Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Diagnosing Hybrid Systems

This paper reports on the findings of an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers which experience abrupt, partial or full failure of component devices. The problem we address is: given a hybrid model of system behavior, a history of executed controller actions, and a history of observations, including an observation of behavior that is aberrant relative to the model of expected behavior, determine what fault occurred to have caused the aberrant behavior. Determining a diagnosis can be cast as a search problem to find the most likely model for the data. Unfortunately, the search space is extremely large. To reduce search space size and to identify an initial set of candidate diagnoses, we propose to exploit techniques originally applied to qualitative diagnosis of continuous systems. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA’s Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.
Measurement of Glucose Uptake in Cultured Cells

Facilitative glucose uptake transport systems are ubiquitous in animal cells and are responsible for transporting glucose across cell surface membranes. Evaluation of glucose uptake is crucial in the study of numerous diseases and metabolic disorders such as myocardial ischemia, diabetes mellitus, and cancer. Detailed in this unit are laboratory methods for assessing glucose uptake into mammalian cells. The unit is divided into five sections: (1) a brief overview of glucose uptake assays in cultured cells; (2) a method for measuring glucose uptake using radiolabeled 3‐O‐methylglucose; (3) a method for measuring glucose uptake using radiolabeled 2‐deoxyglucose (2DG); (4) a microplate method for measuring 2DG‐uptake using an enzymatic, fluorometric assay; and (5) a microplate‐based method using a fluorescent analog of 2DG. © 2015 by John Wiley & Sons, Inc.
A Multi-Layer Intruder Detection System for Multi-Hop Cluster-Based Sensor Networks

Wireless Sensor Networks (WSNs) are playing a fundamental role in emerging pervasive platforms that have potential to host a wide range of next generation civil and military applications. Inexpensive sensor nodes are deployed to the sensing area with little mobility and high density. Furthermore, in many scenarios WSNs are of interest to adversaries and they become susceptible to some types of attacks since they are deployed in open and unprotected environments and are constituted of cheap small devices. Since preventive mechanisms provide no surety to hold the intruder, an intrusion detection system (IDS) running beside preventive mechanism will greatly increase the security of network. Introducing IDS in wireless media is not as similar to that of wired systems due to the differences in capturing audit data and availability of resources. This paper proposes a method of implementing the IDS on cluster-based WSNs using multi-hop data communication.
Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms

While numerous page segmentation algorithms have been proposed in the literature, there is lack of comparative evaluation of these algorithms. In the existing performance evaluation methods, two crucial components are usually missing: 1) automatic training of algorithms with free parameters and 2) statistical and error analysis of experimental results. We use the following five-step methodology to quantitatively compare the performance of page segmentation algorithms: 1) first, we create mutually exclusive training and test data sets with groundtruth, 2) we then select a meaningful and computable performance metric, 3) an optimization procedure is then used to search automatically for the optimal parameter values of the segmentation algorithms on the training data set, 4) the segmentation algorithms are then evaluated on the test data set, and, finally, 5) a statistical and error analysis is performed to give the statistical significance of the experimental results. In particular, instead of the ad hoc and manual approach typically used in the literature for training algorithms, we pose the automatic training of algorithms as an optimization problem and use the simplex algorithm to search for the optimal parameter value. A paired-model statistical analysis and an error analysis are then conducted to provide confidence intervals for the experimental results of the algorithms. This methodology is applied to the evaluation of live page segmentation algorithms of which, three are representative research algorithms and the other two are well-known commercial products, on 978 images from the University of Washington III data set. It is found that the performance indices of the Voronoi, Docstrum, and Caere segmentation algorithms are not significantly different from each other, but they are significantly better than that of ScanSoft's segmentation algorithm, which, in turn, is significantly better than that of X-Y cut.
Seed mediated synthesis of highly mono-dispersed gold nanoparticles in the presence of hydroquinone

Gold nanoparticles (AuNPs) are being studied for several biomedical applications, including drug delivery, biomedical imaging, contrast agents and tumor targeting. The synthesis of nanoparticles with a narrow size distribution is critical for these applications. We report the synthesis of highly mono-dispersed AuNPs by a seed mediated approach, in the presence of tri-sodium citrate and hydroquinone (HQ). AuNPs with an average size of 18 nm were used for the synthesis of highly mono-dispersed nanocrystals of an average size 40 nm, 60 nm, 80 nm and ∼100 nm; but the protocol is not limited to these sizes. The colloidal gold was subjected to UV–vis absorbance spectroscopy, showing a red shift in lambda max wavelength, peaks at 518.47 nm, 526.37 nm, 535.73 nm, 546.03 nm and 556.50 nm for AuNPs seed (18 nm), 40 nm, 60 nm, 80 nm and ∼100 nm respectively. The analysis was consistent with dynamic light scattering and electron microscopy. Hydrodynamic diameters measured were 17.6 nm, 40.8 nm, 59.8 nm, 74.1 nm, and 91.4 nm (size by dynamic light scattering—volume %); with an average poly dispersity index value of 0.088, suggesting mono-dispersity in the size distribution, which was also confirmed by transmission electron microscopy analysis. The advantage of a seed mediated approach is a multi-step growth of nanoparticle size that enables us to control the number of nanoparticles in the suspension, for size ranging from 24.5 nm to 95.8 nm. In addition, the HQ-based synthesis of colloidal nanocrystals allowed control of the particle size and size distribution by tailoring either the number of seeds, amount of gold precursor or reducing agent (HQ) in the final reaction mixture.
Application of Cleaner Technology Concepts in the Arm Coil Assembly Process of Hard Disk Drive Manufacturing

The objectives of this research are to improve raw material and energy consumption efficiency, as well as reduce defects and the use of chemicals in the arm coil assembly process of hard disk drive manufacturing in the case study company by applying the Cleaner Technology concepts. The four main sequential steps used in this research were: (1) pre-assessment, (2) assessment, (3) feasibility study, and (4) implementation. In the first step, raw data, such as process flows, raw material usage and defects data were collected. In the second step, the loss during production and causes of loss were analyzed. Opportunities to reduce raw material, chemical and energy wastage could then be recommended. The next step was to evaluate the feasibility and potential benefits of a particular Cleaner Technology opportunity. Finally, in the last step, after a thorough evaluation and implementation of the opportunities to apply clean technology, the results showed that Arm Coil defects could be reduced by improving the production process using the ECRS technique. was that total costs of approximately 239,702 Thai Baht per month were able to be saved.
Face recognition using modular neural networks and the fuzzy Sugeno integral for response integration

We describe a new approach for face recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces. Also, the method for achieving response integration is based on the fuzzy Sugeno integral. Response integration is required to combine the outputs of all the modules in the modular network. We have applied the new approach for face recognition to a real database of faces of students at our institution. Recognition rates with the modular approach were compared against the monolithic single neural network approach to measure the improvement. The results of the new modular neural network approach were excellent overall and also in comparison to the monolithic approach. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 275–291, 2005.
Advances in the Combined Use of Adult Cell Therapy and Scaffolds for Brain Tissue Engineering

No long-term effective treatments are currently available for brain neurological disorders such as focal and global cerebral ischemia, traumatic brain injury (TBI) and neurodegenerative disorders. Current treatments are primarily focused on limiting the damage and slowing the degeneration and, most of them are only symptomatic while function restoration is rarely achieved. This underlies the need for alternative therapies such as brain cell therapy that allows functional replacement of missing or damaged neurons by transplanting cells that may differentiate into the desired phenotype and integrate the host parenchyma, or alternatively rescue the affected neuronal population. Due to the high cell death that occurs during neurodegenerative processes, brain neurological disorders are an ideal target for cell-based therapies. Several teams tried to prevent the loss of neurons or to replace them, using neuronal precursors and lately, since stem cell discovery, embryonic stem (ES) cells, adult stem cells, tissue-derived stem cells or more recently induced pluripotent stem (iPS) cells (for review see (Abeliovich & Doege, 2009; Joyce et al., 2010; Chen & Xiao, 2011; Loewenbruck & Storch, 2011). These studies demonstrated the potential of cell therapy to repair the injured brain even if poor survival of grafted cells was reported (Brundin et al., 1985; Schierle et al., 1999; Brundin et al., 2000; Isacson et al., 2003; Olanow et al., 2003). In this book chapter, brain cell therapy studies performed with adult nontransformed cells that, by nature, allow the use of autologous tissues for transplantation and overcome the immunological, availability, as well as ethical concerns will be reported. It is important to bear in mind that, for organs with a higher level of complexity such as the brain, cell therapy remains a challenging task. Fortunately, relatively recent medical, biological and technological advances in tissue engineering approaches allow functional
FAST MONTE CARLO ALGORITHMS FOR MATRICES II: COMPUTING A LOW-RANK APPROXIMATION TO A MATRIX∗

In many applications, the data consist of (or may be naturally formulated as) an m×n matrix A. It is often of interest to find a low-rank approximation to A, i.e., an approximation D to the matrix A of rank not greater than a specified rank k, where k is much smaller than m and n. Methods such as the singular value decomposition (SVD) may be used to find an approximation to A which is the best in a well-defined sense. These methods require memory and time which are superlinear in m and n; for many applications in which the data sets are very large this is prohibitive. Two simple and intuitive algorithms are presented which, when given an m×n matrix A, compute a description of a low-rank approximation D∗ to A, and which are qualitatively faster than the SVD. Both algorithms have provable bounds for the error matrix A − D∗. For any matrix X, let ‖X‖F and ‖X‖2 denote its Frobenius norm and its spectral norm, respectively. In the first algorithm, c columns of A are randomly chosen. If the m × c matrix C consists of those c columns of A (after appropriate rescaling), then it is shown that from CTC approximations to the top singular values and corresponding singular vectors may be computed. From the computed singular vectors a description D∗ of the matrix A may be computed such that rank(D∗) ≤ k and such that ‖A−D‖ξ ≤ min D:rank(D)≤k ‖A−D‖2ξ + poly(k, 1/c) ‖A‖ 2 F holds with high probability for both ξ = 2, F . This algorithm may be implemented without storing the matrix A in random access memory (RAM), provided it can make two passes over the matrix stored in external memory and use O(cm + c2) additional RAM. The second algorithm is similar except that it further approximates the matrix C by randomly sampling r rows of C to form a r × c matrix W . Thus, it has additional error, but it can be implemented in three passes over the matrix using only constant additional RAM. To achieve an additional error (beyond the best rank k approximation) that is at most ‖A‖F , both algorithms take time which is polynomial in k, 1/ , and log(1/δ), where δ > 0 is a failure probability; the first takes time linear in max(m,n) and the second takes time independent of m and n. Our bounds improve previously published results with respect to the rank parameter k for both the Frobenius and spectral norms. In addition, the proofs for the error bounds use a novel method that makes important use of matrix perturbation theory. The probability distribution over columns of A and the rescaling are crucial features of the algorithms which must be chosen judiciously.
Improving cache behavior of dynamically allocated data structures

Poor data layout in memory may generate weak data locality and poor performance. Code transformations such as loop blocking or interchanging and array padding have addressed this issue for scientific applications. However many generalist applications do not use data arrays, but dynamically allocated heterogeneous data structures. In this paper, we explore two data layout techniques for dynamically allocated data structures: field reorganization, and instance interleaving. The application of these techniques may be guided by program profiling. This allows significant cache behavior improvements on some applications. To support instance interleaving, we developed a specific memory allocation library called ialloc. An ialloc-like library may be of great help in a toolbox for performance tuning of general-purpose applications.
Dynamics of a Polymer Network Based on Dual Sierpinski Gasket and Dendrimer: A Theoretical Approach

In this paper we focus on the relaxation dynamics of a multihierarchical polymer network built through the replication of the dual Sierpinski gasket in the form of a regular dendrimer. The relaxation dynamics of this multihierarchical structure is investigated in the framework of the generalized Gaussian structure model using both Rouse and Zimm approaches. In the Rouse-type approach, we show a method whereby the whole eigenvalue spectrum of the connectivity matrix of the multihierarchical structure can be determined iteratively, thereby rendering possible the analysis of the Rouse-dynamics at very large generations. Remarkably, the general picture that emerges from both approaches, even though we have a mixed growth algorithm and the monomers interactions are taken into account specifically to the adopted approach, is that the multihierarchical structure preserves the individual relaxation behaviors of its constituent components. The theoretical findings with respect to the splitting of the intermediate domain of the relaxation quantities are well supported by experimental results.
Role of SFRS13A in low‐density lipoprotein receptor splicing

Low‐density lipoprotein receptor (LDLR) is a major apolipoprotein E (APOE) receptor and thereby is critical to cholesterol homeostasis and, possibly, Alzheimer disease (AD) development. We previously identified a single nucleotide polymorphism (SNP), rs688:C>T, that modulates LDLR exon 12 splicing and is associated with cholesterol levels in premenopausal women and with Alzheimer disease in men. To gain additional insights into LDLR splicing regulation, we seek to identify splicing factors that modulate LDLR splicing efficiency. By using an in vitro minigene study, we first found that ectopic expression of SFRS3 (SRp20), SFRS13A (SRp38), SFRS13A‐2 (SRp38‐2), and RBMX (hnRNP G) robustly decreased LDLR splicing efficiency. Although SFRS3 and SFRS13A specifically increased the LDLR transcript lacking exon 11, SFRS13A‐2 and RBMX primarily increased the LDLR isoform lacking both exons 11 and 12. When we evaluated the relationship between the expression of these splicing factors and LDLR splicing in human brain and liver specimens, we found that overall SFRS13A expression was significantly associated with LDLR splicing efficiency in vivo. We interpret these results as suggesting that SFRS13A regulates LDLR splicing efficiency and may therefore emerge as a modulator of cholesterol homeostasis. Hum Mutat 31:1–8, 2010. © 2010 Wiley‐Liss, Inc.
IMECE 2003-43439 IMPROVED VEHICLE CRASHWORTHINESS VIA SHAPE OPTIMIZATION

Advancements in high-performance computing and in nonlinear dynamic structural simulation have made it possible to virtually test potential designs prior to building and testing expensive prototypes. These tools alone, however, still require an engineer to develop a design based on intuition and numerous time-consuming and error-prone iterations. The next level of advancement is software to automate the process of iterating over a large number of design scenarios and intelligently seek optimal values for those parameters that strongly affect product performance and cost. While many design optimization approaches are limited to a small number of continuous design variables, the approach described here leads to a productive search over hundreds of variables at a time. This capability has been implemented in a software product called HEEDS (Hierarchical Evolutionary Engineering Design System). HEEDS uses multiple autonomous agents to hierarchically decompose a problem into subsets with highly decomposed overlapped relationships. Decomposition is effected by using different numbers of design variables, different levels of design variable discretization, and/or other problem-specific divideand-conquer rules. The system combines evolutionary search algorithms with local optimization 1 Copyright © 2003 by ASME techniques. Using ABAQUS/Explicit and LS-DYNA as the finite element solver within the HEEDS optimization environment, this process has been applied to several automotive rail designs, resulting in significant gains in performance along with up to 20% reductions in mass compared to baseline rails designed by experienced engineers. Two example applications of this method are described herein.
The placenta as a site of cytomegalovirus infection in guinea pigs

The development of cytomegalovirus (CMV) infection in the placenta was studied in Hartley guinea pigs inoculated at midgestation, and its role in determining the outcome of fetal CMV infection was assessed. A hematogenous spread of CMV from the mother to the placenta occurred early during the course of the infection. However, the virus remained present in placental tissues long after CMV had been cleared from maternal blood (i.e., 3 and 4 weeks postinoculation). At that time, the virus was able to replicate in placental tissues in the presence of specific maternal antibodies. Viral nucleocapsids were seen within nuclei of trophoblastic cells, and virions were present surrounding infected cells. In addition, typical CMV-induced histopathological lesions bearing CMV antigens were consistently localized at the transitional zone between the capillarized labyrinth and the noncapillarized interlobium. Whenever CMV infection of the fetus occurred, virus was isolated from the associated placenta. Among placental-fetal units with CMV-infected placentas, only 27% of the fetuses were found to be infected. In addition, there was a delay in the establishment of the infection in the fetus in relation to the placenta, although frequencies of virus isolation in placental and fetal tissues peaked at 3 weeks after CMV inoculation. These results suggest that during primary CMV infection of pregnant guinea pigs, the placenta not only serves as a reservoir for CMV but also acts to limit transmission of the virus to the fetus.
Interactions of Carbon Nanotubes and Carbon Nanohorns with a Model Membrane Layer and Lung Surfactant In Vitro

A broader use of carbon nanomaterials increases the risk of their inhalation as aerosol dispersed in the air. Inhaled nanometer-sized particles are known to penetrate to the pulmonary region where they interact with the lung surfactant as the first barrier they meet and eventually penetrate to the surface of the cellular layer. This study presents the results of experimental studies of physicochemical interactions between several types of carbon nanomaterials (nanotubes and nanohorns of various size and surface properties) and lipid layers in two qualitatively different experimental systems: Langmuir trough and pulsated drop tensiometer, both providing complementary possibilities to study interfacial properties of the lipid-rich layer. Quantified alterations in mechanical properties if lipid films (equilibrium compressibility, dynamic surface elasticity, and viscosity) indicate that nanocarbons with different wettability may induce concentration-dependent frustration of the lung surfactant and biological membranes in vivo. The observed effects are discussed not only in relation to health effects from nanoparticle inhalation but also to potential medical applications of engineered carbon nanomaterials.
Searching for partners

A combination of the EMARS reaction and the application of mass spectrometry‐based proteomics techniques promises to permit cell‐surface molecular clustering to be analyzed under physiological conditions [Jiang et al., Proteomics 2012, 12, 54–62]. It is very likely that this approach will provide new insights into a wide range of research areas directed at understanding the cell‐surface interactome.
Quantile causal mediation analysis allowing longitudinal data

Mediation analysis has mostly been conducted with mean regression models. With this approach modeling means, formulae for direct and indirect effects are based on changes in means, which may not capture effects that occur in units at the tails of mediator and outcome distributions. Individuals with extreme values of medical endpoints are often more susceptible to disease and can be missed if one investigates mean changes only. We derive the controlled direct and indirect effects of an exposure along percentiles of the mediator and outcome using quantile regression models and a causal framework. The quantile regression models can accommodate an exposure‐mediator interaction and random intercepts to allow for longitudinal mediator and outcome. Because DNA methylation acts as a complex “switch” to control gene expression and fibrinogen is a cardiovascular factor, individuals with extreme levels of these markers may be more susceptible to air pollution. We therefore apply this methodology to environmental data to estimate the effect of air pollution, as measured by particle number, on fibrinogen levels through a change in interferon‐gamma (IFN‐γ) methylation. We estimate the controlled direct effect of air pollution on the qth percentile of fibrinogen and its indirect effect through a change in the pth percentile of IFN‐γ methylation. We found evidence of a direct effect of particle number on the upper tail of the fibrinogen distribution. We observed a suggestive indirect effect of particle number on the upper tail of the fibrinogen distribution through a change in the lower percentiles of the IFN‐γ methylation distribution.