Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental and Theoretical Combustion Investigations Assisting the Development of New Liquid and Gelled Propellants

Liquid or gelled propellants show various advantages over solid rocket propellants. The usage of the propellants, however, requires additional processing devices to effectively burn them in combustion chambers. The dispersion of the fluids, the evaporation rate and flame reaction mainly influence the combustion characteristics and, therefore, have to be analysed in detail by experimental and experimental studies. The fuels nitromethane and the oxidisers H2O2, as well as mixtures of them were investigated in various experimental equipment applying cinematographic, pyrometric and spectroscopic methods. In strand burner experiments the evaporation and pyrolysis rate the flame structure were studied in an optical pressure cell. The results are the dependence of the burning rate on temperature and pressure (mainly Vieille’s law). Free moving and burning droplets of liquid and gelled mixtures have been investigated. The combined use of high speed cinematography, thermography and spectroscopy allows the observation of main reaction zones and results in temperature distributions over the moving droplet. Gellants like aerosil change the burning behaviour of the droplets concerning stability and increased combustion temperatures. To assist the experimental investigations the reaction kinetics were computed with CHEMKIN, because of most kinetic data of liquid and gelled propellants are known from the literature. The resulting data are used in a one dimensional transient computer code to model combustion. The results give input parameters for droplet combustion modelling which includes phase transition evaporation and gas phase oxidation. An approach concerned the simulation of the combustion of dispersed droplets with hot spot modelling.
Mixture of Topic-Based Distributional Semantic and Affective Models

Typically, Distributional Semantic Models (DSMs) estimate semantic similarity between words using a single-model, where the multiple senses of polysemous words are conflated in a single representation. Similarly, in textual affective analysis tasks, ambiguous words are usually not treated differently when estimating word affective scores. In this work, a semantic mixture model is proposed enabling the combination of word similarity scores estimated across multiple topic-specific DSMs (TDSMs). Based on the assumption that semantic similarity implies affective similarity, we extend this model to perform sentence-level affect estimation. The proposed model outperforms the baseline approach achieving state-of-the-art results for semantic similarity estimation and sentence-level polarity detection.
1 Connected 2 – point function and rooted melonic graphs

Melonic graphs constitute the family of graphs arising at leading order in the 1/N expansion of tensor models. They were shown to lead to a continuum phase, reminiscent of branched polymers. We show here that they are in fact precisely branched polymers, that is, they possess Hausdorff dimension 2 and spectral dimension 4/3.
Expression of KIR2DS1 by decidual natural killer cells increases their ability to control placental HCMV infection

Significance The etiology of pregnancy complications such as miscarriage, preterm birth, and preeclampsia are largely unknown. The combination of the maternal Killer Immunoglobulin-like Receptor-2DS1 (KIR2DS1) with fetal HLA-C was identified as a factor determining risk of pregnancy complications. Thus decidual natural killer cells (dNK) that express high levels of KIR may have a predominant role in pregnancy outcome. Although the dNK effect has been associated with cytokine and growth factor production to promote placentation, here we show that the combination of maternal KIR and HLA-C is also a factor that determines protection against placental human cytomegalovirus (HCMV) infections. Expression of KIR2DS1 by dNK increases the cytotoxic response of dNK to HLA-C2+ HCMV-infected maternal decidual stromal cells, preventing viral spread and placental pathology. The combination of the activating killer cell Ig-like receptor 2DS1 (KIR2DS1) expressed by maternal decidual natural killer cells (dNK) and the presence of its ligand, the HLA-C allotype HLA-C2, expressed by fetal trophoblasts, reduces the risk of developing pregnancy complications. However, no molecular or cellular mechanism explains this genetic correlation. Here we demonstrate that KIR2DS1+ dNK acquired higher cytotoxic function than KIR2DS1− dNK when exposed to human cytomegalovirus (HCMV)-infected decidual stromal cells (DSC), particularly when DSCs express HLA-C2. Furthermore, dNK were unable to degranulate or secrete cytokines in response to HCMV-infected primary fetal extravillous trophoblasts. This emphasizes the immunological challenge to clear placental viral infections within the immune-privileged placenta. Activation of dNK through KIR2DS1/HLA-C2 interaction increases their ability to respond to placental HCMV infection and may limit subsequent virus-induced placental pathology. This mechanism is directly related to how KIR2DS1 expressed by dNK reduces development of severe pregnancy complications such as miscarriages and preterm delivery.
Long Short-Term Memory for Speaker Generalization in Supervised Speech Separation

Speech separation can be formulated as learning to estimate a time-frequency mask from acoustic features extracted from noisy speech. For supervised speech separation, generalization to unseen noises and unseen speakers is a critical issue. Although deep neural networks (DNNs) have been successful in noise-independent speech separation, DNNs are limited in modeling a large number of speakers. To improve speaker generalization, a separation model based on long short-term memory (LSTM) is proposed, which naturally accounts for temporal dynamics of speech. Systematic evaluation shows that the proposed model substantially outperforms a DNN-based model on unseen speakers and unseen noises in terms of objective speech intelligibility. Analyzing LSTM internal representations reveals that LSTM captures long-term speech contexts. It is also found that the LSTM model is more advantageous for low-latency speech separation and it, without future frames, performs better than the DNN model with future frames. The proposed model represents an effective approach for speaker- and noise-independent speech separation.
Enumerating Flow Trees in the FHSV Example 60 1 . Introduction and qualitative discussion of basic ideas

In four dimensional N=2 supergravity theories, BPS bound states near marginal stability are described by configurations of widely separated constituents with nearly parallel central charges. When the vacuum moduli can be dialed adiabatically until the central charges become anti -parallel, a paradox arises. We show that this paradox is always resolved by the existence of “bound state transformation walls” across which the nature of the bound state changes, although the index does not jump. We find that there are two distinct phenomena that can take place on these walls, which we call recombination and conjugation. The latter is associated to the presence of singularities at finite distance in moduli space. Consistency of conjugation and wall-crossing rules near these singularities leads to new constraints on the BPS spectrum. Singular loci supporting massless vector bosons are particularly subtle in this respect. We argue that the spectrum at such loci necessarily contains massless magnetic monopoles, and that bound states around them transform by intricate hybrids of conjugation and recombination.
Internet of Things for Smart Railway: Feasibility and Applications

The explosively growing demand of Internet of Things (IoT) has rendered broadscale advancements in the fields across sensors, radio access, network, and hardware/software platforms for mass market applications. In spite of the recent advancements, limited coverage and battery for persistent connections of IoT devices still remains a critical impediment to practical service applications. In this paper, we introduces a cost-effective IoT solution consisting of device platform, gateway, IoT network, and platform server for smart railway infrastructure. Then, we evaluate and demonstrate the applicability through an in-depth case study related to IoT-based maintenance by implementing a proof of concept and performing experimental works. The IoT solution applied for the smart railway application makes it easy to grasp the condition information distributed over a wide railway area. To deduce the potential and feasibility, we propose the network architecture of IoT solution and evaluate the performance of the candidate radio access technologies for delivering IoT data in the aspects of power consumption and coverage by performing an intensive field test with system level implementations. Based on the observation of use cases in interdisciplinary approaches, we figure out the benefits that the IoT can bring.
A P ] 1 M ay 2 01 1 LARGE TIME WELLPOSDNESS TO THE 3-D CAPILLARY-GRAVITY WAVES IN THE LONG WAVE REGIME

In the regime of weakly transverse long waves, given long-wave initial data, we prove that the nondimensionalized water wave system in an infinite strip under influence of gravity and surface tension on the upper free interface has a unique solution on [0, T/ε] for some ε independent of constant T. We shall prove in the subsequent paper [22] that on the same time interval, these solutions can be accurately approximated by sums of solutions of two decoupled Kadomtsev-Petviashvili (KP) equations.
Smooth and Efficient Policy Exploration for Robot Trajectory Learning

Many policy search algorithms have been proposed for robot learning and proved to be practical in real robot applications. However, there are still hyperparameters in the algorithms, such as the exploration rate, which requires manual tuning. The existing methods to design the exploration rate manually or automatically may not be general enough or hard to apply in the real robot. In this paper, we propose a learning model to update the exploration rate adaptively. The overall algorithm is a combination of methods proposed by other researchers. Smooth trajectories for the robot can be produced by the algorithm and the updated exploration rate maximizes the lower bound of the expected return. Our method is tested in the ball-in-cup problem. The results show that our method can receive the same learning outcome as the previous methods but with fewer iterations.
THE EFFECT OF REAL AND ARTIFICIAL EARNINGS MANAGEMENT ON THE VALUE RELEVANCE OF EARNINGS

The goal of the present research is to study the effect of real and artificial earnings management on the value relevance of earnings in firms accepted in Tehran Stock Exchange. Therefore, 66 companies accepted in Tehran Stock Exchange for the financial period between the years 2007 and 2012, were selected. The companies under investigations belonged to 6 active industries in the Stock Exchange. In the present study we have used Jones (Dechow et al., 1995) adjusted model to measure artificial earnings management and based on the studies carried out by Roychowdhury (2006) we have used three models of: (1) usual level of discretionary expenses estimation (focusing on firm’s selling, general, and administrative expenses) model, (2) usual level of production costs estimation model, and (3) usual level of operational cash flows estimation model to measure real earnings management. To test the research hypotheses we have used a multi-variable regression model. The research results show that real and artificial earnings management has a negative effect on the value relevance of earnings. This means that by increasing real management and also artificial management of earnings, the value relevance of earnings decreases.
A Large Scale Crowd Density Classification Using Spatio-Temporal Local Binary Pattern

Increasing world wide population is leading to dense crowd gathering at public places. Due to mass gathering at large scale, crowd related disaster has been frequently occurred. In order to prevent crowd calamities, automated crowd scene analysis has been a topic of great interest. Density is the status of crowd which is essential to classify in visual surveillance system primarily for security aspects. Most of the existing techniques work on detection and tracking of individuals. Due to fewer pixels per target, multiple occlusion and perspective effects etc., detection and tracking of individuals is a complex task in dense crowd scenarios. This paper presents a novel strategy for large scale crowd density classification powered by dynamic texture analysis. This approach consists of an interest points detection followed by spatio-temporal feature extraction. A rotation invariant spatio-temporal local binary (RIST-LBP) pattern is proposed to extract dynamic texture of the moving crowd. Further, a multi-class support vector regression is adopted for density classification. We also include a tracking step which tracks the selected interest points over the video frames for crow flow estimation. We validate our proposed approach on three different datasets such as PETS, UCF and CUHK which vary in density ranging from low to very dense. The performance of our proposed approach is compared with most commonly used pixel based statistics. Our approach has the advantage of low computational complexity with high efficiency in real world applications of video surveillance.
Knowledge-Based Modeling and Natural Computing for Coordination in Pervasive Environments

In this paper we start with the assumption that coordination in complex systems can be understood in terms of presence and location of information. We propose a modeling framework which supports an integrated view of these two aspects of coordination (which we call knowledge diffusion). For this sake we employ methods from ontological modeling, modal logics, fuzzy logic and membrane computing. We demonstrate how these techniques can be combined in order to support the reasoning about knowledge and very abstract behavioral descriptions. As an example we discuss the notion of distributed action and show how it can be treated in our framework. Finally we exploit the special features of our architecture in order to integrate bio-inspired coordination mechanisms which rely on the exchange of molecules (i.e. uninterpreted messages).
Immune cell response to strenuous resistive breathing: comparison with whole body exercise and the effects of antioxidants

Background/hypothesis Whole body exercise (WBE) changes lymphocyte subset percentages in peripheral blood. Resistive breathing, a hallmark of diseases of airway obstruction, is a form of exercise for the inspiratory muscles. Strenuous muscle contractions induce oxidative stress that may mediate immune alterations following exercise. We hypothesized that inspiratory resistive breathing (IRB) alters peripheral blood lymphocyte subsets and that oxidative stress mediates lymphocyte subpopulation alterations following both WBE and IRB. Patients and methods Six healthy nonathletes performed two WBE and two IRB sessions for 45 minutes at 70% of VO2 maximum and 70% of maximum inspiratory pressure (Pimax), respectively, before and after the administration of antioxidants (vitamins E, A, and C for 75 days, allopurinol for 30 days, and N-acetylcysteine for 3 days). Blood was drawn at baseline, at the end of each session, and 2 hours into recovery. Lymphocyte subsets were determined by flow cytometry. Results Before antioxidant supplementation at both WBE end and IRB end, the natural killer cell percentage increased, the T helper cell (CD3+ CD4+) percentage was reduced, and the CD4/CD8 ratio was depressed, a response which was abolished by antioxidants only after IRB. Furthermore, at IRB end, antioxidants promoted CD8+ CD38+ and blunted cytotoxic T-cell percentage increase. CD8+ CD45RA+ cell percentage changes were blunted after antioxidant supplementation in both WBE and IRB. Conclusion We conclude that IRB produces (as WBE) changes in peripheral blood lymphocyte subsets and that oxidative stress is a major stimulus predominantly for IRB-induced lymphocyte subset alterations.
Incremental ETL Pipeline Scheduling for Near Real-Time Data Warehouses

: We present our work based on an incremental ETL pipeline for on-demand data warehouse maintenance. Pipeline parallelism is exploited to concurrently execute a chain of maintenance jobs, each of which takes a batch of delta tuples extracted from source-local transactions with commit timestamps preceding the arrival time of an incoming warehouse query and calculates ﬁnal deltas to bring relevant warehouse tables up-to-date. Each pipeline operator runs in a single, non-terminating thread to process one job at a time and re-initializes itself for a new one. However, to continuously perform incremental joins or maintain slowly changing dimension tables (SCD), the same staging tables or dimension tables can be concurrently accessed and updated by distinct pipeline operators which work on di � erent jobs. Inconsistencies can arise without proper thread coordinations. In this paper, we proposed two types of consistency zones for SCD and incremental join to address this problem. Besides, we reviewed existing pipeline scheduling algorithms in our incremental ETL pipeline with consistency zones.
Enhanced in vitro transcytosis of simian immunodeficiency virus mediated by vaccine-induced antibody predicts transmitted/founder strain number after rectal challenge.

BACKGROUND The time to acquisition of simian immunodeficiency virus (SIV) infection following low-dose repeated rectal challenge correlated inversely with the number of transmitted/founder strains among macaques vaccinated with ALVAC-SIV/gp120 or gp120 alone. We determined if the ability of postvaccination, prechallenge sera to enhance SIVmac251 transcytosis across epithelial cells was associated with transmitted/founder strain number. METHODS Transcytosis was carried out by exposing sera and SIVmac251 to the apical surface of human endometrial carcinoma (HEC-1A) cells at pH 6.0 and 12 hours later quantifying virus in fluid bathing the basolateral cell surface (maintained at pH 7.4). These conditions allow Fc neonatal receptor (FcRn)-dependent shuttling of virus across cells. RESULTS There was a strong correlation between the amount of virus transcytosed and number of transmitted variants (R = 0.86, P < .0001). We also found that 4 animals who remained uninfected after repeated rectal challenges had lower serum transcytosis activity than did 19 animals who subsequently became infected (P = .003). Using immunohistochemistry, we demonstrated FcRn on columnar epithelial cells facing the lumen of the macaque rectum. CONCLUSIONS Vaccine-induced antibody capable of enhancing transcytosis in vitro via FcRn may play a role in determining transmitted/founder strain number and infection outcomes following in vivo challenge.
A b-Learning Approach for Electrical Engineering Based on Wireless Access to Pedagogical e-Content

This paper describes a novel pedagogical approach and its application to undergraduate Electrical Engineering courses. A b-learning model is proposed where students are subject to different types of interactions and diverse learning experiences characterised by the type learning session and pedagogical agent in use. These include face-to-face teaching sessions in the classroom, standalone e-learning sessions, working groups and supervised laboratory work. A wireless LAN infrastructure, as part of the Portuguese Electronic University Project, is used for the e-learning component of the proposed approach. In practice, this is an application of the “anywhere, at anytime” concept to learning processes by extending classroom boundaries in both space and availability domains. From a technical point a view, the paper addresses the information network infrastructure used and its quality of service in multimedia communications, the econtent generation, its characteristics, requirements and possible adaptation to heterogeneous environments. A student survey was conducted to evaluate technical and pedagogical aspects of the learning process using the proposed model. The results show this is a promising approach towards flexible learning in Higher Education Institutions and also revealed that students tend to adapt rather slowly to emerging pedagogical approaches, though they are truly in favour to use IT in their own learning processes.
Micropropagation of Caper ( Capparis spinosa L . ) from Wild Plants

Caper bush is grown commercially to obtain unopened flower buds (capers) used as a condiment in salads and sauces and in the manufacture of cosmetics and medicines. Capers play an important role in the food industry and have become a costly and a commercially valuable product. The effect of various treatments on the behavior of in vitro consecutive micropropagation stages of Capparis spinosa was studied and a micropropagation protocol was developed. Many media were tested; Murashige and Skoog (MS) at different basal salt strengths, modified MS (1⁄2MSD) and woody plant medium (WPM) for the establishment stage. WPM was found to be the best medium for establishment of mother plants. Multiple shoots were obtained on WPM medium supplemented with 0.8 mgL kinetin in combination with 0.05 mgL indole-3-butyric acid and 0.1 mgL gibberellic acid. High frequency (80%) of rooting was obtained on 1⁄2MS medium supplemented with 5 mgL indole-3-acetic acid. Regenerated plantlets were successfully acclimatized with 63% survival. _____________________________________________________________________________________________________________
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.