Miracle-dz/UniFAR-FTUs huggingface dataset:
Asphalt Modified by Ethylene-Acrylic Acid Copolymer Ionomers: Fundamental Investigations of Mechanical and Rheological Properties

Ethylene-acrylic acid copolymer (EAA) has been demonstrated to be a suitable additive for modifying the properties of asphalt. EAA mixed with metal hydroxides/oxides form EAA-M ionomers, which increase the polarity of EAA, improving its adhesive properties and affecting its mechanical and rheological characteristics. The present work investigates the mechanical and rheological characteristics of asphalt modified by EAA in conjunction with either Ca (OH)2, NaOH, or ZnO. The high-temperature properties of the modified asphalts, including the softening point, and needle penetration, were evaluated. Rheological characteristics of modified asphalts were investigated by rotary rheometer. Moreover, the storage stability at high temperature, morphology and chemical structure were also analyzed. Results indicate that a 4wt% EAA-M ionomer concentration in the base asphalt is adequate for providing the enhanced properties studied. For an equivalent concentration of EAA, the properties of modified asphalts were affected by very small additions of the metal hydroxides/oxides. The best overall mechanical and rheological performance was obtained for EAA-Ca modified asphalt with 4wt% EAA and 2.5wt‰ Ca (OH)2. EAA-Zn modified asphalt provided the most stable high-temperature storage. Compared with 6wt% pure EAA-modified asphalt, which is not stable, the EAA-Zn modified asphalt (2.7wt‰ ZnO) demonstrated reasonable high-temperature storage stability. Compared with asphalt modified with 6wt% pure EAA, the softening point increased from 55.81 °C to 58.05 °C with the addition of 2.7wt‰ NaOH. However, while the mechanical and rheological properties of EAA-Na modified asphalt were very good, its high-temperature storage stability due to the strong reactivity of NaOH, which led to the crosslinking of EAA-Na ionomers, making it difficult to disperse.
A 2020 View of Thymus Stromal Cells in T Cell Development

The thymus is an intricate primary lymphoid organ, wherein bone marrow–derived lymphoid progenitor cells are induced to develop into functionally competent T cells that express a diverse TCR repertoire, which is selected to allow for the recognition of foreign Ags while avoiding self-reactivity or autoimmunity. Thymus stromal cells, which can include all non–T lineage cells, such as thymic epithelial cells, endothelial cells, mesenchymal/fibroblast cells, dendritic cells, and B cells, provide signals that are essential for thymocyte development as well as for the homeostasis of the thymic stroma itself. In this brief review, we focus on the key roles played by thymic stromal cells during early stages of T cell development, such as promoting the homing of thymic-seeding progenitors, inducing T lineage differentiation, and supporting thymocyte survival and proliferation. We also discuss recent advances on the transcriptional regulation that govern thymic epithelial cell function as well as the cellular and molecular changes that are associated with thymic involution and regeneration.
Identification of Important Residues by Computational Alanine Scanning Analysis of Interaction Mechanisms of scFv Anti-p17 Complexes Based on Molecular Dynamics Simulations

Molecular dynamics simulation (MD) coupled with molecular mechanics generalized Born surface area (MM-GBSA) method were performed to probe the binding mechanisms of single chain Fv fragment with p17 epitopes of HIV-1 protease (scFv anti-p17). The results show that van der Waals and electrostatic energy is a main force of the binding. Computational alanine scanning by substitution of the amino acids in binding to alanine and analysis of structure-affinity relation were used to predict the antibody-antigen binding modes. The important residues locate in the hot spot of the surface between scFv and p17 have been identified. This study can contribute significantly to the designs of the novel antibody targeting the p17 peptides. Point mutations and molecular docking between the modeled scFv antibody and p17 peptides have been investigated in some of the key residues to search for novel antibody with the improved binding activity. The experimental results have been compared to see how well of the prediction.
An Empirical Study on the Linkage between Shanghai and Shenzhen Stock Index Futures and Spot Prices

This article studies the linkage between stock index futures and stock index from two aspects, namely price priority and volatility spillover effects. Price priority is the relationship between price priority and lag. By studying price priority, we can know the reaction speed of the stock index futures market and the stock index market to new information, and then determine which market is dominant in asset pricing, thereby providing investors rational decisions. The volatility spillover effect is the conductivity of volatility, which is manifested as the impact of changes in the price of one market on other markets. By studying the volatility spillover effect between stock index futures and stock markets, we can predict the potential risks that may exist in the market. This can also help us make more reasonable investment decisions.
The Integrated Model of Online and Offline Education and Training of Student Activists

Online education and training model is used more and more often in the training of student activists. The online training model has the advantages of flexible schedule, flexible scope adjustment and abundant resources, while it also has the disadvantages such as difficult quality control, unstable resources and insufficient role playing. The reasons for this situation are mainly as follows: inadequate understanding, unreasonable organization and implementation, and passive collection of resources. Measures can be taken for further improvement and optimization, including the establishment of online learning system, adjustment of offline learning system, improvement of online assessment system, improvement of the offline investigation system and other efforts, so as to create an integrated model of online and offline education that trains and examines students' ability of combining knowledge with practice.
An Evaluation of Serverless Data Processing Frameworks

Serverless computing is a promising cloud execution model that significantly simplifies cloud users' operational concerns by offering features such as auto-scaling and a pay-as-you-go cost model. Consequently, serverless systems promise to provide an excellent fit for ad-hoc data processing. Unsurprisingly, numerous serverless systems/frameworks for data processing emerged recently from research and industry. However, systems researchers, decision-makers, and data analysts are unaware of how these serverless systems compare to each other. In this paper, we identify existing serverless frameworks for data processing. We present a qualitative assessment of different system architectures and an experiment-driven quantitative comparison, including performance, cost, and usability using the TPC-H benchmark. Our results show that the three publicly available serverless data processing frameworks outperform a comparatively sized Apache Spark cluster in terms of performance and cost for ad-hoc queries on cold data.
Security Vulnerability Assessment of Power IoT based on Business Security

Power Internet of Things is the application of IoT in smart power grid. Once attacked, it will cause huge losses. Therefore, it is necessary to conduct a security assessment to take defensive measures. However, the traditional vulnerability assessment methods of the power Internet of things mostly focus on the security of the system itself, without considering the impact on business economy and efficiency. This paper proposes a security vulnerability assessment method of power Internet of Things integrating business security. This method first analyzes the security risks faced by the power Internet of Things, and establishes its attack tree model. Then, each leaf node is rated from the three safety features, which are weighted by evaluation and calculation, and the activation probability of each leaf node is calculated. After that, considering the blind attack factor, the activation probability of all nodes in the model is calculated. Finally, the vulnerability of the system and the vulnerability sensitivity of each leaf node are obtained. According to the vulnerability sensitivity, measures are taken to protect the weak links of the system. The effectiveness of the proposed method is verified by experiments on SCADA (supervisory control and data acquisition) system in the power Internet of things.
Improved Adaptive Clutter Removal Algorithm for Indoor Localization Using IR-UWB Radar

In order to locate human targets in highly cluttered indoor environments accurately with impulse radio ultra-wideband (IR-UWB) radar, environmental clutter need to be removed first to reveal the signal reflected from the target. In this paper, we approach the problem with new signal processing procedures. An efficient adaptive clutter removal algorithm based on adaptive variance estimation is proposed to estimate the dynamic clutter in in-door environment. Test results with experimental data in an indoor environment with moving and non-moving targets verified that the proposed method can improve the ranging accuracy of both types of targets.
Blockchain Solutions in Education: A New Cross-Education Blockchain-Based Framework

Variant blockchain-based solutions were proposed during the last 5 years for education. This paper is the first to discuss these solutions and their usage and benefits. However, there is not yet any solution in the literature that issues or validates a certificate which includes courses from different educational institutes. This paper proposes a novel framework for cross-education blockchain-based solution. It collects participating institutes in one blockchain network. Graduates of this solution can join courses in the institutes that provide the latest and strongest contents. The issued certificate is self-validating for all the courses that are obtained from multiple institutes. It is cryptographically coded and securely stored using the blockchain technology. It can be validated instantaneously through one validation portal independently without contacting its issuing institutes. The saved time and resources are oriented toward enhancing students' services. This framework can be extended to fields that require issuing or validating cross-acquired certificates.
Selection of Optimal Strategy for Moving Target Defense Based on Signal Game

This paper analyzes the dynamic and incomplete information characteristics of network offensive and defensive confrontation. In this paper, a signal game is used as the framework, and the defender is used as the signal initiator. The defensive behavior model that induces the signal to interfere with the attack is used to construct the moving target defense of the signal game. Considering the unavoidable misdetection defects of the defense detection system itself, a method for quantifying the benefits of the attack strategy and the defense strategy is proposed, and a refined Bayesian equilibrium algorithm and a prior belief correction algorithm are given. The optimal defense strategy and the optimal induced signal strategy were selected through the relative defense gains and refined Bayesian equilibrium results, respectively. Finally, an example is used to illustrate and verify the feasibility and effectiveness of the model and method. Based on the analysis of experimental data, general rules are summarized.
SPA: Stealthy Poisoning Attack

Deep neural networks are susceptible to trojan attacks due to the lack of their interpretability in training process. The trained models can be purposely polluted by the attackers using training data with special patterns called trojan triggers, which are also called poisoned samples. When the model is put in use, the trojan trigger will be stamped on the testing samples to manipulate the output so as to achieve trojan attack. In previous work that achieved high attack success rate, the fixed patterns of the trojan trigger in poisoned samples make them easily detected and eliminated by defense algorithms. We propose a novel stealthy trojan attack approach called SPA, which exploits the generative adversarial network to generate poisoned samples and models that can be triggered by benign samples without trojan triggers. The generated poisoned samples are stealthy, namely, look natural thus are less likely to be detected easily. Our experiments have shown that SPA can achieve trojan attack success rate as high as 91.74%, with only 7% poisoned samples in public dataset LFW and CASIA. We have experimented with a few defense algorithms such as autodecoder defense and DBSCAN cluster detection and showed the resilience of SPA.
Predicting Vocational Personality Type from Socio-demographic Features Using Machine Learning Methods

This study aimed to apply supervised machine learning techniques to one domain of psychological research: vocational interests. Socio-demographic factors can be considered strong predictors of vocational interests, which might have far-reaching practical implications for professional counselling and social network analysis. The dataset used in this study is a collection of answers to the RIASEC (Holland Codes) psychological test. Different Machine Learning architectures were used to predict RIASEC scales using socio-demographic features. The problem was treated as a multioutput regression task, multiclass and multilabel classification. The following models were used: independent regression, regression chains, three-letter code classification, inferring label relations. Models comparison showed that the models that exploit intercorrelations between RIASEC scales yielded the best results.
Cane bud recognition and positioning method based on machine vision

In order to realize sugarcane bud orientation and positioning seeding, a sugarcane bud tracking and positioning method based on improved GOTURN algorithm is proposed. This method takes the segmented image of sugarcane in natural environment as the object, and uses YCbCr color space to convert the image into binary value. After a series of morphological treatments of expansion and corrosion, the area of the cane bud is obtained by frame selection. Finally, the sugarcane bud is tracked by the GOTURN algorithm, and the width of the frame of the cane bud frame is compared at different angles to define the minimum width of the cane bud For qualified positioning. In this way, the continuous and dynamic intelligent recognition of sugarcane seed characteristics by the sugarcane seeding device is realized.
FDI: A Fast IoT Device Identification Approach

Identificationof IoT devices exposed to the public network brings multiple benefits, such as automated network asset statistics, vulnerability detection based on specific devices, andintelligent perception of network attack behavior. In this paper, we focus on the real-time challenge of large-scale IoT device identification. The current solution of collecting redundant device data neither extract featureswell nor can meet real-time requirements. To address these challenges, we propose a fast IoT device identification approach (FDI), a multiple classifier combination in which each classifier is responsible for analyzingspecific port data.FDI can update the device identification result synchronously in the process of data acquisition.When the result reaches theconfidence threshold, the port data collection will be terminated in advance.Moreover, FDI takes a best-first port scanning strategy, which can further accelerate the device identification process.By experimenting with data obtained from the real world, we demonstrate the fact of port data redundancy and give the optimal range of theconfidence threshold. Experimental resultsshow FDI can increase identification efficiency by 46.67%.
Deciding reachability under persistent x86-TSO

We address the problem of verifying the reachability problem in programs running under the formal model Px86 defined recently by Raad et al. in POPL'20 for the persistent Intel x86 architecture. We prove that this problem is decidable. To achieve that, we provide a new formal model that is equivalent to Px86 and that has the feature of being a well structured system. Deriving this new model is the result of a deep investigation of the properties of Px86 and the interplay of its components.
Comparation of several mapping methods for humanoid robot

In order to realize the goal of humanoid robot working independently in the environment of human life, Robots need to finish the job of perception of the environment and the establishment of a map, so as to finish the job of positioning and navigation. In practical application, there are many different mapping methods for humanoid robots according to different targets. It is an important problem to choose which sensor and how to build a map. This paper discusses the common mapping methods of humanoid robot, and introduces and compares the map building technology of humanoid robot from sensors, map description and construction methods, map using environments and other aspects.
Recent Developments in HLA B27 Anterior Uveitis

There has been steady progress in understanding the pathogenesis, clinical features, and effective treatment of acute anterior uveitis (AU) over the past 5 years. Large gene wide association studies have confirmed that AU is a polygenic disease, with overlaps with the seronegative arthropathies and inflammatory bowel diseases, associations that have been repeatedly confirmed in clinical studies. The role of the microbiome in AU has received increased research attention, with recent evidence indicating that human leukocyte antigen B27 (HLA B27) may influence the composition of the gut microbiome in experimental animals. Extensive clinical investigations have confirmed the typical features of acute AU (AAU) and its response to topical, regional and systemic immunosuppressive treatment. Increased understanding of the role of cytokines has resulted in studies confirming the value of anti-cytokine therapy [anti–tumor necrosis factor (anti-TNF) and interleukin 6 (IL-6) therapy] in severe and recurrent cases of AAU, particularly in subjects with an associated spondyloarthopathy (SpA) and in juvenile idiopathic arthritis (JIA)–associated AAU.
Si‐Encapsulating Hollow Carbon Electrodes via Electroless Etching for Lithium‐Ion Batteries

Remarkable improvements in the electrochemical performance of Si materials for Li‐ion batteries have been recently achieved, but the inherent volume change of Si still induces electrode expansion and external cell deformation. Here, the void structure in Si‐encapsulating hollow carbons is optimized in order to minimize the volume expansion of Si‐based anodes and improve electrochemical performance. When compared to chemical etching, the hollow structure is achieved via electroless etching is more advanced due to the improved electrical contact between carbon and Si. Despite the very thick electrodes (30 ∼ 40 μm), this results in better cycle and rate performances including little capacity fading over 50 cycles and 1100 mA h g−1 at 2C rate. Also, an in situ dilatometer technique is used to perform a comprehensive study of electrode thickness change, and Si‐encapsulating hollow carbon mitigates the volume change of electrodes by adoption of void space, resulting in a small volume increase of 18% after full lithiation corresponding with a reversible capacity of about 2000 mA h g−1.
Protein and Organic-Molecular Crystallography With 300kV Electrons on a Direct Electron Detector

Electron 3D crystallography can reveal the atomic structure from undersized crystals of various samples owing to the strong scattering power of electrons. Here, a direct electron detector DE64 was tested for small and thin crystals of protein and an organic molecule using a JEOL CRYO ARM 300 electron microscope. The microscope is equipped with a cold-field emission gun operated at an accelerating voltage of 300 kV, quad condenser lenses for parallel illumination, an in-column energy filter, and a stable rotational goniometer stage. Rotational diffraction data were collected in an unsupervised manner from crystals of a heme-binding enzyme catalase and a representative organic semiconductor material Ph-BTBT-C10. The structures were determined by molecular replacement for catalase and by the direct method for Ph-BTBT-C10. The analyses demonstrate that the system works well for electron 3D crystallography of these molecules with less damaging, a smaller point spread, and less noise than using the conventional scintillator-coupled camera.
Potential Risk Factors for Aggression and Playfulness in Cats: Examination of a Pooling Fallacy Using Fe-BARQ as an Example

Using a popular method of behaviour evaluation which rates the intensity of behaviour in different contexts, we demonstrate how pooling item scores relating to a given construct can reveal different potential risk factors for the dependent variable depending on how the total score is constructed. We highlight how similar simple total scores can be constructed through very different combinations of constituent items. We argue for the importance of examining individual item score distributions, and the results from different intensity thresholds before deciding on the preferred method for calculating a meaningful dependent variable. We consider simply pooling individual item scores which conflate context with intensity to calculate an average score and assuming this represents a biologically meaningful measure of trait intensity is a fallacy. Specifically using four items that describe intercat aggression and eleven that describe playfulness in cats in Fe-BARQ, we found sex and neuter status, social play and fearfulness were consistently significant predictors for intercat aggression scores; and age, age when obtained, social play and fearfulness were significant predictors of playfulness scores. However, the significance of other factors such as scratching varied with the threshold used to calculate to the total score. We argue that some of these inconsistent variables may be biologically and clinically important and should not be considered random error. Instead they need to be evaluated in the context of other available evidence.
Light Sheet Microscopy Using FITC-Albumin Followed by Immunohistochemistry of the Same Rehydrated Brains Reveals Ischemic Brain Injury and Early Microvascular Remodeling

Until recently, the visualization of cerebral microvessels was hampered by the fact that only short segments of vessels could be evaluated in brain sections by histochemistry. These limitations have been overcome by light sheet microscopy, which allows the 3D analysis of microvasculature in cleared brains. A major limitation of light sheet microscopy is that antibodies do not sufficiently penetrate cleared brains. We herein describe a technique of reverse clearing and rehydration, which after microvascular network analysis allows brain sectioning and immunohistochemistry employing a broad set of antibodies. Performing light sheet microscopy on brains of mice exposed to intraluminal middle cerebral artery occlusion (MCAO), we show that in the early phase of microvascular remodeling branching point density was markedly reduced, more strongly than microvascular length. Brain infarcts in light sheet microscopy were sharply demarcated by their autofluorescence signal, closely corresponding to brain infarcts revealed by Nissl staining. Neuronal survival, leukocyte infiltration, and astrocytic reactivity could be evaluated by immunohistochemistry in rehydrated brains, as shown in direct comparisons with non-cleared brains. Immunohistochemistry revealed microthrombi in ischemic microvessels that were likely responsible for the marked branching point loss. The balance between microvascular thrombosis and remodeling warrants further studies at later time-points after stroke.