Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between Nutrition Intake and Estimated Glomerular Filtration Rate in Korean Adults (30–65 years) from the Korea National Health and Nutrition Examination Survey 2013–2014

This study aimed to examine the relationship between nutrition intake and estimated glomerular filtration rate (eGFR) indicating kidney function in Korean individuals without diabetes or cardiovascular disease. Study participants from the Korea National Health and Nutrition Examination Survey 2013–2014 (n = 4,378, 30–65 years) were classified by their eGFR levels (mL/min/1.732 m2): ≥ 120 (n = 299), 119–105 (n = 789), 104–90 (n = 1,578), 89–60 (n = 1,685), < 60 (n = 27). After adjusted for confounding factors (age, sex, cigarette smoking, alcohol drinking, total caloric intake [TCI], income status, education level, body mass index, and physical activity), blood pressure, low-density lipoprotein (LDL) cholesterol, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, creatinine, fasting glucose, and glycated hemoglobin were higher, and high density lipoprotein cholesterol levels were lower in participants with lower eGFR levels than those with higher eGFR levels. n-3 fatty acid (FA) and n-6 FA (% of TCI/day) intake were also significantly higher in participants with higher eGFR levels than in those with lower eGFR levels. Based on the above results, participants were subdivided into 3 groups according to n-6 FA intake levels (Q1: ≥ 2.93%, n = 1,462; Q2: 2.92%–1.88%, n = 1,463; Q3: < 1.88%, n = 1,453). People consuming higher n-6 FAs, particularly the Q1 group showed higher eGFR levels and lower levels of LDL cholesterol and creatinine. In conclusion, higher intake of n-6 FAs within the range of dietary reference may be beneficial to maintain healthy kidney function.
NEREA: Named Entity Recognition and Disambiguation Exploiting Local Document Repositories

In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes, setting out the main data of these entities. It achieves a high performance thanks to the use of classification resources belonging to the local database. With this aim, the system performs tasks of named entity recognition and disambiguation by using three types of knowledge bases: local classification resources, global databases like DBpedia, and its own catalog created by NEREA. The proposed method has been validated with two different datasets and its operation has been tested in English and Spanish. The working methodology is being applied in a real environment of a media with promising results.
On the numeric stability of Gaussian processes regression for relational reinforcement learning

In this work we investigate the behavior of Gaussian processes as a regression technique for reinforcement learning. When confronted with too many mutually dependant learning examples, the matrix inversion needed for prediction of a new target value becomes numerically unstable. By paying attention to using suitable numerical techniques and employing QR-factorization these instabilities can be avoided. This leads to better and more stable performance of the attached reinforcement learner.
Self-crafting vegetable snacks: testing the IKEA-effect in children

Purpose The purpose of this paper is to test whether the IKEA-effect (Norton et al., 2012) – better liking for self-crafted products than for identical products crafted by others – can be exploited to increase liking and consumption of vegetable snacks in children. Design/methodology/approach A between-subjects experiment was conducted at an after school care facility. In total, 86 children aged four to six either crafted a peacock with vegetables or with non-food objects following an example. After the task, children ate snack vegetables ad libitum, and rated their liking for the vegetables and pride in crafting the peacock. Findings No significant main effect of the vegetable snack creation on consumption and liking was observed. Also, perceived pride did not mediate the effect of self-crafting vegetable snacks on consumption of and liking for vegetables. Research limitations/implications Vegetable consumption did not differ between children who were either simply exposed to vegetable snacks while crafting or those who were crafting the vegetable snacks themselves. The equal consumption might suggest that this is caused by simple exposure, but more research is needed comparing self-crafting and exposure to a condition where there is no initial exposure to vegetables. Originality/value Although the IKEA-effect has been demonstrated in adults, this is one of the first studies evaluating the IKEA-effect in children and as a means to increase liking for a generally disliked product in this target group, i.e. vegetables. The IKEA-effect could not be replicated under these more stringent conditions, where the experimental set-up enabled disentangling exposure and crafting effects.
Wave sequence based identification of sinus rhythm beats on a microcontroller

Holter recorders are currently changing their typical arrhythmia detection focus towards additional ECG evaluation objectives like ST-T-segment or heart rate variability analysis. Such estimations are only applicable when they are related to normal sinus rhythm excitations. However, up to now most approaches do not actively inquire this question but label every beat normal which is not sufficiently pathologic to be identified as abnormal beat. In this work we propose a real time applicable algorithm to identify sinus rhythm beats depending on their characteristic wave sequence regularity. Identification results are evaluated against the AAMI standard conform beat reference annotations in the MIT-BIH Arrhythmia database (Se=93.52%; +P=90.24%), European ST-T-Database (Se=95.09%; +P=99.86%) and the MlT-BIH Normal Sinus Rhythm database (Se=98.56%; +P=99.65%). Additionally we prove the algorithms to be running on an ARM Cortex-M3 microprocessor by detailed execution time and memory usage evaluation. The presented real time applicable algorithm allows an active beat by beat identification of sinus rhythm excitations to continue with comprehensive evaluations which rely on physiological conduction properties.
Application of Time-Delay Estimation to Mixed Reality Multisensor Tracking

JVRB, 11(2014), no. 3. - Spatial tracking is one of the most challenging and important parts of Mixed Reality environments. Many applications, especially in the domain of Augmented Reality, rely on the fusion of several tracking systems in order to optimize the overall performance. While the topic of spatial tracking sensor fusion has already seen considerable interest, most results only deal with the integration of carefully arranged setups as opposed to dynamic sensor fusion setups. A crucial prerequisite for correct sensor fusion is the temporal alignment of the tracking data from several sensors. Tracking sensors are typically encountered in Mixed Reality applications, are generally not synchronized. We present a general method to calibrate the temporal offset between different sensors by the Time Delay Estimation method which can be used to perform on-line temporal calibration. By applying Time Delay Estimation on the tracking data, we show that the temporal offset between generic Mixed Reality spatial tracking sensors can be calibrated. To show the correctness and the feasibility of this approach, we have examined different variations of our method and evaluated various combinations of tracking sensors. We furthermore integrated this time synchronization method into our UBITRACK Mixed Reality tracking framework to provide facilities for calibration and real-time data alignment.
Evaluation of noise annotation lines: using noise to represent thematic uncertainty in maps

Noise annotation lines are a promising technique to visualize thematic uncertainty in maps. However, their potential has not yet been evaluated in user studies. In two experiments, we assessed the usability of this technique with respect to a different number of uncertainty levels as well as the influence of two design aspects of noise annotation lines: the grain and the width of the noise grid. We conducted a web-based study utilizing a qualitative comparison of 2 areas in 150 different maps. We recruited participants from Amazon Mechanical Turk with the majority being nonexperts with respect to the use of maps. Our findings suggest that for qualitative comparisons of “constant uncertainty” (i.e., constant uncertainty per area) in thematic maps, noise annotation lines can be used for up to six uncertainty levels. During comparison of four, six, and eight levels, the different designs of the technique yielded significantly different accuracies. We propose to use the “large noise width, coarse grain” design that was most successful. For “mixed uncertainty” (i.e., uncertainty is not constant per area) we observed a significant decrease in accuracy, but for four levels the technique can still be recommended. This article is a follow-up to our conference paper reporting on preliminary results of the first of the two experiments.
An Independent Navigation Model for Consistent and Personalized Web Navigation

There are various designs to improve website navigation. However, a fundamental issue is still not addressed in all these designs: the consistency of navigation models across different websites. In addition, almost all of the current designs are static and passive; users have to adapt their navigation behaviors to each model and follow it all the time on a particular website. The problem is not easily solved by current usability guidelines. This research proposes a new Independent Web Navigation model to address these problems. The proposed solution separates and models navigational information independently, provides a consistent and personalizable client interface for the complex and unpredictable web, and offers rich functionalities and flexibility for users to easily manage and adapt web navigation to their needs.
INCORPORATING FUZZY AHP IN SCOR MODEL FOR MEASURING SUPPLY CHAIN OPERATIONS PERFORMANCE: A CASE STUDY OF AN EGYPTIAN NATURAL BOTTLED WATER COMPANY

The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not­for­profit purposes without prior permission or charge, provided:
ReKonf: A Reconfigurable Adaptive ManyCore Architecture

The "one-architecture-fits-all" design philosophy is inadequate for catering to the diverse characteristics of applications running on manyCore architectures. After evaluating various configurations of manyCore architectures for a variety of applications, we designed ReKonf, a reconfigurable adaptive manyCore architecture. ReKonf dynamically configures its reconfigurable components to morph into significantly different configurations on the same architecture by continuously monitoring vital parameters of applications. ReKonf adapts the architecture by tracking core utilization, live cache utilization and cache sharing between threads, at runtime without losing execution state. Our evaluation of various applications on a cycle accurate simulator shows that the (reconfigurable) architecture suitable for such applications can be classified into three main variants: Chip Multiprocessor mode; Symmetric Multiprocessor mode; and Clustered mode, with up to 256 processing cores. Our results show that improvements from 32% to 72% over a baseline configuration can be observed by choosing the right configuration for an application. We also propose architecture components that should be reconfigurable in future manyCore architectures.
A novel narrowband interference suppression method for OFDM radar

The interference between automotive radar sensors becomes a major issue with the increasing number of radars integrated in vehicles for comfort and safety functions. The state-of-the-art radars, typically operating with frequency modulated continuous wave (FMCW) modulation, can be regarded as narrowband interferers for a high bandwidth orthogonal frequency division multiplexing (OFDM) radar with comparably short duration of OFDM symbols. In this paper, we analyze the impact of such interferers on OFDM radar, give a signal model of OFDM radar in presence of interference and discuss the effect of signal processing steps on the latter. Additionally, we present an interference suppression algorithm suitable for any type of narrowband interference. We show in simulations that a considerable interference suppression can be achieved and verify the presented algorithm with measurements.
Embedded, Microcontroller-based Web Server

The paper presents a functionally reduced WWW server based on an 8051 microcontroller. The web server can be embedded in a device and used to remotely, over the Internet, inspect and/or control its state. Upon an HTTP request, the server supplies a web page showing the current status of the device’s sensors and actuators. Viewing the page with any popular browser, the user can change the device’s settings and submit another HTTP request. Having received it, the web server will make the changes requested by the user. The server software is functionally limited and aimed at minimal memory requirements. Therefore, a somewhat simplified version of the TCP and IP protocols has been implemented. Nevertheless, the server reliably responds to requests generated by a number of popular web browsers running on a remote host.
Both Structural and Non-Structural Forms of the Readthrough Protein of Cucurbit aphid-borne yellows virus Are Essential for Efficient Systemic Infection of Plants

Cucurbit aphid-borne yellows virus (CABYV) is a polerovirus (Luteoviridae family) with a capsid composed of the major coat protein and a minor component referred to as the readthrough protein (RT). Two forms of the RT were reported: a full-length protein of 74 kDa detected in infected plants and a truncated form of 55 kDa (RT*) incorporated into virions. Both forms were detected in CABYV-infected plants. To clarify the specific roles of each protein in the viral cycle, we generated by deletion a polerovirus mutant able to synthesize only the RT* which is incorporated into the particle. This mutant was unable to move systemically from inoculated leaves inferring that the C-terminal half of the RT is required for efficient long-distance transport of CABYV. Among a collection of CABYV mutants bearing point mutations in the central domain of the RT, we obtained a mutant impaired in the correct processing of the RT which does not produce the RT*. This mutant accumulated very poorly in upper non-inoculated leaves, suggesting that the RT* has a functional role in long-distance movement of CABYV. Taken together, these results infer that both RT proteins are required for an efficient CABYV movement.
IKnowU : A Framework to Generate Multiple Abstract User Interfaces

This research presents a multidisciplinary approach aimed at generating multiple Abstract User Interfaces (AUIs), which are adaptable for different kinds of users, performing different tasks, using specific devices in various physical environments. The UI generation framework, called IKnowU, is based on a unified process for interactive system design, which integrates Software Engineering (SE), and Human-Computer Interaction (HCI) best practices. This framework is supported by KnowiXML, a Knowledge-Based System (KBS) that facilitates the allocation of appropriate visual elements during the generation of AUIs. These AUIs are generated by using problem solving methods studied in Artificial Intelligence (AI). Design knowledge encoded in KnowiXML uniformly manipulates models and UI specifications through the use of an User Interface Description Language (UIDL).
Feedback-aided pilot placement for two-way cooperative-OFDM

We consider the use of channel feedback to determine the pilot placement for channel estimation of two-way OFDM relay links. We propose a feedback technique in which the pilot allocation mechanism is adapted to the doubly-selective channels. The reduction in the SNR due to channel estimation error is considered by the criteria of SNR gap ratio, thus, is utilized to design the pilot placement. A suboptimal but efficient algorithm, called the iterative pilot allocation (IPA) scheme, is proposed, where iterative exchanges between old pilot locations and the location of the worst data subcarrier are done. The efficacy of the proposed IPA scheme is demonstrated through numerical simulations.
Psychic Routing: Upper Bounds on Routing in Private DTNs

We present early work investigating a one-way delay-tolerant communications channel which affords its users perfect unobservability at the price of a limited bitrate. We suggest an unrealizable protocol, Psychic Routing, against which we can compare the performance of concrete DelayTolerant Networking routing schemes. We then use Psychic Routing to evaluate the performance of routing in our perfectly unobservable channel.
piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics

Ecologists and evolutionary biologists rely on an increasingly sophisticated set of statistical tools to describe complex natural systems. One such tool that has gained significant traction in the biological sciences is structural equation models (SEM), a form of path analysis that resolves complex multivariate relationships among a suite of interrelated variables. Evaluation of SEMs has historically relied on covariances among variables, rather than the values of the data points themselves. While this approach permits a wide variety of model forms, it limits the incorporation of detailed specifications. Recent developments have allowed for the simultaneous implementation of non‐normal distributions, random effects and different correlation structures using local estimation, but this process is not yet automated and consequently, evaluation can be prohibitive with complex models. Here, I present a fully documented, open‐source package piecewiseSEM, a practical implementation of confirmatory path analysis for the r programming language. The package extends this method to all current (generalized) linear, (phylogenetic) least‐square, and mixed effects models, relying on familiar r syntax. I also provide two worked examples: one involving random effects and temporal autocorrelation, and a second involving phylogenetically independent contrasts. My goal is to provide a user‐friendly and tractable implementation of SEM that also reflects the ecological and methodological processes generating data.
Live and Learn: Availability Biases and Beliefs about Military Power

Do leaders learn more about military force from firsthand experience with the armed forces or their nation’s last war? Despite a resurgence of interest in the effects of leaders and their beliefs on international conflict and war, we still know little about which mechanisms cause what beliefs. I theorize that although personal experiences in the military may provide less normatively probative lessons for a leader contemplating the initiation of a conflict than his nation’s last war, personal military experiences will have a greater effect on beliefs because they are more directly experienced, vivid and cognitively accessible. I argue that this availability bias should make leaders who have experience in the military but not combat operations develop beliefs that make them more likely to authorize the use of force than leaders with combat experience. I test this hypothesis on Lyndon Johnson and John Kennedy’s beliefs about the use of force in Vietnam with archival evidence and secondary studies. These cases control for the nation’s last war because both experienced the second World War but allow variation in military experiences because Kennedy but not Johnson experienced combat. The results suggest that further attention to leaders’ personal military experiences will yield better predictions about their beliefs and foreign policies.
An Efficient Method for Composing Whole Body Motions of a Humanoid Robot

This paper describes a method for composing whole body motions of a humanoid robot. Recently there have been many studies of controlling a humanoid robot, and robots have been acquiring the ability to perform various tasks. However, it is still complicated work for us to create complex whole body motions like humans. In order to solve this problem, we propose a motion composition method that uses primitive motions. A whole motion sequence is decomposed into several kinds of primitives. By acquiring primitives from a human motion, or editing primitives manually, we can easily generate a motion that can be performed by a robot. We have developed a software platform for motion composition, and generated robot performances of traditional folk dances based on human performances.
ACCURATE MODELLING OF TISSUE PROPERTIES IN DIFFUSE OPTICAL IMAGING OF THE HUMAN BRAIN

Diffuse optical imaging (DOI) is an emerging imaging modality for non-invasive functional medical imaging, using near infrared (NIR) or visible red light. The innovation is to derive functional information about living tissue from measurements of light that has passed through it. Optical imaging can be applied to imaging of tissues as diverse as the central nervous system, female breast, muscle, and joints of fingers. This thesis addresses the application of DOI to studying the human brain. In this thesis, the problems of modelling light propagation in the adult and infant human head, and reconstructing three-dimensional images of functional changes in the brain using optical measurements, are addressed. Difference imaging, where changes from baseline optical parameters rather than absolute parameter values are reconstructed, is considered. The goal was to develop methods for accurate modelling of light propagation and to clarify how specific aspects of the computational modelling affect the reconstruction of functional images from optical measurements of the human brain. Specifically, the significance of anisotropic light propagation in the white matter, and a priori knowledge of the anatomy and the optical properties of the head and brain are studied. Moreover, a generic probabilistic atlas model of the infant head to enhance image reconstruction is developed. Significance of anisotropic light propagation was found to be small in optical imaging of the adult brain. Although anisotropic light propagation may have a larger impact on the measured signal when infants are imaged, results suggest that image reconstruction can be performed without taking anisotropy into consideration. The use of a priori anatomical knowledge was found to significantly improve the accuracy and robustness of image reconstruction in difference imaging. The results suggest that for optimal reconstructions, individual MR imaging based anatomical data should be used when possible. For cases where individual anatomical data is not available, atlas models should be developed. An important consideration is how to obtain the baseline optical parameters of tissue classes in the anatomical model. Literature-derived parameters can be used as a starting point. For optimal results however, methods should be developed for estimating the baseline parameters from measured data.