Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Pattern of Preterm Births in Korea for 2000–2012

The aim of this study was to investigate a seasonal pattern of preterm births in Korea. Data were obtained from the national birth registry of the Korean Statistics Office and included all births in Korea during the period 2000–2012 (n = 6,310,800). Delivery dates were grouped by month of the year or by season (winter [December, January, February], spring [March, April, May], summer [June, July, August], and autumn [September, October, November]). The seasonal patterns of prevalence of preterm births were assessed. The rates of preterm births at 37 weeks were highest twice a year (once in winter and again in summer). The rates of preterm births increased by 13.9% in summer and 7.5% in winter, respectively, than in spring (OR, 1.139; 95% CI, 1.127–1.152, and OR, 1.075; 95% 1.064–1.087, respectively) after controlling for age, the educational level of the parents, maternal parity, and neonatal gender. The pattern for spontaneous preterm births < 34 weeks was similar. In Korea, a seasonal pattern of preterm births was observed, with peak prevalence in summer and winter. A seasonal pattern of preterm births may provide new insights for the pathophysiology of preterm births.
Infusing Neuroscience Into the Study and Prevention of Drug Misuse and Co-Occurring Aggressive Behavior

The etiology of behavioral precursors to substance misuse and aggression is viewed from the perspective of a developmental, multifactorial model of complex disorders. Beginning at conception, genetic and environmental interactions have potential to produce a sequence of behavioral phenotypes during development that bias the trajectory toward high-risk outcomes. One pathway is theorized to emanate from a deviation in neurological development that predisposes children to affective and cognitive delays or impairments that, in turn, generate dysregulatory behaviors. The plasticity of these neurobiological systems is highly relevant to the prevention sciences; their functions are reliant upon environmental inputs and can be altered, for better or for worse, contingent upon the nature of the inputs. Thus, social contextual factors confer significant influence on the development of this neural network and behavioral outcomes by increasing risk for, or protecting against, dysregulatory outcomes. A well-designed intervention can exploit the brain's plasticity by targeting biological and social factors at sensitive time points to positively influence emergent neurobiological functions and related behaviors. Accordingly, prevention research is beginning to focus on perturbations in developmental neural plasticity during childhood that increase the likelihood of risky behaviors and may also moderate intervention effects on behavior. Given that the more complex features of neurobiological functions underlying drug misuse and aggression (e.g., executive cognitive function, coping skills, affect regulation) do not coalesce until early adulthood when prefrontal-limbic brain networks consolidate, it is critical that mechanisms underlying developmental risk factors are identified. An empirically driven prevention approach, thus, may benefit from consideration of (i) the type, effect, and developmental timing of the environmental impact on the brain, and (ii) the type and effect on brain function, and developmental timing of the intervention. This translational approach promises to eventually offer some direction for the design of effective interventions to prevent drug misuse and concomitant aggression.
Data Musicalization

Data musicalization is the process of automatically composing music based on given data as an approach to perceptualizing information artistically. The aim of data musicalization is to evoke subjective experiences in relation to the information rather than merely to convey unemotional information objectively. This article is written as a tutorial for readers interested in data musicalization. We start by providing a systematic characterization of musicalization approaches, based on their inputs, methods, and outputs. We then illustrate data musicalization techniques with examples from several applications: one that perceptualizes physical sleep data as music, several that artistically compose music inspired by the sleep data, one that musicalizes on-line chat conversations to provide a perceptualization of liveliness of a discussion, and one that uses musicalization in a gamelike mobile application that allows its users to produce music. We additionally provide a number of electronic samples of music produced by the different musicalization applications.
Inference of internal loss rates in the MBone

We present MBone experiments that validate an end-to-end measurement technique we call Multicast Inference of Network Characteristics (MINC). MINC exploits the performance correlation experienced by multicast receivers to infer loss rates and other attributes of internal links in a multicast tree. MINC has two important advantages in the Internet context: it does not rely on network collaboration and it scales to very large measurements. In previous work, we laid the foundation for MINC using rigorous statistical analysis and packet-level simulation. Here, we further validate MINC by comparing the loss rates on internal MBone tunnels as inferred using our technique and as measured using the mtrace tool. Inferred values closely matched directly measured values differences were usually well below 1%, never above 3%, while loss rates varied between 0 and 35%.
Marrying stable models with belief update

The need for integration of classical logic with nonmonotonic rules has been gaining importance in areas like the Semantic Web and Open Multi-Agent Systems. A number of researchers addressed this problem by proposing a unified semantics for knowledge bases composed of both first-order formulae and rules (also called hybrid knowledge bases). These semantics have matured over the years, but only provide solutions for the static case when knowledge does not
Pandemrix™ and narcolepsy: A critical appraisal of the observational studies

A link between Pandemrix™ (AS03-adjuvanted H1N1 pandemic influenza vaccine, GSK Vaccines, Belgium) and narcolepsy was first suspected in 2010 in Sweden and Finland following a number of reports in children and adolescents. Initial scepticism about the reported association faded as additional countries reported similar findings, leading several regulatory authorities to restrict the use of Pandemrix™. The authors acknowledge that currently available data suggest an increased risk of narcolepsy following vaccination with Pandemrix™; however, from an epidemiologist's perspective, significant methodological limitations of the studies have not been fully addressed and raise questions about the reported risk estimates. We review the most important biases and confounders that potentially occurred in 12 European studies of the observed association between Pandemrix™ and narcolepsy, and call for further analyses and debate.
Crushing of circular steel tubes filled with nanoporous-materials-functionalized liquid

The crush behaviors of steel tubes filled with nanoporous-materials-functionalized liquids are experimentally investigated under quasi-static and dynamic conditions. Results show that the nanoporous-materials-functionalized liquid can enhance the load-carrying and the energy absorption capacities of thin-walled tubes, as the buckling mode is affected. The effective buckling stress increases with the infiltration pressure, and the overall compressibility is highly dependent on the nanopore volume. A general rule of designing nanoporous-materials-functionalized liquid-filled tubes is proposed. The interaction between the tube wall and the nanoporous-materials-functionalized liquid is analyzed.
Balanced generalized weighing matrices and their applications

Balanced generalized weighing matrices include well-known classical combinatorial objects such as Hadamard matrices and conference matrices; moreover, particular classes of BGW -matrices are equivalent to certain relative difference sets. BGW -matrices admit an interesting geometrical interpretation, and in this context they generalize notions like projective planes admitting a full elation or homology group. After surveying these basic connections, we will focus attention on proper BGW -matrices; thus we will not give any systematic treatment of generalized Hadamard matrices, which are the subject of a large area of research in their own right. In particular, we will discuss what might be called the classical parameter series. Here the nicest examples are closely related to perfect codes and to some classical relative difference sets associated with affine geometries; moreover, the matrices in question can be characterized as the unique (up to equivalence) BGW -matrices for the given parameters with minimum q-rank. One can also obtain a wealth of monomially inequivalent examples and deter mine the q-ranks of all these matrices by exploiting a connection with linear shift register sequences.
Parisi : ' Genotypes ' for neural networks 1 ' Genotypes ' for neural networks

Neural networks are computational models of nervous systems. However, organisms do not possess only nervous systems and other phenotypic traits but also genetic information stored in the nucleus of their cells (genotype). The nervous system is part of the phenotype which is derived from this genotype through a process called development. The information specified in the genotype determines aspects of the nervous system which are expressed as innate behavioral tendencies and predispositions to learn. When neural networks are viewed in the broader biological context of artificial life, they tend to be accompanied by genotypes and to become members of evolving populations of networks in which genotypes are inherited from parents to offspring.
Interactive worker assistance: comparing the effects of in-situ projection, head-mounted displays, tablet, and paper instructions

With increasing complexity of assembly tasks and an increasing number of product variants, instruction systems providing cognitive support at the workplace are becoming more important. Different instruction systems for the workplace provide instructions on phones, tablets, and head-mounted displays (HMDs). Recently, many systems using in-situ projection for providing assembly instructions at the workplace have been proposed and became commercially available. Although comprehensive studies comparing HMD and tablet-based systems have been presented, in-situ projection has not been scientifically compared against state-of-the-art approaches yet. In this paper, we aim to close this gap by comparing HMD instructions, tablet instructions, and baseline paper instructions to in-situ projected instructions using an abstract Lego Duplo assembly task. Our results show that assembling parts is significantly faster using in-situ projection and locating positions is significantly slower using HMDs. Further, participants make less errors and have less perceived cognitive load using in-situ instructions compared to HMD instructions.
Licensed under Creative Commons Attribution Cc by Search Optimization Using Multiobjective Particle Swarm Optimization

The reusability provides many benefits such as increasing productivity, Reliability & Quality along with reducing the cost &development time and if the number of components developed is not according to the requirement then the technique of reusability is of great help. The main problem faced by the CBSE in reusability is to select the component for reuse as before reusing there is need to retrieve it from repository &before retrieving; searching of relevant component from repository needs to be performed. The proposed technique is a hybridization of Multiobjective Particle Swarm Optimization and Pareto Dominance Principle.
Long-term socio-cultural influences on food choice: the case of meat

This paper examines long-term and short-term influences on food choice with a particular reference to the question how current food choices in north-western Europe are shaped by long-term changes in attitude towards the animal origin of meat. The analysis aims to specify whether these changes may become substantial enough to get an impact on the consumption of meat and to contribute to a food system that is more sustainable. Using knowledge from a wide range of disciplines (i.e. psychology, sociology, anthropology, history) the author presents a cascade-like framework that sorts insights on influences on behaviour into a logical order. The framework combines perceptual and rational processes internal to the person with social, organizational, and distal processes (i.e. long-term causes). The framework shows that the various influences on behaviour have their own pace and that these differences in pace are particularly relevant for the proper diagnosis of socio-cultural changes and their linkages with food choice criteria. The paper's main argument is that gaining more insight into these influences may contribute to the pursuit of a system that is more sustainable.
Underlying Mechanisms of Cooperativity, Input Specificity, and Associativity of Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis

Long-term potentiation (LTP) is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF) signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.
On-board image-based vehicle detection and tracking

In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
Opinion Extraction and Summarization on the Web

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sources containing such opinions, e.g., product reviews, forums, discussion groups. and blogs. Techniques are now being developed to exploit these sources to help organizations and individuals to gain such important information easily and quickly. In this paper, we first discuss several aspects of the problem in the AI context, and then present some results of our existing work published in KDD-04 and WWW-05.
Linking Environmental Exposure with Public Health: Dichlorodiphenyltrichloroethane Extracted from Soils and Water of Recently Exposed Communities of Selected Locations in Zambia

Background. In 2000, a Zambian private mining company reintroduced the use of dichlorodiphenyltrichloroethane (DDT) to control malaria in two districts. From 2000 to 2010, DDT had been applied in homes without any studies conducted to ascertain its fate in the environment. We aimed to quantify the presence of DDT and its metabolites in the soil and water around communities where it was recently used. Methods. We collected superficial soil and water samples from drinking sources of three study areas. DDT was extracted by QuEChERS method and solid phase extraction for soils and water, respectively. Analysis was by gas chromatography-mass spectrometry. A revalidated method with limits of detection ranging from 0.034 to 0.04 ppb was used. Results. Median levels of total DDT were found at 100.4 (IQR 90.9–110) and 725.4 ng/L (IQR 540–774.5) for soils and water, respectively. No DDT above detection limits was detected in the reference area. These results are clinically significant given the persistent characteristics of DDT. Conclusion. DDT presence in these media suggests possible limitations in the environmental safeguards during IRS. Such occurrence could have potential effects on humans, especially children; hence, there is a need to further examine possible associations between this exposure and humans.
Endogenous Collateral: Arbitrage and Equilibrium without Bounded Short Sales

We study the implications of the absence of arbitrage in an two period economy where default is allowed and assets are secured by collateral choosen by the borrowers. We show that non arbitrage sale prices of assets are submartingales, whereas non arbitrage purchase prices of the derivatives (secured by the pool of collaterals) are supermartingales. We use these non arbitrage conditions to establish existence of equilibrium, without imposing bounds on short sales. The nonconvexity of the budget set is overcome by considering a continuum of agents. Our results are particularly relevant for the collateralized mortgage obligations(CMO) markets.
Strategy development in tourism destinations : a DEA approach

The aim of the paper is to introduce an application of Data Envelopment Analysis (DEA) for benchmarking the efficiency of (tourism) service production processes on the level of tourism destinations. After a brief discussion of the latest benchmarking research studies in tourism, an efficiency model for tourism service processes will be developed (Parasuraman 2002). The latter postulates simultaneous consideration of major input resources needed to provide destination experiences (Vuorinen et al. 1998; Fuchs et al. 2002). Comprehensive efficiency measures for a benchmarking group comprising a total of 21 Tyrolean destination units are subsequently obtained by the aid of the Data Envelopment Analysis (Charnes et al. 1978). These empirical findings form the basis for a newly applied strategy development approach for tourism destinations. The paper concludes by discussing major strengths and weaknesses of the presented benchmarking approach and gives recommendations for future research. (original abstract)
An E-Valuation Framework for Developing Net-Enabled Business Metrics Through Functionality Interaction

Managers engaged in net-enabled business planning seek metrics to help them analyze the success of their e-business investments. Likewise, researchers require metrics to build analytical models and conduct empirical research on the impact of e-business strategies on firm performance. In this article, the authors develop a comprehensive E-Valuation Framework for identifying net-enabled applications and their resulting user-based functionalities for activities across the value chain. The authors propose that the real value from net-enabled applications can be found in functionality interactions, where one application enables or enhances functionality in another application. The comprehensive framework can be used to generate three types of metrics managers can use to evaluate their net-enabled strategic initiatives. Further, a classification of net-enabled organizations provides the basis for selecting applications critical to a firm's strategic thrusts. We make use of the resource-based view of the firm and real-options analysis to discuss how successful application deployment is based on the resources and assets the firm possesses as well as managing the rollout of an applications portfolio over time. The framework allows managers to map their organization's net-enabled initiatives into a coherent, easily understood visual representation and provides direction for researchers evaluating the efficacy of net-enabled business strategies.
An algorithm for pen-based gesture recognition based on levenshtein distance

In this paper we present a new algorithm for automatic recognition of hand drawn sketches based on the Levenshtein distance. The purpose for drawing sketches in our application is to create graphical user interfaces in a similar manner as the well established paper sketching. The new algorithm is trainable by every user and improves the recognition performance of the techniques which were used before for widget recognition. In addition, this algorithm ay serve for recognizing other types of sketches, such as letters, figures, and commands. In this way, there is no modality disruption at sketching time.
Pediatric lymphoma may develop by "one-step" cell transformation of a lymphoid cell

Lymphomas are a large group of neoplasms developed from lymphoid cells (LCs) in lymph nodes (LNs) or lymphoid tissues (LTs). Some forms of lymphomas, including Burkitt lymphoma (BL), ALK+ anaplastic large cell lymphoma (ALK+-ALCL), and T-cell lymphoblastic lymphoma/leukemia (T-LBL), occur mainly in children and teenagers. Hodgkin's lymphoma (HL) has a peak incidence at age 20s. To understand pediatric lymphoma, we have recently proposed two hypotheses on the causes and the mechanism of cell transformation of a LC. Hypothesis A is: repeated bone-remodeling during bone-growth and bone-repair may be a source of cell injuries of marrow cells including hematopoietic stem cells (HSCs), myeloid cells, and LCs, and thymic involution may be a source of damage to the developing T-cells in thymus. Hypothesis B is: a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. In this paper, we discuss pediatric lymphomas by this hypothesis. Having a peak incidence at young age, BL, T-LBL, ALK+-ALCL, and HL develop more likely as a result of rapid transformation of a LC. In BL, ALK+-ALCL, and HL, the cell transformations may be triggered by severe viral infections. In T-LBL, the cell transformation may be related to thymic involution. Occurring in both adults and children, diffuse large B-cell lymphoma (DLBCL) may develop via slow or accelerated pathway. In conclusion, pediatric lymphoma may develop as a result of "one-step" cell transformation of a LC, and severe viral infections may be the main trigger for the rapid transformation of a LC in a LN/LT.