Miracle-dz/UniFAR-FTUs huggingface dataset:
Protective Effects of Urinary Trypsin Inhibitor on Vascular Permeability Following Subarachnoid Hemorrhage in a Rat Model

Inflammation and apoptosis play important roles in increasing vascular permeability following subarachnoid hemorrhage (SAH). The objective of this study was to evaluate whether urinary trypsin inhibitor (UTI), a serine protease inhibitor, attenuates vascular permeability by its antiinflammatory and antiapoptotic effects after experimental SAH.
Multi-level supply-modulated Chireix outphasing for LTE signals

This work presents a dynamic characterization of a multi-level Chireix outphasing (ML-CO) power amplifier (PA) with modulated signals. The ML-CO technique combines the advantages of envelope tracking and outphasing architectures by limiting the supply modulation to discrete levels (enabled by an efficient power DAC modulator) and using outphasing for fine amplitude control. We describe the development of an experimental test bench able to supply phase- and time-aligned modulated signals for outphasing and supply modulation simultaneously. Pulse characterization is used to design a multilevel memory-less polynomial DPD. The linearized ML-CO PA is demonstrated with 1.4 MHz and 10 MHz LTE signals at 9.7 GHz. For both signals, the average total power consumption is reduced by a factor of two when supply modulation is used. For the 9.3 dB PAR, 1.4 MHz signal the PA operates with 38% average drain efficiency at 0.54 W average output power.
Experimental Analysis of Electro-Discharge Machining Parameters for Minimum Tool Wear Rate on Machinability of Carbon Fiber/Epoxy Composites Using Taguchi Method

Electro-Discharge machining is a process of removal of material through melting or vaporization caused by high frequency spark discharge. A good selection of proper tool (electrode) material is necessary for minimum tool wear rate. Number of techniques are employed for optimization of process parameters but in this paper we used Design of experiments via Taguchi method. This method is used to determine the optimal process parameter in EDM during machining of carbon fiber/epoxy composites with copper-cadmium tool. Four major control factors named as peak current (Ip), gap voltage (Vg), pulse-on-time (Ton) and duty cycle (η) are considered to determine the effect on TWR. In Taguchi method for design of experiment three levels of each parameter has been taken into consideration by using standard L9 orthogonal array and finding out optimal setting of machining parameters for minimum tool wear rate. A relationship among selected process parameters is developed by doing Response surface methodology with the help of MINITAB 15 software. Analysis of Variance (ANOVA) is also employed for obtaining the optimal process parameters.
Issues in Software Cache Coherence

Large scale multiprocessors can provide the computational power needed to solve some of the larger problems of science and engineering today. Shared memory provides an attractive and intuitive programming model that makes good use of programmer time and effort. Shared memory however requires a coherence mechanism to allow caching for performance and to ensure that processors do not use stale data in their computation. Directory-based coherence, which is the hardware mechanism of choice for large scale multiprocessors, can be expensive both in terms of hardware cost and in terms of the intellectual effort needed to design a correct, efficient protocol. For scalable multiprocessor designs with network-based interconnects, software-based coherence schemes provide an attractive alternative. In this paper we evaluate a new adaptive software coherence protocol, and demonstrate that smart software coherence protocols can be competitive with hardware-based coherence for a large variety of programs. We then discuss issues that affect the performance of software coherence protocols and proceed to suggest algorithmic and architectural enhancements that can help improve software coherence performance.
Frequency adaptive repetitive control of grid connected inverter for wind turbine applications

This paper is concerned with the current control of grid connected inverters based on a simple frequency adaptive repetitive controller. A grid-tie inverter is desired to behave as a robust current source inverter with the high gain in order to improve both the reference current tracking and disturbance rejection capabilities. The variations of grid impedance and frequency in distribution networks are some important challenges in the design of grid interfaced converters. In this research, the performance of a frequency adaptive repetitive controller (RC), which is based on the internal model principle (IMP), is investigated for a single-phase grid connected inverter.
SELECTIVE BREEDING FOR HIGH ENDURANCE RUNNING INCREASES HINDLIMB SYMMETRY

Abstract Comparative studies provide correlational evidence of morphological adaptations for high locomotor performance, such as the classical indicators of cursoriality in mammals, long limbs and high metatarsal/femur ratios. More recently, enlarged femoral condyles have been suggested as an adaptation for high endurance running in the genus Homo. Asymmetry of locomotor appendages should adversely affect locomotor abilities, but this has not been studied in a rigorous evolutionary context. We used experimental evolution to test for morphological adaptations associated with high voluntary wheel running in selectively bred lines of mice. Surprisingly, the classical indicators of cursoriality had not evolved in concert with high activity levels. Instead, high runners had larger femoral condyles and reduced directional asymmetry of hindlimb bones. We hypothesize that greater limb symmetry and larger femoral heads are general adaptations associated with sustained, high-speed locomotion.
Inflation from geometrical tachyons

We propose an alternative formulation of tachyon inflation using the geometrical tachyon arising from the time dependent motion of a BPS $D3$-brane in the background geometry due to $k$ parallel $NS$5-branes arranged around a ring of radius $R$. Because of the fact that the mass of this geometrical tachyon field is $\sqrt{2/k}$ times smaller than the corresponding open-string tachyon mass, we find that the slow-roll conditions for inflation and the number of e-foldings can be satisfied in a manner that is consistent with an effective 4-dimensional model and with a perturbative string coupling. We also show that the metric perturbations produced at the end of inflation can be sufficiently small and do not lead to the inconsistencies that plague the open-string tachyon models. Finally we argue for the existence of a minimum of the geometrical tachyon potential which could give rise to a traditional reheating mechanism.
Development of a Humanized Antibody with High Therapeutic Potential against Dengue Virus Type 2

Background Dengue virus (DENV) is a significant public health threat in tropical and subtropical regions of the world. A therapeutic antibody against the viral envelope (E) protein represents a promising immunotherapy for disease control. Methodology/Principal Findings We generated seventeen novel mouse monoclonal antibodies (mAbs) with high reactivity against E protein of dengue virus type 2 (DENV-2). The mAbs were further dissected using recombinant E protein domain I-II (E-DI-II) and III (E-DIII) of DENV-2. Using plaque reduction neutralization test (PRNT) and mouse protection assay with lethal doses of DENV-2, we identified four serotype-specific mAbs that had high neutralizing activity against DENV-2 infection. Of the four, E-DIII targeting mAb DB32-6 was the strongest neutralizing mAb against diverse DENV-2 strains. Using phage display and virus-like particles (VLPs) we found that residue K310 in the E-DIII A-strand was key to mAb DB32-6 binding E-DIII. We successfully converted DB32-6 to a humanized version that retained potency for the neutralization of DENV-2 and did not enhance the viral infection. The DB32-6 showed therapeutic efficacy against mortality induced by different strains of DENV-2 in two mouse models even in post-exposure trials. Conclusions/Significance We used novel epitope mapping strategies, by combining phage display with VLPs, to identify the important A-strand epitopes with strong neutralizing activity. This study introduced potential therapeutic antibodies that might be capable of providing broad protection against diverse DENV-2 infections without enhancing activity in humans.
Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification

The exponential rise in the demand of vision based traffic surveillance systems have motivated academia-industries to develop optimal vehicle detection and classification scheme. In this paper, an adaptive learning rate based Gaussian mixture model (GMM) algorithm has been developed for background subtraction of multilane traffic data. Here, vehicle rear information and road dash-markings have been used for vehicle detection. Performing background subtraction, connected component analysis has been applied to retrieve vehicle region. A multilayered AlexNet deep neural network (DNN) has been applied to extract higher layer features. Furthermore, scale invariant feature transform (SIFT) based vehicle feature extraction has been performed. The extracted 4096-dimensional features have been processed for dimensional reduction using principle component analysis (PCA) and linear discriminant analysis (LDA). The features have been mapped for SVM-based classification. The classification results have exhibited that AlexNet-FC6 features with LDA give the accuracy of 97.80%, followed by AlexNet-FC6 with PCA (96.75%). AlexNet-FC7 feature with LDA and PCA algorithms has exhibited classification accuracy of 91.40% and 96.30%, respectively. On the contrary, SIFT features with LDA algorithm has exhibited 96.46% classification accuracy. The results revealed that enhanced GMM with AlexNet DNN at FC6 and FC7 can be significant for optimal vehicle detection and classification.
Thermal—mechanical coupled simulation of a solid brake disc in repeated braking cycles

Abstract Braking operation is a process that converts kinetic energy and potential energy of a moving automobile into other energies. Most of the mechanical energy is transferred into heat. Frictional heat, which is generated on the interface of the disc and pads, can cause high temperature during the braking process. The objective of the present work is to determine temperature distribution, thermal distortion, and thermal stress in a solid disc by three-dimensional modelling for repeated braking. Braking is applied four times in the present study; the vehicle is decelerated from 100 to 50 kph with 0.6 g, after which the velocity is again accelerated to 100 kph. In order to simulate the friction heat behaviour accurately in repeated braking, the moving heat source, which is defined by time and space variable, is applied on the frictional surface. The temperature field and thermal stress in the disc present a non-uniformity characteristic because of the moving heat source. Temperature and thermal stress of the point on the frictional surface of the brake disc present fluctuation in the braking operation. The coning angle due to the non-uniform radial temperature distribution varies with temperature. Thermal fatigue is also discussed in this article.
Birkhoff-von Neumann input buffered crossbar switches

Previously, we proposed a scheduling algorithm that is capable of providing rate guarantees for input-buffered crossbar switches. The algorithm is based on a decomposition result by Birkhoff (1945) and von Neumann (1953) for a doubly substochastic matrix. An input buffered crossbar switch that uses such an algorithm is called the Birkhoff-von Neumann switch in this paper. For the Birkhoff-von Neumann switch, the rate guarantees are uniformly good for all non-uniform traffic, and it does not require framing or internal speedup. Our objective of this paper is to make the Birkhoff-von Neumann switch more complete and practical. We do so by addressing three topics: providing best-effort services in the Birkhoff-von Neumann switch, hardware implementation of the switch fabric, and multistage Birkhoff-von Neumann switches.
Cancelable Face Recognition Using Random Multiplicative Transform

The generation of cancelable and privacy preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a novel approach for cancelable biometric authentication using random multiplicative transform. The proposed method transforms the original biometric feature vector through element-wise multiplication with a random vector, and the sorted index numbers of the resulting vector in the transformed domain are stored as the biometric template. The changeability and privacy protecting properties of the generated biometric template are analyzed in detail. The effectiveness of the proposed method is well supported by extensive experimentation on a face verification problem.
Vaccine-induced monoclonal antibodies targeting circumsporozoite protein prevent Plasmodium falciparum infection.

Malaria, which is the result of Plasmodium falciparum infection, is a global health threat that resulted in 655,000 deaths and 216 million clinical cases in 2010 alone. Recent phase 3 trials with malaria vaccine candidate RTS,S/AS01 (RTS,S) in children has demonstrated modest efficacy against clinical and severe malaria. RTS,S targets the pre-erythrocytic phase of the disease and induces high antibody titers against the P. falciparum circumsporozoite protein (CSP) and a moderate CD4(+) T cell response. The individual contribution of these adaptive immune responses to protection from infection remains unknown. Here, we found that prophylactic administration of anti-CSP mAbs derived from an RTS,S-vaccinated recipient fully protected mice with humanized livers from i.v. - and mosquito bite–delivered P. falciparum sporozoite challenge. Titers of anti-CSP that conveyed full protection were within the range observed in human RTS,S vaccine recipients. Increasing anti-CSP titers resulted in a dose-dependent reduction of the liver parasite burden. These data indicate that RTS,S-induced antibodies are protective and provide sterilizing immunity against P. falciparum infection when reaching or exceeding a critical plasma concentration.
Conditional Random Fields for Machine Translation System Combination

Minimum Error Rate Training (MERT) as an effective parameters learning algorithm is widely applied in machine translation and system combination area. However, there exists an ambiguity problem in respect to the training goal and it is hard for MERT to tackle, that is different parameters may lead to the same minimum error rate in training but greatly different performances in testing. We propose a novel training objective as the unique goal for training towards, namely partial references, and by use of conditional random fields (CRF) to cast the decoding procedure in system combination as a sequence labeling problem. Experiments on Chinese-English translation test sets show that our approach significantly outperforms the MERT-based baselines with less training time.
Classifying Insects from SEM Images Based on Optimal Classifier Selection and D-S Evidence Theory

SUMMARY In this paper, an insect classiﬁcation method using scanning electron microphotographs is presented. Images taken by a scanning electron microscope (SEM) have a unique problem for classiﬁcation in that visual features di ﬀ er from each other by magniﬁcations. Therefore, direct use of conventional methods results in inaccurate classiﬁcation results. In order to successfully classify these images, the proposed method generates an optimal training dataset for constructing a classiﬁer for each magniﬁca-tion. Then our method classiﬁes images using the classiﬁers constructed by the optimal training dataset. In addition, several images are generally taken by an SEM with di ﬀ erent magniﬁcations from the same insect. Therefore, more accurate classiﬁcation can be expected by integrating the results from the same insect based on Dempster-Shafer evidence theory. In this way, accurate insect classiﬁcation can be realized by our method. At the end of this paper, we show experimental results to conﬁrm the e ﬀ ectiveness of the proposed method.
On the analysis of average time complexity of estimation of distribution algorithms

Estimation of Distribution Algorithm (EDA) is a well-known stochastic optimization technique. The average time complexity is a crucial criterion that measures the performance of the stochastic algorithms. In the past few years, various kinds of EDAs have been proposed, but the related theoretical study on the time complexity of these algorithms is relatively few. This paper analyzed the time complexity of two early versions of EDA, the Univariate Marginal Distribution Algorithm (UMDA) and the Incremental UMDA (IUMDA). We generalize the concept of convergence to convergence time, and manage to estimate the upper bound of the mean First Hitting Times (FHTs) of UMDA (IUMDA) on a well-known pseudo-modular function, which is frequently studied in the field of genetic algorithms. Our analysis shows that UMDA (IUMDA) has O(n) behaviors on the pseudo-modular function. In addition, we analyze the mean FHT of IUMDA on a hard problem. Our result shows that IUMDA may spend exponential generations to find the global optimum. This is the first time that the mean first hitting times of UMDA (IUMDA) are theoretically studied.
Cooperative relay selection scheme based on geographical information

In this paper, we propose a cooperative relay selection scheme based on geographical information in cellular networks. According to the current user equipment density in a certain cell, we firstly choose the potential relays among the user equipments. Then, the localization algorithm based on time difference of arrival has been adapted to locate the potential nodes. Finally, we select the cooperative relay from the potential relays based on the known geographical information. We also give simulation experiments. Simulation results show that the scheme can choose the best cooperative relay to increase effectively the user capacity and to improve system performance as well.
LEM-3 – A LEM Domain Containing Nuclease Involved in the DNA Damage Response in C. elegans

The small nematode Caenorhabditis elegans displays a spectrum of DNA damage responses similar to humans. In order to identify new DNA damage response genes, we isolated in a forward genetic screen 14 new mutations conferring hypersensitivity to ionizing radiation. We present here our characterization of lem-3, one of the genes identified in this screen. LEM-3 contains a LEM domain and a GIY nuclease domain. We confirm that LEM-3 has DNase activity in vitro. lem-3(lf) mutants are hypersensitive to various types of DNA damage, including ionizing radiation, UV-C light and crosslinking agents. Embryos from irradiated lem-3 hermaphrodites displayed severe defects during cell division, including chromosome mis-segregation and anaphase bridges. The mitotic defects observed in irradiated lem-3 mutant embryos are similar to those found in baf-1 (barrier-to-autointegration factor) mutants. The baf-1 gene codes for an essential and highly conserved protein known to interact with the other two C. elegans LEM domain proteins, LEM-2 and EMR-1. We show that baf-1, lem-2, and emr-1 mutants are also hypersensitive to DNA damage and that loss of lem-3 sensitizes baf-1 mutants even in the absence of DNA damage. Our data suggest that BAF-1, together with the LEM domain proteins, plays an important role following DNA damage – possibly by promoting the reorganization of damaged chromatin.
Finding Attributes in the Web Using a Parser

In previous work, we found that a great deal of information about noun attributes can be extracted from the Web using simple text patterns, and that enriching vector-based models of concepts with this information about attributes led to drastic improvements in noun categorization. We extend this previous work by comparing concept descriptions extracted using patterns with descriptions extracted with a parser. Our results show that it is computationally more efficient to use simple text patterns than parsing text.
Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks

In large-scale multiple testing problems, data are often collected from heterogeneous sources and hypotheses form into groups that exhibit different characteristics. Conventional approaches, including the pooled and separate analyses, fail to efficiently utilize the external grouping information. We develop a compound decision theoretic framework for testing grouped hypotheses and introduce an oracle procedure that minimizes the false nondiscovery rate subject to a constraint on the false discovery rate. It is shown that both the pooled and separate analyses can be uniformly improved by the oracle procedure. We then propose a data-driven procedure that is shown to be asymptotically optimal. Simulation studies show that our procedures enjoy superior performance and yield the most accurate results in comparison with both the pooled and separate procedures. A real-data example with grouped hypotheses is studied in detail using different methods. Both theoretical and numerical results demonstrate that exploiting external information of the sample can greatly improve the efficiency of a multiple testing procedure. The results also provide insights on how the grouping information is incorporated for optimal simultaneous inference.
Robust carrier frequency offset estimation for OFDM systems in doppler shift channels

In this paper, a robust carrier frequency offset (CFO) estimation scheme is proposed for OFDM systems in Doppler shift channels. We first present a training symbol (TS) pattern that has the same value so as to provide a wide CFO estimation range and to ensure the accuracy of the CFO estimation. Using the TS, we then present maximum-Likelihood (ML) and averaging least square (LS) algorithms for performing acquisition and tracking steps of the CFO estimation. The performance of the proposed technique is evaluated with respect to a mean-square-error (MSE) and a bit-error-rate (BER) in a typical urban (TU) channel.
Design and development of a microfluidic platform for use with colorimetric gold nanoprobe assays

Júri: Presidente: Doutora Ana Maria Félix Trindade Lobo, Professora Catedrática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Arguentes: Doutor João Lemos Pinto, Professor Catedrático da Universidade de Aveiro Doutora Graça Maria Henriques Minas, Professora Associada da Escola de Engenharia da Universidade do Minho Vogais: Doutor Rodrigo Ferrão de Paiva Martins, Professor Catedrático da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Doutora Elvira Maria Correia Fortunato, Professora Catedrática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Doutor Adélio Miguel Magalhães Mendes, Professor Associado com Agregação da Faculdade de Engenharia da Universidade do Porto Doutor Pedro Miguel Ribeiro Viana Baptista, Professor Associado com Agregação da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Doutora Maria Goreti Ferreira Sales, Professora Adjunta do Instituto Superior de Engenharia do Porto do Instituto Politécnico do Porto Doutor Abel González Oliva, Investigador Auxiliar do Instituto de Tecnologia Química e Biológica António Xavier da Universidade Nova de Lisboa