Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrate ion spikes in ice cores not suitable as proxies for solar proton events

Nitrate ion spikes in polar ice cores are contentiously used to estimate the intensity, frequency, and probability of historical solar proton events, quantities that are needed to prepare for potentially society‐crippling space weather events. We use the Whole Atmosphere Community Climate Model to calculate how large an event would have to be to produce enough odd nitrogen throughout the atmosphere to be discernible as nitrate peaks at the Earth's surface. These hypothetically large events are compared with probability of occurrence estimates derived from measured events, sunspot records, and cosmogenic radionuclides archives. We conclude that the fluence and spectrum of solar proton events necessary to produce odd nitrogen enhancements equivalent to the spikes of nitrate ions in Greenland ice cores are unlikely to have occurred throughout the Holocene, confirming that nitrate ions in ice cores are not suitable proxies for historical individual solar proton events.
Promote Tumor Progression Mechanisms That Modify the Tumor Microenvironment and Can Rab 27 a Supports Exosome-Dependent and-Independent

During progression from single cancer cells to a tumor mass and metastases, tumor cells send signals that can subvert their tissuemicroenvironment. These signals involve solublemolecules and various extracellular vesicles, including a particular type termed exosomes. The specific roles of exosomes secreted in the tumor microenvironment, however, is unclear. The small GTPases RAB27A and RAB27B regulate exocytosis of multivesicular endosomes, which lead to exosome secretion, in human HeLa cells. Here, we used mouse models to show that Rab27a blockade in mammary carcinoma cells decreased secretion of exosomes characterized by endocytic markers, but also ofmatrixmetalloproteinase 9, which is not associatedwith exosomes. Rab27a blockade resulted in decreased primary tumor growth and lung dissemination of a metastatic carcinoma (4T1), but not of a nonmetastatic carcinoma (TS/A). Local growth of 4T1 tumors requiredmobilization of a population of neutrophil immune cells induced by Rab27a-dependent secretion of exosomes together with a specific combination of cytokines and/or metalloproteinases. Our findings offer in vivo validation of the concept that exosome secretion can exert key pathophysiologic roles during tumor formation and progression, but they also highlight the idiosyncratic character of the tumor context. Cancer Res; 72(19); 4920–30. 2012 AACR.
STRUCTURAL HEALTH MONITORING OF WIND TURBINE BLADES USING DIFFERENT NONDESTRUCTIVE TESTING METHODS

The most important part of wind turbine is the blade that must be tested during the fabrication and during the functioning when can be damaged by moisture absorption, fatigue, wind gusts or lightning strikes. The common defects in turbine blades may be faulty microscopic and mesoscopic appeared in matrix, broken fibers can also appear and develop under moderated loads, or cracks and delaminations due to low energy impacts, etc. The paper propose to present the some results obtained from testing of glass fiber reinforced plastic used in the construction of the wind turbine blades as well as the monitoring of the entire scalable blade using different sensors placed on critical location on blade. The sensors are placed on critical location on blade determined by finite element method simulation and a comparison between the complementary methods is done.
Modeling and Forecasting Electric Vehicle Consumption Profiles

The growing number of electric vehicles (EV) is challenging the traditional distribution grid with a new set of consumption curves. We employ information from individual meters at charging stations that record the power drawn by an EV at high temporal resolution (i.e., every minute) to analyze and model charging habits. We identify five types of batteries that determine the power an EV draws from the grid and its maximal capacity. In parallel, we identify four main clusters of charging habits. Charging habit models are then used for forecasting at short and long horizons. We start by forecasting day-ahead consumption scenarios for a single EV. By summing scenarios for a fleet of EVs, we obtain probabilistic forecasts of the aggregated load, and observe that our bottom-up approach performs similarly to a machine-learning technique that directly forecasts the aggregated load. Secondly, we assess the expected impact of the additional EVs on the grid by 2030, assuming that future charging habits follow current behavior. Although the overall load logically increases, the shape of the load is marginally modified, showing that the current network seems fairly well-suited to this evolution.
AN ALGORITHM FOR SMART ROI DETECTION IN ELISPOT EXAMINATION IMAGES ALGORYTM INTELIGENTNEGO WYKRYWANIA OBSZARU ZAINTERESOWANIA W OBRAZACH

In this paper a new approach to automatic Region of Interest (ROI) detection in biomedical microscope images is presented. The aim of the algorithm is to detect the circular contour in the image. To accomplish the task, Canny edge detector and least squares methods are employed. Compared to our previous attempts, the current solution is invulnerable to varying: picture exposition, acquisition device, and image resolution.
Progress in Numerical Modelling of Packed Bed Biomass Combustion

Bio mass is a readily available renewable energy source for heating and power generation which reduces carbon dioxide emissions. Analysis of biomass combustion is complicated due to its large variations and physical structures. The physical and chemical processes involved in biomass combustion are necessary to understand which will improve applications of biomass in various sectors for energy use. Computer simulation of these combustion systems can be helpful to analyse different working conditions and to estimate the field values inside the whole domain. This simulation also helps to solve operation problems and may be used as a design tool. There are only a few numerical simulations of fixed bed biomass combustion employing complete model for both the solid and gas phases. The computational fluid dynamics (CFD) code available can model the gas phase combustion but it can’t model all processes of packed bed biomass conversion. There are some recent works where numerical modelling of biomass combustion in three dimensional moving beds has been performed. In these works the bed contraction due to particle shrinkage is considered. The packed bed is considered as a porous zone and User Defined Functions (UDFs) platform is used to introduce the solid phase variables in the CFD. This article provides a review of the fundamental aspects and emerging trends in numerical modelling of packed bed biomass combustion. The published works on this area are reviewed and summarized in an organized manner with general conclusions. This paper also points the critical challenges to be resolved in future. This will help the researchers to decide the direction of further investigations.
Uncertainty Analysis for VVER-1000 Core Simulations with MCNP / ATHLET

Based on a coupling scheme between the Monte Carlo neutron transport code MCNP6 and the thermo-hydraulics code ATHLET, developed earlier, steady-state calculations for the full-power state of a VVER-1000 reactor core specified within an OECD/NEA benchmark were performed. This was done with the multi-group option of MCNP6, with self-shielded cross sections for all materials generated beforehand with the SCALE 6.1. 2 code system, thus enabling uncertainty and sensitivity analyses with respect to the underlying nuclear data using the XSUSA package. Nuclear data and covariance data are taken from SCALE 6.1. 2. Analyses were also performed with stand-alone MCNP6 calculations, yielding results for radial power distribution not much different from results obtained with continuous-energy data in the framework of the benchmark, confirming the high quality of the multi-group approach. Uncertainties in the radial power distribution are significant, with values of up to 9% for the relative standard deviation in the core center. The obtained uncertainties in the radial power distribution from the coupled MCNP6/ATHLET simulation are very similar to those from the stand-alone MCNP6 calculation, meaning that the thermohydraulic feedback does not significantly change the uncertainties. Sensitivity analyses show that the main contributions to the uncertainties in the radial power distribution are due to uncertainties in the average number on neutrons per fission of 239 Pu.
An investigation of performance characteristics of a pixellated room-temperature semiconductor detector for medical imaging

The operation of any semiconductor detector depends on the movement of the charge carriers, which are created within the material when radiation passes through, as a result of energy deposition. The carrier movement in the bulk semiconductor induces charges on the metal electrodes, and therefore a current on the electrodes and the external circuit. The induced charge strongly depends on the material transport parameters as well as the geometrical dimensions of a pixellated semiconductor detector. This work focuses on the performance optimization in terms of energy resolution, detection efficiency and intrinsic spatial resolution of a room-temperature semiconductor pixellated detector based on CdTe/CdZnTe. It analyses and inter-relates these performance figures for various dimensions of CdTe and CdZnTe detectors and for an energy range spanning from x-ray (25 keV) to PET (511 keV) imaging. Monte Carlo simulations, which integrate a detailed and accurate noise model, are carried out to investigate several CdTe/CdZnTe configurations and to determine possible design specifications. Under the considered conditions, the simulations demonstrate the superiority of the CdZnTe over the CdTe in terms of energy resolution and sensitivity in the photopeak. Further, according to the results, the spatial resolution is maximized at high energies and the energy resolution at low energies, while a reasonable detection efficiency is achieved at high energies, with a 1 × 1 × 6 mm3 CdZnTe pixellated detector.
Weighted graph states and applications to spin chains, lattices and gases

Weighted graph states naturally arise when spin systems interact via an Ising-type interaction. First, we abstractly define the class of weighted graph states and demonstrate its computational accessibility. We show how reduced density matrices of a small number of spins (≈10) can be computed from arbitrarily large systems using weighted graph techniques and projected entangled pair techniques, and we discuss various entanglement measures accessible from these reduced density matrices. Second, we apply these findings to spin chains and lattices with long-range interactions and analytically derive area laws for the scaling of block-wise entanglement. Then, we turn to disordered spin systems, spin gases, which are connected to random weighted graph states and which share their entanglement properties. Finally, we use a spin gas as a bath that introduces decoherence in single as well as multipartite spin systems. The microscopic, exact decoherence model we obtain can operate in different regimes and exhibit non-Markovian features as well as spatially correlated noise effects.
Graphene Aerogel-Directed Fabrication of Phase Change Composites

Although phase change materials have been extensively used for thermal energy storage, various shortcomings such as low thermal conductivity, leakage during work, and short-age of multiple driving ways greatly hinder their practical applications. Among the new materials that can overcome these problems, graphene aerogel has attracted special inter-est owing to its 3D conductive network and extraordinary capillary force. In this chapter, we review recent progress of graphene-aerogel-based phase change composites (PCCs) and provide a brief introduction on the following topics: 1) why graphene aerogels can be used for PCCs, 2) the sol-gel transition synthesis of graphene aerogels, 3) the fabrication of graphene-aerogel-based PCCs, and 4) their applications in thermal energy storage, electric-thermal conversion and storage, solar-thermal conversion and storage, and thermal buffer. Finally, we also discuss the limitation and future development of these graphene-based materials.
Dynamic Stability of Heavy Lift Vessels During Loss of Tandem Load

This study investigates the hazard of capsizing of crane ships after an accidental loss of the lifted load. If this load is lifted by two cranes in a so-called tandem lift, the load generally does not fall off both cranes simultaneously. In fact, both cranes fail rather subsequently and thus there exist a duration where the load and the ship act as a coupled multi-body system. In order to access the coupled dynamics, this study presents a simulation method which computes the motions of a loss of load during a tandem lift. Here, the load and the ship are modeled as two rigid bodies which are connected by a rigid rope. Thus, a fully non-linear multi-body system with 11 degrees-of-freedom is obtained for which the equations of motions are given in minimal coordinates. The hydrodynamic forces are considered by a superposition of non-linear hydrostatic forces, linear radiation forces, and viscous roll damping forces. The radiation forces are efficiently computed by a state-space model which is based on a newly developed time domain identification scheme for multiple degrees-of-freedom systems. Before the proposed simulation method is applied to assess the hazard of capsizing of crane ships after a loss of load during tandem lifts, it has successfully been validated against a series of model tests. It is found that the timing of the subsequently occuring loss of load and the crane configurations can have a large influence on the likelihood of a capsize. Furthermore, current stability criteria are reviewed against a realized direct assessment of the dynamic stability after a loss of load.
Imaging Individual Biomolecules on Surfaces Molecular Structure from High Resolution Scanning Probe Microscopy

Scanning tunneling microscopy produces high resolution images of surfaces and their adsorbates, ultimately reaching atomic resolution. Despite this impressive performance, the scope of this method is greatly limited by sample preparation. STM samples are prepared in ultrahigh vacuum, because they have to be chemically pure at the atomic level. Using electrospray ion beam deposition (ES-IBD), a preparative massspectrometry method based on soft-ionization, mass-selected molecular ion beams, and their soft-landing UHV we recently extended the reach of STM towards bioand macromolecules. Our work shows that STM and ES-IBD are an ideal match, because the precise chemical information of the preparative mass spectrometry perfectly complements the high resolution structural information.
MARX'S VIEW OF ASIAN SOCIETY AND HIS “ASIATIC MODE OF PRODUCTION”

In this article Marx's view of Asian society and his “Asiatic mode of production’ are analysed. It is concluded that Marx at first regarded Asian society as a special society which was stagnant and devoid of history, but that at length he overcame this view, considered that the Asiatic mode of production was begotten out of the dissolution of primitive society and was the earliest form of class society and the specific mode of production preceding the ancient mode of production, and placed this mode of production in the series of historical stages of development. Thereafter the author sets out his own views regarding the logical structure of the genesis, development, and fall of this Asiatic mode of production.
Coherence in machine translation

Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and with no access to extra-sentential context. In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as evidenced via discourse relations. For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text.
Extracting Sparse High-Dimensional Dynamics from Limited Data

Extracting governing equations from dynamic data is an essential task in model selection and parameter estimation. The form of the governing equation is rarely known a priori; however, based on the sparsity-of-effect principle one may assume that the number of candidate functions needed to represent the dynamics is very small. In this work, we leverage the sparse structure of the governing equations along with recent results from random sampling theory to develop methods for selecting dynamical systems from under-sampled data. In particular, we detail three sampling strategies that lead to the exact recovery of first-order dynamical systems when we are given fewer samples than unknowns. The first method makes no assumptions on the behavior of the data, and requires a certain number of random initial samples. The second method utilizes the structure of the governing equation to limit the number of random initializations needed. The third method leverages chaotic behavior in the data to construct a nearly deterministic sampling strategy. Using results from compressive sensing, we show that the strategies lead to exact recovery, which is stable to the sparse structure of the governing equations and robust to noise in the estimation of the velocity. Computational results validate each of the sampling strategies and highlight potential applications.
Theory, modelling and implementation of graphene field-effect transistor

Two-dimensional materials with atomic thickness have attracted a lot of attention from researchers worldwide due to their excellent electronic and optical properties. As the silicon technology is approaching its limit, graphene with ultrahigh carrier mobility and ultralow resistivity shows the potential as channel material for novel high speed transistor beyond silicon. This thesis summarises my Ph.D. work including the theory and modelling of graphene field-effect transistors (GFETs) as well as their potential RF applications. The introduction and review of existing graphene transistors are presented. Multiscale modelling approaches for graphene devices are also introduced. A novel analytical GFET model based on the drift-diffusion transport theory is then developed for RF/microwave circuit analysis. Since the electrons and holes have different mobility variations against the channel potential in graphene, the ambipolar GFET cannot be modelled with constant carrier mobility. A new carrier mobility function, which enables the accurate modelling of the ambipolar property of GFET, is hence developed for this purpose. The new model takes into account the carrier mobility variation against the bias voltage as well as the mobility difference between electrons and holes. It is proved to be more accurate for the DC current calculation. The model has been written in Verilog-A language and can be import into commercial software such as Keysight ADS for circuit simulation. In addition, based on the proposed model two GFET non-Foster circuits (NFCs) are conducted. As a negative impedance element, NFCs find their applications in impedance matching of electrically small antennas and bandwidth improvement of metasurfaces. One of the NFCs studied in this thesis is based on the Linvill’s technique in which a pair of identical GFETs is used while the other circuit utilises the negative resistance of a single GFET. The stability analysis of NFCs is also presented. Finally, a high impedance
An Efficient Complex Event Processing Algorithm based on Multipattern Sharing for Massive Manufacturing Event Streams

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.
Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naive Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naive Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.