties of two-dimensional materials, such as graphene, black phosphorus, molybdenum disulfide (MoS2) and tungsten disulfide, exhibit significant dependence on their lattice orientations, especially for zigzag and armchair lattice orientations. Understanding of the atomic probe motion on surfaces with different orientations helps in the study of anisotropic materials. Unfortunately, there is no comprehensive model that can describe the probe motion mechanism. In this paper, we report a tribological study of MoS2 in zigzag and armchair orientations. We observed a characteristic power spectrum and friction force values. To explain our results, we developed a modified, two-dimensional, stick-slip Tomlinson model that allows simulation of the probe motion on MoS2 surfaces by combining the motion in the Mo layer and S layer. Our model fits well with the experimental data and provides a theoretical basis for tribological studies of two-dimensional materials.

Recent progresses in the synthesis of large-area and stable atomically thin MoS _2 have evoked enormous interest toward the future applications of two-dimensional (2D) electronics. Although considerable theoretical researches have been conducted to examine the zigzag and armchair lattice orientations of MoS _2 , which are closely related to the physical and chemical properties of this material, experimental investigations into these two orientations are still quite rare. In this paper, we present an experimental study on nanofabrication along the zigzag and armchair orientations of monolayer MoS _2 using normal- and phase-mode AFM. After identifying the zigzag and armchair orientations, distinctly different nanofabrication forces along these two orientations are obtained, which are approximately 15.9 nN and 35.8 nN, respectively. To determine the underlying mechanism of this discrepancy, molecular dynamics simulation is performed. The simulated nanofabrication forces along the zigzag and armchair orientations are 12.16 ± 0.59 nN and 21.45 ± 0.74 nN, respectively, in good agreement with the experimentally measured ones. The results provide a better understanding of the zigzag and armchair lattice orientations of monolayer MoS _2 as well as a promising approach to closed-loop fabrication of 2D materials with desirable lattice orientations.

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.

A composable information verification approach is proposed for the secure service chain composition.Based on the secure service component，the security constraints for the component’s composibility is specified and the information flow verification algorithms is proposed.Through the experiments and simulation，it shows that the approach can decrease the verification cost effectively and improve the efficiency of the verification.

The advances in mobile technologies enable mobile devices to cooperate with each other to perform complex tasks to satisfy users’ composite service requirements. However, data with different sensitivities and heterogeneous systems with diverse security policies pose a great challenge on information flow security during the service composition across multiple mobile devices. The qualitative information flow control mechanism based on non-interference provides a solid security assurance on the propagation of customer’s private data across multiple service participants. However, strict discipline limits the service availability and may cause a high failure rate on service composition. Therefore, we propose a distributed quantitative information flow evaluation approach for service composition across multiple devices in mobile environments. The quantitative approach provides us a more precise way to evaluate the leakage and supports the customized disciplines on information flow security for the diverse requirements of different customers. Considering the limited energy feature on mobile devices, we use a distributed evaluation approach to provide a better balance on consumption on each service participant. Through the experiments and evaluations, the results indicate that our approach can improve the availability of composite service effectively while the security can be ensured.

For an unknown environment, how to make a mobile robot identify a target object and locate it autonomously, this is a very challenging question. In this paper, a novel multi-sensor fusion method based on a camera and a laser range finder (LRF) for mobile manipulations is proposed. Although a camera can acquire large quantities of information, it does not directly get the 3D data of the environment. Moreover, the camera image processing is complex and easily influenced from the change in ambient light. In view of the ability of the LRF to directly get the 3D coordinates of the environment and its stability against outside influence, and the superiority of the camera to acquire rich color information, the combination of the two sensors by making use of their advantages is employed to obtain more accurate measurement as well as to simplify information processing. To overlay the camera image with the measurement point cloud of the pitching LRF and to reconstruct the 3D image which includes pixel depth information, the homogeneous transformation model of the system is built. Then, based on the combination of the color features from the camera image and the shape features from the LRF measurement data, the autonomous identification and location of target object are achieved. In order to extract the shape features of the object, a two-step method is introduced, and a sliced point cloud algorithm is proposed for the preliminary classification of the measurement data of the LRF. The effectiveness of the proposed method is validated by the experimental testing and analysis carried out on the mobile manipulator platform. The experimental results show that by this method, the robot can not only identify target object autonomously, but also determine whether it can be operated, and acquire a proper grasping location.

The advent of atomic force microscopy (AFM) has provided a powerful tool for investigating the behaviors of single native biological molecules under physiological conditions. AFM can not only image the conformational changes of single biological molecules at work with sub-nanometer resolution, but also sense the specific interactions of individual molecular pair with piconewton force sensitivity. In the past decade, the performance of AFM has been greatly improved, which makes it widely used in biology to address diverse biomedical issues. Characterizing the behaviors of single molecules by AFM provides considerable novel insights into the underlying mechanisms guiding life activities, contributing much to cell and molecular biology. In this article, we review the recent developments of AFM studies in single-molecule assay. The related techniques involved in AFM single-molecule assay were firstly presented, and then the progress in several aspects (including molecular imaging, molecular mechanics, molecular recognition, and molecular activities on cell surface) was summarized. The challenges and future directions were also discussed.

Joint offset calibration is one of the most important methods to improve the positioning accuracy for industrial robots. This paper presents an efficient method to calibrate industrial robot joint offset. The proposed method mainly relies on a laser pointer mounted on the robot end-effector and a position sensitive device (PSD) located in the work space arbitrarily. A vision based control was employed to aid the laser beam shooting at the center of PSD surface from several initial robot postures. For each shoot, the laser beam was a line in space which can be determined by the robot joint angles and their offsets which were recorded when the laser beam was brought to the center of the PSD surface. Therefore, there are several lines in space parameterized by robot joint offsets only and all these lines were constrained by the same point, that is, the center of the PSD surface. Consequently, an optimization model was formulated and the Levenberg-Marquardt (LM) algorithm was employed to identify the joint offsets. The developed calibration system was implemented on an ABB industrial robot IRB1600 successfully. And the joint offsets of this robot can be calibrated within 6 minutes.

There remain major gaps in our knowledge regarding the detailed mechanisms by which autoantibodies mediate damage at the tissue level. We have undertaken novel strategies at the interface of engineering and clinical medicine to integrate nanoscale visual and structural data using nanorobotic atomic force microscopy with cell functional analyses to reveal previously unattainable details of autoimmune processes in real-time. Pemphigus vulgaris is a life-threatening autoimmune blistering skin condition in which there is disruption of desmosomal cell-cell adhesion structures that are associated with the presence of antibodies directed against specific epithelial proteins including Desmoglein (Dsg) 3. We demonstrate that pathogenic (blister-forming) anti-Dsg3 antibodies, distinct from non-pathogenic (non-blister forming) anti-Dsg3 antibodies, alter the structural and functional properties of keratinocytes in two sequential steps--an initial loss of cell adhesion and a later induction of apoptosis-related signaling pathways, but not full apoptotic cell death. We propose a "2-Hit" model for autoimmune disruption associated with skin-specific pathogenic autoantibodies. These data provide unprecedented details of autoimmune processes at the tissue level and offer a novel conceptual framework for understanding the action of self-reactive antibodies.

Controlling robots by natural language (NL) is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications.

Abstract High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method.

Dynamic service composition provides us with a promising approach to cooperate different sensor nodes in WSN to build complex applications based on their basic functions. Usually multiple nodes located in different regions provide data with different security levels, and it is critical to ensure the security of the information flow in the composite services. However, the energy-limited nature of sensor nodes in WSN poses a significant challenge for the centralized information flow verification with which the verification node needs to consume lots of computation and network resources. In this paper, we specify the security constraints for each service participant to secure the information flow in a service chain based in the lattice model and then present a distributed verification framework that cooperates different service participants to verify their information flow policies distributively. The evaluation results show a significant decrease on the verification cost of the single verification node, which provides a better load balance in each sensor node.

Robotic systems have been widely used in many areas to assist human beings. Mobile manipulators are among the most popular choices. This paper investigates human assistance systems using a mobile manipulator, for example, to guide the blind and to transport objects. Distinct from existing systems, an integrated dynamic model and controller of the mobile manipulator are designed. Singularity, manipulability and safety are all considered in the system design. Furthermore, two human assistance modes – Robot-Human mode and Teleoperator-Robot-Human mode – are designed and analysed. The Teleoperator-Robot-Human mode can integrate human intelligence into the assistance system to further enhance the system efficiency and safety. The experimental results implemented on a mobile manipulator demonstrated the effectiveness of the designed systems.

Distributed data storage is a key technology in the data collection in wireless sensor networks. The storage scheme based on network coding is applied to data collection in wireless sensor networks because of its high reliability and low overhead. However, it is an open problem to reduce data repair communication overhead caused by the failure of storage nodes. This paper focuses on this issue and presents a two-layer distributed data storage scheme. The lower-layer nodes store the encoded data blocks and the upper-layer nodes store the re-encoded blocks that are responsible for failure data recovery. Based on the two-layer data storage scheme, a data repair method is proposed to decrease the repair communication overhead with only sacrificing lower storage overhead. Compared with MSR, interference alignment-based scheme and group interference alignment scheme, the proposed method has lower repair communication overhead. We prove that the proposed method can reduce the repair communication overhead to o ( 1 / k ) times and it is suitable to resource-constrained distributed wireless sensor networks.

The design of bowtie antennas for carbon nanotube (CNT) photodetectors has been investigated. CNT photodetectors have shown outstanding performance by using CNT as sensing element. However, detection wavelength is much larger than the diameter of the CNT, resulting in small fill factor. Bowtie antenna can confine light into a subwavelength volume based on plasmonic resonance, thus integrating a bowtie antenna to CNT photodetectors can highly improve photoresponse of the detectors. The electric field enhancement of bowtie antennas was calculated using the device geometry by considering fabrication difficulties and photodetector structure. It is shown that the electric field intensity enhancement increased exponentially with distance reduction between the CNT photodetector to the antenna. A redshift of the peak resonance wavelength is predicted due to the increase of tip angles of the bowtie antennas. Experimental results showed that photocurrent enhancement agreed well with theoretical calculations. Bowtie antennas may find wide applications in nanoscale photonic sensors.

Sensitive to Apoptosis Gene (SAG), also known as RBX2 (RING box protein-2), is the RING component of SCF (SKP1, Cullin, and F-box protein) E3 ubiquitin ligase. Our previous studies have demonstrated that SAG is an anti-apoptotic protein and an attractive anti-cancer target. We also found recently that Sag knockout sensitized mouse embryonic stem cells (mES) to radiation and blocked mES cells to undergo endothelial differentiation. Here, we reported that compared to wild-type mES cells, the Sag(-/-) mES cells were much more sensitive to all-trans retinoic acid (RA)-induced suppression of cell proliferation and survival. While wild-type mES cells underwent differentiation upon exposure to RA, Sag(-/-) mES cells were induced to death via apoptosis instead. The cell fate change, reflected by cellular stiffness, can be detected as early as 12 hrs post RA exposure by AFM (Atomic Force Microscopy). We then extended this novel finding to RA differentiation therapy of leukemia, in which the resistance often develops, by testing our hypothesis that SAG inhibition would sensitize leukemia to RA. Indeed, we found a direct correlation between SAG overexpression and RA resistance in multiple leukemia lines. By using MLN4924, a small molecule inhibitor of NEDD8-Activating Enzyme (NAE), that inactivates SAG-SCF E3 ligase by blocking cullin neddylation, we were able to sensitize two otherwise resistant leukemia cell lines, HL-60 and KG-1 to RA. Mechanistically, RA sensitization by MLN4924 was mediated via enhanced apoptosis, likely through accumulation of pro-apoptotic proteins NOXA and c-JUN, two well-known substrates of SAG-SCF E3 ligase. Taken together, our study provides the proof-of-concept evidence for effective treatment of leukemia patients by RA-MLN4924 combination.

Traditional synchronization schemes of multimedia applications are based on temporal relationships between inter- and intrastreams. These schemes do not provide good synchronization in the presence of random delay. As a solution, this paper proposes an adaptive content-based synchronization scheme that synchronizes multimedia streams by accounting for content in addition to time. This approach to synchronization is based on the fact that having two streams sampled close in time does not always imply that these streams are close in content. The proposed scheme primary contribution is the synchronization of audio and video streams based on content. The secondary contribution is adapting the frame rate based on content decisions. Testing adaptive content-based and adaptive time-based synchronization algorithms remotely between the American University of Beirut and Michigan State University showed that the proposed method outperforms the traditional synchronization method. Objective and subjective assessment of the received video and audio quality demonstrated that the content-based scheme provides better synchronization and overall quality of multimedia streams. Although demonstrated using a video conference application, the method can be applied to any multimedia streams including nontraditional ones referred to as supermedia like control signals, haptic, and other sensory measurements. In addition, the method can be applied to synchronize more than two streams simultaneously.

In this paper, we develop a robot with the ability to secure human safety in humanrobot collisions arising in our living and working environments. The humansymbiotic service robot using compliant hybrid joints realizes human safety, absorbs impact force, and fulfills task. In unexpected or expected collisions with human, the arising impulse force is attenuated effectively by the proposed physical model. Owing to the displacement of the links, several recovery controls have been developed for the endeffector to maintain its desired task position after the collision. The force attenuation property has been verified through collision simulations and experiments in that the capability of the proposed passive arm in overcoming the limitations of active compliance control has been demonstrated.

In this paper, we develop a robot with the ability to secure human safety in human-robot collisions arising in our living and working environments. The human-symbiotic service robot using compliant hybrid joints realizes human safety, absorbs impact force, and fulfills task. In unexpected or expected collisions with human, the arising impulse force is attenuated effectively by the proposed physical model. Owing to the displacement of the links, several recovery controls have been developed for the end-effector to maintain its desired task position after the collision. The force attenuation property has been verified through collision simulations and experiments in that the capability of the proposed passive arm in overcoming the limitations of active compliance control has been demonstrated.