Miracle-dz/UniFAR-FTUs huggingface dataset:
VHDL Modeling of Particle Swarm Optimization Algorithm

In this paper, we present the design, modeling and analysis of particle swarm optimization (PSO) algorithm in Very- High-Speed Integrated Circuit Hardware Description Language (VHDL). PSO performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. This is especially useful for optimizing IIR and nonlinear adaptive filters. This paper describes the models of PSO algorithm and presents a circuit architecture that facilitates efficient hardware implementations. The results of the function simulation in the Active HDL platform demonstrate the successful design and modeling of the PSO algorithm.
SVDD-based Mechanical Fault Diagnosis for Fiberboard Gluing System

This paper studies the problem that it is difficult to collect the fault information when diagnosing the gluing system of fiberboard. A novel binary-classification method based on support vector data description (SVDD) for fault diagnosis is proposed. Vibration signals are used as data for fault diagnosis, kernel principal component analysis (KPCA) is employed for feature extraction of the normal and fault examples, and SVDD algorithm as classifier is used for fault diagnosis. Experiments show that SVDD algorithm is practical and efficient, and has better identification rate than artificial neural network when lack of unknown fault training examples.
The role of effort in moderating the anxiety – performance relationship: Testing the prediction of processing efficiency theory in simulated rally driving

Abstract We tested some of the key predictions of processing efficiency theory using a simulated rally driving task. Two groups of participants were classified as either dispositionally high or low anxious based on trait anxiety scores and trained on a simulated driving task. Participants then raced individually on two similar courses under counterbalanced experimental conditions designed to manipulate the level of anxiety experienced. The effort exerted on the driving tasks was assessed though self-report (RSME), psychophysiological measures (pupil dilation) and visual gaze data. Efficiency was measured in terms of efficiency of visual processing (search rate) and driving control (variability of wheel and accelerator pedal) indices. Driving performance was measured as the time taken to complete the course. As predicted, increased anxiety had a negative effect on processing efficiency as indexed by the self-report, pupillary response and variability of gaze data. Predicted differences due to dispositional levels of anxiety were also found in the driving control and effort data. Although both groups of drivers performed worse under the threatening condition, the performance of the high trait anxious individuals was affected to a greater extent by the anxiety manipulation than the performance of the low trait anxious drivers. The findings suggest that processing efficiency theory holds promise as a theoretical framework for examining the relationship between anxiety and performance in sport.
Forward Bias Trading in Emerging Markets

This paper investigates the returns to forward bias-trading in dynamic multi-currency strategies in order to empirically assess the limits to speculation hypothesis in foreign exchange markets. The results suggest that bias-trading strategies allow for economically significant excess returns, represent attractive diversification devices, and contain low downside risk. Furthermore, enriching carry-trade portfolios with emerging market currencies results in large diversification gains. Overall, the findings are in line with the widespread use of bias-trading strategies among market professionals and challenge the concept of limits to speculation as an explanation for the forward bias puzzle.
Community-associated methicillin-resistant Staphylococcus aureus colonization burden in HIV-infected patients.

BACKGROUND The epidemic of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has had a disproportionate impact on patients with human immunodeficiency virus (HIV). METHODS We evaluated CA-MRSA colonization burden (number of colonized sites per total number sampled) among HIV-infected and HIV-negative inpatients within 72 hours of hospitalization. From March 2011 through April 2012, we obtained cultures from nasal and extranasal sites (throat, axilla, inguinal, perirectal, and chronic wound if present) and collected risk factor data. RESULTS Of 745 patients (374 HIV-infected, 371 HIV-negative), 15.7% were colonized with CA-MRSA at any site: 20% of HIV and 11% of HIV-negative patients (relative prevalence=1.8, P=.002). HIV-infected patients had a higher prevalence of nasal, extranasal, and exclusive extranasal colonization as well as higher colonization burden. Perirectal and inguinal areas were the extranasal sites most frequently colonized, and 38.5% of colonized patients had exclusive extranasal colonization. Seventy-three percent of isolates were identified as USA300. Among HIV-infected patients, male sex, younger age, and recent incarceration were positively associated whereas Hispanic ethnicity was negatively associated with higher colonization burden. Among HIV-negative patients, temporary housing (homeless, shelter, or substance abuse center) was the only factor associated with higher colonization burden. Predictors of USA300 included HIV, younger age, illicit drug use, and male sex; all but 1 colonized individual with current or recent incarceration carried USA300. CONCLUSIONS HIV-infected patients were more likely to have a higher CA-MRSA colonization burden and carry USA300. In certain populations, enhanced community and outpatient-based infection control strategies may be needed to prevent CA-MRSA cross-transmission and infection.
Screening of a Rhodococcus Cosmid Library , via Gene Homology , to Identify Novel Genes Associated with the Bioconversion of Indene to Indandiol

Non-Scientific Abstract Throughout the past decade, AIDS has become one of the most threatening and prevalent diseases in America. As the number of patients suffering from the potentially fatal disease increases, so does the demand for different types of drug therapy. One of the more successful therapies is Crixivan , a drug produced by MERCK. Although highly effective at slowing the spread of AIDS in the body, it is incredibly difficult and expensive to synthesize in the lab, and thus is hard to obtain at a reasonable price. Alternate means have been employed to solve this problem, the most successful being the genetic engineering of bacteria to produce certain key components for the synthesis of Crixivan. It has been shown that the bacterium Rhodococcus erythropolis I24 can convert molecules of indene to indandiol, a key ingredient in the making of Crixivan. However, the actual gene or genes responsible for this process are still unknown. We sought to identify any and all genes related to this pathway, so that they can be utilized to help speed up the production of Crixivan, and lower the cost of AIDS drug therapy across the nation.
Microgrid control: A comparative study on control strategies for controlling the circulating current

Maintaining power quality in microgrid which is a building block of smart grids very important issue. A small deviation in control variables of any source in a microgrid can lead to circulating current, which can cause malfunction of entire system. Circulating current, plays a major role in affecting the power quality. In this paper two control schemes to control the circulating current are proposed and compared. These control schemes aim at controlling the circulating current and maximizing the microgrid efficiency under islanded mode of operation. A Simulink model is developed and the effectiveness of the two control schemes is tested through simulation in MATLAB. Simulation result reveals that control scheme based on multilevel inverter over isolation diode for controlling the circulating current in microgrid offers better performance and conversion efficiency.
Prior Art Retrieval Using Various Patent Document Fields Contents

In this paper, we report our approach to retrieve patent documents based on the prior art. We use the standard Information Retrieval (IR) techniques which contain indexing and retrieval processes. We use various combinations of document fields for the query formulation. Based on the evaluation summary, we achieve the best result for the combinations of invention-title, description and claims fields in terms of precision and recall.
A Close Binary Nucleus in the Most Oxygen-Poor Planetary Nebula PN G135.9+55.9

We report Far Ultraviolet Spectroscopic Explorer and new deep optical spectroscopic observations of PN G135.9+55.9, the most oxygen-poor planetary nebula located in the Galactic halo. These observations allow us to estimate the gravity of the central star by fitting the profile of the observed H I absorption lines with non-LTE model atmospheres. Our best-fit implies that the central star is still in a pre-white dwarf stage. We also find large variability of the radial velocities of the absorption component of the Balmer lines on a timescale of hours. This is direct evidence that the nucleus of PN G135.9+55.9 is a close binary. The large semiamplitude of the radial velocity variations and the probably short period suggest a massive compact companion, likely a white dwarf. Although our orbital solutions are very preliminary, they indicate that the total mass of the system probably exceeds the Chandrasekhar limit. If confirmed, this would make this binary the potential progenitor of a Type Ia supernova.
Random Sets Approach and its Applications

The random sets approach is heuristic in nature and has been inspired by the growing speed of computations. For example, we can consider a large number of classifiers where any single classifier is based on a relatively small subset of randomly selected features or random sets of features. Using cross-validation we can rank all random sets according to the selected criterion, and use this ranking for further feature selection. Another application of random sets was motivated by the huge imbalanced data, which represent significant problem because the corresponding classifier has a tendency to ignore patterns with smaller representation in the training set. Again, we propose to consider a large number of balanced training subsets where representatives from both patterns are selected randomly. The above models demonstrated competitive results in two data mining competitions.
Piezoelectric-on-Silicon Lateral Bulk Acoustic Wave Micromechanical Resonators

This paper reports on the design, fabrication, and characterization of piezoelectrically-transduced micromechanical single-crystal-silicon resonators operating in their lateral bulk acoustic modes to address the need for high-Q microelectronic-integrable frequency-selective components. A simple electromechanical model for optimizing performance is presented. For verification, resonators were fabricated on 5-mum-thick silicon-on- insulator substrates and use a 0.3-mum zinc oxide film for transduction. A bulk acoustic mode was observed from a 240 mum times 40 mum resonator with a 600-Omega impedance (Q=3400 at P=1 atm) at 90 MHz. A linear resonator absorbed power of -0.5 dBm and an output current of 1.3 mA rms were measured. The same device also exhibited a Q of 12 000 in its fundamental extensional mode at a pressure of 5 torr.
Particle Filtering of Database assisted Positioning Estimates using a novel Similarity Measure for GSM Signal Power Level Measurements

In this paper we present a novel and statistically motivated similarity measure for database assisted positioning of GSM mobile terminals by evaluating signal power level reports which are transmitted regulary. Unlike a simple Euclidian distance measure, the proposed scheme incorporates inherent information about signal power level measurements requested by the serving base station but not reported by the mobile terminal. Furthermore we show how the Monte Carlo method of nonlinear post filtering using particle filtering helps to obtain better position estimates and surprisingly also helps to reduce the computational complexity. Results are presented for real field data.
Exploring multiple sleep modes in on/off based energy efficient HPC networks

Energy efficiency is one of the key challenges in high-performance computing (HPC). The current target of 1 ExaFlop in 20 MW requires a ten-fold improvement in energy efficiency, which is only possible through significant improvements in the energy efficiency throughout the system. Interconnects are particularly inefficient, since their links are always on, consuming full power in order to provide low latency, even though the average interconnect utilization is low. To address the above, the Ethernet standards committee in-charge of 40/100/400Gb Ethernet has opted to include protocols that define low power modes, specifically Fast-Wake, alongside the older Deep-Sleep, to make interconnect links energy proportional. With these standards ratified as recently as March 2014, it is unclear how these low power modes can be used in HPC. While energy efficiency is critical, techniques with excessive performance overheads are unlikely to be adopted in HPC. To this end, this paper performs the first detailed analysis of Fast-Wake mode for link energy savings in the context of HPC. Our results show that a combination of Fast-Wake and Deep-Sleep can reduce link energy savings by up to 70% with less than 1% performance overheads. However, we show how the parameters of these low power modes must be carefully configured to obtain the right trade-offs in energy and performance. We believe that our analysis could benefit interconnect vendors looking to use these low power modes for deployment in HPC.
Coscor framework for DoA estimation on wireless array sensor network

Compressive sensing (CS) is applied to enable real time direction of arrival (DoA) estimation in a wireless array sensor network by significantly reduce the amount of sensor data that needs to be transmitted over wireless channels. This is accomplished by (i) random sampling of data samples collected at sensor arrays, and (ii) transmitting only the sign-bit of the data samples over wireless channels. It is shown that CS based DoA estimation is capable of delivering similar performance as conventional DoA methods while requiring less than 10% of wireless bandwidth to transmit data. This proposed scheme is validated using a prototype sensor array test bed. Preliminary experimental results clearly validate the superior performance of this proposed scheme.
The Java Based Programmable Logic Controller. New Techniques in Control and Supervision of a Flexible Manufacturing Cell.

In this chapter, we explain new techniques and technologies applied to the control of a flexible manufacturing cell. We have proposed a new control platform: a Java based Programmable Logic Controller (PLC). The Java PLC comprises several modules where the real time control, the communication with industrial fieldbuses and the supervision via web technologies have been developed. This control architecture (Piedrafita & Villarroel 2006) and development environment is based on Petri Nets (PNs), Sequential Function Charts (SFCs) and the Real Time Java programming language. Our objective is to explore the application of new control techniques of manufacturing systems, and to test the use of the Java programming language as a platform for those techniques. To demonstrate the practical utility of the techniques, we have applied them to the control of a flexible manufacturing cell. This research follows earlier studies at the University of Zaragoza on the software implementation of PN. In those studies, Ada95 was the implementation language (Garcia & Villarroel 1996). For the current study, Java was chosen for the following reasons: • The possibility of executing the same code on different platforms. • To compare the Ada95 and Java implementations using the same concurrent and realtime characteristics. • Java is a language that is beginning to be used in the development of control and embedded systems. • Java has a real time extension that allows the necessary time predictability required in these types of applications. From the perspective of the software implementation of Discrete Event Control Systems, we have translated into the Java language some classic PN implementation techniques such as Enabled Transitions or Representing Places. We have also developed SFC implementation techniques such as the Deferred Transit Evolution Model and Immediate Transit Evolution Model. In the execution of a Petri net, a task, the coordinator, makes the firing of transitions and updates the marking. We propose a new PN implementation technique called concurrent coordinators, in which centralized and decentralized ideas are merged. The net is decomposed into several subnets following, for example, a functional criterion, and a
Green Infrastructure and the Sustainability Concept: A Case Study of the Greater New Orleans Urban Water Plan

This Thesis is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rightsholder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself.
Input Domain Reduction through Irrelevant Variable Removal and Its Effect on Local, Global, and Hybrid Search-Based Structural Test Data Generation

Search-Based Test Data Generation reformulates testing goals as fitness functions so that test input generation can be automated by some chosen search-based optimization algorithm. The optimization algorithm searches the space of potential inputs, seeking those that are “fit for purpose,” guided by the fitness function. The search space of potential inputs can be very large, even for very small systems under test. Its size is, of course, a key determining factor affecting the performance of any search-based approach. However, despite the large volume of work on Search-Based Software Testing, the literature contains little that concerns the performance impact of search space reduction. This paper proposes a static dependence analysis derived from program slicing that can be used to support search space reduction. The paper presents both a theoretical and empirical analysis of the application of this approach to open source and industrial production code. The results provide evidence to support the claim that input domain reduction has a significant effect on the performance of local, global, and hybrid search, while a purely random search is unaffected.
Affinity-Dependent Cytolytic Activity Human CTLs Mediate Antibody TCR-Like Human Antibodies Expressed on

The permanent genetic programming via gene transfer of autologous T cells with cell surface receptors directed toward tumor-related Ags holds great promise for the development of more-speciﬁc tumor therapies. In this study we have explored the use of Abs directed to MHC-peptide complexes (or TCR-like Abs) to engraft CTLs with exquisite speciﬁcity for cancer cells. First, we afﬁnity matured in vitro a previously selected TCR-like Ab, Fab-G8, which is highly speciﬁc for the peptide melanoma-associated Ag-A1 presented by the HLA-A1 molecule. A combination of L chain shufﬂing, H chain-targeted mutagenesis, and in vitro selection of phage display libraries yielded a Fab-G8 Ab derivative, Fab-Hyb3, with an 18-fold improved afﬁnity yet identical peptide ﬁne speciﬁcity. Fab-G8 and Fab-Hyb3 were expressed on primary human T lymphocytes as cell surface-anchored Fab, demonstrating that T cells expressing the high-afﬁnity Fab-Hyb3 molecule eradicate tumor cells much more effectively. Further-more, the gain in ligand-binding afﬁnity resulted in a 2-log improvement in the detection of peptide/MHC complexes on melanoma-associated Ag-A1 peptide-loaded cells. In summary, an afﬁnity-matured Ab speciﬁcally recognizing a cancer-related peptide/MHC complex was generated and used to improve the tumor cell killing capacity of human T cells. This strategy, based on engraftment of T cells with in vitro engineered Abs, is an attractive alternative to the laborious, and in many cases unsuccessful, generation of highly potent tumor-speciﬁc T lymphocytes. The Journal of Immunology, 2002, 169: 1110–1118.
Measurements of some Relaxed Properties of Simulated Point Processes

This paper presents an algorithmic method for measurements of relaxed statistical properties of point processes (PP). In particular it makes it possible to determ ine the probability density function of the residual waiting time, which is the distance between an arbitrary origin and the first point of the PP posterior to this origin and also the probabilities of count ing n points of the PP in an interval open by an instant independent of the PP. These quantities are rarely accessible from a theoretical calculation. However, in the cases where this is possible, various experiments show quite good agreement with the theory.
Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.
Diagnosis of Heart Diseases with the Help of thePECG

In this paper we present an enhanced version of the system called PECG. It uses a semi-automatic preprocessor to list primitives from a real ECG signal. The main idea that with the help of the system we can infer patient-speciic analyser programs. In the learning process we started from an over-generalized grammar and learnt both the syntax and the semantics of an ECG grammar. We applied this method to infer T and P waves recognizers for each patient. These waves play an important role in ECG analysis.