Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexure-Based 3-PRPS Parallel Micro-/Nanopositioning System

A six-DOF positioning mechanism was proposed in the paper. The mechanism adopts a 3-PRPS configuration and has three parallel limbs, which are distributed in parallel along the circumference of the output platform. To increase the mechanism output linearity and reduce the resolution requirement on actuators, a lever displacement reduction mechanism was exploited to satisfy the demands. The compliance parameterized model of the mechanism was established by using the compliance matrix method(CMM) and the output compliance of the mechanism was analytically calculated to be 8.479e-08m/N. The established model was verified by the finite element analysis(FEA) of ANSYS software package. The FEA result was 6.965e-08m/N. The CMM and FEA results have a model deviation of 21.74%. The output platform can bear the maximum x-axis load of 600N according to the FEA results. To verify the mechanism performance, a prototype was created and a stroke test experiment was conducted. The results show that the stroke is more than ±40μm × ±40µm × ±30µm × ±200μrad × ±200μrad × ±300μrad. The development micro-/nanopositioning platform is effective and expected to be applied to the six-DOF spatial positioning.
An Improved BP Neural Network Algorithm for Text Classification

This paper presents an improved back propagation neural network (BPNN), which highlights a novel feature selection metrics and the combination of Genetic Algorithm (GA) and BPNN. On one hand, feature selection metrics combined deflection speed and square error (DSSE) is designed to reduce dimensionality and optimize the weights of BPNN, which is capable of decreasing the training time. On the other hand, GA is introduced to optimize the hidden layer of BPNN. Experiment results demonstrate that our proposed algorithm reduces training time of 8% and improves the accuracy of prediction of 2.5%.
Silencing the CSF-1 Axis Using Nanoparticle Encapsulated siRNA Mitigates Viral and Autoimmune Myocarditis

Myocarditis is an inflammatory disease of the heart muscle most commonly caused by viral infection and often maintained by autoimmunity. Virus-induced tissue damage triggers chemokine production and, subsequently, immune cell infiltration with pro-inflammatory and pro-fibrotic cytokine production follows. In patients, the overall inflammatory burden determines the disease outcome. Following the aim to define specific molecules that drive both immunopathology and/or autoimmunity in inflammatory heart disease, here we report on increased expression of colony stimulating factor 1 (CSF-1) in patients with myocarditis. CSF-1 controls monocytes originating from hematopoietic stem cells and subsequent progenitor stages. Both, monocytes and macrophages are centrally involved in mediating tissue damage and fibrotic scarring in the heart. CSF-1 influences monocytes via engagement of CSF-1 receptor, and it is also produced by cells of the mononuclear phagocyte system themselves. Based on this, we sought to modulate the virus-triggered inflammatory response in an experimental model of Coxsackievirus B3-induced myocarditis by silencing the CSF-1 axis in myeloid cells using nanoparticle-encapsulated siRNA. siCSF-1 inverted virus-mediated immunopathology as reflected by lower troponin T levels, a reduction of accumulating myeloid cells in heart tissue and improved cardiac function. Importantly, pathogen control was maintained and the virus was efficiently cleared from heart tissue. Since viral heart disease triggers heart-directed autoimmunity, in a second approach we investigated the influence of CSF-1 upon manifestation of heart tissue inflammation during experimental autoimmune myocarditis (EAM). EAM was induced in Balb/c mice by immunization with a myocarditogenic myosin-heavy chain-derived peptide dissolved in complete Freund's adjuvant. siCSF-1 treatment initiated upon established disease inhibited monocyte infiltration into heart tissue and this suppressed cardiac injury as reflected by diminished cardiac fibrosis and improved cardiac function at later states. Mechanistically, we found that suppression of CSF-1 production arrested both differentiation and maturation of monocytes and their precursors in the bone marrow. In conclusion, during viral and autoimmune myocarditis silencing of the myeloid CSF-1 axis by nanoparticle-encapsulated siRNA is beneficial for preventing inflammatory tissue damage in the heart and preserving cardiac function without compromising innate immunity's critical defense mechanisms.
High-Order Proximity Preserved Embedding for Dynamic Networks

Network embedding, aiming to embed a network into a low dimensional vector space while preserving the inherent structural properties of the network, has attracted considerable attention. However, most existing embedding methods focus on the static network while neglecting the evolving characteristic of real-world networks. Meanwhile, most of previous methods cannot well preserve the high-order proximity, which is a critical structural property of networks. These problems motivate us to seek an effective and efficient way to preserve the high-order proximity in embedding vectors when the networks evolve over time. In this paper, we propose a novel method of Dynamic High-order Proximity preserved Embedding (DHPE). Specifically, we adopt the generalized SVD (GSVD) to preserve the high-order proximity. Then, by transforming the GSVD problem to a generalized eigenvalue problem, we propose a generalized eigen perturbation to incrementally update the results of GSVD to incorporate the changes of dynamic networks. Further, we propose an accelerated solution to the DHPE model so that it achieves a linear time complexity with respect to the number of nodes and number of changed edges in the network. Our empirical experiments on one synthetic network and several real-world networks demonstrate the effectiveness and efficiency of the proposed method.
Finite-Time Extended State Observer Design for Nonlinear Systems with Prescribed Performance

In this paper, a finite-time extended state observer design approach is proposed for a class of uncertain single-input single-output nonlinear systems. A novel extended state observer which can achieve finite time convergence is firstly constructed to estimate the total disturbance. A performance function is used and a new error transformation is provided to guarantee the transient and steady performance. By using the backstepping method, the prescribed performance controller is then developed. Simulation results are conducted to validate the effectiveness of the theoretical results.
Automatic Acquisition of Appropriate Codewords Number in BoVW Model and the Corresponding Scene Classification Performance

Scene classification has received significant attention in past few years, particularly in the scene semantic comprehension. The Bag of Visual Words (BoVW) is one of the most widely used models in current scene classification research. Acquisition of appropriate codewords number in BoVw Model is a key step in BoVw model. The classical framework to acquire the appropriate number of codewords needs to cluster the codewords by K-Means for many iterations, rendering its high computation cost. In the current study, an improved framework was proposed to acquire the appropriate number of codewords automatically, and corresponding scene classification performance with Latent Dirichlet Allocation (LDA) topic model was investigated. The experimental results demonstrate the core capability of proposed framework. The computation cost of scene semantic comprehension can be reduced greatly by means of the proposed framework, particularly in amount sequence image processing such as CT, MRI and other medical image analysis.
A Metamaterial based Multiband Frequency Reconfigurable Antenna for Wireless Applications

A metamaterial loaded multiband frequency reconfigurable antenna is reported. The antenna has a size of $0.34\lambda_{l} \times 0.34\lambda_{l} \times 0.02\lambda_{l}$, at a lower frequency of 3.95 GHz. The antenna consists of two metamaterial, namely, Circular Complimentary Split Ring Resonator (CCSRR) and Hexagonal Complimentary Split Ring Resonator (HCSRR), which independently controls the band at 3.95 and 5.72 GHz, respectively. The proposed antenna shows resonance at 3.95, 5.72 and 8.3 GHz, with fractional bandwidths of about 5.12% (3.8-4 GHz), 2.33% (5.51-5.64 GHz) and 4.22% (8.1-8.45 GHz). Frequency reconfiguration in the proposed antenna is achieved by utilizing two metallic switches, by placing them between the rectangular slot present in the radiating part and the ground plane. When both the switches are ON antenna operates at single band. When either of the switch is ON antenna operates at dual band. When both the switches are OFF, antenna operates at triple band. Acceptable gain, stable radiation pattern and good impedance matching are observed across the operating bandwidths.
Hybrid nanocarrier system for guiding and augmenting simvastatin cytotoxic activity against prostate cancer

Abstract Prostate cancer is the most common non-skin cancer among men. Though statins are mainly used as antihyperlipidemic drugs, many studies have reported their proapoptotic and antimetastatic activities on prostate cancer. However, the poor solubility and insufficient delivery of statins in tumor site limit their anticancer activity. The present study introduces an efficient hybrid drug delivery system for the treatment of prostate cancer. The system involves the chemical conjugation of Simvastatin (SMV), a statin compound, to acid-terminated poly(D, L-lactic-co-glycolic acid), PLGA chains followed by its conversion into nanoparticles (NPs), with in situ physical incorporation of more SMV and superparamagnetic iron oxide nanoparticles (SPIONS) into the PLGA NPs. The PLGA-based hybrid nanocarrier system has been designed in such a way to evade the low bioavailability of SMV, confer sustained release of both encapsulated and chemically conjugated SMV, as well as enhancing the anti-cancer effect of the formula via the magnetic targeting with the aid of the encapsulated SPIONS. Magnetism, morphological and physicochemical characterizations, as well as in-vitro release studies were performed. Besides, cytotoxicity on human prostate cancer cell line (PC-3) was evaluated using MTT assay, cell cycle arrest analysis, annexin V/propidium iodide apoptosis assay and ELISA immunoassay for apoptotic enzyme. Optimum PLGA-based hybrid nanocarrier significantly improved the SMV anticancer activity against human prostate cancer cell line through both apoptosis mechanism and retardation of G2-M phase of cell cycle. Also, the up-regulation of the Caspase 3 was aligned with cytotoxicity study’s findings.
Event-Based HVAC Control—A Complexity-Based Approach

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance. Note to Practitioners—When there are multiple rooms in a building, the HVAC control may achieve a significant energy saving and an indoor comfort satisfaction in the same time through exploring the coupling among the rooms. By appropriately defining the events, the size of the event space is usually much smaller than the state space. Therefore, an event-based control is more scalable and preferred in practice. Local events capture the state changes of rooms in a small neighborhood, which leads to a small event space but limited information. Global events capture the state changes of rooms in a large neighborhood, which leads to more information but a large event space. It remains open how to select events in large-scale HVAC control problems, especially when the computing budget is limited. In this paper, we define the complexity of an event-based control policy by the number of neighboring rooms considered. We develop a method based on hypothesis testing to select events with a proper complexity in order to achieve a good system performance. The performance of this method is demonstrated on an HVAC control problem.
Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.
Center of Mass and Friction Coefficient Exploration of Unknown Object for a Robotic Grasping Manipulation

Center of mass (CoM) and friction coefficient (µ) are essential parameters in autonomous robotic manipulation system. However, it is difficult to measure these parameters. CoM is one of the internal properties, which depends on shape and mass distribution of the object. The parameter µ depends on the contact area between the object and robot finger. This paper provides an efficient method to calculate CoM and µ through three grasping manipulations. In the proposed method, a load cell is used to measure the gravity of the object, the omnidirectional slip sensor can obtain the object's rotation angle during the grasping manipulation, the robot can measure the grasping force. The method is applicable to unknown objects with irregular shape and uniformly-distributed mass. Besides, no prior knowledge of the object is needed. Experiments show that this method can accurately obtain position of CoM and the error is acceptable. The calculation of µ is more sensitive to error, but it still has significant values on object grasping manipulations.
Acetoin and 2,3-butanediol from Bacillus amyloliquefaciens induce stomatal closure in Arabidopsis thaliana and Nicotiana benthamiana

Plants live in close association with large communities of microbes, some of which are foliar pathogens that invade tissues, primarily via stomata on the leaf surface. Stomata are considered part of an integral, innate immunity system capable of efficiently preventing pathogens from entering the host plant. Although Bacillus, a typical plant growth-promoting rhizobacterium, is known to induce stomatal closure, the substances participating in this closure and the mechanism involved in its regulation remain poorly understood. Here, we screened a mutant library and conducted site-specific mutagenesis experiments in order to identify such substances. We found that acetoin and 2,3-butanediol from B. amyloliquefaciens FZB42 induced stomatal closure in Arabidopsis thaliana and Nicotiana benthamiana. These two components could function either via root absorption or volatilization to restrict stomatal apertures, but root absorption was more efficient. Both substances invoked the salicylic acid and abscisic acid signaling pathways to close the stomata and stimulated accumulation of hydrogen peroxide and nitric oxide. The results present comprehensive evidence of how soil rhizobacteria may affect plant stomata, in a way that reinforces the evolved mutualism between the two groups of organisms, and provide potential alternative avenues of research towards reducing the incidence of disease in crops.
Two-phase rate adaptation strategy for improving real-time video QoE in mobile networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience (QoE) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video QoE. First, to measure and assess video QoE, we provide a continuous QoE prediction engine modeled by RNN recurrent neural network. Different from traditional QoE models which consider the QoE-aware factors separately or incompletely, our RNN-QoE model accounts for three descriptive factors (video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-QoE can follow the subjective QoE quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time QoE compared with classical rate adaption methods.
Sensorless High-Precision Position Correction Strategy for a 100 kW@20 000 r/min BLDC Motor With Low Stator Inductance

This paper focuses on the sensorless high-precision position correction strategy for a 100 kW@20 000 r/min brushless dc (BLdc) motor with low stator inductance (0.051 mH). Two key points related to the sensorless were studied. The one is the rotor position detection with high-frequency interference. The other one is the commutation compensation for the high-speed BLdc motor with low stator inductance in whole speed range. In order to filter out the high-frequency interference, the main factors, which that can influence the rotor position detection and the commutation accuracy were analyzed. In order to compensate the commutation error in high-precision and whole speed range, the relationship between the commutation error angle and the phase current deviation was derived. It is noted that the commutation time and the phase current error can exactly reflect the commutation error angle. Then, a novel sensorless high-precision position correction strategy based on the uncommutation current was proposed. Finally, the experiment platform based on a 100 kW@20 000 r/min BLdc motor was built, and the validity and efficiency of the proposed strategy were proved.
Approximate Order-Sensitive k-NN Queries over Correlated High-Dimensional Data

The k Nearest Neighbor (k-NN) query has been gaining more importance in extensive applications involving information retrieval, data mining, and databases. Specifically, in order to trade off accuracy for efficiency, approximate solutions for the k-NN query are extensively explored. However, the precision is usually order-insensitive, which is defined on the result set instead of the result sequence. In many situations, it cannot reasonably reflect the query result quality. In this paper, we focus on the approximate k-NN query problem with the order-sensitive precision requirement and propose a novel scheme based on the projection-filter-refinement framework. Basically, we adopt PCA to project the high-dimensional data objects into the low-dimensional space. Then, a filter condition is inferred to execute efficient pruning over the projected data. In addition, an index strategy named OR-tree is proposed to reduce the I/O cost. The extensive experiments based on several real-world data sets and a synthetic data set are conducted to verify the effectiveness and efficiency of the proposed solution. Compared to the state-of-the-art methods, our method can support order-sensitive k -NN queries with higher result precision while retaining satisfactory CPU and I/O efficiency.
Implementation Scheme of Orbital Refueling Using Microsate IIite

The orbital refueling in space can prolong the lifetime of spacecraft and reduce the cost of space applications. Therefore, it is an important direction for the development of space technology in the future. In this paper, a scheme using microsatellite to operate orbital refueling for the GEO spacecraft is designed. The process of rendezvous and refueling are emphatically described. In addition, the key technologies of microsatellite propulsion, control, observation are analyzed and prospected. At last, the commercial prospect of this scheme is analyzed.
TrustEmbed: A Framework to Predict Ratings with Trust Network

An excellent recommendation system, which gives good predictions of ratings on items like movies or books, keeps people away from the overloaded information. With the accumulation of social data, researchers begin to use social information to solve the problem of rating prediction, rather than rely solely on rating data. In this paper, network embedding technology is first introduced into rating prediction with user's trust information, and a new framework named TrustEmbed is proposed. It combines social data and rating data, and represents items in low-dimensional vectors. Several rating prediction algorithms are compared based on the FilmTrust dataset, and TrustEmbed gets the smallest rating error. Furthermore, TrustEmbed's performance with different parameter values is tested. It is found that appropriate values of parameters can make RMSE and MAE achieve small values together.