Miracle-dz/UniFAR-FTUs huggingface dataset:
Spacecraft attitude tracking control based on MPC and fractional-order sliding mode control

Considering the problem of robust optimal finite-time attitude tracking for spacecraft, a unique characteristic of two advanced control methods, nonlinear fractional order sliding mode control (FOSMC) and linear model predictive control (MPC), combined in a two-layer controller was introduced. This method solved the global stability and robust attitude tracking problems of a group of reaction wheel-driven spacecraft in near-polar orbit under the action of unknown disturbances and uncertainties. The FOSMC controller guaranteed the robustness of the three-axis attitude tracking at suitable target points in the presence of disturbances and uncertainties. The two-layer controller part took into account the advantages of MPC technique to predict the attitude motive and constraints to provide the optimal target point required by the FOSMC controller. Finally, the reliability and superiority of the proposed method were verified by simulation results.
Collision detection and proximity sensors in 3D simulations of mechanical systems

In this thesis we discuss two challenges in 3D simulations of certain mechanical systems. Firstly we discuss collision detection, secondly we discuss how to simulate proximity sensors. The discussed and proposed methods and algorithms are evaluated and implemented in SimulationTool, a tool developed by Philips Healthcare to simulate interventional X-Ray machines. For collision detection we describe the GJK-algorithm. To increase the performance of collision detection we split the process in a broad phase and narrow phase. In the broad phase we check for an intersection between the Bounding Volumes (BVs), volumes that entirely contain an object. We test for an intersection between the two objects only if the BVs collide. To model proximity sensors we propose methods for two types of sensors: a distance sensor and a capacitive sensor. The distance sensor is comparable to a parking sensor in a car. The model for the distance sensor computes the shortest distance to any object in its conic sensing volume. Capacitive sensors generate an electric field surrounding the sensor. Any presence or displacement of nearby conductive objects changes the electric field, which results in a change in the output voltage of the sensor. In this thesis we describe and validate a model to simulate this sensor. Based on the output voltages, the interventional X-Ray machines may be restricted in their movement in certain directions. The proposed model approximates the capacitance of objects close to the sensor. Based on the capacitance of these objects the model predicts the output of the sensor.
Prescribed Performance RISE-based Control of Euler-Lagrange Systems under Saturation

This paper spotlight on the problem of output tracking for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unmatched nonlinearities and non-vanishing disturbances. Most existing prescribed performance control (PPC) can only guarantee uniformly ultimately bounded (UUB) stability, while the robust integral of the sign of the error (RISE) based control, although is able to realize asymptotic stability, does not ensure transient behavior. For this reason, a new method about the neural network (NN) based adaptive control embedded with the RISE technique is presented based on the idea of PPC. Moreover, the constraints imposed on the inputs are addressed by the hyperbolic tangent function, resulting in a solution capable of guaranteeing zero-error tracking with prescribed transient performance. Finally, a numerical simulation is performed to show the validity of presented strategy.
Fragmentation-Aware Routing, Core and Spectrum Assignment in Multi-Core Fiber based SDM-EON

The Space Division Multiplexed-Elastic Optical Network (SDM-EON) has many parallel spatial channels, increasing the spectral capacity manifolds. The routing constraints in SDM-EON tend to leave the spectrum fragmented, leading to inefficient resource utilization. This work presents a Fragmentation Aware-resource provisioning technique to mitigate fragmentation effects while keeping the crosstalk-related impairments within thresh-old values. The proposed approach performs better than the benchmark technique in terms of connection blocking ratio and network resource utilization.
PFCA: An Intelligent Method of Multidimensional User Profile Construction and Application

User profile plays an important role in analyzing high influencers in the era of big data. For example, the profile can help the political strategists to scrutinize the performance of a candidate or party and overcome their weakness long before election day. In this work, we propose a novel profile construction and application model (PFCA) that aims to discover the overall character of high influencers, and apply PFCA to Donald Trump, one of the most influential people recently, to identify Trump’s comprehensive personalities. We first collect and preprocess profile data from static and dynamic aspects of multi-source data, and then categorize multi-source data topics and emotions. Next, we attempt to construct the profile from four aspects: fundamental characteristic, behavior characteristic, value characteristic and situation characteristic, to explore the correlation among these characteristics. Finally, PFCA is verified by the outcomes of the 2016 and 2020 U.S. presidential elections, and is evaluated by relevant experts, which proves that PFCA is relatively effective and efficient to profile high influencers. Moreover, PFCA can be further applied to related analysis and prediction in the 2024 U.S. presidential election.
Going Emotional and Negative: An Effective Way to Attract Attention

Recent years have witnessed the booming of social media. The growing users of social media attracts politicians to be involved to promote their political insights. Previous studies have proved that emotional expressions help politicians diffuse information more quickly. However, the few focus on the changes of politician’s emotional intensity and their driving factors. In this study, we find that political status, such as preparing for election campaign or being in office, as well as topics that make a politician anxious influence his emotional intensity. Taking Donald Trump as an example, we find that he has gradually become emotional and negative since running for the 2016 US presidential election until leaving office in 2020. The topics of election, trade and healthcare always make him emotional. The reactions of tweets show that Trump’s emotionality has successfully attracted more public attention. ‘Going emotional and negative’ has become an effective self-marketing strategy for Donald Trump.
Loss Function Entropy Regularization for Diverse Decision Boundaries

Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without any ground-truth annotation? In this paper, we will attempt to modify the contrastive learning objectives to automatically train a self-complementing ensemble to produce a state-of-the-art prediction on the CIFAR10 and CIFAR100-20 task. This paper will present a remarkably simple method to modify a single unsupervised classification pipeline to automatically generate an ensemble of neural networks with varied decision boundaries to learn a larger feature set of classes. Loss Function Entropy Regularization (LFER), are regularization terms to be added upon the pre-training and contrastive learning objective functions, gives us a gear to modify the entropy state of the output space of unsupervised learning, thereby diversifying the latent representation of decision boundaries of neural networks. Ensemble trained with LFER have higher successful prediction accuracy for samples near decision boundaries. LFER is a effective gear to perturb decision boundaries, and has proven to be able to produce classifiers that beat state-of-the-art at contrastive learning stage. Experiments show that LFER can produce an ensemble where each have accuracy comparable to the state-of-the-art, yet have each have varied latent decision boundaries. It allows us to essence perform meaningful verification for samples near decision boundaries, encouraging correct classification of near-boundary samples. By compounding the probability of correct prediction of a single sample amongst an ensemble of neural network trained, our method is able to improve upon a single classifier by denoising and affirming correct feature mappings.
Tightly Coupled GNSS, INS and Visual Odometry for Accurate and Robust Vehicle Positioning

High-precise vehicle positioning in urban environment is very crucial for modern transportation system. However, the traditional GNSS positioning cannot fully meet the requirement under complex environment. Thus, multi-sensor integrated navigation is inevitable. In this paper, a tightly coupled data fusion system of GNSS, INS and Visual Odometry is proposed to achieve accurate and robust vehicle positioning. The error state Extended Kalman Filter is employed for the multi-sensor integration. In order to make the most use of GNSS information, both pseduorange and carrier phase measurements are tightly coupled via the double-differenced RTK model. The Multi-State Constraint Kalman Filter is adopted to further integrate the stereo visual odometry. The proposed integration system is evaluated by field experiments in complex urban environment. The results show that the GNSS/INS/VO integrated navigation system provides more accurate and continuous positioning results than GNSS alone in GNSS-challenged environment.
Spatio-temporal Travel Volume Prediction and Spatial Dependencies Discovery Using GRU, GCN and Bayesian Probabilities

Travel volumes prediction is an essential topic for urban studies. With accurate estimation, researchers could re-duce the traffic congestion, promote residents’ living quality, and ultimately improve the urban design. Most previous studies only utilised historical travel volume records for prediction, while few of them noticed the impact of spatial factors on travel volumes distributions. In this study, we proposed a Spatio-temporal prediction model (STPM) to forecast passengers’ travel volume. The model quantitatively extract the spatial correlations among the trips. It is designed using Graph Convolutional Network, Gate Recurrent Unit, and Bayesian Probabilities. We conducted two case studies in New York, USA, and Ningbo, China. The prediction results showed our model is superior compared with baseline methods. We also applied Pearson Correlation method to further investigate the spatial correlation of the areas. Results showed that zones with similar activities have strong spatial correlation. Besides, travel patterns and the related activities were revealed.
Adaptive fault-tolerant control of air-breathing hypersonic vehicles with variable geometry inlet*

A novel adaptive fault-tolerant controller is proposed for a switched model of air-breathing hypersonic vehicle with variable geometry inlet (AHV-VGI). The VGI with movable translation fairing is used to capture sufficient air mass flow of scramjet to provide sufficient thrust. A switched model is firstly established with respect to different flight envelop, and the actuator fault and backlash nonlinearity are considered. In order to avoid the control discontinuity or oscillation caused by the sudden change of switching signal. An adaptive fault-tolerant controller based on barrier Lyapunov function is then designed to steer velocity and altitude tracking errors into predefined arbitrarily small residual sets, wherein the effects of actuator fault and backlash nonlinearity are compensated successfully by estimating their upper bounds. Different from other existing switching control strategies, the proposed controller does not contain switching information. Simulations on the switched model are carried out to prove the effectiveness of the designed controller.
Action at a distance: Defects in division plane positioning in the root meristematic zone affect cell organization in the differentiation zone

Cell division plane orientation is critical for plant and animal development and growth. TANGLED1 (TAN1) and AUXIN-INDUCED-IN-ROOT-CULTURES9 (AIR9) are division-site localized microtubule-binding proteins required for division plane positioning. tan1 and air9 Arabidopsis thaliana single mutants have minor or no noticeable phenotypes but the tan1 air9 double mutant has synthetic phenotypes including stunted growth, misoriented divisions, and aberrant cell-file rotation in the root differentiation zone. These data suggest that TAN1 plays a role in nondividing cells. To determine whether TAN1 is required in elongating and differentiating cells in the tan1 air9 double mutant, we limited its expression to actively dividing cells using the G2/M-specific promoter of the syntaxin KNOLLE (pKN:TAN1-YFP). Unexpectedly, in addition to rescuing division plane defects, pKN:TAN1-YFP rescued root growth and the root differentiation zone cell file rotation defects in the tan1 air9 double mutant. This suggests that defects that occur in the meristematic zone later affect the organization of elongating and differentiating cells. Summary Statement Expression of TAN1 in the root meristematic zone rescues cell file rotation defects in tan1 air9 mutants, suggesting defects that occur in mitosis may influence organization of nondividing cells.
Autophagy modulates apical growth and development in the moss Physcomitrium patens

Different to root hairs and pollen tubes, Physcomitrium patens apical growing protonemal cells have the singularity that they continue to undergo cell divisions as the plant develops, allowing to study autophagy in the context of a multicellular apical growing tissue coupled to development. Herein, we showed that the core autophagy machinery is present in the moss P. patens, and deeply characterized the growth and development of wild-type, atg5 and atg7 loss-of-function mutants under optimal and nutrient-deprived conditions. Our results showed that the growth of the different morphological and functional protonemata apical growing cells, chloronema and caulonema, is differentially modulated by this process. These differences depend on the protonema cell type and position along the protonemal filament, and growth condition. As a global plant response, the absence of autophagy triggers the spread of the colony through protonemata growth at the expense of a reduction in buds and gametophore development, and thus the adult gametophytic and reproductive phases. Altogether this study provides valuable information indicating that autophagy has roles during apical growth with differential responses within the cell types of the same tissue and contributes to life cycle progression and thus the development of the 2D and 3D tissues of P. patens. HIGHLIGHT Autophagy is differentially induced in protonemal cells, and contributes to apical growth, life cycle progression, and thus the development of the 2D and 3D tissues of P. patens.
Vγ9Vδ2 T cells are potent inhibitors of SARS-CoV-2 replication and exert effector phenotypes in COVID-19 patients

Vγ9Vδ2 T cells play a key role in the innate immune response to viral infections, including SARS-CoV-1 and 2, and are activated through butyrophilin (BTN)-3A. Here, the objectives were to: 1) characterize the effects of SARS-CoV-2 infection on the number, phenotype, and activation of Vγ9Vδ2 T cells in infected patients, and 2) assess the effects of in vitro SARS-CoV-2 infection on the expression of BTN3A and its impact on the activation and response of Vγ9Vδ2 T cells to an anti-BTN3A antibody. Blood Vγ9Vδ2 T cells decreased in clinically mild SARS-CoV-2 infections compared to healthy volunteers (HV). This decrease was maintained up to 28 days and in the recovery period. Terminally differentiated Vγ9Vδ2 T cells tend to be enriched on the day of diagnosis, 28 days after and during the recovery period compared to HV. Furthermore, these cells showed cytotoxic and inflammatory activities as shown by TNFα, IFNγ and CD107a/b increase following anti-BTN3A activation. Moreover, BTN3A upregulation and Vγ9Vδ2 T cell infiltration were observed in a lung biopsy from a fatal SARS-CoV-2 infection, as compared to HV. In vitro, SARS-CoV-2 infection significantly increased BTN3A expression in macrophages and lung cell lines. The activation via BTN3A enhanced the anti-SARS-CoV-2 Vγ9Vδ2 T cells cytotoxicity and IFN-γ and TNFα in SARS-CoV-2 infected patient. Increasing concentrations of anti-BTN3A were accompanied by an inhibition of viral replication. Altogether, these data suggest that Vγ9Vδ2 T cells are important in the immune response against SARS-CoV-2 infection and that activation by an anti-BTN3A antibody may enhance their response. KEY POINTS SARS-CoV-2 mediates upregulation of the key receptor of Vγ9Vδ2 T cells BTN3A on lung tissues and cell lines as well as monocytes During SARS-CoV-2 infection, Vγ9Vδ2 are differentiated and efficiently degranulate and secrete cytokines upon activation with BTN3A mAb
mTORC1 activity is essential for disease progression in chronic lymphocytic leukemia

The precise role of mechanistic target of rapamycin complex 1 (mTORC1) during chronic lymphocytic leukemia (CLL) pathogenesis remains to be elucidated. Targeted deletion of mTORC1 component Raptor in adult mice reveals that mTORC1 function is essential for initiation and maintenance of CLL. Raptor-deficient bone marrow-derived PKCα-KR transduced haemopoietic progenitors failed to generate a CLL-like disease in vitro, due to an inability to overcome the mTORC1-mediated block in B cell lineage commitment. Induction of Raptor-deficiency in NSG mice transplanted with Mx1-Raptor BM-derived PKCα-KR transduced cells after disease was established, revealed a reduced CLL-like disease load and a significant increase in survival in the mice. Interestingly in mice transplanted with an aggressive CLL-like disease, rapamycin treatment reduced disease burden more effectively than AZD2014 (dual mTORC1/2 inhibitor), indicating a skew towards mTORC1 sensitivity with more aggressive leukemic disease. Rapamycin efficiently targeted the translation elongation axis eEF2/eEF2K downstream of mTORC1, resulting in eEF2 inactivation through induction of eEF2T56 phosphorylation. Rapamycin treatment of primary CLL cells halted proliferation, modulated eEF2K/eEF2 phosphorylation and inhibited MCL1 expression. Our studies demonstrate that mTORC1 plays an essential role in leukemia progression in vitro and in vivo in our CLL mouse model, with evidence for increased rapamycin sensitivity in aggressive secondary CLL transplants. Furthermore, the suppression of translation elongation through inactivation of eEF2 may offer a novel therapeutic target for blocking CLL progression.
Structural Basis of MicroRNA Biogenesis by Dicer-1 and Its Partner Protein Loqs-PB

In animals and plants, Dicer enzymes collaborate with double-stranded RNA-binding proteins to convert precursor-microRNAs (pre-miRNAs) into miRNA duplexes. We report six cryo-EM structures of Drosophila Dicer-1 and its partner Loqs-PB. The structures show Dicer-1•Loqs-PB (1) before binding pre-miRNA, (2) after binding and in a catalytically competent state, (3) after nicking one arm of the pre-miRNA, (4) following complete dicing and initial product release. Our reconstructions suggest that pre-miRNA binds a rare, open conformation of the Dicer-1•Loqs-PB heterodimer, enabling conformational proofreading. The Dicer-1 dsRBD and three Loqs-PB dsRBD domains form a tight belt around the pre-miRNA, distorting the RNA helix to place the scissile phosphodiester bonds in the RNase III active sites. Pre-mRNA cleavage shifts the RNA- binding domains and tightens Dicer-1, promoting product release. Our data suggest a model for how the Dicer-1•Loqs-PB complex effects a complete cycle of pre-miRNA recognition, stepwise endonuclease cleavage, and sequential product release.
Extensive de novo activity stabilizes epigenetic inheritance of CG methylation in Arabidopsis transposons

Cytosine methylation within CG dinucleotides (mCG) can be epigenetically inherited over many generations. Such inheritance is thought to be mediated by a semiconservative mechanism that produces binary present/absent methylation patterns. However, we show here that in Arabidopsis thaliana h1ddm1 mutants, intermediate heterochromatic mCG is stably inherited across many generations and is quantitatively associated with transposon expression. We develop a mathematical model that estimates the rates of semiconservative maintenance failure and de novo methylation at each transposon, demonstrating that mCG can be stably inherited at any level via a dynamic balance of these activities. We find that DRM2 – the core methyltransferase of the RNA-directed DNA methylation pathway – catalyzes most of the heterochromatic de novo mCG, with de novo rates orders of magnitude higher than previously thought, whereas chromomethylases make smaller contributions. Our results demonstrate that stable epigenetic inheritance of mCG in plant heterochromatin is enabled by extensive de novo methylation.
NIPBL and WAPL balance cohesin activity to regulate chromatin folding and gene expression

The relationship between cohesin-mediated chromatin looping and gene expression remains unclear. We investigated the roles of NIPBL and WAPL, two regulators of cohesin activity, in chromatin folding and transcription in human cells. Consistent with their opposing roles in cohesin regulation, depletion of these factors showed opposite effects on levels of chromatin-bound cohesin and spatial insulation of neighboring domains. We find that NIPBL or WAPL depletion each alter the expression of ~2,000 genes, most of which are uniquely sensitive to either regulator. We find that each set of differentially expressed genes are enriched at chromatin loop anchors and clustered within the genome, suggesting there are genomic regions sensitive to either more or less cohesin. Remarkably, co-depletion of both regulators rescued chromatin misfolding and gene misexpression compared to either single knockdown. Taken together, we present a model in which the relative, rather than absolute, levels of NIPBL and WAPL are required to balance cohesin activity in chromatin folding to regulate transcription.
Release of VAMP5‐positive extracellular vesicles by retinal Müller glia in vivo

Cell-cell interactions in the central nervous system are based on the release of molecules mediating signal exchange and providing structural and trophic support through vesicular exocytosis and the formation of extracellular vesicles. The specific mechanisms employed by each cell type in the brain are incompletely understood. Here, we explored the means of communication used by Müller cells, a type of radial glial cells in the retina, which forms part of the central nervous system. Using immunohistochemical, electron microscopic, and molecular analyses, we provide evidence for the release of distinct extracellular vesicles from endfeet and microvilli of retinal Müller cells in adult mice in vivo. We identify VAMP5 as a Müller cell-specific SNARE component that is part of extracellular vesicles and responsive to ischemia, and we reveal differences between the secretomes of immunoaffinity-purified Müller cells and neurons in vitro. Our findings suggest extracellular vesicle-based communication as an important mediator of cellular interactions in the retina.