Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Hospital-Based Diabetes Case Management: An Example From a Northern Taiwan Regional Hospital

&NA; The aim of this study was to design, implement and evaluate disease outcomes at a regional hospital-based case management program of care for patients with type 2 diabetes. A medical team and practice guidelines were established in line with the health insurance strategy of Taiwan's Bureau of National Health Insurance (BNHI) and American Diabetes Association (ADA) Standards of Care for Diabetes (2003 edition). Also, a set of self-care booklets was designed suitable for use by the subject group. The study was prospective and followed the patients from enrollment to one year. Patient outcomes were determined based on laboratory examinations and recorded self-care behavior. Data were collected at enrollment and over 4 follow-up times within a one year period. Generalized Estimating Equation (GEE) multiple linear regression and logistic regression were used for repeated measurements and adjustments of the effects of specific prognostic factors. Sixty subjects diagnosed with type 2 diabetes (mean duration 3.25 years) were recruited. All participants were married with a mean age of 52.5 years. A majority (58.3%) was male and 65% were ethnic Hakka. Self-care knowledge and behavior accomplishment rates were: taking medications by oneself, 91.3% (knowing medicines, 25.4%); hypoglycemia management, 23.3%; monitoring blood sugar, 46.7%; exercise, 35.8%; diet management, 51.7% and foot care, 92.8%. Significantly improved ADA diabetes care standard items included HbA1C (p < .0001), fasting glucose (p < .01) and triglycerides (p < .05). The study incorporated evidence-based guidelines, public health insurance strategies and self-care booklets into a protocol to provide comprehensive care. The implemented diabetes program achieved diabetes care goals and improved patient self-care.
Tactile bump display using electro-rheological fluid

This study proposes a novel technique to display tactile sensation using electro-rheological fluid (ERF). ERF changes its rheological characteristics according to the electric field applied. The ERF used in this study generates relatively high yield stress and behaves as a solid when subjected to a strong electric field. Using this solid-liquid phase transition, we propose a novel device which provides a tactile sensation, i.e., tactile bump display. Applying electric field at a specific position in an ERF chamber, the corresponding ERF behaves as solid at the position. This solid-state ERF gives tactile sensation like a physical bump to a user. We fabricate in this study a prototype, which may realize the above-mentioned idea, and characterize it. We obtained the following results by experiments. First, the tactile bump display could make the users recognize their finger position on a flat surface by creating a tactile bump. Second, we confirmed that the tactile bump might significantly improve the accuracy and the precision of touch typing.
Object-oriented finite element and inverse analysis to determine elastic-plastic properties of an arc-sprayed composite coating

The characterization of thermal sprayed coatings is often limited to microstructural analysis to evaluate the coatings morphology. Indentation is commonly used to determine the mechanical properties of different kinds of engineering materials. However, due to the complex structure of thermal sprayed coatings few results have been obtained so far. In this paper instrumented microindentation and meshing in the OOF2 program was combined with a inverse analysis to determine elastic-plastic properties of an arc-sprayed FeCNiBSi-(W/Ti)C Coating.
A New Coupled Metric Learning for Real-time Anomalies Detection with High-Frequency Field Programmable Gate Arrays

Billions of internet end-users and device to device connections contribute to the significant data growth in recent years, large scale, unstructured, heterogeneous data and the corresponding complexity present challenges to the conventional real-time online fraud detection system security. With the advent of big data era, it is expected the data analytic techniques to be much faster and more efficient than ever before. Moreover, one of the challenges with many modern algorithms is that they run too slowly in software to have any practical value. This paper proposes a Field Programmable Gate Array (FPGA) -based intrusion detection system (IDS), driven by a new coupled metric learning to discover the inter- and intra-coupling relationships against the growth of data volumes and item relationship to provide a new approach for efficient anomaly detections. This work is experimented on our previously published NetFlow-based IDS dataset, which is further processed into the categorical data for coupled metric learning purpose. The overall performance of the new hardware system has been further compared with the presence of conventional Bayesian classifier and Support Vector Machines classifier. The experimental results show the very promising performance by considering the coupled metric learning scheme in the FPGA implementation. The false alarm rate is successfully reduced down to 5% while the high detection rate (=99.9%) is maintained.
Piezoresistive Carbon-based Hybrid Sensor for Body-Mounted Biomedical Applications

For body-mounted sensor applications, the evolution of soft condensed matter sensor (SCMS) materials offer conformability andit enables mechanical compliance between the body surface and the sensing mechanism. A piezoresistive hybrid sensor and compliant meta-material sub-structure provided a way to engineer sensor physical designs through modification of the mechanical properties of the compliant design. A piezoresistive fiber sensor was produced by combining a thermoplastic elastomer (TPE) matrix with Carbon Black (CB) particles in 1:1 mass ratio. Feedstock was extruded in monofilament fiber form (diameter of 300 microns), resulting in a highly stretchable sensor (strain sensor range up to 100%) with linear resistance signal response. The soft condensed matter sensor was integrated into a hybrid design including a 3D printed metamaterial structure combined with a soft silicone. An auxetic unit cell was chosen (with negative Poisson’s Ratio) in the design in order to combine with the soft silicon, which exhibits a high Poisson’s Ratio. The hybrid sensor design was subjected to mechanical tensile testing up to 50% strain (with gauge factor calculation for sensor performance), and then utilized for strain-based sensing applications on the body including gesture recognition and vital function monitoring including blood pulse-wave and breath monitoring. A 10 gesture Natural User Interface (NUI) test protocol was utilized to show the effectiveness of a single wrist-mounted sensor to identify discrete gestures including finger and hand motions. These hand motions were chosen specifically for Human Computer Interaction (HCI) applications. The blood pulse-wave signal was monitored with the hand at rest, in a wrist-mounted. In addition different breathing patterns were investigated, including normal breathing and coughing, using a belt and chest-mounted configuration.
Resilience: A Dynamic Perspective

Identifying characteristics that distinguish youth who achieve adaptive outcomes in the face of adversity from those who do not has furthered our understanding of developmental psychopathology. However, accumulating evidence indicates that particular characteristics rarely serve exclusively risk or protective functions, that individuals who seem resilient on one index often do not seem so on other indices, and that individuals often are not equally resilient across contexts. These findings call for a dynamic conceptualisation of resiliency that can account for why the ways children cope with stressors vary across domain, development, and context. We organise resiliency research into a framework based on a recently proposed dynamic conceptualisation of personality (Mischel & Shoda, 1995). This framework assumes that understanding why some children show resilience in the face of adversity whereas others show difficulties requires identifying: (a) the content of and relational structure among relevant psychological mediators such as competencies, expectancies, values, and goals; and (b) the relation between these psychological mediators and relevant features of the environment. To illustrate the potential of this approach to further our understanding of resiliency, we examine and reconsider the link between IQ and conduct problems.
How far is SLAM from a linear least squares problem?

Most people believe SLAM is a complex nonlinear estimation/optimization problem. However, recent research shows that some simple iterative methods based on linearization can sometimes provide surprisingly good solutions to SLAM without being trapped into a local minimum. This demonstrates that hidden structure exists in the SLAM problem that is yet to be understood. In this paper, we first analyze how far SLAM is from a convex optimization problem. Then we show that by properly choosing the state vector, SLAM problem can be formulated as a nonlinear least squares problem with many quadratic terms in the objective function, thus it is clearer how far SLAM is from a linear least squares problem. Furthermore, we explain that how the map joining approaches reduce the nonlinearity/nonconvexity of the SLAM problem.
Constructing Risk Measures from Uncertainty Sets

We illustrate the correspondence between uncertainty sets in robust optimization and some popular risk measures in finance and show how robust optimization can be used to generalize the concepts of these risk measures. We also show that by using properly defined uncertainty sets in robust optimization models, one can construct coherent risk measures and address the issue of the computational tractability of the resulting formulations. Our results have implications for efficient portfolio optimization under different measures of risk.
Partially Distribution-Free Learning of Regular Languages from Positive Samples

Regular languages are widely used in NLP today in spite of their shortcomings. Efficient algorithms that can reliably learn these languages, and which must in realistic applications only use positive samples, are necessary. These languages are not learnable under traditional distribution free criteria. We claim that an appropriate learning framework is PAC learning where the distributions are constrained to be generated by a class of stochastic automata with support equal to the target concept. We discuss how this is related to other learning paradigms. We then present a simple learning algorithm for regular languages, and a self-contained proof that it learns according to this partially distribution free criterion.
Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.
A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections

An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.
l-ARABINOSE-SENSITIVE, l-RIBULOSE 5-PHOSPHATE 4-EPIMERASE-DEFICIENT MUTANTS OF ESCHERICHIA COLI

Englesberg, E. (University of Pittsburgh, Pittsburgh, Pa.), R L. Anderson, R. Weinberg, N. Lee, P. Hoffee, G. Huttenhauer, and H. Boyer. l-Arabinose-sensitive, l-ribulose 5-phosphate 4-epimerase-deficient mutants of Escherichia coli. J. Bacteriol. 84:137–146. 1962—l-Arabinose-negative mutants of Escherichia coli B/r, ara-53 and ara-139, are deficient in the enzyme l-ribulose 5-phosphate 4-epimerase; ara-53, further analyzed, accumulates large quantities of l-ribulose 5-phosphate when incubated with l-arabinose. The mutant sites are closely linked to the left of the previously ordered l-arabinose mutant sites, and probably represent the structural gene for l-ribulose 5-phosphate 4-epimerase (gene D) in the l-arabinose operon. The inducible levels of l-arabinose isomerase and l-ribulose 5-phosphate 4-epimerase vary correspondingly as a result of mutation in the structural gene for l-ribulokinase (gene B), further substantiating the dual structural and regulatory function of this gene locus. Ara-53 and ara-139 are strongly inhibited by l-arabinose and give rise to l-arabinose-resistant mutants. The one resistant mutant analyzed still lacks the 4-epimerase but is deficient in l-ribulokinase and has increased l-arabinose isomerase activity, a characteristic of a type of mutation in the B gene. It is proposed that accumulation of l-ribulose 5-phosphate is responsible for the inhibition, and that mutation to resistance will involve mutation in the A, B, C, permease, or repressor genes, thus providing a direct method for isolating these types of l-arabinose-negative mutants. Glucose prevents and cures the l-arabinose inhibition.
A Critical Distance Analysis of the Effect of Crack Length on Toughness and Fatigue in Compact Bone

The plane strain fracture toughness, KIC, has a constant value for long cracks, but when specimens containing short cracks are tested it is often found that the measured toughness is less than the long-crack value, and tends to decrease with decreasing crack length. A similar effect occurs when measuring the cyclic stress intensity range, ΔK, corresponding to a given rate of crack growth da/dN. Some experimental data are available in the published literature to show that cortical bone displays these short-crack effects for crack lengths of the order of millimeters or less. The hypothesis of the present work was that these effects can be predicted using an approach known as the Theory of Critical Distances. This is the first time that this approach has been used to predict short crack effects in bone.Copyright © 2008 by ASME
A simple Formalism for Calculation and Verification of Dose in Asymmetric X-ray Fields

A simple method for dose calculations in the center of asymmetrically collimated fields is presented. We use existing base data for symmetric fields and a measured correction factor that allows for off-axis variations of the incident primary beam fluence and beam quality. Our model does not require any considerations of collimator and phantom scatter factors. This algorithm is verified by comparing its predictions with measurements of a series of asymmetric fields at a Clinac 600C with 6 MV photons and a Clinac 2300C/D with 6 and 18 MV photons. Calculated and measured doses agree mostly within 1% for all energies. Mean deviations were found to be less than 0.5%; a maximum deviation of 1.09% was observed in one case.
Formation of Adeno-Associated Virus Circular Genomes Is Differentially Regulated by Adenovirus E4 ORF6 and E2a Gene Expression

ABSTRACT A central feature of the adeno-associated virus (AAV) latent life cycle is persistence in the form of both integrated and episomal genomes. However, the molecular processes associated with episomal long-term persistence of AAV genomes are only poorly understood. To investigate these mechanisms, we have utilized a recombinant AAV (rAAV) shuttle vector to identify circular AAV intermediates from transduced HeLa cells and primary fibroblasts. The unique structural features exhibited by these transduction intermediates included circularized monomer and dimer virus genomes in a head-to-tail array, with associated specific base pair alterations in the 5′ viral D sequence. In HeLa cells, the abundance and stability of AAV circular intermediates were augmented by adenovirus expressing the E2a gene product. In the absence of E2a, adenovirus expressing the E4 open reading frame 6 gene product decreased the abundance of AAV circular intermediates, favoring instead the linear replication form monomer (Rfm) and dimer (Rfd) structures. In summary, the formation of AAV circular intermediates appears to represent a new pathway for AAV genome conversion, which is consistent with the head-to-tail concatemerization associated with latent-phase persistence of rAAV. A better understanding of this pathway may increase the utility of rAAV vectors for gene therapy.
A Survey of Urban Reconstruction

This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved.
Improving Gaussian processes classification by spectral data reorganizing

We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitioned into two parts: along the feature with the highest frequency bandwidth. Secondly, for each part of the data, only the spectrally homogeneous features are chosen and used (the rest discarded) for GP classification. In this way, anisotropy of the data is lessened from the frequency point of view. Tests on synthetic data as well as real datasets show that our approach is effective and outperforms automatic relevance determination (ARD).
Light and electron microscopic studies of the distribution of microtubule‐associated protein 2 in rat brain: A difference between dendritic and axonal cytoskeletons

A specific antiserum was used to ascertain the distribution of microtubule‐associated protein 2 (MAP2) in the rat brain at the light and electron microscope levels. Light microscopy showed MAP2 to be present only in neurons, and only in the dendrites and the perikaryon of each cell. This same polarized distribution pattern was found in the Purkinje, Golgi, basket, stellate, and granule cells of the cerebellum, and also in neurons of the hippocampus, the olfactory bulb, and the midbrain. While labelling of the dendritic arborization was extensive and intense, MAP2 density tended to decrease in the proximal dendritic trunk. Particularly in large neurons (e.g., Purkinje, Golgi, and pyramidal cells), staining was reproducibly weaker in the cell body than in the main dendrites. Dendritic contours generally appeared smooth, without any evidence of staining of dendritic spines. An electron microscope examination of the cerebellum confirmed the presence of MAP2 reactivity in neurons and its absence from axons and non‐neuronal cells. MAP2 in dendrites was associated with microtubules, while MAP2 in neuronal perikarya was associated with polyribosomes. There was no evidence of specific staining in dendritic spines and in postsynaptic densities. MAP2 is a novel dendritic marker and labels part of a specific dendritic cytoskeleton, different from that in axons and non‐neuronal cells.
An ACT-R Model of Memory Applied to Finding the Optimal Schedule of Practice

When a person confronts the task of memorizing a collection of facts some questions have to be answered about how to optimize their learning. Perhaps the most important of these questions is how the practices for each item should be scheduled. The most basic suggestion from psychology is that the practices should be spaced as widely as possible. Since Bahrick (1979) it has been clear that wide spacing intervals result in better performance after a long delay. However, while wide spacing is likely to help, it may also be important to consider item differences when making practice scheduling decisions. Memory items (such as the Japanese-English pairedassociates we have used in this research) are not equally difficult for each subject to learn. In postexperimental interviews subjects often mention that some items were easier to remember because they could find some mediator to connect the cue to the response. Further, the data shows that some items are on average easier for all subjects. Perhaps the cue (the Japanese word) for these items is fairly close to some common English word that can be used to mediate between the cue and response. It is useful to consider how we might optimize learning by attending to this variability in item difficulty. For instance, if we knew an item was easier we could schedule it less often, while if it was more difficult we could schedule it more often. This may have two benefits. First, if we did not use this procedure and instead gave the items equal practice we might expect that some items would be overlearned (the easier items) and some items would be learned poorly (the harder items). This uneven learning might be undesirable. Second, by saving practices on easy items and then distributing them to hard items it might be possible to achieve gains in performance. To attend to this variability requires a model of memory that does two things. First, it needs to describe accurately how practice in general leads to recall performance by characterizing memory effects due to the frequency, recency, and spacing of practices. Second, to optimize performance for individual items, the model needs to be able to characterize both initial average difficulty for each item (estimated by fitting data from prior experiments, from now on referred to as item difficulty) and the difficulty particular subjects have with each item (which will need to be estimated based on performance with the item over the course of each subject’s learning, from now on referred to as subject/item difficulty).