Miracle-dz/UniFAR-FTUs huggingface dataset:
Confined disordered strictly jammed binary sphere packings.

Disordered jammed packings under confinement have received considerably less attention than their bulk counterparts and yet arise in a variety of practical situations. In this work, we study binary sphere packings that are confined between two parallel hard planes and generalize the Torquato-Jiao (TJ) sequential linear programming algorithm [Phys. Rev. E 82, 061302 (2010)] to obtain putative maximally random jammed (MRJ) packings that are exactly isostatic with high fidelity over a large range of plane separation distances H, small to large sphere radius ratio α, and small sphere relative concentration x. We find that packing characteristics can be substantially different from their bulk analogs, which is due to what we term "confinement frustration. " Rattlers in confined packings are generally more prevalent than those in their bulk counterparts. We observe that packing fraction, rattler fraction, and degree of disorder of MRJ packings generally increase with H, though exceptions exist. Discontinuities in the packing characteristics as H varies in the vicinity of certain values of H are due to associated discontinuous transitions between different jammed states. When the plane separation distance is on the order of two large-sphere diameters or less, the packings exhibit salient two-dimensional features; when the plane separation distance exceeds about 30 large-sphere diameters, the packings approach three-dimensional bulk packings. As the size contrast increases (as α decreases), the rattler fraction dramatically increases due to what we call "size-disparity" frustration. We find that at intermediate α and when x is about 0.5 (50-50 mixture), the disorder of packings is maximized, as measured by an order metric ψ that is based on the number density fluctuations in the direction perpendicular to the hard walls. We also apply the local volume-fraction variance σ(τ)(2)(R) to characterize confined packings and find that these packings possess essentially the same level of hyperuniformity as their bulk counterparts. Our findings are generally relevant to confined packings that arise in biology (e.g., structural color in birds and insects) and may have implications for the creation of high-density powders and improved battery designs.
The Journey toward the Patient-Centered Medical Home: A Grounded, Dynamic Theory of Primary Care Transformation

Introduction: This dissertation develops a grounded and dynamic theory of primary care transformation (PCT) in a health service delivery organization (HSDO) implementing the Patient-Centered Medical Home model, in the United States of America. The focus of this theory is on the structural facilitators and challenges to achieving and sustaining high quality primary care. Methodology: Fieldwork performed included semi-structured interviews conducted across the HSDO (n=82), direct observations (n=10 clinics) and archival review. This dissertation utilizes newly-developed methods for theory development and validation, in concert with existing system dynamics methods; with an improved potential to integrate findings across quantitative and qualitative research directions. Results: My theory illuminates how the actions of various stakeholders (medical assistants, managers, clinicians and patients) interact with each other and with the fundamental characteristics of primary care service delivery to create diverse transformation trajectories. Two types of leverage points are identified: policies and preferences. The latter are more difficult to modify as they require changing stakeholders’ mental models. It is the combination of policies and preferences interacting within the system structure that produces hoped-for and feared transformation trajectories. There is no policy that induces success regardless of stakeholder preferences. There are some preferences that induce success or failure regardless of the policies being implemented. Conclusion: Sustaining success requires understanding the system structure within which policies and preferences operate – how decisions are made, their consequences, and the delays involved. Otherwise, transformation risks being overwhelmed by unintended consequences, misunderstood system behavior or impatience. This work presents an improved understanding of what PCT involves, and of how operational and cognitive aspects intersect. Overall, this work is more than a study of transformation. It presents theory, methods and a case for the development of an integrative methodology and paradigm.
Is mindful awareness effective on hope, burnout and self-efficacy among school counsellors in Turkey?

ABSTRACT The mediational role of counsellor burnout and dispositional hope between mindful awareness and counsellor self-efficacy has been examined among 243 professional school counsellors from 72 cities out of 81 in Turkey. With structural equation modelling and a bootstrap method, the results have revealed that counsellor burnout and dispositional hope fully mediated the relationship between mindful awareness and counsellor self-efficacy. Limitations, directions for future research, and practical implications for the counselling field are also discussed.
Abrupt Sensor Fault Diagnosis Based on Wavelet Network

The possible faults of a sensor may be classified as abrupt (sudden) faults and incipient (slowly developing) faults. This paper focuses on the abrupt faults of a sensor. Due to the limited number of scales, a single wavelet amplitude map has not enough scales to describe all details of the signal. The sampling grid in the scale direction is rather sparse, Some of the fault information will be leaked under such sparse grid. To make up for the deficiency of scalar orthogonal wavelet transform in the application of abrupt fault diagnosis, multiwavelet packets transform was introduced into the field of abrupt fault diagnosis. The distribution differences of the signal energy on decomposed multiwavelet scales of the signal before and after the fault occurring are extracted as the fault feature and used as the input of multi-dimensional wavelet network. A new model-free diagnostic method for isolating abrupt sensor faults is developed based on a proposed algorithm of multi-dimensional wavelet network constructing. The method has been proved to be quite effective in the detection of sensor abrupt fault.
Using a Temperature Gradient against the Time in Polyacrylamide Gel Electrophoresis May Eliminate the Need for Stacking Gels

Background and Objectives: Making stacking gels for polyacrylamide gels in the laboratory by conventional methods is laborious and time consuming. Considering the role of temperature in polyacrylamide gels with respect to electrical resistance and viscosity, we assumed that decreasing the temperature would cause an increase in electrical resistance and viscosity. Ultimately, a downward temperature gradient imposed in the first phase of polyacrylamide gel running time would supposedly improve the migration of macromolecules. This project analyzed the effect of temperature gradient on the migration of macromolecules in the continuous gels (without stacking) and compared it with results obtained using stacking gels. Material and Methods: Electrical resistance was calculated using Ohm’s law. Subsequently, to examine the effect of temperature change on macromolecules separation, conformation sensitive gel electrophoresis (CSGE) was used as a model and specimens were run under three different conditions, one of which was prepared with a stacking gel. Results: The electrical resistance showed an inverse relationship with temperature in this study. Separation of the DNA molecules in the continuous gels (with no stacking) was comparable with the conventional method (with stacking). Conclusion: Using a temperature gradient against time may be an alternative method for stacking gels.
A Discovery Resource of Rare Copy Number Variations in Individuals with Autism Spectrum Disorder

The identification of rare inherited and de novo copy number variations (CNVs) in human subjects has proven a productive approach to highlight risk genes for autism spectrum disorder (ASD). A variety of microarrays are available to detect CNVs, including single-nucleotide polymorphism (SNP) arrays and comparative genomic hybridization (CGH) arrays. Here, we examine a cohort of 696 unrelated ASD cases using a high-resolution one-million feature CGH microarray, the majority of which were previously genotyped with SNP arrays. Our objective was to discover new CNVs in ASD cases that were not detected by SNP microarray analysis and to delineate novel ASD risk loci via combined analysis of CGH and SNP array data sets on the ASD cohort and CGH data on an additional 1000 control samples. Of the 615 ASD cases analyzed on both SNP and CGH arrays, we found that 13,572 of 21,346 (64%) of the CNVs were exclusively detected by the CGH array. Several of the CGH-specific CNVs are rare in population frequency and impact previously reported ASD genes (e.g., NRXN1, GRM8, DPYD), as well as novel ASD candidate genes (e.g., CIB2, DAPP1, SAE1), and all were inherited except for a de novo CNV in the GPHN gene. A functional enrichment test of gene-sets in ASD cases over controls revealed nucleotide metabolism as a potential novel pathway involved in ASD, which includes several candidate genes for follow-up (e.g., DPYD, UPB1, UPP1, TYMP). Finally, this extensively phenotyped and genotyped ASD clinical cohort serves as an invaluable resource for the next step of genome sequencing for complete genetic variation detection.
Advanced conceptual network usage in library database queries

This paper describes the generic principles of Decomate-II’s Concept Browser. It discusses the three main problems in using a thesaurus or conceptual network to help users formulate database queries: the network maintenance problem, the network navigation problem, and the network-to-database mapping problem. Possible solutions to all these problems are proposed, based on previous experiences with concept network systems. Care is taken to keep the resulting system suitable for production use in an existing library environment based on Boolean keyword retrieval from a large collection with uncontrolled vocabulary.
(QuarterlyJournal of Experimental Psychology, in press) Are coffee and toffee served in a cup? Ortho-phonologically mediated associative priming

We report three masked associative priming experiments with the lexical decision task that explore whether the initial activation flow of a visually presented word activates the semantic representations of that word’s orthographic/phonological neighbors. The predictions of cascades and serial/modular models of lexical processing differ widely in this respect. Using a masked priming paradigm (SOA=50 ms), words preceded by ortho-phonologically mediated associated “neighbors” (oveja-MIEL, the Spanish for sheep-HONEY; note that oveja is a phonological neighbor of abeja, the Spanish for bee) were recognized more rapidly than words preceded by an unrelated word prime (Experiments 1 and 3). Furthermore, the magnitude of the orthophonologically mediated priming effect (oveja-MIEL) was similar to the magnitude of the associative priming effect (abeja-MIEL). With visible primes and a 250 ms SOA, only the directly associated words showed a priming effect (Experiment 2). These findings pose some problems for a modular account and are more easily interpreted in terms of cascaded models.
Efficient allocation of balancing and ramping costs

Electricity market designs must evolve to incorporate efficiently the large-scale penetration of renewable energy sources and demand side participation. Current renewable energy support schemes and fixed tariff systems fail to address two major power system operation issues and therefore create additional system costs. First, unlike other commodity markets, the provision of reserve capacity is required for reliable operation. Second, power system operation is stressed by increasing flexibility requirements. One way to reduce these costs is to share the costs of ramping and reserve capacity between consumers, producers and the system operator. We present a market mechanism that reduces the cost of reserve capacity and the cost of ramping in a Pareto-efficient manner. Simulation studies demonstrate the cost reduction that this mechanism achieves as compared with a benchmark approach.
SUB-PIXEL PRECISION IMAGE MATCHING FOR DISPLACEMENT MEASUREMENT OF MASS MOVEMENTS USING NORMALISED CROSS-CORRELATION

This study evaluates the performance of two fundamentally different approaches to achieve sub-pixel precision of normalised crosscorrelation when measuring surface displacements on mass movements from repeat optical images. In the first approach, image intensities are interpolated to a desired sub-pixel resolution using a bi-cubic interpolation scheme prior to the actual displacement matching. In the second approach, the image pairs are correlated at the original image resolution and the peaks of the correlation coefficient surface is then located at the desired sub-pixel resolution using three techniques, namely bi-cubic interpolation, parabola fitting and Gaussian fitting. Both principal approaches are applied to three typical mass movement types: rockglacier creep, glacier flow and rock sliding. Their performance is evaluated in terms of matching accuracy and in reference to the images of the resolution they are expected to substitute. Our results show that intensity interpolation using bi-cubic interpolation (first approach) performs best followed by bi-cubic interpolation of the correlation surface (second approach). Both Gaussian and parabolic peak locating perform weaker. By increasing the spatial resolution of the matched images by intensity interpolation using factors of 2 to 16, 40% to 80% reduction in mean error could be achieved in reference to the same resolution original image.
ON THE X-RAY OUTBURSTS OF TRANSIENT ANOMALOUS X-RAY PULSARS AND SOFT GAMMA-RAY REPEATERS

We show that the X-ray outburst light curves of four transient anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs), namely, XTE J1810−197, SGR 0501+4516, SGR 1627−41, and CXOU J164710.2−455216, can be produced by the fallback disk model that was also applied to the outburst light curves of persistent AXPs and SGRs in our earlier work. The model solves the diffusion equation for the relaxation of a disk that has been pushed back by a soft gamma-ray burst. The sets of main disk parameters used for these transient sources are very similar to each other and to those employed in our earlier models of persistent AXPs and SGRs. There is a characteristic difference between the X-ray outburst light curves of transient and persistent sources. This can be explained by the differences in the disk surface density profiles of the transient and persistent sources in quiescence indicated by their quiescent X-ray luminosities. Our results imply that a viscous disk instability operating at a critical temperature in the range of ∼1300–2800 K is a common property of all fallback disks around AXPs and SGRs. The effect of the instability is more pronounced and starts earlier for the sources with lower quiescent luminosities, which leads to the observable differences in the X-ray enhancement light curves of transient and persistent sources. A single active disk model with the same basic disk parameters can account for the enhancement phases of both transient and persistent AXPs and SGRs. We also present a detailed parameter study to show the effects of disk parameters on the evolution of the X-ray luminosity of AXPs and SGRs in the X-ray enhancement phases.
Learning Horn Expressions with LogAn-H

The paper introduces LOGAN-H —a system for learning first-order function-free Horn expressions from interpretations. The system is based on an algorithm that learns by asking questions and that was proved correct in previous work. The current paper shows how the algorithm can be implemented in a practical system, and introduces a new algorithm based on it that avoids interaction and learns from examples only. The LOGAN-H system implements these algorithms and adds several facilities and optimizations that allow efficient applications in a wide range of problems. As one of the important ingredients, the system includes several fast procedures for solving the subsumption problem, an NP-complete problem that needs to be solved many times during the learning process. We describe qualitative and quantitative experiments in several domains. The experiments demonstrate that the system can deal with varied problems, large amounts of data, and that it achieves good classification accuracy.
A Novel Data Query Method for Wireless Sensor Networks

In this paper, we propose an energy-efficient data query method for wireless sensor networks (WSNs). Our method is flexible and adaptive. To solve the cluster head coverage problem in our method, we propose virtual grid clustering (VGC) protocol in which the clusters are constructed only in the query area when they are needed. The clusters are destroyed after the query ends. Theoretical analysis and simulations show that our method can save significant amount of energy comparing to routing tree protocol and LEACH.
Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context

This paper deals with the statistical segmentation of multisensor images. In a Bayesian context, the interest of using hidden Markov random fields, which allows one to take contextual information into account, has been well known for about 20 years. In other situations, the Bayesian framework is insufficient and one must make use of the theory of evidence. The aim of the authors' work is to propose evidential models that can take into account contextual information via Markovian fields. They define a general evidential Markovian model and show that it is usable in practice. Different simulation results presented show the interest of evidential Markovian field model-based segmentation algorithms. Furthermore, an original variant of generalized mixture estimation, making possible the unsupervised evidential fusion in a Markovian context, is described. It is applied to the unsupervised segmentation of real radar and SPOT images showing the relevance of the proposed models and corresponding segmentation methods in real situations.
Chi-Sim: A New Similarity Measure for the Co-clustering Task

Co-clustering has been widely studied in recent years. Exploiting the duality between objects and features efficiently helps in better clustering both objects and features. In contrast with current co-clustering algorithms that focus on directly finding some patterns in the data matrix, in this paper we define a (co-)similarity measure, named X-Sim, which iteratively computes the similarity between objects and their features. Thus, it becomes possible to use any clustering methods (k-means, .) to co-cluster data. The experiments show that our algorithm not only outperforms the classical similarity measure but also outperforms some co-clustering algorithms on the document-clustering task.
Collaborative Information Visualization Using a Multi-projection System and Mobile Devices

The wide availability of database systems and low cost of hardware allow enterprises and researchers the opportunity to store large data collections. The challenge then became the understanding of these data. To overcome this problem Information Visualization (IV) techniques have been employed to amplify the human cognitive ability through graphical data representations, that show properties and relationships from these data. This work presents an approach to overcome the visual scalability by using a Multi-projection system, allowing the exploration of large datasets. Additionally, this approach allows collaborative interaction and exploration by using mobile devices like tablets and smart phones.
Genetic and Immunological Factors Involved in Natural Resistance to HIV-1 Infection

Infection with Human immunodeficiency virus type-1 (HIV-1) induces severe alterations of the immune system leading to an increased susceptibility to opportunistic infections and malignancies. However, exposure to the virus does not always results in infection. Indeed, there exist individuals who have been repeatedly exposed to HIV-1 but do not exhibit clinical or serological evidence of infection, known as exposed seronegative individuals. Many studies have focused on the different mechanisms involved in natural resistance to HIV-1 infection, and have reported several factors associated with this phenomenon, including the presence of genetic polymorphisms in the viral coreceptors, innate and adaptive immune cells with particular phenotypic and functional features, and molecules such as antibodies and soluble factors that play an important role in defense against infection by HIV-1. The study of these factors could be the key for controlling this viral infection. This review summarizes the main mechanisms involved in resistance to HIV-1 infection.
Modularization techniques for active rules design

Active database systems can be used to establish and enforce data management policies. A large amount of the semantics that normally needs to be coded in application programs can be abstracted and assigned to active rules. This trend is sometimes called “knowledge independence” a nice consequence of achieving full knowledge independence is that data management policies can then effectively evolve just by modifying rules instead of application programs. Active rules, however, may be quite complex to understand and manage: rules react to arbitrary event sequences, they trigger each other, and sometimes the outcome of rule processing may depend on the order in which events occur or rules are scheduled. Although reasoning on a large collection of rules is very difficult, the task becomes more manageable when the rules are few. Therefore, we are convinced that modularization, similar to what happens in any software development process, is the key principle for designing active rules; however, this important notion has not been addressed so far. This article introduces a modularization technique for active rules called stratification; it presents a theory of stratification and indicates how stratification can be practically applied. The emphasis of this article is on providing a solution to a very concrete and practical problem; therefore, our approach is illustrated by several examples.
Exploring Social Contexts along the Time Dimension: Temporal Analysis of Named Entities

Exploring the evolution of social contexts with time can provide unique insights into human social dynamics. Several social contexts and relationships can be mined from unstructured text articles that describe social phenomena. In contrast to structured graphs of social networks, named entity recognition is a task that attempts to classify elements in unstructured textual items into predefined categories, such as organizations, people, locations, quantities, and temporal expressions. State of the art systems have approached the quality of human annotators on static documents for multiple languages. The problem of constructing and linking corresponding entities across topics and documents still exists. During a temporal sequence, entities fluctuate in frequency over time, and the set of entities in the present document can differ from the beginning and end. Furthermore, with user-generated content available on most major news sites, different viewpoints and entity relationships are generated by different users. This paper describes the Sequencer system for the temporal analysis of named entities in news articles between media reported stories and user generated content.